environmental risk assessment case study

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• Case Studies

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Arkansas River, CO

Willimantic river, ct, little floyd river, ia, long creek, me, presumpscot river, me, groundhouse river, mn, bogue homo, ms, little scioto river, oh, touchet river, wa, lake washington, wa, clear fork watershed, wv, elk hills, ca (terrestrial), upper arkansas river, co (terrestrial), birds of prey (terrestrial).

These fourteen (14) case studies illustrate how assessors have developed and interpreted evidence to determine causes of biological impairments. They provide examples of how to organize an assessment report, analyze data, and present results. Most of the cases assess rivers and streams, but a few assess terrestrial ecosystems.

The process for identifying causes of biological impairments continues to improve. As a result you will note differences among the case studies. In some examples, comments have been inserted by the U.S. EPA editor or the authors. These comments are not meant to indicate errors in the analyses. Rather, they suggest alternative approaches that users may apply in future assessments.

The full list of case studies are listed in the box to the right. The dots displayed in the map below show the approximate locations of where these case studies occurred.

Many of the following links exit the EPA web site

This case study used several evidence lines to show that metal exposure impaired benthic macroinvertebrates.

Effect : Altered benthic invertebrate assemblage Sources : Mining wastes Probable causes : Mixed metals Report : Arkansas River Case Study: Using Strength of Evidence Analysis. p. 4-11 in U.S. EPA (2000) Stressor Identification Guidance Document . U.S. Environmental Protection Agency, Washington DC. EPA/822/B-00/025. Guidance, presentations, other : Clements WH (1994) Benthic invertebrate community responses to heavy metals in the Upper Arkansas River Basin, Colorado. Journal of the North American Benthological Society 13:30-44. Clements WH, Kiffney PM (1994) Integrated laboratory and field approach for assessing impacts of heavy metals at the Arkansas River, Colorado. Environmental Toxicology and Chemistry 13:397-404. Clements WH, Carlisle DM, Lazorchak JM, Johnson PC (2000) Heavy metals structure benthic communities in Colorado mountain streams. Ecological Applications 10:626-638. Kiffney PM, Clements WH (1994) Structural responses of benthic macroinvertebrate communities from different stream orders to zinc. Environmental Toxicology and Chemistry 13:389-395. Kiffney PM, Clements WH (1994) Effects of heavy metals on a macroinvertebrate assemblage from a Rocky Mountain stream in experimental microcosms. Journal of the North American Benthological Society 13:511-523. Nelson SM, Roline RA (1996) Recovery of a stream macroinvertebrate community from mine drainage disturbance. Hydrobiologia 339:73-84.

A screening assessment from a one-day workshop led to additional sampling. This sampling discovered an illicit toxic source, remediation of which led to improved aquatic life. This experience led the State to develop a causal assessment program. In turn, this program led the State to address impervious surface effects on stream condition.

Effect : Altered benthic invertebrate assemblage Sources : Impervious surfaces, upstream impoundments, concrete channels, waste water treatment facility, industrial outfalls Probable causes : Primarily a toxic effluent; secondarily sediment, altered food resources, increased temperature Report : Bellucci C, Hoffman G, Cormier S (2009) An Iterative Approach for Identifying the Causes of Reduced Benthic Macroinvertebrate Diversity in the Willimantic River, Connecticut . U.S. Environmental Protection Agency, Cincinnati, OH. EPA/600/R-08/144. TMDL : CTDEP (2001) Total Maximum Daily Load Analysis for the Upper Willimantic River (PDF) (16 pp, 382 K, About PDF ) . Connecticut Department of Environmental Protection, Stafford CT.

This case study illustrates the difficulties of assigning specific cause to biological impairment. Challenges included data collected in different ways, small discrimination between acceptable and impaired streams, and the presence of multiple stressors. This case study demonstrates several strategic techniques to address these challenges.

Effect : Altered fish and benthic invertebrate assemblages and a fish kill Sources : Row crop agriculture, hog production, wastewater treatment facility Probable causes : Primarily substrate alteration; secondarily nutrient enrichment and episodic toxic ammonia concentrations Manuscript : Haake DM, Wilton T, Krier K, Stewart AJ, Cormier SM (2010) Causal assessment of biological impairment in the Little Floyd River, Iowa, USA. Human and Ecological Risk Assessment 16(1):116-148. Report : Haake D, Wilton T, Krier K, Isenhart T, Paul J, Stewart A, Cormier S (2008) Stressor Identification in an Agricultural Watershed: Little Floyd River, Iowa .  U.S. Environmental Protection Agency, Cincinnati, OH. EPA/600/R 08/131. TMDL : IA DNR (2005) Total Maximum Daily Load For Sediment and Dissolved Oxygen, Little Floyd River, Sioux and O’Brien Counties, Iowa (PDF)   (32 pp, 378 K, About PDF ) . Iowa Department of Natural Resources, TMDL & Water Quality Assessment Section.

This detailed assessment illustrates the complexity of urban systems affected by many causes.

Effect : Altered benthic invertebrate assemblage, extirpated brook trout fishery Sources : Commercial and industrial area, airport, dairy Probable causes : Decreased dissolved oxygen, altered flow regime, decreased large woody debris, increased temperature and increased toxicity due to ionic strength Report : U.S. EPA (2007) Causal Analysis of Biological Impairment in Long Creek, a Sandy-Bottomed Stream in Coastal Southern Maine (Final Report) . U.S. Environmental Protection Agency, Washington DC. EPA/600/R-06/065F.

This is one of first two Stressor Identification case studies. The study was performed prior to development of the SI Guidance, and it informed guidance development. The weight of evidence was heavily influenced by the lack of co-occurrence of the effect with other candidate causes and by manipulations at a pulp mill on the Androscoggin River. Reductions in total suspended solids at the pulp mill led to recovery.

Effect : Altered benthic invertebrate assemblage Sources : Impoundment, paper and pulp mill Probable cause : Total suspended solids with floc Report : Presumpscot River, Maine. Ch. 6 in U.S. EPA (2000) Stressor Identification Guidance Document . U.S. Environmental Protection Agency, Washington DC. EPA/822/B-00/025. TMDL : U.S. EPA (1998) New England’s Review of the Presumpscott River TMDL Memo (PDF)   (12 pp, 14.1.Mb, About PDF ) . [Last accessed 02/03/10] Guidance, presentations, other : Presumpscot River Plan Steering Committee (2002) Cumulative Impacts to Environmental Conditions on the Presumpscot River and its Shorelands (PDF)  (DRAFT – As distributed at the June 2002 Public Meetings) (102 pp, 1.3 Mb, About PDF ) . [Last accessed 02/02/10]

This screening assessment was done during a two-and-a-half day workshop. Findings were used to mount a more extensive watershed-scale assessment with additional data collection. Results of the screening assessment were confirmed and additional causes were characterized. The State adopted the Stressor Identification process and developed their own guidance and training materials.

Effect : Altered benthic invertebrate assemblage Sources : Waste water treatment facility, agriculture Probable causes : Sediment, nutrients Report : Lane C, Cormier S (2004) Screening Level Causal Analysis and Assessment of an Impaired Reach of the Groundhouse River, Minnesota. U.S. Environmental Protection Agency, Cincinnati OH. TMDL : Minnesota Pollution Control Agency (2009) Groundhouse River Total Maximum Daily Loads for Fecal Coliform and Biota (Sediment) Impairments (PDF)   (377 pp, 9.3 Mb, About PDF ) .  [Last accessed 01/31/10]  Guidance, presentations, other :  Minnesota Pollution Control Agency (2009) Brown's Creek Impaired Biota TMDL - Stressor Identification  (229  pp, 1.9Mb, About PDF ) .[Last accessed 03/31/12]

This assessment was one of the first cases undertaken by the State. It resulted in the State's streamlined stressor identification process. The State performed more than 700 court-ordered causal assessments for total maximum daily load (TMDL) development. A standard candidate cause list and screening levels developed at the program's beginning increased assessment speeds.

Effect : Altered benthic invertebrate assemblage Sources : Forestry, agriculture, reservoir Probable causes : Primarily dissolved oxygen and altered food resources Report : Hicks M, Whittington K, Thomas J, Kurtz J, Stewart A, Suter GW II, Cormier S (2010) Causal Assessment of Biological Impairment in the Bogue Homo River, Mississippi Using the U.S. EPA's Stressor Identification Methodology . U.S. Environmental Protection Agency, Cincinnati OH. EPA/600/R-08/143. TMDL : MDEQ (2005) Phase 1: Total Maximum Daily Load Biological Impairment Due to Organic Enrichment/Low Dissolved Oxygen and Nutrients: The Bogue Homo River, Pascagoula Basin, Jones County, Mississippi (PDF)   (44 pp, 681 K, About PDF ) . Mississippi Department of Environmental Quality, Office of Pollution Control, Jackson MS. Guidance, presentations, other : MDEQ (2004) Draft Stressor Identification for the Bogue Homo River, Forrest and Perry Counties, Mississippi. Mississippi Department of Environmental Quality, Office of Pollution Control, Jackson MS.

This is one of the first two Stressor Identification case studies. In addition to the original case, alternate formats for organizing data are presented in CADDIS.

Effects : Altered fish and benthic invertebrate assemblages Sources : Channelized stream, creosote plant and treatment facility, industrial waste site, waste water treatment facilities Probable causes : Altered habitat, PAHs, metal and ammonia toxicity in different segments Manuscripts : Norton SB, Cormier SM, Suter GW II, Subramanian B, Lin ELC, Altfater D, Counts B (2002) Determining probable causes of ecological impairment in the Little Scioto River, Ohio, USA. Part 1: Listing candidate causes and analyzing evidence. Environmental Toxicology and Chemistry 21(6):1112-1124. Cormier SM, Norton SB, Suter GW II, Altfater D, Counts B (2002) Determining the causes of impairments in the Little Scioto River, Ohio. Part 2: Characterization of causes. Environmental Toxicology and Chemistry 21(6):1125-1137. Report : Little Scioto River, Ohio. Ch. 7 in U.S. EPA (2000) Stressor Identification Guidance Document . U.S. Environmental Protection Agency, Washington DC. EPA/822/B-00/025. Guidance, presentations, other : Ohio EPA (2008) Biological and Water Quality Study of the Little Scioto River (PDF) (59 pp, 1.04Mb, About PDF ). Ohio Environmental Protection Agency, Columbus OH. [Last accessed 02/02/10]

This screening causal assessment was a novel application of the Stressor Identification process for several reasons. It involved a long river stretch, in an arid watershed of the northwestern U.S. It also marked the first use of endangered salmonids as a Stressor Identification endpoint. Specific alteration of the invertebrate assemblage aided analysis.

Effect : Altered benthic invertebrate assemblages and extirpation of salmonids Sources : Wheat and irrigated agriculture, impoundments, logging, cattle raising Probable causes : Primarily water temperature and sedimentation; secondarily toxics, low dissolved oxygen, alkaline pH, reduced detritus, reduced flow and reduced habitat complexity Manuscript : Wiseman CD, LeMoine M, Cormier S (2010) Assessment of probable causes of reduced aquatic life in the Touchet River, Washington, USA. Human and Ecological Risk Assessment 16(1):87-115. Report : Wiseman CD, LeMoine M, Plotnikoff R, Diamond J, Stewart A, Cormier S (2009) Identification of Most Probable Stressors to Aquatic Life in the Touchet River, Washington . U.S. Environmental Protection Agency, Cincinnati OH. EPA/600/R 08/145. TMDL : Washington Department of Ecology. Walla Walla River Basin TMDL Water Quality Improvement Report (2007) and the Walla Walla Watershed TMDL Water Quality Implementation Plan (2008) with links to stressor-specific TMDLs. [Last accessed 05/27/18]  Guidance, presentations, other : Adams K (2010) Guidance for Stressor Identification of Biologically Impaired Aquatic Resources in Washington State . Washington State Department of Ecology, Olympia WA. Publication No. 10-03-036.

This is a brief synopsis of a historically important causal assessment of a eutrophic system. Evidence of world-wide consistency of association established general causality. Modeling was important in establishing specific causality.

Effect : Cyanobacteria blooms Sources : Waste water inputs Probable causes : Phosphorus Report : Lake Washington Case Study. p. 4-13 in U.S. EPA (2000) Stressor Identification Guidance Document .  U.S. Environmental Protection Agency, Washington DC. EPA/822/B-00/025. Guidance, presentations, other : Summarized from Lehman JT (1986) Control of eutrophication in Lake Washington: Case Study. pp. 301-316 in Ecological Knowledge and Environmental Problem-Solving: Concepts and Case Studies. National Academy Press, Washington DC.

This case addresses a moderately sized drainage with several tributaries. Stressor-response relationships derived from field data prior to the assessment provided the primary evidence.

Effect : Altered benthic invertebrate assemblage Sources : Mining, logging, agriculture, and residential development. Probable causes : Sulfate/conductivity, organic and nutrient enrichment, acid mine drainage, residual metals (particularly aluminum) at moderately acidic pH, excess sediment, and multiple stressors Report : Gerritsen J, Zheng L, Burton J, Boschen C, Wilkes S, Ludwig J, Cormier S (2010) Inferring Causes of Biological Impairment in the Clear Fork Watershed, West Virginia . U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Cincinnati OH. EPA/600/R-08/146. TMDL : WVDEP (2006) Appendix 1. Clear Fork (PDF)   (14 pp, 372 K, About PDF ) in Total Maximum Daily Loads for Selected Streams in the Coal River Watershed, West Virginia. Prepared by Water Resources and TMDL Center, Tetra Tech, Inc., Charleston WV. Guidance, presentations, other : WVDEP (1997) An Ecological Assessment of the Coal River Watershed. West Virginia Department of Environmental Protection, Division of Water Resources, Watershed Assessment Program. Report number - 5050009 – 1997, pp. 93.

This case study deals with a contaminated terrestrial site and an endangered wildlife population. This study illustrates the importance of spatial and temporal scales of causes and effects. Based on mathematical modeling to link causes with population changes, it reverses a prior assessment’s findings.

Effect : Decline in abundance of the endangered San Joaquin Kit Fox Sources : Petroleum drilling, wastes, vehicles and drought Probable causes : Predation and accidents Report : U.S. EPA (2008) Analysis of the Causes of a Decline in the San Joaquin Kit Fox Population on the Elk Hills, Naval Petroleum Reserve #1, California . U.S. Environmental Protection Agency, Cincinnati OH. EPA/600/R-08/130.

This case study applied Stressor Identification to a highly mineralized area of the Colorado Rocky Mountains. Evaluated impairments were reduced vegetation, plant growth and species richness in meadows irrigated with Upper Arkansas River water. This study demonstrates aspects of the assessment process that may differ between aquatic and terrestrial systems.

Effect : Reduced plant growth and plant species richness Sources : Mining, smelting, agriculture Probable causes : Extrinsic metal with decreased pH (floodplain); extrinsic metal (irrigated meadows) Report : Kravitz M (2011) Stressor Identification (SI) at Contaminated Sites: Upper Arkansas River, Colorado . U.S. Environmental Protection Agency, Cincinnati OH. EPA/600/R-08/029.

This synopsis explains that the link between DDT and peregrine falcon decline was not initially recognized. The connection was made by re-examining the impairment description. Eventually it was recognized that the specific effect was reproductive failure due to eggshell thinning.

Effect : Decline of birds of prey Probable causes : DDT/DDE Report : Revisiting the Impairment in the Case of DDT. p. 5-2 in U.S. EPA (2000) Stressor Identification Guidance Document . U.S. Environmental Protection Agency, Washington DC. EPA/822/B-00/025. Guidance, presentations, other : Blus LJ, Henny CF (1997) Field studies on pesticides and birds: unexpected and unique relations. Ecological Applications 7:1125-1132.  Grier JW (1982) Ban of DDT and subsequent recovery of reproduction in bald eagles. Science 218:1232-1234.

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NGFS Occasional Papers Case Studies of Environmental Risk Analysis Methodologies

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• Published on 09/10/2020
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ORIGINAL RESEARCH article

Implementation of chemical health, safety, and environmental risk assessment in laboratories: a case-series study.

• 1 Department of Occupational Health, Research Center of Health Sciences and Technologies, Semnan University of Medical Sciences, Semnan, Iran
• 2 Social Determinants of Health Research Center, Semnan University of Medical Sciences, Semnan, Iran
• 3 Student Research Committee, Semnan University of Medical Sciences, Semnan, Iran
• 4 Department of Occupational Health and Safety, Memorial University of Newfoundland, St. John's, NL, Canada

Introduction: Characterizing risks associated with laboratory activities in universities may improve health, safety, and environmental management and reduce work-related diseases and accidents. This study aimed to develop and implement a chemical risk assessment method to determine and prioritize more hazardous chemicals in the academic laboratories.

Methods: A case-series study was conducted at five academic laboratories and research facilities of an Iranian medical sciences university in 2021. A risk assessment was developed and implemented in three phases to identify, evaluate, and classify potential risks and hazards. The approach provided an innovative tool for evaluating and prioritizing risks in chemical laboratories. Hazards were classified on a five-level scale. The technique reviewed both quantitative and qualitative data and pieces of evidence using Laboratory Safety Guidance (OSHA), Occupational Hazard Datasheet (ILO), the standards of the American Conference of Governmental Industrial Hygienists (ACGIH), International Agency for Research on Cancer (IARC), and National Fire Protection Agency (NFPA) codes.

Results: Overall, the frequency of risks rated from “moderate” to “very high” levels was determined for the health hazards (9.3%), environmental hazards (35.2%), and safety hazards (20.4%). Hydrochloric acid had a high consumption rate in laboratory operations and received the highest risk levels in terms of potential hazards to employees' health and the environment. Nitric acid, Sulfuric acid, Formaldehyde, and Sodium hydroxide were assessed as potential health hazards. Moreover, Ethanol and Sulfuric acid were recognized as safety hazards. We observed adequate security provisions and procedures in academic laboratory operations. However, the lack of awareness concerning health, safety, environmental chemical hazards, and inappropriate sewage disposal systems contributed to the increasing levels of laboratory risk.

Conclusions: Chemicals used in laboratory activities generate workplace and environmental hazards that must be assessed, managed, and risk mitigated. Developing a method of rating health, safety, and environmental risks related to laboratory chemicals may assist in defining and understanding potential hazards. Our assessment suggested the need for improving the risk perception of individuals involved in handling chemicals to prevent exposure from workplace duties and environmental pollution hazards.

Introduction

Laboratories and research facilities are considered a fundamental part of universities playing a crucial role in preparing students and researchers to obtain skills that are valuable in their future careers ( 1 ). The presence of numerous chemicals in laboratories has faced safety and health managers with challenges in estimating their risks and hazards. The chemicals and equipment that are used by laboratory personnel and students present a number of serious, sometimes life-threatening hazards and accidents. Laboratory managers are responsible to protect their personnel and students from exposure to chemical, biological, and physical hazards ( 2 ). Therefore, the presented risk assessment method for the academic laboratories and applying prevention and mitigation measures in this study enable the laboratory managers to do their responsibility to their personnel and students.

A survey by OSHA has reported that the potential hazards associated with conducting research at laboratories in academic institutions were 11 times more dangerous as compared to commercial laboratories in a range of industrial sectors with labs ( 3 ). Literature review on the safety and health of laboratories in higher education institutions has shown many laboratory incidents leading to fatalities and injuries caused by fires, explosions, and equipment resulting in debilitating injuries and death ( 4 ). Previous studies on health-related hazards have reported both acute and chronic poisonings following exposure to various chemicals in laboratory environments ( 5 ). Moreover, laboratory wastewater consists of hazardous chemicals that have been considered a substantial environmental threat ( 6 ). In the United States, about 18% of occupational accidents in higher education institutions were related to laboratory environments and in approximately one-third of accidents, students were the main victims ( 7 – 9 ). A review of reported cases in the literature evidence suggests that the trend of accidents was on the rise in academic laboratories over the past several years ( 10 , 11 ). Lack of awareness of various safety and health hazards has triggered accidents, mainly related to the unsafe work practices of chemicals and equipment in laboratories ( 12 ).

Integrated health, safety, and environmental risk assessment would be beneficial in understanding risks, evaluating hazards, and planning a strategy to prevent accidents in laboratories ( 13 , 14 ). International occupational safety and health organizations have developed standards and instructions to prevent and control hazards in laboratory environments. Training of students and laboratory workers provided a culture of safety, health, and environmental consciousness in dealing with laboratory risks and hazards ( 15 ). Although risk assessment has shown to be an efficient approach to identify and introduce appropriate measures to manage risks and hazards, workplace risk levels may differ based on tasks and unsafe acts even in the same work environment. In essence, the laboratory risk assessment should be implemented for individual specific laboratory settings and each work task and role to effectively apply controls ( 16 ). Obtaining objective and comprehensive data concerning risks and hazards has presented challenges for health, safety, and risk management professionals in chemical laboratories. Planning a risk assessment requires the definition of an assessing project with an educated team. Hazard prediction and recognition are the beginning or first step to measure the strength of the impact of a threat ( 2 ).

Many research activities are performed in chemical laboratories at universities, which are seldom assessed by occupational safety and health engineers ( 11 ). This study performed an integrated health, safety, and environmental risk assessment to determine the level of risks for potential workplace exposure in terms of different jobs and work duties in academic lab settings. The process includes prediction, recognition, classification, and evaluation of risks and hazards in chemical laboratories. The plan for adequate measures to prevent and mitigate risks and fitness of work to laboratory personnel and the student will be discussed.

Design and Setting of the Study

A cross-sectional design and action research were applied to develop and conduct a comprehensive risk assessment to determine a range of health, safety, and environmental risks associated with the activities in academic laboratories. This study was implemented at five medical and health sciences laboratories affiliated to Semnan University in 2021.

Suggested Steps of Risk Assessment

Figure 1 demonstrates the methodology steps proposed for assessing risks in chemical laboratories in university environments. These include developing an integrated risk approach, collecting information to categorize risk factors, calculating risk levels, and proposing health, safety, and environmental measures.

Figure 1 . Flowchart of the suggested methodology for risk assessment.

Development of an Integrated Risk Assessment Approach

Our methodology is based on the use of a structured checklist to integrate the process of predicting and recognizing hazards, evaluating the risks posed by hazards, and managing the risks of hazards in the context of the university laboratory. This technique reviewed both quantitative and qualitative data regarding chemicals, environment, and activities associated with the specific processes, and judgments were confined to a particular laboratory process in isolation.

Recognition of potential risks and hazards in laboratory environments and activities was based on checklists, walk-through observation, and interviews with working individuals in laboratories. We developed a combined behavior-based and process-based checklists to conduct a broader risk assessment for identifying the risk level of work practices and mitigating the associated risks. The study tool was adopted from Laboratory Safety Guidance (OSHA), Occupational Hazard Datasheet (ILO), and the Princeton University Laboratory Safety Manual. The tool consisted of 131 items, which were used to assess working areas, emergency planning, required information and documentation, personal protective equipment, electrical hazards, chemical storage and use, flammable liquids, compressed gases, disposal of chemicals used in the lab, ventilation requirements, security, and training.

Collecting Information to Categorize Risk Factors

We identified and grouped chemical exposure and hazards according to their properties, work procedures, and occupational potential exposure scenarios by using frequency and work behavior in the laboratories studied.

Calculating Risk Levels

The laboratory hazard risk rating of a chemical was estimated by multiplying the severity of consequence value by the likelihood of incidence value. For this step, we assembled literature on hazard properties for each chemical from reliable resources to obtain a review of a clear understanding of the safety and health controls. The pieces of literature were reviewed for exposure limits and carcinogenicity of chemical substances as identified by the standards of American Conference of Governmental Industrial Hygienists (ACGIH), Immediately Dangerous to Life or Health Concentrations (IDLH) of toxic substances, and National Fire Protection Agency (NFPA) codes ( 17 , 18 ).

We used an assessment matrix to conduct a comparative analysis concerning “the severity of consequence” and “the probability of incidence” to determine the risk rating for individual health, safety, and environmental hazards. Our estimates of hazard risk ratings were used to categorize risk into varying levels of risk by applying standard linear scaling. Table 1 demonstrates the matrix of risk levels and expectations of responses required to improve safety and health in the laboratory (ISO 31000) ( 19 ).

Table 1 . Establishing a laboratory hazard and process matrix-based risk system with standard linear scaling (values 1–5) to determine the risk score.

Proposing Health, Safety, and Environmental Measures

The prevention and mitigation of health, safety, and environmental risk measures were proposed based on calculated risk scores.

In this study, we used a checklist to recognize potential risks and hazards in the laboratory settings. Health, safety, and environmental hazards associated with common chemical laboratory activities and workflow and the percentage of compliance and non-compliance with laboratory guidelines are shown in Table 2 .

Table 2 . Results of hazard analysis checklist based on work processes and behaviors evaluated in university chemical laboratories and verified frequency of compliance and non-compliance with health, safety, and environmental guidelines.

