4.1 Article

Combined Burden of Heat and Particulate Matter Air Quality in WA Agriculture

Journal

JOURNAL OF AGROMEDICINE
Volume 26, Issue 1, Pages 18-27

Publisher

TAYLOR & FRANCIS INC
DOI: 10.1080/1059924X.2020.1795032

Keywords

Rural air pollution; heat index; wildfire smoke; agriculture; heat stress

Funding

  1. National Institute for Occupational Safety and Health [CDC/NIOSH 5U54OH007544-17]
  2. National Institute of Environmental Health Sciences [NIH/NIEHS P30 ES007033]

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The study evaluated the combined burden of heat and air quality exposure in Washington State agriculture, finding significant spatial and temporal variability in high heat and PM(2.5) exposures. Counties with the largest agricultural populations were at highest risk for concurrent high heat and PM(2.5) exposures during the third quarter, and limited access to local air quality information was observed in certain rural areas.
Objectives To evaluate the combined burden of heat and air quality exposure in Washington State agriculture by (1) characterizing the spatiotemporal pattern of heat and PM(2.5)exposures during wildfire seasons; (2) describing the potential impact of these combined exposures on agricultural worker populations; and (3) identifying data gaps for addressing this burden in rural areas. Methods: We combined county-level data to explore data availability and estimate the burden of heat and PM(2.5)co-exposures for Washington agricultural workers from 2010 to 2018. Quarterly agricultural worker population estimates were linked with data from a weather station network and ambient air pollution monitoring sites. A geographical information system displayed counties, air monitoring sites, agricultural crops, and images from a smoke dispersion model during recent wildfire events. Results: We found substantial spatial and temporal variability in high heat and PM(2.5)exposures. The largest peaks in PM(2.5)exposures tended to occur when the heat index was around 85 degrees F and during summers when there were wildfires. Counties with the largest agricultural populations tended to have the greatest concurrent high heat and PM(2.5)exposures, and these exposures tended to be highest during the third quarter (July-September), when population counts were also highest. Additionally, we observed limited access to local air quality information in certain rural areas. Conclusion: Our findings inform efforts about highest risk areas, times of year, and data availability in rural areas. Understanding the spatiotemporal pattern of exposures is consistent with the precision agriculture framework and is foundational to addressing equity in rural agricultural settings.

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