Search Results (259)

Eutrophication driven by nitrogen and phosphorus discharge remains a critical global environmental challenge. This study developed a sustainable strategy for synergistic nutrient removal and recovery by fabricating MgO-coated biochar (Mg-MBC600) through co-pyrolysis of municipal sludge and sunflower stalk (300–700 °C). Systematic investigations revealed temperature-dependent adsorption performance, with optimal nutrient removal achieved at 600 °C pyrolysis. The Mg-MBC600 composite exhibited enhanced physicochemical properties, including a specific surface area of 156.08 m²/g and pore volume of 0.1829 cm³/g, attributable to magnesium-induced structural modifications. Advanced characterization confirmed the homogeneous dispersion of MgO nanoparticles (~50 nm) across carbon matrices, forming active sites for chemisorption via electron-sharing interactions. The maximum adsorption capacities of Mg-MBC600 for nitrogen and phosphorus reached 84.92 mg/L and 182.27 mg/L, respectively. Adsorption kinetics adhered to the pseudo-second-order model, indicating rate-limiting chemical bonding mechanisms. Equilibrium studies demonstrated hybrid monolayer–multilayer adsorption. Solution pH exerted dual-phase control: acidic conditions (pH 3–5) favored phosphate removal through Mg₃(PO₄)₂ precipitation, while neutral–alkaline conditions (pH 7–8) promoted NH₄⁺ adsorption via MgNH₄PO₄ crystallization. XPS analysis verified that MgO-mediated chemical precipitation and surface complexation dominated nutrient immobilization. This approach establishes a circular economy framework by converting waste biomass into multifunctional adsorbents, simultaneously addressing sludge management challenges and enabling eco-friendly wastewater remediation. Full article

Industrial wastewater management remains a critical barrier to achieving Sustainable Development Goal 6 (SDG 6) in many developing countries, where regulatory frameworks exist but affordable and scalable treatment solutions are lacking. In Pakistan, the textile sector is a leading polluter, with untreated effluents routinely discharged into rivers and agricultural lands despite stringent National Environmental Quality Standards (NEQS). This study presents a pilot-scale case from Faisalabad’s Khurrianwala industrial zone, where a decentralized, nature-based bioreactor was piloted to bridge the gap between policy and practice. The system integrates four treatment stages—anaerobic digestion (AD), floating treatment wetland (FTW), constructed wetland (CW), and sand filtration (SF)—and was further intensified via nutrient amendment, aeration, and bioaugmentation with three locally isolated bacterial strains (Acinetobacter junii NT-15, Pseudomonas indoloxydans NT-38, and Rhodococcus sp. NT-39). The fully intensified configuration achieved substantial reductions in total dissolved solids (TDS) (46%), total suspended solids (TSS) (51%), chemical oxygen demand (COD) (91%), biochemical oxygen demand (BOD) (94%), nutrients, nitrogen (N), and phosphorus (P) (86%), sulfate (26%), and chloride (41%). It also removed 95% iron (Fe), 87% cadmium (Cd), 57% lead (Pb), and 50% copper (Cu) from the effluent. The bacterial inoculants persist in the system and colonize the plant roots, contributing to stable bioremediation. The treated effluent met the national environmental quality standards (NEQS) discharge limits, confirming the system’s regulatory and ecological viability. This case study demonstrates how nature-based systems, when scientifically intensified, can deliver high-performance wastewater treatment in industrial zones with limited infrastructure—offering a replicable model for sustainable, SDG-aligned pollution control in the Global South. Full article

Harmful Algal Blooms (HABs) are a growing environmental concern due to their adverse impacts on aquatic ecosystems, human health, and economic activities. These blooms are driven by a combination of factors, including nutrient enrichment, environmental factors, and hydrological conditions, leading to the excessive growth of algae. HABs produce toxins that threaten aquatic biodiversity, contaminate drinking water, and cause economic losses in fisheries and tourism. The causes of HABs are multifaceted, involving interactions between environmental factors such as temperature, light availability, and nutrient levels. Agricultural runoff, wastewater discharge, and industrial pollution introduce excessive nitrogen and phosphorus into water bodies, fueling bloom formation. Climate change further exacerbates the problem by altering precipitation patterns, increasing water temperatures, and intensifying coastal upwelling events, all of which create favorable conditions for HAB proliferation. This review explores the causes, ecological consequences, and potential mitigation strategies for HABs. Effective monitoring and detection methods, including satellite remote sensing, molecular biotechnology, and artificial intelligence-driven predictive models, offer promising avenues for early intervention. Sustainable management strategies such as nutrient load reductions, bioremediation, and regulatory policies can help mitigate the adverse effects of HABs. Public awareness and community involvement also play a crucial role in preventing and managing HAB events by promoting responsible agricultural practices, reducing waste discharge, and supporting conservation efforts. By examining existing literature and case studies, this study underscores the urgent need for comprehensive and interdisciplinary approaches to regulate HABs. Full article

