Main content area

Modeling variation in 1,3-dichloropropene emissions due to soil conditions and applicator practices

Brown, Colin R., Kandelous, Maziar, Sartori, Fabio, Collins, Christopher, Spurlock, Frank
The Science of the total environment 2019 v.678 pp. 768-779
1,3-dichloropropene, application methods, applicators, decision making, emissions, fumigants, fumigation, models, pathogens, pests, public health, soil, soil quality, California
The fumigant 1,3-dichloropropene (1,3-D) is widely used for control of soil-borne pests and pathogens, but post-application emissions may lead to off-site transport and possible human exposure. The fraction of applied material emitted into the atmosphere and the magnitude of peak emissions are two quantities used by regulators to protect public health and are typically based on field estimates. However, the current body of field studies covers only a narrow subset of the broad range of application practices and soil conditions under which applications are performed and is subject to an unknown level of estimation error. Here we use the HYDRUS model to estimate cumulative and peak emissions of 1,3-D for 17 application methods used in California. The simulations are parameterized with soils data from 16 fields sampled immediately prior to fumigation in order to establish a representative distribution of initial soil conditions. The results demonstrate a wide range in cumulative emissions, with mean losses of initial applied mass between 10 and 58% over two weeks depending on application method. Emissions are highly variable in response to soil conditions, with coefficients of variation ranging from 16 to 54% for cumulative flux and 26 to 67% for peak three-hour flux depending on application method. The simulated distributions show similarities to the available field study estimates in terms of the mean and spread of distributions, particularly in the case of cumulative emissions, indicating that the modeling approach could be a useful tool to support regulatory decision-making in cases where field data is limited.