Spatiotemporal analysis of water quality using multivariate statistical techniques and the water quality identification index for the Qinhuai River basin, East China

Monitoring water quality is indispensable for the identification of threats to water environment and later management of water resources. Accurate monitoring and assessment of water quality have been long-term challenges. In this study, multivariate statistical techniques (MST) and water quality identification index (WQII) were applied to analyze spatiotemporal variation in water quality and determine the major pollution sources in the Qinhuai River, East China. A rotated principal component analysis (PCA) identified three potential pollution sources during the wet season (mixed pollution, physicochemical, and nonpoint sources of nutrients) and the dry season (nutrient, primary environmental, and organic sources) and they explained 81.14% of the total variances in the wet season and 78.42% of total variances in the dry season. The result of redundancy analysis (RDA) showed that population density, urbanization, and wastewater discharge are the main sources of organic pollution, while agricultural fertilizer consumption and industrial wastewater discharge are the main sources of nutrients such as nitrogen and phosphorus. The water quality of the Qinhuai River basin was determined to be mainly Class III (slightly polluted) and Class IV (moderately polluted) based on WQII. Temporally, the change trend of WQII showed that water quality gradually deteriorated between 1990 and 2005, improved between 2006 and 2010, and then deteriorated again. Spatially, the WQII distribution map showed that areas with more developed urbanization were relatively more polluted. Our results show that MST and WQII are useful tools to help the public and decision makers to evaluate the water quality of aquatic environment

A Hybrid Wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series

Value Assessment of Artificial Wetland Derived from Mining Subsided Lake: A Case Study of Jiuli Lake Wetland in Xuzhou

Mining subsided lakes are major obstacles for ecological restoration and resource reuse in mining regions. Transforming mining subsided lakes into artificial wetlands is an ecological restoration approach that has been attempted in China in recent years, but a value assessment of the approach still needs systematic research. This paper considers Jiuli Lake wetland, an artificial wetland derived from restoration of a mining subsided lake in plain area, as a case study. A value assessment model for the artificial wetland was established based on cost–benefit analysis by means of field monitoring, social surveys, GIS geostatistics, raster calculation methods, etc. Empirical analysis and calculations were performed on the case study region. The following conclusions were drawn: (1) after ecological restoration, ecosystem services of Jiuli Lake wetland which has become a national level wetland park yield positive values; (2) the improved environment of the Jiuli Lake wetland has a spillover effect on the price of surrounding land, resulting in land price appreciation; (3) using GIS geostatistics and raster calculation methods, the impact range, strength, and value of the spillover effect can be explicitly measured; (4) through the establishment of a value assessment model of the artificial wetland, incomes of the ecological restoration was found to be sufficient to cover the implementation costs, which provides a research foundation for economic feasibility of ecological restoration of mining subsided lakes

Analysis of the Lake-Effect on Precipitation in the Taihu Lake Basin Based on the GWR Merged Precipitation

Based on the high-density gauged rainfall, the geographically weighted regression (GWR) was used to fuse the daily precipitation of rain gauges with those of Multi-source Weighted-Ensemble Precipitation V2.1 (MSWEP V2.1) and a new merged daily precipitation was generated (referred to as GWR merged precipitation, denoted by GWRMP). Then, the precipitation accuracy at 0.1° × 0.1° grid scale and the lake-effect on precipitation in the Taihu Lake Basin were investigated. Results show that GWRMP is characterized with higher precision and stronger spatial recognition ability compared with MSWEP in the whole basin at 0.1° × 0.1° grid scale, and lake area with a relatively sparse network of rain gauges is no exception. Topography is the most important influencing factor of rainfall in the Taihu Lake Basin, the Pearson correlation coefficient (r) between DEM and the main precipitation type (EOF-1) in the whole basin is 0.64, resulting in a rainy area in the southwestern mountain, and less rain at plain and lake area based on the GWRMP. The multi-year average precipitation in the lake upwind area is 8.31% lower than that in the downwind area. Different with the influence mechanism of precipitation in the southwestern mountainous area characterized by high consistency between the spatial distribution of precipitation and the climatic elements derive from the ERA5 meteorological reanalysis data (|r| > 0.6), there is a lower consistency in the lake downwind area (|r| < 0.5) and no consistency in the lake upwind area at the 0.25° × 0.25° grid scale. The southeast monsoon is deduced as the most important factor affecting the procedure of lake-effect on precipitation in the Taihu Lake Basin. The distribution of wind direction and wind speed determines the dynamic changes of surface water vapor to a certain extent, and the lake-effect on precipitation is most likely occurs in July