Our survey of laboratory activities showed that work with compressed gases and flammable liquids was in acceptable compliance with security considerations and safe work procedures. However, the above half of non-compliance was related to the preparation in emergency response situations, not using personal protective equipment, poor inappropriate chemical disposal, treatment of waste products, and awareness and training. The lack of written emergency action plans, chemical hygiene lab procedures, and Safety Data Sheet (SDS) were identified to contribute to operational risks in chemical laboratory activities. The unsafe acts by the lab staff related to waste effluent disposal management mainly included risk factors of improper disposal containment and methods for experiment waste. We observed a lack of compliance in emergency response plans that are mainly associated with inadequate knowledge of staff and students about how to identify the location of fire extinguishers, how to request emergency assistance, and how to communicate potential leak, fire, and explosion scenarios. The unsafe conditions, such as aging electrical cords and plugs and contact with incorrectly grounded devices, were identified to increase operational risks of instruments in laboratories. Additionally, obstructed fire alarm pull stations or inappropriate layout of fire extinguishers in the lab environments increases the reaction time in the occurrence of accidents. Almost all individuals involved in handling chemicals in the laboratories reported they had not received the proper chemical safety training. Our onsite observations showed the unsafe storage of chemicals, which may lead to leakage and increase the possibility of exposure and accidents or high potential for injuries and damages. Students and laboratory workers were more likely not to choose the safe course of action concerning the use of personal protective equipment. For example, a common unsafe act was working in university labs without wearing face and eye or respiratory protection. The absence of proper Protective Personal Equipment (PPE) leads to unsafe exposure and subsequent injury. Furthermore, in chemical laboratories, the users frequently violated safe work procedures during transporting or setting up the experiment or apparatus. We identified many facilities and experiments in compliance with environment, health, and safety codes for handling flammable liquids and compressed gases in chemical laboratories. However, any deviation from the intended experimental steps in laboratory operations could result in severe consequences. The survey evaluated comprehensive health, safety, and environmental hazards of 54 chemicals used in chemical laboratories ( Figure 2 ). The proposed class-based risk assessment involves five levels of classes. The fourth- and fifth-level classes characterize the main risk factors.

Figure 2 . Frequency of chemicals at estimated risk level classes in university laboratory activities.

A total of 44 risk factors were predicted and recognized as the “high” or “very high” level assessment classes. Potential health hazards recognized at the “very high” level were more frequent when compared to safety and environmental hazards, respectively, accounting for 9.2, 3.7, and 1.8% of the total number of hazards at the “very high” level class. Moreover, the chemicals with the level of “high” risk contributed to a greater number of environmental hazards (35.2%) followed by safety hazards (20.4%) and health hazards (11.1%). The identified health, safety, and environmental hazards of chemicals at the intermediate level were, respectively, 20.4, 13, and 18.5% of the total number of third-level categories, implying that prevention and control actions are required to manage the risks. Additionally, the mean value of 29.7% of the assessed chemicals had very low and low health risk levels. These mean values for safety and environmental hazards were 31.5 and 22.3%, respectively.

Overall, using chemicals in laboratory operations produced a wide range of risk levels. Cyclohexane, Nitric acid, Sulfuric acid, Formaldehyde, and Sodium Hydroxide were classified as “very high” risk levels with a score estimated at 25, accounting for 9.3% of potential hazards to health. Many chemicals (35.2%) were classified at the “high” risk levels involved in environmental hazards. In contrast, few chemicals (1.8%) presented a “very high” risk level to the environment. Table 3 demonstrates the potential health, safety, and environmental hazards of the studied chemicals and the relevant calculated risk scores.

Table 3 . Health, safety, and environmental risk assessment matrix of common chemicals used in university laboratories.

Our risk assessment showed that 25.9% of the laboratory chemicals might be associated with heavy potential exposure as scored at 5 or 4. Moreover, more than half of the laboratory chemicals (25.9%) contributed to the high level of severity outcomes. The results demonstrated that Ethanol and Sulfuric acid presented a “very high” risk level (scored at 25) in safety risk assessment. Furthermore, 27.8 and 44.4% of chemicals were rated high scores of probability and severity, respectively, in the safety risk assessment. Hydrochloric acid was the only chemical that was ranked at the “very high” level in the environmental risk assessment, with a score estimated at 25.

Discussions

This study assessed health, safety, and environmental risks in academic laboratories that use chemicals for educational and research activities. The variability of chemical use in academic laboratories might lead to various health, safety, and environmental risk factors. Our findings agree with prior research that suggested that educational and research laboratories of academic institutions need to assess their vulnerabilities and plan their own risk mitigation accordingly ( 20 ).

Our risk assessment indicated that the percentage of health hazards at the “very high” risk level was higher when compared to the safety and environmental hazards. Overall, the mean values of 13.6, 12.4, and 18.5% of the assessed chemicals were classified in “moderate” to “very high” categories of health, safety, and environmental hazards, respectively. Therefore, health and safety rules must be considered strictly as a priority by the people who work with chemicals in laboratories for reducing the risk of chemical-related diseases and accidents ( 21 ). In this study, the laboratory health and safety checklist showed that most non-compliance was linked to the chemical storage and training/awareness sections. The main faults in chemical storage were related to the labeling of cabinets to indicate chemical class and the labeling of chemical containers, particularly when chemicals are transferred from their original containers. Additionally, quantities of chemicals in storage were inconsistent with short-term needs of the assessed laboratories. All of these non-compliances in chemical storage may result in extensive fire or explosion in the laboratories of academic settings. Omidvari et al. found similar results in their study at Azad University in Iran, which reported fire risk and accidents in educational buildings, particularly in laboratories ( 22 ).

Due to the importance of training and awareness in reducing exposures, accidents, and injuries, all laboratory workers, such as faculty, staff, and students, should receive laboratory standard training. The training programs should involve chemical safety programs, chemical emergency action plans, and laboratory security plans. After holding the training courses, it should be ensured that the laboratory workers know who and when to use personal protective equipment, how to use emergency equipment, such as eyewashes and safety showers, where SDSs are kept, spill control procedures, emergency procedures, and chemical waste procedures. The previous studies recommended the periodic training courses for laboratory staff and approved the laboratory safety and security curriculum in most faculties in order to increase awareness, safety, and security culture among laboratory workers and allow them to distinguish what to do before, during, and after emergencies ( 9 , 23 – 25 ).

Moreover, the general work environment, emergency planning, and required information for chemical laboratories were the other parts of the checklist that involved the highest numbers of non-compliance in this study. Not only allocating one room of the chemistry laboratory to a chemical warehouse has been increased the safety risk but also the layout of chemicals was not in accordance with safety principles and standards for practice. For instance, the chemical storage was not at “least 18 below the sprinkler head or at least 24” below the ceiling. In at least 2 laboratories, not considering the 5S principles for work environment and storage of materials, such as paper goods, plastic containers, boxes, and empty containers, that would fuel to the burning fire was major non-compliance violation. Additionally, the alternative exits, chemicals material safety data sheet (MSDS), safety instructions, Self-Contained Breathing Apparatus (SCBA), and required special security systems or controls to limit access were not available in the assessed laboratories. The lack of an emergency action plan was the other major fault in this study. The findings of this study and similar research studies provide useful information to plan and develop an emergency action plan for the prevention and mitigation of the emergencies and their harmful consequences in the laboratories of academic institutions ( 26 – 28 ). The prevention and mitigation measures should be prioritized for implementation in accordance with available funds and other resources. Prior studies reported low-cost interventions that might involve reducing major risks and their consequences. Planning a safe layout for gas cylinders or fire extinguishers, providing the SDS for all chemicals used in laboratories, using chemical labeling of cabinets and containers, and non-structural mitigation measures are recommended ( 29 , 30 ).

In the domain of environmental risk assessment, 44.5% of chemicals were classified in “very low” and “low' risk levels, but 55.5% of them were ranked “intermediate” to “very high” risk degrees. The most important chemical environment-related hazard was waste disposal. The lack of an individual sewage system for laboratories and releasing chemicals into the urban sewage system can contaminate the underground water with hazardous chemicals. Previous studies assessed a high level of environmental risk in underground water reservoirs related to hazardous chemical effluents from academic laboratories ( 31 , 32 ).

Conclusions

This chemical health, safety, and environmental risk assessment was developed and conducted according to the standards and guidelines set by the international occupational health and safety organizations. The applied approach revealed the significant risks associated with chemicals used at the university laboratories. The instrument developed for this study will be put into good use in helping health and safety engineers to identify and classify potential risks of laboratory operations to health, safety, and environment. Prevention and mitigation measures should be based on detailed risk assessment methods to minimize identified hazards and provide a safe environment to reduce and/or eliminate the occurrence of diseases and injury in laboratories.

Universities should provide training courses in the curriculum on health and safety in laboratories, particularly for new students at the first of each semester, and periodic similar training courses for faculty and staff plays a key role in increasing awareness and risk perception for considering significant risks at the laboratories. Furthermore, inspecting and assessing the laboratories and research facilities by standard laboratory checklists routinely and removing the non-compliance operations at the earliest time are essential in providing a safe work environment.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the corresponding author, without undue reservation.

Ethics Statement

This study was approved by the Ethics Committee Review Board at Semnan university of Medical Sciences (IR.SEMUMS.REC.1398.131). All the participants signed a consent form and were informed on the purpose of the study prior to interview as per local protocol on research ethics.

Author Contributions

AD: material preparation, conceptualization, methodology, investigation, writing—reviewing, and editing. MJ: material preparation, conceptualization, and data collection. FF: analysis, interpretation, first draft of the manuscript, conceptualization, and investigation. All authors contributed to the study conception, design, investigation, reviewed and commented on previous versions of the manuscript, and read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank all the staff laboratories that participated and collaborated in this study and Semnan University of Medical Sciences and Health Services for their support to conduct this research.

1. Husin S, Mohamad AB, Abdullah S, Anuar N. Implementation of chemical health risk assessment in teaching laboratories. Asian Soc Sci. (2012) 8:184–91. doi: 10.5539/ass.v8n16p184

CrossRef Full Text | Google Scholar

2. Ozdemir Y, Gul M, Celik E. Assessment of occupational hazards and associated risks in fuzzy environment: a case study of a university chemical laboratory. Hum Ecolog Risk Assess Int J. (2017) 23:895–924. doi: 10.1080/10807039.2017.1292844

3. Evans J,. Safety First? (2014). ChemistryWorld . Available online at: http://www.rsc.org/chemistryworld/2014/05/safety-first (accessed July 19, 2019).

4. Malakout J, Arsang Jang S, Mosaferchi S, Hasely F, Azizi F, Mahdinia M, et al. Health risk assessment of occupational exposure to hazardous chemicals in laboratories of qom university of medical sciences. Iran Occup Health J. (2014). 11:13–25. doi: 10.29252/johe.6.3.171

5. Mohammadfam I, Abdolahi F, Karimi S. Assessment and risk management in the laboratories of the school of public health, a medical university using the ACHiL technique. J Occup Hyg Eng. (2018) 5:20–7. doi: 10.21859/johe.5.2.20

6. Chartres N, Bero LA, Norris SL. A review of methods used for hazard identification and risk assessment of environmental hazards. Environ Int. (2019) 123:231–9. doi: 10.1016/j.envint.2018.11.060

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Van Noorden R. Safety survey reveals lab risks: questionnaire suggests researchers not as safe as they feel. Nature. (2013) 493:9–11. doi: 10.1038/493009a

8. Wu T-C, Liu C-W, Lu M-C. Safety climate in university and college laboratories: Impact of organizational and individual factors. J Safety Res. (2007) 38:91–102. doi: 10.1016/j.jsr.2007.01.003

9. Simmons H, Matos B, Simpson S. Analysis of injury data to improve safety and training. J Chem Health and Saf. (2017) 24:21–8. doi: 10.1016/j.jchas.2016.03.004

10. Confino A. Collective Memory and Cultural History: Problems of Method. The American Historical Review. (1997) 102:1386–1403. doi: 10.2307/2171069

11. Shu C, Lin M. Planning, Managing and Designing in Laboratories Safety and Health . Taipei: Gau-Lih Book Co. (2004).

Google Scholar

12. Oshima Y. Characteristics of chemical risks in academic research laboratory. J Environ Saf. (2016) 7:95–7. doi: 10.11162/daikankyo.E15PROCK03

13. Report on Commission on Risk Assessment and Risk Management Symposium on a Public Health Approach to Environmental Health Risk Management . Washington, DC (1997).

14. Pourang N, Esmaeili F, Ranjbarian M. Assessment of potential safety hazards of three fisheries research centers laboratories staff by failure modes and effect analysis (FMEA) and the relevant statistical methods. Iran J Health Environ. (2017) 10:87–102. Available online at: http://ijhe.tums.ac.ir/article-1-5781-en.html

15. Kinderlerer J. Risk and the law: scientists do not necessarily like the restrictions and regulations of biotechnology, but they need to accept that lawmakers must reflect the wishes and demands of the majority of the population, who are increasingly aware of the potential risks. EMBO Rep. (2004) 5:S66–70. doi: 10.1038/sj.embor.7400259

16. Ménard AD, Trant JF. A review and critique of academic lab safety research. Nat Chem. (2019) 1–9. doi: 10.1038/s41557-019-0375-x

17. Nation U. Globally Harmonized System of Classification and Labelling of Chemicals (GHS) . New York, NY: United Nation (2019).

18. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. (2019) 47:D1102–9. doi: 10.1093/nar/gky1033

19. Dehdashti A, Fatemi F, Jannati M, Asadi F, Kangarloo MB. Applying health, safety, and environmental risk assessment at academic settings. BMC Public Health. (2020) 20:1328. doi: 10.1186/s12889-020-09419-5

20. Salazar-Escoboza MA, Laborin-Alvarez JF, Alvarez-Chavez CR, Noriega-Orozco L, Borbon-Morales C. Safety climate perceived by users of academic laboratories in higher education institutes. Saf Sci. (2020) 121:93–9. doi: 10.1016/j.ssci.2019.09.003

21. Gatae F, Jasim A, Hassan S. Safety assessment in chemical and biological laboratories at basra university, Iraq. Biochem Cell Arch. (2020) 20:3565–8.

22. Omidvari M, Mansouri N, Nouri J. A pattern of fire risk assessment and emergency management in educational center laboratories. Saf Sci. (2015) 73:34–42. doi: 10.1016/j.ssci.2014.11.003

23. Pazzi V, Morelli S, Pratesi F, Sodi T, Valori L, Gambacciani L, et al. Assessing the safety of schools affected by geo-hydrologic hazards: the geohazard safety classification (GSC). Int J Disast Risk Reduct. (2016) 15:80–93. doi: 10.1016/j.ijdrr.2015.11.006

24. U.S. Department of Education, Office of Elementary and Secondary Education, Office of Safe and Healthy Students . Guide for Developing High-Quality Emergency Operations Plans for Institutions of Higher Education. Washington, DC (2013).

25. Mohammadfam I, Mirzaei Aliabadi M, Soltanian AR, Tabibzadeh M, Mahdinia M. Investigating interactions among vital variables affecting situation awareness based on Fuzzy DEMATEL method. Int J Ind Ergon. (2019) 74:102842. doi: 10.1016/j.ergon.2019.102842

26. Ho C-C, Chen M-S. Risk assessment and quality improvement of liquid waste management in Taiwan University chemical laboratories. Waste Manag. (2018) 71:578–588. doi: 10.1016/j.wasman.2017.09.029

27. Wang J-J. Study on the context of school-based disaster management. Int J Disaster Risk Reduct. (2016) 19:224–34. doi: 10.1016/j.ijdrr.2016.08.005

28. Nanda R, Raina SK. Integrating disaster risk reduction in school curriculum: A vision statement by a joint working group of university and medical teachers. Int J Disaster Risk Reduct. (2019) 33:495–7. doi: 10.1016/j.ijdrr.2018.09.011

29. Mckevitt W, Timler P, Lo K. Nonstructural damage from the Northridge Earthquake. Can J Civil Eng. (2011) 22:428–37. doi: 10.1139/l95-051

30. Abd Ali A, Shaalan N, Al-Dahhan W, Hairunisa N, Yousif E. A Technical evaluation of a chemistry laboratory: a step forward for maintaining safety measures. Orient J Phy Sci. (2017) 2:68–71. doi: 10.13005/OJPS02.01.06

31. Hanifa MR, Elzagheid M. Waste Chemicals and solvents-profiling and recovery at academic chemical laboratories. Int J Chem Sci. (2019) 17:301. doi: 10.37532/0972-768X.2019.17(1).301

32. Dehdashti A, Hafezi R. Health, safety and environmental risk assessment in an academic laboratory: a case study. Iran Occup Health. (2015) 12:66–86. Available online at: http://ioh.iums.ac.ir/article-1-1188-en.html

Keywords: chemicals, risk assessment, academic, laboratories, safety

Citation: Fatemi F, Dehdashti A and Jannati M (2022) Implementation of Chemical Health, Safety, and Environmental Risk Assessment in Laboratories: A Case-Series Study. Front. Public Health 10:898826. doi: 10.3389/fpubh.2022.898826

Received: 17 March 2022; Accepted: 21 April 2022; Published: 14 June 2022.

Reviewed by:

Copyright © 2022 Fatemi, Dehdashti and Jannati. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alireza Dehdashti, dehdasht@semums.ac.ir ; dehdasht@yahoo.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 15 November 2021

Role of epidemiology in risk assessment: a case study of five ortho-phthalates

• Maricel V. Maffini   ORCID: orcid.org/0000-0002-3853-9461 1 ,
• Birgit Geueke   ORCID: orcid.org/0000-0002-0749-3982 2 ,
• Ksenia Groh 3 ,
• Bethanie Carney Almroth   ORCID: orcid.org/0000-0002-5037-4612 4 &
• Jane Muncke   ORCID: orcid.org/0000-0002-6942-0594 2  

Environmental Health volume  20 , Article number:  114 ( 2021 ) Cite this article

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The association between environmental chemical exposures and chronic diseases is of increasing concern. Chemical risk assessment relies heavily on pre-market toxicity testing to identify safe levels of exposure, often known as reference doses (RfD), expected to be protective of human health. Although some RfDs have been reassessed in light of new hazard information, it is not a common practice. Continuous surveillance of animal and human data, both in terms of exposures and associated health outcomes, could provide valuable information to risk assessors and regulators. Using ortho-phthalates as case study, we asked whether RfDs deduced from male reproductive toxicity studies and set by traditional regulatory toxicology approaches sufficiently protect the population for other health outcomes.

We searched for epidemiological studies on benzyl butyl phthalate (BBP), diisobutyl phthalate (DIBP), dibutyl phthalate (DBP), dicyclohexyl phthalate (DCHP), and bis(2-ethylhexyl) phthalate (DEHP). Data were extracted from studies where any of the five chemicals or their metabolites were measured and showed a statistically significant association with a health outcome; 38 studies met the criteria. We estimated intake for each phthalate from urinary metabolite concentration and compared estimated intake ranges associated with health endpoints to each phthalate’s RfD.

For DBP, DIBP, and BBP, the estimated intake ranges significantly associated with health endpoints were all below their individual RfDs. For DEHP, the intake range included associations at levels both below and above its RfD. For DCHP, no relevant studies could be identified. The significantly affected endpoints revealed by our analysis include metabolic, neurodevelopmental and behavioral disorders, obesity, and changes in hormone levels. Most of these conditions are not routinely evaluated in animal testing employed in regulatory toxicology.

We conclude that for DBP, DIBP, BBP, and DEHP current RfDs estimated based on male reproductive toxicity may not be sufficiently protective of other health effects. Thus, a new approach is needed where post-market exposures, epidemiological and clinical data are systematically reviewed to ensure adequate health protection.

Peer Review reports

Non-communicable diseases (NCD) are a global burden to public health [ 1 ]. Nutritional shortcomings and lifestyle factors have been associated with increased incidence of diabetes and obesity, but current evidence indicates that exposures to environmental chemical contaminants also play a role in the development of NCDs [ 2 ]. In the US, cardiovascular diseases and mental health conditions impose the highest economic burden followed by cancer, diabetes, and chronic respiratory diseases [ 3 ]. Of particular concern are exposures during gestation and early childhood [ 4 ] . A recent review [ 5 ] proposed incorporating environmental health risk factors when estimating global burden of disease, including air pollutants, neurotoxicants, endocrine disrupting chemicals, and climate-related factors. To do this successfully, the components of risk assessment such as exposure sources and levels, as well as data about chemical effects and associated health outcomes, are required [ 6 ].

One source of chemical exposure is plastic. With a global production of almost 360 million metric tons in 2018 [ 7 ], manufacturing, use, and disposal of plastic materials pose major safety concerns. Leachate from landfills, migration from consumer products (e.g., food packaging, toys, flooring, textiles), and air pollution from burning plastic materials are just some of the sources of chemical contamination affecting humans and the environment [ 8 , 9 , 10 ] . Because information on chemicals present in plastics is difficult to obtain and their hazards often remain unknown, Groh and colleagues [ 11 ] published a comprehensive database with more than 900 chemicals likely associated with plastic packaging as part of the Hazardous Chemicals in Plastic Packaging (HCPP) project. The authors also ranked the chemicals based on hazards to human and environmental health according to the United Nations’ Globally Harmonized System of Classification and Labelling of Chemicals [ 12 ] The 63 chemicals that ranked highest for human health concerns underwent a tiered prioritization [ 13 ] based on biomonitoring data, endocrine disrupting properties, and their regulatory status under the European Chemicals Regulation REACH. This prioritization approach identified five ortho-phthalates (referred to as phthalates in this article) for which the risk to human health was considered the highest: benzyl butyl phthalate (BBP, CAS 85-68-7); dibutyl phthalate (DBP, CAS 84-74-2); diisobutyl phthalate (DIBP, CAS 84-69-5); bis(2-ethylhexyl) phthalate (DEHP, CAS 117-81-7); dicyclohexyl phthalate (DCHP, CAS 84-61-7).

Phthalates are highly abundant plastic additives used primarily as plasticizers to soften materials and make them flexible [ 14 ]. Human biomonitoring shows widespread exposure to phthalates [ 15 ] from diverse sources including food which could be contaminated from its packaging as well as other food contact materials such as conveyor belts and tubing used in food processing [ 16 , 17 , 18 , 19 , 20 ]. Personal care products and building materials also contribute to human exposure to phthalates [ 21 , 22 ].

Several regulatory authorities have assessed the toxicity of BBP, DBP, DIBP, DEHP, and DCHP [ 23 , 24 , 25 ] and established the amount of each chemical above which the risk to human health increases. Regulatory agencies give different names to these so-called ‘safe’ levels including derived no-effect level (DNEL) used by the European Chemical Agency (ECHA) [ 26 ], acceptable daily intake (ADI) used by International Programme on Chemical Safety [ 27 ], total dietary intake (TDI) used by the European Food Safety Authority (EFSA) [ 28 ] and reference dose (RfD) used by the U.S. Environmental Protection Agency (EPA) [ 29 ]. Although the nomenclature is different, the meaning is similar, namely, exposures above established amounts of chemicals are not safe. For simplicity, we use the term RfD throughout the article.

The established RfDs for the five phthalates are the result of risk assessments of mostly animal studies showing adverse effects on male reproductive development due to the anti-androgenic properties of these chemicals. These risk assessments’ results have led to the restriction of some uses of these phthalates. In 2008, the Congress of the United States banned the use of DEHP, BBP and DBP in children’s toys and childcare articles [ 30 ] and in 2017, the Consumer Protection Safety Commission increased the list of prohibited phthalates to eight [ 23 ]. Similarly, the European Union has also listed DEHP and DBP in its authorization list under REACH and more than a dozen phthalates are included in the candidate list for authorization [ 31 ]. The observed decline in human exposure to restricted phthalates in industrialized countries over the years [ 15 , 32 , 33 ] have been attributed to these regulatory measures. Notably there are yet no major restrictions to uses in food contact materials (e.g., packaging, processing equipment), pharmaceuticals, and medical devices.

Epidemiological data published in the last 15 years indicate that in some cases exposure to phthalates is still a cause of concern to human health. For example, recent publications by the U.S. Environmental Protection Agency (EPA) show a strong association between exposure to low concentrations of DEHP, DBP, and DIBP and increased risk of diabetes [ 34 ], and between exposure to DEHP and DBP and male reproductive effects such as reduced semen quality and testosterone levels [ 35 ]. Several small- and large-scale human studies have also shown phthalates to associate in a dose-dependent manner with negative effects on neurodevelopment [ 36 , 37 ], metabolic function [ 38 ] and female reproduction [ 39 ]. Therefore, we aimed to investigate whether regulatory safe levels of phthalates are protective of the public for other relevant health outcomes in addition to male reproductive development. We conducted a targeted literature search of human studies showing association between any of the five phthalates, BBP, DBP, DIBP, DEHP, and DCHP and health effects. Furthermore, we back-estimated daily intake for each phthalate that showed a statistically significant association with health effects, and compared these estimated intake values to the individual RfD.

