2015 Progress Report: Pesticide Exposure PathwaysEPA Grant Number: R834514C002
Subproject: this is subproject number 002 , established and managed by the Center Director under grant R834514
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: University of Washington Center for Child Environmental Health Risks Research (2010)
Center Director: Faustman, Elaine
Title: Pesticide Exposure Pathways
Investigators: Faustman, Elaine , Yost, Michael
Current Investigators: Faustman, Elaine
Institution: University of Washington
EPA Project Officer: Callan, Richard
Project Period: October 1, 2010 through September 24, 2016
Project Period Covered by this Report: August 25, 2014 through September 24,2015
RFA: Children's Environmental Health and Disease Prevention Research Centers (with NIEHS) (2009) RFA Text | Recipients Lists
Research Category: Children's Health , Health
Since 1998, researchers of the University of Washington Center for Child Environmental Health Risks Research (the Center) have been using a multi-disciplinary research approach working in the lab, in the field, and in the community to understand the mechanisms that define children’s susceptibility to pesticides, identify the implications of this susceptibility for development and learning, and partner with our communities to translate our findings into risk communication, risk management, and prevention strategies.
The specific objectives of the two field-based projects—the CBPR project and the pesticide exposure pathways research project—are to:
- Improve our understanding of critical pathways of potential pesticide exposure for children; and
- Apply culturally appropriate interventions to reduce children’s exposure to pesticides.
There are no changes to the specific aims.
Specific aims addressed (and referenced publications) between September 25, 2009, and July 31, 2014:
- Determine the dominant transport mechanism for the proximity pathway in a community setting
- Establish the spatial extent of the proximity pathway
- Estimate pesticide residues in the community by combining land-use regression with models of spray drift, volatilization and pest-pressure
- Assess the Assess the importance of proximity through land-use regression estimates of exposure
- Monitor indoor/outdoor pesticide levels to assess infiltration into indoor environments
- Assess the relative importance of the take-home vs. proximity pathway
- Evaluate the effect of personal activities on exposures in families living far from applied fields.
Specific aims addressed between August 1, 2014, and July 31, 2015:
4. Assess the importance of proximity through land-use regression estimates of exposure.
Previously, we have demonstrated elevated concentrations of outdoor chlorpyrifos (CPF) and its more potent transformation product CPF-oxon in outdoor air samples in the Yakima Valley during dormant spray applications periods (Armstrong, 2014). These exposures are strongly associated with proximity to crops (pome fruit and stone fruit) using ground-applications of CPF. In addition, we have shown that the importance of proximity persists beyond short distances and that living within 2 km of a field is strongly (R2 = 0.55) predictive of outdoor concentrations of CPF and CPF-oxon.
A land-use regression (LUR) exposure model was built to describe the relationship between agricultural land-use characteristics and chlorpyrifos air sampling results from households in the CHC Yakima Valley cohort that were instrumented with PUF-PAS (polyurethane foam passive air sampling) disks (described in previous annual progress reports). Using the instrumented households model, monthly outdoor chlorpyrifos concentrations are now being estimated for non-instrumented households using the same measures of agricultural activity. The exposure model building process and predictions are being prepared for submission to a peer-reviewed journal.
The study region consisted of approximately 2,000 km2 of mostly rural space with several clusters of residential communities in the Yakima Valley. Agricultural land-use was estimated within 125, 250, 500, 1,000, 2,000, 4,000, and 8,000 meter buffers of study households. Buffer sizes were considered scientifically reasonable due to the volatilization behavior of chlorpyrifos from treated crops and previous pesticide use exposure models. Land use within given buffers was for area of all crops, area of tree fruit orchards, and estimated mass of chlorpyrifos applied during that year. The buffers were then intersected with the field parcel shapefile, which generated polygons that could be summed across crop type for each household. The resulting crop type and area associated with each household was used in conjunction with the pesticide-crop type matrix for chlorpyrifos to estimate the amount of chlorpyrifos use. Measured distances were also available for nearest crop of any type, nearest tree fruit orchard, and elevation.
The relationship between outdoor concentrations was modeled using our detailed land use data and updated chlorpyrifos usage maps (see Figure 1). Because the dataset of spatial covariates for chlorpyrifos air concentrations provides a group of highly-correlated spatial covariates, we employed Partial Least Squares (PLS) regression, as described by Sampson, et al. (2013). Briefly, PLS "finds a small number of linear combinations of the GIS covariates that most efficiently account for variability in the measured concentrations" (Bergen, et al., 2013). PLS regressions were computed and cross-validated using the pls package in R. After the three highest-scoring PLS components were determined, occupational status as a farmworker and temperature were added to the model. Occupational status was included simply to confirm that farmworkers were thought to reside closer to chlorpyrifos application sites, on average. Daily temperatures were included to capture the effect of chlorpyrifos volatilization and transport. The final model was expressed as: Log(CPF) ~ β0 + β1 C1 + β2 C2 + + β3 C3 + β4T, where CPF was chlorpyrifos air concentration, C1, C2, and C3 were PLS components with the highest scores, and T was monthly mean temperature recorded by the nearest weather station.
