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Pesticides are essential in modern agriculture for controlling pests and enhancing food production. However, concerns about their human and environmental health impacts have broadened discussions on their use, regulation, ethics, and sustainability. Scientific research, media coverage, input from corporations, governments, and non-governmental organizations (NGOs) shape public opinions and potentially influence regulatory decisions. This project analyzed pesticide-related discussions on Twitter/X from 2013 to 2022, focusing on information influence and propagation among individuals and organizations, advancing over prior research that looked at topic frequency, trends, and geography. Using a validated snowball sampling method, we collected over 3 million tweets from 1 million users and identified key network influencers, ie, information hubs, analyzing their content, popularity, and characteristics. Machine learning and a tailored information flow score were used to explore the dynamics of information flow and sentiment across hubs. Our analysis revealed that organizational hubs, particularly NGOs and media, were more active and had higher follower-to-following ratios than individual influencers. NGOs and media also dominated the pesticide-related discourse, while individual influencers had a lesser role. Information sources were unevenly distributed, with a dominance of retweets, news, and media posts, and a low prevalence of scientific sources. Information flow was high through NGOs, academia, and individuals, but poor from government accounts. Pesticide-focused hubs were more active and targeted in their information dissemination, with public sentiment largely negative. By delving deeper into the dynamics of information dissemination and influence networks, this study provides insights that emphasize 1) the need for better communication strategies to integrate diverse stakeholder perceptions and values, and 2) prioritizing the dissemination of credible scientific information, while also addressing sectoral disparities. Together, they can help policymakers and industry stakeholders build trust, promote transparency, and advance sustainable pesticide regulation., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Society of Environmental Toxicology and Chemistry.)

One of the practical contributions of epidemiology studies is to inform risk assessment and management to protect public health. However, there is a perception among some that environmental and occupational epidemiology is falling short of satisfying the needs of risk assessors. The specific reasons for this are not clearly understood. To help identify the points of dissatisfaction and possible areas for mutual learning, we conducted a survey of risk assessors, seeking their opinions of epidemiology research. We present a few quantitative measures and a thematic analysis of responses to open-ended questions. Survey results suggest that some risk assessors (with some adamant exceptions) believe that epidemiology has great potential to contribute to risk assessment but can be deficient in many ways. For example, respondents identified the lack of full disclosure of methods, deficiencies in exposure assessment, the absence of comprehensive uncertainty analyses, and the failure to investigate or explore thresholds of effects as some of the common shortcomings. These could be straightforward to address. Respondents also brought up a wide collection of more complicated and subtle concerns that could lead to further improvement of useful results. We identify areas where mutually-educating interdisciplinary dialogue seems particularly promising. Epidemiology research is expensive, and risk management decisions even more so; therefore, it is desirable for the risk assessment and epidemiologic communities to work toward making epidemiologic research more useful for informing decisions., Competing Interests: The authors declare no financial conflicts of interest other than their employment relationships with companies that might have some unidentified goals that conflict with the goals of this work. IB and CVP disclose activism on behalf of better bias analyses and other improvements in the conduct of epidemiology, belief in existence of thresholds in biology, and sincere desire to make risk assessment more quantitative. While there is no apparent way that the financing of this project would create any conflict of interest regarding the project goals, design, or analysis, we also disclose that CVP was contracted for this project by HESI, IB subsumed it into his academic duties, SD worked on it as part of her employment at HESI, and the other authors volunteered their time., (© 2024 The Authors.)

