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Episodes of fine-particulate matter (PM

Some patients with colon cancer eventually develop metastasis during treatment, and the 5‑year survival rate of patients with metastatic colon cancer remains relatively low, which is most likely due to the ineffectiveness of the current standard treatment. Systemic treatment for patients with colon cancer has expanded from chemotherapy to targeted therapy and immunotherapy. Immunotherapy holds promise in the treatment of colon cancer. The present study revealed the role of innate immune receptor helicase DExD/H‑box helicase 58 (DDX58), which encodes retinoic acid‑inducible gene‑I (RIG‑I), in colon cancer. It was demonstrated that colon cancer patients with a low protein expression of DDX58/RIG‑I had a worse 5‑year survival rate of patients compared with patients with a high expression of DDX58/RIG‑I. The activation of DDX58/RIG‑I inhibited the proliferation, migration and invasion of colon cancer cells, as well as tumor growth in a nude mouse xenograft model of colon cancer. To investigate the mechanisms of action of DDX58/RIG‑I in colon cancer, the role of signal transducer and activator of transcription 3 (STAT3)/cystathionine‑γ‑lyase (CSE) signaling in the up‑ or downregulation of DDX58 was examined. The data demonstrated that DDX58 regulated the STAT3/CSE signaling pathway by interacting with STAT3 and consequently affecting the proliferation of tumor cells in colon cancer. In addition, the RIG‑I agonist, SB9200, induced proliferation, migration and invasion of human colon cancer. On the whole, the present study demonstrates that DDX58/RIG‑I affects the proliferation of tumor cells by regulating STAT3/CSE signaling in colon cancer. The findings presented herein suggest that DDX58/RIG‑I activation may be an effective treatment strategy, and DDX58/RIG‑I agonists may be potential therapeutic candidates for colon cancer.

Analysis of the spatial variations in river networks and the related influencing factors is crucial for the management and protection of basins. To gain insight into the spatial variations and influencing factors of river networks between large basins, in this study, three river basins from north to south in China (Songhua River Basin, Yellow River Basin and Pearl River Basin) were selected for investigation. First, based on a digital elevation model, different river networks with six drainage accumulation thresholds of three basins were extracted using ArcGIS. The optimal networks were determined through fitting the relationship between the accumulation threshold and related drainage density. Then, we used two indicators, drainage density and water surface ratio, to characterize the spatial variations of three basins. Finally, Pearson’s correlation coefficients were calculated between those two indicators and natural/human influencing factors. The results showed that drainage density and water surface ratio decreased from north to south in China and were negatively correlated with natural/human influencing factors. Drainage density was more influenced by natural factors than by human factors, while the opposite was true for water surface ratio. These findings may provide some basis for the management and protection of the river network.

Parameter uncertainty inherent in reservoir operation affects operation model robustness and has been considered in conventional operation focusing on improving hydropower generation. With more attention paid to ecological environment protection recently, riverine ecosystem protection requires environmental flow (e-flow) management to sustain a near-natural flow regime. Whether there is e-flow management in reservoir operation has an impact on the uncertainty of reservoir operation, but parameter uncertainty was rarely considered in reservoir operation with e-flow management. In this study, a framework is proposed for performing parameter uncertainty analysis in reservoir operation associated with e-flow management. Both e-flow requirements and hydropower generation are considered in reservoir operation to sustain the harmonious development between ecological environment and human society. To compare the effect of different e-flow managements on the uncertainty of reservoir operation, three e-flow management scenarios are set. The Metropolis-Hastings algorithm of Markov Chain Monte Carlo (MCMC) sampling approach was applied for parameter estimation and uncertainty quantification. We used this framework in a case study of Nuozhadu hydropower station on the Lancang River in southern China to test its effectiveness. The results demonstrated that parameter uncertainty greatly affects the robustness of reservoir operation model. The comparison of reservoir operation under different e-flow management scenarios shows that more detailed e-flow management can effectively reduce uncertainty in reservoir operation and sustain the near-natural flow regime in a river.

