Water Quality Analysis of the Yangtze and the Rhine River: A Comparative Study Based on Monitoring Data from 2007 to 2018

Chiral pesticides levels in peri-urban area near Yangtze River and their correlations with water quality and microbial communities

Pesticides are considered to be the second-largest non-point source pollution in water. Our research assayed the river network of typical agricultural areas in the middle and lower Yangtze River as the study area. Pesticides residues in aquatic environment were determined by QuEChERS, combined with high-performance liquid chromatography tandem mass spectrometry, or gas chromatograph-mass spectrometer. At chiral pesticides' levels, we detected pesticides contents in water, classified and counted the types of pesticides, and analyzed their environmental risk assessment. Furthermore, potential correlations between chiral pesticides concentrations and water quality indicators were assayed. Additionally, we explored their relations with microbial communities at species levels. Enantiomers of Diclofop-methyl, Ethiprole, Difenoconazole and Epoxiconazole were enantioselectively distributed. More interestingly, due to various chiral environment of the sampling site, the enantiomers of Tebuconazole Acetochlor, Glufosinate ammonium and Bifenthrin had completely different distributions at different sites. Based on that, the chiral pesticides Diclofop-methyl, Bifenthrin, Ethiprole, Tebuconazole and Difenoconazole are enantioselective to the risk of aquatic environment. Generally, enantiomeric selectivity had high positive correlations with total nitrogen and phosphorus. Then we found that chiral fate behavior of Tebuconazole and Paichongding in water might be affected by prokaryotes. In addition, the chiral behavior of Diclofop-methyl, Propiconazole, Difenoconazole, and Tebuconazole isomers in water might be negatively affected by eukaryotes. That research helped us to comprehensively understand the impact of non-point source pollution of chiral pesticides in aquatic environment and provided basic data support for developing biological and water quality indicators for monitoring pollution in aquatic environment.

What will the water quality of the Yangtze River be in the future?

The long-term prediction of water quality is important for water pollution control planning and water resource management, but it has received little attention. In this study, the water quality trend in the Yangtze River is found to stabilize at most monitoring stations under environmental protection activities. Based on the physical mechanism and stochastic theory, a novel river water quality prediction model combining pollution source decomposition (including local point, local nonpoint and upstream sources) and time series decomposition (including trend, seasonal and residential components) is developed. The observed water quality data from 76 monitoring stations in the Yangtze River, including permanganate index (COD_Mn) and total phosphorus (TP), are used to drive this model to make long-term water quality predictions. The results show that this model has an acceptable accuracy. In the future, the concentration of COD_Mn will meet the water quality targets at most stations in the Yangtze River, but the concentration of TP will not be able to meet the water quality target at 28.5 % of the stations. Furthermore, the prediction value of COD_Mn is 62.2 % lower than the target on average. However, the prediction value of TP is only 24.4 % lower than the target on average, and it will exceed the water target by >50 % at some stations. This model has the potential to be widely used for long-term water quality prediction in the future.

Spatiotemporal variations of water quality and their driving forces in the Yangtze River Basin, China, from 2008 to 2020 based on multi-statistical analyses

Water quality deterioration is a prominent issue threatening water security worldwide. As the largest river in China, the Yangtze River Basin is facing severe water pollution due to intense human activities. Analyzing water quality trends and identifying the corresponding driver factors are important components of sustainable water quality management. Thus, spatiotemporal characteristics of the water quality from 2008 to 2020 were analyzed by using a Mann-Kendall test and rescaled range analysis (R/S). In addition, multi-statistical analyses were used to determine the main driving factors of variation in the permanganate index (COD_Mn), ammonia nitrogen (NH₃-N) concentration, and total phosphorus (TP) concentration. The results showed that the mean concentrations of NH₃-N and TP decreased from 0.31 to 0.16 mg/L and 0.16 to 0.07 mg/L, respectively, from 2008 to 2020, indicating that the water quality improved during this period. However, the concentration of COD_Mn did not reduce remarkably. Based on R/S analysis, the NH₃-N concentration was predicted to continue to decrease from 2020 to 2033, whereas the COD_Mn concentration was forecast to increase, highlighting an issue of great concern. In terms of spatial distribution, water quality in the upstream was better than that of the mid-downstream. Multi-statistical analyses revealed that the temporal variation in water quality was predominantly influenced by tertiary industry (TI), the nitrogen fertilizer application rate (N-FAR), the phosphate fertilizer application rate (P-FAR), and the irrigation area of arable land (IAAL), with contribution rates of 15.92%, 14.65%, 3.46%, and 2.84%, respectively. The spatial distribution of COD_Mn was mainly influenced by TI, whereas that of TP was primarily determined by anthropogenic activity factors (e.g., N-FAR, P-FAR). This study provides deep insight into water quality evolution in the Yangtze River Basin that can guide water quality management in this region.

Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization

Undeniably, there is a link between water resources and people's lives and, consequently, economic development, which makes them vital in health and the environment. Proper water quality forecasting time series has a crucial role in giving on-time warnings for water pollution and supporting the decision-making of water resource management. The principal aim of this study is to develop a novel and cutting-edge ensemble data intelligence model named the weighted exponential regression and hybridized by gradient-based optimization (WER-GBO). Indeed, this is to reach more meticulous sodium (Na⁺) prediction monthly at Maroon River in the southwest of Iran. This developed model has advantages over other previous methodologies thanks to the following merits: (i) it can improve the performance and ability by mixing the outputs of four distinct data intelligence (DI) models, i.e., adaptive neuro-fuzzy inference system (ANFIS), least square support vector regression (LSSVM), Bayesian linear regression (BLR), and response surface regression (RSR); (ii) the proposed model can employ a Cauchy weighted function combined with an exponential-based regression model being optimized by GBO algorithm. To evaluate the performance of these models, diverse statistical indices and graphical assessment including error distributions, box plots, scatter-plots with confidence bounds and Taylor diagrams were conducted. According to obtained statistical metrics and verified validation procedures, the proposed WER-GBO resulted in promising accuracy compared to other models. Furthermore, the outcomes revealed the WER-GBO (R = 0.9712, RMSE = 0.639, and KGE = 0.948) reached more accurate and reliable results than other methods such as the ANFIS, LSSVM, BLR, and RSR for Na prediction in this study. Hence, the WER-GBO model can be considered a constructive technique to forecast the water quality parameters.

An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.

The importance of organic carbon as a coadjutant in the transport of pollutants

Dissolved organic carbon (DOC) is a physicochemical parameter widely used in the evaluation of surface water quality; however, its role as an agent of transport and transference of pollutants sometimes is still disregarded. The heterogeneous composition of DOC, predominantly composed of humin, humic and fulvic acids, renders it an inherent capacity to bind to organic and inorganic pollutants. This is an important feature when the knowledge of present and future conditions of aquatic environments is of concern. Some authors concluded that DOC is a controlling agent of mobility of metals, phosphorus, herbicides, and pesticides, among others. Nevertheless, some physical and chemical conditions in the water column and in the sediment can immobilize the contaminants and make the DOC less soluble, which will hamper the formation of DOC-pollutant complexes. This mini review is intended to present the importance of DOC quantification and some information on its association with water contaminants, which could render them unavailable for uptake.

Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control.

A Comprehensive Evaluation Model of Ammonia Pollution Trends in a Groundwater Source Area along a River in Residential Areas

In this study, a comprehensive evaluation model of ammonia pollution trends in a groundwater source area along a river in residential areas is proposed. It consists of coupling models and their interrelated models, including (i) MODFLOW and (ii) MT3DMS. The study area is laid in a plain along a river, where a few workshops operate and groundwater is heavily contaminated by domestic pollutants, agricultural pollutants, and cultivation pollutants. According to the hydrogeological conditions of the study area and the emissions of ammonia calculated in the First National Pollution Source Census Report in China, this study calibrates and verifies the prediction model. The difference between the observed water level and the calculated water level of the model is within the confidence interval of the test. This means that the model is reliable and that it can truly reflect changes in the groundwater flow field and can be directly used to simulate the migration of ammonia. The simulation results show that, after 20 years, the center of the ammonia pollution plume will gradually flow east along with the groundwater over time, mainly affecting the groundwater, which is less than 200 m from the river, and the ammonia content near wells at a maximum extent of less than 0.3 mg/L.

Seasonal variations of water quality response to land use metrics at multi-spatial scales in the Yangtze River basin

Land use pattern is increasingly regarded as an important determinant of environmental quality and regional ecosystems. Understanding the correlation between land use metrics and water quality is essential to improve water pollution prediction and provide guidance for land use planning. Here, we examined the land use metrics and water quality parameters (i.e., dissolved oxygen, DO; pH; ammonia nitrogen NH₄⁺-N; permanganate index, COD_Mn), as well as their relationships in the Yangtze River basin. The DO and pH exhibited the notable spatio-temporal variability, suggesting that anthropogenic land uses (farmland and urban land) greatly impacted riverine water quality. The catchment and riparian scales respectively showed a high potential in explaining water quality in the dry and wet seasons. The land use metrics were tightly linked to water quality in the dry season, indicating that intensive farming activities led to high loadings of agriculture-related chemicals and thus water quality deterioration. Our results provided useful information regarding riverine water quality response to land use metrics at multi-spatial scales.