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Mine tailings ponds that contain heavy metals are sources of potential risk to human security and ecosystem health. China particularly faces challenge of accidental water pollution risk from more than 8869 mine tailings ponds in serve by 2015, some of which are close to residential areas and other important infrastructures within 1 km downstream. To address watershed-scale risk assessment of accidental water pollution from mine tailings ponds, a Bayesian Network-based Risk Dynamic Simulation (BN-RDS) model was proposed to simulate “sources/stressors-receptors-endpoints” risk routes. An accidental water pollution convection-diffusion simulation was coupled to Bayesian Networks to perform the risk dynamic simulation and risk evolution quantification at watershed-scale. This method was applied to the risk assessment of 23 tailings dams in 12 sub-watersheds covering the Guanting Reservoir basin (the major backup drinking water source for Beijing) in Zhangjiakou City, China. The result indicated that ecosystem health and property security were the endpoints at the highest risk in the overall watershed. Spatially, the combined risk distribution map showed the risk was higher in the downstream of the Guanting Reservoir Watershed and in its two tributary basins (the Qingshui River and the Longyang River). This research highlighted a probabilistic approach to accidental water pollution risk assessment of tailings ponds with verifiable and tangible results for risk managers and stakeholders.

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower-level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper- and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities.

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions . It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 10 8 CNY to [22,290.84, 27,492.57] × 108 CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios.

Water security is essential for sustainable economic and social development. To ensure water security, water engineering projects play an important role in regional developing program. The coastal city—Zhuhai City suffers a challenge from both future increasing water demand and salinity intrusion, To keep water security of the city, several alternative projects are designed. However, as the high cost of the projects and the potential adverse influence, a final projects portfolio, as well as their construction order has not be decided. To assist a proper decision of projects portfolio for local water security, this study proposed a combined method with one-vote veto and Analytic Hierarchy Process, to evaluate every projects portfolio from various perspectives. The results show: 1) to satisfy future water demand of 150 thousands m3/h, it is recommended to adopt a projects portfolio including expanding existing reservoirs and constructing new reservoirs, as its higher capacity utilization (96.87%), relatively lower construction and operation costs (8.59 billion and 57 million respectively), and lower ecological impact; 2) to satisfy future water demand of 160 thousands m3/h or more, transferring water from other cities should be an indispensable part for water security; 3) channel reservoir is not recommended as its high construction and operation costs, and high ecological impact. This study evaluated different alternative projects for cities' water security and provided a strong support for local government to decide their long-term projects portfolio and their construction order. The proposed method may be readily applied in other regions.

An integrated approach was developed through incorporating a copula-based violation risk analysis into a programming model for optimizing industrial structures of urban clusters in uncertain conditions. Also, this approach can be used to support decision making about promoting advanced manufacturing sectors (AMSs) and mitigating industrial energy intensity. The advantages and the improvements of this approach lie in (a) reflecting the relationships between energy consumption and economic benefits in industrial sectors, and (b) incorporating the violation risk of the targets in energy-intensity mitigation into an optimizing model. A case study was conducted to illustrate the application of this approach in the Pearl River Delta of China, a highly urbanized area that includes the cities of Guangzhou, Shenzhen, Zhuhai, Dongguan, Huizhou, Zhaoqing, Foshan, and Jiangmen. The results indicated that under the desired industrial structures, violation risk of energy-intensity mitigation in the urban cluster of the Pearl River Delta would be indistinctive in the single-city perspective. Also, except Dongguan, Jiangmen and Zhaoqing, the cities of the urban cluster would achieve the goal for developing AMSs, based on the Industrial Plans for the 13th Five Year.

Polyethylene terephthalate (PET) is a widely used plastic material that may cause significant environmental pollution. China is a major global producer and consumer of PET. Previous studies have focused on the effects of toxic elements from PET (e.g., antimony leached from PET products) on the environment. However, detailed information about PET, particularly about the PET production, trade, use, and recycling in China, is limited. This study developed a network model of PET flows in China, including the production, market trade, manufacturing and use, and waste management and recycling stages. Based on this network model, the characteristics of PET flows during three periods of development for the PET industry were analyzed. The results show that the fiber and bottle manufacturing industries are the industries with the largest PET in-use stocks. The PET flows showed different characteristics in the terms of waste import, recycling, and disposal (mechanical recycling, chemical recycling, incineration, landfill, and discarding) in the different periods of PET industrial development. Notably, the amount of discarded PET was significant, and the treatment of waste PET would probably be a challenge in the future. Policies for improving the PET cycling system were provided on the basis of the study results to promote the management and sustainable utilization of PET materials.