Targeted literature search

We searched the Public Library of Medicine for human studies on phthalates published between 2003 and 2019. Search terms included compounds’ full name, abbreviation, and chemical abstracts service (CAS) numbers in combination with human exposure, epidemiological studies and metabolites among others. See Supplemental Materials for additional information. This targeted search aimed at obtaining information on the five phthalates including concentration of parent phthalates or metabolites in any bodily fluid, description of measured endpoint, and statistical significance of the association between health endpoint and concentration measured. When a study met these criteria, we extracted the following data: 1) population sampled and population in which the endpoints were measured (e.g., men; pregnant women/children; children, etc.); age; gestational age where appropriate; 2) metabolite or parent compound concentration as percentile, geometric mean or other available concentration measure; 3) concentration at which metabolite(s) or parent compound had a statistically significant correlation with an endpoint; 4) statistically significant endpoint and outcome (e.g., increase/decrease; positive/negative association). We used the studies that met the criteria described above to perform the analysis and controlled for quality, specifically, whether the studies included controls for covariates and confounders such as race, maternal/paternal age, child’s sex, IQ, socioeconomic status, smoking, physical activity, caloric intake, etc.; however, we did not control for potential bias.

Intake estimation from urinary concentration

From the studies that met the inclusion criteria, we identified the lowest phthalate metabolite concentration that was associated with a statistically significant endpoint. Concentration data were expressed in various ways including geometric means of a population, percentiles, and average of urine collections per individual visits. We established the following assumptions: 1) the 25th percentile concentration was considered equivalent to a no-observed-adverse-effect level when concentrations were expressed as quartiles, meaning that only concentrations at or greater than the 25th percentile were included; 2) unless specified in the studies, logistic regressions were considered linear.

For each phthalate, we estimated intake using urinary concentration of its metabolite(s), daily urine volume, body weight (bw), and creatinine correction values for the different populations assessed [ 40 , 41 ]. In the case of DEHP, we considered the individual excretion of its four metabolites over time and expressed it as percent of the parent phthalate’s intake as described previously [ 42 , 43 ]. We used the following mean percentage excretion values for DEHP metabolites: 6% for monoethylhexyl phthalate (MEHP), 11% for mono-(2-ethyl-5-oxohexyl phthalate (5oxo MEHP), 15% for mono(2-ethyl-5-hydroxyhexyl) phthalate (5OH MEHP) and 14% for mono-(2-ethyl-5-carboxypentyl) phthalate (5cx MEHP). For DBP, DIBP and BBP, we followed the European Chemical Agency (ECHA) assumption of 100% elimination of the parent compound as phthalate monoesters [ 25 ]. We used the following formula:

Intake (μg/ bw (kg)/d) = Metabolite concentration (μg/L) x (Vol (L)/day) x (1/bw (kg)) x (1/% elimination)

In cases when creatinine correction was needed, concentration of urinary metabolite in microgram per gram (μg/g) creatinine was multiplied by the urinary concentration of creatinine in gram per liter (g/L). The Supplemental Materials include an example of the intake calculations and the assumptions made for each population (children, pregnant women, non-pregnant women, men) regarding body weight, daily urine volume, and creatinine excretion.

Regulation of priority phthalates

The uses of and exposure to phthalates are regulated in the European Union and the United States [ 23 , 24 , 25 , 44 , 45 ]. We chose the regulatory limits set by ECHA and the US Consumer Protection Safety Commission (CPSC) to compare against the estimated intakes associated with health endpoints because these safe levels have been reaffirmed or established in the last 5 years using current scientific evidence. In addition, both assessments target products that are commonly used by children, a susceptible population as highlighted by government regulatory agencies [ 23 , 25 , 46 ]. Regulatory RfDs are commonly expressed as the amount of chemical a person is safe to consume per kilogram of body weight per day, over their expected lifetime. Table  1 summarizes the RfD for BBP, DBP, DIBP and DEHP and the health endpoint selected by ECHA to establish each reference dose. Because ECHA did not establish an RfD for DCHP, we used a regulatory limit set by the US CPSC, i.e., less than 0.1% DCHP per weight of the final product for children’s toys and articles [ 23 ]. This assessment was also based on DCHP’s anti-androgenic effects (i.e., reduced anogenital distance) observed in male rodents [ 54 ].

We identified 38 out of 64 publications that met our selection criteria (Table  2 ). The studies included longitudinal and cross-sectional studies; small cohorts (e.g., patients at fertility clinics; under-represented urban populations) and nationally representative cohorts such as the National Health and Nutrition Examination Survey (NHANES) of the U.S. Center for Disease Control and Prevention; and prenatal exposure studies where phthalates were measured in the mothers but the health outcomes were assessed in their children months or years after birth. Supplemental Materials Table S1 lists the 26 publications that did not meet our criteria and therefore were not included in this case study.

All 38 studies reported phthalate metabolites measured in urine. DEHP was the phthalate most frequently assessed. There were 12 studies on mother-child pairs evaluating prenatal exposure effects, 12 women-only studies, six men-only studies, eight children studies evaluating postnatal exposure effects, and two studies including both men and women. A few studies included more than one population (e.g., children and adults) and only one study was a prospective mother-child study. It is worth noting that none of the studies included evaluation of DCHP, neither as a parent compound nor its metabolite. The lack of epidemiological studies on DCHP is likely due to the fact that the urinary concentration of DCHP metabolite has been found to be consistently below the limit of detection at the 75th percentile in the NHANES 1999–2010 period [ 83 ] and, when measured, the frequency of detection has been low (e.g., less than 10% of the population tested) [ 33 , 83 ].

Table 1 lists the range of exposure for each phthalate and their association with significant endpoints. All phthalates measured in urine as metabolites of DEHP, DBP, BBP and DIBP showed significant associations with reproductive (male and female), neurodevelopmental, behavioral, hormonal, and metabolic endpoints at estimated intake values well below their respective RfDs.

Figure  1 shows the estimated intake distribution per phthalate compared to the respective RfD. DEHP had the widest range of estimated intakes associated with statistically significant endpoints: 0.03–242.5 μg/kg-bw/d (Table 1 , Fig.  1 ). The highest estimate was almost seven times greater than the RfD (35 μg/kg-bw/d) which is an indication that some individuals could already be exposed to unsafe levels of the chemical as judged by the current regulatory limits. As shown in Table 1 , the highest DEHP intake was associated with decreased semen quality [ 47 ]. On the lower end, DEHP was associated with significantly lower number of ovarian antral follicles (a measure of remaining oocytes supply) [ 39 ] at an estimated intake three-orders of magnitude lower than the RfD (0.03 and 35 μg/kg-bw/d, respectively).

Schematic representation of the range of estimated intake for individual phthalates (solid light-colored bars) associated with statistically significant endpoints (small circles) in relation to their respective reference doses (RfD; large circles). Each small circle corresponds to an endpoint significantly associated with an estimated intake. The lowest metabolite concentrations measured in urine that were found to be associated with statistically significant endpoints were 0.03, 0.19, 0.06 and 0.08 μg/L for DEHP, DBP, BBP and DIBP, respectively. See Supplemental Table S2 for additional data. DEHP: diethylhexyl phthalate; DBP: dibutyl phthalate; BBP: butylbenzyl phthalate; DIBP: diisobutyl phthalate

For DBP, DIBP and BBP, the ranges of intake associated with statistically significant endpoints were all below their respective RfDs. (Fig. 1 ). The lowest estimated intake for DBP (0.19 μg/kg-bw/d) was associated with decreased sperm motility and semen concentration [ 48 ] while the highest intake (2.86 μg/kg-bw/d) was associated with decreased concentration of total thyroid hormone thyroxine (T4) and free T4 (fT4) in women [ 49 ]. The lowest DIBP intake measured in pregnant women (0.08 μg/kg-bw/d) was associated with decrease in masculine play behavior in boys [ 52 ] and the highest intake (0.51 μg/kg-bw/d) also measured in pregnant women, was significantly associated with increased occurrence of eczema in children [ 53 ]. The range of estimated intake for BBP associated with significant endpoints showed the greatest difference with the RfD. The lowest intake of 0.06 μg/kg-bw/d was associated with increased levels of steroid hormone binding globulin (SHBG) in children [ 50 ]. SHBG is a protein that transports estrogen and testosterone in the blood and regulates their access to tissues [ 84 ]. The highest estimated intake for BBP (0.6 μg/kg-bw/d) was associated with increased body mass index and waist circumference in men and women [ 51 ]. These intakes are eight-thousand to five-thousand times lower than BBP’s RfD of 500 μg/kg-bw/d.

The four phthalates for which we found data are known to affect male reproductive development due to their anti-androgenic properties which are the basis of their regulation. However, other systems are also affected at exposure levels similar to those associated with anti-androgenicity as seen in Table 2 . Our analysis shows the 10 lowest estimated intakes were significantly associated with endpoints measured in women and children. Many of these endpoints relate to endocrine function and neurobehavioral development in children as well as female reproductive system (Table  3 ).

Prenatal exposures to DEHP, DBP, BBP and DIBP were significantly associated with a diverse set of negative outcomes in the neurological system, and all endpoints were associated with intakes well below the RfD for each phthalate. Supplemental Table S2 shows that children born to mothers exposed to phthalates during pregnancy display delayed psychomotor and mental development [ 65 , 66 ]; decreased intellectual, memory and executive function development [ 36 ]; and behavioral changes associated with both delinquency and externalization [ 64 ] as well as withdrawn personalities and internalization of problems [ 65 , 68 ]. Increased odds of attention deficit hyperactivity disorder [ 69 ] and decreased masculine behavior in boys [ 52 ] were also observed.

We identified three major systems associated with metabolic function that were affected by phthalates: thyroid, pancreas, and fat tissue (Supplemental Table S3 ). DEHP, DBP and BBP were associated with decreased levels of triiodothyronine (T3) in men and children as young as 4 years of age. DEHP was also associated with decreased levels of free T4 in women [ 56 ] and men [ 82 ] and decreased thyroid stimulating hormone (TSH) in men [ 47 ].

DIBP and DEHP intakes were positively associated with insulin resistance in children [ 72 , 78 ] and men and women [ 60 , 77 ]. The effect of DEHP on fat tissue was more diverse. For instance, in adults, body mass index (BMI) was negatively associated with DEHP levels in men and women [ 59 ], while Hatch et al. [ 51 ] reported a positive correlation in women). Maternal DEHP levels were inversely associated with their daughters’ BMI at a young age (4–7 years) [ 67 ] and Zang and colleagues also observed a negative association between DEHP levels and obesity in 8–10-year-old girls [ 74 ]. DBP and BBP showed a positive correlation with obesity in boys [ 74 ], BMI and waist circumference in women and men [ 51 ].

All the estimated intakes were below their respective RfDs, except for the reduction in TSH level in men that was associated with the highest DEHP intake of 242.55 μg/kg-bw/d [ 47 ].

Both, the male and female reproductive systems and their associated hormones, were negatively affected by the four phthalates (Supplemental Table S4 ). DEHP, DBP and DIBP intakes were associated with reduced number of antral follicles in women [ 39 ] and DEHP, DBP and BBP with delayed puberty in girls [ 73 ]. DEHP and DBP were associated with decreased number of fertilized eggs and total oocytes, and lower quality of oocytes [ 57 ]. DEHP and DIBP showed a negative association with trophoblast differentiation genes [ 58 ]. DEHP was also associated with decreased levels of inhibin [ 61 ], a critical hormone in reproductive functions [ 85 ], and showed inconsistent association with gestational length [ 62 , 63 ].

In adult men, DEHP, DBP and BBP all had a negative association with semen quality including concentration and sperm motility [ 47 , 48 , 79 ]. DEHP was associated with decreased total and free testosterone and estradiol, as well as increased levels of SHBG [ 80 ]. DEHP also had a positive association with testosterone/estradiol ratio [ 81 ]. In boys gestationally exposed to known levels of phthalates, DEHP and DIBP were negatively associated with free and total testosterone and estradiol [ 50 ]. DEHP, DBP and BBP were associated with increased SHBG. DBP was associated with decreased levels of dehydroepiandrosterone [ 50 ]. Finally, DEHP was also associated with reduced anogenital distance in boys [ 70 ].

This case study shows that low dose exposures to BBP, DBP, DIDP and DEHP are associated with health endpoints in organs and systems not usually assessed in regulatory toxicology studies. These endpoints differ markedly from the well-studied effects of phthalates on male reproductive development. Furthermore, there are significant physiological effects (i.e., early biological perturbations that may lead to overt effects) and disorders that may require clinical interventions later in life associated with estimated intake levels lower than the current RfD. We also observed that some individuals appear to be exposed to levels of DEHP higher than its RfD. This may be the case if there are yet to be identified exposure routes and sources, or if the metabolism or excretion of DEHP is altered. Overall, these data, although with limitations, show weaknesses in a chemical regulation framework that is in need of improvement.

Some of the limitations are, first, this study is similar to a mapping of evidence; it is not a systematic review that must follow stricter protocols and methods. Second, our approach aimed to capture as many publications as possible. However, although we used broad search terms, we may still have missed relevant publications. Third, we trust the integrity and quality of the journal peer-reviewed conducted for each of the studies we included. However, we understand the peer review process is not perfect. An example of this less-than-perfect process is the lack of clarity or data that prevented us to include an additional 26 human studies as shown in Table S1. Importantly, only six studies were excluded because of the lack of statistical significance, hence, the body of evidence is consistent with the associations. Fourth, in some cases, data interpretation had to be based on information that was available. Although we contacted authors from some of the studies that did not meet our criteria to obtain additional data, only a few responded to our request and were willing to share additional data. Fifth, the number of subjects in the studies varied from less than 100 to thousands of people; although the population size as such could be a limitation, strong and weak statistical significance was observed in all cases. As all but one study was cross-sectional, we are mindful about implying that they show causality. Lastly, some assumptions made in our calculations may have been outdated. For example, the EPA handbook on exposure is from 2011. Although it is our understanding that the agency and others continue to use this handbook in their analysis, we cannot rule out that parameters such as body weight by age range may have changed in the last decade and could have affected our estimates.

Overall, the case study we present here specifically aimed to use strong human data to perform a first examination of a hypothesis, namely that the current animal-based testing methods to estimate “safe” exposure levels of chemicals could be significantly underestimating actual human health risk if epidemiological data are not considered. Following the initial confirmatory findings presented here, this hypothesis will serve as a basis to guide further testing and more detailed assessments in a follow-up work.

The protection of public health from detrimental effects of environmental chemical exposures should ideally incorporate the expertise from two sides: the risk assessors and the healthcare community, including epidemiologists. On the one hand, risk assessment relies on evaluating exposure to a chemical and using animal models to identify which organ(s) would be affected, in order to find a dose that would cause no harm. On the other hand, the medical community is confronted with a wide range of health outcomes in the human population—from acute to chronic and from subtle to clinically defined—and tries to identify what caused them, whether environmental chemical exposure or otherwise, in order to support prevention. But there is a disconnect between these bookends of environmental health which hinders effective protection of the public from chemical exposures. In 2017, the US National Academy of Sciences [ 86 ] recommended that for evaluating evidence of low dose effects, regulators should surveil for signals indicating an adverse outcome in a human population or evidence that a particular low dose effect may not be detectable with traditional toxicity testing. The authors stated that one way to seek out information is by conducting regular surveys of the scientific literature. Our limited case study of five phthalates shows that many of the health effects observed to occur in humans at very low exposure levels are not traditionally evaluated in animal toxicology testing. Metabolic, neurodevelopmental and behavioral disorders, obesity, levels of hormones and transport proteins are just a few examples of endpoints not commonly included in toxicity testing guidelines despite their relevance to human health. It is also important to point out that traditional toxicology studies only infrequently evaluate a dose-effect relationship using chemical levels relevant to human exposures occurring at different life-stages. Rather, assumptions of safe levels are commonly made based on adult non-pregnant animal data. These omissions thus result in significant gaps in chemicals regulation that may put human health at risk [ 87 ].

The current chemical risk assessment approach to establish an RfD used by most regulatory agencies around the world combines a dose that did not cause an adverse effect in animal studies using high exposure doses and safety factors (also known as uncertainty factors) to account for incomplete data and variability between and within species. Although not routinely, regulatory ‘safe’ levels have been reviewed. For example, ECHA lowered the derived no effect level for DIBP from 420 to 8.3 μg/kg bw/d in 2016 [ 25 ]; similarly, the European Food Safety Authority (EFSA) lowered the tolerable daily intake of bisphenol A from 50 to 4 μg/kg bw/d in 2015 [ 88 ]. In both cases, new scientific information was available at the time the agencies were responding to requests for reassessment of those chemicals. However, we would argue that, in addition to specific requests made to regulatory agencies, a more systematic reevaluation of RfDs could be incorporated into the risk assessment and management processes. For example, a post-market RfD reassessment could be triggered by 1) human studies showing associations between exposure and endpoints previously not measured; 2) information on reported uses or biomonitoring indicating increased exposures due to chemical production volumes or reduced exposure due to abandoned uses; or 3) new hazard information. Lastly, this information surveillance should not be the exclusive responsibility of the regulatory agencies; rather, companies with approved chemical uses should submit new available information that could potentially raise questions about the safety of their product and agencies should establish a mechanism to enforce this requirement.

Both, scientific information and market behavior, are dynamic. Advances in science and technology allow scientists to develop new methods to measure chemicals in humans and gain new knowledge and understanding of chemicals’ interactions with physiological systems at different life stages. To account for these developments, epidemiological and clinical studies together with chemical biomonitoring data should be evaluated at regular intervals as recommended by the NAS [ 86 ] in order to check whether an RfD review is warranted to better protect public health. We are cognizant that this approach, although promising, is not without shortcomings. For instance, biomonitoring data alone cannot account for all sources of exposure. For chemicals like phthalates, with many sources ranging from the diet to personal care products and house dust, it may be challenging to design mitigating strategies to reduce the most significant sources of exposure. However, well designed surveys and a better understanding of materials’ composition may help identifying the major exposure sources for various populations as it was described by Lioy and colleagues [ 89 ].

As implied earlier, the RfD represents a concept of ‘safety’, a bright line between ‘no risk’ or ‘safe’ when the exposure estimate is below the established number and ‘risk’ or ‘unsafe’ when the value is greater than the RfD. In reality, it is far more complicated, namely, chemical hazard information and populations’ background exposures from multiple chemicals, health conditions and life-stages change with time. In its 2009 Science and Decisions report [ 90 ], the NAS recognized this complexity and recommended a progression away from the current concept of ‘safety’ and towards dose-response methods that quantify risk at doses used in animal experiments as well as lower doses representing human exposures. As much as two-thirds of the human population suffers from chronic diseases that cannot be explained by genetic causes alone [ 91 ] and it is becoming increasingly apparent that life-long chemical exposures can contribute to this burden [ 5 ]. Yet, for the great majority, chemicals are not evaluated for their contribution to common chronic ailments in the human population [ 92 , 93 ]. As a consequence, the current work on toxicology and epidemiology is inundated with disconnected data that misses the bigger picture: better protection of the entire human population’s health. Perhaps it is time to reconsider the status quo to ensure adequate population health protection. Issues to be interrogated may include, among others, strategies for proper assessment of the risk of developmental exposures; use of early biomarkers of health effects; integration of evidence from different data streams including predictive modeling, in vitro, animals and humans; development of new and redesign of old testing protocols; optimization of in vitro testing to minimize the use of laboratory animals; design of protocols to more efficiently monitor human exposures.

Conclusions

Phthalates have been used in many products for many decades. There are substantial animal and human data available which allowed us to use these substances in case studies such as this one. However, a similar question could be raised for many other chemicals with a growing body of human biomonitoring data and evidence of human health effects [ 94 ].

To set the course for a better, more efficient and health protective risk assessment of chemicals, a dialogue should be established between risk assessors, the medical community, and academic researchers. Until a profound modernization of the risk assessment and management of chemicals occurs, human studies should be taken into account to identify whether the health risk of chemicals already in the marketplace, such as phthalates, should be reassessed.

Abbreviations

First trimester

Second trimester

Third trimester

Acceptable daily intake

Benzyl butyl phthalate

Body mass index

Body weight

Chemical abstract service

Consumer products safety commission

Dibutyl phthalate

Dicyclohexyl phthalate

Bis(2-ethylhexyl) phthalate

Diisobutyl phthalate

Derived no-effect level

European chemical agency

European food safety authority

Environmental protection agency

Homeostatic model assessment of insulin resistance

Intelligence quotient

Non-communicable disease

National health and nutrition examination survey

Registration, evaluation, authorization and restriction of chemicals

• Reference dose

Steroid hormone binding globulin

Triiodothyronine

Tolerable daily intake

Thyroid stimulating hormone

United States

World Health Organization: Global status report on noncommunicable diseases. 2014.

Landrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, Basu N, et al. The lancet commission on pollution and health. Lancet. 2017;391:462–512.

Chen S, Kuhn M, Prettner K, Bloom DE. The macroeconomic burden of noncommunicable diseases in the United States: estimates and projections. PLoS One. 2018;13(11):e0206702.

Article   Google Scholar  

Balbus JM, Barouki R, Birnbaum LS, Etzel RA, Gluckman PD, Grandjean P, et al. Early-life prevention of non-communicable diseases. Lancet. 2013;381(9860):3–4.

Shaffer RM, Sellers SP, Baker MG, de Buen KR, Frostad J, Suter MK, et al. Improving and expanding estimates of the global burden of disease due to environmental health risk factors. Environ Health Perspect. 2019;127(10):105001.

Muncke J, Backhaus T, Geueke B, Maffini MV, Martin OV, Myers JP, et al. Scientific challenges in the risk assessment of Food contact materials. Environ Health Perspect. 2017;125(9):095001.

PlasticsEurope: Plastics - the Facts 2019. In.; 2019.

Kawagoshi Y, Tsukagoshi Y, Fukunaga I. Determination of estrogenic activity in landfill leachate by simplified yeast two-hybrid assay. JEM. 2002;4(6):1040–6.

CAS   Google Scholar  

Biedermann-Brem S, Biedermann M, Pfenninger S, Bauer M, Altkofer W, Rieger K, et al. Plasticizers in PVC toys and childcare products: what succeeds the phthalates? Market survey 2007. Chromatographia. 2008;68(3):227–34.

Article   CAS   Google Scholar  

Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, et al. Plastic waste inputs from land into the ocean. Science. 2015;347(6223):768–71.

Groh KJ, Backhaus T, Carney-Almroth B, Geueke B, Inostroza PA, Lennquist A, et al. Overview of known plastic packaging-associated chemicals and their hazards. Sci Total Environ. 2019;651(Pt 2):3253–68.

United Nations Economic Commission for Europe: Globally harmonized system of classification and labelling of chemicals (GHS). 2015. https://unece.org/about-ghs .

Geueke B, Inostroza PA, Maffini M, Backhaus T, Carney-Almroth B, Groh KJ, et al. Prioritization approaches for hazardous chemicals associated with plastic packaging. Food Packaging Forum Zurich. 2018:1–14.

Nerin C, Canellas E, Vera P. Plasticizer migration into foods. In: Reference Module in Food Science. Amsterdam: Elsevier; 2018.

Frederiksen H, Nielsen O, Koch HM, Skakkebaek NE, Juul A, Jørgensen N, et al. Changes in urinary excretion of phthalates, phthalate substitutes, bisphenols and other polychlorinated and phenolic substances in young Danish men; 2009–2017. Int J Hyg Environ Health. 2020;223(1):93–105.

Husøy T, Martínez MA, Sharma RP, Kumar V, Andreassen M, Sakhi AK, et al. Comparison of aggregated exposure to di(2-ethylhexyl) phthalate from diet and personal care products with urinary concentrations of metabolites using a PBPK model - results from the Norwegian biomonitoring study in EuroMix. Food Chem Toxicol. 2020;143:111510.

Geueke B, Muncke J. Substances of very high concern in food contact materials: migration and regulatory background. Packag Technol Sci. 2018;31(12):757–69.

Van Holderbeke M, Geerts L, Vanermen G, Servaes K, Sioen I, De Henauw S, et al. Determination of contamination pathways of phthalates in food products sold on the Belgian market. Environ Res. 2014;134:345–52.

Schecter A, Lorber M, Guo Y, Wu Q, Yun SH, Kannan K, et al. Phthalate concentrations and dietary exposure from food purchased in New York state. Environ Health Perspect. 2013;121(4):473–94.

Guart A, Bono-Blay F, Borrell A, Lacorte S. Effect of bottling and storage on the migration of plastic constituents in Spanish bottled waters. Food Chem. 2014;156:73–80.

Koch HM, Lorber M, Christensen KL, Pälmke C, Koslitz S, Brüning T. Identifying sources of phthalate exposure with human biomonitoring: results of a 48 h fasting study with urine collection and personal activity patterns. Int J Hyg Environ. 2013;216(6):672–81.

Hammel SC, Levasseur JL, Hoffman K, Phillips AL, Lorenzo AM, Calafat AM, et al. Children's exposure to phthalates and non-phthalate plasticizers in the home: the TESIE study. Environ Int. 2019;132:105061.

Consumer Protection Safety Commission. Prohibition of Children's Toys and Child Care Articles Containing Specified Phthalates. In: 16 CFR 1307. United States: Federal Register; 2017. p. 49938–82.