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Authors of this manuscript are employees of Syngenta Crop Protection LLC. Syngenta Crop Protection LLC is a provider of agricultural science and technology, including crop protection chemical products such as propiconazole., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The appropriate use of human biomonitoring data to model population chemical exposures is challenging, especially for rapidly metabolized chemicals, such as agricultural chemicals. The objective of this study is to demonstrate a novel approach integrating model predicted dietary exposures and biomonitoring data to potentially inform regulatory risk assessments. We use lambda-cyhalothrin as a case study, and for the same representative U.S. population in the National Health and Nutrition Examination Survey (NHANES), an integrated exposure and pharmacokinetic model predicted exposures are calibrated to measurements of the urinary metabolite 3-phenoxybenzoic acid (3PBA), using an approximate Bayesian computing (ABC) methodology. We demonstrate that the correlation between modeled urinary 3PBA and the NHANES 3PBA measurements more than doubled as ABC thresholding narrowed the acceptable tolerance range for predicted versus observed urinary measurements. The median predicted urinary concentrations were closer to the median measured value using ABC than using current regulatory Monte Carlo methods.

Synthetic pesticides are important agricultural tools that increase crop yield and help feed the world's growing population. These products are also highly regulated to balance benefits and potential environmental and human risks. Public perception of pesticide use, safety, and regulation is an important topic necessitating discussion across a variety of stakeholders from lay consumers to regulatory agencies since attitudes toward this subject could differ markedly. Individuals and organizations can perceive the same message(s) about pesticides differently due to prior differences in technical knowledge, perceptions, attitudes, and individual or group circumstances. Social media platforms, like Twitter, include both individuals and organizations and function as a townhall where each group promotes their topics of interest, shares their perspectives, and engages in both well-informed and misinformed discussions. We analyzed public Twitter posts about pesticides by user group, time, and location to understand their communication behaviors, including their sentiments and discussion topics, using machine learning-based text analysis methods. We extracted tweets related to pesticides between 2013 and 2021 based on relevant keywords developed through a "snowball" sampling process. Each tweet was grouped into individual versus organizational groups, then further categorized into media, government, industry, academia, and three types of nongovernmental organizations. We compared topic distributions within and between those groups using topic modeling and then applied sentiment analysis to understand the public's attitudes toward pesticide safety and regulation. Individual accounts expressed concerns about health and environmental risks, while industry and government accounts focused on agricultural usage and regulations. Public perceptions are heavily skewed toward negative sentiments, although this varies geographically. Our findings can help managers and decision-makers understand public sentiments, priorities, and perceptions and provide insights into public discourse on pesticides. Integr Environ Assess Manag 2023;19:1581-1599. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)., (© 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).)

Environmental epidemiology has proven critical to study various associations between environmental exposures and adverse human health effects. However, there is a perception that it often does not sufficiently inform quantitative risk assessment. To help address this concern, in 2017, the Health and Environmental Sciences Institute initiated a project engaging the epidemiology, exposure science, and risk assessment communities with tripartite representation from government agencies, industry, and academia, in a dialogue on the use of environmental epidemiology for quantitative risk assessment and public health decision making. As part of this project, four meetings attended by experts in epidemiology, exposure science, toxicology, statistics, and risk assessment, as well as one additional meeting engaging funding agencies, were organized to explore incentives and barriers to realizing the full potential of epidemiological data in quantitative risk assessment. A set of questions was shared with workshop participants prior to the meetings, and two case studies were used to support the discussion. Five key ideas emerged from these meetings as areas of desired improvement to ensure that human data can more consistently become an integral part of quantitative risk assessment: 1) reducing confirmation and publication bias, 2) increasing communication with funding agencies to raise awareness of research needs, 3) developing alternative funding channels targeted to support quantitative risk assessment, 4) making data available for reuse and analysis, and 5) developing cross-disciplinary and cross-sectoral interactions, collaborations, and training. We explored and integrated these themes into a roadmap illustrating the need for a multi-stakeholder effort to ensure that epidemiological data can fully contribute to the quantitative evaluation of human health risks, and to build confidence in a reliable decision-making process that leverages the totality of scientific evidence., (© 2022 The Authors.)

