Assessment of Water Quality of Four Mahoning River Sub-watersheds, Northeast Ohio PDF Download

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Assessment of Water Quality of Four Mahoning River Sub-watersheds, Northeast Ohio

Assessment of Water Quality of Four Mahoning River Sub-watersheds, Northeast Ohio PDF Author: Sohaib Alam
Publisher:
ISBN:
Category : Water
Languages : en
Pages : 0

Book Description
The water quality of the historically industrial Mahoning River continues to be impacted by non-point pollution sources from the watershed. We evaluated the current surface water quality of four important Mahoning River sub-watersheds: Meander Creek, Crab Creek, Lower Mosquito Creek, and Yellow Creek using the National Sanitation Foundation water quality index (NSF-WQI). Water quality parameters measured include, temperature, dissolved oxygen, pH, ammonia, soluble reactive phosphates, total phosphorus, nitrate-nitrogen, total suspended solids, volatile solids, total dissolved solids, total coliform, Escherichia coli and biochemical oxygen demand. All of the samples were taken during the summer at low flow conditions. NSF-WQI of four sub-watersheds are compared along with their land cover types identified as residential, commercial, industrial, forested, and agricultural areas. The NSF-WQI for Lower Mosquito Creek was found to be the highest at 70 and Crab Creek having the lowest at 64.5, respectively being in good and medium according to NSF-WQI range (0-100). Also, the Post-hoc Tukey HSD test indicates Crab Creek is statistically significantly lower than Lower Mosquito Creek. Land cover type of residential in Lower Mosquito Creek had the highest NSF-WQI at 72 and agriculture land cover in Crab Creek had the lowest at 53, respectively being in good and medium NSF-WQI range. When comparing NSF-WQI of overall land cover types, residential had the highest at 70 and agriculture had the lowest at 64, while forest and industrial were same at 67, respectively being in good and rest in medium NSF-WQI range. Tukey HSD test indicates the NSF-WQI for residential land cover is statistically significantly higher than agriculture land cover. The principal component analysis depicts a weak correlation with water variables and NSF-WQI of the sub-watersheds.