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.

Reliable assessment of personal exposure to air pollution remains a challenge due to the limitations of monitoring technology. Recent technology developments, such as reductions in the size and cost of samplers as well as incorporation of continuous sensors for location, activity, and exposure (i.e., global positioning systems [GPS], accelerometers, and low-cost pollutant sensors), have advanced our ability to assess personal exposure to air pollution. This study evaluated the upgraded Ultrasonic Personal Aerosol Sampler (UPAS v2.1 PLUS) as a tool for quantifying time-integrated indoor and personal exposure to particulate matter (PM) and black carbon (BC) among a panel of participants in California's Central Valley and exploring personal exposures in different microenvironments using time/location-resolved PM data. Three field campaigns demonstrated that filter-derived PM₁₀, PM_2.5, PM₁₀ BC, and PM_2.5 BC concentrations measured using the UPAS were linear, unbiased, and precise compared to those measured using conventional personal sampling equipment. Time-resolved PM, GPS, and light intensity data from the UPAS allowed for personal PM_2.5 exposure assessment across microenvironments. The majority of daily PM_2.5 exposure occurred inside the home. Participants with higher out-of-home PM_2.5 exposures received those exposures primarily in agricultural and in-transit environments, in accordance with their self-reported occupational exposures. This study demonstrated the UPAS v2.1 PLUS is a reliable and valid tool for characterizing indoor air pollution and personal exposures in both temporal and spatial dimensions. Its enhanced capabilities should reduce the burden of personal activity logging in the field and enable accurate and precise estimation of exposures for epidemiological and community-based research.

California’s San Joaquin Valley experiences some of the worst particulate matter (PM) air pollution in the U.S., but PM_2.5 and PM₁₀ exposures in agricultural communities are understudied. We collaborated with rural residents living adjacent to large-scale agricultural production and processing activities to assess 24-h-average personal and indoor PM_2.5 and PM₁₀ concentrations during different seasons. We visited 35 participants from 18 households during December 2023, May 2024, and the September 2024 harvest season to collect PM samples and survey data. Mixed effects linear regression models (with random effects for participant or household) assessed associations between natural log-transformed PM concentrations and regional ambient PM, harvest season, as well as participant/household characteristics. Participants were mostly female (69%) and Hispanic/Latino(a) (100%). Median household distance to processing facility silos was 633 m. Median personal exposures to PM_2.5 and PM₁₀ were 11.1 and 45.5 µg m⁻³. Median indoor PM_2.5 and PM₁₀ levels were 12.9 and 24.3 µg m⁻³. Overall, 29% of personal and indoor PM_2.5 samples and 33% of personal and indoor PM₁₀ samples exceeded WHO 24-h air quality guidelines (15 µg m⁻³ PM_2.5, 45 µg m⁻³ PM₁₀). The factors most strongly associated with personal and indoor PM were household members working in agriculture and regional ambient PM measures.

In 2020, California banned the sale and agricultural use of chlorpyrifos, an organophosphate pesticide (OP) associated with neurotoxicity and other adverse health outcomes. We primarily assessed changes in chlorpyrifos associated with this policy and secondarily explored how other OP exposures changed. The participants were from California’s Central Valley, 18 years or older, and English- or Spanish-speaking. The surveys and urine samples were collected pre-ban (December 2020) and post-ban (February–April 2022). The urine samples were analyzed for a chlorpyrifos-specific metabolite (TCPy), six dialkyl phosphates (DEP, DMTP, DETP, DMDTP, DMP, DEDTP), and total DE and DM. The pre- and post-ban metabolite concentrations were compared via Wilcoxon signed-rank tests and natural log-transformed paired differences in linear mixed effects regression, adjusted for covariates. Forty-nine participants had repeated biomarker data. The mean age of the study population was 46.8 years (SD: 16), 61% female, 67% Spanish-speaking, 100% Hispanic/Latino(a), and 47% had less than a high school education. Six urinary metabolites (TCPy, DEP, DMP, DMTP, total DE and total DM) had sufficient variation for further analysis, while DMDTP, DEDTP, and DETP were undetected. The paired differences in adjusted models showed statistically significant increases in TCPy and DMP associated with the policy change (e.g., TCPy estimated ratio of geometric means: 4.53 (95% CI 2.66, 7.69)) Reductions in metabolites of chlorpyrifos exposure were not observed following California’s chlorpyrifos ban, suggesting ongoing exposure to chlorpyrifos from other sources.

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 (β: 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 (β: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.

Most air pollution epidemiology studies rely on outdoor exposure data from various sources, such as reference monitors, low-cost monitors, models, or Earth observations. However, people spend 90 % of their time indoors, with 70 % of that time spent at home, which may result in misclassification of air pollution exposure when using data reflecting ambient concentrations. In this study, we evaluated methods to predict residential indoor black carbon (BC) from outdoor BC, PM2.5, and housing characteristics to support future efforts in estimating personal air pollution exposure. Households from the Healthy Start cohort in Denver, CO hosted paired indoor/outdoor low-cost air samplers for one-week periods during spring 2018, summer 2018, and winter 2019. Participants completed questionnaires about housing characteristics like building type, flooring, and heating and cooling methods. Filters were analyzed for BC using transmissometry. Ridge, LASSO and ordinary least squares regression (OLS) techniques were used to build predictive models of indoor BC given the available set of covariates. Leave-one-out cross-validation was used to assess the predictive accuracy of each model. We hypothesized that Ridge and LASSO will obtain improved predictive performance over the OLS model due to regularization. A total of 27 households participated, with 39 paired measurements available after data cleaning. All winter data were excluded due to high variability and incomplete sampling times for outdoor measurements. Performance issues suggested insufficient weatherproofing of monitors for low temperatures. The Ridge regression showed the best predictive performance. The final inference model included outdoor PM2_.5, hard floors, and the presence of pets in the home, accounting for approximately 28 % of the variability in indoor BC concentrations measured in participant homes. In the absence of indoor monitoring, household characteristics like flooring and the presence of pets can help predict indoor levels of BC.