Some states and localities restrict siting of new oil and gas (O&G) wells relative to public areas. Colorado includes a 500-foot exception zone for building units, but it is unclear if that sufficiently protects public health from air emissions from O&G operations. To support reviews of setback requirements, this research examines potential health risks from volatile organic compounds (VOCs) released during O&G operations.We used stochastic dispersion modeling with published emissions for 47 VOCs (collected on-site during tracer experiments) to estimate outdoor air concentrations within 2,000 feet of hypothetical individual O&G facilities in Colorado. We estimated distributions of incremental acute, subchronic, and chronic inhalation non-cancer hazard quotients (HQs) and hazard indices (HIs), and inhalation lifetime cancer risks for benzene, by coupling modeled concentrations with microenvironmental penetration factors, human-activity diaries, and health-criteria levels.Estimated exposures to most VOCs were below health criteria at 500-2,000 feet. HQs were < 1 for 43 VOCs at 500 feet from facilities, with lowest values for chronic exposures during O&G production. Hazard estimates were highest for acute exposures during O&G development, with maximum acute HQs and HIs > 1 at most distances from facilities, particularly for exposures to benzene, 2- and 3-ethyltoluene, and toluene, and for hematological, neurotoxicity, and respiratory effects. Maximum acute HQs and HIs were > 10 for highest-exposed individuals 500 feet from eight of nine modeled facilities during O&G development (and 2,000 feet from one facility during O&G flowback); hematologic toxicity associated with benzene exposure was the critical toxic effect. Estimated cancer risks from benzene exposure were < 1.0 × 10 -5 at 500 feet and beyond. Implications : Our stochastic use of emissions data from O&G facilities, along with activity-pattern exposure modeling, provides new information on potential public-health impacts due to emissions from O&G operations. The results will help in evaluating the adequacy of O&G setback distances. For an assessment of human-health risks from exposures to air emissions near individual O&G sites, we have utilized a unique dataset of tracer-derived emissions of VOCs detected at such sites in two regions of intense oil-and-gas development in Colorado. We have coupled these emission stochastically with local meteorological data and population and time-activity data to estimate the potential for acute, subchronic, and chronic exposures above health-criteria levels due to air emissions near individual sites. These results, along with other pertinent health and exposure data, can be used to inform setback distances to protect public health.

Composites made with engineered nanomaterials (nanocomposites) have a wide range of applications, from use in basic consumer goods to critical national defense technologies. Carbon nanotubes (CNTs) are a popular addition in nanocomposites because of their enhanced mechanical, thermal, and electrical properties. Concerns have been raised, though, regarding potential exposure and health risks from nanocomposites containing CNTs because of comparisons to other high aspect ratio fibers. Assessing the factors affecting CNT release from composites is therefore paramount for understanding potential exposure scenarios that may occur during product handling and manipulation. Standardized methods for detecting and quantifying released CNTs, however, have not yet been developed. We therefore evaluated experimental approaches deployed by various researchers, with an emphasis on characterizing free versus composite bound CNTs. From our analysis of published studies characterizing CNT releases from nanocomposites, we found that the qualitative and quantitative methods used across studies varied greatly, thus limiting the ability for objective comparison and evaluation of various release factors. Nonetheless, qualitative results indicated that factors such as composite type, CNT functionalization, and energy input during manipulation (i.e., grinding) may affect CNT release. Based on our findings, we offer several recommendations for future product testing and assessment of potential exposure and health risks associated with CNT nanocomposites.