Sanitary landfill leachate treatment was evaluated using magnetite-catalyzed ozone, an upflow anaerobic sludge blanket (UASB) reactor, and microalgae, both individually and in combination, to improve biodegradability and remove organic matter, solids, metals, and nutrients. Leachates were characterized before and after each treatment, and their impacts on methanogenic activity, aerobic toxicity, and the BOD₅/COD ratio were assessed. Magnetite-catalyzed ozone pretreatment enhanced biodegradability, enabling an optimal coupling point with the UASB at 40 min when the specific methanogenic activity reached 0.22 g CH₄-COD/(gVSS·d). The UASB achieved COD removal rates of up to 75%, but high concentrations were maintained in the effluent with low ammoniacal nitrogen and phosphorus removal rates. Microalgae promoted nutrient removal, reducing total nitrogen and phosphorus by up to 65% and 70%, respectively, although with lower efficiency in terms of organic matter removal. Process coupling demonstrated that ozonation followed by UASB application improved anaerobic degradation, whereas the use of microalgae after biological treatment optimized the final effluent quality. Despite the improvements achieved, the final values for some parameters still exceeded the discharge limits, indicating the need for operational adjustments or additional treatments to ensure effective purification. Full article

With rapid economic and social development, eutrophication in coastal areas is currently one of the most severe environmental problems worldwide. However, our understanding of the response of the phytoplankton community structure to the intensification of coastal eutrophication is still relatively limited. Here, seasonal phytoplankton, environmental factors, and nutrients were investigated in 2009 and 2019 in Zhanjiang Bay, where eutrophication has intensified in recent years, to analyze the variation in nutrient structure and its impact on the phytoplankton community. The results revealed that the DIP and DSI concentrations in 2019 were higher than those in 2009. However, dissolved inorganic nitrogen (DIN) has decreased substantially over the past several decades, which is due mainly to the decrease in anthropogenic nitrogen emissions, the substantial increase in the intrusion of high-salinity seawater, and the high-phosphorus wastewater discharged from urban industries. This resulted in a decrease in phytoplankton cell abundance, phytoplankton composition, and species diversity (H′) in 2019 compared with 2009. In addition, the superior N transport, storage, and response strategy to a low N supply of diatoms, especially Skeletonema and Chaetoceros, might benefit the growth of diatoms under low DIN conditions. The lower DIN/DIP ratio in 2019 favored the growth of diatoms, especially Skeletonema and Chaetoceros, leading diatoms to dominate the phytoplankton assemblage. This study demonstrates how changes in nutrient structure alter the community structure of phytoplankton, providing new insights into deepening our understanding of eco-environmental evolution. Full article

Headwater streams serve as a crucial link between forest and downstream aquatic ecosystems and also act as crucial agents in carbon (C) and nutrient storage and flux. These aquatic systems play a pivotal role in regulating biogeochemical cycles. Plant litter is an important contributor of nutrients to headwater streams, having significant impacts on downstream ecosystems. However, current research predominantly focuses on the dynamics of plant litter C and nutrients such as nitrogen and phosphorus, and we know little about those of nutrients such as sodium (Na). In this study, we conducted a comprehensive evaluation of the annual dynamics of plant litter Na storage within a subtropical headwater stream. This study took place over a period of one year, from March 2021 to February 2022. Our results showed that (1) the average annual concentration and storage of litter Na was 538.6 mg/kg and 2957.6 mg/m², respectively, and litter Na storage exhibited a declining trend from stream source to mouth, while demonstrating significantly higher values during the rainy season compared to the dry season; (2) plant litter type had significant impacts on Na concentration and storage, with leaf, twig, and fine woody debris accounting for the majority of litter Na storage; and (3) hydrological (precipitation, discharge) and physicochemical (water temperature, flow velocity, pH, dissolved oxygen, alkalinity) factors jointly affected Na storage patterns. Overall, the results of this study clearly reveal the dynamic characteristics of Na storage in plant litter in a subtropical forest headwater stream, which contributes to a more comprehensive understanding of the role of headwater streams in nutrient cycling and the dynamic changes of nutrients along with hydrological processes. This research will enhance our predictive understanding of nutrient cycling at the watershed scale. Full article

