Search

Traditional blue water resources assessment and management may not meet the needs of sustainable water resource utilization; ignoring the number of green water resources will underestimate the availability of water resources. To rationally allocate and scientifically manage the limited water resources, it is necessary to divide the rich and poor flow situation of blue water and green water. The MIKE SHE-MIKE HYDRO integrated coupled model was selected and used in the Yalong River basin to ascertain the blue and green water in the hydrological cycle. The model was calibrated by matching simulated discharge against observed streamflow discharge at the Tongziling Station. At the same time, the research analyzed the component of green water and the total amount of blue water or green water on a temporal scale. The set pair analysis (SPA) was introduced to classify blue water and green water, which can not only understand the amount and distribution characteristics of water resources in the Yalong River Basin but also rationally allocate the total of water resources in the basin from the perspective of the regional water cycle. Furthermore, according to the situation of blue water and green water in the basin, the related policies are formulated to realize the efficient utilization of water resources in the Yalong River basin.

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds.

As the total water resources consumption control and carbon mitigation continuous improvement, the weak water-carbon incorporate management is increasingly exposed. In this study, a water-carbon nexus assessment framework is proposed to analyze the nexus relationship between water consumption and carbon emission, and distinguishes the coupled water-carbon transmission intensity and the transfer paths under regional and industrial scales. According to the practical input-output table, water consumption, and carbon emission information, the framework is applied to Beijing-Tianjin-Hebei urban agglomeration (BTHUA), a population, resource, and trade intensive area of China. Inter-regional/intra-regional water consumption and carbon emission transfer fluxes between sectors, the pairwise ecological relationship, and the water-carbon nexus were analyzed. Results indicated that the water-carbon transfer indexes from Hebei to Beijing and Tianjin were 161.85 kg/m

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.

Microplastic deposition in subtropical lakes and the influences of human activities remain to be deeply and fully understood. Owing to the intensification of urban construction and population growth, urban lakes serving as significant freshwater resources for sustainable development of the regional economy are becoming degraded, especially due to microplastic pollution. To understand the deposition characteristics of microplastics in lake sediments from the China's subtropical city, six sediment core samples were collected from Xinghu Lake of Guangdong Province. Here, we analyzed the morphological characteristics of microplastics from the perspective of microstructure, and investigated the temporal and spatial distribution patterns of microplastics on the macroscopic scale. The deposition characteristics of microplastics in the past 64 years and the influence of socio-economic factors on the accumulation of microplastics were further clarified through the isotope composition of cesium-137 and lead-210 in the subtropical urban area with intense human activities. The results showed that the microplastic concentration of sediment cores in Xinghu Lake was 523 ± 140 particles/kg. The average sizes of microplastics in the five sub-lakes (i.e., Bohai, Zhongxin, Li, Qinglian, and Xiannü Lakes) of Xinghu Lake were 668, 642, 727, 708 and 646 μm, respectively. There were 25 polymers in sediment cores of Xinghu Lake. Rayon, polypropylene, polyethylene terephthalate and polypropylene-polyethylene copolymer were the main types, and the microplastics have the aging phenomenon or mechanical abrasion. The average deposition rates of sediment and microplastics were 0.6 cm/a and 106 particles/(kg·a) in Xinghu Lake, respectively. Meanwhile, the urban expansion and economic growth, as indicated by the increase in the urban area, population and gross domestic product, all played an essential role in the accelerated accumulation of microplastics in sediment cores of Xinghu Lake.

In this paper, a hybrid method integrated unbiased grey model (UGM) and artificial neural network (ANN) into an interval two-stage fuzzy credibility-constrained programming (ITFCP) framework is proposed for water resources allocation of the Yalong River research area. Through the grey correlation analysis and the eXtreme Gradient Boosting (XGboost) algorithm, the economic and social indicators are related to the water demands of different water sectors in different regions can be obtained for building water demand prediction model. According to the unbiased grey prediction of the socio-economic development data of each region in the Yalong River Basin (YRB), water demand prediction models are constructed by using neural network. The establishment of a hybrid two-stage interval fuzzy credibility-constrained programming model can analyze the uncertainties existing in the process of water resources allocation. Taking 2020, 2025, and 2030 as the planning years, the developed model studies and reveals the system benefits at different credibility levels, the water shortage of each user in sub-regions and the water resources allocation situation to provide suggestion for managers to optimize the allocation of water resources. Compared to the previous methods, this integrated model can help decision-makers set management policies more sustainably and profitably.

In this research, an interval two-stage stochastic fuzzy-interval credibility constraint programming (ISFICP) method was developed for water resources allocation among multiple water users under complexities and uncertainties. The method could reflect the multiple complexities of water resources management, also trade-offs between the system benefits and violation risks. Dongjiang River (DJR) Basin, which supplies water to several core cities in south China such as Guangzhou, Shenzhen, and Hong Kong, was applied as the real demonstrative case. The water resources system of DJR Basin is particularly complex due to it is the primary source water for Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Through considering multiple complexities and uncertainties of the water resources system, such as natural, economic, and social conditions, ISFICP was developed to obtain potential water-allocation schemes. Probabilistic distribution, fuzzy-interval sets (FIS), and discrete intervals were introduced to represent the multiple uncertainties associated with the multiple complexities. The results indicated that the model could provide practical schemes for local decision-makers under multiple scenarios such as flow levels, credibility levels, and recycling rates.

