Surface water quality for irrigation and industrial purposes: a comparison between the south and north sides of the Wei River Plain (northwest China).

Surface water is extensively used for irrigation and industrial purposes in the Wei River Plain. However, the surface water shows different characteristics in the southern and northern zones of the Wei River Plain. This study aims to investigate the differences in surface water quality between the southern and northern zones of the Wei River Plain and their influencing factors. To ascertain the hydrochemistry and its governing factors, graphical methods, ion plots, and multivariate statistical analyses were employed. The quality of the irrigation water was assessed using various irrigation water quality indices. In addition, water foaming, corrosion, scaling, and incrustation risks were determined to evaluate water quality for industrial uses. The spatial distribution of water quality was done using GIS models. This research revealed that the concentrations of EC, TH, TDS, HCO3-, Na+, Mg2+, SO42- and Cl- on the north side of the plain were twice as high as those on the south side. On both sides of the Wei River Plain, water‒rock interactions, ion exchange, and considerable evaporation were observed. Gypsum, halite, calcite, and dolomite all dissolve to produce significant anions and cations in the water, according to ion correlation analysis. However, additional sources of contaminants led to higher concentrations in the surface water on the north side than on the south side. Surface water in the south of the Wei River Plain has superior quality to that in the north, according to the overall findings of irrigation water and industrial water quality assessments. The findings of this study will boost better water resource management policies for the plain.

Variation and multi-time series prediction of total hardness in groundwater of the Guanzhong Plain (China) using grey Markov model.

Total hardness (TH) is an important index representing the water suitability for domestic purpose. TH is represented mainly by Ca2+ and Mg2+ which are essential elements for human bone development. Between 2000 and 2015, the TH values of groundwater in major cities of the Guanzhong Plain varied significantly. The study was carried out to investigate TH variation over 16 years and to examine how effective the grey Markov model was in predicting TH concentrations in time series datasets. The hydrochemical parameters determining TH concentration and their origins were investigated using statistical analysis and geochemical models. The grey Markov model, which is effective in short time series prediction, was used to forecast the multi-time series of TH. The findings demonstrated a prevalence of HCO3- and SO42- in the groundwater types combined with calcite precipitation, gypsum, and dolomite dissolution that increased the concentration of Ca2+, Mg2+, and HCO3-, influencing TH variation. The predicted TH values of the eight monitoring wells for the year 2016 were 1213.66, 124.30, 203.66, 103.01, 349.56, 251.23, 453.31, and 471.81 mg/L, respectively. Datasets with low TH variation were more accurately predicted than datasets with high TH variation. This was especially observed on sample B557 where TH concentration in 2010 was 400.33 mg/L and suddenly dropped to 90.1, 82.6, 85.1, 87.6, and 75.1 mg/L in 2011, 2012, 2013, 2014, and 2015, respectively. The study also shows that the Markov chain model can optimize the GM(1,1) model and improve the prediction accuracy significantly. All samples in Weinan City and one sample in Xi'an City showed a significant decrease in TH concentration. Except one sample in Xi'an City, TH concentrations tended to rise in the other cities (Baoji, Xianyang) of the Guanzhong Plain. This study verified the reliability of the grey Markov model in terms of forecasting time series datasets with high variability, and the results can be referential to similar studies in the world.

Groundwater Pollution Source Identification and Apportionment Using PMF and PCA-APCS-MLR Receptor Models in Tongchuan City, China.

Potential sources of groundwater pollution in Tongchuan City, China, were qualitatively identified based on 14 key water quality indicators of 59 groundwater samples, and the contribution of each source to groundwater quality was quantitatively evaluated. Groundwater pollution sources were analyzed using PMF and PCA-APCS-MLR models, and their applicability in groundwater pollution assessment in Tongchuan City was tested. Results indicate that both models identified four sources of groundwater contamination. Natural evolution was the main cause of groundwater pollution in the study area, followed by the coal industry, agriculture, and urbanization. Although the spatial distribution of pollution sources in the two models differed, the urbanized area to the east of the study area was more severely affected by sewage discharge, the west was more obviously affected by the coal industry, and the north was mainly polluted by agriculture. Both of the fitting results of the two models are good, but R2 values obtained by the PMF model (0.4440-0.9991) were generally higher than those obtained by the PCA-APCS-MLR model (0.5180-0.9530), indicating that PMF model results were more accurate than the PCA-APCS-MLR model.