Assessing the effects of chemical mixtures using a Bayesian network-relative risk model (BN-RRM) integrating adverse outcome pathways (AOPs) in three Puget Sound watersheds

Chemical mixtures are difficult to assess at the individual level, but more challenging at the population level. There is still little insight of the molecular pathway for numerous chemical mixtures. We have conducted a regional-scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a Bayesian Network- Relative Risk Model (BN-RRM) incorporating a molecular pathway. We used this BN-RRM framework in a case study with organophosphate pesticide (OP) mixtures (diazinon, chlorpyrifos, and malathion) in three watersheds (Lower Skagit, Nooksack, Cedar) in the state of Washington (USA). Puget Sound Chinook salmon (Oncorhynchus tshawytscha) Evolutionary Significant Units (ESU) were chosen as population endpoints. These populations are a valuable ecosystem service in the Pacific Northwest because they benefit the region as a species that provide protection of biodiversity and are spiritually and culturally treasured by the local tribes. Laetz et al. (2009, 2013) indicated that organophosphate pesticide mixtures act synergistically to salmon and impair neurological molecular activity which leads to a change in swimming behavior and mortality, which then leads to changes in population productivity. Exposure response curves were generated for OP mixtures to connect the molecular pathway. Ecological stressors from dissolved oxygen and temperature were also included in our risk analysis. Synergism within the mixtures as well as increasing temperature and decreasing dissolve oxygen content lead to increasing risk to Puget Sound Chinook salmon populations. This research demonstrates a probabilistic approach with a multiple stressor framework to estimate the effects of mixtures through a molecular pathway and predict impacts to these valuable ecosystem services

Using metapopulation models to estimate the effects of pesticides and environmental stressors to Spring Chinook salmon in the Yakima River Basin, WA

Population-level endpoints provide ecological relevance to Ecological Risk Assessments (ERAs), because this is the level at which environmental management decisions are made. However, many population-level risk assessments do not reflect the spatial and temporal heterogeneity of the populations they represent, and thus preclude an understanding of how population dynamics and viability are affected by toxicants on a regional scale. We have developed a probabilistic ERA (specifically, a Bayesian Network-Relative Risk Model (BN-RRM)) that integrates an Adverse Outcome Pathway (AOP) framework, to quantify the sub-lethal and lethal effects of toxicants and environmental stressors on the metapopulation dynamics of salmonids. As a case study for developing this model, we have examined the impacts of organophosphate (OP) insecticides, water temperature, and dissolved oxygen on the Spring Chinook (Oncorhynchus tshawytscha) salmon metapopulation in the Yakima River Basin (YRB), Washington. A stochastic Matrix Metapopulation Model was developed using demographic data for three Spring Chinook salmon populations and one supplemental hatchery population in the YRB. Site specific data on OP contaminated habitats utilized by various salmonid life stages were incorporated into the metapopulation model by incrementally reducing survival parameters based on levels of exposure. Exposure scenarios were simulated for 200 replications of 50-year population projections using RAMAS Metapop©, and the results were incorporated into the BN-RRM. The results of this modeling effort indicated that small, wild Spring Chinook populations in the YRB have a greater probability of altered population dynamics when exposed to stressors than larger, supplemented populations. Additionally, the results indicated a seasonal effect of the stressors, with summer conditions posing a greater risk to salmon populations than winter conditions. This probabilistic ERA framework shows promise for estimating the spatiotemporal impacts of stressors on ESA-listed species (i.e., Pacific salmon) at the metapopulation level, where population dynamics and spatial structure create complex risk dynamics

Assessing the Effects of Chemical Mixtures using a Bayesian Network-Relative Risk Model (BN-RRM) Integrating Adverse Outcome Pathways (AOPs)

There are long-standing uncertainties about toxicity of chemical mixtures to populations. Laboratory toxicity tests have confirmed synergistic and antagonistic effects to individuals, but not to populations.We will conduct a regional scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a new Bayesian Network- Relative Risk Model (BN-RRM) incorporating an Adverse Outcome Pathway (AOP). We started applying this new BN-RRM framework in a case study with organophosphate pesticide mixtures (diazinon, chlorpyrifos, and malathion). Acetylcholinesterase inhibition (AChE) was chosen the molecular initiating event and the Puget Sound Chinook salmon (Oncorhynchus tshawytscha) and Coho salmon (Oncorhynchus kisutch) Evolutionary Significant Units (ESU) were chosen as population endpoints. Dose-response equations will be generated from the mixtures, integrated into the new BN-RRM framework and then overall risk will be calculated for the populations. Preliminary results indicate that organophosphate pesticide mixtures act synergistically and impair olfactory function that lead to loss of antipredator, homing and reproductive behavior which lead to changes in population age structure and patch dynamics. Assessing mixtures through this new BN-RRM framework is an innovative method of predicting effects to populations. This research will demonstrate a probabilistic approach to synthesize the effects of mixtures and predict impacts to populations