EFSA Panel on Food contact materials E, aids P, Silano V, Barat Baviera JM, Bolognesi C, Chesson A, Cocconcelli PS, et al. Update of the risk assessment of di-butylphthalate (DBP), butyl-benzyl-phthalate (BBP), bis (2-ethylhexyl) phthalate (DEHP), di-isononylphthalate (DINP) and di-isodecylphthalate (DIDP) for use in food contact materials. EFSA J. 2019;17(12):e05838.

Google Scholar  

European Chemical Agency Committee for Risk Assessment: Opinion on an Annex XV dossier proposing restrictions on FOUR PHTHALATES (DEHP, BBP, DBP, DIBP). ECHA/RAC/RES-O-0000001412-86-140/F. 2017.

European Commision: Registration, evaluation, authorisation and restriction of chemicals (REACH). EC 1907/2006; 2006.

Joint FAO/WHO Expert Committee on Food Additives: Principles and methods for the risk assessment of chemicals in food. Environmental Health Criteria 2009.

European Food Safety Authority. Glossary [ https://www.efsa.europa.eu/en/glossary-taxonomy-terms ].

US Environmental Protection Agency Integrated Risk Information System (IRIS). Glossary [ https://sor.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?details=&glossaryName=IRIS%20Glossary ].

Consumer Product Safety Improvement Act of 2008. 122 STAT 3016 2008.

ECHA Authorization List [ https://echa.europa.eu/authorisation-list ].

Zota AR, Calafat AM, Woodruff TJ. Temporal trends in phthalate exposures: findings from the National Health and nutrition examination survey, 2001-2010. Environ Health Perspect. 2014;122(3):235–41.

Schwedler G, Rucic E, Lange R, Conrad A, Koch HM, Pälmke C, et al. Phthalate metabolites in urine of children and adolescents in Germany. Human biomonitoring results of the German Environmental Survey GerES V, 2014–2017. Int J Hyg Environ Health. 2020;225:113444.

Radke EG, Galizia A, Thayer KA, Cooper GS. Phthalate exposure and metabolic effects: a systematic review of the human epidemiological evidence. Environ Int. 2019;132:104768.

Radke EG, Braun JM, Meeker JD, Cooper GS. Phthalate exposure and male reproductive outcomes: a systematic review of the human epidemiological evidence. Environ Int. 2018;121(Pt 1):764–93.

Factor-Litvak P, Insel B, Calafat AM, Liu X, Perera F, Rauh VA, et al. Persistent associations between maternal prenatal exposure to phthalates on child IQ at age 7 years. PLoS One. 2014;9(12):e114003.

Radke EG, Braun JM, Nachman RM, Cooper GS. Phthalate exposure and neurodevelopment: a systematic review and meta-analysis of human epidemiological evidence. Environ Int. 2020;137:105408.

James-Todd TM, Chiu YH, Messerlian C, Mínguez-Alarcón L, Ford JB, Keller M, et al. Trimester-specific phthalate concentrations and glucose levels among women from a fertility clinic. Environ Health. 2018;17(1):55.

Messerlian C, Souter I, Gaskins AJ, Williams PL, Ford JB, Chiu YH, et al. Urinary phthalate metabolites and ovarian reserve among women seeking infertility care. Hum Reprod. 2016;31(1):75–83.

US Environmental Protection Agency: Exposure factors handbook 2011 edition (final report). Washington, DC 2011.

Barr DB, Wilder LC, Caudill SP, Gonzalez AJ, Needham LL, Pirkle JL. Urinary creatinine concentrations in the U.S. population: implications for urinary biologic monitoring measurements. Environ Health Perspect. 2005;113(2):192–200.

Koch HM, Bolt HM, Preuss R, Angerer J. New metabolites of di(2-ethylhexyl)phthalate (DEHP) in human urine and serum after single oral doses of deuterium-labelled DEHP. Arch Toxicol. 2005;79(7):367–76.

Anderson WA, Castle L, Hird S, Jeffery J, Scotter MJ. A twenty-volunteer study using deuterium labelling to determine the kinetics and fractional excretion of primary and secondary urinary metabolites of di-2-ethylhexylphthalate and di-iso-nonylphthalate. Food Chem Toxicol. 2011;49(9):2022–9.

US Food and Drug Administration. Cosmetic Ingredients: Phthalates [ https://www.fda.gov/cosmetics/cosmetic-ingredients/phthalates ].

US Environmental Protection Agency. Assessing and Managing Chemicals under TSCA: Risk Management for Phthalates [ https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/risk-management-phthalates ].

Neal-Kluever A, Aungst J, Gu Y, Hatwell K, Muldoon-Jacobs K, Liem A, et al. Infant toxicology: state of the science and considerations in evaluation of safety. Food Chem Toxicol. 2014;70:68–83.

Wang YX, Zhou B, Chen YJ, Liu C, Huang LL, Liao JQ, et al. Thyroid function, phthalate exposure and semen quality: exploring associations and mediation effects in reproductive-aged men. Environ Int. 2018;116:278–85.

Hauser R, Meeker JD, Duty S, Silva MJ, Calafat AM. Altered semen quality in relation to urinary concentrations of phthalate monoester and oxidative metabolites. Epidemiology. 2006;17(6):682–91.

Huang PC, Kuo PL, Guo YL, Liao PC, Lee CC. Associations between urinary phthalate monoesters and thyroid hormones in pregnant women. Hum Reprod. 2007;22(10):2715–22.

Ferguson KK, Peterson KE, Lee JM, Mercado-García A, Blank-Goldenberg C, Téllez-Rojo MM, et al. Prenatal and peripubertal phthalates and bisphenol a in relation to sex hormones and puberty in boys. Reprod Toxicol. 2014;47:70–6.

Hatch EE, Nelson JW, Qureshi MM, Weinberg J, Moore LL, Singer M, et al. Association of urinary phthalate metabolite concentrations with body mass index and waist circumference: a cross-sectional study of NHANES data, 1999-2002. Environ Health. 2008;7:27.

Swan SH, Liu F, Hines M, Kruse RL, Wang C, Redmon JB, et al. Prenatal phthalate exposure and reduced masculine play in boys. Int J Androl. 2010;33(2):259–69.

Soomro MH, Baiz N, Philippat C, Vernet C, Siroux V, Nichole Maesano C, et al. Prenatal exposure to phthalates and the development of eczema phenotypes in male children: results from the EDEN mother-child cohort study. Environ Health Perspect. 2018;126(2):027002.

US Consumer Product Safety Commission: Chronic Hazard Advisory Panel on Phthalates and Phthalates Alternatives. cpsc.gov/chap ; 2014.

Huang PC, Tsai CH, Liang WY, Li SS, Huang HB, Kuo PL. Early phthalates exposure in pregnant women is associated with alteration of thyroid hormones. PLoS One. 2016;11(7):e0159398.

Johns LE, Ferguson KK, Soldin OP, Cantonwine DE, Rivera-González LO, Del Toro LV, et al. Urinary phthalate metabolites in relation to maternal serum thyroid and sex hormone levels during pregnancy: a longitudinal analysis. Reprod Biol Endocrinol. 2015;13:4.

Machtinger R, Gaskins AJ, Racowsky C, Mansur A, Adir M, Baccarelli AA, et al. Urinary concentrations of biomarkers of phthalates and phthalate alternatives and IVF outcomes. Environ Int. 2018;111:23–31.

Adibi JJ, Whyatt RM, Hauser R, Bhat HK, Davis BJ, Calafat AM, et al. Transcriptional biomarkers of steroidogenesis and trophoblast differentiation in the placenta in relation to prenatal phthalate exposure. Environ Health Perspect. 2010;118(2):291–6.

Yaghjyan L, Sites S, Ruan Y, Chang SH. Associations of urinary phthalates with body mass index, waist circumference and serum lipids among females: National Health and nutrition examination survey 1999-2004. Int J Obes. 2015;39(6):994–1000.

Kim JH, Park HY, Bae S, Lim YH, Hong YC. Diethylhexyl phthalates is associated with insulin resistance via oxidative stress in the elderly: a panel study. PLoS One. 2013;8(8):e71392.

Du YY, Guo N, Wang YX, Hua X, Deng TR, Teng XM, et al. Urinary phthalate metabolites in relation to serum anti-Müllerian hormone and inhibin B levels among women from a fertility center: a retrospective analysis. Reprod Health. 2018;15(1):33.

Boss J, Zhai J, Aung MT, Ferguson KK, Johns LE, McElrath TF, et al. Associations between mixtures of urinary phthalate metabolites with gestational age at delivery: a time to event analysis using summative phthalate risk scores. Environ Health. 2018;17(1):56.

Adibi JJ, Hauser R, Williams PL, Whyatt RM, Calafat AM, Nelson H, et al. Maternal urinary metabolites of Di-(2-Ethylhexyl) phthalate in relation to the timing of labor in a US multicenter pregnancy cohort study. Am J Epidemiol. 2009;169(8):1015–24.

Huang HB, Kuo PH, Su PH, Sun CW, Chen WJ, Wang SL. Prenatal and childhood exposure to phthalate diesters and neurobehavioral development in a 15-year follow-up birth cohort study. Environ Res. 2019;172:569–77.

Whyatt RM, Liu X, Rauh VA, Calafat AM, Just AC, Hoepner L, et al. Maternal prenatal urinary phthalate metabolite concentrations and child mental, psychomotor, and behavioral development at 3 years of age. Environ Health Perspect. 2012;120(2):290–5.

Kim Y, Ha EH, Kim EJ, Park H, Ha M, Kim JH, et al. Prenatal exposure to phthalates and infant development at 6 months: prospective mothers and Children's environmental health (MOCEH) study. Environ Health Perspect. 2011;119(10):1495–500.

Buckley JP, Engel SM, Braun JM, Whyatt RM, Daniels JL, Mendez MA, et al. Prenatal phthalate exposures and body mass index among 4- to 7-year-old children: a pooled analysis. Epidemiology. 2016;27(3):449–58.

Philippat C, Nakiwala D, Calafat AM, Botton J, De Agostini M, Heude B, et al. Prenatal exposure to nonpersistent endocrine disruptors and behavior in boys at 3 and 5 years. Environ Health Perspect. 2017;125(9):097014.

Engel SM, Villanger GD, Nethery RC, Thomsen C, Sakhi AK, Drover SSM, et al. Prenatal phthalates, maternal thyroid function, and risk of attention-deficit hyperactivity disorder in the Norwegian mother and child cohort. Environ Health Perspect. 2018;126(5):057004.

Swan SH, Sathyanarayana S, Barrett ES, Janssen S, Liu F, Nguyen RH, et al. First trimester phthalate exposure and anogenital distance in newborns. Hum Reprod. 2015;30(4):963–72.

Boas M, Frederiksen H, Feldt-Rasmussen U, Skakkebæk NE, Hegedüs L, Hilsted L, et al. Childhood exposure to phthalates: associations with thyroid function, insulin-like growth factor I, and growth. Environ Health Perspect. 2010;118(10):1458–64.

Smerieri A, Testa C, Lazzeroni P, Nuti F, Grossi E, Cesari S, et al. Di-(2-ethylhexyl) phthalate metabolites in urine show age-related changes and associations with adiposity and parameters of insulin sensitivity in childhood. PLoS One. 2015;10(2):e0117831.

Kasper-Sonnenberg M, Wittsiepe J, Wald K, Koch HM, Wilhelm M. Pre-pubertal exposure with phthalates and bisphenol a and pubertal development. PLoS One. 2017;12(11):e0187922.

Zhang Y, Meng X, Chen L, Li D, Zhao L, Zhao Y, et al. Age and sex-specific relationships between phthalate exposures and obesity in Chinese children at puberty. PLoS One. 2014;9(8):e104852.

Trasande L, Sathyanarayana S, Trachtman H. Dietary phthalates and low-grade albuminuria in US children and adolescents. Clin J Am Soc Nephrol. 2014;9(1):100–9.

Trasande L, Sathyanarayana S, Spanier AJ, Trachtman H, Attina TM, Urbina EM. Urinary phthalates are associated with higher blood pressure in childhood. J Pediatr. 2013;163(3):747–753.e741.

Meeker JD, Ferguson KK. Relationship between urinary phthalate and bisphenol a concentrations and serum thyroid measures in U.S. adults and adolescents from the National Health and nutrition examination survey (NHANES) 2007-2008. Environ Health Perspect. 2011;119(10):1396–402.

Trasande L, Spanier AJ, Sathyanarayana S, Attina TM, Blustein J. Urinary phthalates and increased insulin resistance in adolescents. Pediatrics. 2013;132(3):e646–55.

Duty SM, Silva MJ, Barr DB, Brock JW, Ryan L, Chen Z, et al. Phthalate exposure and human semen parameters. Epidemiology. 2003;14(3):269–77.

Mendiola J, Meeker JD, Jørgensen N, Andersson AM, Liu F, Calafat AM, et al. Urinary concentrations of di(2-ethylhexyl) phthalate metabolites and serum reproductive hormones: pooled analysis of fertile and infertile men. J Androl. 2012;33(3):488–98.

Meeker JD, Calafat AM, Hauser R. Urinary metabolites of di(2-ethylhexyl) phthalate are associated with decreased steroid hormone levels in adult men. J Androl. 2009;30(3):287–97.

Meeker JD, Calafat AM, Hauser R. Di(2-ethylhexyl) phthalate metabolites may alter thyroid hormone levels in men. Environ Health Perspect. 2007;115(7):1029–34.

Center for Disease Control and Prevention: Fourth National Report on Human Exposure to Environmental Chemicals. 2019.

Hammond GL. Diverse roles for sex hormone-binding globulin in reproduction. Biol Reprod. 2011;85(3):431–41.

Luisi S, Florio P, Reis FM, Petraglia F. Inhibins in female and male reproductive physiology: role in gametogenesis, conception, implantation and early pregnancy. Hum Reprod Update. 2005;11(2):123–35.

National Academies of Sciences E, Medicine: Application of systematic review methods in an overall strategy for evaluating low-dose toxicity from endocrine active chemicals. 2017.

Maffini M, Vandenberg L. Closing the gap: improving additives safety evaluation to reflect human health concerns. Environ Risk Assess Remediat. 2017;1(3):26–33.

EFSA Panel on Food Contact Materials E, Flavourings, Aids P. Scientific opinion on the risks to public health related to the presence of bisphenol a (BPA) in foodstuffs. EFSA J. 2015;13(1):3978.

Lioy PJ, Hauser R, Gennings C, Koch HM, Mirkes PE, Schwetz BA, et al. Assessment of phthalates/phthalate alternatives in children's toys and childcare articles: review of the report including conclusions and recommendation of the chronic Hazard advisory panel of the consumer product safety commission. J Expo Sci Environ Epidemiol. 2015;25(4):343–53.

National Academy of Sciences: Science and Decisions - Advancing Risk Assessment. In. National Academies of Science, Engineering, and Medicine; 2009.

Rappaport SM, Smith MT. Epidemiology. Environment and disease risks. Science. 2010;330(6003):460–1.

US Food and Drug Administration: Guidance for Industry and Other Stakeholders: Redbook 2000. Toxicological Principles for the Safety Assessment of Food Ingredients 2007.

European Chemical Agency: Framework, Read-Across Assessment. 2017.

Madia F, Worth A, Whelan M, Corvi R. Carcinogenicity assessment: addressing the challenges of cancer and chemicals in the environment. Environ Int. 2019;128:417–29.

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Acknowledgments

The authors are grateful to Dr. Leonardo Trasande for his expert advice and guidance and to those investigators that shared detailed data not included in the public versions of their publications.

This work was funded in part by a grant from MAVA Foundation and by the Food Packaging Forum (FPF). BG and JM are employees of FPF. FPF receives unconditional donations for unrestricted funding, as well as project-related grants, and all funding sources are listed.

https://www.foodpackagingforum.org/about-us/funding . Neither the board of FPF, nor MAVA Foundation interfered with the authors’ freedom to design, conduct, interpret and publish this information.

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MVM: Conceptualization, Methodology, Investigation and Original draft. BG: Validation, Visualization, Review and Editing. KG: Validation, Review and Editing. BCA: Review and Editing. JM: Conceptualization, Review and Editing, Project administration, Funding acquisition. The author(s) read and approved the final manuscript.

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Maffini, M.V., Geueke, B., Groh, K. et al. Role of epidemiology in risk assessment: a case study of five ortho-phthalates. Environ Health 20 , 114 (2021). https://doi.org/10.1186/s12940-021-00799-8

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Environmental risk assessment of human pharmaceuticals in the European Union: A case study with the β-blocker atenolol

Affiliation.

• 1 German Federal Environment Agency (UBA), Wörlitzer Platz 1, 06813 Dessau, Germany. [email protected]
• PMID: 19886730
• DOI: 10.1897/IEAM_2009-050.1

β-Adrenergic receptor blockers (β-blockers) are applied to treat high blood pressure, ischemic heart disease, and heart rhythm disturbances. Due to their widespread use and limited human metabolism, β-blockers are widely detected in sewage effluents and surface waters. β-Adrenergic receptors have been characterized in fish and other aquatic animals, so it can be expected that physiological processes regulated by these receptors in wild animals may be affected by the presence of β-blockers. Because ecotoxicological data on β-blockers are scarce, it was decided to choose the β-blocker atenolol as a case study pharmaceutical within the project ERAPharm. A starting point for the assessment of potential environmental risks was the European guideline on the environmental risk assessment of medicinal products for human use. In Phase I of the risk assessment, the initial predicted environmental concentration (PEC) of atenolol in surface water (500 ng L−1) exceeded the action limit of 10 ng L−1. Thus, a Phase II risk assessment was conducted showing acceptable risks for surface water, for groundwater, and for aquatic microorganisms. Furthermore, atenolol showed a low potential for bioaccumulation as indicated by its low lipophilicity (log KOW = 0.16), a low potential for exposure of the terrestrial compartment via sludge (log KOC = 2.17), and a low affinity for sorption to the sediment. Thus, the risk assessment according to Phase II-Tier A did not reveal any unacceptable risk for atenolol. Beyond the requirements of the guideline, additional data on effects and fate were generated within ERAPharm. A 2-generation reproduction test with the waterflea Daphnia magna resulted in the most sensitive no-observed-effect concentration (NOEC) of 1.8 mg L−1. However, even with this NOEC, a risk quotient of 0.003 was calculated, which is still well below the risk threshold limit of 1. Additional studies confirm the outcome of the environmental risk assessment according to EMEA/CHMP (2006). However, atenolol should not be considered as representative for other β-blockers, such as metoprolol, oxprenolol, and propranolol, some of which show significantly different physicochemical characteristics and varying toxicological profiles in mammalian studies.

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Implementation of Chemical Health, Safety, and Environmental Risk Assessment in Laboratories: A Case-Series Study

• Farin Fatemi , A. Dehdashti , M. Jannati
• Published in Frontiers in Public Health 14 June 2022
• Environmental Science, Chemistry

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The emission characteristics of vocs and environmental health risk assessment in the plywood manufacturing industry: a case study in shandong province.

Graphical Abstract

1. Introduction

2. materials and methods, 2.1. sample collection, 2.2. data analysis, 2.2.1. calculation of emission factors, 2.2.2. voc source profile calculation method, 2.2.3. estimation of reactivity of ofp-based voc emission inventory, 2.2.4. vocs health risk assessment, 3. results and discussion, 3.1. voc emission factors, 3.2. fugitive emission characteristics of vocs, 3.2.1. general characteristics of vocs, 3.2.2. characteristic components, 3.3. vocs souce profiles of the plywood manufacturing industry, 3.4. ozone formation potential (ofp) of vocs, 3.5. health risk assessment of vocs, 3.5.1. non-cancer toxic risk of vocs, 3.5.2. cancer toxic risk of vocs, 4. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• FAO. Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/faostat/zh/#data/FO (accessed on 20 June 2024).
• MEP. China Ecological and Environmental Statistics Annual Report. Available online: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/202305/P020230529570623593284.pdf (accessed on 20 June 2024).
• Chen, Z.-W.; Ting, Y.-C.; Huang, C.-H.; Ciou, Z.-J. Sources-oriented contributions to ozone and secondary organic aerosol formation potential based on initial VOCs in an urban area of Eastern Asia. Sci. Total Environ. 2023 , 892 , 164392. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• He, L.; Duan, Y.; Zhang, Y.; Yu, Q.; Huo, J.; Chen, J.; Cui, H.; Li, Y.; Ma, W. Effects of VOC emissions from chemical industrial parks on regional O 3 -PM 2.5 compound pollution in the Yangtze River Delta. Sci. Total Environ. 2024 , 906 , 167503. [ Google Scholar ] [ CrossRef ]
• Lyu, X.; Guo, H.; Wang, Y.; Zhang, F.; Nie, K.; Dang, J.; Liang, Z.; Dong, S.; Zeren, Y.; Zhou, B.; et al. Hazardous volatile organic compounds in ambient air of China. Chemosphere 2020 , 246 , 125731. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, L.; Zhu, X.; Yu, Y.; Niu, W.; Zheng, Y.; Zhang, Y.; Lin, J. Priority Control of Hazardous Air Pollutants in Beijing-Tianjin-Hebei and Its Surrounding Areas. Res. Environ. Sci. 2021 , 34 , 194–201. [ Google Scholar ]
• You, G.; Lu, S.; Jin, Z.; Ren, J.; Sun, R.; Li, J.; Hou, W.; Xie, S. Emission Factors and Source Profiles of Volatile Organic Compounds in the Petroleum Refining Industry through On-Site Measurement from Multiple Refineries. Environ. Sci. Technol. Lett. 2024 , 11 , 230–236. [ Google Scholar ] [ CrossRef ]
• You, G.; Liu, H.; Sun, R.; Jin, Z.; Li, J.; Zhou, X.; Lu, S.; Xie, S. Characterizing VOCs emissions of five packaging and printing enterprises in China and the emission reduction potential of this industry. J. Clean. Prod. 2023 , 420 , 138445. [ Google Scholar ] [ CrossRef ]
• Gu, X.; Chen, K.; Cai, M.; Yin, Z.; Liu, X.; Li, X. Study on the Fingerprint and Atmospheric Activity of Volatile Organic Compounds from Typical Industrial Emissions. Int. J. Environ. Res. Public Health 2023 , 20 , 3517. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liang, C.; Feng, B.; Wang, S.; Zhao, B.; Xie, J.; Huang, G.; Zhu, L.; Hao, J. Differentiated emissions and secondary organic aerosol formation potential of organic vapor from industrial coatings in China. J. Hazard. Mater. 2024 , 466 , 133668. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Xiao, H.; Zhang, J.; Hou, Y.; Wang, Y.; Qiu, Y.; Chen, P.; Ye, D. Process-specified emission factors and characteristics of VOCs from the auto-repair painting industry. J. Hazard. Mater. 2024 , 467 , 133666. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• CNFPIR. China National Forest Products Industry Association. Available online: http://www.cnfpia.org/ (accessed on 20 June 2024).
• Mellouki, A.; Wallington, T.J.; Chen, J. Atmospheric Chemistry of Oxygenated Volatile Organic Compounds: Impacts on Air Quality and Climate. Chem. Rev. 2015 , 115 , 3984–4014. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Venecek, M.A.; Carter, W.P.L.; Kleeman, M.J. Updating the SAPRC Maximum Incremental Reactivity (MIR) scale for the United States from 1988 to 2010. J. Air Waste Manag. Assoc. 2018 , 68 , 1301–1316. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hu, K.; Liu, Z.; Wang, M.; Zhang, B.; Lin, H.; Lu, X.; Chen, W. VOCs Fugitive Emission Characteristics and Health Risk Assessment from Typical Plywood Industry in the Yangtze River Delta Region, China. Atmosphere 2021 , 12 , 1530. [ Google Scholar ] [ CrossRef ]
• MEE. Technical Guidelines for the Source Emission Inventories of Atmospheric Volatile Organic Compounds (Trial). Available online: https://www.mee.gov.cn/gkml/hbb/bgg/201408/W020140828351293705457.pdf (accessed on 20 August 2024).
• Lu, D.; Lu, S.; Shao, M.; Wang, L.; Ren, J. Emission characteristics of volatile organic compounds (VOCs)from typical plywood industry. China Environ. Sci. 2020 , 40 , 1924–1931. [ Google Scholar ]
• Zhao, R.; Huang, L.; Zhang, J.; Ouyang, F. Emissions characteristics of volatile organic compounds (VOCs) from typical industries of solvent use in Chengdu City. Acta Sci. Circumstantiae 2018 , 38 , 1147–1154. [ Google Scholar ]
• Zhao, F.; Peng, Y.; Huang, L.; Li, Z.; Tu, W.; Wu, B. Fugitive emissions of volatile organic compounds from the pharmaceutical industry in China based on leak detection and repair monitoring, atmospheric prediction, and health risk assessment. J. Environ. Sci. Health Part A Toxic/Hazard. Subst. Environ. Eng. 2023 , 58 , 647–660. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, H.; Hao, R.; Xie, X.; Li, G.; Wang, X.; Wu, W.; Zhao, H.; Zhang, Z.; Fang, L.; Hao, Z. Emission characteristics, risk assessment and scale effective control of VOCs from automobile repair industry in Beijing. Sci. Total Environ. 2023 , 860 , 160115. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cheng, L.; Wei, W.; Guo, A.; Zhang, C.; Sha, K.; Wang, R.; Wang, K.; Cheng, S. Health risk assessment of hazardous VOCs and its associations with exposure duration and protection measures for coking industry workers. J. Clean. Prod. 2022 , 379 , 134919. [ Google Scholar ] [ CrossRef ]
• US EPA. Integrated Risk Information System (IRIS). Available online: https://iris.epa.gov/AtoZ/?list_type=alpha (accessed on 20 July 2024).
• US EPA. Provisional Peer-Reviewed Toxicity Values (PPRTVs). Available online: https://www.epa.gov/pprtv (accessed on 20 July 2024).
• International Agency for Research on Cancer (IARC). Available online: https://www.iarc.who.int/ (accessed on 20 July 2024).
• GBZ/T 298-2017 ; Guidelines for Occupational Health Risk Assessment of Chemicals in the Workplace. Chinese Standard: Beijing, China, 2017. Available online: http://www.nhc.gov.cn/ewebeditor/uploadfile/2017/10/20171026154012835.pdf (accessed on 20 July 2024).

