Offset integrity reduces environmental risk: Using lessons from biodiversity and carbon offsetting to inform water quality offsetting in the catchments of the Great Barrier Reef.

Environmental offsetting has been developed as a mechanism to facilitate the benefits from economic development while avoiding or minimizing environmental harm. This is achieved by compensating for environmental impacts at one location by generating equivalent environmental improvements elsewhere. However, experience with biodiversity and carbon offsetting indicates it can be difficult to ensure the integrity of offsets. Under recent legislation in the catchments of the Great Barrier Reef (GBR), Australia, it is mandatory for water quality emissions from new or expanded point source development to be offset by reducing pollution elsewhere, frequently through reducing non-point source pollution (NPSP). Therefore, informed by experience with biodiversity and carbon offsetting, we summarised sources of uncertainty in NPSP reduction that would influence water quality offset integrity; estimated the maximum potential demand for water quality offsets from sewage treatment plants, the largest point source emitter of total nitrogen (TN) in the GBR catchments, between 2018 and 2050; and discussed the implications of both on the ability of offsetting to counterbalance the impact of economic development in catchments where nitrogen loads have a large influence on the health of important GBR ecosystems. The catchments surrounding the population centres of Cairns and Mackay had both a potentially high future demand for nitrogen water quality offsets and nitrogen loads with a strong influence on the health of the GBR. Consequently, any low integrity water quality offsets in these catchments could jeopardise progress toward the water quality improvements needed to ensure the continued health of the GBR. Water quality offsetting has numerous strengths as a policy instrument however substantial uncertainties remain related to environmental outcomes. Until further research can reduce these uncertainties, water quality offsets that are implemented near increased point source emissions and have a high certainty of effectiveness may provide a balance between scientific rigour and policy workability.

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks.

The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the landfill needs more precise management. To solve this problem, based on the "pressure-state-response" framework, a method for the identification and evaluation of groundwater pollution around the landfill was constructed. The LPI method was used to assess the contamination potential of the leachate. The comprehensive quality of groundwater was evaluated by the entropy-AHP water quality assessment method, sodium adsorption ratio and sodium percentage. The probabilistic health risks of groundwater were assessed based on a Monte Carlo algorithm. The sources of pollutants were identified by comprehensively using the PCA-APCS-MLR model and the PMF model. Finally, the self-organizing map algorithm and the Kmeans algorithm were integrated to enhance the precision of groundwater management and control measures. The results showed that the leachate of the landfill was in the mature stage, and the concentration of inorganic substances was relatively high. Leachate had the potential to contaminate surrounding groundwater. The groundwater quality of 68.14% of the study area was in the poor or lower level. The groundwater near the landfill was unsuitable not only for drinking but also for irrigation purposes. Cl- was the main non-carcinogenic risk factor. Reducing pollutant concentration and controlling exposure time are effective strategies for mitigating health risks caused by high-concentration pollutants (Cl-, NO3-) and low-concentration pollutants (F-), respectively. The groundwater around the landfill was jointly affected by six pollution sources. The PMF model has better analytical ability in mixed pollution areas. The groundwater in the study area was divided into five clusters, of which cluster Ⅰ was significantly affected by leachate, and cluster Ⅴ had the lowest pollution and health risk.

Causal diagnostic model and governance strategies to reduce pollution from ship accidents in Chinese waters.

Ship transportation is a primary mode for global trade and cargo transport, yet even minor discrepancies can lead to ship accidents, causing severe secondary environmental pollution. Maritime accidents involve complex and numerous factors. Formal Concept Analysis (FCA) can identify the key contributing factors and their impact levels by eliminating homogenization factors in maritime accidents. This study constructs an innovative FCA model of ship accidents in Chinese waters, utilizing 172 ship accident reports released by the China Maritime Safety Administration. The analysis reveals seven reduced sets and 23 diagnostic rules of ship accidents. Results show that failed ship registration/security inspection, deficient nautical data and instruments, and management issues are the most critical factors. Three accident chains are identified and corresponding mitigation strategies are proposed to reduce potential pollution from ship accidents. These strategies offer significant reference value for preventing ship accidents and reducing their environmental impact in China and globally.

Unlocking policy effects: Water resources management plans and urban water pollution.