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle–Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN‐RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN‐RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50‐y timeframes. There were 3 primary conclusions drawn from the model‐ building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2020;16:28–42. © 2019 SETA

Evaluation of the public health impacts of traffic congestion: a health risk assessment

Background: Traffic congestion is a significant issue in urban areas in the United States and around the world. Previous analyses have estimated the economic costs of congestion, related to fuel and time wasted, but few have quantified the public health impacts or determined how these impacts compare in magnitude to the economic costs. Moreover, the relative magnitudes of economic and public health impacts of congestion would be expected to vary significantly across urban areas, as a function of road infrastructure, population density, and atmospheric conditions influencing pollutant formation, but this variability has not been explored. Methods: In this study, we evaluate the public health impacts of ambient exposures to fine particulate matter (PM2.5) concentrations associated with a business-as-usual scenario of predicted traffic congestion. We evaluate 83 individual urban areas using traffic demand models to estimate the degree of congestion in each area from 2000 to 2030. We link traffic volume and speed data with the MOBILE6 model to characterize emissions of PM2.5 and particle precursors attributable to congestion, and we use a source-receptor matrix to evaluate the impact of these emissions on ambient PM2.5 concentrations. Marginal concentration changes are related to a concentration-response function for mortality, with a value of statistical life approach used to monetize the impacts. Results: We estimate that the monetized value of PM2.5-related mortality attributable to congestion in these 83 cities in 2000 was approximately $31 billion (2007 dollars), as compared with a value of time and fuel wasted of$60 billion. In future years, the economic impacts grow (to over $100 billion in 2030) while the public health impacts decrease to$13 billion in 2020 before increasing to $17 billion in 2030, given increasing population and congestion but lower emissions per vehicle. Across cities and years, the public health impacts range from more than an order of magnitude less to in excess of the economic impacts. Conclusions: Our analyses indicate that the public health impacts of congestion may be significant enough in magnitude, at least in some urban areas, to be considered in future evaluations of the benefits of policies to mitigate congestion

A Systematic Review of Carcinogenic Outcomes and Potential Mechanisms from Exposure to 2,4-D and MCPA in the Environment

Chlorophenoxy compounds, particularly 2,4-dichlorophenoxyacetic acid (2,4-D) and 4-chloro-2-methylphenoxy)acetic acid (MCPA), are amongst the most widely used herbicides in the United States for both agricultural and residential applications. Epidemiologic studies suggest that exposure to 2,4-D and MCPA may be associated with increased risk non-Hodgkins lymphoma (NHL), Hodgkin’s disease (HD), leukemia, and soft-tissue sarcoma (STS). Toxicological studies in rodents show no evidence of carcinogenicity, and regulatory agencies worldwide consider chlorophenoxies as not likely to be carcinogenic or unclassifiable as to carcinogenicity. This systematic review assembles the available data to evaluate epidemiologic, toxicological, pharmacokinetic, exposure, and biomonitoring studies with respect to key cellular events noted in disease etiology and how those relate to hypothesized modes of action for these constituents to determine the plausibility of an association between exposure to environmentally relevant concentrations of 2,4-D and MCPA and lymphohematopoietic cancers. The combined evidence does not support a genotoxic mode of action. Although plausible hypotheses for other carcinogenic modes of action exist, a comparison of biomonitoring data to oral equivalent doses calculated from bioassay data shows that environmental exposures are not sufficient to support a causal relationship. Genetic polymorphisms exist that are known to increase the risk of developing NHL. The potential interaction between these polymorphisms and exposures to chlorophenoxy compounds, particularly in occupational settings, is largely unknown

A Systematic Framework for Collecting Site-Specific Sampling and Survey Data to Support Analyses of Health Impacts from Land-Based Pollution in Low- and Middle-Income Countries

