Md Abu Bakar Siddique
Bangladesh Council of Scientific and Industrial Research
Abu Reza Md. Towfiqul Islam
Begum Rokeya University
Md Saddam Hossain
Mawlana Bhashani Science and Technology University
Rahat Khan
Bangladesh Atomic Energy Commission
Ahedul Akbor
Bangladesh Council of Scientific and Industrial Research
Md Hasanuzzaman
Begum Rokeya University
Md Wasiq Mamun Sajid
Begum Rokeya University
Md Younus Mia
Mawlana Bhashani Science and Technology University
Javed Mallick
King Khalid University
M Safiur Rahman
Bangladesh Atomic Energy Commission
Md Mostafizur Rahman
Jahangirnagar University
Md Bodrud-Doza
BRAC
Surface water quality index, Principal component analysis, Factor analysis, Entropy theory, Dhaleswari River, Bangladesh
Dhaleshwari River in Hemayetpur of Savar subdistrict, Bangladesh
Socio-economic and Policy
Water quality, Pollution
In this study, Dhaleshwari River in Hemayetpur of Savar subdistrict, situated at the northwest part of Dhaka city, Bangladesh was investigated. The city accommodated with approximately1.4 million of inhabitants. Our study area lies at the latitudes of 23°51'30.0024'' N and longitudes of 90°16'0.0120'' E with the land surface of an elevation of ~15m (Banglapedia 2018). The Dhaleshwari river is divided into two tributaries: the name Dhaleshwari remains for the northern upstream branch which combines at the southern downstream part at Manikganj district with its other branch namely Kaligonga river (Hasan et al. 2020). The divided branches are merged again before combining with the Shitalakshya river and ultimately merging with the Meghna river, which ends up in the delta of the Ganges-Brahmaputra-Meghna rivers (Ahsan et al., 2019). Three most prominent seasons are observed in the studied area: (1) the pre-monsoon season (March-June: hot and humid rainy season with the temperature reaching up to 40°C), (2) the monsoon or wet season (July-October: very wet with temperatures ~30°C), and (3) the post-monsoon or dry season (November-February: winter season with temperature 10 to 20°C). Average 2000 mm rainfall occurs in the rainy season with average 75% humidity and 60% cloud cover (Rahman et al., 2020). The study area is comprised of Pleistocene alluvium-soil with a gentle slope heading from west to east. Land usages are mostly associated with agriculture (24.3%), agricultural laborer (12.8%), wage laborer (4.44%), forestry, cattle breeding, and fishing (1.90%), industry (1.37%), service (20.7%), commerce (17.4%), transport (3.96%), construction (1.66%), and others (11.5%). The total cultivable land is ~16,750 hectares with fallow land of ~10,550 hectares (Ahsan et al. 2019). Total 50 river-water samples were collected from 5 different sampling stations (denoted as S1, S2, S3, S4, & S5) of the studied area during the monsoon or wet season (July-October: total 25 samples) and post-monsoon or dry season (November-February: total 25 samples) in the year of 2018 following the standard guidelines (APHA 2012). Sampling stations were chosen horizontally to cover the entire industrial area including the main effluent discharged points (S2) where the subsequent stations were ~0.5km apart from each other. From each sampling station, five samples (composite samples of two/three independent collections) were collected (one sample form the center of the sampling point and other four samples around the sampling points were collected) which were separated from each other by 100-200m. Polyethylene plastic bottles (of 1000 mL) preconditioned with 5% conc. HNO3 and rinsed with double-deionized water (Ahsan et al. 2019) were utilized to collect the river-water samples. Before the sampling, these sampling bottles were rinsed at least 3 times with the water samples to be collected at each sampling station. For collecting water samples, pre-prepared sampling bottles were submerged at ~10cm underneath the water surface of the river. After sampling, the samples were immediately acidified with 2 mL conc. HNO3 per 1000 mL of samples (Habib et al. 2020; Ahsan et al. 2019), after then the bottles were screwed carefully and marked with the respective identification numbers. The same number of duplicate samples were also collected without acidification and labeled accordingly for determining the anions and some physicochemical parameters of the Dhaleswari River. All samples were then placed in an ice bath, carried to the