Water resource management, as a foundation for supporting sustainable urban development, has garnered increasing attention from scholars. Developing effective water resource management plans is a major challenge faced by countries worldwide. This study uses the 2015 Water Pollution Control and Prevention Action Plan (WPCAP) in China as a natural experiment and employs a Difference-in-Differences (DID) model to estimate the relationship between WPCAP and urban water pollution from 2010 to 2021. The findings are as follows: 1) WPCAP reduces water pollution. 2) WPCAP decreases water pollution in high-policy-pressure cities but increases water pollution in low-policy-pressure cities within a 60 km radius, particularly having a significantly negative impact on water pollution in low-policy-pressure cities with low altitude. 3) optimizing industrial and domestic water use, as well as enhancing sewage treatment capabilities, are crucial pathways through which WPCAP reduces water pollution. Additionally, WPCAP significantly improves water pollution control capabilities in cities with abundant water resources, large cities, and industrialized cities. 4) although WPCAP's ability to control water pollution increases management costs, it also raises residential income and promotes population growth. These findings have important implications for the sustainable development of water resources in emerging countries, including China.

The Brazilian National Plan to Combat Marine Litter: A critical assessment.

The Brazilian National Plan to Combat Marine Litter was launched in March 2019 and in its context, 577 campaigns were carried out to clean up beaches, rivers, estuaries and mangroves, until January 2023, at an estimated cost of US$ 16,733,000.00. The volume collected in four years represented only 0.0041 % of the total solid waste discharged annually in the maritime waters under the country's jurisdiction. The direct benefit to society was only US$ 173,751.41 at market value for the litter collected. Litter lesser than 2.5 cm was left out, not been characterized. The results achieved deserve reflection and critical evaluation, in order to allow improvements, serving as reference. The participation of around 43,000 people in cleanup campaigns is an indication of environmental citizenship and an asset that cannot be overlooked by managers, demanding focus, tangible results and benefits in terms of environmental quality, well-being and savings on public cleaning services costs.

Establishing an affordable solar-floating Fe2O3@A1-xRx-TiO2 photo-Fenton catalytic system through the cyclic utilization of iron waste to de-pollute textile water contamination.

This study focuses on developing a cost-effective Fe2O3 catalyst from oilfield iron waste to create a floating heterogeneous photo-Fenton system with anatase/rutile(A/R) TiO2 heterophase photocatalyst (cork-Fe2O3@A1-xRx-TiO2) for treating textile pollution in sunlight. Through controlling sol-gel (SG) microwave heating technique, the A/R ratio of A1-xRx-TiO2 crystal is tuned (A/R ratio = 1.13 and Eg = 3.02 eV) to improve adsorption-photocatalytic removal of anionic/cationic dyes with an apparent kinetic rate (kapp) of 0.0074 min-1 under UV-visible irradiation. The developed cork-Fe2O3@A53.1R46.8-TiO2 floated system also outperforms the classical photo-Fenton with Fe/H2O2 benchmark, showing a 2-fold enhancement in textile dye degradation (kapp = 0.216 min-1 and space-time yield (SY) of 1.7*10-4 mol/E.g at pH 5.65) with high stability over four reuse cycles. The formation of Fe2O3@A53.1R46.8-TiO2 Type-II heterojunction is confirmed by optical and electrochemical analyses, allowing the acceleration of direct electron transfer mechanism and oxidative degradation of dyes during photo-Fenton reaction. As a case study, the cork-Fe2O3@A53.1R46.8-TiO2 system demonstrates a high capability for efficient mineralization of textile pollution in a real effluent, achieving 82 ± 2% reduction in the total organic contents at an operational cost of 2.61 $/kg.m3 in sunlight. Thus, this research addresses challenges in conventional Fenton chemistry, iron waste recycling, and catalyst retention, offering new insights for sustainable treatment of textile effluents and environmental protection.

The Missing Piece of the Puzzle: How Different Water-related Public Concerns Contribute to Water Governance in China.

Public participation is crucial to improving the performance of water governance systems, especially in a governance model dominated by a top-down hierarchical structure. Public concerns, as a vital form of public participation, have been acknowledged as an essential component in contributing to water governance. However, few studies explore the varying effects of diverse public concerns in leading to different water governance outcomes. This study addresses this gap by exploring the direct and indirect effects of water-related public concerns on water pollution control and governments' pollution mitigating actions, using citizens' water-related posts crawled from China's social media. Results show that public water-related concerns contribute to water governance both through its direct effects on reducing water pollution and indirect effects by promoting policy actions. Specifically, the concerns related to water pollution hazards, water pollution monitoring, prevention products, and water pollution control measures have more positive impacts on water pollution reduction compared to other types of concern. Meanwhile, public concerns demonstrate stronger effects in triggering economic-related and infrastructure-related water pollution mitigation actions. This study provides nuanced insights to understand the role of public participation in improving water governance, the findings of which are insightful to enhance accountability of water governance systems through a bottom-up approach.

