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. 2022 Dec 21;195(1):219. doi: 10.1007/s10661-022-10821-2

Table 2.

Ordinary least squares and spatial regression results for each pollutant in the wet season

Model R2 AIC Equation
E. coli OLS 0.44 93.75 1.45 + 0.0052(developed_250m)—0.055(mean_slope_250m) + 0.12(std_dev_slope_250m)—0.0022(soil_100m) + 0.072(stream_order)
SL 0.50 87.97 0.0042(developed_250m) + 0.11(std_dev_slope_250m)—0.049(mean_slope_250m) + 0.86 + 0.35(W_ecoli_wet) + 0.070(stream_order)—0.0018(soil_100m)
SE 0.49 88.17 1.47 + 0.0051(developed_250m) + 0.10(std_dev_slope_250m)—0.045(mean_slope_250m) + 0.39(LAMBDA_ecoli_wet)—0.0025(soil_100m) + 0.066(stream_order)
Lead OLS 0.15 -209.42 0.12 + 0.00015(pipe_length) + 0.0011(mean_elev_100m) + 0.022(std_dev_slope_250m)—0.0097(mean_slope_250m)—0.00097(impervious_100m)
SL 0.22 -212.50 0.00015(pipe_length) + 0.022(std_dev_slope_250m) + 0.33(W_lead_wet)—0.0097(mean_slope_250m) + 0.0011(mean_elev_100m) + 0.12*—0.00097(impervious_100m)**
SE 0.22 -213.57 0.12 + 0.00014(pipe_length) + 0.023(std_dev_slope_250m) + 0.0010(mean_elev_100m)—0.0094(mean_slope_250m) + 0.33(LAMBDA_lead_wet)—0.00087(impervious_100m)*
Nitrate OLS 0.14 -131.64 0.46—0.0025(developed_250m) + 0.0030(impervious_100m)
SL 0.19 -135.79 0.14—0.0021(developed_250m) + 0.0028(impervious_100m) + 0.33(W_nitrate_wet)
SE 0.18 -136.60 0.44—0.0022(developed_250m) + 0.0025(impervious_100m) + 0.28(LAMBDA_nitrate_wet)
Orthophosphate OLS 0.13 -881.09 0.014—9.21E-5(mean_elev_250m) + 5.12E-5(soil_100m) + 0.0029(std_dev_elev_100m) + 4.23E-5(developed_250m)
SL 0.32 -899.98 0.54(W_ortho_wet) + 0.0077—7.01E-5(mean_elev_250m) + 0.0019(std_dev_elev_100m)* + 2.22E-5(developed_250m)** + 1.76E-5(soil_100m)**
SE 0.33 -903.33 0.66(LAMBDA_ortho_wet) + 0.021—7.25E-5(mean_elev_250m) + 0.0014(std_dev_elev_100m)** + 2.67E-5(developed_250m)**—1.81E-5(soil_100m)**
Total suspended solids OLS 0.08 25.06 1.03—0.0033(impervious_250m) + 0.00029(pipe_length)—0.061(std_dev_slope_100m) + 0.044(std_dev_slope_250m)
SL 0.12 26.54 0.89—0.061(std_dev_slope_100m) + 0.00028(pipe_length)—0.0030(impervious_250m) + 0.046(std_dev_slope_250m) + 0.13(W_tss_wet)**
SE 0.12 24.67 1.02—0.061(std_dev_slope_100m) + 0.00029(pipe_length)—0.0032(impervious_250m) + 0.047(std_dev_slope_250m) + 0.12(LAMBDA_tss_wet)**
Zinc OLS 0.33 42.87 0.0060(developed_250m) + 0.47—0.0073(impervious_100m)—0.032(mean_slope_100m) + 0.069(std_dev_slope_250m) + 0.00029(pipe_length) + 0.0018(soil_250m)
SL 0.46 27.05 0.47(W_zinc_wet) + 0.0046(developed_250m)—0.0054(impervious_100m) + 0.062(std_dev_slope_250m)—0.024(mean_slope_100m) + 0.00026(pipe_length) + 0.0011(soil_250m)** + 0.12**
SE 0.47 26.56 0.59(LAMBDA_zinc_wet) + 0.0054(developed_250m) + 0.47 + 0.056(std_dev_slope_250m)—0.0042(impervious_100m)—0.019(mean_slope_100m)* + 0.00023(pipe_length)* + 0.00097(soil_250m)**

Pollutant concentrations were transformed using log10(concentration + 1). Explanatory variables for each regression are listed in order of significance. R2 values are equivalent to adjusted R2 for OLS only. AIC = Akaike Information Criteria, OLS = Ordinary least squares, SL = Spatial lag, SE = spatial error; W_“pollutant”_“ season”= spatial lag coefficient; LAMBDA_“pollutant”_“season” = spatial error coefficient. Table adapted from Mainali and Chang (2018) (Mainali & Chang, 2018)

*insignificant at the 90% confidence level

**insignificant at the 95% confidence level