Habitat is of great importance in, determining the trophic transfer of pollutants in freshwater ecosystems; however, the major factors influencing chemical trophodynamics in pelagic and benthic food webs remain unclear. This study investigated the levels of p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), polycyclic aromatic hydrocarbons (PAHs), 3 and substituted PAHs (s-PAHs) in 2 plankton species, 6 invertebrate species, and 10 fish species collected from Lake Dianchi in southern China. Relatively high concentrations of PAHs and s-PAHs were detected with total concentrations of 11.4-1400 ng/g wet weight (ww) and 5.3-115 ng/g ww, respectively. Stable isotope analysis and stomach content analysis were applied to quantitatively determine the trophic level of individual organisms and discriminate between pelagic and benthic pathways, and the trophodynamics of the detected compounds in the two food webs were assessed. P,p'-DDE was found to exhibit relatively higher trophic magnification rate in the pelagic food web than in the benthic food web. In contrast, PAHs and s-PAHs exhibited greater dilution rates along the trophic levels in the pelagic food web. The lower species differences of pollutants accumulated in benthic organisms compared to pelagic organisms is attributable to extra uptake via ingested sediment in benthos. The average uptake proportions of PAHs and s-PAHs via ingested sediment in benthic biotas were estimated to be 31-77%, and that of p,p'-DDE was 46%. The uptake routes are of importance for assessing the trophic magnification potentials of organic pollutants, especially in eutrophic freshwater ecosystems.

The industrial sector is a major source of China's major CO2 emissions. In 2012, the emissions embedded in the final-use-induced intermediate CO2 flows contributed to approximately 91.2% of the total emissions. Hence, controlling CO2 emissions along supply chains could have a significant contribution in achieving the climate mitigation goal to which the Chinese government has been committed. In this study, we first extracted final-use induced CO2 transfer networks from input-output tables, and then applied the power-of-pull approach to the networks to identify the key sectors significantly affecting the CO2 emissions of each targeted sector's supply chains. Our results showed that each sector itself has significant power of pulling their emissions. Energy and raw material providers have played an essential role in pulling other sectors' emissions in the past years. The results of all sectors can easily construct a network reflecting the system's characteristics. And the power-of-pull approach could easily be integrated with the structural path analysis. Our proposed approach has the potential in helping policy making by offering a new perspective.

The present study investigated the abundance, richness, diversity, and community composition of denitrifiers (based on nirS and nosZ genes) in the stratified water columns and sediments in eutrophic Dianchi Lake and mesotrophic Erhai Lake using quantitative PCR assay and high-throughput sequencing analysis. Both nirS- and nosZ denitrifiers were detected in waters of these two lakes. Surface water showed higher nosZ gene density than bottom water, and Dianchi Lake waters had larger nirS gene abundance than Erhai Lake waters. The abundance of sediment nirS- and nosZ denitrifiers in Dianchi Lake was larger than that in Erhai Lake. nirS richness and diversity and nosZ richness tended to increase with increasing sediment layer depth in both lakes. The distinct structure difference of sediment nirS- and nosZ denitrifier communities was found between in Dianchi Lake and Erhai Lake. These two lakes also differed greatly in water denitrifier community structure. Moreover, phylogenetic analysis indicated the presence of several different groups of nirS- or nosZ denitrifiers in both lakes. The novel nirS denitrifiers were abundant in both Dianchi Lake and Erhai Lake, while most of the obtained nosZ sequences could be affiliated with known genera.

Nutrients loading reduction in watershed is essential for lake restoration from eutrophication. The efficient and optimal decision-making on loading reduction is generally based on water quality modeling and the quantitative identification of nutrient sources at the watershed scale. The modeling process is influenced inevitably by inherent uncertainties, especially by uncertain parameters due to equifinality. Therefore, the emerging question is: if there is parameter uncertainty, how to ensure the robustness of the optimal decisions? Based on simulation-optimization models, an integrated approach of pattern identification and analysis of robustness was proposed in this study that focuses on the impact of parameter uncertainty in water quality modeling. Here the pattern represents the discernable regularity of solutions for load reduction under multiple parameter sets. Pattern identification is achieved by using a hybrid clustering analysis (i.e., Ward-Hierarchical and K-means), which was flexible and efficient in analyzing Lake Bali near the Yangtze River in China. The results demonstrated that urban domestic nutrient load is the most potential source that should be reduced, and there are two patterns for Total Nitrogen (TN) reduction and three patterns for Total Phosphorus (TP) reduction. The patterns indicated different total reduction of nutrient loads, which reflect diverse decision preferences. The robust solution was identified by the highest accomplishment with the water quality at monitoring stations that were improved uniformly with this solution. We conducted a process analysis of robust decision-making that was based on pattern identification and uncertainty, which provides effective support for decision making with preference under uncertainty. (C) 2017 Elsevier B.V. All rights reserved.