laboratory, and preserved in a refrigerator at 4ºC on the same day until the analysis (Islam et al. 2020c; Ahsan et al. 2019). All water samples were analyzed at the Institute of National Analytical Research and Service (INARS, ISO/IEC 17025: 2017 accredited laboratory), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh. Before analysis, samples were allowed to reach ambient room temperature and shaken well for homogeneity. For metal analysis, water sample (100 mL) was taken into 250 mL cleaned glass-beaker by calibrated pipette, and was acidified with 5 mL of conc. HNO3. For digestions, samples were then heated on a hot plate (150-180 °C), and volume was reduced to ~25-30 mL. Samples were then allowed to cool and were then transferred to 100 mL cleaned and calibrated volumetric flask by rinsing the beaker at least three times with double deionized water and then the flask was filled up to the mark (100 mL) with deionized water. Samples were then filtered and preserved in cleaned and dried nontransparent polyethylene plastic bottle (250 mL) with proper labeling for the determination of metals content. Statistical approaches were performed by SPSS statistical package (version 25) for Windows. Kolmogorov-Smirnov test was run to check data normality & homogeneity of surface water for both dry and wet seasons. Pearson’s correlation matrix was calculated to check the association between the analyzed hydro-chemical variables. The coefficient of determination (R2) was utilized to estimate the goodness of fit of the tested models. Root mean square error (RMSE) of the model was computed to assess the predictive capability of all the models. A paired sample t-test was run to establish the statistical differences in seasons’ concentrations of physicochemical parameters.
In Review: Environmental Science and Pollution Research, Version 1, 07 June, 2021
We developed a well-establish IWQIs and EWQIs for surface water suitability for agricultural purposes concerning representative variables suggested by FAO-29 standard and a well-accepted method, namely, NSFWQI and entropy theory. Seasonality to spatiotemporal changes in physicochemical parameters in surface water during both seasons has been investigated in this work. Spatial patterns of water quality indices were appraised using Moran’s autocorrelation index. The outcomes of Shannon entropy theory implied that Mg, Cr, TDS, and Cl− for the dry season and Cd, Cr, Cl− and SO42− for the wet season were recognized as the main pollutants, triggering water quality degradation. PCA and FA have been employed efficiently in two crucial parts, e.g., least variable selection and allocating their weights on one hand. Shannon entropy theory has been employed efficiently in two steps, i.e., entropy weight of all variables and their information entropy on the other hand to develop EWQI-1 and EWQI-2 for both the seasons. Both PCA and FA identified the weights for the preliminary sixteen variables added in computing IWQI-1 and IWQI-2, respectively, with the final method being suggested. The IWQIs exhibited an analogous trend with the EWQIs model that implied water quality classes varied from poor to good qualities. The PCA/FA lessened the dimensionality of multiple parameters to develop a well-demonstrative LDS of EC, TDS, Cr, Zn, Mg, SO42−, Pb, Cd, Cl−, and NO3− for dry season and Pb, Mn, Cr, Co, Cu, Mg, Zn, EC, TDS, and NO3− for the wet season to be added in introducing IWQImin-1, IWQImin-2, and IWQImin-3 for both seasons. The performance of IWQIs is depicted comparatively higher than EQWIs because of inclusiveness. The IWQImin with weights come from PCA developed in good measuring and predicting water quality compared to weights originated from FA. The variables chosen in the computation of IWQImin can be evidently estimated, which will considerably reduce monitoring time and cost of data collection and analysis of a huge number of variables. On a spatial scale, IWQImin-3 was statistically negatively correlated with the wet season (Moran’s I value > 0). Our research has also identified the physicochemical variables including NO3−, Mg, Cl−, Pb and Cr may influence the irrigation water quality for the dry season and Mn, Pb, Mg, and Cr for the wet season using RF model in the Dhaleshwari River basin, hence justifying further large basin-scale analysis.
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