Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution.

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

Mitigation Measures Could Aggravate Unbalanced Nitrogen and Phosphorus Emissions from Land-Use Activities.

Socioeconomic factors and mitigation potentials are essential drivers of the dynamics of nutrient emissions, yet these drivers are rarely examined at broad spatiotemporal scales. Here, we combine material flow analysis and geospatial analysis to examine the past and future changes of nitrogen and phosphorus emissions in China. Results show that anthropogenic nitrogen and phosphorus emissions increased by 17% and 32% during 2000-2019, respectively. Meanwhile, many regions witnessed decreasing nitrogen emissions but rising phosphorus discharged to waterbody, leading to a 20% decrease in the nitrogen/phosphorus ratio. In addition to many prominent factors like fertilizer use, the increasing impervious land area around cities is a notable factor driving the emissions, indicating the urgency to limit building expansion, especially in North China Plain and other less-developed regions. Improving land-use efficiency and consuming behaviors could reduce nitrogen and phosphorus emissions by 65-77% in 2030, but the nitrogen/phosphorus ratio will increase unintendedly due to larger reduction potentials for phosphorus, which may deteriorate the aquatic ecosystem. We highlight that nitrogen and phosphorus emissions should be reduced with coordinated but differentiated measures by prioritizing nitrogen reduction through cropland and food-system management.

Hybrid life-cycle and hierarchical archimedean copula analyses for identifying pathways of greenhouse gas mitigation in domestic sewage treatment systems.

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 CO2eq 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.

Downscaling of environmental indicators: A review.

Environmental indicators at different scales are important for environmental management, daily life, and scientific research. Because of the lack of statistics below a national scale for many environmental indicators, scholars have developed various downscaling methods to obtain finer-scale and diverse forms of data for different environmental indicators. However, the existing downscaling methods for environmental indicators are diverse and fragmented. Here, we reviewed the downscaling methods by reclassifying the environmental indicators from a life cycle perspective into five categories: natural resources use and related attributes; material and energy consumption; environmental discharge; climate change; and environmental footprints. We first provide a general introduction to downscaling theory in the environmental field, including definitions, techniques, and evolution. We then elaborate on downscaling methods and make an inventory of the five categories of environmental indicators. We summarize the downscaling methods commonly applied to specific indicators, scale transformation, the strengths and limitations of corresponding methods, and provide specific examples. Next, we discuss ways to select or construct downscaling methods based on four principles: objective orientation, data accessibility, model feasibility, and model adjustment. Finally, we explore the future direction of downscaling and provide insights for improving downscaling for environmental indicators. In this review, we generalize and clarify the downscaling techniques for environmental indicators, which will help facilitate the appropriate selection of downscaling methods by researchers.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China.

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Uncovering the risk spillover of agricultural water scarcity by simultaneously considering water quality and quantity.

Not only insufficient water quantity but also inadequate water quality can pose constraints on agricultural production and result in potential economic losses. Such economic losses in agriculture may adversely impact downstream producers through reduced input supplies. In this study, we developed an index assessing potential economic losses in agriculture under both quantity- and quality-induced water scarcity, termed integrated Agricultural Water Scarcity Risk (AWSR). Combining integrated AWSR with a multi-regional input-output model, we estimated the spillovers of integrated AWSR along supply chains. Our results showed that the intersectoral transmission of virtual integrated AWSR (sectoral spillovers in terms of integrated AWSR) were 5 times the virtual quantity-based AWSR. Pollution significantly intensifies the indirect supply-chain repercussions of agricultural water scarcity. Moreover, we identified some primary virtual integrated AWSR exporters (e.g., Jiangsu-vegetables and Shandong-swine, of which the integrated AWSR had considerable spillover effects on downstream sectors) and importers (e.g., Henan-chemical industry and Henan-textiles, which were vulnerable to upstream integrated AWSR), that could not be detected in quantity-based AWSR results. This study underscores the importance of water quality in the assessments of AWSR. Strategies to mitigate the spillovers of AWSR might be inefficient without the consideration of water quality.

