Spatial distribution, ecological risk, and human health assessment of heavy metals in lake surface sections - a case study of Qinghai Lake, China.

Qinghai Lake is the largest inland saltwater lake in China, with a drainage area of 29,661 km2. This study sought to conduct an ecological and human health risk assessment of metals and heavy metals, including copper, as well as investigate their concentration, distribution, and source distribution. In terms of seasonal variation, the increases in Fe, Cr, As, Pb, and Hg were relatively large, and the spatial distribution of metals presented a three-level stepped distribution trend, gradually increasing from east to west. By further exploring the source and migration path of pollutants, our study found that the source of metals in the sediments of Qinghai Lake is mainly controlled by five rivers entering the lake. Enrichment factor (EF) calculations indicated that the metal accumulation or enrichment capacity of the three central points in Qinghai Lake Basin was strong. Interestingly, the enrichment capacity of Cu and Zn was the strongest among all metals but occurred at low and medium concentration levels, respectively. The Igeo and [Formula: see text] ecological risk assessment results indicated that the individual metals posed little to no ecological risks to the Qinghai Lake Basin. However, the multi-element environmental risk comprehensive index (RI) indicated that Hg (RI = 147.97) represented a slight ecological hazard, Mn (RI = 181.13) posed moderate ecological hazards, and Zn (RI = 386.66) posed strong ecological hazards. The human health risk assessment results showed that the heavy metals in the surface sediments of Qinghai Lake currently do not pose a threat to human health. This information may facilitate the implementation of more stringent monitoring programs in the aquatic ecosystem by the relevant regulatory authorities.

Adsorption and desorption of heavy metals at water sediment interface based on bayesian model.

Estuarine areas are not only the main gathering point for human sewage but also the place where one-way and two-way fluids interact, thus forming a complex and changeable geochemical physical field. Here, heavy metals (HMs) are adsorbed and desorbed due to physical, chemical, and biochemical processes. However, the adsorption and desorption behavior of HMs in the aquatic environment is complex, and physicochemical processes occurring in the estuarine sediment-water interface control the direction and boundaries of the system. This study analyzed the migration and distribution of HMs in rivers and lakes, and established a Bayesian network model to quantitatively understand the impact of nutrients and key environmental factors on the adsorption-desorption behavior of HMs in lake and estuaries, as well as the competitive relationship between environmental factors. The influence of environmental factors and the occurrence of HMs are both important model inputs. Our findings indicated that the migration risk of Cd in Qinghai Lake was high. Environmental factors such as Cation exchange capacity (CEC), Organic matter (OM), Soluble fluoride (SFL), and pH play the most important role in the adsorption and desorption of HMs. Our findings also indicated that the exchange and activity of HMs in sediments were much higher than in the overlying water. The organic matter content was the most complex environmental factor affecting HMS adsorption and desorption at the water-sediment interface. Additionally, the mass concentration of dissolved oxygen (DO) has a linear relationship with bioavailable HMs in river and lake sediments, but has no linear relationship with the concentration of water-soluble HMs. Interestingly, there are synergistic effects between environmental factors, which directly or indirectly affect the release of bioavailable HMs. However, it is important to determine whether the effects of different environmental factors on the exchange of bioavailable HMs are negative or positive. Our findings suggested that Bayesian network (BN) signals (positive or negative) could provide insights into the transfer direction of metals in the water-sediment interface.

Too ill to cure? - An uncertainty-based probabilistic model assessment on one of China's most eutrophic lakes.

Eutrophication is a global challenge, which is exemplified by the tremendous efforts but little results in restoring the sixth largest and also one of the most eutrophic freshwater lakes in China, Lake Dianchi. Considering large parametric uncertainties in water quality modeling, the traditionally used deterministic water quality model is expanded to a probabilistic model to explore the Lake Dianchi's potential responses to different levels of pollutant load reductions. The results show that, given the long pollution history and severe pollution state in Lake Dianchi, a minimum pollution load reduction by half (base year 2003) is required to maintain the water quality state as it is now in 40 years. At least a 60% nutrient load reduction is required to generate any likelihood of water quality improvement, however, the system stabilizes quickly after about 10 years, which may explain why tremendous investments have generated little results. 80% of nutrient load reduction for 40 years has 95% probability of meeting the TN target but only a below 50% (45%) probability in meeting the TP target, and even less to meet water quality target for Chla. The feasibility of ever reaching the Chinese drinking water standards for total phosphorous and total nitrogen is questionable.