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Du, W.; Xie, H.; Li, J.; Guan, X.; Li, M.; Wang, H.; Wang, X.; Zhang, X.; Zhang, Q. The Emission Characteristics of VOCs and Environmental Health Risk Assessment in the Plywood Manufacturing Industry: A Case Study in Shandong Province. Sustainability 2024 , 16 , 7350. https://doi.org/10.3390/su16177350

Du W, Xie H, Li J, Guan X, Li M, Wang H, Wang X, Zhang X, Zhang Q. The Emission Characteristics of VOCs and Environmental Health Risk Assessment in the Plywood Manufacturing Industry: A Case Study in Shandong Province. Sustainability . 2024; 16(17):7350. https://doi.org/10.3390/su16177350

Du, Weiyan, Huan Xie, Jiao Li, Xu Guan, Miaomiao Li, Haolin Wang, Xinfeng Wang, Xin Zhang, and Qingzhu Zhang. 2024. "The Emission Characteristics of VOCs and Environmental Health Risk Assessment in the Plywood Manufacturing Industry: A Case Study in Shandong Province" Sustainability 16, no. 17: 7350. https://doi.org/10.3390/su16177350

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Implementation of Chemical Health, Safety, and Environmental Risk Assessment in Laboratories: A Case-Series Study

Farin fatemi.

1 Department of Occupational Health, Research Center of Health Sciences and Technologies, Semnan University of Medical Sciences, Semnan, Iran

Alireza Dehdashti

2 Social Determinants of Health Research Center, Semnan University of Medical Sciences, Semnan, Iran

Mohammadreza Jannati

3 Student Research Committee, Semnan University of Medical Sciences, Semnan, Iran

4 Department of Occupational Health and Safety, Memorial University of Newfoundland, St. John's, NL, Canada

Associated Data

The raw data supporting the conclusions of this article will be made available by the corresponding author, without undue reservation.

Introduction

Characterizing risks associated with laboratory activities in universities may improve health, safety, and environmental management and reduce work-related diseases and accidents. This study aimed to develop and implement a chemical risk assessment method to determine and prioritize more hazardous chemicals in the academic laboratories.

A case-series study was conducted at five academic laboratories and research facilities of an Iranian medical sciences university in 2021. A risk assessment was developed and implemented in three phases to identify, evaluate, and classify potential risks and hazards. The approach provided an innovative tool for evaluating and prioritizing risks in chemical laboratories. Hazards were classified on a five-level scale. The technique reviewed both quantitative and qualitative data and pieces of evidence using Laboratory Safety Guidance (OSHA), Occupational Hazard Datasheet (ILO), the standards of the American Conference of Governmental Industrial Hygienists (ACGIH), International Agency for Research on Cancer (IARC), and National Fire Protection Agency (NFPA) codes.

Overall, the frequency of risks rated from “moderate” to “very high” levels was determined for the health hazards (9.3%), environmental hazards (35.2%), and safety hazards (20.4%). Hydrochloric acid had a high consumption rate in laboratory operations and received the highest risk levels in terms of potential hazards to employees' health and the environment. Nitric acid, Sulfuric acid, Formaldehyde, and Sodium hydroxide were assessed as potential health hazards. Moreover, Ethanol and Sulfuric acid were recognized as safety hazards. We observed adequate security provisions and procedures in academic laboratory operations. However, the lack of awareness concerning health, safety, environmental chemical hazards, and inappropriate sewage disposal systems contributed to the increasing levels of laboratory risk.

Conclusions

Chemicals used in laboratory activities generate workplace and environmental hazards that must be assessed, managed, and risk mitigated. Developing a method of rating health, safety, and environmental risks related to laboratory chemicals may assist in defining and understanding potential hazards. Our assessment suggested the need for improving the risk perception of individuals involved in handling chemicals to prevent exposure from workplace duties and environmental pollution hazards.

Laboratories and research facilities are considered a fundamental part of universities playing a crucial role in preparing students and researchers to obtain skills that are valuable in their future careers ( 1 ). The presence of numerous chemicals in laboratories has faced safety and health managers with challenges in estimating their risks and hazards. The chemicals and equipment that are used by laboratory personnel and students present a number of serious, sometimes life-threatening hazards and accidents. Laboratory managers are responsible to protect their personnel and students from exposure to chemical, biological, and physical hazards ( 2 ). Therefore, the presented risk assessment method for the academic laboratories and applying prevention and mitigation measures in this study enable the laboratory managers to do their responsibility to their personnel and students.

A survey by OSHA has reported that the potential hazards associated with conducting research at laboratories in academic institutions were 11 times more dangerous as compared to commercial laboratories in a range of industrial sectors with labs ( 3 ). Literature review on the safety and health of laboratories in higher education institutions has shown many laboratory incidents leading to fatalities and injuries caused by fires, explosions, and equipment resulting in debilitating injuries and death ( 4 ). Previous studies on health-related hazards have reported both acute and chronic poisonings following exposure to various chemicals in laboratory environments ( 5 ). Moreover, laboratory wastewater consists of hazardous chemicals that have been considered a substantial environmental threat ( 6 ). In the United States, about 18% of occupational accidents in higher education institutions were related to laboratory environments and in approximately one-third of accidents, students were the main victims ( 7 – 9 ). A review of reported cases in the literature evidence suggests that the trend of accidents was on the rise in academic laboratories over the past several years ( 10 , 11 ). Lack of awareness of various safety and health hazards has triggered accidents, mainly related to the unsafe work practices of chemicals and equipment in laboratories ( 12 ).

Integrated health, safety, and environmental risk assessment would be beneficial in understanding risks, evaluating hazards, and planning a strategy to prevent accidents in laboratories ( 13 , 14 ). International occupational safety and health organizations have developed standards and instructions to prevent and control hazards in laboratory environments. Training of students and laboratory workers provided a culture of safety, health, and environmental consciousness in dealing with laboratory risks and hazards ( 15 ). Although risk assessment has shown to be an efficient approach to identify and introduce appropriate measures to manage risks and hazards, workplace risk levels may differ based on tasks and unsafe acts even in the same work environment. In essence, the laboratory risk assessment should be implemented for individual specific laboratory settings and each work task and role to effectively apply controls ( 16 ). Obtaining objective and comprehensive data concerning risks and hazards has presented challenges for health, safety, and risk management professionals in chemical laboratories. Planning a risk assessment requires the definition of an assessing project with an educated team. Hazard prediction and recognition are the beginning or first step to measure the strength of the impact of a threat ( 2 ).

Many research activities are performed in chemical laboratories at universities, which are seldom assessed by occupational safety and health engineers ( 11 ). This study performed an integrated health, safety, and environmental risk assessment to determine the level of risks for potential workplace exposure in terms of different jobs and work duties in academic lab settings. The process includes prediction, recognition, classification, and evaluation of risks and hazards in chemical laboratories. The plan for adequate measures to prevent and mitigate risks and fitness of work to laboratory personnel and the student will be discussed.

Design and Setting of the Study

A cross-sectional design and action research were applied to develop and conduct a comprehensive risk assessment to determine a range of health, safety, and environmental risks associated with the activities in academic laboratories. This study was implemented at five medical and health sciences laboratories affiliated to Semnan University in 2021.

Suggested Steps of Risk Assessment

Figure 1 demonstrates the methodology steps proposed for assessing risks in chemical laboratories in university environments. These include developing an integrated risk approach, collecting information to categorize risk factors, calculating risk levels, and proposing health, safety, and environmental measures.

Flowchart of the suggested methodology for risk assessment.

Development of an Integrated Risk Assessment Approach

Our methodology is based on the use of a structured checklist to integrate the process of predicting and recognizing hazards, evaluating the risks posed by hazards, and managing the risks of hazards in the context of the university laboratory. This technique reviewed both quantitative and qualitative data regarding chemicals, environment, and activities associated with the specific processes, and judgments were confined to a particular laboratory process in isolation.

Recognition of potential risks and hazards in laboratory environments and activities was based on checklists, walk-through observation, and interviews with working individuals in laboratories. We developed a combined behavior-based and process-based checklists to conduct a broader risk assessment for identifying the risk level of work practices and mitigating the associated risks. The study tool was adopted from Laboratory Safety Guidance (OSHA), Occupational Hazard Datasheet (ILO), and the Princeton University Laboratory Safety Manual. The tool consisted of 131 items, which were used to assess working areas, emergency planning, required information and documentation, personal protective equipment, electrical hazards, chemical storage and use, flammable liquids, compressed gases, disposal of chemicals used in the lab, ventilation requirements, security, and training.

Collecting Information to Categorize Risk Factors

We identified and grouped chemical exposure and hazards according to their properties, work procedures, and occupational potential exposure scenarios by using frequency and work behavior in the laboratories studied.

Calculating Risk Levels

The laboratory hazard risk rating of a chemical was estimated by multiplying the severity of consequence value by the likelihood of incidence value. For this step, we assembled literature on hazard properties for each chemical from reliable resources to obtain a review of a clear understanding of the safety and health controls. The pieces of literature were reviewed for exposure limits and carcinogenicity of chemical substances as identified by the standards of American Conference of Governmental Industrial Hygienists (ACGIH), Immediately Dangerous to Life or Health Concentrations (IDLH) of toxic substances, and National Fire Protection Agency (NFPA) codes ( 17 , 18 ).

We used an assessment matrix to conduct a comparative analysis concerning “the severity of consequence” and “the probability of incidence” to determine the risk rating for individual health, safety, and environmental hazards. Our estimates of hazard risk ratings were used to categorize risk into varying levels of risk by applying standard linear scaling. Table 1 demonstrates the matrix of risk levels and expectations of responses required to improve safety and health in the laboratory (ISO 31000) ( 19 ).

Establishing a laboratory hazard and process matrix-based risk system with standard linear scaling (values 1–5) to determine the risk score.

Proposing Health, Safety, and Environmental Measures

The prevention and mitigation of health, safety, and environmental risk measures were proposed based on calculated risk scores.

In this study, we used a checklist to recognize potential risks and hazards in the laboratory settings. Health, safety, and environmental hazards associated with common chemical laboratory activities and workflow and the percentage of compliance and non-compliance with laboratory guidelines are shown in Table 2 .

Results of hazard analysis checklist based on work processes and behaviors evaluated in university chemical laboratories and verified frequency of compliance and non-compliance with health, safety, and environmental guidelines.

Our survey of laboratory activities showed that work with compressed gases and flammable liquids was in acceptable compliance with security considerations and safe work procedures. However, the above half of non-compliance was related to the preparation in emergency response situations, not using personal protective equipment, poor inappropriate chemical disposal, treatment of waste products, and awareness and training. The lack of written emergency action plans, chemical hygiene lab procedures, and Safety Data Sheet (SDS) were identified to contribute to operational risks in chemical laboratory activities. The unsafe acts by the lab staff related to waste effluent disposal management mainly included risk factors of improper disposal containment and methods for experiment waste. We observed a lack of compliance in emergency response plans that are mainly associated with inadequate knowledge of staff and students about how to identify the location of fire extinguishers, how to request emergency assistance, and how to communicate potential leak, fire, and explosion scenarios. The unsafe conditions, such as aging electrical cords and plugs and contact with incorrectly grounded devices, were identified to increase operational risks of instruments in laboratories. Additionally, obstructed fire alarm pull stations or inappropriate layout of fire extinguishers in the lab environments increases the reaction time in the occurrence of accidents. Almost all individuals involved in handling chemicals in the laboratories reported they had not received the proper chemical safety training. Our onsite observations showed the unsafe storage of chemicals, which may lead to leakage and increase the possibility of exposure and accidents or high potential for injuries and damages. Students and laboratory workers were more likely not to choose the safe course of action concerning the use of personal protective equipment. For example, a common unsafe act was working in university labs without wearing face and eye or respiratory protection. The absence of proper Protective Personal Equipment (PPE) leads to unsafe exposure and subsequent injury. Furthermore, in chemical laboratories, the users frequently violated safe work procedures during transporting or setting up the experiment or apparatus. We identified many facilities and experiments in compliance with environment, health, and safety codes for handling flammable liquids and compressed gases in chemical laboratories. However, any deviation from the intended experimental steps in laboratory operations could result in severe consequences. The survey evaluated comprehensive health, safety, and environmental hazards of 54 chemicals used in chemical laboratories ( Figure 2 ). The proposed class-based risk assessment involves five levels of classes. The fourth- and fifth-level classes characterize the main risk factors.

Frequency of chemicals at estimated risk level classes in university laboratory activities.

A total of 44 risk factors were predicted and recognized as the “high” or “very high” level assessment classes. Potential health hazards recognized at the “very high” level were more frequent when compared to safety and environmental hazards, respectively, accounting for 9.2, 3.7, and 1.8% of the total number of hazards at the “very high” level class. Moreover, the chemicals with the level of “high” risk contributed to a greater number of environmental hazards (35.2%) followed by safety hazards (20.4%) and health hazards (11.1%). The identified health, safety, and environmental hazards of chemicals at the intermediate level were, respectively, 20.4, 13, and 18.5% of the total number of third-level categories, implying that prevention and control actions are required to manage the risks. Additionally, the mean value of 29.7% of the assessed chemicals had very low and low health risk levels. These mean values for safety and environmental hazards were 31.5 and 22.3%, respectively.

Overall, using chemicals in laboratory operations produced a wide range of risk levels. Cyclohexane, Nitric acid, Sulfuric acid, Formaldehyde, and Sodium Hydroxide were classified as “very high” risk levels with a score estimated at 25, accounting for 9.3% of potential hazards to health. Many chemicals (35.2%) were classified at the “high” risk levels involved in environmental hazards. In contrast, few chemicals (1.8%) presented a “very high” risk level to the environment. Table 3 demonstrates the potential health, safety, and environmental hazards of the studied chemicals and the relevant calculated risk scores.

Health, safety, and environmental risk assessment matrix of common chemicals used in university laboratories.

Our risk assessment showed that 25.9% of the laboratory chemicals might be associated with heavy potential exposure as scored at 5 or 4. Moreover, more than half of the laboratory chemicals (25.9%) contributed to the high level of severity outcomes. The results demonstrated that Ethanol and Sulfuric acid presented a “very high” risk level (scored at 25) in safety risk assessment. Furthermore, 27.8 and 44.4% of chemicals were rated high scores of probability and severity, respectively, in the safety risk assessment. Hydrochloric acid was the only chemical that was ranked at the “very high” level in the environmental risk assessment, with a score estimated at 25.

Discussions

This study assessed health, safety, and environmental risks in academic laboratories that use chemicals for educational and research activities. The variability of chemical use in academic laboratories might lead to various health, safety, and environmental risk factors. Our findings agree with prior research that suggested that educational and research laboratories of academic institutions need to assess their vulnerabilities and plan their own risk mitigation accordingly ( 20 ).

Our risk assessment indicated that the percentage of health hazards at the “very high” risk level was higher when compared to the safety and environmental hazards. Overall, the mean values of 13.6, 12.4, and 18.5% of the assessed chemicals were classified in “moderate” to “very high” categories of health, safety, and environmental hazards, respectively. Therefore, health and safety rules must be considered strictly as a priority by the people who work with chemicals in laboratories for reducing the risk of chemical-related diseases and accidents ( 21 ). In this study, the laboratory health and safety checklist showed that most non-compliance was linked to the chemical storage and training/awareness sections. The main faults in chemical storage were related to the labeling of cabinets to indicate chemical class and the labeling of chemical containers, particularly when chemicals are transferred from their original containers. Additionally, quantities of chemicals in storage were inconsistent with short-term needs of the assessed laboratories. All of these non-compliances in chemical storage may result in extensive fire or explosion in the laboratories of academic settings. Omidvari et al. found similar results in their study at Azad University in Iran, which reported fire risk and accidents in educational buildings, particularly in laboratories ( 22 ).

Due to the importance of training and awareness in reducing exposures, accidents, and injuries, all laboratory workers, such as faculty, staff, and students, should receive laboratory standard training. The training programs should involve chemical safety programs, chemical emergency action plans, and laboratory security plans. After holding the training courses, it should be ensured that the laboratory workers know who and when to use personal protective equipment, how to use emergency equipment, such as eyewashes and safety showers, where SDSs are kept, spill control procedures, emergency procedures, and chemical waste procedures. The previous studies recommended the periodic training courses for laboratory staff and approved the laboratory safety and security curriculum in most faculties in order to increase awareness, safety, and security culture among laboratory workers and allow them to distinguish what to do before, during, and after emergencies ( 9 , 23 – 25 ).

Moreover, the general work environment, emergency planning, and required information for chemical laboratories were the other parts of the checklist that involved the highest numbers of non-compliance in this study. Not only allocating one room of the chemistry laboratory to a chemical warehouse has been increased the safety risk but also the layout of chemicals was not in accordance with safety principles and standards for practice. For instance, the chemical storage was not at “least 18 below the sprinkler head or at least 24” below the ceiling. In at least 2 laboratories, not considering the 5S principles for work environment and storage of materials, such as paper goods, plastic containers, boxes, and empty containers, that would fuel to the burning fire was major non-compliance violation. Additionally, the alternative exits, chemicals material safety data sheet (MSDS), safety instructions, Self-Contained Breathing Apparatus (SCBA), and required special security systems or controls to limit access were not available in the assessed laboratories. The lack of an emergency action plan was the other major fault in this study. The findings of this study and similar research studies provide useful information to plan and develop an emergency action plan for the prevention and mitigation of the emergencies and their harmful consequences in the laboratories of academic institutions ( 26 – 28 ). The prevention and mitigation measures should be prioritized for implementation in accordance with available funds and other resources. Prior studies reported low-cost interventions that might involve reducing major risks and their consequences. Planning a safe layout for gas cylinders or fire extinguishers, providing the SDS for all chemicals used in laboratories, using chemical labeling of cabinets and containers, and non-structural mitigation measures are recommended ( 29 , 30 ).

In the domain of environmental risk assessment, 44.5% of chemicals were classified in “very low” and “low' risk levels, but 55.5% of them were ranked “intermediate” to “very high” risk degrees. The most important chemical environment-related hazard was waste disposal. The lack of an individual sewage system for laboratories and releasing chemicals into the urban sewage system can contaminate the underground water with hazardous chemicals. Previous studies assessed a high level of environmental risk in underground water reservoirs related to hazardous chemical effluents from academic laboratories ( 31 , 32 ).

This chemical health, safety, and environmental risk assessment was developed and conducted according to the standards and guidelines set by the international occupational health and safety organizations. The applied approach revealed the significant risks associated with chemicals used at the university laboratories. The instrument developed for this study will be put into good use in helping health and safety engineers to identify and classify potential risks of laboratory operations to health, safety, and environment. Prevention and mitigation measures should be based on detailed risk assessment methods to minimize identified hazards and provide a safe environment to reduce and/or eliminate the occurrence of diseases and injury in laboratories.

Universities should provide training courses in the curriculum on health and safety in laboratories, particularly for new students at the first of each semester, and periodic similar training courses for faculty and staff plays a key role in increasing awareness and risk perception for considering significant risks at the laboratories. Furthermore, inspecting and assessing the laboratories and research facilities by standard laboratory checklists routinely and removing the non-compliance operations at the earliest time are essential in providing a safe work environment.

Data Availability Statement

Ethics statement.

This study was approved by the Ethics Committee Review Board at Semnan university of Medical Sciences (IR.SEMUMS.REC.1398.131). All the participants signed a consent form and were informed on the purpose of the study prior to interview as per local protocol on research ethics.

Author Contributions

AD: material preparation, conceptualization, methodology, investigation, writing—reviewing, and editing. MJ: material preparation, conceptualization, and data collection. FF: analysis, interpretation, first draft of the manuscript, conceptualization, and investigation. All authors contributed to the study conception, design, investigation, reviewed and commented on previous versions of the manuscript, and read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank all the staff laboratories that participated and collaborated in this study and Semnan University of Medical Sciences and Health Services for their support to conduct this research.

Investigation of wildfire risk and its mapping using GIS-integrated AHP method: a case study over Hoshangabad Forest Division in Central India

• Published: 24 August 2024

Cite this article

• Mohd Amin Khan   ORCID: orcid.org/0000-0002-0633-9625 1 ,
• Amitesh Gupta 2 ,
• Pritee Sharma 1 &
• Arijit Roy   ORCID: orcid.org/0000-0001-5581-9257 3  

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Increasing wildfire risk is a major menace to the subtropical biodiversity. However, regional plan may not oblige the local management in wildfire prevention in a locality where people are majorly depending on forest resource and the area undergoes significant human encroachment. Addressing that, the current study focuses one such area, Hoshangabad Forest Division (HFD), located in Central India, where wildfires have damaged the local biodiversity and surrounding socio-economic activities in previous years. While several studies found alarming increase in wildfires across different regions in India, this study found a non-significant increasing trend (slope =  ~ 1 incidence/year) of MODIS active fire points over HFD area. Positive and significant spatial autocorrelation among the fire locations was found (Moron's I = 0.11), which indicates that fire often ignite over few specific places and spread to neighboring areas. Using Getis-Ord Gi statistics, four significant hotspots were distinguished, where 47% of total fire occurrences had occurred and the remaining were observed scattered across HFD. To assess wildfire risk within this locality, Analytical Hierarchical Process was used, where eight physical and six socio-economic factors were integrated in GIS environment. The model achieved a ROC-AUC score of 0.76 while validated with wildfire records from the MODIS. 32 and 28% of HFD area were found under high and very high-risk, respectively, where 78% of wildfire incidents had occurred during 2001–2022. Additionally, specific areas within HFD (western, central west, and south-western areas) are identified as facing higher risks of wildfires. This study shows that wildfire risk assessment at local-scale may differ what observed a regional scale and provide a promenade for area-specific wildfire prevention plan. It also suggests enhanced monitoring near forest edges, immediate action to protect teak trees, and prescribed fire management to reduce dry fuel in forests and roadsides. Thus, the study provides valuable insights into wildfire management and contributes to more specialized research and methodological advancement in this field.

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Abdo, H. G., Almohamad, H., Al Dughairi, A. A., & Al-Mutiry, M. (2022). GIS-based frequency ratio and analytic hierarchy process for forest fire susceptibility mapping in the western region of Syria. Sustainability, 14 (8), 4668. https://doi.org/10.3390/su14084668

Article   Google Scholar  

Abedi Gheshlaghi, H. (2019). Using GIS to develop a model for forest fire risk mapping. Journal of the Indian Society of Remote Sensing, 47 (7), 1173–1185. https://doi.org/10.1007/s12524-019-00981-z

Adab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65 (3), 1723–1743. https://doi.org/10.1007/s11069-012-0450-8

Afreen, S., Sharma, N., Chaturvedi, R. K., Gopalakrishnan, R., & Ravindranath, N. H. (2011). Forest policies and programs affecting vulnerability and adaptation to climate change. Mitigation and Adaptation Strategies for Global Change, 16 (2), 177–197. https://doi.org/10.1007/s11027-010-9259-5

Aftergood, O. S. R., & Flannigan, M. D. (2022). Identifying and analyzing spatial and temporal patterns of lightning-ignited wildfires in Western Canada from 1981 to 2018. Canadian Journal of Forest Research, 52 (11), 1399–1411. https://doi.org/10.1139/cjfr-2021-0353

Ajin, R., Loghin, A.-M., Vinod, P., & Jacob, M. (2016). Forest Fire risk zone mapping using RS and GIS techniques: A study in Achankovil Forest Division, Kerala, India. Journal of Earth, Environment and Health Sciences, 2 (3), 109. https://doi.org/10.4103/2423-7752.199288

Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Rosan, T. M., Vedovato, L. B., Wagner, F. H., Silva, C. V. J., Silva Junior, C. H. L., Arai, E., Aguiar, A. P., Barlow, J., Berenguer, E., Deeter, M. N., Domingues, L. G., Gatti, L., Gloor, M., Malhi, Y., Marengo, J. A., Miller, J. B., Saatchi, S. (2018). 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nature Communications , 9 (1), 536. https://doi.org/10.1038/s41467-017-02771-y

Babu, K. S., Roy, A., & Aggarwal, R. (2018). Mapping of forest fire burned severity using the sentinel datasets. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42 , 469–474.

