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China is facing severe pressure on its water resources and water environments. Calculating the industrial water efficiency of each province is an important index for the central government to evaluate local governments. In the traditional water resources evaluation index, the industrial water use efficiency and pollutant discharge are evaluated separately. In this paper, we collected industrial input data, output data and pollutant discharge data with a four-stage data envelopment analysis to calculate China's industrial water use efficiency with and without considering pollutant discharge, and then analyzed the factors influencing the industrial water use efficiency. The results show that the eastern coastal provinces of China have the highest water use efficiency and are less affected by pollutant discharge than other provinces. The industrial water use efficiency of the central and western provinces is lower than that of the other provinces, and the industrial water use efficiency in the central provinces is greatly affected by pollutant discharge. Factor endowment, economic development level, scientific and technological progress, industrial structure, proportion of foreign investment, water consumption per 10000 yuan of value-added by industry, industrial sewage treatment capacity and educational investment have a significant influence on the industrial water use efficiency of China. We suggest that the government strengthen the construction of sewage plants and other related infrastructure in central provinces when conducting the industrial transfer of heavy polluting enterprises. [ABSTRACT FROM AUTHOR]

The deterioration of water quality has become a primary environmental concern worldwide. Understanding the status of water quality and identifying the influencing factors are important for water resources management. However, reported analyses have mostly been conducted in small and focused areas. It is still unclear if factors driving spatial variation in water quality would be different in extended spatial scales. In this paper, we analyzed spatial pattern of inland surface water quality in China using a dataset with four water quality parameters (i.e., pH, DO, NH4+-N and CODMn) and the water quality level. We tested the effects of anthropogenic (i.e., land use and socio-economic) and natural (i.e., climatic and topographic) factors on spatial variation in water quality. The study concluded that the overall inland surface water quality in China was at level III (fair). Water quality level was strongly correlated with CODMn and NH4+-N concentration. In contrast to reported studies that suggested land use patterns were the determinants of inland surface water quality, this study revealed that both anthropogenic and natural factors played important roles in explaining spatial variation of inland surface water quality in China. Among the tested explanatory variables, mean elevation within watershed appeared as the best predictor for pH, while annual precipitation and mean air temperature were the most important explanatory variables for CODMn and DO, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watershed. Compared to studies at smaller spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales of consideration. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are largely influenced by natural variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality. [ABSTRACT FROM AUTHOR]

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning. [ABSTRACT FROM AUTHOR]

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

Wetlands are recognized as one of the most important natural environments for humans. At the same time, heavy metal pollution has an important impact on wetlands. China's Raoyanghe Wetland is one of the most important natural wild species gene banks in China. Eight heavy metal elements (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in surface layer and deep layer soils were analyzed using statistical-, pollution index-, and Nemerow index-based methods, the Hakanson potential ecological risk index method, and principal component and cluster analyses. The results showed that the maximum concentrations of heavy metals exceeded the background values in the core area and buffer zone of the wetland, but the heavy metal content of the soils was generally low and did not exceed 30%. With the exception of Hg, heavy metal concentrations showed strong spatial differentiation. The differences between the surface layer and deep layer soils of the core area were smaller than in the buffer zone. With the exception of Cd, a clear vertical zonation in the buffer zone soils was observed, showing greater evidence of external influences in this zone than the core. With the exception of partial surface soils, which indicated a safe level of pollution in the core area, all other soils were classified as having a ‘mild’ level of pollution. Thus, the wetland is moderately polluted, with both the core area and the buffer zone presenting a low level of potential ecological risk. According to the results of the present study, heavy metal contaminants in the wetland soils were found to be derived mainly from the natural sources. [ABSTRACT FROM AUTHOR]

Nitrate pollution of surface and groundwater systems is a major problem globally. For some time now wetlands have been considered potential systems for improving water quality. Nitrate dissolved in water moving through wetlands can be removed through different processes, such as the denitrification process, where heterotrophic facultative anaerobic bacteria use for respiration, leading to the production of nitrogen (N2) and nitrous oxide (N2O) gases. Nitrate removal and emission of N2O in wetlands can vary spatially, depending on factors such as vegetation, hydrology and soil structure. This study intended to provide a better understanding of the spatial variability and processes involved in removal and emission of N2O in riparian wetland soils. We designed a laboratory experiment simulating surface water flow through soil columns collected from different sites dominated by different plant species within a wetland. Water and gas samples for and N2O analyses were collected every 5 days for a period of 30 days. The results revealed significant removal of in all the soil columns, supporting the role of riparian wetland soils in removing nitrogen from surface runoff. Nitrate removal at 0 and 10cm depths in sites dominated by Phragmites australis and Carex schnimdtii was significantly higher than in the site dominated by Calamagrostis epigeio. Nitrous oxide emissions varied spatially and temporally with negative flux observed in sites dominated by P. australis and C. schnimdtii. These results reveal that in addition to the ability of wetlands to remove , some sites within wetlands are also capable of consuming N2O, hence mitigating not only agricultural nitrate pollution but also climate change. [ABSTRACT FROM AUTHOR]

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions. [ABSTRACT FROM AUTHOR]

As a huge threat to the public health, China’s air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants’ hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O3, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O3 is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM10 and O3, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants’ evolutionary process, but also shed lights on the application of complex network methods into geographic issues. [ABSTRACT FROM AUTHOR]

