Hydrological management for improving nutrient assimilative capacity in plant-dominated wetlands: A modelling approach.

Wetland eutrophication is a global environmental problem. Besides reducing pollutant emissions, improving nutrient assimilative capacity in wetlands is also significant for preventing eutrophication. Hydrological management can improve nutrient assimilative capacity in wetlands through physical effects on the dilution capacity of water body and ecological effects on wetland nutrient cycles. The ecological effects are significant while were rarely considered in previous research. This study focused on the ecological effects of hydrological management on two crucial nutrient removal processes, plant uptake and biological denitrification, in plant-dominated wetlands. A dual-objective optimization model for hydrological management was developed to improve wetland nitrogen and phosphorus assimilative capacities, using upstream reservoir release as water regulating measure. The model considered the interactions between ecological processes and hydrological cycles in wetlands, and their joint effects on nutrient assimilative capacity. Baiyangdian Wetland, the largest freshwater wetland in northern China, was chosen as a case study. The results found that the annual total assimilative capacity of nitrogen (phosphorus) was 4754 (493) t under the optimal scheme for upstream reservoir operation. The capacity of nutrient removal during the summer season accounted for over 80% of the annual total removal capacity. It was interesting to find that the relationship between water inflow and nutrient assimilative capacity in a plant-dominated wetland satisfied a dose-response relationship commonly describing the response of an organism to an external stressor in the medical field. It illustrates that a plant-dominated wetland shows similar characteristics to an organism. This study offers a useful tool and some fresh implications for future management of wetland eutrophication prevention.

A universal calibrated model for the evaluation of surface water and groundwater quality: Model development and a case study in China.

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.

Evaluating the relationship between groundwater quality and land use in an urbanized watershed.

Understanding the impact of urbanization on groundwater quality is critical. Effective water management requires understanding the relationship between land use and water quality. The study's goals were to compare the effects of land use, identify the types of land that impact hydrochemistry, and define how different land use affects water quality. For this purpose, the comparative relationship between groundwater quality, land use classes and landscape metrics were established for the years 2016 and 2021. Water samples were collected from 42 wells, and different hydro-chemical variables were considered to calculate the water quality index (WQI). The WQI value in 2016 ranged from 26.49 to 151.03 and 29.65 to 155.62 in 2021. The results indicate that the water quality in most parts of the study area is moderate for drinking and domestic purpose use. The google earth engine platform was used and radiometrically corrected and orthorectified Sentinel-2 satellite images were processed to classify land use classes for selected years. Five buffer zones were established within a 2-km watershed along each well site, and the effects of land use types and landscape metrics on water quality in the buffer zones were analyzed. Results revealed that the effects of land use types on water quality were mainly reflected in buffer 1 (B1), buffer 4 (B4), buffer 5 (B5) in 2016 and B1, buffer 3 (B3), and B5 in 2021. The impacts of landscape-level metrics on water quality are mainly reflected in buffer 2 (B2) and B3 in 2021, while at the class-level, they are mainly reflected in B1 and B4 in 2021. The redundancy analysis revealed that different hydro-chemical variables behaved differently with the land use classes and landscape metrics in the various buffer zones.

Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan.

Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.

Impacts of biochars on bacterial community shifts and biodegradation of antibiotics in an agricultural soil during short-term incubation.