Babu, K. V., Roy, S. A., & Prasad, P. R. (2016). Forest fire risk modeling in Uttarakhand Himalaya using TERRA satellite datasets. European Journal of Remote Sensing, 49 (1), 381–395. https://doi.org/10.5721/EuJRS20164921

Behera, S., Prusty, B. K., Behera, M. D., & Kale, M. P. (2023). Characterizing fuel flammability in a tropical dry community forest in Eastern India using laboratory and remote sensing based approaches. Tropical Ecology . https://doi.org/10.1007/s42965-023-00309-6

Belikova, M. Y., Baranovskiy, N. V., Karanin, A. V., Bazarov, A. V., Sychev, R. S., & Glebova, A. V. (2022). Analysis of Spatial Distribution Processes for Forest Fires Near the Railway Infrastructure Using Clustering: Case Study. International Journal on Engineering Applications (IREA) , 10 (6), Article 6. https://doi.org/10.15866/irea.v10i6.22224

Bhuyan, M. J., & Deka, N. (2022). Delineation of groundwater potential zones at micro-spatial units of Nagaon district in Assam, India, using GIS-based MCDA and AHP techniques. Environmental Science and Pollution Research . https://doi.org/10.1007/s11356-022-24505-4

Bhuyan, M. J., Deka, N., & Saikia, A. (2024). Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP). Risk Analysis, 44 (4), 817–832. https://doi.org/10.1111/risa.14191

Cetin, M., Isik Pekkan, Ö., Ozenen Kavlak, M., Atmaca, I., Nasery, S., Derakhshandeh, M., & Cabuk, S. N. (2023). GIS-based forest fire risk determination for Milas district. Turkey. Natural Hazards, 119 (3), 2299–2320. https://doi.org/10.1007/s11069-022-05601-7

Cetin, M., Kaya, A. Y., Elmastas, N., Adiguzel, F., Siyavus, A. E., & Kocan, N. (2024). Assessment of emergency gathering points and temporary shelter areas for disaster resilience in Elazıg. Turkey. Natural Hazards, 120 (2), 1925–1949. https://doi.org/10.1007/s11069-023-06271-9

Chandramouli, C., & General, R. (2011). Census of india 2011. Provisional Population Totals. New Delhi: Government of India , pp. 409–413.

Chas-Amil, M. L., Touza, J., & García-Martínez, E. (2013). Forest fires in the wildland–urban interface: A spatial analysis of forest fragmentation and human impacts. Applied Geography, 43 , 127–137. https://doi.org/10.1016/j.apgeog.2013.06.010

Costa-Saura, J. M., Bacciu, V., Ribotta, C., Spano, D., Massaiu, A., & Sirca, C. (2022). Predicting and Mapping Potential Fire Severity for Risk Analysis at Regional Level Using Google Earth Engine. Remote Sensing , 14 (19), Article 19. https://doi.org/10.3390/rs14194812

Davis, E. J., Moseley, C., Nielsen-Pincus, M., & Jakes, P. J. (2014). The community economic impacts of large wildfires: A case study from Trinity County. California. Society and Natural Resources, 27 (9), 983–993. https://doi.org/10.1080/08941920.2014.905812

Dennison, P. E., Brewer, S. C., Arnold, J. D., & Moritz, M. A. (2014). Large wildfire trends in the western United States, 1984–2011. Geophysical Research Letters, 41 (8), 2928–2933. https://doi.org/10.1002/2014GL059576

Doerr, S. H., & Santín, C. (2016). Global trends in wildfire and its impacts: Perceptions versus realities in a changing world. Philosophical Transactions of the Royal Society B: Biological Sciences, 371 (1696), 20150345. https://doi.org/10.1098/rstb.2015.0345

Donegan, H. A., Dodd, F. J., & McMaster, T. B. M. (1992). A new approach to AHP decision-making. The Statistician, 41 (3), 295. https://doi.org/10.2307/2348551

Erden, T., & Karaman, H. (2012). Analysis of earthquake parameters to generate hazard maps by integrating AHP and GIS for Küçükçekmece region. Natural Hazards and Earth System Sciences, 12 (2), 475–483. https://doi.org/10.5194/nhess-12-475-2012

Ertugrul, M., Ozel, H. B., Varol, T., Cetin, M., & Sevik, H. (2019). Investigation of the relationship between burned areas and climate factors in large forest fires in the Çanakkale region. Environmental Monitoring and Assessment, 191 (12), 737. https://doi.org/10.1007/s10661-019-7946-6

Ertugrul, M., Varol, T., Ozel, H. B., Cetin, M., & Sevik, H. (2021). Influence of climatic factor of changes in forest fire danger and fire season length in Turkey. Environmental Monitoring and Assessment, 193 (1), 28. https://doi.org/10.1007/s10661-020-08800-6

Article   CAS   Google Scholar  

Fernandes, P. M., & Botelho, H. S. (2003). A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire, 12 (2), 117–128. https://doi.org/10.1071/wf02042

Flannigan, M. D., Wotton, B. M., Marshall, G. A., De Groot, W. J., Johnston, J., Jurko, N., & Cantin, A. S. (2016). Fuel moisture sensitivity to temperature and precipitation: Climate change implications. Climatic Change, 134 (1–2), 59–71. https://doi.org/10.1007/s10584-015-1521-0

Gabban, A., San-Miguel-Ayanz, J., & Viegas, D. X. (2008). A comparative analysis of the use of NOAA-AVHRR NDVI and FWI data for forest fire risk assessment. International Journal of Remote Sensing, 29 (19), 5677–5687. https://doi.org/10.1080/01431160801958397

Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24 (3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

Ghosh, A., & Kar, S. K. (2018). Application of analytical hierarchy process (AHP) for flood risk assessment: A case study in Malda district of West Bengal. India. Natural Hazards, 94 (1), 349–368. https://doi.org/10.1007/s11069-018-3392-y

Gupta, A., Bhatt, C. M., Roy, A., & Chauhan, P. (2020a). COVID-19 lockdown a window of opportunity to understand the role of human activity on forest fire incidences in the Western Himalaya. India. Current Science, 119 (2), 390–398.

Gupta, A., Pradhan, B., & Maulud, K. N. A. (2020b). Estimating the impact of daily weather on the temporal pattern of COVID-19 outbreak in India. Earth Systems and Environment, 4 (3), 523–534. https://doi.org/10.1007/s41748-020-00179-1

Gupta, A., Roy, A., & Chauhan, P. (2023). Space-based observation of early summer wildfire event and its environmental proxies during 2021 in Eastern Peninsular India. Arabian Journal of Geosciences, 16 (7), 433. https://doi.org/10.1007/s12517-023-11544-5

Hussin, Y. A., Matakala, M., & Zagdaa, N. (2008). The application of remote sensing and GIS in modelling forest fire hazard in Mongolia. In ISPRS 2008 : Proceedings of the XXI congress : Silk road for information from imagery : the International Society for Photogrammetry and Remote Sensing, 3–11 July, Beijing, China. Comm. VIII, WG VIII/2. Beijing : ISPRS, 2008. pp. 289–294 (pp. 289–294). International Society for Photogrammetry and Remote Sensing (ISPRS). http://www.isprs.org/proceedings/XXXVII/congress/8_pdf/2_WG-VIII-2/22.pdf

Ionita, M., Nagavciuc, V., Scholz, P., & Dima, M. (2022). Long-term drought intensification over Europe driven by the weakening trend of the Atlantic Meridional Overturning Circulation. Journal of Hydrology: Regional Studies, 42 , 101176. https://doi.org/10.1016/j.ejrh.2022.101176

ISFR. (2021). Forest Survey of India, Ministry of Environment, Forest, and Climate Change (MoEFCC), Government of India. https://fsi.nic.in/forest-report-2021-details

Jafari Goldarag, Y., Mohammadzadeh, A., & Ardakani, A. S. (2016). Fire risk assessment using neural network and logistic regression. Journal of the Indian Society of Remote Sensing, 44 (6), 885–894. https://doi.org/10.1007/s12524-016-0557-6

Jain, M., Saxena, P., Sharma, S., & Sonwani, S. (2021). Investigation of forest fire activity changes over the central India domain using satellite observations during 2001–2020. GeoHealth , 5 (12), e2021GH000528.

Jain, P., Wang, X., & Flannigan, M. D. (2017). Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015. International Journal of Wildland Fire, 26 (12), 1009. https://doi.org/10.1071/WF17008

Jaiswal, R. K., Mukherjee, S., Raju, K. D., & Saxena, R. (2002). Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 4 (1), 1–10. https://doi.org/10.1016/S0303-2434(02)00006-5

Jiménez-Ruano, A., Rodrigues Mimbrero, M., Jolly, W. M., & De La Riva Fernández, J. (2019). The role of short-term weather conditions in temporal dynamics of fire regime features in mainland Spain. Journal of Environmental Management, 241 , 575–586. https://doi.org/10.1016/j.jenvman.2018.09.107

Kale, M. P., Ramachandran, R. M., Pardeshi, S. N., Chavan, M., Joshi, P. K., Pai, D. S., Bhavani, P., Ashok, K., & Roy, P. S. (2017). Are Climate Extremities changing forest fire regimes in India? An analysis using MODIS fire locations during 2003–2013 and gridded climate data of India Meteorological Department. Proceedings of the National Academy of Sciences, India Section a: Physical Sciences, 87 (4), 827–843. https://doi.org/10.1007/s40010-017-0452-8

Kant Sharma, L., Kanga, S., Singh Nathawat, M., Sinha, S., & Chandra Pandey, P. (2012). Fuzzy AHP for forest fire risk modeling. Disaster Prevention and Management: An International Journal, 21 (2), 160–171. https://doi.org/10.1108/09653561211219964

Kantarcioglu, O., Kocaman, S., & Schindler, K. (2023). Artificial neural networks for assessing forest fire susceptibility in Türkiye. Ecological Informatics, 75 , 102034. https://doi.org/10.1016/j.ecoinf.2023.102034

Kayet, N., Chakrabarty, A., Pathak, K., Sahoo, S., Dutta, T., & Hatai, B. K. (2020). Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk (FFR) mapping in Melghat Tiger Reserve (MTR) forest. Journal of Forestry Research, 31 (2), 565–579. https://doi.org/10.1007/s11676-018-0826-z

Kendall, M. (1975). Rank correlation methods . Charles Griffin & Co.

Google Scholar  

Khan, H. U. (2013). Hoshangabad Forest Division’s Action Plan 2013–14 to 2022–23. Madhya Pradesh Forest Department , (380).

Kodandapani, N., Cochrane, M. A., & Sukumar, R. (2008). A comparative analysis of spatial, temporal, and ecological characteristics of forest fires in seasonally dry tropical ecosystems in the Western Ghats. India. Forest Ecology and Management, 256 (4), 607–617. https://doi.org/10.1016/j.foreco.2008.05.006

Kumar, A., Kumar, G., Saikia, P., Khare, P. K., & Khan, M. L. (2022). Spatial pattern of tree diversity and impacts of ecological disturbances on forest structure in tropical deciduous forests of Central India. Biotropica, 54 (6), 1363–1375. https://doi.org/10.1111/btp.13068

Kumar, M., Nisha Phukon, S., & Singh, H. (2021). The role of communities in sustainable land and forest management. In P. Kumar Shit, H. R. Pourghasemi, P. P. Adhikary, G. S. Bhunia, & V. P. Sati (Eds.), Forest Resources Resilience and Conflicts (pp. 305–318). Elsevier. https://doi.org/10.1016/B978-0-12-822931-6.00024-1

Kumar, S., & Kumar, A. (2022). Hotspot and trend analysis of forest fires and its relation to climatic factors in the western Himalayas. Natural Hazards, 114 (3), 3529–3544. https://doi.org/10.1007/s11069-022-05530-5

Kumar, S., Meenakshi, D. B., Vandana, G., & Kumar, A. (2015). Identifying triggers for forest fire and assessing fire susceptibility of forests in Indian western Himalaya using geospatial techniques. Natural Hazards, 78 (1), 203–217. https://doi.org/10.1007/s11069-015-1710-1

Kumari, B., & Pandey, A. C. (2020). Geo-informatics based multi-criteria decision analysis (MCDA) through analytic hierarchy process (AHP) for forest fire risk mapping in Palamau Tiger Reserve, Jharkhand state. India. Journal of Earth System Science, 129 (1), 204. https://doi.org/10.1007/s12040-020-01461-6

Lamat, R., Kumar, M., Kundu, A., & Lal, D. (2021). Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: A case study in the mountainous terrain of Northeast India. SN Applied Sciences, 3 (4), 425. https://doi.org/10.1007/s42452-021-04391-0

Lasslop, G., & Kloster, S. (2017). Human impact on wildfires varies between regions and with vegetation productivity. Environmental Research Letters, 12 (11), 115011. https://doi.org/10.1088/1748-9326/aa8c82

Liu, Z., Yang, J., Chang, Y., Weisberg, P. J., & He, H. S. (2012). Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Global Change Biology, 18 (6), 2041–2056. https://doi.org/10.1111/j.1365-2486.2012.02649.x

Madhya Pradesh Forest Department. (2017). Jurisdictional Sub-National Redd+ Program for Hoshangabad District of Madhya Pradesh State, India (Version 1.3). U.S. Agency for International Development . https://pdf.usaid.gov/pdf_docs/PA00N73S.pdf

Maffei, C., Lindenbergh, R., & Menenti, M. (2021). Combining multi-spectral and thermal remote sensing to predict forest fire characteristics. ISPRS Journal of Photogrammetry and Remote Sensing, 181 , 400–412. https://doi.org/10.1016/j.isprsjprs.2021.09.016

Mallick, J., Talukdar, S., Alsubih, M., Salam, R., Ahmed, M., Kahla, N. B., & Shamimuzzaman, Md. (2021). Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theoretical and Applied Climatology, 143 (1–2), 823–841. https://doi.org/10.1007/s00704-020-03448-1

Mamgain, S., Roy, A., Karnatak, H. C., & Chauhan, P. (2023). Satellite-based long-term spatiotemporal trends of wildfire in the Himalayan vegetation. Natural Hazards, 116 (3), 3779–3796.

Mamuji, A. A., & Rozdilsky, J. L. (2019). Wildfire as an increasingly common natural disaster facing Canada: Understanding the 2016 Fort McMurray wildfire. Natural Hazards, 98 (1), 163–180. https://doi.org/10.1007/s11069-018-3488-4

Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13 (3), 245. https://doi.org/10.2307/1907187

Memisoglu Baykal, T. (2023). GIS-based spatiotemporal analysis of forest fires in Turkey from 2010 to 2020. Transactions in GIS, 27 (5), 1289–1317. https://doi.org/10.1111/tgis.13066

Mohammad, L., Mondal, I., Bandyopadhyay, J., Pham, Q. B., Nguyen, X. C., Dinh, C. D., & Al-Quraishi, A. M. F. (2022). Assessment of spatio-temporal trends of satellite-based aerosol optical depth using Mann-Kendall test and Sen’s slope estimator model. Geomatics, Natural Hazards and Risk, 13 (1), 1270–1298. https://doi.org/10.1080/19475705.2022.2070552

Mohammadi, F., Bavaghar, M. P., & Shabanian, N. (2014). Forest fire risk zone modeling using logistic regression and GIS: An Iranian case study. Small-Scale Forestry, 13 (1), 117–125. https://doi.org/10.1007/s11842-013-9244-4

Mohanty, A., & Mithal, V. (2022). Managing Forest Fires in a Changing Climate (p. 24). Council on Energy, Environment and Water New Delhi.

Mohd, A., Pritee, S., & Mohanasundari, T. (2024). Analysing the Escalation of Forest Fire in India: Exploring Causal Factors and Mitigation Strategies. Journal of Tropical Forest Science , 36 (2), 215–223. https://doi.org/10.26525/jtfs2024.36.2.215

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37 (1/2), 17. https://doi.org/10.2307/2332142

Narayanaraj, G., & Wimberly, M. C. (2012). Influences of forest roads on the spatial patterns of human- and lightning-caused wildfire ignitions. Applied Geography, 32 (2), 878–888. https://doi.org/10.1016/j.apgeog.2011.09.004

Nasiri, V., Sadeghi, S. M. M., Bagherabadi, R., Moradi, F., Deljouei, A., & Borz, S. A. (2022). Modeling wildfire risk in western Iran based on the integration of AHP and GIS. Environmental Monitoring and Assessment, 194 (9), 644. https://doi.org/10.1007/s10661-022-10318-y

Nezval, V., Andrášik, R., & Bíl, M. (2022). Vegetation fires along the Czech rail network. Fire Ecology, 18 (1), 15. https://doi.org/10.1186/s42408-022-00141-8

Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., Ajin, R. S., & Kuriakose, S. L. (2021). Application of GIS and AHP method in forest fire risk zone mapping: A study of the Parambikulam Tiger Reserve, Kerala, India. Journal of Geovisualization and Spatial Analysis, 5 (1), 14. https://doi.org/10.1007/s41651-021-00082-x

Nuthammachot, N., & Stratoulias, D. (2021). Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: Method and case study. Environment, Development and Sustainability, 23 (12), 17443–17458. https://doi.org/10.1007/s10668-021-01394-0

Ozenen Kavlak, M., Cabuk, S. N., & Cetin, M. (2021). Development of forest fire risk map using geographical information systems and remote sensing capabilities: Ören case. Environmental Science and Pollution Research, 28 (25), 33265–33291. https://doi.org/10.1007/s11356-021-13080-9

Parajuli, A., Gautam, A. P., Sharma, S. P., Bhujel, K. B., Sharma, G., Thapa, P. B., Bist, B. S., & Poudel, S. (2020). Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics, Natural Hazards and Risk, 11 (1), 2569–2586. https://doi.org/10.1080/19475705.2020.1853251

Paveglio, T. B., Kooistra, C., Hall, T., & Pickering, M. (2016). Understanding the effect of large wildfires on residents’ well-being: What factors influence wildfire impact? Forest Science, 62 (1), 59–69. https://doi.org/10.5849/forsci.15-021

Povak, N. A., Hessburg, P. F., & Salter, R. B. (2018). Evidence for scale‐dependent topographic controls on wildfire spread. Ecosphere , 9 (10). https://doi.org/10.1002/ecs2.2443

Pozo, R. A., Galleguillos, M., González, M. E., Vásquez, F., & Arriagada, R. (2022). Assessing the socio-economic and land-cover drivers of wildfire activity and its spatiotemporal distribution in south-central Chile. Science of the Total Environment, 810 , 152002. https://doi.org/10.1016/j.scitotenv.2021.152002

Prestemon, J. P., Pye, J. M., Butry, D. T., Holmes, T. P., & Mercer, D. E. (2002). Understanding broadscale wildfire risks in a human-dominated landscape. Forest Science, 48 (4), 685–693. https://doi.org/10.1093/forestscience/48.4.685

Ray, T., Malasiya, D., Verma, A., Purswani, E., Qureshi, A., Khan, M. L., & Verma, S. (2023). Characterization of spatial–temporal distribution of forest fire in Chhattisgarh, India, using MODIS-based active fire data. Sustainability , 15 (9), Article 9. https://doi.org/10.3390/su15097046

Reddy, C. S., Bird, N. G., Sreelakshmi, S., Manikandan, T. M., Asra, M., Krishna, P. H., Jha, C. S., Rao, P. V. N., & Diwakar, P. G. (2019). Identification and characterization of spatio-temporal hotspots of forest fires in South Asia. Environmental Monitoring and Assessment, 191 (S3), 791. https://doi.org/10.1007/s10661-019-7695-6

Rodrigues, M., Jiménez, A., & de la Riva, J. (2016). Analysis of recent spatial–temporal evolution of human driving factors of wildfires in Spain. Natural Hazards, 84 (3), 2049–2070. https://doi.org/10.1007/s11069-016-2533-4

Roshani, Sajjad, H., Rahaman, M. H., Rehman, S., Masroor, M., & Ahmed, R. (2022). Assessing forest health using remote sensing-based indicators and fuzzy analytic hierarchy process in Valmiki Tiger Reserve, India. International Journal of Environmental Science and Technology . https://doi.org/10.1007/s13762-022-04512-1

Roy, P. S. (2003). Forest fire and degradation assessment using satellite remote sensing and geographic information system. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 361 , 400.

Saaty, T. L. (1980). The analytic hierarchy process . Mcgrawhill international.

Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145 (1), 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8

Saha, S. (2002). Anthropogenic fire regime in a deciduous forest of central India. Current Science , 1144–1147.

Salavati, G., Saniei, E., Ghaderpour, E., & Hassan, Q. K. (2022). Wildfire risk forecasting using weights of evidence and statistical index models. Sustainability , 14 (7), Article 7. https://doi.org/10.3390/su14073881

Santín, C., & Doerr, S. H. (2016). Fire effects on soils: The human dimension. Philosophical Transactions of the Royal Society B: Biological Sciences, 371 (1696), 20150171. https://doi.org/10.1098/rstb.2015.0171

Santos, S. M. B. dos, Bento-Gonçalves, A., Franca-Rocha, W., & Baptista, G. (2020). Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices. Geosciences , 10 (3), Article 3. https://doi.org/10.3390/geosciences10030106

Santos, F. L. M., Nogueira, J., Souza, R. A. F. D., Falleiro, R. M., Schmidt, I. B., & Libonati, R. (2021). Prescribed burning reduces large, high-intensity wildfires and emissions in the Brazilian Savanna. Fire, 4 (3), 56. https://doi.org/10.3390/fire4030056

Sari, F. (2023). Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models. Journal of Forestry Research, 34 (2), 355–371. https://doi.org/10.1007/s11676-022-01502-4

Satendra, & Kaushik, A. D. (2014). Forest fire disaster management. National Institute of Disaster Management, Ministry of Home Affairs, New Delhi. https://nidm.gov.in/pdf/pubs/forest%20fire.pdf

Satir, O., Berberoglu, S., & Donmez, C. (2016). Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk, 7 (5), 1645–1658. https://doi.org/10.1080/19475705.2015.1084541

Schmerbeck, J., & Fiener, P. (2015). Wildfires, ecosystem services, and biodiversity in tropical dry forest in India. Environmental Management, 56 (2), 355–372. https://doi.org/10.1007/s00267-015-0502-4

Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63 (324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934

Silva, I. D. B., Valle, M. E., Barros, L. C., & Meyer, J. F. C. A. (2020). A wildfire warning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing, 89 , 106075. https://doi.org/10.1016/j.asoc.2020.106075

Sivrikaya, F., & Küçük, Ö. (2022). Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68 , 101537. https://doi.org/10.1016/j.ecoinf.2021.101537

Sofan, P., Bruce, D., Schroeder, W., Jones, E., & Marsden, J. (2020). Assessment of VIIRS 375 m active fire using tropical peatland combustion algorithm applied to Landsat-8 over Indonesia’s peatlands. International Journal of Digital Earth, 13 (12), 1695–1716. https://doi.org/10.1080/17538947.2020.1791268

Srivastava, S. K., Saran, S., de By, R. A., & Dadhwal, V. K. (2014). A geo-information system approach for forest fire likelihood based on causative and anti-causative factors. International Journal of Geographical Information Science, 28 (3), 427–454. https://doi.org/10.1080/13658816.2013.797984

Stephenson, C., Handmer, J., & Betts, R. (2013). Estimating the economic, social and environmental impacts of wildfires in Australia. Environmental Hazards, 12 (2), 93–111.

Sunar, F., & Özkan, C. (2001). Forest fire analysis with remote sensing data. International Journal of Remote Sensing, 22 (12), 2265–2277. https://doi.org/10.1080/01431160118510

Surbhi Singh, S., & Jeganathan, C. (2024). Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh. India. Advances in Space Research, 73 (6), 2969–2987. https://doi.org/10.1016/j.asr.2023.12.054

Suryabhagavan, K. V., Alemu, M., & Balakrishnan, M. (2016). GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: A case study in Harenna forest, southwestern Ethiopia. Tropical Ecology, 57 (1), 33–43.