Ecofriendly reservoir operation is an important tool for sustainable water resource management in regulated rivers. Optimization of reservoir operation is potentially affected by the stochastic characteristics of inflows. However, inflow stochastics are not widely incorporated in ecofriendly reservoir operation optimization. The reasons might be that computational cost and unsatisfactory performance are two key issues for reservoir operation under uncertainty inflows, since traditional simulation methods are usually needed to evaluate over many realizations and the results vary between different realizations. To solve this problem, a noisy genetic algorithm (NGA) is adopted in this study. The NGA uses an improved type of fitness function called sampling fitness function to reduce the noise of fitness assessment. Meanwhile, the Monte Carlo method, which is a commonly used approach to handle the stochastic problem, is also adopted here to compare the effectiveness of the NGA. Degree of hydrologic alteration and water supply reliability, are used to indicate satisfaction of environmental flow requirements and human needs. Using the Tanghe Reservoir in China as an example, the results of this study showed that the NGA can be a useful tool for ecofriendly reservoir operation under stochastic inflow conditions. Compared with the Monte Carlo method, the NGA reduces ~90% of the computational time and obtains higher water supply reliability in the optimization.

Dam impoundment causes significant phosphorus (P) retention in reservoirs, leading to the emergence of legacy P release as a water quality threat under external pollution load reduction measures. Reservoir operations impact sediment P release through their complex influence on hydrodynamic and biogeochemical processes, but applicatory reservoir operation strategies for legacy P management have rarely been proposed. This study introduces a reservoir operation optimization approach for balancing economic objectives (i.e., water supply and hydropower generation) and P release mitigation. A coupled hydrodynamic-eutrophication-sediment model based on the Environmental Fluid Dynamics Code platform is used to simulate sediment P release under multiple reservoir operation scenarios. The resultant simulation data are used to train a dynamic artificial neural network (ANN) to model the response of P release to reservoir operations. By coupling the ANN with a reservoir operation model, we develop a multi-objective optimization model to refine reservoir operations for a three-tier tradeoff (i.e., water supply, hydropower generation and P release mitigation). China's Danjiangkou Reservoir is chosen as a case study to generate optimal operation rules. The optimal operation scheme decreases multi-year averaged annual P release by 19% (from 6688 tons to 5436 tons) while inversely increasing annual economic loss caused by water supply shortages by 14% and decreasing hydropower generation by 5%. Pareto-optimal solutions are subsequently generated to analyze tradeoffs between water supply/hydropower generation and P release mitigation. This study demonstrates the efficiency of using reservoir operations for legacy P management and offers an optimization tool for reservoir operation decision-making processes.

Assessment of flow regime alteration is significant for the scientific protection and restoration of rivers. The range of variability approach (RVA) and its revised versions, based on a suite of Indicators of Hydrological Alteration (IHA), have been widely used for flow regime alteration assessment. In these methods, the IHA of each specific year are treated separately, and their individual alterations are used to reflect the alteration of flow regime. However, it is known that one hydrological year is an important hydrological period, and the alteration of inner characteristics of one hydrological year (ICOHY), collectively characterized by the 32 IHA values of the year, can also lead to serious ecological degradation of rivers. In other words, treating the 32 IHA individually and separately in RVA and its revisions can not reflect the alteration of ICOHY. To overcome this limitation, a revised RVA is proposed. In the revised method, the 32 IHA values for each specific year are considered as a set, and the alteration of the set (SA) is measured by the set pair analyses method. The assessment of individual alteration (IA) of each IHA is carried out according to RVA. SA and IA are incorporated into a single index to reflect the overall flow regime alteration (OA). The proposed method is applied to a case study of the Xi Dayang Reservoir in China to test its effectiveness. The results suggest that the RVA underestimates the alteration degree and that the new method could give a more comprehensive assessment.

The water resources carrying capacity (WRCC) is fundamental in aiding sustainable socioeconomic regional development, and increasing attention is being paid to WRCC forecasting. The challenge of WRCC forecasting lies in the complex characteristics of nonlinear multiple indicator time series data. To address this problem, this study proposed a feedforward neural network (FNN) based on normalization and error correction for WRCC forecasting. Firstly, units and values of multiple indicators for use in the WRCC were normalized simultaneously to allow the data to be treated as a single equivalent indicator. Thus, the high-dimensional forecasting model was simplified to a low-dimensional model. To improve the effectiveness of the model, an error correction method was then adopted to correct forecasted WRCC values according to similar corresponding sample values. Two simple types of structured FNN were used to address the overfitting problem, and the bipolar sigmoid function was used as the activation function of hidden nodes. The final calibrated model was expressed as an equation rather than a programming language, thus making it easier to use. Yantai, a city in Shandong Province, was selected as a case study to validate this proposed method. Results showed that the mean relative absolute errors of these two simplified FNN models are 1.23% and 1.18%, respectively. Compared to other models, this model has been shown to be feasible and simple to use for WRCC forecasting.