With the rapid economic growth and social development in China, conflicts over water resources between human and nature are continuously increasing which is attracting the attention of researchers. At the same time, discharge of water pollutants and exploitation of water resources pose a daunting challenge to the sustainable development of economy and society. China consists of 34 provincial administrative regions having similar or different characteristics in the levels of economic development, water resource endowment, water environmental capacity and water environmental pressure. Among these, it is meaningful to analyze spatial similarities and variations in water environmental carrying capacity (WECC), which contributes to carrying out different and scientific strategies for the management of water environment and for sustainable economic and social development in China. An index system is established to quantify WECC from the perspectives of carrying capacity, environmental pressure, vulnerability of water environment and exploitation and utilization potential. The k-means clustering method is applied to conduct the similarity combination based on the quantification of 4 integrated indicators using catastrophe progression method. The silhouette coefficient is introduced to measure the quality of clustering and to determine the optimal clustering number. The obtained results indicate that carrying condition of water environment becomes more and more better and exploitation and utilization potential of WECC is decreasing gradually from the east to the west in China, and there are more overload in the north provinces and less in the south. In addition, water environmental vulnerability in the west is higher than that of central and eastern provinces in China. The optimal clustering number is 4 obtained by calculating the silhouette coefficient. Also, 31 provinces are categorized into 4 sub-areas i.e. key protected area, controlled development area, optimized development area and prioritized development area. The suggestions on the corresponding bidirectional regulation to different sub-areas are also put forward to provide a scientific reference to rational distribution of economic development, elaborate management of water environment as well as regional sustainable development in the future.

Sediment nitrogen (N) cycling is an important biological removal process for N permanently and driven by N-cycling microbial community. There is a growing interest in interactions between submerged vegetation (SV) and sediment N-cycling bacterial community, because of the close link between rooted aquatic plants and the sediment microbes. However, the effects of SV on the sediment N-cycling bacterial community are still controversial. Furthermore, the discrimination of direct and indirect effects of SV on the N-cycling bacterial community remains unclear. Here, we investigated the biomass and species richness of SV and determined the corresponding environment factors (water quality and sediment properties) in Honghu Lake (China). We also used functional genes as markers to unveil the bacterial diversity and community composition and abundance in lake sediments. Our results showed that biomass and species richness of SV affected the composition, diversity and abundance of sediment N-cycling bacterial communities through improving lake water quality and sediment properties. With the increasing richness and abundance of SV, the diversity of most N-cycling bacterial assemblages including nitrifying, denitrifying and DNRA bacteria decreased, while the abundance increased. However, the anammox bacterial assemblage in sediments showed inverse trends. Sediment carbon vs. nitrogen (C:N) ratio negatively affected the abundance of amoA and nirS + nirK + nosZ bacterial assemblages. Additionally, due to the presence of SV, positive interactions among N-cycling bacterial assemblages were found, such as amoA and nrfA bacterial assemblages. Overall, our findings confirmed the significant effects of SV on the N-cycling bacterial community structure and abundance. Moreover, the direct effects of SV on the N-cycling bacterial community and the indirect effects through altering the sediment C were clarified in our study. Our results casted a new light on the negative effects of high C:N ratio. From the study, we made a conclusion that the better SV develops, the greater nitrogen removal occurs in lake sediments.

An integrated indicator system was developed for determining synthetic environmental responses under multiple types of coastal reclamation engineering in the Yellow River estuary, China. Four types of coastal engineering works were analyzed, namely port construction, petroleum exploitation, fishery and aquaculture, and seawall defense. In addition, two areas with limited human disturbances were considered for comparison. From the weights of the response value for each indicator, port construction was determined to be the primary impact contributor among the four engineering works studies. Specifically, hydrodynamic conditions, ecological status, economic costs, and engineering intensity were on average 72.78%, 65.03%, 75.03%, and 66.35% higher than those of other engineering types. Furthermore, fishery and aquaculture impact on water quality was 42.51% higher than that of other engineering types, whereas seawall defense impact on landscape variation was 51.75% higher than that of other engineering types. The proposed indicator system may provide effective coastal management in future.

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin's social and economic growth with protecting the region's ecosystems.

Grey water footprint (GWF) is an essential indicator describing the effects of aquatic pollutants on water quantity, which is significantly influenced by interregional trade. Existing studies on the impacts of interregional trade on GWF in China have deviations. That is, traditional GWF is determined by the pollutant with the highest pollutant-specific GWF, but overlooks the influences of coexisting compounds. Coexisting compounds have large influences on GWF quantification. However, the impacts of interregional trade on the improved GWF in China have not been well characterized. This study integrates the multi-regional input-output analysis and an improved GWF quantification method considering multiple pollutants, to investigate the impacts of interregional trade on the GWF in China. Compared with traditional GWF, the improved GWF of Chinese regions decreases by 19-35%. Up to 35% of China's improved GWF is related to commodities that are produced outside of regions where they are finally consumed. Beijing-Tianjin and coastal regions outsource GWF to the central and northern regions. Sectors contributing the most to GWF outsourcing include corn, cattle, swine, poultry, and other animal husbandry. This study provides more accurate region-specific and sector-specific results for policy decisions on water resource conservation in China.

