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Electricity consumption and anaerobic reactions cause direct and indirect greenhouse gas (GHG) emissions within domestic sewage treatment systems (DSTSs). GHG emissions in DSTSs were influenced by the sewage quantity and the efficacy of treatment technologies. To address combined effects of these variables, this study presented an approach for identifying pathways for GHG mitigation within the DSTSs of cities under climate change and socio-economic development, through combining life cycle analysis (LCA) and the Hierarchical Archimedean copula (HAC) methods. The approach was innovative in the following aspects: 1) quantifying the GHG emissions of the DSTSs; 2) identifying the correlations among temperature changes, socioeconomic development, and domestic sewage quantity, and 3) predicting the future fluctuations in GHG emissions from the DSTSs. The effectiveness of the proposed approach was validated through its application to an urban agglomeration in the Pearl River Delta (PRD), China. To identify the potentials of GHG mitigation in the DSTSs, two pathways (i.e., general and optimized) were proposed according to the different technical choices for establishing facilities from 2021 to 2030. The results indicated that GHG emissions from the DSTS in the PRD were [3.01, 4.96] Mt CO 2 eq in 2021, with substantial contributions from Shenzhen and Guangzhou. Moreover, GHG emissions from the sewage treatment facilities based on Anaerobic-Anoxic-Axic (AAO) technology were higher than those based on other technologies. Under the optimized pathway, GHG emissions, contributed by the technologies of Continuous Cycle Aeration System (CASS) and Oxidation Ditch (OD), were the lowest. Through the results of correlation analysis, the impact of socioeconomic development on domestic sewage quantities was more significant than that of climate change. Domestic sewage quantities in the cities of the PRD would increase by 4.10%–28.38%, 17.14%–26.01%, and 18.15%–26.50% from 2022 to 2030 under three Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. These findings demonstrated that the capacities of domestic sewage treatment facilities in most cities of the PRD should be substantially improved from 0.12 to 2.99 times between 2022 and 2030. Under the optimized pathway, the future GHG emissions of the CASS method would be the lowest, followed by the OD method. • The correlations between domestic sewage quantity, socioeconomic development, and climate change were focused on. • A hybrid approach based on LCA and HAC was proposed for GHG mitigation in domestic sewage treatment systems. • A typical region of China was adopted for illustrating applicability of the developed approach. • Two pathways were proposed according to the different choices for technical facilities from 2022 to 2030. [ABSTRACT FROM AUTHOR]

Urban non-point source (NPS) pollution management is receiving increased attention because of the environmental and ecological consequences of urbanization. We developed a Storm Water Management Model (SWMM)-based interval double-sided chance-constrained programming (SWMM–IDCCP) model for the optimal design of low impact development (LID) during urban NPS pollution management at a community scale. Pollution control efficiency and economic costs related to LID practices were considered in the SWMM–IDCCP model, simultaneously addressing uncertainties in pollutant output and decision-making processes. Specifically, by combining SWMM, Monte Carlo simulation, interval linear programming, and double-sided chance-constrained programming, the SWMM–IDCCP model can evaluate export loads of urban NPS pollutants and pollution control efficiencies of LID practices and analyze the corresponding uncertainties. Concurrently, the proposed model can provide reliable and optimal implementation schemes for LID practices under different scenarios, dealing with uncertainties such as discrete intervals and double-sided random parameters. The proposed model was applied to the Dongguan University of Technology in Dongguan City, South China. The random distribution characteristics of pollution load were identified. The ranges of pollution load for total suspended solids, chemical oxygen demand, total nitrogen, and total phosphorus were 76,430–165,698, 44,366–74,155, 2118–3,830, and 97–177 kg in 2021, respectively. Probability distribution characteristics of pollution control efficiencies of three LID practices (i.e., bio-retention cell, green roof, and permeable pavement) for the four urban NPS pollutants were obtained. Using the SWMM-IDCCP model, LID configuration schemes were obtained under diverse pollution control targets and confidence levels, taking into account variations in the decision preference of different administrators. This research indicates that the SWMM-IDCCP model can be used to manage urban NPS pollution at a community scale under uncertainty. [ABSTRACT FROM AUTHOR]