Anaerobic ammonium-oxidizing (anammox) process can play an important role in freshwater nitrogen cycle. However, the distribution of anammox bacteria in freshwater lake and the associated environmental factors remain essentially unclear. The present study investigated the temporal and spatial dynamics of sediment anammox bacterial populations in eutrotrophic Dianchi Lake and mesotrophic Erhai Lake on the Yunnan Plateau (southwestern China). The remarkable spatial change of anammox bacterial abundance was found in Dianchi Lake, while the relatively slight spatial shift occurred in Erhai Lake. Dianchi Lake had greater anammox bacterial abundance than Erhai Lake. In both Dianchi Lake and Erhai Lake, anammox bacteria were much more abundant in summer than in spring. Anammox bacterial community richness, diversity, and structure in these two freshwater lakes were subjected to temporal and spatial variations. Sediment anammox bacterial communities in Dianchi Lake and Erhai Lake were dominated by Candidatus Brocadia and a novel phylotype followed by Candidatus Kuenenia; however, these two lakes had distinct anammox bacterial community structure. In addition, trophic status determined sediment anammox bacterial community structure.

Microbial methanogenesis in sediment plays a crucial role in CH4 emission from freshwater lake ecosystems. However, knowledge of the layer-depth-related changes of methanogen community structure and activities in freshwater lake sediment is still limited. The present study was conducted to characterize the methanogenesis potential in different sediment-layer depths and the vertical distribution of microbial communities in two freshwater lakes of different trophic status on the Yunnan Plateau (China). Incubation experiments and inhibitor studies were carried out to determine the methanogenesis potential and pathways. 16S rRNA and mcrA genes were used to investigate the abundance and structure of methanogen and archaeal communities, respectively. Hydrogenotrophic methanogenesis was mainly responsible for methane production in sediments of both freshwater lakes. The layer-depth-related changes of methanogenesis potential and the abundance and community structure of methanogens were observed in both Dianchi Lake and Erhai Lake. Archaeal 16S rRNA and mcrA genes displayed a similar abundance change pattern in both lakes, and the relative abundance of methanogens decreased with increasing sediment-layer depth. Archaeal communities differed considerably in Dianchi Lake and Erhai Lake, but methanogen communities showed a slight difference between these two lakes. However, methanogen communities illustrated a remarkable layer-depth-related change. Order Methanomicrobiales was the dominant methanogen group in all sediments, while Methanobacteriales showed a high proportion only in upper layer sediments. The trophic status of the lake might have a notable influence on the depth-related change pattern of methanogenesis activity, while the methanogen community structure was mainly influenced by sediment depth.

The distribution of archaeal community and the associated environmental variables in constructed wetland (CW), especially in free water surface flow CW (FWSF-CW), remain poorly understood. The present study explored the spatial and temporal dynamics of archaeal community in an FWSF-CWused for surface water treatment and evaluated the driving environmental variables. The archaeal density varied considerably among sites and seasons, ranging from 3.37 x 10(8) to 3.59 x 10(9) 16S rRNA gene copies per gram dry sediment/soil. The archaeal population density was adversely affected by high temperatures and tended to be lower during summer than during spring and winter. Moreover, considerable spatio-temporal variations of archaeal richness, diversity and community structure also occurred in the FWSF-CW. Higher nutrient contents correlated with a lower archaeal richness and diversity. Nitrate and carbon/nitrogen ratiowere found to play important roles in shaping the overall archaeal community structure. Euryarchaeota and Bathyarchaeota were the dominant archaeal phyla in wetland sediments, while Thaumarchaeota tended to be dominant in wetland soils. In addition, the wetland archaeal community was related to vegetation type. (C) 2017 Elsevier B.V. All rights reserved.