Connecting blue-green infrastructure elements to reduce combined sewer overflows.

By infiltrating and retaining stormwater, Blue-Green Infrastructure (BGI) can help to reduce Combined Sewer Overflows (CSOs), one of the main causes of urban water pollution. Several studies have evaluated the ability of individual BGI types to reduce CSOs; however, the effect of combining these elements, likely to occur in reality, has not yet been thoroughly evaluated. Moreover, the CSO volume reduction potential of relevant components of the urban drainage system, such as detention ponds, has not been quantified using hydrological models. This study presents a systematic way to assess the potential of BGI combinations to mitigate CSO discharge in a catchment near Zurich (Switzerland). Sixty BGI combinations, including four BGI elements (bioretention cells, permeable pavement, green roofs, and detention ponds) and four different implementation rates (25%, 50%, 75%, and 100% of the available sewer catchment area) are evaluated for four runoff routing schemes. Results reveal that BGI combinations can provide substantial CSO volume reductions; however, combinations including detention ponds can potentially increase CSO frequency, due to runoff prolongation. When runoff from upstream areas is routed to the BGI, the CSO discharge reductions from combinations of BGI elements differ from the cumulative CSO discharge reductions achieved by individual BGI types, indicating that the sum of effects from individual BGI types cannot accurately predict CSO discharge in combined BGI scenarios. Moreover, larger BGI implementation areas are not consistently more cost-effective than small implementation areas, since the additional CSO volume reduction does not outweigh the additional costs. The best-performing BGI combination depends on the desired objective, being CSO volume reduction, CSO frequency reduction or cost-effectiveness. This study emphasizes the importance of BGI combinations and detention ponds in CSO mitigation plans, highlighting their critical factors-BGI types, implementation area, and runoff routing- and offering a novel and systematic approach to develop tailored BGI strategies for urban catchments facing CSO challenges.

Unraveling agricultural water pollution despite an ecological policy in the Ayeyarwady Basin.

BACKGROUND: The Ayeyarwady Basin in Myanmar, a critical economic zone, faces severe ecological degradation due to unsustainable agricultural practices. These practices pose significant threats to human health and marine biodiversity. Environmental threats persist despite the Myanmar government's efforts to implement biodiversity protection policies. This research explores the limited compliance with environmental protection policies among farmers in the Ayeyarwady Basin and its implications for sustainable agricultural practices and ecological conservation. METHODS: This research employs an exploratory phenomenological approach, utilizing semi-structured, in-depth interviews with government officials and farmers (N = 30). The data collected were subjected to thematic analysis using Atlas 23. RESULTS: Preliminary findings reveal a gap in farmers' awareness and understanding of these policies, hindered by insufficient financing, poor communication infrastructure, and uncoordinated policy monitoring. These factors and existing unrest contribute to a top-down policy approach that neglects frontline stakeholders. The study suggests the need for clear stakeholder roles, adequate policy financing, and diverse communication strategies to effectively implement environmental policies and protect human and marine life. CONCLUSIONS: Environmental policy shortcomings in Myanmar are attributable to governmental oversight and insufficient stakeholder engagement. To mitigate pollution and safeguard river basin ecosystems, the government must delineate stakeholder responsibilities, allocate appropriate policy funding, and adopt varied communication approaches with farmers.

Tide of change: Urgency of a national marine litter policy in India.

The rise of plastics in the 20th century revolutionized modern life but inadvertently exacerbated the marine litter crisis. The proliferation of wastes such as single-use plastics has escalated pollution along the coastline, demanding coordinated, decisive, and unified action. While the absence of specific national marine litter policies is concerning, there is growing recognition of urgency to address this issue. A group of experts and stakeholders was involved through a multi-staged workshop to assess the generated information based on scientific evidence and formulate a framework for the National Marine Litter Policy (NMLP). This paper proposes policy options (4 targets & 20 strategies) to address marine litter pollution, especially plastics, and aims to elucidate the urgency and significance of implementing an NMLP as a comprehensive strategy to combat plastics pollution. Implementing dedicated policies and action plans tailored to the unique challenges faced by each country is a vital step towards sustainable oceans.