This paper establishes an explicable integrated machine learning model for predicting the discharge water quality in a circulating cooling water system of a power plant. The performance differences between three deep learning models, a Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN), and traditional machine learning models, namely eXtreme Gradient Boosting (XGboost) and Support Vector Machine (SVM), were evaluated and compared. The TCN model has high fitting accuracy and low error in predicting ammonia nitrogen, nitrate nitrogen, total nitrogen, chemical oxygen demand (COD), and total phosphorus in the effluent of a circulating cooling tower. Compared to other traditional machine learning models, the TCN has a larger R² (maximum 0.911) and lower Root Mean Square Error (RMSE, minimum 0.158) and Mean Absolute Error (MAE, minimum 0.118), indicating the TCN has better feature extraction and fitting performance. Although the TCN takes additional time, it is generally less than 1 s, enabling the real-time prediction of drainage water quality. The main water quality indices have the greatest causal inference relationship with those of makeup water, followed by the concentration ratio, indicating that concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen, and COD have a more decisive impact. Shapley Additive Explanations (SHAP) analysis further reveals that the concentration ratio has a weaker decisive impact on circulating cooling water drainage quality. The results of this study facilitate the optimization of industrial water resource management and offer a feasible technical pathway for water resource utilization in power plants. Full article

To address the critical challenges of wind erosion mitigation and sustainable soil management in the fragile agroecosystem of the black soil region in the foothills of the Daxing’anling Mountains, this study evaluated five tillage practices—conventional ridge tillage (CP), no tillage with straw removal (NT), no tillage with straw mulching (R+NT), autumn strip tillage with straw mulching (R+ST_A), and spring strip tillage with straw mulching (R+ST_S)—across two landforms: gently sloped uplands and flat depressions. The results demonstrated that R+ST_S achieved superior performance across both landscapes, exhibiting a 42.99% reduction in the wind erosion rate, a 48.88% decrease in soil sediment discharge, and a 52.26% reduction in the soil creep amount compared to CP. These improvements were mechanistically linked to the enhanced surface microtopography (aerodynamic roughness increased by 1.8–2.3 fold) and optimized straw coverage (68–72%). R+ST_S also enhanced the topsoil fertility, increasing the total nitrogen (TN), soil organic carbon (SOC), alkaline nitrogen (AN), available phosphorus (AP), and rapidly available potassium (AK) by 22.07%, 12.94%, 14.92%, 32.94%, and 9.52%, respectively. Furthermore, it improved maize emergence and its yield by 10.04% and 9.99% compared to R+NT. Mantel tests and SEM revealed strong negative correlations between erosion and nutrients, identifying nitrogen availability as the key yield driver. R+STS offers a sustainable strategy for erosion control and productivity improvement in the black soil region. Full article

Sustainable agriculture necessitates innovative solutions to enhance plant growth while optimizing resource efficiency. Plasma discharge generates reactive oxygen and nitrogen species (NH₄⁺, NO₃⁻, and NO₂⁻), which form plasma-activated water upon dissolution, affecting the nutritional solution pH and electrical conductivity (EC) and, consequently, plant development. Four treatments were applied, resulting from combining high or low plasma discharge intensities at 45 or 60 min spray intervals: low plasma discharge with a 45 min interval (T1), low plasma discharge with a 60 min interval (T2), high plasma discharge with a 45 min interval (T3), and high plasma discharge with a 60 min interval (T4). The experiment followed a 4 × 5 × 2 factorial design comprising the four treatments, five replications per treatment, and two independent experimental repeats, resulting in forty experimental units. Each unit contained 12 lettuce plants, for a total of 480 plants. The multivariate analysis of variance confirmed statistically significant treatment effects. The combination of high plasma intensity and a 45 min spray interval significantly increased the growth parameters and yield as compared with the other treatments. In particular, compared with T1, which produced the lowest values across all measured parameters, T3 resulted in a 97% increase in leaf area, a 72% increase in stem diameter, a 49% increase in leaf number, a 44% increase in leaf width, and a 30% increase in leaf length. Additionally, T3 increased edible yield by 210% and total biomass production by 203% compared with T1. These results demonstrate the combined effect of plasma intensity and spraying frequency in optimizing plant development in aeroponic systems. As far as mineral uptake is concerned, T3 increased the nitrogen, potassium, phosphorus, calcium, and magnesium concentrations by 18.2%, 16.7%, 32.3%, 20.2%, and 11.2%, respectively, compared with T1. The regression analysis further validated the robustness of the findings, indicating plasma intensity to be a dominant factor. Enhanced mineral uptake (N, P, K, Ca, and Mg) and consistent growth trends across treatments highlighted the significance of plasma technology in optimizing plant growth, yield, and nutrient absorption, suggesting it is a sustainable and efficient approach to modern agriculture. Full article