The article examines the detailed spatial and temporal distributions of coastal reclamation in the northwest coast of Bohai Bay experiencing rapid coastal reclamation in China from 1974 to 2010 in annual intervals. Moreover, soil elements properties and spatial distribution in reclaimed area and inform the future coastal ecosystems management was also analyzed. The results shows that 910.7 km2 of coastal wetlands have been reclaimed and conversed to industrial land during the past 36 years. It covers intertidal beach, shallow sea and island with a percentage of 76.0%, 23.5% and 0.5%, respectively. The average concentration of Mn is 686.91mg/kg and the order of concentration of heavy metal are Cr>Zn>As>Ni>Cu>Pb>Cd>Hg. We used the "space for time substitution" method to test the soil properties changes after reclamation. The potential ecological risk of heavy metal is in low level and the risk of Cd and As is relatively higher. The ecosystem-based coastal protection and management are urgent to support sustainable coastal ecosystems in Bohai bay in the future. [ABSTRACT FROM AUTHOR]

Air and soil pollution from mining activities has been considered as a critical issue to the health of living organisms. However, few efforts have been made in distinguishing the main pathway of organism genetic damage by heavy metals related to mining activities. Therefore, we investigated the genetic damage of Leymus chinensis leaf cells, the air particulate matter (PM) contents, and concentrations of the main heavy metals (Pb, Cd, Cr, Hg) in soil and foliar dust samples collected from seven experiment points at the core mining area and one control point 20 kilometers away from the core mining area in Inner Mongolia in 2013. Comet assay was used to test the genetic damage of the Leymus chinensis leaf cells; the Tail DNA% and Tail Moment were used to characterize the genetic damage degree of the plant cells. The comet assay results showed that the cell genetic damage ratio was up to 77.0% in experiment points but was only 35.0% in control point. The control point also had the slight Tail DNA% and Tail Moment values than other experiment groups. The cell damage degree of the control group was 0.935 and experiment groups were 1.299–1.815. The geo-accumulation index and comperehensive pollution index(CPI) were used to characterize heavy metal pollution in foliar dust samples, and single factor pollution index and CPI were used to characterize the heavy metal pollution in soil samples. The CPIfoliar dust of control group was 0.36 and experiment groups were 1.45–2.57; the CPIsoil of control group was 0.04 and experiment groups were 0.07–0.12. The results of correlation analyze showed that Air Quality Index (AQI) -CPIfoliar dust(r = 0.955**)>Damage degree-CPIfoliar dust(r = 0.923**)>Damage degree-AQI(r = 0.908**)>Damage degree-CPIsoil (r = 0.824*). The present research proved that mining activity had a high level of positive correlation with organism genetic damage caused by heavy metals through comparing with the control point; soil and atmosphere were both the important action pathway for heavy metal induced genetic damage in mining area. Furthermore, heavy metal contents in foliar dust showed a higher positive correlation with genetic damage than when compared with soil. This means the heavy metal contents that L.chinensis absorbed through respiration from the atmosphere could make more serious genetic damage than when absorbed by root systems from soil in the mining area. This study can provide theoretical support for research on plant genetic damage mechanisms and exposure pathways induced by environmental pollution. [ABSTRACT FROM AUTHOR]

Foods produced on soils impacted by Pb-Zn mining activities are a potential health risk due to plant uptake of the arsenic (As) associated with such mining. A field survey was undertaken in two Pb-Zn mining-impacted paddy fields in Guangdong Province, China to assess As accumulation and translocation, as well as other factors influencing As in twelve commonly grown rice cultivars. The results showed that grain As concentrations in all the surveyed rice failed national food standards, irrespective of As speciation. Among the 12 rice cultivars, “SY-89” and “DY-162” had the least As in rice grain. No significant difference for As concentration in grain was observed between the rice grown in the two areas that differed significantly for soil As levels, suggesting that the amount of As contamination in the soil is not necessarily the overriding factor controlling the As content in the rice grain. The iron and manganese plaque on the root surface curtailed As accumulation in rice roots. Based on our results, the accumulation of As within rice plants was strongly associated with such soil properties such as silicon, phosphorus, organic matter, pH, and clay content. Understanding the factors and mechanisms controlling As uptake is important to develop mitigation measures that can reduce the amount of As accumulated in rice grains produced on contaminated soils. [ABSTRACT FROM AUTHOR]

The total concentrations and chemical forms of heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in the surface sediments of the Laizhou Bay and the surrounding marine area of the Zhangzi Island (hereafter referred to as Zhangzi Island for short) were obtained and multiple indices and guidelines were applied to assess their contamination and ecological risks. The sedimentary conditions were fine in both of the two studied areas according to the marine sediment quality of China. Whereas the probable effects level guideline suggested that Ni might cause adverse biological effects to occur frequently in some sites. All indices used suggested that Cd posed the highest environmental risk in both the Laizhou Bay and the Zhangzi Island, though Cd may unlikely be harmful to human and ecological health due to the very low total concentrations. The enrichment factor (EF) showed that a substantial portion of Cr was delivered from anthropogenic sources, whereas the risk assessment code (RAC) indicated that most Cr was in an inactive state that it may not have any adverse effect either. Moreover, the results of EF and geoaccumulation index were consistent with the trend of the total metal concentrations except for Cd, while the results of RAC and potential ecological risk factor did not follow the same trend of their corresponding total metal concentrations. We also evaluated the effects of using different indices to assess the environmental impact of these heavy metals. [ABSTRACT FROM AUTHOR]