Dual risk-aversion programming for regional industrial structure adjustment with water-energy nexus: A case study of Tianjin, China.

The water-energy nexus (WEN) system is a large-scale complex system that comes with diverse forms of risks owing to many challenges in the process of maintaining economic-resource-environmental sustainability. First, the rapidly increasing demand for water and energy subjects many regions to the high risk of water and energy shortages. Second, decision makers face difficulties in weighing system benefits and loss risks under a series of stricter water-energy policies. To handle the aforementioned dual risks of WEN, in this study we propose copula-based stochastic downside risk-aversion programming (CSDP) for regional water-energy management. CSDP integrates the superiority of the copula analysis method and downside risk-aversion programming into a framework, which can not only reveal the risk interactions between water resources and energy demand by using copula functions under different probability distributions, even previously unknown correlations, but also control economic risk, tackle systemic uncertainties, and provide an effective linkage between system stability and conflicting economic benefits. The proposed model was applied to a water-energy system case study in Tianjin City, China. Optimal solutions for various water resources and energy demand copulas associated with different scenarios and hierarchical risk levels were examined in the CSDP model. The results showed that water resources have a greater influence than energy on industrial structure adjustment in Tianjin, with consequent effects on system benefits, optimal output value schemes, and environmental protection strategies. In addition, the tertiary industry provides a new opportunity for economic growth based on a large amount of water-energy consumption, and its potential resources and water-air pollution risks also deserve extensive attention.

A coupling simulation and optimization method developed for environmental-economic management of Lake watershed.

Many lake basins are facing the challenge of mitigating water shortage and water pollution while maintaining economic growth. Existing planning method for water pollution control often focus on how to alleviate water pollution effectively at the lowest cost, but rarely pay attention to the dynamic feedback and synergy effects between water pollution abatement, water conservation and social economy. This article proposes a method which consists of system dynamics model (SDM), Soil and Water Assessment Tool (SWAT) and objective programming model (OPM). It could be used to create insights on basin-wide water problems from a systematic perspective. The case study on Yilong Lake Watershed evaluates the proposed measures in existing local planning, calculates the optimal scheme, and discusses issues including the uncertainty of effectiveness, choice between recycling sewage and transferring outside, and the necessity of restricting food processing industry. This method could be improved on the simulation of social and industrial economy, the simulation of water cycle, and the spatial planning.

Multiperspective-driven factorial metabolic network analysis framework for energy-water nexus vulnerability assessment and management-policy simulation.

Energy and water are rapidly consumed as the most basic strategic resources of various nations. It is of vital importance to systematically explore the environmental and economic impacts of energy-water co-management policies. This study is to develop a multiperspective-driven factorial metabolic network analysis framework (MPDF) to (a) investigate the direct/indirect/total resource consumption response mechanisms induced by changes in production and consumption; (b) explore the factor interactions of different policies in diverse energy and water metabolic networks by initiating factorial analysis; (c) quantify the economic effects of co-management policies by proposing multiple vulnerability indicators. A typical energy-dependent region, Shanxi Province, China was selected as a case study. The results indicated that the production- and consumption-oriented policies have various guidelines for reducing direct and indirect energy-water consumption. Significant interactions in simulation results suggest synergistic effects across sectors. Considering that Shanxi's energy-water nexus economic vulnerability is as high as 2.22%, it is recommended to prioritize the allocation of resources to sectors with significant factor effects to avoid economic losses. Implementing corresponding resource conservation policies for light industry, machinery manufacturing, construction can reduce water consumption by 18.8%. The findings are expected to provide a solid scientific basis for formulating co-management strategies to alleviate resource scarcities.

An improved calibration and uncertainty analysis approach using a multicriteria sequential algorithm for hydrological modeling.

Hydrological models are widely used as simplified, conceptual, mathematical representatives for water resource management. The performance of hydrological modeling is usually challenged by model calibration and uncertainty analysis during modeling exercises. In this study, a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method was proposed to improve the efficiency and performance of hydrological modeling with high reliability. To evaluate the performance and feasibility of the proposed method, two case studies were conducted in comparison with two other methods, sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE). The results indicated that the MS-CUA method could quickly locate the highest posterior density regions to improve computational efficiency. The developed method also provided better-calibrated results (e.g., the higher NSE value of 0.91, 0.97, and 0.74) and more balanced uncertainty analysis results (e.g., the largest P/R ratio values of 1.23, 2.15, and 1.00) comparing with other traditional methods for both case studies.