Nutrients function as essential biological substrates for coastal phytoplankton growth and serve as pivotal indicators in marine environmental monitoring. The intensification of land-based nutrient sources inputs has exacerbated eutrophication in Chinese coastal water, while mechanistic understanding of differential self-purification processes among distinct land-based source nutrients (river source, domestic source, aquaculture source, and industrial source) remains limited, constraining accurate assessment of bay’s self-purification capacity. This study conducted incubation experiments in Tieshan Bay (TSB) during Summer (June 2023) and winter (January 2024), systematically analyzing the self-purification process of nutrients and associated environmental drivers. Distinct source-specific patterns emerged: river inputs exhibited maximal dissolved inorganic nitrogen (DIN) 1.390 ± 0.74 mg/L, whereas industrial discharges showed peak dissolved inorganic phosphorus (DIP) 4.88 ± 1.45 mg/L. Chlorophyll a (Chl-a) concentrations varied markedly across sources, ranging from 34.97 ± 23.37 μg/L (domestic source) to 86.63 ± 77.08 μg/L (river source). First-order kinetics demonstrated significant source differentiation (p < 0.05). River-derived DIN exhibited the highest attenuation coefficient (−0.3244 ± 0.17 d⁻¹), contrasting with industrial-sourced DIP showing maximum depletion (−0.4332 ± 0.20 d⁻¹). Correlation analysis indicated that summer was significantly associated with the impacts of three key control factors pH, dissolved oxygen, and turbidity on nutrient dynamics (p < 0.05), whereas winter exhibited a stronger dependence on salinity. These parameters collectively may modulate microbial degradation pathways and particulate matter adsorption capacities. These findings establish quantitative thresholds for coastal nutrient buffering mechanisms, highlighting the necessity for source-specific eutrophication mitigation frameworks. The differential self-purification efficiencies underscore the importance of calibrating pollution control strategies according to both anthropogenic discharge characteristics and regional hydrochemical resilience, which is of key importance for ensuring the traceability and control of land-based sources of pollution into the sea and the scientific utilization of the self-purification capacity of the bay water body. Full article

Environmental monitoring is essential to assess and, if possible, anticipate the consequences of various marine economic developments. This study describes progress in environmental monitoring by developing and applying a eutrophication index (EI) for marine protected areas (MPAs). The EI combines available data, such as biological oxygen demands, dissolved inorganic nitrogen and phosphorus, and chlorophyll-a, with the weighting factors calculated from principal component analysis to assess environmental quality. Its effectiveness was tested using nearly three decades of environmental data (since 1996) from the Nha Trang MPA in Vietnam. The EI revealed clear trends in environmental quality. In the period 1996–2006, environmental conditions deteriorated, negatively impacting aquaculture. In the later period, 2007–2024, improved environmental protection policies, technological developments, expanding tourism, and heightened public awareness contributed to a reversal of this trend. During the earlier period, the EI indicated poor environmental quality (Level V), while in the later years, it improved significantly, approaching Level II. This study also identified the spatial eutrophication patterns and helped to determine the causes of specific eutrophication levels. These included port development, aquaculture activities, and domestic waste discharge. These findings highlight the close relationship between environmental quality and economic activities in the bay. Overall, the new EI and its sensitivity maps enhance environmental monitoring capabilities. They provide valuable tools for decision-makers, aiding in the strategic planning of marine economic development, ecosystem protection, and sustainable resource use. The approach supports long-term environmental stewardship and more informed, adaptive management of coastal and marine areas. Full article

Intensive farming in urban suburbs often causes habitat loss, soil erosion, wastewater discharge, and agricultural productivity decline, threatening long-term benefits for the local community. We developed a nature-based solution for sustainable land restoration by establishing “Green Treasure Island” (GTI). The aim of this study is to evaluate the ecological restoration effectiveness of GTI and explore its feasibility and replicability for future applications. The core eco-functional zone of GTI—a 7 hm² forested wetland—embedded a closed-loop framework that integrates land consolidation, ecological restoration, and sustainable land utilization. The forested wetland efficiently removed 65% and 74% of dissolved inorganic nitrogen and phosphorus from agricultural runoff, raised flood control capacity by 22%, and attracted 48 bird species. Additionally, this biophilic recreational space attracted over 3400 visitors in 2022, created green jobs, and promoted local green agricultural product sales. Through adaptive management and nature education activities, GTI evolved into a landmark that represents local natural–social characteristics and serves as a publicly accessible natural park for both rural and urban residents. This study demonstrates the feasibility of creating GTI for improving ecosystem services, providing a practical, low-cost template that governments and local managers can replicate in metropolitan rural areas worldwide to meet both ecological and development goals. Full article

