A system dynamics simulation-based strategic analysis of integrated water resources utilization and management in Shenzhen city.

As one of the most rapidly developing cities in China, Shenzhen grapples with an increasing challenge in managing water resources due to escalating conflicts with its soaring water demand. This study established a system dynamics (SD) model based on a causal loop diagram to explore the intricate interconnections within the urban water resources system. Through simulating water supply and demand in Shenzhen from 2021 to 2035, the model identified key sensitive factors and examined various utilization scenarios for multiple water resources. Results indicated that water scarcity posed a significant obstacle to Shenzhen's development. To tackle this challenge, several effective measures should be implemented, including enhancing water conservation capabilities, developing seawater resources, promoting water reuse, optimizing the economic structure, and managing population growth. Prioritizing water conservation efforts and maximizing the utilization of seawater resources were regarded as the most impactful strategies in alleviating the water crisis. Furthermore, the relationship between water conservation capabilities and seawater utilization scale was analyzed using the SD model, contributing to the development of a comprehensive water resources management strategy. The findings from this study would provide insights into robust methods for allocating water resources, thereby enhancing sustainable water management strategies applicable to regions facing similar challenges.

Aluminum phytotoxicity in acidic environments: A comprehensive review of plant tolerance and adaptation strategies.

Aluminum (Al), a non-essential metal for plant growth, exerts significant phytotoxic effects, particularly on root growth. Anthropogenic activities would intensify Al's toxic effects by releasing Al3+ into the soil solution, especially in acidic soils with a pH lower than 5.5 and rich mineral content. The severity of Al-induced phytotoxicity varies based on factors such as Al concentration, ionic form, plant species, and growth stages. Al toxicity leads to inhibited root and shoot growth, reduced plant biomass, disrupted water uptake causing nutritional imbalance, and adverse alterations in physiological, biochemical, and molecular processes. These effects collectively lead to diminished plant yield and quality, along with reduced soil fertility. Plants employ various mechanisms to counter Al toxicity under stress conditions, including sequestering Al in vacuoles, exuding organic acids (OAs) like citrate, oxalate, and malate from root tip cells to form Al-complexes, activating antioxidative enzymes, and overexpressing Al-stress regulatory genes. Recent advancements focus on enhancing the exudation of OAs to prevent Al from entering the plant, and developing Al-tolerant varieties. Gene transporter families, such as ATP-Binding Cassette (ABC), Aluminum-activated Malate Transporter (ALMT), Natural resistance-associated macrophage protein (Nramp), Multidrug and Toxic compounds Extrusion (MATE), and aquaporin, play a crucial role in regulating Al toxicity. This comprehensive review examined recent progress in understanding the cytotoxic impact of Al on plants at the cellular and molecular levels. Diverse strategies developed by both plants and scientists to mitigate Al-induced phytotoxicity were discussed. Furthermore, the review explored recent genomic developments, identifying candidate genes responsible for OAs exudation, and delved into genome-mediated breeding initiatives, isolating transgenic and advanced breeding lines to cultivate Al-tolerant plants.

A novel method of identifying estuary high-nutrient zones for water quality management.

Coastal shallow waters are highly vulnerable to pollution, often leading to the development of intricate eutrophication zones. However, accurately determining these areas poses a significant challenge due to the complex interplay of estuarine hydrodynamics and nutrient transformation. To address such issue, a novel method was proposed to identify high-nutrient zones through calculating the continuous zonation of released tracers when their instantaneous concentrations declined to 1/e of their initial values. The method was well tested using idealized estuary models with varying shape parameters, water depths and river discharges. The results consistently revealed that the boundaries of high-nutrient zones fell within the mixed zone, characterized by salinity levels of 10- 20 psu. In Shenzhen Bay, a typical shallow bay, distinct differences were observed in the concentrations of dissolved inorganic nitrogen (DIN) and PO43-. Both the 20 psu isohaline and the proposed method effectively identified the partition boundary of high DIN and PO43- in 2001-2010, but only the newly proposed method demonstrated accuracy in delineating the actual high-nutrient zone during the continuous nutrient reduction period from 2010 to 2020. This study provides a practical and feasible approach that can serve as an auxiliary decision-making tool for managing estuarine water environments, and it has potential to facilitate the implementation of timely and effective measures for pollution control.