Performance, comparison and utilization of reduced sulfur (-2) compounds (S2-, FeS and SCN-) in autotrophic denitrification process by thiosulfate-driven autotrophic denitrifier.

The coexistence of reduced sulfur (-2) compounds (S2-, FeS and SCN-) are found in some industrial wastewaters due to pre-treatment of Fe(II) salts. These compounds as electron donors have attracted increasing interest in autotrophic denitrification process. However, the difference of their functions still remain unknown, which limit efficient utilization in autotrophic denitrification process. The study aimed to investigate and compare utilization behavior of these reduced sulfur (-2) compounds in autotrophic denitrification process activated by thiosulfate-driven autotrophic denitrifiers (TAD). Results showed that the best denitrification performance was observed in SCN-; while the reduction of nitrate was significantly inhibited in S2- system and the efficient accumulation of nitrite was observed in FeS system with cycle experiments continuing. Additionally, intermediates containing sulfur were produced rarely in SCN- system. However, the utilization of SCN- was limited obviously in comparison with S2- in coexistence systems. Moreover, the presence of S2- increased the accumulation peak of nitrite in coexistence systems. The biological results indicated that the TAD utilized rapidly these sulfur (-2) compounds, in which genus of Thiobacillus, Magnetospirillum and Azoarcus might play main roles. Moreover, Cupriavidus might also participate in sulfur oxidation in SCN- system. In conclusion, these might be attributed to the characteristics of sulfur (-2) compounds including the toxicity, solubility and reaction process. These findings provide theoretical basis for regulation and utilization of these reduced sulfur (-2) compounds in autotrophic denitrification process.

The efficient utilization of thiocyanate on simultaneous removal of ammonium and nitrate through thiosulfate-driven autotrophic denitrifiers and anammox.

The efficient utilization of thiocyanate remain be an important bottleneck in the low-cost nitrogen removal for wastewaters containing thiocyanate. The study aimed to investigate the feasibility of thiocyanate in removal of nitrate and ammonium through anammox (AN) and thiosulfate-driven autotrophic denitrifiers (TSAD). The results showed that removal of nitrate and ammonium were achieved rapidly utilizing thiocyanate, which was attributed to degradation of thiocyanate by TSAD and cooperation with AN. The utilization efficiency of thiocyanate in nitrogen removal was increased by 250% due to the microbial cooperation. Excess thiocyanate and ammonium did not influence the nitrogen removal amount. However, the nitrogen removal were affected obviously by the biomass ratio (XAN/XTSAD) between AN and TSAD Moreover, the dynamics related to removal of pollutants was described successfully by a modified Monod model with time constraints. These findings offer an insight for efficient utilization of thiocyanate in nitrogen removal via microbial cooperation.

Optimal configuration of low impact development practices for the management of urban runoff pollution under uncertainty.

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.

Can improving the spatial equity of urban green space mitigate the effect of urban heat islands? An empirical study.

The role of urban green space (UGS) in mitigating the urban heat island (UHI) effect has been demonstrated in a growing body of literature. However, the potential influence of the spatial equity of UGS distribution on the UHI effect has largely been overlooked. The present study aims to identify this potential influence using the spatial equity of UGS and the land surface temperature (LST) as measures of UGS spatial distribution and UHIs, respectively. A comprehensive spatial distribution map of UGS was generated by combining the UGS coverage fraction data within urban impervious pixels and the green cover data outside urban impervious pixels. Then, the spatial equity of UGS distribution across all urban impervious pixels was determined using the Gini coefficient. In addition, an LST map was derived using the thermal infrared spectral bands of Landsat 8 OLI/TIRS products. A case study of Dongguan, a highly urbanized city in China, showed that (1) the distribution of both UGS and LSTs were spatially aggregated in all the towns of the city, (2) the LST of urban impervious pixels was negatively correlated with the area of surrounding UGS, and (3) the Gini coefficient of UGS was positively correlated with the proportion of hot and cool areas, but negatively correlated with the proportion of medium-hot and medium-cool areas. These findings indicate that increasing the amount of UGS is beneficial to the reduction of urban average LSTs, while promoting the spatial equity of UGS distribution is conducive to reducing the spatial aggregation of LSTs within urban areas, thereby improving the overall urban thermal environment. Therefore, as a nature-based solution, promoting the spatial equity of UGS distribution could enhance the overall cooling effect of UGS more effectively at the city scale, and thus further underpin the sustainable development of the urban environment.