Socioeconomic development, leading to significant changes in land-use patterns, has further influenced the output of regional nonpoint-source (NPS) pollution. Multiple uncertainties exist in the processes of land-use changes and NPS pollution export. These uncertainties can deeply affect the management of regional land-use patterns and control of NPS pollution. In this research, an integrated land-use prediction and optimization (ILUPO) model based on system dynamics, export coefficient, interval linear programming, and fuzzy parameter programming models was proposed. The ILUPO model can provide future land-use patterns and NPS pollution loads, and also help optimize the patterns under multiple pollution reduction scenarios. Interval and fuzzy uncertainties in the processes of land-use changes and NPS pollution output can be effectively addressed. The developed model was applied to a water source area in the central part of northern Guangdong Province in South China. For the prediction period 2020-2030 under the high-speed development scenario, results show that cropland area would decrease, while grassland and waterbody areas would increase. In contrast, these three types of land-use would show opposite variation trends under the low-speed development scenario. Construction land area would decrease, while forestland area would increase under both the low-speed and high-speed development scenarios. Variation of the predicted land-use patterns would lead to an increase of total nitrogen loads under each of the scenario, while the total phosphorus loads would show relatively complex variation trends. Regional land-use patterns should be further optimized to mitigate NPS pollution. However, the pollution loads in the study area cannot be reduced by5% through land-use adjustment. Because cropland would still be the critical source of NPS pollution after optimization, strictly controlling the areas of cropland would be important for the management of such pollution in the research area. In addition, certain areas of grassland and waterbody would need to be converted into cropland and construction land to balance the economic benefit of the system and NPS pollution control. Multiple results obtained from the model under different scenarios of pollution reduction targets and α-cut levels can provide decision-making supports for the local policy makers. The developed ILUPO model can yield insights useful for the planning and adjustment of regional land-use patterns while considering NPS pollution control under conditions of uncertainty.

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.

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.

Wetland eutrophication is a global environmental problem. Besides reducing pollutant emissions, improving nutrient assimilative capacity in wetlands is also significant for preventing eutrophication. Hydrological management can improve nutrient assimilative capacity in wetlands through physical effects on the dilution capacity of water body and ecological effects on wetland nutrient cycles. The ecological effects are significant while were rarely considered in previous research. This study focused on the ecological effects of hydrological management on two crucial nutrient removal processes, plant uptake and biological denitrification, in plant-dominated wetlands. A dual-objective optimization model for hydrological management was developed to improve wetland nitrogen and phosphorus assimilative capacities, using upstream reservoir release as water regulating measure. The model considered the interactions between ecological processes and hydrological cycles in wetlands, and their joint effects on nutrient assimilative capacity. Baiyangdian Wetland, the largest freshwater wetland in northern China, was chosen as a case study. The results found that the annual total assimilative capacity of nitrogen (phosphorus) was 4754 (493) t under the optimal scheme for upstream reservoir operation. The capacity of nutrient removal during the summer season accounted for over 80% of the annual total removal capacity. It was interesting to find that the relationship between water inflow and nutrient assimilative capacity in a plant-dominated wetland satisfied a dose-response relationship commonly describing the response of an organism to an external stressor in the medical field. It illustrates that a plant-dominated wetland shows similar characteristics to an organism. This study offers a useful tool and some fresh implications for future management of wetland eutrophication prevention.

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.

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.

Occurrence of temperature anomaly has greatly affected natural cycles of water resources in Lancang River basin in China, which is the upper reach of Mekong River. An integrated spatiotemporal decomposition and analysis method was proposed for the identification of temperature-variation patterns under changing climatic conditions in the basin. This method was based on the combination of S-mode empirical orthogonal function analysis, IDW interpolation, liner regression, weighted moving average and Mann Kendall methods. Results indicated that the first two modes extracted nearly 80% of spatiotemporal variations in temperature. Temperature in the whole basin followed the same variation trend through the first mode analysis. Sensitive areas were mainly located in the southwest of the basin, which occupied nearly half of the basin. The associated time series presented that the basin appeared transition from cold periods to warm periods. Temperature increased significantly over the period of 1960 to 2009 at annual and seasonal scales, particularly over 1990s. At the same time, the most significant rising occurred in winter and the least in summer. In the second mode, a west-east inverse phase pattern of temperature variations was a distinct feature in most of the basin. Temporal trend indicated that the increasing trend in the west region was slightly stronger than that in the east. This was particularly the case of edge areas almost vertical juncture with monsoons. This research is not only helpful in improving understanding of temperature response to global warming in the basin but also provides a basis for basin management.

In this study, a large-scale inexact two-stage waste-load allocation (ITS-WLA) model is developed to identify desired decision alternatives for supporting economic development and water quality management. In this model, uncertainties expressed as both probability distributions and discrete intervals are incorporated within a general optimization framework. A two-step interactive solution algorithm is used to generate interval solutions, and four scenarios are developed to examine tradeoffs between system benefits and system-failure risks. The ITS-WLA model applied to a case study in Xingshan County, China. Because of the extensive impacts of the Three Gorges Reservoir (TGR), the largest newly built reservoir worldwide, Xingshan County is facing many challenges in formulating development strategies. It is thus desired to develop effective decision-support tools to help local decision makers identify strategies to balance economic development and water-quality protection. This study represents a fi...

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.

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk.A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.