Dynamic metabolism network simulation for energy-water nexus analysis: A case study of Liaoning Province, China.

Faced with gradually serious energy and water crisis, the exploration of energy-water nexus metabolism relationships could provide a new direction for achieving the resources conservation and consumption reduction. In this paper, a comprehensive assessment for energy-water metabolic network is proposed to search the dynamic evolution and quantify the sectoral metabolic processes in Liaoning Province, China. Input-output analysis (IOA) and ecological network analysis (ENA) are integrated to clarify the embodied linkages and the complicated system interactions in the energy-water nexus network. The coefficients of "water-related energy" and "energy-related water" are explored through the system dynamics model (SDM) based on the consecutive input-output tables from 2012 to 2018. In addition, energy-water nexus efficiency index (EWEI) is constructed to evaluate the metabolism circulation efficiency of energy-water nexus network. The results indicate that (a) the energy and water outflow sectors mainly concentrate in primary industries and tertiary industries, while inflow sectors mainly concentrate in secondary industries; (b) the sectoral relationship provides effective pathways to implement collaborative resource saving and efficiency improvement measures, and the key sectors with the highest correlation with other sectors in energy-water nexus are AGR (agriculture), MIN (mining) and WRC (wholesale, retail and catering services). (c) the EWEI shows a gradually downward trend and the state of the energy system would determine the health of the energy-water nexus metabolism more strongly than water system.

Plateau river research: spatial-temporal variations of δ18O and δd in the water of the Yarlung Tsangpo River and their controlling factors.

Stable isotope technology has been widely used to trace sources and evolution of water bodies, relationships between different water bodies and pollution sources. Based on δ18O and δD data from the Yarlung Tsangpo River in 2017, this paper analyzes the composition characteristics of δ18O and δD in the river water during the low-flow, high-flow and normal-flow seasons of the entire Yarlung Tsangpo River and further reveals the spatial evolution and influencing factors. The results show that the values of δ18O, δD and d-excess were different in time and space. The δ18O, δD and d-excess values of the Yarlung Tsangpo River in the low-flow season were significantly higher than in the high-flow season. This was mainly due to weak evaporation enrichment and precipitation supply effects on the river water during the wet season. From the perspective of season change, the d-excess in the low-season is significantly higher than in the high-flow season and the normal-flow season; from the perspective of spatial change, the d-excess shows the same spatial variation trend as the δ18O, which first decreases and then increases. Based on the investigation of δ18O and δD in the main and Branch rivers of the Yarlung Tsangpo River Basin, it was found that the upper reaches of the basin were mainly supplied by snowmelt, the lower reaches were mainly supplied by rainfall.

Plateau River research: ecological risk assessment of surface sediments in the Yarlung Tsangpo River.

To fully understand the environmental quality of sediments in the Yarlung Tsangpo River Basin, surface sediments of the mainstream of the Yarlung Tsangpo River and its five major tributaries were studied. In 2017, a total of 201 samples from the Yarlung Tsangpo River and its tributaries were collected during three water seasons. Fifteen trace elements (including Cd and heavy metals such as Cr, Cu, Pb, and As), which have great environmental effects, were analyzed. The results show that titanium, manganese, and chromium are the main heavy metals in the sediments of the Yarlung Zangbo River, accounting for 51%, 10%, and 7% of the total of 15 heavy metal elements measured, respectively. There were no significant differences among the heavy metal contents in the sediments of Yarlung Tsangpo River among three water seasons, but there were significant spatial variations. During the same period, the concentration of each element in the tributary sediments was generally higher than that in the mainstream. According to three different ecological risk assessment models, it is calculated that the heavy metal elements in the surface sediments of the river basin are at low or no pollution risk.

Water environment in the Tibetan Plateau: heavy metal distribution analysis of surface sediments in the Yarlung Tsangpo River Basin.

To fully understand the environmental quality of sediments in the Yarlung Tsangpo River Basin, surface sediments of the main stream of the Yarlung Tsangpo River and its five major tributaries were studied. In 2016, a total of 201 water samples from the Yarlung Tsangpo River and its tributaries were collected during three water seasons. Fifteen trace elements (including Cd and heavy metals such as Cr, Cu, Pb, and As), which have great environmental effects, were analyzed. The results showed that Ti, Mn, and Cr were the main heavy metals in Yarlung Tsangpo River sediments, accounting for 51%, 10%, and 7% of the total heavy metals, respectively. There were no significant differences among the heavy metal contents in the sediments of Yarlung Tsangpo River among three water seasons, but there were significant spatial variations. During the same period, the concentration of each element in the tributary sediments was generally higher than that in the main stream. According to three different ecological risk assessment models, it is calculated that the heavy metal elements in the surface sediments of the river basin are at low or no pollution risk.

