Spatio-temporal variations in chemical-physical water quality parameters influencing water reuse for irrigated agriculture in tropical urbanized deltas

Haldar, Kamonashish; Kujawa-Roeleveld, Katarzyna; Dey, Priyanka; Bosu, Shanchita; Datta, Dilip Kumar; Rijnaarts, H.H.M.

doi:10.1016/j.scitotenv.2019.134559

Cited by 58 publications

(22 citation statements)

References 48 publications

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“…In addition, Kriging only works for normal distributions, while IDW has the ability to handle parameters that are not normally distributed [47]. The IDW method assumes that the values of the unsampled points are more similar to the values of the closer sampled points [48]. Since the change of water quality is continuous, and the water quality was greatly affected by closer observation points, IDW was used in this study.…”

Section: Idw Methodsmentioning

confidence: 99%

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Yang

Zhao

Wang

et al. 2020

IJERPH

217

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This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin’anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin’anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin’anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin’anjiang River were analyzed. We found that the overall water quality of Xin’anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin’anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin’anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.

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Section: Idw Methodsmentioning

confidence: 99%

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Yang

Zhao

Wang

et al. 2020

IJERPH

217

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“…For this study, a correlation coefficient greater than or equal to 0.7 (r ≥ 0.7) was defined as a strong correlation. Correlation coefficient values between 0.4 and 0.7 (0.4 ≤ r < 0.7) were considered moderately strong, and below 0.4 (r < 0.4) were considered weak ( Haldar et al., 2020 ). Principal component analysis (PCA) was also performed to provide a rough overview of the reduced dimensions of parameters and sample clustering using Origin 2021.…”

Section: Methodsmentioning

confidence: 99%

Spatial variation of dissolved organic nitrogen in Wuhan surface waters: Correlation with the occurrence of disinfection byproducts during the COVID-19 pandemic

et al. 2021

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Intensified sanitization practices during the recent coronavirus disease-2019 (COVID-19) led to the release of chlorine-based disinfectants in surface water, potentially triggering the formation of disinfection byproducts (DBPs) in the presence of dissolved organic nitrogen (DON). Thus, a comprehensive investigation of DON's spatial distribution and its association with DBP occurrence in the surface water is urgently needed. In this study, a total of 51 water samples were collected from two rivers and four lakes in May 2020 in Wuhan to explore the regional variation of nitrogen (N) species, DON's compositional characteristics, and the three classes of DBP occurrence. In lakes, 53.0% to 86.3% of N existed as DON, with its concentration varying between 0.3–4.0 mg N/L. In contrast, NO 3 − -N was the dominant N species in rivers. Spectral analysis revealed that DON in the lakes contained higher humic and fulvic materials with higher A 254 , A 253 /A 203, SUVA 254 , and P III+IV /P I+II+V ratios, while rivers had higher levels of hydrophilic compounds. Trihalomethanes (THMs) were the most prevalent DBPs in the surface waters, followed by N-nitrosamines and haloacetonitriles (HANs). The levels of N-nitrosamines (23.1–97.4 ng/L) increased significantly after the outbreak of the COVID-19 pandemic. Excessive DON in the surface waters was responsible for the formation of N-nitrosamines. This study confirmed that the presence of DON in surface water could result in DBP formation, especially N-nitrosamines, when disinfectants were discharged into surface water during the COVID-19 pandemic.

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“…However, the temporal trends for pH and TDS in the dry season were slightly higher than those in the wet season. This might be due to the intense evaporation that occurs in the dry season and dilution of excessive rainfall in the wet season [ 7 ].…”

Section: Resultsmentioning

confidence: 99%

“…and anthropogenic factors (domestic and industrial wastewater, agricultural activities, urban development, etc.) [ 6 , 7 ]. Su et al [ 8 ] found that the water quality of the Qiantang River in eastern China was affected mainly by domestic sewage and agricultural pollution, industrial wastewater discharge, vehicle exhaust, sand mining, and mineral weathering.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns in River Water Quality and Pollution Source Apportionment in the Arid Beichuan River Basin of Northwestern China Using Positive Matrix Factorization Receptor Modeling Techniques

Xiao

Zhang

Niu

et al. 2020

IJERPH

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Deteriorating surface water quality has become an important environmental problem in China. In this study, river water quality was monitored in July (wet season) and October (dry season) 2019 at 26 sites, and a water quality index (WQI) and positive matrix factorization (PMF) model were used to assess surface water quality and identify pollution sources in the Beichuan River basin, Qinghai Province, China. The results showed that 53.85% and 76.92% of TN, 11.54% and 34.62% of TP, 65.38% and 76.92% of Fe, and 11.54% and 15.38% of Mn samples in the dry and wet seasons, respectively, exceeded the Chinese Government’s Grade III standards for surface water quality. The spatial variation in water quality showed that it gradually deteriorated from upstream to downstream as a result of human activity. The temporal variation showed that water quality was poorer in the wet season than in the dry season because of the rainfall runoff effect. The PMF model outputs showed that the primary sources of pollution in the wet season were mineral weathering and organic pollution sources, domestic and industrial sewage, and agricultural and urban non-point pollution sources. However, in the dry season, the primary sources were mineral weathering and organic pollution sources, industrial sewage, and domestic sewage. Our results suggest that the point pollution sources (domestic and industrial sewage) should be more strictly controlled, as a priority, in order to prevent the continued deterioration in water quality.

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Spatio-temporal variations in chemical-physical water quality parameters influencing water reuse for irrigated agriculture in tropical urbanized deltas

Cited by 58 publications

References 48 publications

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Spatial variation of dissolved organic nitrogen in Wuhan surface waters: Correlation with the occurrence of disinfection byproducts during the COVID-19 pandemic

Spatiotemporal Patterns in River Water Quality and Pollution Source Apportionment in the Arid Beichuan River Basin of Northwestern China Using Positive Matrix Factorization Receptor Modeling Techniques

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