Prediction of Agricultural Contaminant Concentrations in Ambient Air

Cryer, Steven A.; Wesenbeeck, Ian van

doi:10.5772/intechopen.86091

Cited by 1 publication

(1 citation statement)

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“…The development and functionality of SOFEA4 is described in detail by Cryer and van Wesenbeeck. 9 Zou et al 10 studied the sensitivity of AERMOD-simulated SO 2 concentrations to variations in dispersion coefficients, meteorological data, and terrain data at 3 locations in Texas, and found that while variations in meteorology had the most significant effect on simulated concentrations, the dispersion coefficients and terrain data had very limited influence. Long 11 examined the sensitivity of AERMOD-simulated concentrations of SO 2 to variations in 8 model input parameters, including albedo, Bowen ratio, surface roughness, cloud cover, solar radiation, and height at which ambient temperature is measured using meteorological data from the San Francisco Bay Area.…”

Section: Introductionmentioning

confidence: 99%

Modeling 1,3-D Concentrations in Ambient Air in High Use Airsheds of the United States

Wesenbeeck¹,

Cryer

Helle

et al. 2019

Air, Soil and Water Research

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Soil fumigants, such as 1,3-dichloropropene (1,3-D), are used on a variety of different crops in high use areas in the United States, including the Pacific Northwest, the mid-Atlantic coast, and the Southeast coastal plains. Contaminant concentrations in air are often required for environmental exposure and human risk assessment. The SOil Fumigant Exposure Assessment (SOFEA) model, originally developed to explore volatile pesticide exposure and bystander risk, has recently been upgraded using AERMOD, the US Environmental Protection Agency’s (EPA) recommended regulatory air dispersion model to predict short-, medium-, and long-term pesticide concentrations in air resulting from representative agronomic practices in large airsheds. Modeling air concentrations has several advantages over monitoring such as the ability to predict concentrations at multiple locations and airsheds at a much greater temporal frequency than could be practically accomplished through monitoring alone. The agricultural modeling tool presented herein was parameterized using 1,3-D application data (mass, date, and depth applied, location, etc) obtained from growers in each study area, and local weather data and hourly concentrations of 1,3-D in ambient air were simulated for large airsheds. The human equivalent concentrations (HECs) for acute, short-term, sub-chronic, and chronic exposure of 1,3-D were not exceeded in any of the study areas investigated. These simulated 1,3-D concentrations are used to assess human exposure and risk, which considers human-life-stage-specific exposure factors, including residential mobility, time-activity patterns, and age-specific inhalation rates and body weights.

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Section: Introductionmentioning

confidence: 99%