Tariq, A., Jiango, Y., Lu, L., Jamil, A., Al-ashkar, I., Kamran, M., & Sabagh, A. E. (2023). Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars. Geomatics, Natural Hazards and Risk, 14 (1), 2190856. https://doi.org/10.1080/19475705.2023.2190856

Tiwari, A., Shoab, M., & Dixit, A. (2021). GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: A comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques. Natural Hazards, 105 (2), 1189–1230. https://doi.org/10.1007/s11069-020-04351-8

Tyukavina, A., Potapov, P., Hansen, M. C., Pickens, A. H., Stehman, S. V., Turubanova, S., Parker, D., Zalles, V., Lima, A., Kommareddy, I., Song, X.-P., Wang, L., & Harris, N. (2022). Global trends of forest loss due to fire from 2001 to 2019. Frontiers in Remote Sensing, 3 , 825190. https://doi.org/10.3389/frsen.2022.825190

Vadrevu, K., Eaturu, A., & Badarinath, K. (2009). Fire risk evaluation using multicriteria analysis—A case study. Environmental Monitoring and Assessment, 166 , 223–239. https://doi.org/10.1007/s10661-009-0997-3

Vadrevu, K. P., Csiszar, I., Ellicott, E., Giglio, L., Badarinath, K. V. S., Vermote, E., & Justice, C. (2013). Hotspot analysis of vegetation fires and intensity in the Indian Region. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6 (1), 224–238. https://doi.org/10.1109/JSTARS.2012.2210699

Vadrevu, K. P., Lasko, K., Giglio, L., Schroeder, W., Biswas, S., & Justice, C. (2019). Trends in vegetation fires in South and Southeast Asian Countries. Scientific Reports, 9 (1), 7422. https://doi.org/10.1038/s41598-019-43940-x

van der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N., & Dolman, A. J. (2008). Climate controls on the variability of fires in the tropics and subtropics. Global Biogeochemical Cycles , 22 (3). https://doi.org/10.1029/2007GB003122

Van Hoang, T., Chou, T. Y., Fang, Y. M., Nguyen, N. T., Nguyen, Q. H., Xuan Canh, P., Ngo Bao Toan, D., Nguyen, X. L., & Meadows, M. E. (2020). Mapping Forest Fire Risk and Development of Early Warning System for NW Vietnam Using AHP and MCA/GIS Methods. Applied Sciences , 10 (12), 4348. https://doi.org/10.3390/app10124348

Whitman, E., Sherren, K., & Rapaport, E. (2015). Increasing daily wildfire risk in the Acadian Forest Region of Nova Scotia, Canada, under future climate change. Regional Environmental Change, 15 (7), 1447–1459. https://doi.org/10.1007/s10113-014-0698-5

Zeren Cetin, I., Ozel, H. B., & Varol, T. (2020). Integrating of settlement area in urban and forest area of Bartin with climatic condition decision for managements. Air Quality, Atmosphere & Health, 13 (8), 1013–1022. https://doi.org/10.1007/s11869-020-00871-1

Zeren Cetin, I., Varol, T., & Ozel, H. B. (2023a). A geographic information systems and remote sensing–based approach to assess urban micro-climate change and its impact on human health in Bartin. Turkey. Environmental Monitoring and Assessment, 195 (5), 540. https://doi.org/10.1007/s10661-023-11105-z

Zeren Cetin, I., Varol, T., Ozel, H. B., & Sevik, H. (2023b). The effects of climate on land use/cover: A case study in Turkey by using remote sensing data. Environmental Science and Pollution Research, 30 (3), 5688–5699. https://doi.org/10.1007/s11356-022-22566-z

Zhang, Z., Feng, Z., Zhang, H., Zhao, J., Yu, S., Du, W., Zhang, Z., Feng, Z., Zhang, H., Zhao, J., Yu, S., & Du, W. (2017). Spatial distribution of grassland fires at the regional scale based on the MODIS active fire products. International Journal of Wildland Fire, 26 (3), 209–218. https://doi.org/10.1071/WF16026

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Acknowledgements

The authors are thankful to Madhya Pradesh Forest Department for proving the essential data about the study area and Indian Institute of Remote Sensing (IIRS) Dehradun for providing free access GIS data. We are also thankful to Ms. Shailja Mamgain and Mr. Prince from the Disaster Management Studies Lab at IIRS for their valuable technical support in the realm of GIS software assistance. Futher, we extend our gratitude to Ms. Monika for her review of the manuscript, focusing on both the English language and writing style.

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Khan, M.A., Gupta, A., Sharma, P. et al. Investigation of wildfire risk and its mapping using GIS-integrated AHP method: a case study over Hoshangabad Forest Division in Central India. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05225-w

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An earthquake-triggered avalanche in Nepal in 2015 was exacerbated by climate variability and snowfall anomalies

• Yu Zhuang 1 , 2 ,
• Binod Dawadi 3 , 4 ,
• Jakob Steiner 5 , 6 ,
• Rajesh Kumar Dash   ORCID: orcid.org/0000-0002-5486-5697 7 ,
• Yves Bühler   ORCID: orcid.org/0000-0002-0815-2717 1 , 2 ,
• Jessica Munch 1 , 2 &
• Perry Bartelt 1 , 2  

Communications Earth & Environment volume  5 , Article number:  465 ( 2024 ) Cite this article

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On 25 April 2015, the Gorkha earthquake triggered a large rock-ice avalanche and an air blast disaster in the Langtang Valley, Nepal. More than 350 people were killed or left missing. Here we reconstruct the evolution of the Langtang avalanche-air blast using field investigations and numerical modeling and examine the influence of two primary climate-related phenomena: snowfall anomalies and warm temperatures. Our findings suggest a deep snow cover fosters the formation of a dispersed avalanche, which increases the mobility and destructive power of the powder cloud air blast. Elevated air temperatures intensify meltwater production and lubricate the flowing mass. Both mechanisms contributed to the Langtang disaster. Our study underscores the essential impact of snow cover and air temperature on the risk assessment of high-altitude rock-ice avalanches, highlighting how seasonal and climatic variations affect avalanche runout and air blast dynamics.

Introduction

Large rock-ice avalanches are geophysical mass flows composed of a mixture of rock and ice. They can be extremely hazardous due to their extremely high velocity and long runout 1 , 2 , 3 . In earthquake-prone regions globally changing climate appears to be exacerbating these hazards 4 . Scientists have long recognized rapid global climate change favors the instability of mountains in glacial and periglacial areas 5 , 6 , 7 . Nevertheless, to our knowledge, no hazard risk assessment considers how the impact of climate change contributes to their destructive potential, especially in terms of runout and flow regime transitions, such as the formation of hazardous air blasts.

One phenomenon of climate change is frequent snowfall anomalies in high-altitude regions. In the past decades, the duration of snowfall decreased, while snowfall intensity showed an increasing trend 8 , 9 , 10 . The thick snow cover arising from snowfall anomalies is an important mass source of the avalanche core. This snow entrainment process amplifies the avalanche volume 11 , 12 , lubricates the avalanche movement 13 , and exacerbates the formation of a rock-ice-snow powder avalanche 14 . This type of avalanche often generates powerful air blasts capable of causing damage and human fatalities far beyond the reach of avalanche core 15 .

Another feature of the ongoing climate change problem is warming 16 , 17 , moreover, how changing snow and air temperatures will change avalanche flow dynamics. In existing rock-ice avalanche models, the sliding mass is treated as a thermally insulated system, ignoring the effect of ambient environment 18 , 19 . Frictional shearing 20 , entrainment process 13 , and particle collisions 21 are heat energy sources that change the avalanche temperature and produce meltwater. It is important to note that avalanche snow (ice) exists near its melting point and frictional heating can easily supply the necessary energy input needed to produce meltwater. The porous-medium structure of avalanches and the dispersive movement of granular particles allow the intake and outburst of ambient air 14 . This interaction with the ambient environment is inevitable during avalanche movement and can either enhance or hinder the heating process. The temperature difference between the avalanche and ambient air leads to a heat exchange that greatly influences meltwater production and the flow regime of the avalanche core.

One striking example is the rock-ice avalanche of Langtang (2015), which was triggered by the Gorkha earthquake in a warm season, releasing several ice masses well above the snowline 22 . The thick snow cover amplified the avalanche volume and formed a rock-ice-snow powder avalanche. Fujita et al. 23 attributed the massive destruction caused by the avalanche directly to a snow cover anomaly. The air blast nearly destroyed the Langtang village and flattened a forest on the valley counter-slope 24 .

In this paper, we reconstruct the evolution of the Langtang avalanche and generated air blasts based on documented field measurements and numerical modeling. We further conduct simulations with different snow cover depths and air temperatures to investigate the impact of snow entrainment and ambient environment, indicating how seasonal and climatic influences may affect the danger of rock-ice avalanches. The study focuses exclusively on changes in runout and flow regimes under different snow cover and air temperature conditions, rather than variability in avalanche occurrence due to climate change. Our primary goal is to quantify the danger arising from rock-ice-snow avalanches, containing both a dense flow core and dust cloud, in different mountain conditions that can be eventually associated with changing climate scenarios.

Langtang avalanche

On 25 April 2015, the Gorkha earthquake (Mw7.9) triggered a large rock-ice avalanche in the Langtang Valley, Nepal. Details of the Langtang avalanche are well recorded in existing literature. Thus, here we briefly introduce the essential information concerning this disastrous event. The avalanche was initiated as a multi-source ice avalanche (initiated in several release areas) of ~3.5 × 10 6  m 3 , and the release areas are located over 6000 m a.s.l. (Fig.  1a, b ). Satellite images (April–May 2015) indicated an evident snowline at 4000–4500 m a.s.l. Four anomalous snowfall events occurred during the previous winter (October 2014–April 25, 2015), resulting in a thick snow cover on the mountain surface 23 . Field investigation reveals that the released ice mass entrained snow (over 4500 m a.s.l.) and debris cover (below 4500 m a.s.l.) along the travel path (Fig.  1c ) 23 . According to pre- and post-event digital surface models 25 , we knew that the avalanche involved a total volume of 14.38 × 10 6  m 3 of rock, ice, and snow mass. Of this, a total of 6.95 × 10 6  m 3 accumulated in the Langtang Valley 25 . The Langtang village is located on the valley floor (Fig.  1d, e ) and was not struck directly by the avalanche core. The air blast generated by the avalanche destroyed large parts of the village and caused over 350 deaths. This is confirmed by field observations showing that many houses constructed of stone slabs were flattened or destroyed by the air blast 24 (Supplementary Fig.  1a ). Furthermore, the air blast impacted an area of 1 km up and down the valley and flattened a forest on the opposite mountain (Fig.  1e , Supplementary Fig.  1b ), as described by Kargel et al. 22 .

a The release area from Langtang Lirung. The size of released materials helps estimate the initial volume. b The direction from where additional ice was released from around Langtang II. Both are shown in detail in the right part of the figure. c The plateau where the rock entrainment occurred. d The headwall above the village (height ~500 m). e Shows the view angle of the photo as an inset where destroyed houses ( e1 ), blown-over trees ( e2 ), and deposited ice–debris mix on the far side of the valley ( e3 ) are shown. Images taken from the helicopter by D.F. Breashears/GlacierWorks.

Two local meteorological stations record the air temperature: Kyanjing station at 3862 m a.s.l. (6.3 km away from the Langtang village) and Yala Base camp station at 5058 m a.s.l. (10.2 km from the Langtang village) 26 . The average measured temperature on 25 April was −0.65 to 9.05 °C at Kyanjing station (2012–2014, 2016–2018) and −8.10 to 0.18 °C at Yala Base camp station (2013, 2016–2018). Another pluviometer at 4831 m a.s.l. recorded an air temperature of −0.4° to 3.6° within 30 min before the avalanche occurred, indicating a warm environment 23 . The Gorkha earthquake occurred at 11:56 Nepal Standard Time, therefore a high temperature (9 and 0 °C at 3862 and 5058 m a.s.l., arising from the recorded max value) is applied for the following analysis, indicating an air temperature gradient of ~0.75 °C per 100 m elevation difference. According to Fujita et al. 23 , the snow depth at Yala station was ~1.5 m. Using this snow depth value, combined with the location of the snow line, we derived a preliminary estimate of the snow cover gradient. Based on this estimate, the snow depth at the release area was calibrated, ensuring that the entrained and total avalanche volumes match the measured data.

Langtang avalanche and the generated air blast

Modeling results of the Langtang avalanche are presented in Fig.  2 . The snow depth at the release area is estimated to be 3 m, and a gradient of −0.15 m per 100 m is determined with this depth value and the location of the snow line. The modeled released ice volume is 3.65 × 10 6  m 3 , and the sliding mass entrained snow and rock materials of 11.20 × 10 6 m 3 during the movement process (simulated total volume of 14.85 × 10 6 m 3 ). The error between the simulated and actual volumes (initial volume of 3.50 × 10 6 and total volume of 14.38 × 10 6  m 3 ) is within 5%. Two main deposit areas are observed in the simulation: the platform at 4500 m a.s.l. and the Langtang Valley (Fig.  2a ). The depth of deposits in the Langtang Valley is over 30 m, as observed by Fujita et al. 23 . The simulated avalanche volume and the deposit area match these observations. The calculated avalanche core reached the maximum velocity of over 90 m s −1 at 5000–5500 m a.s.l. When the avalanche passed the Langtang Valley, immediately before hitting the toe of the opposite mountain, it was traveling at 57 m s −1 (Fig.  2b ), matching the velocity estimated by the run-up equation (63 m s −1 , Kargel et al. 22 ).

a Final deposit distribution of the avalanche core, matching the observations (red lines). b Maximum velocity of the avalanche core. c Maximum mean pressure of the air blast. d and e Powder pressure profile at the Langtang village (point A in c ) and the forest at the mountain toe (point B in c ). The satellite image arises from Planet (Images ©2021 Planet Labs PBC).

Figure  2c shows the dynamic pressure of the Langtang avalanche-induced air blast. The air blast shows an impact area far beyond the avalanche core, covering the whole Langtang village and the forest on the opposite mountainside. In the Langtang village, the dynamic pressure of the air blast exceeds 15 kPa but decreases gradually to 6 kPa as it moves over the village (a rule of thumb used by avalanche engineers is that a 1 kPa air blast will violently blow a human to the ground at impact). The turbulent fluctuations greatly magnify the air blast pressure. At a specific location (simulation point) near Langtang village (point A in Fig.  2c ), the maximum pressure reaches 28 kPa (Fig.  2d ), nearly double the pressure arising from the mean velocity. Such high pressures are capable of destroying houses, as observed. On the opposite mountain slope, the pressure of the air blast reaches a mean of 10 kPa and a maximum of 18 kPa at the mountain toe (Fig.  2e ) but decreases substantially as it climbs up the mountain. Tree-breakage calculations follow the method proposed by Feistl et al. 27 . Trees on the opposite mountain face are Abies and Rhododendron species with an average diameter of 0.16 m 23 . Using a bending strength of 72 MPa, modeling results indicate a tree-breakage area of about 0.8 km 2 , extending 1 km up and down the valley from the deposit center and 550 m up the mountain (Fig.  2c ). The calculated tree-breakage area essentially matches the observations 22 .

Impact of snow entrainment

We set up scenarios to investigate the impact of snow cover on the dynamics of rock-ice avalanches and air blasts. The snow cover at the release area is set at 0–3 m in depth with a constant gradient of −0.15 m per 100 m. The air temperature remains fixed. In the RAMMS model, the snow cover is adjusted based on the slope angle and curvature along with the elevation gradient to produce a realistic snow distribution on mountainous terrain. Snow cover distributions are shown in Fig.  3a–d .

a – d Snow cover distribution in different scenarios. Red dotted line represents the calculated domain. e – h Impacts of snow cover on the avalanche extent. i – l Impacts of snow cover on the air blast dynamics. m – p Pressure profile at the Langtang village (Point A in i ) in cases of different snow covers. The satellite image arises from Planet (Images ©2021 Planet Labs PBC).

Modeling results indicate a longer runout distance and higher mobility in the case of a thick snow cover (Fig.  3e–h ). When the snow cover at the release area reaches 3 m, which corresponds to the modeling results in Fig.  2 , the calculated avalanche dynamics match the observed conditions well. In the case without considering snow entrainment (snow cover = 0 m), the rock-ice avalanche stops before hitting the opposite mountain. The sliding mass primarily deposits at the 4500 m a.s.l. platform, with only a little mass reaching the Langtang Valley. For the air blast hazards, the cloud dynamics are again related to the thickness of the snow cover. Both the impact area and dynamic pressure of the air blast decrease in the case of a thin snow cover (Fig.  3i–p ). When there is no snow cover, the mean dynamic pressure at the Langtang village is only 2.5 kPa, and only a few trees are damaged on the opposite mountain (Fig.  3l, p ), showing minor destruction compared with the actual event. This implies that the entrained snow facilitates the formation of a dispersed powder avalanche and is a primary factor that led to the Langtang disaster. Our numerical results strongly support the hypothesis of Fujita et al. 23 .

Impacts of ambient temperature

Further scenarios of different air temperatures are designed to investigate the impact of temperature variation on the danger arising from rock-ice avalanches, as presented in Fig.  4 . The air temperature at 3862 m a.s.l. (Kyanjing meteorological station) is set −1 to 19 °C with a constant gradient of 0.75 °C per 100 m. Here the snow cover depth remains fixed. Modeling results indicate that the avalanche core shows higher mobility in the case of a warm environment. When the air temperature at 3862 m a.s.l. reaches −1 °C, the heat transfer with the cold air restricts the melting of snow and ice. The meltwater is 74,000 t at the end of the event (Fig.  4g ), and the maximum water content in the avalanche core is ~600 mm m −3 (Fig.  4d ). In this scenario, relatively small amounts of material deposit in the Langtang Valley (Fig.  4a ) compared with the actual conditions. When the air temperature is 19 °C at 3862 m a.s.l., a warm environment, the produced meltwater reaches 170,000 t (Fig.  4i ), more than twice the amount calculated for a cold environment. The maximum water content in the avalanche core reaches over 1800 mm m −3 (Fig.  4f ), a high value. The rheological relationship applied here is an ever-decreasing Coulomb friction resistance with increasing water content 13 :

where \({\mu }_{\Phi }\) is the dry friction coefficient, \({\mu }_{\min }\) is the fully lubricated friction coefficient, representing the lowest friction, \({m}_{{\rm {w}}}\) is the water height in the flow and \({m}_{0}\) is the reference height. The friction decreases from \({\mu }_{\Phi }\) (dry avalanche) to \({\mu }_{\min }\) (fully lubricated flow) with the increase in water content. In the case of a warm environment (Fig.  4f ), the abundant amount of meltwater at the avalanche front provides lubrication (decreases the friction, Supplementary Fig.  2 ) leading to a fluid-like flow regime in the Langtang Valley and thus a long runout distance (Fig.  4c ). The avalanche moved over 1.5 km downstream of the Langtang Valley. For the Langtang avalanche, due to the low temperature of the released materials, the meltwater appears at ~20 s after the avalanche initiation and accumulates in the frontal lobe of the avalanche deposits (Fig.  4e, h ).

a – c Impacts of air temperature on the avalanche extent. d – f Impacts of air temperature on the water content distribution within the avalanche core. g – i Impacts of air temperature on meltwater production. Here, the air temperature is set as −1, 9, and 19 °C at 3862 m a.s.l. with a constant temperature gradient. The satellite image arises from Planet (Images ©2021 Planet Labs PBC).

Discussion and implications

Scientists suggest that ongoing climate change favors the initiation of rock-ice avalanches, but its impacts on avalanche formation and flow are rarely quantified 3 , 6 , 28 . With this purpose, using the Langtang avalanche, we numerically investigate two important phenomena arising from climate change to the destructive potential of rock-ice avalanches: snowfall anomalies and global warming.

Snowfall anomalies

For a rock-ice avalanche, snowfall anomalies will exacerbate the avalanche mobility and lead to an air blast problem. Anomalous snowfall and the associated thick snow cover favor snow entrainment in the mountain environment. This process can magnify the avalanche volume and change the flow behavior. Compared with ice and rock mass, snow is easily entrained because of the low shear stress threshold 29 . Snow is a material of low friction and can decrease the equivalent Coulomb frictional coefficient (in Eq. ( 13 )) of the avalanche core. One primary consequence of the snow entrainment is the increment of flow height, which increases the normal and shear stress acting on the ground surface. Combined with the entrainment process 12 , the additional shearing work attributable to the shear stress increment exacerbates the heat energy production within the avalanche core, therefore favoring meltwater production. The lubrication effect of the meltwater greatly enhances the avalanche mobility. Furthermore, the flowing resistance is a function of fluctuation energy \({R}_{\Phi }\) (Eq. ( 13 )), again related to the shearing work and the flow height. Chute experiments 30 indicate that friction decreases in proportion to the increase in the gravitational work rate or the shearing work rate. Without considering the entrainment process, there is no mass source for avalanches in motion. For a finite-sized avalanche, shear gradients within the avalanche core cause the flow height to decrease. The shearing work rate and production of fluctuation energy subsequently decline, and friction increases. The avalanche begins to starve and decelerate, eventually stopping on the slope.

As for the air blast hazard, the process of snow entrainment facilitates the formation of a rock–ice–snow avalanche with a dense granular core consisting of rock and ice fragments and a dust cloud of suspendable particles. The initial mass and momentum of the air blast are known to arise from the air movements caused by displacing air in the avalanche core 14 . The entrained snow is, therefore, an important mass/momentum source to transfer mass and momentum to the powder cloud, magnifying its impact area and dynamic pressure. In the case without snow entrainment, the Langtang avalanche-induced air blast hardly destroys the Langtang village and the forest (Fig.  3l ), principally different from the actual conditions. This implies air blast hazards could show a higher destructive force after an intense snowfall event. We suggest the previous snowfall anomalies and the snow entrainment were a primary factor that caused the Langtang disaster.

Heat exchange with air temperature

For rock-ice avalanches, the generation of heat energy and the phase change processes are of paramount interest due to the unique properties of ice and snow 18 , 31 . The components of snow and ice reside close to their melting points, and the frictional heat generated during the avalanche propels these materials toward a phase transition. This process is tempered by the ambient air temperature. Recorded videos 3 and our detailed investigations into avalanche evolution provide compelling evidence of the physical interaction between the avalanche core and the ambient air temperature. The avalanche core entrains the ambient air during the movement process and outbursts the dust-mixed air to generate the powder cloud. The Reynolds number of natural rock-ice avalanches 32 reaches 10 4 −10 6 . Such a turbulent structure facilitates the mixture of the granular particles and entrained air, therefore exacerbating a heat exchange between the avalanche core with the ambient environment. Our modeling results of the Langtang avalanche suggest a warm air temperature amplifies the meltwater production and lubricates the flowing mass.

For simplification and engineering applications, we used an experimentally based heat transfer relationship for sphere particles 33 . Though the impacts of particle shape and porosity are ignored, this relationship is sensitive to the particle size. More precisely, the particle surface area. According to the heat transfer relationship (Eq. ( 12 )):

The heat transfer is proportional to r −1.5 – r −2.0 . In this study, due to the lack of accurate particle size distribution, particle sizes for snow (7 cm), ice (10 cm), and rock (30 cm) are determined based on our practical experience with numerous historical avalanches and the existing literature 34 , 35 . This implies heat energy exchange between snow particles with air reaches 8.9–18.4 times rock particles of the same volume. For the Langtang avalanche, the volume of the entrained snow accounts for >70% of the total volume 23 . The process from avalanche initiation to deposition lasts ~3 min, and the air temperature in the Langtang Valley reaches ~14 °C. The small-sized snow particles, prolonged movement, and warm air temperature intensify the heat exchange and meltwater production, resulting in a flow-like movement in the Langtang Valley. Sensitivity analysis indicates a longer runout distance, more meltwater production, and a highly fluid flow regime of avalanches in a warm environment. The impacts of heat transfer with the ambient air on meltwater production and avalanche runout can be even higher than the contribution of snowpack temperature (see extra simulations presented in Supplementary Fig.  3 ).

Notably, the Gorkha earthquake and Langtang avalanche occurred at noon, in a warm environment. If the avalanche occurs at night, a cold environment hinders the meltwater production and leads to a smaller runout and impact area that is similar to the scenario depicted in Fig.  4a . We are not able to predict when a catastrophic earthquake will occur, but our analysis of air temperature impacts indicates that the destructive potential of rock-ice avalanches is not only related to large-scale global warming but also to short-term diurnal temperature.

Risk assessments of rock-ice avalanches are a primary concern in high-altitude mountainous regions. Confronted with the problem of extreme and rare events and the lack of historical documentation, avalanche dynamics modeling will play a key role in assessing the safety of settlements, transportation routes, and hiking trails. To our knowledge, the impacts of climate change on the destructive potential of rock–ice avalanches are multifaceted, but rarely considered. We suggest the snowfall anomalies and warm air temperature greatly contributed to the Langtang disaster.

Conclusions

At noon on April 25, 2015, the Mw 7.9 Gorkha earthquake precipitated a large rock–ice avalanche in the Langtang Valley. The resultant air blasts obliterated the Langtang village, a nearby forest, and led to the tragic loss of over 350 lives. Through meticulous field investigations and advanced numerical modeling, we have identified two principal factors contributing to the severity of the Langtang disaster: anomalous snowfall and elevated temperatures. The substantial snow cover facilitated snow entrainment, promoting the development of a dispersed powder avalanche. This entrained snow significantly increased the avalanche volume, acted as a lubricant for the flowing mass, and amplified the destructive power of the air blast. Furthermore, the warm air temperature, in conjunction with the turbulent structure of the avalanche core, intensified heat exchange between the granular particles and the entrained air. This thermal process enhanced meltwater production, consequently altering the avalanche’s mobility. These mechanisms were crucial in intensifying the Langtang disaster. We emphasize the critical finding that incorporating the effects of snow cover and air temperature factors heavily influenced by seasonal and climatic changes—is essential when performing hazard analyses for high-altitude rock-ice avalanches. Our results reveal the inherent fragility of natural systems, particularly the mobility and destructive potential of rock-ice avalanches. Minor variations in temporal factors, climatic conditions, or the antecedent state of an event can either intensify or mitigate disastrous outcomes. This insight compels us to ponder how many disasters have been averted or caused by seemingly insignificant changes in the natural environment.