Due to frequent and serious waterlog and environmental pollution in cities in recent years, urban stormwater management (USM) has become an essential issue of urban sustainable development in China. Low Impact Development (LID) technologies are effective and popular measures of USM, and can be used to reduce urban waterlog and the associated environmental pollution. In this paper, in order to identify an optimal strategy of LID technologies based on economic efficiencies and environmental performances, the construction costs and environmental benefits of the four LID technologies (including grass swales, bioretention cells, green roofs, and permeable pavement) were analyzed. Then, due to the uncertain feature of rainfall, concentration of pollutants and many social-economic factors, an inexact fuzzy chance-constrained programming (IFCCP) model was developed. Multiple uncertainties that can be expressed as interval parameters, fuzzy sets, and stochastic distribution can be addressed effectively and incorporated directly into the modeling process. Based on the analysis of the precipitation probability distribution and land use data, the developed model was applied to a university campus with relatively high building density and plot ratio in Beijing. Then, optimal solutions of multiple precipitation probabilities ( p m ) under varying USM goals were obtained. The four LID technologies were chosen to meet multiple USM goals based on multiple p m values. When   p m = 0.1 , 0.05 , 0.02 and   0.015 , the total construction investments would be 1.46 to 4.66, 1.46 to 5.25, 2.31 to 8.54 and 3.95 to 12.7 million yuan, respectively. The relationship between construction costs and p m represented the relationship between economic benefit and system risk. The results indicated that USM was influenced by the rainfall capacity, construction costs would increase with the increase of precipitation, and the risks of constraint violation would decrease as the construction costs increase. When considering the four LID technologies during the planning stage of USM, the preferred selection order would be bioretention cells, permeable pavements, grass swales and green roofs. Therefore, the solutions under each p m level could provide the references for desired USM plans. [ABSTRACT FROM AUTHOR]

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. • A simulation-based interval stochastic bi-level programming model was developed. • System hierarchical structure and multi-objective characteristics were considered. • The stochastic characteristics of nutrient export can be reflected. • The model can reflect uncertainties in both simulation and optimization processes. • The model can support watershed water quality management. [ABSTRACT FROM AUTHOR]

The problem of fresh water scarcity is one of the major constraints for regional development. Rapid urbanization and rising economic prosperity has further exacerbated problems with not only water quantity, but also water quality. Hence, sustainable utilization of water resources should be a priority, especially in water stressed areas. However, most current studies on optimal water allocation models are based on physical water, which cannot fully reveal the water flows embedded in imported and exported raw materials and products throughout all production processes. Thus, they fail to achieve sustainability goals. Therefore, the objective of this study was to set up an optimal water allocation model for industrial sectors based on water footprint accounting. In this paper, first the internal and external blue and gray water footprints were evaluated. The results indicated that the external water footprint was the major contributor to Dalian's water footprint, which shared around 72.58% of the total. Among all the industrial sectors, the chemical industry and chemical production sector had the highest total WF. Second, analyses of water productivity intensity of the WF and physical water for the industrial sectors were conducted. The comparison of the results revealed that physical water productivity intensity failed to explain the embedded water inflows in the production processes among the regions and sectors. Third, an optimization model based on the water footprint accounting results was thus set up. The optimal consequences indicated that a water allocation plan could meet both the water requirements for the blue and gray water footprints of the industrial sectors, and their target output goals as well. Therefore, this optimal allocation model is both rational and applicable for sustainable water utilization in future water management strategy formulations. [ABSTRACT FROM AUTHOR]

With the studies of ecological stoichiometry changing from global to regional and smaller scales, it is of great significance to know how, for a single species in a typical ecosystem, the carbon (C), nitrogen (N) and phosphorus (P) stoichiometry varies with the seasons and responds to environmental stresses. In this study, taking T. chinensis ( Tamarix chinensis Lour.) in the Laizhou Bay coastal wetland as a test object, seasonal variations in leaf C, N and P stoichiometry and their correlations with soil salt were analyzed. The results showed that leaf C, N and P contents and the ratios of C:N, C:P and N:P of T. chinensis exhibited large variations. The leaf C, N and P contents were 388.56–472.07, 18.39–32.86 and 1.02–3.16 g kg −1 , respectively. Also, the ratios of C:N, C:P and N:P were 11.83–24.78, 125.98–425.98 and 8.62–23.66, respectively. The seasonal variation of the leaf C content presented an increasing trend while the leaf N and P contents indicated a trend of first increasing and later decreasing. However, a trend contrary to the leaf N and P was observed for the ratios of leaf C:N, C:P and N:P. There was a significant negative correlation between leaf P content and soil salt. Conversely, a positive correlation was found between the ratios of leaf C:P, N:P and soil salt. Generally, T. chinensis in the Laizhou Bay wetland took a defensive life-history strategy during the growth process. The growth of T. chinensis was limited by N at the beginning of the growing season, and by P in the middle and final stages of the growing season. [ABSTRACT FROM AUTHOR]

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 by >5% 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. • An integrated model is proposed for land-use prediction and optimization. • System dynamics of land-use patterns affected by multiple factors can be reflected. • Uncertainties in land-use change and NPS pollution export processes can be addressed. • Optimal land-use patterns can be obtained to support land-use decision-making. [ABSTRACT FROM AUTHOR]