Co-occurrence patterns and assembly processes of abundant and rare bacterioplankton in plain river network areas of eastern China

In the context of anthropogenic impacts on riverine ecosystems globally, understanding the response of bacterioplankton to anthropogenic stress is important for human and environmental health. Bacterioplankton communities are critical for maintaining ecosystem stability, but little is known about their co-occurrence networks and assembly processes in intense human-impacted plains river networks. By applying cooccurrence networks, variance partitioning, and null model analysis, we investigated the mechanisms of interaction and assembly of abundant (>1% relative abundance) and rare (<0.01%) taxa at 32 sites in river networks along an urbanization gradient (urban, suburban, and agricultural areas). Our results show that interactions between the bacterioplankton communities were more complex in urban areas than in other areas, and that environmental factors (primarily fluoride, nitrate nitrogen, and dissolved organic carbon) as well as spatial factors (i.e., Moran’s Eigenvector Maps) explained most of the variation in bacterioplankton communities. Abundant taxa showed stronger spatial turnover than rare taxa, indicating that spatial factors played a greater role in the assemblage of abundant taxa. Land use types, especially impervious surfaces, had a unique influence on rare taxa, but contributed less to community change than other factors. In addition, rare taxa were mainly influenced by deterministic processes, whereas abundant taxa were more influenced by stochastic processes, especially in agricultural areas. These findings suggest that the shares of abundant and rare taxa mediate disturbances in local environmental conditions in anthropogenic river network areas, while deterministic processes play a greater role in shaping bacterioplankton communities. Overall, our study provides important insights for environmental monitoring and management in areas of intense human activity, emphasizing the need to consider both abundant and rare taxa when assessing the impact of human activities on riverine ecosystems

Unveiling the influence of specialists and generalists on Macroinvertebrate assemblage heterogeneity in lake Taihu

The loss of biodiversity in the era of fast environmental change is often revealed as biotic homogenization. In the large and nutrient-rich Lake Taihu located in eastern China, we evaluated the degree of specialization among organisms and the impact of environmental factors on macroinvertebrate assemblages. First, we measured the niche widths of macroinvertebrate species in respect to environmental conditions and utilized the Outlying Mean Index (OMI) analysis to categorize them into specialists and generalists. The effects of environmental factors on assemblages of macroinvertebrate taxa identified by varying niche widths were then investigated using the lake dataset. Within our study lake, habitats display considerable environmental gradients, resulting in habitat heterogeneity linked to several habitats, and have no barriers to species migration. Generalist macroinvertebrate taxa dominated the macroinvertebrate assemblages in our study lake, but some differences were evident among the different habitat types of the lake. The macrophyte-dominated habitat (MDH) displayed the highest species richness for both generalists (11 species) and specialists (8 species), while the algal-dominated habitat (ADH) had lower richness but higher overall abundances. Generalist species showed the largest abundances, exhibiting considerable spatial heterogeneity among the sampled sites. Our results showed that generalists shape macroinvertebrate assemblages through their abundance and richness variation in large shallow lakes. Furthermore, strong environmental gradients and a high degree of habitat connectivity within large lakes allow specialist and intermediate species to maintain high richness in resource-rich habitats, in addition to generalist species also maintaining high abundance in such lakes

Numerical Simulations of Non-Point Source Pollution in a Small Urban Catchment: Identification of Pollution Risk Areas and Effectiveness of Source-Control Measures

Urban non-point source pollution is becoming a serious issue under the context of rapid urbanization and its impacts on surface hydrologic processes. The identification of non-point source risk areas and the effectiveness of source-control measures provides important first steps to improve the degrading aquatic environment but is challenged by the complex dynamics and variabilities of surface pollutants in urban environments. In this study, we investigate the spatial and temporal variabilities of non-point source pollution in a small urban catchment based on numerical simulations and in-situ samplings. Our results show that residential, industrial, and commercial land contribute to the most pollutant loadings and are the main constituents of the pollution risk area. Rainfall duration and intensity are the main factors in determining the temporal variations of urban non-point source pollution. There is no correlation between early drought days and pollution load. Numerical simulations show that it is more effective to increase urban vegetation coverage than to enhance road cleaning for effective non-surface pollution control. For enhanced road cleaning, it is more effective to improve the frequency of road cleaning than its efficiency. Our results provide important guidance for effective controls of non-point source pollution as well as the establishment of long-term surface pollutant monitoring network in complex urban environments