A Copula-based interval linear programming model for water resources allocation under uncertainty.

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.

High discharge intensified low net ecosystem productivity, hypoxia, and acidification at three outlets of the Pearl River Estuary, China.

Clarifying the influence of hydrological variations on ecological function is a topic of considerable interest in watershed ecological flow assessment and water resource management. Net ecosystem productivity (NEP) is a useful composite indicator of ecosystem function, reflecting material cycling and energy flow. However, the effects of hydrological variations on NEP, especially the influence mechanism, remain unclear due to the complex environmental characteristics in estuaries. We analysed the spatial-temporal variability of the aquatic environment and NEP through in-situ monitoring and field sampling from December 2018 to January 2020 at three outlets (Humen, Jiaomen, and Hongqimen) with different hydrological conditions in the Pearl River Estuary (PRE), China, and explored the influence mechanism of hydrological variation on NEP. The 155 groups of effective metabolism values were estimated using Odum's open-water method. The daily ecosystem respiration (ER) was higher than the gross primary production (GPP); therefore, water bodies were dominated by net heterotrophy at the three outlets. The daily NEP (-4.34 ± 1.40 mg O2 L-1d-1), O2 concentration (5.2 ± 1.02 mg L-1), and pH (7.53±0.24) were lowest at Humen, which also had the largest discharge and tide volume, deepest water depth, and widest channel. Seasonally, the NEP in the summer (-3.30 ± 1.39 mg O2 L-1d-1) and autumn (-3.19 ± 1.60 mg O2 L-1d-1) was lower than those in the spring (-1.56 ± 1.92 mg O2 L-1d-1) and winter (-2.17 ± 1.50 mg O2 L-1d-1). The inhibitory effect of increased discharge on the metabolic rate exceeded the stimulation provided by seasonal factors, such as increased temperature and solar radiation. The scour and dilution effect caused by discharge increase reduced chlorophyll a concentration; meanwhile, the increase in turbidity resulted in a decrease in the photosynthetic rate and GPP. ER was stimulated by heterotrophic microorganisms and high total suspended solids, resulting in a decrease in O2 and endogenous organics, thus causing the low NEP, hypoxia, and acidification phenomenon. Our results suggest that lengthening the discharge pulse period in summer and autumn will further decrease NEP and increase the area of hypoxia and acidification at the three outlets in the PRE.

Prediction and optimization of regional land-use patterns considering nonpoint-source pollution control under conditions of uncertainty.

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.

A feasibility study of metal sulfide (FeS and MnS) on simultaneous denitrification and chromate reduction.

Metal sulfide-based biological process is considered as a promising biotechnology for next-generation wastewater treatment. However, it is not clear if simultaneous bio-reduction of nitrate and chromate was achievable in this process. This study aimed to evaluate the feasibility of metal sulfides (FeS and MnS) on simultaneous denitrification and chromate reduction in autotrophic denitrifying column bioreactors. Results showed that simultaneous reduction of nitrate and chromate was achieved using metal sulfides (FeS and MnS) as electron donors, in which sulfate was the sole soluble end-product. Apart from the sulfur element in the metal sulfides, Fe(II) and Mn(II) were also involved in nitrate and chromate reduction as indicative by the formation of their oxidative states compounds. In microbial communities, SHD-231 and Thiobacillus were the most predominant bacteria, which might have played important roles in simultaneous denitrification and chromate reduction. Compared to FeS, MnS showed a higher performance on nitrate and chromate removal, which could also reduce the toxic inhibition of chromate on nitrate reduction. According to results of XRD and XPS, as well as a lower sulfate production in the FeS system, FeS might have been covered easily to hydroxides due to its bio-oxidation, which limited mass transfer efficiency and bio-availability of FeS. The findings in this study offered insights in the development of promising approaches for the treatment of toxic and hazardous compounds using metal sulfide.