Most heavy metal exposure and pollution results from multiple industrial activities, including metal processing in refineries, and microelectronics. These issues pose a great threat to human health, ecological balance, and even societal stability. During 2012–2017, China, in particular, faced the challenge of 23 heavy metals accidents, six of which were extraordinarily serious accidents. Accidental environmental pollution is rarely caused by a single heavy metal, but rather by heavy metal mixtures. To address the need for a joint exposure risk assessment for heavy metal mixed pollution accidents at the watershed scale, a Copula-based exposure risk dynamic simulation model was proposed. A coupled hydrodynamic and accidental heavy metal exposure model is constructed for an hourly simulation of the exposure fate of heavy metals from each risk source once accidental leakage has occurred. The Copula analysis was introduced to calculate the dual heavy metal joint exposure probability in real time. This method was applied to an acute Cr6+-Hg2+ joint exposure risk assessment for 43 electroplating plants in nine sub-watersheds within the Dongjiang River downstream basin. The results indicated seven risk sources (i.e., S1, S4, H18, H23, H27–H28, and H34) that presented relatively high exposure risk to their surrounding sub-watersheds. Spatially, the acute exposure risk level was highest in the tributary basin (sub-watershed XW) than in the mainstream (sub-watershed DW2) and the river network (sub-watershed RW) of the lower reaches of the Dongjiang River. This research highlights an effective probabilistic approach for performing a joint exposure risk analysis of heavy metal mixed pollution accidents at the watershed scale.

To identify the key contributors of urban carbon emissions as well as the acting paths, it is necessary to analyze the carbon flows from a systematic perspective. Thus, the concept of urban carbon metabolism was introduced in this paper and correspondingly input-output analysis (IOA) and ecological network analysis (ENA) were combined to conduct the carbon metabolism analysis. Concretely speaking, the urban IO table was compiled based on the provincial one and then the direct and embodied urban carbon flows were accounted. Subsequently, the carbon metabolic network model was established, through which the characteristics of the metabolic network were further analyzed to better reveal the contributors and influencing factors of carbon emissions. Dongguan, a city famous as the "factory of the world", was chosen as the case. The results indicate that the total direct and embodied carbon flows were mainly concentrated in manufacture. Manufacture was found to be major factors affecting other compartments through indirect interplay. A trophic hierarchical structure was found, where compartments can be classified into primary producers, secondary producers, primary consumers and secondary consumers according to their metabolic characteristics in use of energy. Electricity, gas & water were defined as secondary producer, and its self-induced carbon flows accounted for more than 95% of the carbon flow conversion within this compartment. By further comparing the metabolic characteristics in Dongguan with that of Guangdong Province and other cities, measures were suggested to heighten energy utilization efficiency and promote positive interactions among compartments to promote the carbon emission reduction in Dongguan.

The Laoguanhe River is a major tributary of the Danjiang River, which is the source water source for the Danjiangkou Reservoir (i.e., the source reservoir for the middle route of the South-to-North Water Diversion Project in China). This study was intended to provide scientific decision support to help manage the water environment in the Laoguanhe River and maintain high water quality levels for the water diversion project. Risk assessment based on water quality simulation was undertaken in this research. The QUAL2Kw model was used to improve the simulations of the water quality of the Laoguanhe River under sparse data conditions. Latin hypercube sampling (LHS) was used to improve flow inputs for QUAL2Kw. The model was used to calculate the water quality risks under several scenarios based on a Markov stochastic process to reflect uncertainty. The simulation results indicated that the water quality of the downstream section of the Laoguanhe River could meet the basic requirements for exporting water to the Danjiangkou Reservoir.