Toxic effects of lead (Pb), cadmium (Cd) and tetracycline (TC) on the growth and development of Triticum aestivum: A meta-analysis.

The environmental issue of lead (Pb), cadmium (Cd), and tetracycline (TC) contamination in cereal crops has become a growing concern worldwide. An in-depth understanding of this issue would be of importance to promote effective management strategies for heavy metals and antibiotics worldwide. The present study was conducted to assess the toxic effects of heavy metals (Cd, Pb) and antibiotics (TC) on Triticum aestivum (T. aestivum, common wheat) based on studies conducted in the past 22 years. Data pertaining to the growth and development of T. aestivum were extracted and analyzed from 89 publications spanning from 2000 to 2022. Our results showed that Pb, Cd and TC significantly reduced growth and development by 11 %, 9 %, and 5 %, respectively. Additionally, significant accumulation of Cd (42 %) and Pb (17 %) was observed in T. aestivum samples, although there was little change in TC accumulation, which showed limited absorption, accumulation, and translocation of TC in wheat plants. Pb had the greatest impact on the yield of T. aestivum, followed by Cd, while TC had no apparent effect. Furthermore, exposure to Cd, Pb and TC reduced the photosynthetic rate due to chlorophyll reduction, with Cd having the most pronounced effect (58 %), followed by Pb (37 %) and TC (8 %). Cd exposure also significantly enhanced gaseous exchange (37 %) compared to TC and Pb, which reduced gaseous exchange by 4 % and 10 %, respectively. However, the treatments with TC (>50-100 mgL-1), Pb (>1000-2000 mg L-1) and Cd (>500-1000 mg L-1) increased the defense system of T. aestivum samples by 38 %, 15 %, and 11 %, respectively. The obtained findings have significant implications for risk assessment, pollution prevention, and remediation strategies to address soil contamination from Pb, Cd and TC in farmland.

Development of biocatalytic microbial ecosystem (FPUS@RODMs@In-PAOREs) for rapid and sustainable degradation of various refractory organics.

The removal of diverse refractory organics from complex industrial wastewater continues to be a challenge. Although biological treatments are commonly employed, only partial degradation and increasing emergence of nitrogenous compounds, i.e., nitrate (NO3) and nitrite (NO2) would pose severe toxicity to the intact microbes. Herein, an efficient biocatalytic microbial ecosystem (BCME) was designed over a porous bio-carrier made of a functional polyurethane sponge (FPUS). The BCME comprised a unique set of organisms (RODMs) with novel metabolism, efficiently degrading highly-concentrated aromatics. Strategic enzyme immobilization was utilized to introduce in-situ production and aggregation of the oxidation and reduction enzymes (In-PAOREs) onto the FPUS, thereby ensuing sustained functions of the RODMs community. The developed FPUS@RODMs@In-PAOREs system was found to enhance the refractory organics removal rate to 4 kg/m3/day, and it would be attributed to the enzymatic catalysis of refractory organics (2000 mg/L) accompanied by the removal of COD (1200 mg/L) and nitrogenous compounds (200 mg/L). Besides, the fluctuating concentration of extra polymeric substances (EPS) played a dual role through enhancing adhesion, promoting the development of a functional microbial ecosystem, and creating an EPS gradient within the FPUS bio-carrier. This differential distribution of enzymes was established to significantly boost biocatalysis activity reaching 400 U/g VSS.

Strategic analysis on development of simultaneous adsorption and catalytic biodegradation over advanced bio-carriers for zero-liquid discharge of industrial wastewater.