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood.characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses. (C) 2017 Elsevier B.V. All rights reserved.

Practical and optimal reduction of watershed loads under deep uncertainty requires sufficient search alternatives and direct evaluation of robustness. These requirements contribute to the understanding of the tradeoff between cost and robustness; while they are not well addressed in previous studies. This study thereby (a) uses preconditioning technique in Evolutionary Algorithm to reduce unnecessary search space, which enables a sufficient search; and (b) derives Robustness Index (RI) as a second-tier optimization objective function to achieve refined solutions (solved by GA) that address both robustness and optimality. Uncertainty-based Refined Risk Explicit Linear Interval Programming is used to generate alternatives (solved by Controlled elitist NSGA-II). The robustness calculation error is also quantified. Proposed approach is applied to Lake Dianchi, China. Results demonstrate obvious improvement in robustness after conducting sufficient search and negative robustness-optimality trade-offs, and provides a detailed characteristic of robustness that can serve as references for decision-making.

This study developed a framework for combining multi-regional input-output analysis and network indicators to assess the interregional CO2 flows in China. The interregional CO2 flows of eight regions were calculated and visualized based on a multiregional input-output (MRIO) model for China. The focus of the research was intermediate use. The results of the network indicators showed that refined petroleum, coke, nuclear fuel and chemical products (07), and basic metals and fabricated metal sectors (09) played key roles in the complex networks. and these sectors in most regions controlled a large share of CO2 transfer by functioning as key hubs and authorities. They along with commerce, transport, storage, and post (16) acted as agents that brokered the CO2 flows within and between regions. The roles of some other industrial sectors were also identified, e.g., construction (15) functioned as the largest authority. The results demonstrated the importance and effectiveness of network indicators for identifying the characteristics of CO2 emissions embedded in the domestic supply chain, and provided new information relevant to policy implementation.

Lake eutrophication is associated with excessive anthropogenic nutrients (mainly nitrogen (N) and phosphorus (P)) and unobserved internal nutrient cycling. Despite the advances in understanding the role of external loadings, the contribution of internal nutrient cycling is still an open question. A dynamic mass-balance model was developed to simulate and measure the contributions of internal cycling and external loading. It was based on the temporal Bayesian Hierarchical Framework (BHM), where we explored the seasonal patterns in the dynamics of nutrient cycling processes and the limitation of N and P on phytoplankton growth in hyper-eutrophic Lake Dianchi, China. The dynamic patterns of the five state variables (Chla, TP, ammonia, nitrate and organic N) were simulated based on the model. Five parameters (algae growth rate, sediment exchange rate of N and P, nitrification rate and denitrification rate) were estimated based on BHM. The model provided a good fit to observations. Our model results highlighted the role of internal cycling of N and P in Lake Dianchi. The internal cycling processes contributed more than external loading to the N and P changes in the water column. Further insights into the nutrient limitation analysis indicated that the sediment exchange of P determined the P limitation. Allowing for the contribution of denitrification to N removal, N was the more limiting nutrient in most of the time, however, P was the more important nutrient for eutrophication management. For Lake Dianchi, it would not be possible to recover solely by reducing the external watershed nutrient load; the mechanisms of internal cycling should also be considered as an approach to inhibit the release of sediments and to enhance denitrification. (C) 2017 Elsevier Ltd. All rights reserved.

A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system investment and probability of constraint satisfaction, which were selected from 23 sets of Pareto solutions out of 120 solution sets. Decision-makers can select optimal decisions from the solutions above in accordance with the actual conditions of different sub-watersheds under various engineering measures. The relationship between system investment and risk demonstrated that system investment increased rapidly when the probability level of constraint satisfaction was higher than 0.9, but it reduced significantly if appropriate risk was permitted. Evaluation of robustness of the optimal scheme indicated that the Pareto solution obtained from the model provided the ideal option, since the solutions were always on the Pareto frontier under various distributions and mean values of the random parameters. The application of the multi-objective chance-constrained programming to optimize the reduction of watershed nutrient loads in Lake Qilu indicated that it is also applicable to other environmental problems or study areas that contain uncertainties.