A Bayesian network-based risk assessment (BN-RA) model was developed to assess the risk of hazmat transportation by identifying, modeling, and quantitatively calculating the risk on the Middle Route of the South-to-North Water Transfer Project (MRSNWTP) in China. First, we selected seven parameters from five categories of impact factors (i.e., human, vehicle, tank, weather, and road environment) as quintessential risk factors for accidents. Second, we used the developed BN-RA model to predict the probability of accidents. Third, using bidirectional inference in the BN approach, we analyzed and ranked the importance of the effects of these factors. The developed model was subsequently applied to assess the risks of major bridges crossing canals with different pavement grades and traffic flow levels both at present and in the future for the Beijing-Shijiazhuang Section of the MRSNWTP. The results indicated the following: (1) Although the overall potential risk of hazmat transportation accidents on all bridges in the Beijing-Shijiazhuang Section was fairly low (e.g., 0.08 %), the impacts cannot be ignored because of the potential for huge losses. (2) According to the analysis of many factors that may affect accidents, the driving patterns of drivers exerted the strongest influence on the probability of an accident, followed by vehicle conditions and lighting conditions. (3) If a vehicle were to fail, the highest probability (0.17 %) of an accident would arise if it were traveling on a road with no street lighting and poor road conditions at night. (4) Assuming that a vehicle was in good condition, the highest probability (0.12 %) of an accident arised when the vehicle suddenly encountered poor road conditions with no lights on a foggy night. (5) The predicted probabilities of accidents on Bridge TCWRR (short for the Tang County West Ring Road Bridge) in the short (i.e., the year 2017), medium (i.e., the year 2022) and long terms (i.e., the year 2027) were 3.25 × 10−4, 5.37 × 10−4, and 8.89 × 10−4, respectively. For Bridge DNR (short for the Dian Bridge on the North Road), these values were 8.64 × 10−6, 1.02 × 10−5, and 1.21 × 10−5, respectively. Based on the risk assessment results, to lower the accident probability and avoid the serious consequences resulting from hazmat transportation accidents, we developed an appropriate emergency response program to reduce potential hazards. This research resolved the problems of randomness and uncertainty associated with hazmat transportation in the MRSNWTP and can provide a reference for the effective prevention of hazmat transportation accidents and scientific decision-making in risk management.

The Miyun Reservoir plays a pivotal role in providing drinking water for the city of Beijing. In this research, ecological network analysis and scenario analysis were integrated to explore soil nitrogen cycling of chestnut and Chinese pine forests in the upper basin of the Miyun Reservoir, as well as to seek favorable fertilization modes to reduce agricultural non-point source pollution. Ecological network analysis results showed that (1) the turnover time was 0.04 to 0.37 year in the NH4+ compartment and were 15.78 to 138.36 years in the organic N compartment; (2) the Finn cycling index and the ratio of indirect to direct flow were 0.73 and 11.92 for the chestnut forest model, respectively. Those of the Chinese pine forest model were 0.88 and 29.23, respectively; and (3) in the chestnut forest model, NO3− accounted for 96% of the total soil nitrogen loss, followed by plant N (2%), NH4+ (1%), and organic N (1%). In the Chinese pine forest, NH4+ accounted for 56% of the total soil nitrogen loss, followed by organic N (34%) and NO3− (10%). Fertilization mode was identified as the main factor affecting soil N export. To minimize NH4+ and NO3− outputs while maintaining the current plant yield (i.e., 7.85e0 kg N/year), a fertilization mode of 162.50 kg N/year offered by manure should be adopted. Whereas, to achieve a maximum plant yield (i.e., 3.35e1 kg N/year) while reducing NH4+ and NO3− outputs, a fertilization mode of 325.00 kg N/year offered by manure should be utilized. This research is of wide suitability to support agricultural non-point source pollution management at the watershed scale.

In this paper, an improved multi-scale integrated modeling framework has been established to evaluate coastal reclamation intensity (CRI). About 7 indicators are considered, including ecological degradation intensity (EDI), hydrodynamic disturbance (IHD), engineering types, water quality, economic investment, population growth, and reclaimed land area. Meanwhile, an integrated framework enhanced methods in terms of (a) measuring intensity of ecological degradation process under multi-scale impact, (b) developing the indicator system of CRI, and discussing the driving forces and trends of coastal reclamation, (c) determining fuzzy preference relations of weight and calculating the specific value of CRI with the case study areas of Yellow River estuary from 2000 to 2015. As the result, the CRI has been expanded unceasingly in recent years. The total growth rate from 2000 to 2015 is about 37.97%. It is concluded that CRI has climbed to a higher intensity level in resent 15years.

Source water areas are facing many potential water pollution risks. Risk assessment is an effective method to evaluate such risks. In this paper an integrated model based on k-means clustering analysis and set pair analysis was established aiming at evaluating the risks associated with water pollution in source water areas, in which the weights of indicators were determined through the entropy weight method. Then the proposed model was applied to assess water pollution risks in the region of Shiyan in which China's key source water area Danjiangkou Reservoir for the water source of the middle route of South-to-North Water Diversion Project is located. The results showed that eleven sources with relative high risk value were identified. At the regional scale, Shiyan City and Danjiangkou City would have a high risk value in term of the industrial discharge. Comparatively, Danjiangkou City and Yunxian County would have a high risk value in terms of agricultural pollution. Overall, the risk values of north regions close to the main stream and reservoir of the region of Shiyan were higher than that in the south. The results of risk level indicated that five sources were in lower risk level (i.e., level II), two in moderate risk level (i.e., level III), one in higher risk level (i.e., level IV) and three in highest risk level (i.e., level V). Also risks of industrial discharge are higher than that of the agricultural sector. It is thus essential to manage the pillar industry of the region of Shiyan and certain agricultural companies in the vicinity of the reservoir to reduce water pollution risks of source water areas.