Degradation of isofenphos in cornfields with conservation tillage practices 1

Abstract

Isofenphos (Amaze®, Oftanol®, 1‐methylethyl 2‐[[ethoxy‐[(l‐methylethyl)aminol phosphinothloyl]oxy]benzoate) was applied at planting to study the effects of four different tillage treatments (no‐tillage, fall chisel plow, Paraplow®, and fall moldboard plow) on isofenphos degradation rates and routes in cornfields over two growing seasons. Soil samples were taken at intervals extending over 69 days each growing season. Tillage treatment had no significant effect on isofenphos degradation rates and products. However, the repeated application of isofenphos had a very significant effect on lsofenphos degradation. Degradation was much more rapid the second year. A laboratory experiment comparing sterile and nonsterlle soils, with and without isofenphos history, confirmed enhanced mlcrobial degradation resulting from two consecutive years of isofenphos application. In the first year, isofenphos oxon was found at greater amounts relative to the second year. Soil bioassays conducted on soils collected from the field at different dates in the second year showed that effective control of larvae was no longer present 21 days after isofenphos application in the field.     

A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of \(\text {NO}_{x}\) in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated \(R^2\) of approximately \(0.7\) at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.