By Russell L.; Shamim, Mah; Jackson, Scott H. Jones
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Additional info for Describing the Behavior and Effects of Pesticides in Urban and Agricultural Settings
1 m, the same as the depth of the house lots, and produced a random distribution of rain droplets, closely mimicking natural rainfall patterns (Figure 2c). 9 L stainless steel collection basin located below a sampling shed (Figure 2d). 4 m and housed the runoff collection basin, refrigerator, an Isco 6712 autosampler (placed inside a refrigerator with its temperature monitored by a Campbell Scientific Incorporated [CSI] CS107 temperature probe), CSI CR1000 data-logging system, and various other electronic components.
Chem. 2010, 29, 1203–1208. 7. ; Gan, J. Water Res. 2012, 46, 645–652. 8. Trask, J. ; Harbourt, C. ; Lam, C. Environ. Toxicol. Chem. 2014, 33, 302–307. 9. Davidson, P. ; Jones, R. ; Harbourt, C. ; Goodwin, G. ; Sliz, G. E. Environ. Toxicol. Chem. 2014, 33, 52–60. 10. Laskowsky, D. A. Rev. Environ. Contam. Toxicol. 2002, 174, 49–170. 1. 25 Chapter 3 Determining Critical Factors Controlling Off-Site Transport of Pyrethroids in the Urban Environment Paul S. Miller,1 J. Malia Andrus,2 Paul C. Davidson,3 Russell L.
To assess whether factors selected by the MARS methodology may be representative of another, correlated factor, the degree of correlation between potentially influential factors was calculated. For correlations between continuous variables, the Pearson correlation coefficient was used. For relationships between factor and continuous variables, Kruskal-Wallis tests were conducted, and for relationships between factors, the chi-squared metric was calculated. Results Model Performance The results of all MARS model predictions alongside actual results are shown in Figure 5-9 for each surface and plot within each surface.