Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (ß: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (ß:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Factors affecting urinary organophosphate pesticide metabolite levels among Californian agricultural community members.

Organophosphate (OP) pesticides are widely used in California for agricultural pest and weed control despite their well-documented adverse health effects among infants, children, and adults. We sought to identify factors affecting urinary OP metabolites among families living in high-exposure communities. Our study included 80 children and adults who lived within 61 m (200 ft) of agricultural fields in the Central Valley of California in January and June 2019, which are pesticide non-spraying and spraying seasons, respectively. We collected one urine sample per participant during each visit to measure dialkyl phosphate (DAP) metabolites; these were coupled with in-person surveys to identify health, household, sociodemographic, pesticide exposure, and occupational risk factors. We used a data-driven, best subsets regression approach to identify key factors that influenced urinary DAPs. Participants were mostly Hispanic/Latino(a) (97.5 %), over half were female (57.5 %), and most households reported having a member who worked in agriculture (70.6 %). Among the 149 urine samples suitable for analysis, DAP metabolites were detected in 48.0 % and 40.5 % of samples during January and June, respectively. Total diethyl alkylphosphates (EDE) were only detected in 4.7 % (n = 7) of samples, but total dimethyl alkylphosphates (EDM) were detected in 41.6 % (n = 62) of samples. No differences were observed in urinary DAP levels by visit month or by occupational exposure to pesticides. Best subsets regression identified several individual- and household-level variables that influenced both urinary EDM and total DAPs: the number of years spent living at the current address, household use of chemical products to control mice/rodents, and seasonal employment status. Among adults only, we identified educational attainment (for total DAPs) and age category (for EDM) as significant factors. Our study found consistent urinary DAP metabolites among participants, regardless of spraying season, and identified potential mitigating factors that members of vulnerable populations can implement to protect their health against OP exposure.

Factors Associated with Levels of Organophosphate Pesticides in Household Dust in Agricultural Communities.

Organophosphate (OP) pesticides are associated with numerous adverse health outcomes. Pesticide use data are available for California from the Pesticide Use Report (PUR), but household- and individual-level exposure factors have not been fully characterized to support its refinement as an exposure assessment tool. Unique exposure pathways, such as proximity to agricultural operations and direct occupational contact, further complicate pesticide exposure assessment among agricultural communities. We sought to identify influencing factors of pesticide exposure to support future exposure assessment and epidemiological studies. Household dust samples were collected from 28 homes in four California agricultural communities during January and June 2019 and were analyzed for the presence of OPs. Factors influencing household OPs were identified by a data-driven model via best subsets regression. Key factors that impacted dust OP levels included household cooling strategies, secondary occupational exposure to pesticides, and geographic location by community. Although PUR data demonstrate seasonal trends in pesticide application, this study did not identify season as an important factor, suggesting OP persistence in the home. These results will help refine pesticide exposure assessment for future studies and highlight important gaps in the literature, such as our understanding of pesticide degradation in an indoor environment.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020.

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R2 of 0.83 and a root-mean-square error of 0.15 µg/m3 for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.