Comparison of regional air dispersion simulation and ambient air monitoring data for the soil fumigant 1,3-dichloropropene

- Air dispersion model validation limited by availability of relevant monitoring data
- Compared model to long term continuous measurements of 1,3-D concentrations in air
- SOFEA2 model adequately predicted timing and magnitude of 1,3-D at nine locations.
- Modeled concentrations during periods of atmospheric stability (low mixing height) were improved.
- SOFEA2 is useful tool for estimating airborne levels of 1,3-D and other soil fumigants.

SOFEA v2.0 is an air dispersion modeling tool used to predict acute and chronic pesticide concentrations in air for large air sheds resulting from agronomic practices. A 1,3-dichloropropene (1,3-D) air monitoring study in high use townships in Merced County, CA, logged 3-day average air concentrations at nine locations over a 14.5 month period. SOFEA, using weather data measured at the site, and using a historical CDPR regulatory assumption of a constant 320 m mixing height, predicted the general pattern and correct order of magnitude for 1,3-D air concentrations as a function of time, but failed to estimate the highest observed 1,3-D concentrations of the monitoring study. A time series and statistical comparison of the measured and modeled data indicated that the model underestimated 1,3-D concentrations during calm periods (wind speed < 1 m/s), such that the annual average concentration was under predicted by approximately 4.7-fold, and the variability was not representative of the measured data. Calm periods are associated with low mixing heights (MHs) and are more prevalent in the Central Valley of CA during the winter months, and thus the assumption of a constant 320 m mixing height is not appropriate. An algorithm was developed to calculate the MH using the air temperature in the weather file when the wind speed was < 1 m/s. When the model was run using the revised MHs, the average of the modeled 1,3-D concentration Probability Distribution Function (PDF) was within 5% of the measured PDF, and the variability in modeled concentrations more closely matched the measured dataset. Use of the PCRAMMET processed weather data from the site (including PCRAMMET MH) resulted in the global annual average concentration within 2-fold of measured data. Receptor density was also found to have an effect on the modeled 1,3-D concentration PDF, and a 50 × 50 receptor grid in the nine township domain captured the measured 1,3-D concentration distribution much better than a 3 × 3 receptor grid (i.e., simulated receptors at the nine monitoring locations). Comparison of the monitored and simulated PDF for 72-h 1,3-D concentrations indicated that SOFEA slightly over predicts the 1,3-D concentration distribution at all percentiles below the 99th with slight under prediction of the 99-100th percentile values. This suggests that without further refinement, the SOFEA2 model, based upon field validation observations, will result in representative but conservative estimates of lifetime exposure to 1,3-D for bystanders in 1,3-D use areas.

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