Summary statistics indicate that among the 181 households in the CHC 3 cohort, 125, 125, and 79 were located within 2,000 meters of cherry, apple, and pear orchards, respectively. Within the same distance, arithmetic mean area (km2) for any crop type was largest for field corn [Mean 1.52 (SD: 1.15)], hops [0.55 (0.87)], and alfalfa hay [0.50 (0.40)]. Among tree fruit, mean area was largest for cherries [0.39 (0.58)], apples [0.32 (0.51)], and pears [0.31 (0.48)]. Preliminary univariate model results confirm our previous finding (Armstrong, 2014) that residences proximal to fields experience higher monthly outdoor levels of chlorpyrifos than non-proximal households. The 2,000 and 4,000 meter buffer sizes were found to be most predictive of the relationship between location and outdoor CPF concentration. On average, each 10 meter increase in residential distance from the nearest tree fruit orchard resulted in a 0.09% reduction in geometric mean outdoor CPF air concentration (ng/m3) (95% CI: 0.05, 0.14) (R2 = 0.48). Residential outdoor CPF air concentration was weakly correlated with mass of CPF applied within 250 meters (1.34, 95% CI: 1.10, 1.65, R2 = 0.12), but more strongly correlated with mass applied within 2000 meters (1.008, 95% CI: 1.004, 1.012, R2 = 0.55).
Our PLS LUR model adequately captured (R2 = 0.61) the variability in the 2011 outdoor air concentrations of CPF based on proximity to parcel level crop maps. Component 1 loaded heavily for distance from nearest tree fruit orchard and mass of chlorpyrifos applied within the largest buffers, component 2 loaded heavily for elevation and distance from nearest crop of any type, and component 3 loaded heavily for area of pears and corn within largest buffers. Geographic covariates that were most important for explaining chlorpyrifos variability were related to larger buffers (1-4 km).
The discrepancy of larger buffers being more predictive might be attributable to the fact that fewer houses (n = 8) had any chlorpyrifos applied within the smaller buffer compared to the larger buffer (n = 23), but it might also indicate that the off-target movement of airborne chlorpyrifos occurs over greater distances than previously thought. Farmworker status does not appear to be predictive of measured outdoor CPF levels in the full model. Previous significant findings related to farmworker status were likely due to farmworkers living closer to applied fields on average in the sample of homes.
Future efforts will continue to focus on validating the final PLS LUR exposure model for chlorpyrifos and explore the impact of joint effect exposures on children’s health.
Other future work aims to investigate the short-term and long-term joint effect of exposures to (1) chlorpyrifos (and its oxon), (2) PM2.5, a criteria air pollutant with important sources in the Yakima Valley, including biomass burning and animal feed lot operations; and (3) nitrogen oxides, with important motor vehicle sources. Exposure to these pollutants has been associated with oxidative stress, increased inflammatory responses, asthma symptoms, including wheeze, and neurological outcomes such as delayed psychomotor development, decreased cognitive abilities, and risk of developing ADD/ADHD. Yakima Valley provides a unique location within which to study the single and joint effect of these pollutants on children’s health and development because (1) these pollutants are heterogeneously distributed over time and space within the geographic study area due to their highly localized sources and (2) the short-term concentrations of CP and criteria pollutants NO2 and PM2.5 are not correlated in time or space due to their distinct sources and temporal trends. This independence in concentrations makes it possible to include both in epidemiological models and still retain the ability to make valid inference on the models created.
- Armstrong JL, Dills RL, Yu J, Yost MG, and Fenske RA. 2014. A sensitive LC-MS/MS method for measurement of organophosphorus pesticides and their oxygen analogs in air sampling matrices. Journal of Environmental Science and Health, Part B. 49(2): 102-108.
- Bergen S, Sheppard L, Sampson PD, Kim SY, Richards M, Vedal S, Kaufman JD, Szpiro AA. 2013. A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference. Environ Health Perspect. Sep;121(9):1017-25.
- Sampson PD, Richards M, Szpiro AA, Bergen A, Sheppard L, Larson TV, Kaufman JD. 2013. A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ. Aug 1 75: 383-392
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other subproject views:||All 33 publications||12 publications in selected types||All 11 journal articles|
|Other center views:||All 507 publications||224 publications in selected types||All 175 journal articles|
||Loftus C, Yost M, Sampson P, Arias G, Torres E, Vasquez VB, Bhatti P, Karr C. Regional PM2.5 and asthma morbidity in an agricultural community: a panel study. Environmental Research 2015;136:505-512.||
Supplemental Keywords:children's health, risk assessment, pesticide exposure, age-related differences, pesticides, children's vulnerablity, biological markers, agricultural community, RFA, Health, Scientific Discipline, INTERNATIONAL COOPERATION, ENVIRONMENTAL MANAGEMENT, Biochemistry, Children's Health, Environmental Policy, Biology, Risk Assessment, pesticide exposure, age-related differences, pesticides, children's vulnerablity, biological markers, agricultural community
Progress and Final Reports:Original Abstract
Main Center Abstract and Reports:R834514 University of Washington Center for Child Environmental Health Risks Research (2010)
Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R834514C001 Community-Based Participatory Research
R834514C002 Pesticide Exposure Pathways
R834514C003 Molecular Mechanisms
R834514C004 Genetic Susceptibility