Background: The objective of this evaluation is to understand the human health impacts of mountaintop removal (MTR) mining, the major method of coal mining in and around Central Appalachia. MTR mining impacts the air, water, and soil and raises concerns about potential adverse health effects in neighboring communities; exposures associated with MTR mining include particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), metals, hydrogen sulfide, and other recognized harmful substances., Methods: A systematic review was conducted of published studies of MTR mining and community health, occupational studies of MTR mining, and any available animal and in vitro experimental studies investigating the effects of exposures to MTR-mining-related chemical mixtures. Six databases (Embase, PsycINFO, PubMed, Scopus, Toxline, and Web of Science) were searched with customized terms, and no restrictions on publication year or language, through October 27, 2016. The eligibility criteria included all human population studies and animal models of human health, direct and indirect measures of MTR-mining exposure, any health-related effect or change in physiological response, and any study design type. Risk of bias was assessed for observational and experimental studies using an approach developed by the National Toxicology Program (NTP) Office of Health Assessment and Translation (OHAT). To provide context for these health effects, a summary of the exposure literature is included that focuses on describing findings for outdoor air, indoor air, and drinking water., Results: From a literature search capturing 3088 studies, 33 human studies (29 community, four occupational), four experimental studies (two in rat, one in vitro and in mice, one in C. elegans), and 58 MTR mining exposure studies were identified. A number of health findings were reported in observational human studies, including cardiopulmonary effects, mortality, and birth defects. However, concerns for risk of bias were identified, especially with respect to exposure characterization, accounting for confounding variables (such as socioeconomic status), and methods used to assess health outcomes. Typically, exposure was assessed by proximity of residence or hospital to coal mining or production level at the county level. In addition, assessing the consistency of findings was challenging because separate publications likely included overlapping case and comparison groups. For example, 11 studies of mortality were conducted with most reporting higher rates associated with coal mining, but many of these relied on the same national datasets and were unable to consider individual-level contributors to mortality such as poor socioeconomic status or smoking. Two studies of adult rats reported impaired microvascular and cardiac mitochondrial function after intratracheal exposure to PM from MTR-mining sites. Exposures associated with MTR mining included reports of PM levels that sometimes exceeded Environmental Protection Agency (EPA) standards; higher levels of dust, trace metals, hydrogen sulfide gas; and a report of increased public drinking water violations., Discussion: This systematic review could not reach conclusions on community health effects of MTR mining because of the strong potential for bias in the current body of human literature. Improved characterization of exposures by future community health studies and further study of the effects of MTR mining chemical mixtures in experimental models will be critical to determining health risks of MTR mining to communities. Without such work, uncertainty will remain regarding the impact of these practices on the health of the people who breathe the air and drink the water affected by MTR mining., (Published by Elsevier Ltd.)

Many epidemiology studies have investigated associations of perfluorooctanoate (PFOA) exposures with a variety of adverse health outcomes for participants in the C8 Health Project. The exposure concentrations (i.e., air and groundwater) used in these studies were determined primarily based on participant's residential locations. However, for residential addresses that could not be geocoded to the street level, the exposure concentrations were assigned based on population-weighted ZIP code centroid, which may result in exposure mischaracterization. The aim of this current study is to evaluate the potential impact of mischaracterized exposure concentrations due to geocoding uncertainty on the predicted serum PFOA concentrations and the epidemiological association between PFOA exposure and preeclampsia. For both workplace addresses and incompletely geocoded residential addresses, we used Monte Carlo (MC) simulation to assign alternate geographic locations within the reported ZIP code (instead of population-weighted ZIP code centroids) and the corresponding exposure concentrations. We found that mischaracterization of residential exposure due to population-weighted ZIP code centroid assignment had no significant impact on the serum PFOA concentration predictions and the epidemiological association of PFOA exposure with preeclampsia. In contrast, the uncertainty in workplace exposure moderately impacted the rank exposure among the participants. We observed a 41% increase in the average adjusted odds ratio of preeclampsia occurrence that may be due to differing proportions of cases (64.3%) and controls (54.5%) with workplace address geocodes during pregnancy. This finding suggests that differential exposure mischaracterization can be reduced by obtaining accurate exposure information such as street addresses and tap water consumption, for both workplaces and residences. The analysis we present is one approach for estimating the potential impacts of positional errors in a geocoding-based exposure assessment on exposure estimates and epidemiological study results., (Copyright © 2016 Elsevier Inc. All rights reserved.)

