*4.7. Pollutant Loadings*

Pollutant loading calculations were obtained from quantifying flow and water quality data. To well represent the loadings with each respected watershed, the pollutant loadings were based on the watershed area for the three watersheds. Table 4 shows the results for the unit area loading rates for each watershed reflecting which of the three watersheds has the highest loading. The HWMD watershed shows higher results with respect to the flow, water quality parameters, and the overall watershed area, where both NPS and PS pollution are potential attributes of these elevated results. These data are not representative of the whole profile of the watersheds. More data should be quantified to better distinguish which watershed contributes the most to water impairments to the LLM.


**Table 4.** Summary of the pollutant loading (kg/km2/year) for the three watersheds.

<sup>1</sup> Bacteria loading unit is in MPN/km2/year. Source: SWQMIS.

The pollutant loadings per unit area distribution for each water quality parameter were provided with respect to each watershed area (Figure 7). Methods for calculating the loadings for each pollutant can be found in the USEPA Handbook for Developing Watershed Plans to Restore Our Waters [36]. These loadings were generated automatically through ArcGIS properties to show the difference among pollutant loadings. Bacteria load-

a.

ings per unit area were determined to be slightly higher for the IBWCNF watershed than RVD. However, IBWCNF has more potential NPS and PS sources for bacteria than RVD. The mean value for the bacteria loadings in IBWCNF and RVD was 12.4 (kg/km2/year) and 12.3 (kg/km2/year); respectively. This can be explained by the fact that the main bacterial sources in both watersheds come from agricultural activities. The ratio in cultivated crops in IBWCNF was slightly higher than RVD. IBWCNF covered 59% of cultivated crops, while RVD covered 52%. Additionally, the flow volume in RVD was higher than IBWCNF. The average flow rate in RVD was 2.57 CMS, while in IBWCNF it was 2.38 CMS. This could be the reason why the bacteria loadings in both watersheds have a minor difference. TKN results proved to be higher for the HWMD, which support the relative contribution of the TLAP to this watershed. Nitrate and nitrite and chlorophyll-a concentrations were high in the HWMD, corresponding to the significant presence of urban areas in the watershed. Ammonia results showed to be higher in the IBWCNF watershed, supporting the identification of a substantial percentage of agricultural lands. The HWMD had the highest loadings for TP and organic nitrogen, supporting the presence of MSWs. Figure 6a–g reflects the loading with respect to the subwatersheds of the three North and Central Watersheds. The HWMD watershed was identified to be higher in all the water quality parameters due to the high flow recordings in this watershed. *Sustainability* **2021**, *13*, x FOR PEER REVIEW 17 of 23

**Figure 7.** Spatial distribution of the pollutant loading for different parameters in the North and Central Watersheds: (**a**) bacteria, (**b**) ammonia, (**c**) total nitrogen, (**d**) organic nitrogen, (**e**) total phosphorus, (**f**) nitrite + nitrate, and (**g**) chlorophyll-**Figure 7.** Spatial distribution of the pollutant loading for different parameters in the North and Central Watersheds: (**a**) bacteria, (**b**) ammonia, (**c**) total nitrogen, (**d**) organic nitrogen, (**e**) total phosphorus, (**f**) nitrite + nitrate, and (**g**) chlorophyll-a.

vided acceptable results to characterize the North and Central Watersheds.

The cyberinfrastructure and REON website contributed significantly to this study in portraying relevant characteristics of each of the North and Central Watersheds. The REON website not only collects distinct information into one single source but also allows the stakeholders within each watershed to assess the watershed characteristics. Therefore, this platform is an innovative tool that supports effective watershed characterization. ArcGIS automated hydrology tools have shown to have satisfactory results in delineating watersheds. Overall, the study showed that the watershed delineation process used pro-

Although the HWMD watershed was not the highest regarding the urban areas, it is considered higher in NPS pollution with respect to the entire area of the North and Central Watersheds. Urban areas have more impact on the HWMD in comparison to the other watersheds regarding the overall watershed areas. This finding suggests that urban areas in this watershed are linked to the presence of bacteria and chlorophyll-a. Based on the water quality data obtained, only chlorophyll-a levels were higher than the other watershed levels. The high levels of chlorophyll-a relate to the HWMD watershed in extensive urban areas. Based on the total PS pollution found in the North and Central Watersheds,