The rise of small-scale and localized economic activities in low- and middle-income countries (LMICs) has led to increased exposures to contaminants associated with these processes and the potential for resulting adverse health effects in exposed communities. Risk assessment is the process of building models to predict the probability of adverse outcomes based on concentration-response functions and exposure scenarios for individual contaminants, while epidemiology uses statistical methods to explore associations between potential exposures and observed health outcomes. Neither approach by itself is practical or sufficient for evaluating the magnitude of exposures and health impacts associated with land-based pollution in LMICs. Here we propose a more pragmatic framework for designing representative studies, including uniform sampling guidelines and household surveys, that draws from both methodologies to better support community health impact analyses associated with land-based pollution sources in LMICs. Our primary goal is to explicitly link environmental contamination from land-based pollution associated with specific localized economic activities to community exposures and health outcomes at the household level. The proposed framework was applied to the following three types of industries that are now widespread in many LMICs: artisanal scale gold mining (ASGM), used lead-acid battery recycling (ULAB), and small tanning facilities. For each activity, we develop a generalized conceptual site model (CSM) that describes qualitative linkages from chemical releases or discharges, environmental fate and transport mechanisms, exposure pathways and routes, populations at risk, and health outcomes. This upfront information, which is often overlooked, is essential for delineating the contaminant zone of influence in a community and identifying relevant households for study. We also recommend cost-effective methods for use in LMICs related to environmental sampling, biological monitoring, survey questionnaires, and health outcome measurements at contaminated and unexposed reference sites. Future study designs based on this framework will facilitate consistent, comparable, and standardized community exposure, risk, and health impact assessments for land-based pollution in LMICs. The results of these studies can also support economic burden analyses and risk management decision-making around site cleanup, risk mitigation, and public health education

Association of per- and polyfluoroalkyl substance exposure with fatty liver disease risk in US adults.

Background & aimsPer- and polyfluoroalkyl substances (PFAS) are widespread pollutants with demonstrated hepatotoxicity. Few studies have examined the association between PFAS and fatty liver disease (FLD) risk in an adult population.MethodsIn this cross-sectional study of participants from the 2017-2018 National Health and Nutrition Examination Survey, serum PFAS were measured, and FLD cases were ascertained by vibration-controlled transient elastography. Logistic regression models were used to examine the association between circulating PFAS levels and FLD risk. Analyses were stratified into non-alcoholic FLD and alcoholic FLD risk groups by alcohol intake status, as well as controlling for other risk factors, including personal demographics, lifestyle factors, and related health factors.ResultsAmong 1,135 eligible participants, 446 had FLD. For FLD risk, the multivariable-adjusted odds ratio per log-transformed SD increase (ORSD) in perfluorohexane sulfonate (PFHxS) was 1.13 (95% CI 1.01-1.26). The association between PFHxS and FLD appeared stronger among individuals with obesity or high-fat diets (both p interaction <0.05). When limiting the analysis to 212 heavy drinkers (≥2 drinks/day for women and ≥3 drinks/day for men), significantly higher risk of alcoholic FLD was found for higher levels of perfluorooctanoic acid (ORSD 1.79; 95% CI 1.07-2.99), PFHxS (ORSD 2.06; 95% CI 1.17-3.65), and perfluoroheptane sulfonic acid (ORSD 1.44; 95% CI 1.00-2.07), and marginally significant higher risk for total PFAS (ORSD 2.12; 95% CI 0.99-4.54). In never or light drinkers, we did not observe any significant association between PFAS and non-alcoholic FLD. Significant positive associations were found for PFAS with aspartate aminotransferase, gamma-glutamyl transaminase, total bilirubin, and albumin (β ranged from 0.008 to 0.101, all p <0.05).ConclusionsHigher serum PFAS was moderately associated with FLD risk and worse liver function in the general population, and among those with independent risk factors, including heavy alcohol intake, obesity, or high-fat diets, PFAS increased the risk. These results suggest synergistic effects on hepatic steatosis between PFAS exposures as measured through biomonitoring data and lifestyle risk factors in a nationally representative US population.Impact and implicationsThe per- and polyfluoroalkyl substances (PFAS) may convey higher risk for chronic liver disease in humans. Among 1,135 US adults in the 2017-2018 National Health and Nutrition Examination Survey, we found that higher serum PFAS was associated with higher fatty liver disease risk and worse liver function, especially among those with liver disease risk factors, including heavy alcohol intake, obesity, or high-fat diets. Continuously monitoring PFAS in the population and examining how they potentiate risk to the liver are essential