Quantitative source apportionment of water solutes and CO2 consumption of the whole Yarlung Tsangpo River basin in Tibet, China.

The Tibetan Plateau is known as the "Asian water tower," and the Yarlung Tsangpo (YT) River is the largest river that originates in and flows across the southern Tibetan Plateau. Although the solute source of YT River has been extensively and qualitatively analyzed, there is a lack of the quantitative analysis for the whole basin and seasonal variation of hydrochemical characteristics. Here, 212 samples obtained in the mainstream and tributary of YT River in different (wet, normal, and dry) periods were used for the solute apportionment using the mass balance model. The results showed that the solutes in YT River water were mainly derived from the carbonate and silicate rock weathering that accounts 42.2% and 26.9% in the total solute source, respectively, as the complex geological conditions in the basin. A part of the ions (7.5%) was also originated from the atmospheric precipitation as the abundant rainfall in the wet period. Meanwhile, the contribution of solute sources had no significantly seasonal variation in the upstream, whereas it had significantly seasonal variation in the downstream with the tropical climate and heavy rainfall. Importantly, the rock weathering of the basin could consume so much atmospheric CO2 (0.54% of the consumption at global with the only 0.16% of the global surface area) that could mitigate the global warming, which followed an increasing trend from upstream to downstream. The quantitative analysis of the solute source for YT River fills in the gaps in the chemically characteristic cognition of the basin. It is beneficial for the water resource management for the Asian. The proportion of solute sources in the YT River and its tributaries for wet (W), normal (N), and dry (D) periods.

An inexact multistage fuzzy-stochastic programming for regional electric power system management constrained by environmental quality.

Electric power system involves different fields and disciplines which addressed the economic system, energy system, and environment system. Inner uncertainty of this compound system would be an inevitable problem. Therefore, an inexact multistage fuzzy-stochastic programming (IMFSP) was developed for regional electric power system management constrained by environmental quality. A model which concluded interval-parameter programming, multistage stochastic programming, and fuzzy probability distribution was built to reflect the uncertain information and dynamic variation in the case study, and the scenarios under different credibility degrees were considered. For all scenarios under consideration, corrective actions were allowed to be taken dynamically in accordance with the pre-regulated policies and the uncertainties in reality. The results suggest that the methodology is applicable to handle the uncertainty of regional electric power management systems and help the decision makers to establish an effective development plan.

Internal cycling, not external loading, decides the nutrient limitation in eutrophic lake: A dynamic model with temporal Bayesian hierarchical inference.

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.

A DPSIR model for ecological security assessment through indicator screening: a case study at Dianchi Lake in China.

Given the important role of lake ecosystems in social and economic development, and the current severe environmental degradation in China, a systematic diagnosis of the ecological security of lakes is essential for sustainable development. A Driving-force, Pressure, Status, Impact, and Risk (DPSIR) model, combined with data screening for lake ecological security assessment was developed to overcome the disadvantages of data selection in existing assessment methods. Correlation and principal component analysis were used to select independent and representative data. The DPSIR model was then applied to evaluate the ecological security of Dianchi Lake in China during 1988-2007 using an ecological security index. The results revealed a V-shaped trend. The application of the DPSIR model with data screening provided useful information regarding the status of the lake's ecosystem, while ensuring information efficiency and eliminating multicollinearity. The modeling approach described here is practical and operationally efficient, and provides an attractive alternative approach to assess the ecological security of lakes.

An integrated system dynamics model developed for managing lake water quality at the watershed scale.