We recently utilized a suite of environmental fate and transport models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations, and also assessed the association of those concentrations with preeclampsia for participants in the C8 Health Project (a cross-sectional study of over 69,000 people who were environmentally exposed to PFOA near a major U.S. fluoropolymer production facility located in West Virginia). However, the exposure estimates from this integrated model relied on default values for key independent exposure parameters including water ingestion rates, the serum PFOA half-life, and the volume of distribution for PFOA. The aim of the present study is to assess the impact of inter-individual variability and epistemic uncertainty in these parameters on the exposure estimates and subsequently, the epidemiological association between PFOA exposure and preeclampsia. We used Monte Carlo simulation to propagate inter-individual variability/epistemic uncertainty in the exposure assessment and reanalyzed the epidemiological association. Inter-individual variability in these parameters mildly impacted the serum PFOA concentration predictions (the lowest mean rank correlation between the estimated serum concentrations in our study and the original predicted serum concentrations was 0.95) and there was a negligible impact on the epidemiological association with preeclampsia (no change in the mean adjusted odds ratio (AOR) and the contribution of exposure uncertainty to the total uncertainty including sampling variability was 7%). However, when epistemic uncertainty was added along with the inter-individual variability, serum PFOA concentration predictions and their association with preeclampsia were moderately impacted (the mean AOR of preeclampsia occurrence was reduced from 1.12 to 1.09, and the contribution of exposure uncertainty to the total uncertainty was increased up to 33%). In conclusion, our study shows that the change of the rank exposure among the study participants due to variability and epistemic uncertainty in the independent exposure parameters was large enough to cause a 25% bias towards the null. This suggests that the true AOR of the association between PFOA and preeclampsia in this population might be higher than the originally reported AOR and has more uncertainty than indicated by the originally reported confidence interval., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Uncertainty in exposure estimates from models can result in exposure measurement error and can potentially affect the validity of epidemiological studies. We recently used a suite of environmental models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations and assess the association with preeclampsia from 1990 through 2006 for the C8 Health Project participants., Objectives: The aims of the current study are to evaluate impact of uncertainty in estimated PFOA drinking-water concentrations on estimated serum concentrations and their reported epidemiological association with preeclampsia., Methods: For each individual public water district, we used Monte Carlo simulations to vary the year-by-year PFOA drinking-water concentration by randomly sampling from lognormal distributions for random error in the yearly public water district PFOA concentrations, systematic error specific to each water district, and global systematic error in the release assessment (using the estimated concentrations from the original fate and transport model as medians and a range of 2-, 5-, and 10-fold uncertainty)., Results: Uncertainty in PFOA water concentrations could cause major changes in estimated serum PFOA concentrations among participants. However, there is relatively little impact on the resulting epidemiological association in our simulations. The contribution of exposure uncertainty to the total uncertainty (including regression parameter variance) ranged from 5% to 31%, and bias was negligible., Conclusions: We found that correlated exposure uncertainty can substantially change estimated PFOA serum concentrations, but results in only minor impacts on the epidemiological association between PFOA and preeclampsia., Citation: Avanasi R, Shin HM, Vieira VM, Savitz DA, Bartell SM. 2016. Impact of exposure uncertainty on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population. Environ Health Perspect 124:126-132; http://dx.doi.org/10.1289/ehp.1409044.

Assessments of potential exposure to fullerenes and their derivatives in the environment are important, given their increasing production and use. Our study focused on fate processes that determine the movement and bioavailability of fullerenes in soil. We evaluated the sorption, biodegradation, and plant uptake of C60 fullerene using (14)C-labeled C60 solutions in water produced by either solvent exchange with tetrahydrofuran or sonication/extended mixing in water. Organic carbon appeared to have an important influence on C60 soil sorption. The log Koc values for (14)C60 were equivalent for sandy loam and silt loam (3.55 log[mL/g]) but higher for loam (4.00 log[mL/g]), suggesting that other factors, such as pH, clay content and mineralogy, and cation exchange capacity, also influence C60 soil sorption. There was little (14)CO2 production in the silt loam or the sandy loam soil after 754 and 328 days, respectively, suggesting high resistance of C60 to mineralization in soil. Plant uptake was generally low (∼7%), with most of the uptaken (14)C accumulating in the roots (40-47%) and smaller amounts of accumulation in the tuber (22-23%), stem (12-16%), and leaves (18-22%). Our results indicate that C60 released to the environment will not be highly bioavailable but will likely persist in soil for extended periods.