Water Quality Index (WQI) of Shitalakshya River Near Haripur Power Station, Narayanganj, Bangladesh

The present investigation is aimed at understanding the water quality parameters and the findings of a water quality index (WQI) to assess the characteristics of the Shitalakshya River near Haripur power station, Narayanganj for five different years (2013-2018) considering monsoon, pre-monsoon, post-monsoon seasonal variations. In this study, three different methods were used to evaluate the WQI named as; Weighted Arithmetic Index Method, Canadian Council of Ministers of the Environment (CCME) WQI Method and National Sanitation Foundation (NSF) Method. Essential parameters i.e. dissolved oxygen, pH, chloride, turbidity, color, biochemical oxygen demand, total dissolved solids, Silica, Iron, electrical conductivity, Phosphate were considered for calculating the WQI. According to Weighted Arithmetic Index Method, the WQI value varied from 80 to 286 for the last five years. From the National Sanitation Foundation Method, the WQI value was found within 36 to 56 for the study duration. The WQI value was varied from 3 to 16 according to the Canadian Council of Ministers of the Environment Water Quality Index Method. Based on WQI values, the Shitalakhya river water was being classified as poor water for the above-mentioned different years. Among the different parameters, mostly turbidity, electrical conductivity, TSS, Iron were the parameters that caused the situation worst. Journal of Engineering Science 12(3), 2021, 45-55

Priority Pollutants Monitoring and Water Quality Assessment in the Siret River Basin, Romania

The Integrated Water Resources Management regulations aim to ensure a good status of surface water quality and its sustainable use. Water quality monitoring of various water users supports the identification of pollution sources and their environmental impacts. The priority pollutants generated by wastewater discharges from municipal, industrial wastewater treatment plants or agricultural areas are of great interest due to their eco-toxicological effects and bio-accumulative properties. The aim of this study was to monitor the priority organic and inorganic pollutants from the Siret River basin, in Romania, with the purpose of assessing the surface water quality status and evaluating it by the Water Quality Index (WAWQI) method. The monitoring of inorganic priority pollutants (e.g., As, Cd, Hg, Ni, Pb) and organic priority pollutants (e.g., Naphthalene, Anthracene, Phenanthrene, Fluoranthene, Benzo(a)anthracene, Benzo(b)fluoranthene, Benzo(k)fluoranthene, Benzo(a)pyrene, Benzo(ghi)perylene, Indeno(1,2,3-cd)pyrene, α, β, and γ-Hexachlorocyclohexane, and Di-2-ethyl-hexyl-phthalate) was conducted within the Siret River basin, during the period 2015–2020. With this purpose, 21 sampling points (18 river sections and 3 lakes) were considered to assess the water quality. The results of this study proved that the water quality within the Siret River basin is generally classified in the 2nd or 3rd class. The spatial distribution of the water quality index values, using ARCGIS, also highlighted the fact that the water quality is mostly unsuitable for drinking water supplies, being influenced by the quality of its main tributaries, as well as by the effluent of wastewater treatment plants.

Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices With Efficient Machine Learning Technique

Water is an indispensable resource for human production and life. The evaluation of water quality by scientific method that provides sufficient support for the regeneration and recycling utilization of water resources. At present, water quality is mainly evaluated by water quality index (WQI) with weighted entropy value, which comprehensively considers the influence of different relevant environmental factors on the water quality. The calculation process is very complicated and time-consuming. In this paper, the method of correlation analysis is used to select the best combination of relevant environmental factors to assist the prediction model. Two typical kinds of machine learning methods are adopted and compared to realize the prediction of entropy water quality index (EWQI). After the better framework of prediction model is selected, four different kinds of optimization algorithms are used to optimize the prediction model to realize non-linear regression prediction and classification of water quality. According to the results of evaluation indicators, the framework of SVM is more suitable for realizing the prediction of EWQI. Meanwhile, the optimization algorithm of DE-GWO show great potential to improve the performance of SVM, which can make further contribution to the rational use and protection of water resources.