This study investigated the effects of applying different biochars to soil on shifts in the bacterial community, the biodegradation of antibiotics, and their relationships. In total, nine biochars were applied to agricultural soil contaminated with 16 antibiotics. Clustering analysis showed that the responses of bacteria at the genus level to biochars were highly dependent on the biochar feedstock rather than the pyrolysis temperature. Among the antibiotics tested in the study, the biodegradation percentage was lower for tetracyclines (TCs, 6-14%) than sulfonamides (SAs, 8-26%) and quinolones (QLs, 8-24%). For specific individual antibiotics from the same class with similar structures, the high adsorption affinity of soil particles for antibiotics due to hydrophobic interactions (logKow) and electrostatic interactions (pKa) resulted in low biodegradation percentages for antibiotics in the soil. The biodegradation of TCs was affected more by the biochar type (effect size: -10% to 42%) than those of QLs (-26% to 14%) and SAs (-24% to 22%). According to the relationships determined between the bacterial taxonomic composition and biodegradation of antibiotics, Steroidobacter from the phylum Proteobacteria has significant positive correlations with the biodegradation of all SAs (p < 0.01), thereby indicating that Steroidobacter had a high capacity for biodegrading SAs. Significant positive correlations were also detected (p < 0.05) between specific genera (Iamia, Parviterribacter, and Gaiella) from the phylum Actinobacteria and the biodegradation of SAs. No significant positive correlations were found between bacterial genera and the biodegradation percentages for QLs and TCs, possibly due to the specific microorganisms involved in these biodegradation processes. The results in this study provide insights into the biodegradation mechanisms of antibiotics in soil and they may facilitate the development of strategies for the bioremediation of antibiotic-contaminated soil.

Sustainable Water Resource Management of Regulated Rivers under Uncertain Inflow Conditions Using a Noisy Genetic Algorithm.

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.

Responses of Chlorella vulgaris exposed to boron: Mechanisms of toxicity assessed by multiple endpoints.

Boron (B) has been widely used and contaminated the aquatic ecosystem. However, knowledge of the effects of sodium pentaborate pentahydrate (SPP) on algae remains limited. This study aimed to assess SPP toxicity using multiple endpoints, specially detecting the intracellular metal ion concentrations, malondialdehyde (MDA) content and extracellular polymeric substance (EPS) classes for the very first time during SPP exposure to Chlorella vulgaris (C. vulgaris). Our findings indicated that the inhibitory effects of SPP on C. vulgaris may be related to nutrient absorption and utilization. The changes in intracellular starch grains, MDA and the protein-like substances in EPS probably acted as a defense mechanism, helping to alleviate the toxic effects. This work may contribute to the understanding of the mechanism of SPP toxicity in algae. Further studies may focus on the effects of B on speciation of metallic ions and the interaction of B with metallic ions on aquatic organisms.

Effects of surface-modified biochars and activated carbon on the transformation of soil inorganic nitrogen and growth of maize under chromium stress.

Elevated chromium (Cr) level is challenging agricultural production and affecting soil biochemical process. This study evaluated the effect of amendments including surface-modified biochars (HBC: acid washing, Fe(III)-HBC: ferric iron loading, nZVI-HBC: nanoscale zero-valent iron loading) and activated carbon on hexavalent chromium (Cr(VI)) removal in soil and on N cycling enzyme activities, transformation of soil inorganic nitrogen, and growth of maize under Cr stress. The results showed that amendments increased Cr(VI) removal by 72.9%-96.34% at three levels of spiked Cr(VI) (low: 125 mg kg-1, moderate: 250 mg kg-1, high: 500 mg kg-1). Under low Cr stress, amendments generally significantly decreased urease and nitrite reductase activities but increased nitrate reductase activity (p < 0.05). The NH4+-N content had a significant positive correlation with urease activity (p < 0.01), while both NO2--N and NO3--N were absent correlations with N cycling enzyme studied. Amendments decreased NH4+-N/NO3--N ratio under low Cr stress but increased it under moderate Cr stress, although the difference was not significant. Under high Cr stress, only Fe(III)-HBC significantly increased NH4+-N/NO3--N ratio (p < 0.05). The decrease and increase of NH4+-N/NO3--N ratios indicate the enhancement of nitrification and denitrification, respectively. The increase in Cr(VI) removal by amendments contributed to the increase in the migration of NO3--N from roots to shoots. Amendments (except for nZVI-HBC in soil under low Cr stress) increased maize height by 20%-59%. Under low Cr stress, however, nZVI-HBC significantly decreased maize height by 65% (p < 0.05), indicating the toxic effect of nZVI on maize growth overwhelmed low Cr stress.

Is water shortage risk decreased at the expense of deteriorating water quality in a large water supply reservoir?