How to obtain industrial waste data at the county scale: Two downscaling models and their application in Dongguan, China.

The availability of environmental emission data is critical in evaluation of countries' ecological security and the implementation of environmental management. However, access to environmental emission data at the county level is not provided by statistical publications and bulletins. Therefore, in this paper, we develop two novel data downscaling models, an environmental Kuznets curve downscaling model (EKCDM) and a scale model (SM), to obtain county-level environmental emission data. The EKCDM relies on the EKC hypothesis and the assumption that the same model applies across scales, whereas the SM depends on the assumption that the share of a region's environmental pollution is equivalent to its share of economic output. Subsequently, environmental emission data above the county scale can be obtained through model transformation and simple calculations. By verifying and analyzing the official data with the one obtained through downscaling at municipal level and above, we verify the feasibility of the models, after which we apply the models to extrapolate information on the industrial waste of the counties of Dongguan city in Guangdong Province, China. We find that the EKCDM should be given priority in most cases, especially for the quadratic parameter model, and that the SM can be adopted when per capita gross domestic product differs greatly between adjacent levels of administrative units. In general, scholars can synthesize the characteristics of these two models, and obtain more accurate data by supplementing and verifying one with the other. Compared with other downscaling methods, our methods require far less data and the concepts are easily understood, which makes them more feasible and increases applicability. This study provides scholars with powerful tools to explore the relationship between industrial pollution and economic development in depth by obtaining industrial waste data at the county scale, thereby supporting scientific research and policy design.

Spatial Variation of the Effect of Multidimensional Urbanization on PM2.5 Concentration in the Beijing-Tianjin-Hebei (BTH) Urban Agglomeration.

Atmospheric PM2.5 pollution has become a prominent environmental problem in China, posing considerable threat to sustainable development. The primary driver of PM2.5 pollution in China is urbanization, and its relationship with PM2.5 concentration has attracted considerable recent academic interest. However, the spatial heterogeneity of the effect of urbanization on PM2.5 concentration has not been fully explored. This study sought to fill this knowledge gap by focusing on the Beijing-Tianjin-Hebei (BTH) urban agglomeration. Urbanization was decomposed into economic urbanization, population urbanization, and land urbanization, and four corresponding indicators were selected. A geographically weighted regression model revealed that the impact of multidimensional urbanization on PM2.5 concentration varies significantly. Economically, urbanization is correlated positively and negatively with PM2.5 concentration in northern and southern areas, respectively. Population size showed a positive correlation with PM2.5 concentration in northwestern and northeastern areas. A negative correlation was found between urban land size and PM2.5 concentration from central to southern regions. Urban compactness is the dominant influencing factor that is correlated positively with PM2.5 concentration in a major part of the BTH urban agglomeration. On the basis of these findings, BTH counties were categorized with regard to local policy recommendations intended to reduce PM2.5 concentrations.

Is the Water System Healthy in Urban Agglomerations? A Perspective from the Water Metabolism Network.

Urban agglomerations are a primary spatial focus of socioeconomic activity and inherently include large volumes of embodied water. We have applied the concept of water metabolism health to comprehensively evaluate the overall operation of water systems in urban agglomerations and propose an innovative assessment framework. In particular, we constructed a water metabolism network (WMN) model to simulate a water system in which different cities and sectors are integrated, combining a newly compiled multiregional input-output (MRIO) table of water flow with ecological network analysis (ENA). A case study considering the Pearl River Delta (PRD) urban agglomeration in 2015 demonstrates that its network is well synergic but highly dependent, with considerable negative effects. Highly developed cities in southeastern of the PRD exhibit higher embodied water productivity and robustness but impose considerable negative effects on the water system. We found the agricultural sector to be a dominant controller of the network; the construction and service sectors represent the primary beneficiaries with strong competition. We suggest measures at various scales to improve water utilization efficiency and promote positive interactions between components, thus improving water metabolism system health for urban agglomerations.