• An integrated modeling approach is developed. • Multiple modeling methods were combined. • The approach is applied in a City Cluster of China. • Coordination for WEF and economy is analyzed under multiple scenarios. In this research, an integrated approach is developed through the incorporation of systems dynamics (SD), catastrophe progression (CP), and coupled coordination degree (CCD) analysis within a general modelling framework. The developed SD-CP-CCD is then employed to simulate and evaluate relationships among the water-energy-food (WEF) and the economic systems. It will improve traditional methods for WEF nexus analysis through a) incorporating urban WEF connections and changes into the cross-system coupling analysis framework, b) evaluating its coordination with economic growth and a multi-scale and regional disparity, and c) promoting policy recommendations to ensure the guarantee of urban WEF under economic growth. The approach is then verified through WEF nexus system of Urban Agglomerations of Pearl River Delta City Cluster (PRDCC) in China. Temporal and spatial characteristics of CCD in the region were analyzed. The results revealed their performance in cities with multiple economic backgrounds. Also, the fragile factors of each city were reflected by multi-scale exploration. Regional inequality and spatial agglomeration effects were considered. The results showed that CCD has noticeable temporal and spatial differences. Geographically, CCD varied from uncoordinated to high-level coordination. The eastern cities were much better coordinated than the western cities. Due to the increasing pressure of population and economic growth in the future, the upward trend of CCD would occur in most cities. The vulnerability factors of each city were explored. Simultaneously, apparent spatial inequality and agglomeration patterns were observed, with a downward trend over time. The temporal and spatial patterns of CCD revealed in this study indicated that PRDCC policymakers should formulate policies that suit the characteristics of cities and reduce the targeted regional imbalances. [ABSTRACT FROM AUTHOR]

The urbanization process has seen an accelerated increase in recent decades, leading to urban runoff pollution becoming more prominent. However, uncertainty of the pollution output and complexity of management systems have made controlling urban runoff pollution challenging. Therefore, it is necessary to propose advanced modeling methods for these challenges. This research presents an integrated urban runoff pollution management (IURPM) model for optimal configuration of low impact development (LID) practices under multiple uncertainties. The IURPM model combines the hybrid land-use prediction and improved pollution estimation models with interval parameter, stochastic parameter, and multi-objective programming. The proposed IURPM model can not only predict the output characteristics, but also provide optimal configuration schemes for the LID practices in the management of urban runoff pollution under multiple scenarios. In addition, uncertainties expressed as discrete intervals and probability density function in the management systems can be effectively addressed. A case study of the IURPM model was conducted in Dongguan City, South China. Results show that considerable amounts of urban runoff pollutants would export from Dongguan City by 2025. The export loads and pollution output flux per unit area would have significant spatial heterogeneity. The results further indicate that population size, gross domestic product, and regional area size are expected to play important roles in the pollution export, while impervious surface coverage and population density would likely have great influences on the output flux of urban runoff pollution. Based on the model findings, multiple LID practices should be adopted in Dongguan City to reduce the urban runoff pollution loads. Using the IURPM model, multiple LID implementation schemes can be obtained under different pollution reduction scenarios and significance levels, that can provide decision-making support for urban water environmental management, considering variations in the policymaker's decision-making preferences. This study demonstrates that the IURPM model can be applied to the optimal configuration of LID practices for the management of urban runoff pollution under uncertainty., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Water scarcity tends to be aggravated by increase in water demand with the trend of socio-economic development. Thus, non-stationary characteristics of water demand should be identified in water resources allocation (WRA) to alleviate the potential influences from water shortages. In this study, a Copula-based interval linear programming model was established for regional WRA. Through combining correlation analysis and an interval linear programming model, this model can: 1) identify interactions between water demand and socio-economic development levels based on Copula functions, 2) explore variations in water shortage with consideration of multiple risk tolerance levels of decision-makers based on Copula sampling, and 3) obtain desired strategies for WRA through an interval linear programming model. Also, Dalian City in China was selected as a case study area to verify the effectiveness of the model for WRA to five water users (i.e., agricultural sector, industrial sector, public service sector, domestic residents, and ecological environment). Considering multiple tolerance levels of decision-makers to water shortage risk, three scenarios (i.e., S1 to S3), indicating 20%, 40%, and 60% of their low, medium, and high tolerance levels, were proposed. The results showed that the correlation between the amount of water demand and indicators of socio-economic development can be described by Clayton and Gaussian Copula functions. The total water supply of Dalian in 2030 would increase by 2.06%-2.65%, compared with the one in 2025. The allocation of water resources across districts was influenced by varied water demand, energy consumption, and risk tolerance levels. Compared with the amount of water allocation in 2025, the contribution of transferred water sources would increase by 6.71% and 7.04% under S1 and S2 in 2030, respectively, and decrease by 14.31% under S3. With the increase of risk tolerance levels of decision-makers, the amount of water supply in Dalian City would gradually decrease., (Copyright © 2022 Elsevier Ltd. All rights reserved.)