Reservoir operations affect both the quantity and quality of stored and discharged water. Hedging rules (HRs) are commonly used in water supply reservoir operations to ration water delivery and decrease water shortage risk. However, the increased carryover storage with hedging may aggravate reservoir eutrophication through complex effects on hydrodynamic, temperature, light, nutrient, and sediment conditions. The influencing mechanisms are unclear and require further investigation. This study applies a mathematical modeling approach to comparing the effects of standard operation policy (SOP) and HR, discussing the processes and driving factors, and exploring the relationship between water shortage and water quality indicators. We simulate reservoir operation by SOP and optimize HR to generate water supply schedules, and run a quasi-3D water quality model to simulate reservoir hydrodynamic conditions, nutrient cycles, water-sediment exchanges, and algal dynamics under various water supply schedules. The Danjiangkou Reservoir, the water source for China's South-North Water Transfer Project, is used as a case study. The HR for this reservoir decreases its water shortage risk from 22% under SOP to 8%. Modeling results find that the HR increases sediment phosphorus (P) release by 285.3 tons (5.7%) annually as a consequence of extended reservoir submerged area and aggravated hypolimnetic hypoxia. Increased P release can support algal growth, but this effect is set off by the enhancement of light limiting effect caused by higher storages under HR, consequently decreasing the annual mean chlorophyll a concentration in the deep reservoir by 18%. The HR also improves the horizontal mixing of water by changing the hydraulic retention time and flow velocity field, which mitigates algal bloom risks in the surrounding shallow-water zones but deteriorates water quality of the release to downstream. The water quality analysis offers implications for reservoir managers to coordinate their efforts in mitigating risks of water shortage and water quality degradation.

Development of an integrated indicator system to assess the impacts of reclamation engineering on a river estuary.

An integrated indicator system was developed for determining synthetic environmental responses under multiple types of coastal reclamation engineering in the Yellow River estuary, China. Four types of coastal engineering works were analyzed, namely port construction, petroleum exploitation, fishery and aquaculture, and seawall defense. In addition, two areas with limited human disturbances were considered for comparison. From the weights of the response value for each indicator, port construction was determined to be the primary impact contributor among the four engineering works studies. Specifically, hydrodynamic conditions, ecological status, economic costs, and engineering intensity were on average 72.78%, 65.03%, 75.03%, and 66.35% higher than those of other engineering types. Furthermore, fishery and aquaculture impact on water quality was 42.51% higher than that of other engineering types, whereas seawall defense impact on landscape variation was 51.75% higher than that of other engineering types. The proposed indicator system may provide effective coastal management in future.

Industrial water resources management based on violation risk analysis of the total allowable target on wastewater discharge.

To improve the capabilities of conventional methodologies in facilitating industrial water allocation under uncertain conditions, an integrated approach was developed through the combination of operational research, uncertainty analysis, and violation risk analysis methods. The developed approach can (a) address complexities of industrial water resources management (IWRM) systems, (b) facilitate reflections of multiple uncertainties and risks of the system and incorporate them into a general optimization framework, and (c) manage robust actions for industrial productions in consideration of water supply capacity and wastewater discharging control. The developed method was then demonstrated in a water-stressed city (i.e., the City of Dalian), northeastern China. Three scenarios were proposed according to the city's industrial plans. The results indicated that in the planning year of 2020 (a) the production of civilian-used steel ships and machine-made paper & paperboard would reduce significantly, (b) violation risk of chemical oxygen demand (COD) discharge under scenario 1 would be the most prominent, compared with those under scenarios 2 and 3,

Microbial nitrification, denitrification and respiration in the leached cinnamon soil of the upper basin of Miyun Reservoir.

