Nitrogen and phosphorous support the ecosystem by supplying nutrients to algae and aquatic plants. Having them in excess results in the eutrophication of waters creating quality problems. In the past, nitrogen has been widely investigated for wells in the context of groundwater flow. However, a national-scale nitrogen assessment in rivers and streams has not received enough attention. In this study, the Wilcoxon rank-sum test, a non-parametric hypothesis testing method, has been applied to nitrogen concentration in the form of nitrate-nitrogen and nitrite-nitrogen in rivers and streams of the Contiguous United States. This approach was particularly chosen because of the non-normal and positively skewed nitrogen levels occurring in the surface flow. This method was able to identify the impaired body of waters as well as quantify the confidence, significance, and errors involved. The Northern Appalachians (NAP), Northern Plains (NPL), and Xeric (XER) ecoregions were identified as the ecoregions worsening in the nitrogen-nitrate condition with NAP, and XER needed immediate action. The nitrite-nitrogen condition did not pose an immediate threat, so mitigation efforts should focus more on nitrate-nitrogen remediation. It was shown that the method was superior to the two-sample t-test by yielding lower type II errors.

The ideal framework for water infrastructure design in any region relies on rainfall characteristics of that region, which is defined through rainfall intensity-duration-frequency (IDF) curves. The current IDF curves are based on historical observations of precipitation. However, with the help of numerical models, more up-to-date IDF curves can be developed to reflect the current precipitation regime. Here, a weather research and forecasting (WRF) model was applied to produce the precipitation data for Arizona from 1950 to 2017. A total of 20 weather forecasting scenarios were simulated by changing the microphysics schemes to improve precipitation forecasting accuracy. The National Severe Storm Laboratory (NSSL) scheme with cloud condensation nuclei (CCN) improved the coefficient of determination by 10% and was selected as the optimum forecasting scenario. The IDF curves were then constructed based on the modeled data and annual maximum series analysis for each climate division in Arizona. The comparison between updated IDF curves and historical IDF curves showed that incorporating up-to-date precipitation data resulted in lower rainfall intensities for short durations.

This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (${\mathrm{R}}^2$), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with ${\mathrm{R}}^2$ = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with ${\mathrm{R}}^2$ = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.

Nitrogen and phosphorous support the ecosystem by supplying nutrients to algae and aquatic plants. Having them in excess results in the eutrophication of waters creating quality problems. In the past, nitrogen has been widely investigated for wells in the context of groundwater flow. However, a national-scale nitrogen assessment in rivers and streams has not received enough attention. In this research, the Wilcoxon rank sum test, as a non-parametric hypothesis testing method, has been applied to nitrogen concentration in the form of nitrate-nitrogen and nitrite-nitrogen in rivers and streams of the Contiguous United States. This approach was particularly selected because of the non-normal and positively skewed nitrogen levels occurring in the surface flow. This method was able to identify the impaired body of waters as well as quantify the confidence, significance, and errors involved. The Northern Appalachians (NAP), Northern Plains (NPL), and Xeric (XER) ecoregions were worsening in the nitrogen-nitrate condition with NAP, and XER needed immediate actions. The nitrite-nitrogen condition did not pose an immediate threat, so mitigation plans should focus more on nitrate-nitrogen remediation. It was shown that the method was superior to the two-sample t-test by yielding lower type II errors.