Dynamic model of rock–ice avalanche

General framework.

We apply a depth-averaged model to simulate the flow dynamics of the rock-ice avalanche and the generated air blast 19 , 36 . The avalanche core Φ and the air blast Π are described by two distinct depth-averaged layers. The core layer comprises a granular ensemble of rock, ice, and snow particles capable of dispersion and compression, changing the interstitial air space of the core. The core movement is strongly influenced by the surface terrain and allows for both air intake and outburst. During the outburst process, the avalanche core transmits mass and momentum to the cloud Π and creates a turbulent structure in the cloud 14 . The cloud is, therefore, modeled as a turbulent flow, including suspensions of ice and rock dust that are transferred from the avalanche core. The governing equations for both the avalanche core and dust cloud are solved using well-established finite volume schemes within the software rapid mass movement simulation (RAMMS) 36 .

Avalanche core Φ

Here we follow the avalanche core model 19 , 36 . The model involves some essential processes of an avalanche flow, including entrainment of path materials 37 and meltwater production 19 . In the model, the movement of the avalanche core Φ is described by three state valuables: the so-called co-volume height \({\hat{h}}_{\Phi }\) , the dispersed or flowing height \({h}_{\Phi }\) and the depth-averaged velocity \({\vec{u}}_{\Phi }\) that parallel to the slope surface. The co-volume represents the densest packing of ice/rock granules found in the deposit area, corresponding to a co-volume density \({\hat{\rho }}_{\Phi }\) . Because the avalanche core is a multiphase flow of mixed components, the co-volume height \({\hat{h}}_{\Phi }\) is defined as

where \({\rho }_{{\rm {i}}}\) , \({\rho }_{{\rm {s}}}\) , \({\rho }_{{\rm {r}}}\) , \({\rho }_{{\rm {w}}}\) , \({\hat{h}}_{{{\rm{i}}}}\) , \({\hat{h}}_{{{\rm{s}}}}\) , \({\hat{h}}_{{{\rm{r}}}}\) , \({\hat{h}}_{{{\rm{w}}}}\) are the material density and co-volume height of ice, snow, rock, and water, respectively. The model assumes both constant density and velocity profiles of each material. The primary governing equations are as follows:

Equations ( 4 ) and ( 7 ) represent the mass and momentum balances of the avalanche core, which involves the avalanche entrainment \({\dot{M}}_{\Sigma \to \Phi }\) and mass/momentum transfer to the cloud \({\dot{M}}_{\Phi \to \Pi }\) . Equation ( 5 ) describes the dispersive movement of the avalanche core. The term \({\mathbb{D}}(t,{k}_{z},{\dot{k}}_{z},{\ddot{k}}_{z})\) represents the variation of core height due to dispersive pressure effects; we employ the model presented in Buser and Bartelt 38 . Equation ( 6 ) is the mass balance of each phase in the core, including rock, ice, snow, and water. The mass change of each phase arises from the entrainment process, air blast initiation, and phase change. Equations ( 8 ) and ( 9 ) show the balance of fluctuation energy and heat energy. Equation ( 10 ) describes the production and transport of meltwater in the avalanche core. The meltwater arises from both the ice melting and snow melting. The model assumes the mean core temperature \({T}_{\Phi }\) never exceeds the melting temperature of \({T}_{{\rm {m}}}\) until all the ice and snow melt. The heat energy applied for meltwater production is presented in Eq. ( 11 ). The parameters included in Eqs. ( 4 )–( 11 ) are well described by Bartelt et al. 19 and Munch et al. 39 and are listed in Table  1 .

In the heat energy balance of Eq. ( 9 ), the heat transfer from avalanche to the ambient, entrained air is newly involved, defined as

where \({A}_{{{\rm{s}}}}=n\pi {r}^{2}\) , \(n=\frac{{3\rho }_{\Phi }{h}_{\Phi }A}{4{\rho }_{g}\pi {r}^{3}}\) is the particle number, \({\rho }_{{{\rm{g}}}}\) is the particle density, r is the particle radius, \({T}_{\Phi }-{T}_{\Lambda }\) represents the temperature difference between the avalanche core \({T}_{\Phi }\) and the ambient air \({T}_{\Lambda }\) . \(H=\frac{{{{\rm{Nu}}}}\cdot {k}_{{{\rm{a}}}}}{2r}\) is the experimentally based heat transfer coefficient 33 , \({k}_{{{\rm{a}}}}=\) 0.0257 W m −1  K −1 is the thermal conductivity, \({{{Nu}}}=2+0.6{{\mathrm{Re}}}^{\frac{1}{2}}{\Pr }^{\frac{1}{3}}\) is the dimensionless Nusselt number, \({Re}=\frac{2{ur}}{{\nu }_{{{\rm{a}}}}}\) is the Reynolds number, \(Pr =\frac{{c}_{{{\rm{a}}}}{\nu }_{{{\rm{a}}}}}{{k}_{{{\rm{a}}}}}\) is the Prandtl number of the air, \({c}_{{{\rm{a}}}}\) is the specific heat capacity of the air.

A fundamental feature of the avalanche core model is the partitioning of a primary dissipative process, shearing, into the production of heat \({E}_{\Phi }\) (internal energy) and non-directional kinetic energy \({R}_{\Phi }\) (granular temperature). The work done by shearing \({\dot{W}}_{\Phi }={S}_{\Phi }{{\rm{||}}}{\vec{u}}_{\Phi }{{\rm{||}}}\) is divided into microscopic and macroscopic random energy (heat and granular fluctuations) by parameter \({\alpha }_{\Phi }\) 38 . Shearing is controlled by the process-based Voellmy-type rheology 40 :

where ( \({\mu }_{\Phi }\) , \({\xi }_{\Phi }\) ) are the Coulomb and turbulent friction coefficients, respectively, defined as functions of the fluctuation energy \({R}_{\Phi }\) , temperature \({T}_{\Phi }\) and water content \({m}_{{{\rm{w}}}}\) . The flow friction \({S}_{\Phi }\) is dependent on the dispersive properties of the random energy \({R}_{\Phi }\) , avalanche mobility, and, therefore, the formation of the powder cloud is strongly influenced by the shearing process.

Powder cloud Π

A similar set of partial differential equations is proposed to describe the movement of the powder cloud Π. To track the mass changes of different materials in the cloud, an improvement of the model is to suggest the powder cloud as a mixture of a rock powder cloud \({\Pi }_{{{\rm{r}}}}\) and an ice powder cloud \({\Pi }_{{\rm{{i}}}}\) . Therefore, the cloud density depends on the volumetric ratio of rock and ice within the cloud. We begin by presenting the mass balance equations:

Equations ( 14 ) and ( 15 ) represent the mass balance of the rock powder cloud \({\Pi }_{{{\rm{r}}}}\) and ice powder cloud \({\Pi }_{{{\rm{i}}}}\) , respectively, and Eq. ( 16 ) described the mass balance of the total powder cloud Π. Similar to the core, ( \({\hat{h}}_{\Pi {{\rm{r}}}}\) , \({\hat{h}}_{\Pi {{\rm{i}}}}\) , \({\hat{h}}_{\Pi }\) ) represents the initial height of \({\Pi }_{{{\rm{r}}}}\) , \({\Pi }_{{{\rm{i}}}}\) and Π, respectively, and are given by the initial cloud density ( \({\hat{\rho }}_{\Pi {{\rm{r}}}}\) , \({\hat{\rho }}_{\Pi {{\rm{i}}}}\) and \({\hat{\rho }}_{\Pi }\) ) before blowing out from the avalanche core \(\Phi\) . The true cloud height \({h}_{\Pi }\) is affected by clouds ejected from the core ( \({\dot{M}}_{\Phi \to \Pi {{\rm{r}}}}\) , \({\dot{M}}_{\Phi \to \Pi {{\rm{i}}}}\) ) and ambient air entrainment \({\dot{M}}_{\Lambda \to \Pi }\) . Due to this air entrainment, the cloud height increases during the propagation process and the density decreases to \({\rho }_{\Pi }\) , satisfying \({\rho }_{\Pi }={\rho }_{{{\rm{i}}}}\frac{{\nu }_{i}{\hat{h}}_{\Pi {{\rm{i}}}}}{{h}_{\Pi }+{\nu }_{i}{\hat{h}}_{\Pi {{\rm{i}}}}+{\nu }_{r}{\hat{h}}_{\Pi {{\rm{r}}}}}+{\rho }_{{{\rm{r}}}}\frac{{\nu }_{r}{\hat{h}}_{\Pi {{\rm{r}}}}}{{h}_{\Pi }+{\nu }_{i}{\hat{h}}_{\Pi {{\rm{i}}}}+{\nu }_{r}{\hat{h}}_{\Pi {{\rm{r}}}}}+{\rho }_{\Lambda }\frac{{h}_{\Pi }}{{h}_{\Pi }+{\nu }_{i}{\hat{h}}_{\Pi {{\rm{i}}}}+{\nu }_{r}{\hat{h}}_{\Pi {{\rm{r}}}}}\) , \({\rho }_{{{\rm{i}}}}\)  = 971 kg m −3 is the ice density, \({\rho }_{{{\rm{r}}}}\)  = 2500 kg m −3 is the rock density, \({\rho }_{\Lambda }\)  = 1.225 kg m −3 is the air density ( \({\nu }_{{{\rm{i}}}}=\frac{{\hat{\rho }}_{\Pi {{\rm{i}}}}-{\rho }_{\Lambda }}{{\rho }_{{{\rm{i}}}}-{\rho }_{\Lambda }}\) , \({\nu }_{{{\rm{r}}}}\)  =  \(\frac{{\hat{\rho }}_{\Pi {{\rm{r}}}}-{\rho }_{\Lambda }}{{\rho }_{{{\rm{r}}}}-{\rho }_{\Lambda }}\) ) represents the solid fraction in the initial ice and rock powder cloud.

The momentum balance of the powder cloud is

The mixed ice/rock powder cloud moves with a mean velocity vector \({\vec{u}}_{\Pi }\) . The cloud is driven by the initial momentum transferred from the avalanche core \({\dot{M}}_{\Phi \to \Pi }{\vec{u}}_{\Phi }\) and the gravity \(\frac{\left({\rho }_{\Pi }-{\rho }_{\Lambda }\right)}{{\rho }_{\Pi }}\vec{G}\) .

Another important improvement of the proposed model is the inclusion of turbulence. The instantaneous air blast velocity \({\widetilde{u}}_{\Pi }\) is written as the sum of a mean \({\vec{u}}_{\Pi }\) and a fluctuating component \({\vec{u}{\prime} }_{\Pi }\) . The energy associated with the fluctuation of the granules \({R}_{\Pi }\left(x,y,z,t\right)\) can be written as 41

The fluctuation energy is divided into three orthogonal components in the x , y , z directions. Here the velocity fluctuation is assumed to be isotropic, and thus \({R}_{\Pi ,x}={R}_{\Pi ,y}={R}_{\Pi ,z}=\frac{1}{3}{R}_{\Pi }\) . The balance equation of the fluctuation energy can be written as:

We suggest the fluctuation energy has three sources: fluctuation energy that is created in the avalanche core and transported to the cloud \({\dot{M}}_{\Phi \to \Pi }{R}_{\Phi }\) , internal shearing \({\dot{W}}_{\Pi }=\left[{S}_{\Pi }\right]{{\rm{||}}}{\vec{u}}_{\Pi }{{\rm{||}}}\) and air entrainment \(\frac{1}{2}{\rho }_{\Lambda }{\dot{M}}_{\Lambda \to \Pi }{u}_{\Pi }^{2}\) . The fluctuation energy \({R}_{\Pi }\) of the cloud has a short lifetime and its dissipation is controlled by a decay coefficient \({\beta }_{\Pi }\) 15 .

In the air blast model, the air entrainment \({\dot{M}}_{\Lambda \to \Pi }\) and drag resistance \({S}_{\Pi }\) are defined as a contribution of both laminar and turbulent parts. Air entrainment is suggested as a function of the turbulent velocity 42 , 43 , the square root of the turbulent energy \({R}_{\Pi }\) : \({\dot{M}}_{\Lambda \to \Pi }={\alpha }_{{{\rm{L}}}}\left({\rho }_{\Pi }-{\rho }_{\Lambda }\right)+{\alpha }_{{{\rm{T}}}}\sqrt{{R}_{\Pi }{\hat{h}}_{\Pi }}\left({\rho }_{\Pi }-{\rho }_{\Lambda }\right)\) . The drag resistance is suggested as a direct function of the average velocity and turbulent energy: \({S}_{\Pi }={\rho }_{\Pi }({\mu }_{{{\rm{L}}}}{u}_{\Pi }+{\mu }_{{{\rm{T}}}}{R}_{\Pi }{\hat{h}}_{\Pi })\) . The parameters ( \({\alpha }_{{\rm{{L}}}}\) , \({\alpha }_{{{\rm{T}}}}\) ) and ( \({\mu }_{{\rm{{L}}}}\) , \({\mu }_{{{\rm{T}}}}\) ) are sets of laminar/turbulent parameters controlling air entrainment and drag resistance. These parameters, along with the turbulent decay parameter \({\beta }_{\Pi }\) control the magnitude of the avalanche air blast.

The vertical profiles of the velocity, density, and dynamic pressure, which are greatly influential to assess the air blast hazard, refer to Zhuang et al. 15 . The velocity profile follows a parabolic form and is determined using the boundary turbulent velocity values and the mean value. The density profile follows a linear profile decreasing from the bottom to the top of the cloud. Therefore, the vertical profile of the total pressure is written as \(P\left(z\right)=\frac{1}{2}\cdot {\rho }_{\Pi }\left(z\right)\cdot {{u}_{\Pi }\left(z\right)}^{2}=\frac{1}{2}\cdot {\rho }_{\Pi }\left(z\right)\cdot {[{\bar{u}}_{\Pi }(z)+{u{\prime} }_{\Pi }\left(z\right)]}^{2}\) . Here, we assume the worst case is that the fluctuation velocity is always in the same direction as the laminar mean velocity. In this study, we applied DEM arising from SPOT satellite images to do the simulation, referring to Ragettli et al. 44 .

Tree-breakage calculation

The assessment of air blast-induced tree breakage follows the method proposed by Feistl et al. 27 . The tree bending stress arising from the air blast loading is

where \({c}_{\Pi }\) is the drag coefficient, \({\widetilde{u}}_{\Pi }\) is the instantaneous air blast velocity, which is the sum of the mean velocity \({\vec{u}}_{\Pi }\) and turbulent velocity \(u{\prime}\) , \({\rho }_{\Pi }\) is the cloud density, ( \(w\) , \(H\) , \(d\) ) represents the stem diameter, height and effective crown width of trees, respectively, \(\gamma\) is slope angle. The tree breakage occurs when the bending stress \({\sigma }_{\Pi }\) exceeds the experienced tree strength.

Data availability

The dataset of air temperature used in this study is available at: https://doi.org/10.6084/m9.figshare.26178256 .

Code availability

The RAMMS::RockIce model used in this study is available at https://ramms.ch/ .

Huggel, C., Caplan-Auerbach, J., Waythomas, C. F. & Wessels, R. L. Monitoring and modeling ice-rock avalanches from ice-capped volcanoes: a case study of frequent large avalanches on Iliamna Volcano, Alaska. J. Volcanol. Geotherm. Res. 168 , 114–136 (2007).

Article   CAS   Google Scholar  

Schneider, D. et al. Insights into rock‐ice avalanche dynamics by combined analysis of seismic recordings and a numerical avalanche model. J. Geophys. Res.: Earth Surf. 115 , F04026 (2010).

Google Scholar  

Shugar, D. H. et al. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science 373 , 300–306 (2021).

Gruber, S. et al. Review article: Inferring permafrost and permafrost thaw in the mountains of the Hindu Kush Himalaya region. Cryosphere 11 , 81–99 (2017).

Article   Google Scholar  

Ballesteros-Cánovas, J. A., Trappmann, D., Madrigal-González, J., Eckert, N. & Stoffel, M. Climate warming enhances snow avalanche risk in the Western Himalayas. Proc. Natl Acad. Sci. USA 115 , 3410–3415 (2018).

Kääb, A. et al. Massive collapse of two glaciers in western Tibet in 2016 after surge-like instability. Nat. Geosci. 11 , 114–120 (2018).

Fan, X. M. et al. Imminent threat of rock-ice avalanches in High Mountain Asia. Sci. Total Environ. 836 , 155380 (2022).

Bai, L. et al. Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow-dominated region of China. Int. J. Climatol. 39 , 5702–2719 (2019).

Lin, W. & Chen, H. Changes in the spatial–temporal characteristics of daily snowfall events over the Eurasian continent from 1980 to 2019. Int. J. Climatol. 42 , 1841–1853 (2022).

Marshall, A. M., Link, T. E., Robinson, A. P. & Abatzoglou, J. T. Higher snowfall intensity is associated with reduced impacts of warming upon winter snow ablation. Geophys. Res. Lett. 47 , e2019GL086409 (2020).

Sovilla, B., Burlando, P. & Bartelt, P. Field experiments and numerical modeling of mass entrainment in snow avalanches. J. Geophys. Res.: Earth Surf. 111 , F03007 (2006).

Vera Valero, C., Wikstroemjones, K., Bühler, Y. & Bartelt, P. Release temperature, snow-cover entrainment and the thermal flow regime of snow avalanches. J. Glaciol. 61 , 173–184 (2015).

Vera Valero, C., Wever, N., Christen, M. & Bartelt, P. Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout. Nat. Hazards Earth Syst. Sci. 18 , 869–887 (2018).

Bartelt, P., Buser, O., Vera Valero, C. & Bühler, Y. Configurational energy and the formation of mixed flowing powder snow and ice avalanches. Ann. Glaciol. 57 , 179–188 (2016).

Zhuang, Y. et al. Tree blow-down by snow avalanche air blasts: dynamic magnification effects and turbulence. Geophys. Res. Lett. 50 , e2023GL105334 (2023).

Vinnikov, K. Y. & Grody, N. C. Global warming trend of mean tropospheric temperature observed by satellites. Science 302 , 269–272 (2003).

Hansen, J. et al. Global temperature change. Proc. Natl Acad. Sci. USA 103 , 14288–14293 (2006).

Pudasaini, S. P. & Krautblatter, M. A two-phase mechanical model for rock-ice avalanches. J. Geophys. Res.: Earth Surf. 119 , 2272–2290 (2014).

Bartelt, P., Christen, M., Bühler, Y. & Buser, O. Thermomechanical modelling of rock avalanches with debris, ice and snow entrainment. Numer. Methods Geotech. Eng. IX. 2 , 1047–1054 (2018).

Pudasaini, S. P., & Hutter, K. Avalanche Dynamics: Dynamics of Rapid Flows of Dense Granular Avalanches (Springer-Verlag, Berlin, 2007).

Kisuka, F., Hare, C. & Wu, C. Y. Heat generation during oblique particle impact. Powder Technol. 422 , 118481 (2023).

Kargel, J. S. et al. Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake. Science 351 , aac8353 (2016).

Fujita, K. et al. Anomalous winter-snow-amplified earthquake-induced disaster of the 2015 Langtang avalanche in Nepal. Nat. Hazards Earth Syst. Sci. 17 , 749–764 (2017).

Zhuang, Y., Xu, Q., Xing, A., Bilal, M. & Gnyawali, K. R. Catastrophic air blasts triggered by large ice/rock avalanches. Landslides 20 , 53–64 (2023).

Lacroix, P. Landslides triggered by the Gorkha earthquake in the Langtang valley, volumes and initiation processes. Earth Planets Space 68 , 1–10 (2016).

Steiner, J. F. et al. Multi-year observations of the high mountain water cycle in the Langtang catchment, Central Himalaya. Hydrol. Process. 35 , e14189 (2021).

Feistl, T. et al. Forest damage and snow avalanche flow regime. Nat. Hazards Earth Syst. Sci. 15 , 1275–1288 (2015).

Yang, Q., Su, Z., Cheng, Q., Ren, Y. & Cai, F. High mobility of rock-ice avalanches: insights from small flume tests of gravel–ice mixtures. Eng. Geol. 260 , 105260 (2019).

Föhn, P. M. B., Camponovo, C. & Krüsi, G. Mechanical and structural properties of weak snow layers measured in situ. Ann. Glaciol. 26 , 1–6 (1998).

Bartelt, P., Buser, O. & Platzer, K. Starving avalanches: frictional mechanisms at the tails of finite-sized mass movements. Geophys. Res. Lett. 34 , L20407 (2007).

Sansone, S., Zugliani, D. & Rosatti, G. A mathematical framework for modelling rock-ice avalanches. J. Fluid Mech. 919 , A8 (2021).

Dong, Z. B. & Su, L. J. Flow regimes and basal normal stresses in rock–ice avalanches by experimental rotating drum tests. Cold Reg. Sci. Technol. 218 , 104081 (2024).

Ranz, W. & Marshall, W. Evaporation from drops. Chem. Eng. Prog. 48 , 141–146 (1952).

CAS   Google Scholar  

Bartelt, P. & McArdell, B. W. Granulometric investigations of snow avalanches. J. Glaciol. 55 , 829–833 (2009).

Gnyawali, K. R., Xing, A. G. & Zhuang, Y. Dynamic analysis of the multistaged ice-rock debris avalanche in the Langtang valley triggered by the 2015 Gorkha earthquake, Nepal. Eng. Geol. 265 , 105440 (2020).

Christen, M., Kowalski, J. & Bartelt, P. Numerical simulation of dense snow avalanches in three-dimensional terrain. Cold Reg. Sci. Technol. 63 , 1–14 (2010).

Frank, F. et al. Debris-flow modeling at Meretschibach and Bondasca catchments, Switzerland: sensitivity testing of field-data-based entrainment model. Nat. Hazards Earth Syst. Sci. 17 , 801–815 (2017).

Buser, O. & Bartelt, P. Production and decay of random kinetic energy in granular snow avalanches. J. Glaciol. 55 , 3–12 (2009).

Munch, J., Bartelt, P. & Christen, M. Multi-component avalanches for rock-and ice-falls to potential debris flow transition modelling. E3S Web Conf. 415 , 01017 (2023).

Salm, B. Flow, flow transition and runout distances of flowing avalanche. Ann. Glaciol. 18 , 221–226 (1993).

Richard, G. L. & Gavrilyuk, S. L. A new model of roll waves: comparison with Brock’s experiments. J. Fluid Mech. 698 , 374–405 (2012).

Vallet, J., Turnbull, B., Joly, S. & Dufour, F. Observations on powder snow avalanches using videogrammetry. Cold Reg. Sci. Technol. 39 , 153–159 (2004).

Turnbull, B. & McElwaine, J. N. A comparison of powder-snow avalanches at Valle´e de la Sionne, Switzerland, with plume theories. J. Glaciol. 53 , 30–40 (2007).

Ragettli, S., Bolch, T. & Pellicciotti, F. Heterogeneous glacier thinning patterns over the last 40 years in Langtang Himal, Nepal. Cryosphere 10 , 2075–2097 (2016).

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Acknowledgements

The authors are grateful to the late D.F. Breashears from GlacierWorks for the imagery taken from the helicopter, allowing us to identify the release areas of where the glaciers detached. This work is funded by the RAMMS project.

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Yu Zhuang, Yves Bühler, Jessica Munch & Perry Bartelt

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Binod Dawadi

Kathmandu Center for Research and Education, Chinese Academy of Sciences-Tribhuvan University, Kathmandu, Nepal

Himalayan University Consortium, Lalitpur, Nepal

Jakob Steiner

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Contributions

Yu Zhuang designed the work, built the model, did the simulation, analyzed the results, and wrote the manuscript. Binod Dawadi, Rajesh Dash, and Yves Bühler did the field investigation and helped analyze the results. Jakob Steiner contributed to the paper revision and provided the release volume estimates, air temperature information, and DEM. Yves Bühler did an image analysis of the Langtang avalanche and created the satellite images. Jessica Munch helped with the numerical simulation. Perry Bartelt conceived the ideas, built the model, did the simulation, wrote and edited the manuscript.

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Correspondence to Yu Zhuang .

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Zhuang, Y., Dawadi, B., Steiner, J. et al. An earthquake-triggered avalanche in Nepal in 2015 was exacerbated by climate variability and snowfall anomalies. Commun Earth Environ 5 , 465 (2024). https://doi.org/10.1038/s43247-024-01624-z

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Received : 09 April 2024

Accepted : 13 August 2024

Published : 27 August 2024

DOI : https://doi.org/10.1038/s43247-024-01624-z

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