Dam impoundment causes significant phosphorus (P) retention in reservoir sediment. This makes sediment P release a serious water quality threat in areas where external P loads are being controlled. Reservoir operation strategies affect sediment P retention and release through complex influence on hydrodynamic, temperature, algal and sediment conditions. However, the spatiotemporal response of P retention and release to reservoir operations remains unclear. This study uses a high-fidelity hydrodynamic-eutrophication-sediment model to investigate spatiotemporal variation in sediment P retention and release in a large reservoir in China (i.e., the Danjiangkou Reservoir) as well as associated impacts caused by reservoir operations. Modeling results reveal significant sediment P release in the reservoir (5213 tons annually), from June to October in particular. The intermittent increase in submerged soil area surrounding the reservoir is the dominant factor for P release during the initial stage of this period, while an acute decrease in bottom oxygen conditions caused by thermal stratification in the deep-water zone is the dominant factor during the middle and latter stages. Moreover, the inherent complexity of reservoir P cycling along with the large number of state variables limit the usage of high-fidelity P models in optimizing reservoir operations. To resolve this problem, we apply a dynamic artificial neural network (i.e., nonlinear autoregressive network with exogenous inputs) to develop a surrogate model to predict the response of sediment P release to reservoir operations. The surrogate model is successful in predicting the annual time series of P release with a dramatic reduction in computational burden. It can easily be coupled with a reservoir operation optimization model, thereby enabling operators to identify optimal operation rules to support reservoir socioeconomic functions while mitigating the threats from sediment P legacy.

In this study, a thio functionalized polyacrylonitrile (PAN) fiber was promptly prepared with microwave irradiation which exhibited high removal efficiency for Hg2+. The modified fiber (PANMW-Thio) was synthesized by initially cross-linked with diethylenetriamine and then further reacted with Na2S to introduce thio ligands onto the interface of PAN fiber. Following characterization of PANMW-Thio fiber were performed by XRD, FTIR, SEM and elemental analysis. The Hg2+ uptake performance by modified fiber prepared with MW irradiation was compared with that of conventional heating in terms of adsorption kinetics, adsorption isotherm and adsorption thermodynamics. The results revealed that PANMW-Thio exhibited much higher adsorption amount for Hg2+ with the maximum uptake amount of 357 mg/g. Moreover, multiple operation parameters in the dynamic adsorption test were also investigated, and the maximum adsorption amount of PANMW-Thio fiber was obtained at initial Hg2+ concentration of 100 mg/L, bed height of 15 cm and 10 mL/min flow rate, respectively. Dynamic experiment results also proved that the capacities of PANMW-Thio fiber improved when initial concentration and bed height increased and flow rate decreased. Furthermore, the Thomos, Yoon-Nelson and BDST models were utilized to analyze experiment data, while Thomas model exhibited best fitness with the breakthrough curves. Finally, the regeneration ability of PANMW-Thio fiber in column was studied using 0.05 M HCl as the desorption eluent and the adsorption capacity remained well after five reuse cycles. This rapid preparation method and good adsorption performance of prepared adsorbent promise a new solution for Hg2+ pollution remediation in water.

In the present study, a phosphorylated polyacrylonitrile (PAN) fiber adsorbent was rapidly prepared with microwave (MW) assisted method and further applied to capture U(VI) ions from aqueous solution. FTIR and SEM/EDS characterizations confirmed the satisfactory grafting of phosphonic group on the surface of PAN fiber and the integrities of fiber were well preserved. The results indicated that MW assisted method exhibited much higher efficiency during the modification process in terms of shorter reaction time and higher phosphonic content as compared to conventional heating method. The physical property of the modified fiber was well maintained according to TGA/DTA results. The kinetic and isothermal adsorption experiments confirmed that the uptake process of PANMW-Phos fibers for U(VI) followed both pseudo-second-order and Langmuir model, indicating that the adsorption process was controlled by chemisorption mechanism. The maximum adsorption amount for prepared adsorbent towards U(VI) reached at 241.4 mg/g based on the Langmuir calculation, which was relatively higher than other fibrous adsorbent. Moreover, the functional fiber (PANMW-Phos) revealed selective adsorption ability for U(VI) from solution that contained multiple competing metal ions in pH range of 3.0–6.0. Thermodynamic adsorption results further verified the spontaneous and endothermic nature of the binding process. Additionally, the PANMW-Phos showed satisfied desorption and reusability in five times application. The effective extraction of U(VI) ions was mainly attributed to the inner surface complexation mechanism between phosphonic groups and U(VI) ion. The feasible preparation method, higher capacities, excellent selectivity and promising recyclability endow the phosphorylated fiber as suitable adsorbent for extraction of U(VI) from aqueous solution.