Management optimization of nonpoint source pollution considering the risk of exceeding criteria under uncertainty.

Management of nonpoint source (NPS) pollution is highly important in watershed water environmental and ecological security. However, the many complexities and uncertainties that exist in the processes of export and management of NPS pollution exert substantial influences on the reliability of multiple management practices. This study developed an inexact multiobjective possibilistic mean-variance mixed-integer programming (IMPMMP) model for NPS pollution management through optimization of watershed land use pattern and livestock production structure. By coupling interval parameter programming, mixed-integer programming, multiobjective programming, and an export coefficient model within a general possibilistic mean-variance model framework, the IMPMMP model deals effectively with system uncertainties and complexities. Moreover, the risk of exceeding criteria (REC) in NPS pollution management systems can be considered. The proposed IMPMMP model was applied to a real-world case study in the Xinfengjiang Reservoir watershed in South China. Results showed that the preference of decision makers regarding land use adjustment plays a decisive role in determining model feasibility. The area provided for each land use type that could be adjusted has to reach a certain threshold to achieve the goals of reduced pollution load and REC control. The NPS pollution loads after optimization would be exported primarily from different land uses and the human population. Compared with NPS nitrogen pollution management, it is more difficult to reduce the NPS phosphorus load and to manage the corresponding REC through adjustment of the land use pattern and livestock production structure. Moreover, it is difficult to simultaneously reduce the NPS nitrogen and phosphorus pollution loads and REC in each subbasin. The model, which can provide policy makers with a series of schemes for optimization of land use pattern and livestock production structure, has satisfactory applicability and could be used for watershed NPS pollution management.

Analysis of urban carbon metabolism characteristics based on provincial input-output tables.

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.

Net heterotrophy and low carbon dioxide emissions from biological processes in the Yellow River Estuary, China.

Although estimates of total CO2 emissions from global estuaries are gradually decreasing, current numbers are based on limited data and the impacts of anthropogenic and seasonal disturbances have not been studied extensively. Our study estimates annual and seasonal CO2 fluxes in China's Yellow River Estuary (YRE) which incorporated spatiotemporal variations and the effects of water and sediment regulation (WSR). Aquatic metabolism was estimated using Odum's open water dissolved oxygen methods and used to represent the production and assimilation of CO2. Net ecosystem production (NEP) was used to represent the CO2 flux from biological activities and estimate the major CO2 emitters in the YRE. According to our measurements, the annual CO2 release was 6.14 ± 33.63 mol C m-2 yr-1 from 2009 to 2013 and the annual CO2 efflux from the 1521.3 km2 of estuarine surface area was 0.11 ± 0.61 Tg C yr-1 in the YRE. High CO2 emissions in autumn were balanced by high CO2 sequestration in summer, leading to a lower than expected annual net CO2 efflux. The system is an atmospheric CO2 source in spring and winter, near neutral in early summer, a large sink in late summer after WSR, and finally a large atmospheric CO2 source in autumn. Discharge events and seasonality jointly affect estuarine CO2 flux. High CO2 sequestration in summer is due mainly to a combination of high water temperature, chlorophyll a levels, dissolved inorganic carbon, and solar radiation and low turbidity, discharge, and chemical oxygen demand (COD) after WSR. WSR supports the high gross primary productivity rate which exceeds the increase in ecosystem respiration. Although the YRE, as a whole, is a source of atmospheric CO2, the amount of CO2 released is lower than the average estuarine value of mid-latitude regions. Our findings therefore suggest that global CO2 release from estuarine systems is overestimated if spatiotemporal variations and the effects of anthropogenic disturbance are excluded. The NEP method is effective for estimating the CO2 flux, especially in estuaries where CO2 variation is mainly due to biological processes.