In the context of water resource management and pollution control, the characterization of water quality impairments and identification of dominant pollutants are of critical importance. In this study, water quality impairment was assessed on the basis of 7 hydrochemical variables that were monitored bimonthly at 17 sites in 2010 along the rural-suburban-urban portion of the Wen-Rui Tang River in eastern China. Seven methods were used to assess water quality in the river system. These methods included single-factor assessment, water quality grading, comprehensive pollution index, the Nemerow pollution index, principle component analysis, fuzzy comprehensive evaluation, and comprehensive water quality identification index. Our analysis showed that the comprehensive water quality identification index was the best method for assessing water quality in the Wen-Rui Tang River due to its ability to effectively characterize highly polluted waters with multiple impairments. Furthermore, a guideline for the applications of these methods was presented based on their characteristics and efficacy. Results indicated that the dominant pollutant impairing water quality was total nitrogen comprised mainly of ammonium. The temporal variation of water quality was closely related to precipitation as a result of dilution. The spatial variation of water quality was associated with anthropogenic influences (urban, industrial, and agriculture activities) and water flow direction (downstream segments experiencing cumulative effects of upstream inputs). These findings provide valuable information and guidance for water pollution control and water resource management in highly polluted surface waters with multiple water quality impairments in areas with rapid industrial growth and urbanization.

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the rural-suburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R ²), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R ², and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

Integrated source apportionment and risk assessment of metals is of great importance for contamination source control and remediation at the regional watershed scale. To identify metal sources and source-specific ecological risks, sediments were collected in the Wen-Rui Tang urban watershed for metal analysis. Risk assessment showed considerable and extremely high risk for Cu and Cd with large spatial variation. Positive matrix factorization model extracted three main sources with reasonable prediction efficacy for metal concentrations. Due to different toxicity coefficients for various metals, the low concentration contribution of source factor 2 (27.2%) contributed 83.7% of total risk, while a high concentration loading for factor 3 (40.8%) only contributed 4.6% to total risk. Predicted source-specific risk was similar to determined risk level for Cr, Ni and Pb; however, Cu and Cd were predicted with decreased risk, while Zn had increased risk. Triangular fuzzy number (TFN) coupled with stochastic simulation showed elevated trend in risk simulation for Cd and Cu when compared with determined risk. The uncertainties for risk evaluation appear to result from spatial variations in metal concentrations. Source apportionment and specific-risk assessment results suggest that different strategies may be required to address mitigation of elevated metal concentrations versus ecological risk.

Many different approaches have been suggested for the assessment of organic pollution and related water quality variables. However, many methods used to assess organic pollution cannot qualitatively estimate the degree of importance of each pollutant. Water quality identification indices can provide comprehensive water quality information, which is both qualitative and quantitative, especially for areas worse than Grade V of the National Surface Water Quality Standard of China. However, this method considers every indicator equally, without considering the degree of importance of each parameter. Consequently, in this study, the entropy weight method was combined with the water quality identification index method. The improved comprehensive water quality identification index was applied to a water quality data-set for the Wen-Rui Tang River in China, which was generated in 2009 during six months of monitoring at 16 different sites and included seven parameters. The results indicated that the improved comprehensive water quality identification index provided an objective evaluation of each water quality indicator. Understanding the significance of each water quality parameter will help to implement more effective water quality improvement plans.

The gross primary production (GPP) in tropical terrestrial ecosystems plays a critical role in the global carbon cycle and climate change. The strong 2015–2016 El Niño event offers a unique opportunity to investigate how GPP in the tropical terrestrial ecosystems responds to climatic forcing. This study uses two GPP products and concurrent climate data to investigate the GPP anomalies and their underlying causes. We find that both GPP products show an enhanced GPP in 2015 for the tropical terrestrial ecosystem as a whole relative to the multiyear mean of 2001–2015, and this enhancement is the net result of GPP increase in tropical forests and decrease in nonforests. We show that the increased GPP in tropical forests during the El Nino event is consistent with increased photosynthesis active radiation as a result of a reduction in clouds, while the decreased GPP in nonforests is consistent with increased water stress as a result of a reduction of precipitation and an increase of temperature. These results reveal the strong coupling of ecosystem and climate that is different in forest and nonforest ecosystems and provide a test case for carbon cycle parameterization and carbon-climate feedback simulation in models.