The grey water footprint (GWF) is effective in helping to evaluate the sustainable and efficient use of water resources. Traditionally, GWFs have been used to assess individual pollutants wherein only the highest GWF value is typically used. However, multiple pollutants coexist in water, and the characteristics of water resources are defined collectively by all contaminants present. To date, few studies have attempted to specifically consider impacts of multiple pollutants on GWFs; however, GWFs based on multiple pollutants could provide a global picture of spatiotemporal changes in water quality as a whole. Accordingly, this study proposes an improved method to assess GWFs under multiple pollutants. The proposed method is to initially determine a wide range of GWFs, after which different GWF values are refined using a binary search algorithm until meeting a given water quality target. One commonly used method (the Water Quality Index (WQI) from the Canadian Council of Ministers of the Environment (CCME)) was used in this study to assess water quality based on multiple pollutants. We also calculated the water pollution level (WPL) based on associative GWFs and compared results to test the effectiveness of the improved method. China was used as a case study to illustrate this improved method. Results showed that compared to the traditional method, the improved method could reduce 25% of values of GWFs while maintaining a “Good” water quality ranking. Even under a 50% reduction, the improved method was still able to maintain a “Fair” ranking. Based on the traditional method, regions where WPLs were >1 totaled approximately 63.59% in 31 regions throughout China; however, corresponding results under different water quality targets based on the improved method were approximately 46.54% and 36.87%. The improved method provides an efficient tradeoff between water quality and quantity management. It has also shown that existing studies which only considered individual pollutants may have overestimated GWFs and WPLs values.

Water quality evaluation is an important issue in environmental management. Various methods have been used to evaluate the quality of surface water and groundwater. However, all previous studies have used different evaluation models for surface water and groundwater, and the models must be recalibrated due to changes in monitoring indicators in each evaluation. Water quality managers would benefit from a universal and effective model based on a simple expression that would be suitable for all cases of surface water and groundwater, and which could therefore serve as a standard method for a region or country. To meet this requirement, we attempted to develop a universal calibrated model based on the radial basis function neural network. In the new model, the units and values of the evaluation indicators for surface water and groundwater are normalized simultaneously to make the data directly comparable. The model's training inputs comprise the normalized value in each of a water quality indicator's grades (e.g., the nitrate contents defined in a regulatory standard for grades I to V) for all evaluation indicators. The central vector of the Gaussian function is used as the average of the evaluation indicators' normalized standard values for the five grades. The final calibrated model is expressed as an equation rather than in a programming language, and is therefore easier to use. We used the model in a Chinese case study, and found that the model was feasible (it compared well with the results of other models) and simple to use for the evaluation of surface water and groundwater quality.

In this study, an environmental-friendly modeling system was developed and applied to an agriculture nonpoint source (AGNPS) management in Ulansuhai Nur watershed. In this system, water environmental capacity, credibility-based chance-constrained programming (CCCP), and AGNPS optimization models were integrated into a general modeling framework. It could be used to calculate water environmental capacity of total nitrogen and total phosphorus in Ulansuhai Nur watershed, which could consequentially provide input data for the developed AGNPS optimization model. Also, the inherent uncertainties in estimating water environmental capacities that can be expressed as possibilistic distributions were reflected and addressed based on computational results of three widely used methods. Such uncertainties were consequentially transferred to the proposed CCCP model based on the adoption of multiple credibility satisfactory levels, significantly facilitating objectivity reflection of decision alternatives. The developed modeling system was then applied to Ulansuhai Nur watershed of Inner Mongolia, a semi-arid river basin in northwestern China. Optimal strategies for AGNPS management in Ulansuhai Nur watershed were generated with consideration of the maximum total agricultural income under multiple policy scenarios. The results showed that the total agricultural income would increase with point source pollution being cut down, and would decrease with rising credibility levels, representing decreasing system violation risks. It was indicated that the higher of total nitrogen/phosphorus discharge being less than water environmental capacity of Ulansuhai Nur, the lower the total agriculture incomes. The proposed methods could help decision makers establish various production patterns with cost-effective agriculture nonpoint source management schemes in the basin of Ulansuhai Nur, and gain in-depth insights into the trade-offs between total agricultural incomes and system reliabilities.