Leached cinnamon soil is the main agricultural soil distributed in the North China Plain. In this research, leached cinnamon soil samples were collected in the upper basin of Miyun Reservoir (northeast of Beijing, China). The BaPS method (Barometric Process Separation) was applied to measure nitrification, denitrification and respiration rates. The rates of nitrification, denitrification and respiration were 0-120.35 µg N/kg SDW h, 0-246.86 µg N/kg SDW h and 0.17-225.85 µg C/kg SDW h (Soil Dry Weight, SDW), respectively. The emission rates of CO2 and NxOy through nitrification, denitrification and respiration were 1.00-547.80 and 6.00-4850.65 µmol/h, respectively. The analysis of relationships between nitrification, denitrification and respiration rates indicated that these three microbial processes were interacted, which posed impacts on soil nitrogen availability. As indicated by the results, C:N ratio coupled with content could be taken as the indicators of content, which is usually the predominant form of N available to plants growing in soil. Results showed that content was the highest (i.e., >62.4 mg/kg) when C:N ratio was 5.30-8.40, meanwhile content was 3.71-4.39 mg/kg. Nevertheless, content was the lowest (i.e., <6.40 mg/kg) when C:N ratio was 9.2-12.10, meanwhile content was 3.41-4.35 mg/kg.

An enhanced export coefficient based optimization model for supporting agricultural nonpoint source pollution mitigation under uncertainty.

In this research, an export coefficient based dual inexact two-stage stochastic credibility constrained programming (ECDITSCCP) model was developed through integrating an improved export coefficient model (ECM), interval linear programming (ILP), fuzzy credibility constrained programming (FCCP) and a fuzzy expected value equation within a general two stage programming (TSP) framework. The proposed ECDITSCCP model can effectively address multiple uncertainties expressed as random variables, fuzzy numbers, pure and dual intervals. Also, the model can provide a direct linkage between pre-regulated management policies and the associated economic implications. Moreover, the solutions under multiple credibility levels can be obtained for providing potential decision alternatives for decision makers. The proposed model was then applied to identify optimal land use structures for agricultural NPS pollution mitigation in a representative upstream subcatchment of the Miyun Reservoir watershed in north China. Optimal solutions of the model were successfully obtained, indicating desired land use patterns and nutrient discharge schemes to get a maximum agricultural system benefits under a limited discharge permit. Also, numerous results under multiple credibility levels could provide policy makers with several options, which could help get an appropriate balance between system benefits and pollution mitigation. The developed ECDITSCCP model can be effectively applied to addressing the uncertain information in agricultural systems and shows great applicability to the land use adjustment for agricultural NPS pollution mitigation.

Microbial bioavailability of 2,2',4,4'-Tetrabromodiphenyl ether (BDE-47) in natural sediments from major rivers of China.

Microbial degradation plays a crucial role in eliminating polybrominated diphenyl ethers (PBDEs) in environments. However, the microbial bioavailability of PBDEs in aquatic sediments is not well understood. In this work, the bioavailability of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47), a typical PBDE congener, to PBDE-degrading microorganisms in natural sediments from six Chinese rivers under anaerobic conditions was investigated. The contents of black carbon (BC) and total organic carbon (TOC) in the six sediment samples were in the range of 0.025%-0.30% and 0.03%-3.38%, respectively. BDE-47 desorption from various sediments was fitted well with the first-order three-compartment desorption model. The desorbing fraction of sediment-associated BDE-47 at each desorption time interval exhibited a significant negative correlation with the BC content (p < 0.01). In the sediments, the anaerobic debromination of BDE-47 by microorganisms underwent a stepwise debromination pathway generating mainly three lower brominated congeners (BDE-28, -17 and -4). The microbial debromination ratio of BDE-47 ranged from 4.21% to 7.89% in various sediments after 120 d incubation anaerobically, and it negatively correlated with the content of sediment BC significantly (p < 0.01). However, the desorbing fraction and microbial debromination ratio of BDE-47 only showed weak correlations with the TOC content in sediments (p > 0.05). Furthermore, there was a significant positive correlation of desorbing fraction of BDE-47 from sediments with its microbial debromination ratio (p < 0.01) as well as with the level of its three lower brominated products (p < 0.05) after the first 20 d incubation. This study suggests that the BDE-47 bioavailability to microorganisms in anaerobic river sediments is mainly influenced by the content of sediment BC which controls the desorbing fraction of sediment-associated BDE-47.