Quantitative assessment of metal sources in sediments is essential for implementation of source control and remediation strategies. This study investigated metal contamination in sediments to assess potential ecological risks and quantify pollutant sources of metals (Cu, Zn, Pb, Cd, Cr, Co and Ni) in the Wen-Rui Tang River watershed. Total and fraction analysis indicated high pollution levels of metals. Zinc and Cd posed high ecological risk based on the risk assessment code, with the highest ecological risk found in the southwestern of the watershed. The positive matrix factorization (PMF) model was highly effective in predicting total metal concentrations and identified three contributing metal sources. An agricultural source (factor 1) contributed highly to Cu (74.1%) and Zn (42.5%), and was most prominent in the west and south-central portions of the watershed. Cd (93.5%) showed a high weighting with industrial sources (factor 2) with a hot spot in the southwest. Factor 3 was identified as a mixed natural and vehicle traffic source that showed large contribution to Cr (65.2%), Ni (63.9%) and Pb (50.7%). Spatial analysis indicated a consistent pattern between PMF-identified factors and suspected metal sources at the watershed scale demonstrating the efficacy of the PMF modeling approach for watershed analysis.

Metal contamination in aquatic environments is a severe global concern to human health and aquatic ecosystems. This study used several risk assessment indices, to evaluate metal (Cu, Zn, Pb, Cd and Cr) environmental risk of riverine surface sediments across the rural-urban interface of the rapidly developing Wen-Rui Tang River watershed in eastern China. Risk assessments were determined for 38 sites based on the potential ecological risk index (RI), consensus-based sediment quality guidelines (SQGs) and risk assessment code (RAC). Land-use cluster analysis showed that sediments were severely contaminated, especially for Cd, whose concentrations were ∼100 times higher than background levels and had a high proportion in the bioaccessible fraction. According to RI, ErCd was identified with extremely high risk potential, resulting in the highest ecological risk of Cluster 4 (industrial). Similarly, risk within Cluster 4 (industrial) was also ranked highest by SQGs assessment due to the high proportion of industrial land use. Zinc was determined with high risk due to its high concentration compared to its effect range medium (ERM) value. Discrepancies in predicting environmental risks from metals among the three indices were mainly attributed to the contrasting definitions of these metrics. Environmental risk uncertainty derived from spatial variation was further estimated by Monte Carlo simulation and ranked as: Zn > Cd > Cr > Pb > Cu. This comprehensive environmental risk assessment provides important information to guide remediation strategies for management of metal contamination at the watershed scale.

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R² values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R² ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.

Conservation tillage has been widely adopted in agricultural lands worldwide and is considered a potential strategy for climate change mitigation through enhanced carbon sequestration. However, conservation tillage may alter soil N2O emissions, which may diminish the potential climate change mitigation benefits. Based on 212 observations from 40 publications, a meta-analysis was conducted to quantitatively assess the effects of climate regimes, initial soil properties, and type/duration of agricultural practices on soil N2O emission following application of conservation tillage. Overall, conservation tillage significantly increased soil N2O emission by 17.8% compared to conventional tillage. The greatest increase in N2O emission was observed from soils in tropical climates (70.1%) experiencing short-term (29.3%) application of conservation tillage. Soil pH and clay content significantly influenced N2O emission, while overall soil texture and soil organic carbon (SOC) were not effective predictors of soil N2O emission following conservation tillage. According to the categorical meta-analysis, agricultural practices, including water, residue, and rotation managements and crop types, significantly affected soil N2O emission following conservation tillage. Conservation tillage induced N2O emissions were mitigated with rain-fed cropping systems, residue removal, crop rotation and cultivation of beans and some vegetables. Significant categorical variables affecting N2O emission were mainly attributed to soil aeration and substrate availability, which were important factors affecting nitrification and denitrification processes. Overall, the conservation tillage induced N2O emission factor (EFad) increased by 0.40%, suggesting an attenuation of climate change benefits from increased N2O emission. Our meta-analysis provides a scientific basis for assessing the effects of conservation tillage on N2O emissions and provides site-specific information to mitigate N2O emissions associated with conservation tillage practices.

Trend and change-point analyses of water quality time series data have important implications for pollution control and environmental decision-making. This paper developed a new approach to assess trends and change-points of water quality parameters by integrating locally weighted polynomial regression (LWPR) and segmented regression (SegReg). Firstly, LWPR was used to pretreat the original water quality data into a smoothed time series to represent the long-term trend of water quality. Then, SegReg was used to identify the long-term trends and change-points of the smoothed time series. Finally, statistical tests were applied to determine the significance of the long-term trends and change-points. The efficacy of this approach was validated using a 10-year record of total nitrogen (TN) and chemical oxygen demand (COD_Mn) from Shanxi Reservoir watershed in eastern China. Results showed that this approach was straightforward and reliable for assessment of long-term trends and change-points on irregular water quality datasets. The reliability was verified by statistical tests and practical considerations for Shanxi Reservoir watershed. The newly developed integrated LWPR-SegReg approach is not only limited to the assessment of trends and change-points of water quality parameters but also has a broad application to other fields with long-term time series records.