Leachates generated from the degradation of green waste and biosolids from urban wastewater treatment plants (WWTPs) pose significant environmental concerns due to high concentrations of organic pollutants and heavy metals. This study proposes a hybrid treatment strategy combining electrocoagulation (EC) and UVC-activated TiO₂ photocatalysis to remediate leachates produced in laboratory-scale biocells. Initial characterization revealed critical pollutant levels: COD (1373 mg/L), BOD₅ (378 mg/L), total phosphorus (90 mg/L), ammoniacal nitrogen (201 mg/L), and metals such as Ni, Pb, and Mn levels all exceeding those set out in the Ecuadorian discharge regulations. Optimized EC achieved removal efficiencies of 62.6% for COD, 44.4% for BOD₅, 89.8% for phosphorus, and 86.2% for color. However, residual contamination necessitated a subsequent photocatalytic step. Suspended TiO₂ under UVC irradiation removed up to 81.8% of the remaining COD, 88.7% of the ammoniacal nitrogen, and 94.4% of the phosphorus. Levels of heavy metals such as Zn, Fe, Pb, Mn, and Cu were reduced by over 80%, while Cr⁶⁺ was nearly eliminated. SEM–EDS analysis confirmed successful TiO₂ immobilization on sand substrates, revealing a rough, porous morphology conducive to catalyst adhesion; however, heterogeneous titanium distribution suggests the need for improved coating uniformity. These findings confirm the potential of the EC–TiO₂/UVC hybrid system as an effective and scalable approach for treating complex biocell leachates with reduced chemical consumption. Full article

Non-point source (NPS) pollution from agriculture accounts for more than 20% of the total pollution load in the Republic of Korea, with the highest nutrient balance among OECD countries. Rice paddy fields are among the most important NPSs because of their large area, intensive fertilizer use, intensive use of irrigation water, and subsequent drainage. Therefore, the use of controlled drainage in paddy fields (Test) was evaluated for reduction in the discharged volumes and pollutant loads in drainage and stormwater runoff in comparison to plots using traditional drainages (Control). The results show that the loads were highly variable and that the reductions in the annual load of biochemical oxygen demand (BOD), suspended solid (SS), total nitrogen (T-N), total phosphorus (T-P), and total organic carbon (TOC) in the Test compared to that of the Control were 31.0 ± 28.9%, 83.5 ± 11.8%, 65.4 ± 12.2%, 69.1 ± 21.7%, and 64.9 ± 12.9%, respectively. It was shown that discharge in the post-harrowing and transplanting drainage (HD) was predominantly responsible for the total loads; therefore, the load reduction in HD was evaluated further at additional sites. The reduction at all studied sites was highly variable and as follows: 30.0 ± 33.6%, 70.9 ± 24.6%, 32.2 ± 45.5%, 45.7 ± 37.0%, and 27.0 ± 71.5%, for BOD, SS, T-N, T-P, and TOC, respectively. It was also demonstrated that controlled drainage contributed significantly to reducing the loads and volume of stormwater runoff from paddy fields. Correlations between paddy field conditions and multiple regression showed that the loads were significantly related to paddy water quality. The results of this study strongly suggest that controlled drainage is an excellent alternative for reducing the discharge of NPS pollutants from paddy fields. It is also suggested that the best discharge control would be achieved by combinations of various discharge mitigation alternatives, such as the management of irrigation, drainage, and fertilization, as well as drainage treatment, supported by more field tests, identification of the fates of pollutants, effects of rainfall, and climate changes. Full article

Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added costs. As a result, they are rarely monitored, risking improper effluent discharge. Quick, cost-effective assessments of effluent quality could significantly improve plant operation and maintenance. Addressing the special challenges faced by such wastewater treatment systems, artificial intelligence (AI)-based soft sensors and virtual instruments have been developed to forecast effluent quality with the help of a water quality parameter that is inexpensively, easily, and immediately measurable with a hand-held device. In this study, advanced artificial neural network (ANN)-based soft sensors were developed to enhance the monitoring and management of effluent quality in five modular wastewater treatment plants in Bangalore. The models serve as virtual instruments for the measurement of total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), using the wastewater turbidity as the input parameter. By using these AI models, operators can better anticipate and manage water quality, ultimately contributing to more efficient and effective wastewater treatment operations. This innovative approach represents a significant advancement in wastewater treatment technology providing a practical and efficient solution to streamline monitoring and enhance overall plant performance. Full article