With rapid industrial development, millions of tons of industrial wastewater are produced that contain highly toxic, carcinogenic, mutagenic compounds. These compounds may consist of high concentration of refractory organics with plentiful carbon and nitrogen. To date, a substantial proportion of industrial wastewater is discharged directly to precious water bodies due to the high operational costs associated with selective treatment methods. For example, many existing treatment processes rely on activated sludge-based treatments that only target readily available carbon using conventional microbes, with limited capacity for nitrogen and other nutrient removal. Therefore, an additional set-up is often required in the treatment chain to address residual nitrogen, but even after treatment, refractory organics persist in the effluents due to their low biodegradability. With the advancements in nanotechnology and biotechnology, novel processes such as adsorption and biodegradation have been developed, and one promising approach is integration of adsorption and biodegradation over porous substrates (bio-carriers). Regardless of recent focus in a few applied researches, the process assessment and critical analysis of this approach is still missing, and it highlights the urgency and importance of this review. This review paper discussed the development of the simultaneous adsorption and catalytic biodegradation (SACB) over a bio-carrier for the sustainable treatment of refractory organics. It provides insights into the physico-chemical characteristics of the bio-carrier, the development mechanism of SACB, stabilization techniques, and process optimization strategies. Furthermore, the most efficient treatment chain is proposed, and its technical aspects are critically analysed based on updated research. It is anticipated that this review will contribute to the knowledge of academia and industrialist for sustainable upgradation of existing industrial wastewater treatment plants.

Revisiting China's domestic greenhouse gas emission from wastewater treatment: A quantitative process life-cycle assessment.

The wastewater treatment industry could alleviate water pollution but consume a large amount of energy and resources. China has over 5000 centralized domestic wastewater treatment plants and produces an unignorable amount of greenhouse gases (GHG). By considering the wastewater treatment, wastewater discharge, and sludge disposal processes, and employing the modified process-based quantification method, this study quantifies wastewater treatment's on-site and off-site GHG emissions across China. Results showed that the total GHG emission was 67.07 Mt CO2-eq in 2017, with approximately 57% of on-site emissions. The top seven cosmopolis and metropolis (top 1%) emitted nearly 20% of the total GHG emission, while their emission intensity was relatively low due to the huge population. This means that a high urbanization rate may be a feasible way to mitigate GHG emissions in the wastewater treatment industry in the future. Furthermore, GHG reduction strategies can also focus on process optimization and improvement at WWTPs as well as the nationwide promotion of onsite thermal conversion technologies for sludge management.

Flood risk assessment by using an interpretative structural modeling based Bayesian network approach (ISM-BN): An urban-level analysis of Shenzhen, China.

With increasingly uncertain environmental conditions under global change, it is rather important for water security management to evaluate the flood risk, which is influenced by the compound effect of severe weather events and strong anthropogenic activities. In this paper, a risk assessment model in the framework of Bayesian network (BN) was proposed through incorporating with the Interpretative Structural Modeling method (ISM), which would produce an integrated ISM-BN model for reliable flood assessments. The ISM is employed to identify the relations among multiple risk factors, and then helps to configure the BN structure to conduct a risk inference. The established model was further demonstrated in Shenzhen city of China to perform an urban-level risk analysis of the flood disaster, and the Enhanced Water Index (EWI) was introduced to derive model parameters for training and verification. The obtained results of risk assessment lead to an accuracy of 76% with the Area Under ROC Curve (AUC) of 0.82, and spatial distribution of risk levels also showed a satisfactory performance. In addition, it was found that the maximum daily rainfall among ten risk factors play a key part in flood occurrence, while the elevation and storm frequency are also sensitive indicators for the study area. Besides, the spatial flood risk map generated under various design rainfall scenarios would contribute to identifying potential areas that are worth paying particular attention. Thus, the developed assessment model would be a useful tool for supporting flood risk governance to achieve reliable urban water security.

Network evolution and risk assessment of the global phosphorus trade.

Phosphorus is an essential element for food production, but the distribution of its global reserve is highly uneven. With the increasing demand for products from all sectors of the phosphorus supply chain, the international phosphorus material trade is becoming increasingly intensive. However, the evolution of the global phosphorus trade network and potential supply risks caused by the trade structure and trade stability are rarely evaluated. By employing the complex network theory, a phosphorus material trade network and a quantitative evaluation index of the trade risk using the external supply risks are proposed to evaluate the supply risk in different countries from 2000 to 2020. According to the network analysis of global phosphorus trades for phosphate rock, phosphorus fertilizer and phosphoric acid, the number of trading countries and trading links has generally increased during the last twenty years. However, the trade structure was found to be significantly altered due to the stresses on the phosphorus reserve scarcity and trade restrictions from countries such as the United States and China. Correspondingly, Morocco has become the largest phosphorus-exporting country since 2016, while India was the world's largest phosphorus-importing country between 2008 and 2015. The topological network characteristics indicate that the phosphorus trade is well connected and more stable over time, but high supply risks were also identified, especially in developing countries in Africa within their phosphate rock and phosphorus fertilizer trade, which might threaten their food security. The obtained findings would be helpful for phosphorus trading countries to manage their trade risks in a timely manner.