Soil erosion risk assessment is an essential foundation for the planning and implementation of soil and water conservation projects. The commonality among existing studies is that they considered different indicators (e.g., rainfall and slope) in order to determine the soil erosion risk, however, the majority of studies in China neglect one important indicator, namely the slope aspect. It is widely accepted that the vegetation and distribution of rainfall differs according to the different slope aspects (such as sunny slope and shady slope) and these attributes will accordingly influence the soil erosion. Thus, existing studies neglecting this indicator cannot reflect the soil erosion well. To address this problem, a flexible soil erosion risk assessment method that supports decision makers in identifying priority areas in soil and water conservation planning was developed in the present study. Firstly, in order to verify the impact of the slope aspect on soil erosion, field investigations were conducted, and its impact on the characteristics of the community in the study area was analyzed. Secondly, six assessment indicators were selected, including slope gradient, precipitation, NDVI, land use, soil texture and slope aspect. Next, a developed multi-criteria decision analysis (MCDA) method based on the Choquet integral was adopted to assess the soil erosion risk. The MCDA method, combining objective data with subjective assessment based on Choquet integral, could solve the weight problem encountered when using the quantitative method. The parameters required can be modified according to the soil erosion types, assessment scales, and data availability. The synergistic and inhibitory effects among the soil erosion parameters were also considered in the assessment. Finally, the soil erosion risk results in the Xinshui River watershed revealed that more attention should be paid to the slope of farmland and grassland during the planning and management of soil and water conservation projects. The methodology used in the current study can support decision makers in planning and implementing soil and water conservation measures in regions with different erosion types.

Lake eutrophication is a serious environmental problem worldwide, caused by both natural processes and anthropogenic influences. For effective lake eutrophication management, a variety of models have been designed to investigate the complex interrelationship between physical, chemical and biological factors and processes in lakes. However, these models are inconvenient for predictions of lake eutrophication in practical application. To date, very few studies have focused on lake eutrophication in terms of anthropogenic influences (such as sewage emissions and agricultural practices), which are more readily regulated than natural processes. Effective lake eutrophication prediction models should base primarily on facile controlled or predictable indicators. Therefore, to meet this requirement, we designed simple predictive models to determine the interrelationship between the trophic states of lakes and nutrient inputs, which is a direct measurement to characterize anthropogenic influences. Lake Taihu (China) was used as a representative eutrophic water body to assess the accuracy of the proposed predictive models. Firstly, to comprehensively understand the role of nutrient input indicators (NIIs) during eutrophication, Pearson correlation coefficient analysis was conducted on 7 NIIs and 38 types of algae. Secondly, based on the NIIs identified with high correlation coefficients, we modified three commonly used growth models (Gompertz, logistic and Richards) to describe the lake's trophic state. A particle swarm optimization algorithm (PSOA) was used to optimize model variables. Results showed that the mean absolute percentage errors of the optimized Gompertz, logistic and the Richards models were 2.95%, 2.88% and 2.17%, respectively. Finally, these optimized models were used to predict lake eutrophication under several different nutrient input scenarios. Two adjustment scenarios revealed remarkably satisfied trophic states (including roughly oligotrophic and even ultra-oligotrophic) by the 2030s. Our results show that these established models are a simple way to support lake restoration projects by setting realistic and effective targets.

Environmental flow (e-flow) release criteria are key parameters in water resources management and riverine ecosystem protection. The previous methods for e-flow criterion determination are based on the historical flow time series without the consideration of flow uncertainty. Due to low possibility of reoccurrence of the historical flows and the uncertainty of future flows, the flow uncertainty needs to be integrated in the process of determining e-flow release criteria. In this research, a new method is proposed to determine the optimal e-flow release criteria under flow uncertainty accounting for both the human and riverine ecosystem needs. In the new method, the scenario tree method is applied to generate the scenarios of flows, which can cover most of possible flow conditions and can effectively reflect the uncertainty of flows; the Range of Variability Approach (RVA), a most commonly used method to assess the flow regime alteration, is refined by incorporating the uncertainty of flows. The Tang River in Northern China is taken as a case study to test the effectiveness of the new method. The results show that the previous method obviously overestimates the optimal e-flow release criteria and the new method can get more suitable criteria that are suitable for both human and riverine ecosystems.