A reliable system simulation to relate socioeconomic development with water environment and to comprehensively represent a watershed's dynamic features is important. In this study, after identifying lake watershed system processes, we developed a system dynamics modeling framework for managing lake water quality at the watershed scale. Two reinforcing loops (Development and Investment Promotion) and three balancing loops (Pollution, Resource Consumption, and Pollution Control) were constituted. Based on this work, we constructed Stock and Flow Diagrams that embedded a pollutant load model and a lake water quality model into a socioeconomic system dynamics model. The Dianchi Lake in Yunnan Province, China, which is the sixth largest and among the most severely polluted freshwater lakes in China, was employed as a case study to demonstrate the applicability of the model. Water quality parameters considered in the model included chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP). The business-as-usual (BAU) scenario and three alternative management scenarios on spatial adjustment of industries and population (S1), wastewater treatment capacity construction (S2), and structural adjustment of agriculture (S3), were simulated to assess the effectiveness of certain policies in improving water quality. Results showed that S2 is most effective scenario, and the COD, TN, and TP concentrations in Caohai in 2030 are 52.5, 10.9, and 0.8 mg/L, while those in Waihai are 9.6, 1.2, and 0.08 mg/L, with sustained development in the watershed. Thus, the model can help support the decision making required in development and environmental protection strategies.

Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction.

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely "optimal" solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions.

Evaluating anthropogenic N inputs to diverse lake basins: A case study of three Chinese lakes.

The environmental degradation of lakes in China has become increasingly serious over the last 30 years and eutrophication resulting from enhanced nutrient inputs is considered a top threat. In this study, a quasi-mass balance method, net anthropogenic N inputs (NANI), was introduced to assess the human influence on N input into three typical Chinese lake basins. The resultant NANI exceeded 10,000 kg N km(-2) year(-1) for all three basins, and mineral fertilizers were generally the largest sources. However, rapid urbanization and shrinking agricultural production capability may significantly increase N inputs from food and feed imports. Higher percentages of NANI were observed to be exported at urban river outlets, suggesting the acceleration of NANI transfer to rivers by urbanization. Over the last decade, the N inputs have declined in the basins dominated by the fertilizer use but have increased in the basins dominated by the food and feed import. In the foreseeable future, urban areas may arise as new hotspots for nitrogen in China while fertilizer use may decline in importance in areas of high population density.

[Study on the stages of major sediments in Dianchi lake].

The statistical technique self-organizing maps (SOM) was applied for stages analysis of Lake Dianchi sediments, southwestern China. The dataset of nine pollutants, including Cd, Cr, Cu, Hg, Pb, Sn, Zn, TP and KN, was observed and collected for 10 monitoring sites from 1991 to 2010. The results show that the 20 years' study could be divided into 4 stages. In stage one (1991 to 1995) , concentrations of sediments are relativity low. In the second stage (1996 to 2001) , concentrations of most sediments are higher than the stage before and show increasing trends. In the following stage (2002 to 2006), majority of the observed sediments exhibit fluctuation characteristics. Nevertheless, different concentration patterns exist among different pollutants, the concentration climaxes of most pollutants have been observed during the year 1996 to 1999 and 2005 to 2007. According to the relevant information gathering from yearly lake pollution load, the national survey of pollution sources etc., the reason for the above stages and concentration patterns of observed sediments are analyzed. The result shows that sediment concentration is sensitive to human activities in the basin, such as pollution emission as well as controlling.

Quantitative evaluation of lake eutrophication responses under alternative water diversion scenarios: a water quality modeling based statistical analysis approach.

China is confronting the challenge of accelerated lake eutrophication, where Lake Dianchi is considered as the most serious one. Eutrophication control for Lake Dianchi began in the mid-1980s. However, decision makers have been puzzled by the lack of visible water quality response to past efforts given the tremendous investment. Therefore, decision makers desperately need a scientifically sound way to quantitatively evaluate the response of lake water quality to proposed management measures and engineering works. We used a water quality modeling based scenario analysis approach to quantitatively evaluate the eutrophication responses of Lake Dianchi to an under-construction water diversion project. The primary analytic framework was built on a three-dimensional hydrodynamic, nutrient fate and transport, as well as algae dynamics model, which has previously been calibrated and validated using historical data. We designed 16 scenarios to analyze the water quality effects of three driving forces, including watershed nutrient loading, variations in diverted inflow water, and lake water level. A two-step statistical analysis consisting of an orthogonal test analysis and linear regression was then conducted to distinguish the contributions of various driving forces to lake water quality. The analysis results show that (a) the different ways of managing the diversion projects would result in different water quality response in Lake Dianchi, though the differences do not appear to be significant; (b) the maximum reduction in annual average and peak Chl-a concentration from the various ways of diversion project operation are respectively 11% and 5%; (c) a combined 66% watershed load reduction and water diversion can eliminate the lake hypoxia volume percentage from the existing 6.82% to 3.00%; and (d) the water diversion will decrease the occurrence of algal blooms, and the effect of algae reduction can be enhanced if diverted water are seasonally allocated such that wet season has more flows.