Environmental risks of disposable face masks during the pandemic of COVID-19: Challenges and management.

Since the COVID-19 outbreak in early 2020, face mask (FM) has been recognized as an effective measure to reduce the infection, increasing its consumption across the world. However, the large amount of at-home FM usage changed traditional medical waste management practices, lack of improper management. Currently, few studies estimate FM consumption at a global scale, not to say a comprehensive investigation on the environmental risks of FM from a life cycle perspective. Therefore, global FM consumption and its associated environmental risks are clarified in the present study. Our result shows that 449.5 billion FMs were consumed from January 2020 to March 2021, with an average of 59.4 FMs per person worldwide. This review also provides a basis to understand the environmental risk of randomly disposed of FM and highlights the urgent requirement for the attention of FMs waste management to prevent pollution in the near future.

Real-time prediction of river chloride concentration using ensemble learning.

Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R2 with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.

Characterization of elevation and land cover dependent trends of NDVI variations in the Hexi region, northwest China.

In the arid Hexi Corridor of northwest China, vegetation cover plays a pivotal role in sustaining the unique terrestrial ecosystem. In this paper, vegetation changes during growth season from April to October were investigated through examining the trends in the Normalized Difference Vegetation Index (NDVI) across the Hexi region. Based on the GMMIS NDVI 3g.v1 dataset, NDVI trend and its dependency on elevation and land cover were analyzed for the period 1982-2015 according to multiple statistical tests. Results showed that NDVI exhibited a significantly increasing trend in ∼70% of the vegetated area, in contrast with a negative trend only in 2.85%. The resulting distinct groups with respect to decreasing, increasing and no trends presented significant differences in elevation and land cover composition, and the correlation between elevation, land cover and NDVI trend magnitude was subjected to precipitation and temperature change. The elevation and grassland cover were found to mainly account for variations in NDVI trend, and increase in elevation and various types of land cover excluding impervious and bare land would facilitate the trend magnitude. The dependency of NDVI trend on elevation and land cover was very vulnerable to increasing air temperature, which triggered an improvement in the vegetable activity to adapt to climate change, especially grass and forest. The contribution of crop and shrub to NDVI change was sensitive to precipitation trend change, but the crop was primarily influenced by human activities. The identified patterns of vegetation change would help to gain insights into the adapting mechanism of the fragile ecosystems in arid areas to changing environmental conditions.

Groundwater level prediction using a SOM-aided stepwise cluster inference model.

Accurate groundwater level (GWL) prediction can contribute to sustaining reliable water supply to domestic, agricultural and industrial uses as well as ecological services, especially in arid and semi-arid areas. In this paper, a regional GWL modeling framework was first presented through coupling both spatial and temporal clustering techniques. Specifically, the self-organizing map (SOM) was applied to identify spatially homogeneous clusters of GWL piezometers, while GWL time series forecasting was performed through developing a stepwise cluster multisite inference model with various predictors including climate conditions, well extractions, surface runoffs, reservoir operations and GWL measurements at previous steps. The proposed modeling approach was then demonstrated by a case of an arid irrigation district in the western Hexi Corridor, northwest China. Spatial clustering analysis identified 6 regionally representative central piezometers out of 30, for which sensitivity and uncertainty analysis were carried out regarding GWL predictions. As the stepwise cluster tree provided uncertain predictions, we added an AR(1) error model to the mean prediction to forecast GWL 1 month ahead. Model performance indicators suggest that the modeling system is a useful tool to aid decision-making for informed groundwater resource management in arid areas, and would have a great potential to extend its applications to more areas or regions in the future.

Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability.

Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications.

Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach.

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10(9) $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.