The development of multifunctional materials for water pollution remediation using pollen and sporopollenin.

Pollen is a promising material for water treatment owing to its renewable nature, abundant sources, and vast reserves. The natural polymer sporopollenin, found within pollen exine, possesses a distinctive layered porous structure, mechanical strength, and stable chemical properties, which can be utilized to prepare sporopollenin exine capsules (SECs). Leveraging these attributes, pollen or SECs can be used to develop water pollution remediation materials. In this review, the structure of pollen is first introduced, followed by the categorization of various methods for extracting SECs. Then, the functional expansion of pollen adsorbents, with an emphasis on their recyclability, reusability, and visual sensing capabilities, as opposed to mere functional group modification, is discussed. Furthermore, the progress made in utilizing pollen as a biological template for synthesizing catalysts is summarized. Intriguingly, pollen can also be engineered into self-propelled micromotors, enhancing its potential application in adsorption and catalysis. Finally, the challenges associated with the application of pollen in water pollution treatment are discussed. These challenges include the selection of environmentally friendly, non-toxic reagents in synthesizing pollen water remediation products and the large-scale application after synthesis. Moreover, the multifunctional synthesis and application of different water remediation products are prospected.

Determinants and action paths of transboundary water pollution collaborative governance: A case study of the Yangtze River Basin, China.

With rapid economic growth, the issue of water pollution has become increasingly prominent, and there is a consensus that river basin management systems and cross-regional management coordination mechanisms need improving. In this study, 171 transboundary sections of the Yangtze River Basin were matched with the data of 57 cities to construct panel data from 2015 to 2021. Based on the four-dimensional framework of environment-determination-process-resources, the dynamic qualitative comparative analysis (QCA) method is used to identify the key influencing factors and action paths of water pollution collaborative governance effects. The results show that a single antecedent condition is not necessary to achieve efficient collaborative governance effects, and only the "number of collaborative governance" and "scale of collaboration" conditions played important roles. There are five paths that can achieve efficient collaborative governance effects: economy-oriented, ecology-oriented, technology-oriented, government-oriented, and all-oriented. Additionally, heterogeneous results show that the impact of the regional governance intention on efficient collaborative governance effect is limited in the middle and upstream sections of the Yangtze River Basin, while the downstream sections are more dependent on the basic condition of the basin. The results can help promote effective cross-regional collaboration in the Yangtze River Basin, provide scientific basis for regions to formulate targeted governance measures, and provide models for governance in other regions.

Navigating agricultural nonpoint source pollution governance: A social network analysis of best management practices in central Pennsylvania.

The Chesapeake Bay watershed is representative of governance challenges relating to agricultural nonpoint source pollution and, more generally, of sustainable resources governance in complex multi-actor settings. We assess information flows around Best Management Practices (BMPs) undertaken by dairy farmers in central Pennsylvania, a subregion of the watershed. We apply a mixed-method approach, combining Social Network Analysis, the analysis of BMP-messaging (i.e. information source, flow, and their influences), and qualitative content analysis of stakeholders' interviews. Key strategic actors were identified through network centrality measures such as degree of node, betweenness centrality, and clustering coefficient. The perceived influence/credibility (by farmers) of BMP-messages and their source, allowed for the identification of strategic entry points for BMP-messages diffusion. Finally, the inductive coding process of stakeholders' interviews revealed major hindrances and opportunities for BMPs adoption. We demonstrate how improved targeting of policy interventions for BMPs uptake may be achieved, by better distributing entry-points across stakeholders. Our results reveal governance gaps and opportunities, on which we draw to provide insights for better tailored policy interventions. We propose strategies to optimize the coverage of policy mixes and the dissemination of BMP-messages by building on network diversity and actors' complementarities, and by targeting intervention towards specific BMPs and actors. We suggest that (i) conservation incentives could target supply chain actors as conservation intermediaries; (ii) compliance-control of manure management planning could be conducted by accredited private certifiers; (iii) policy should focus on incentivizing inter-farmers interaction (e.g. farmers' mobility, training, knowledge-exchange, and engagement in multi-stakeholders collaboration) via financial or non-pecuniary compensation; (iv) collective incentives could help better coordinate conservation efforts at the landscape or (sub-)watershed scale; (v) all relevant stakeholders (including farmers) should be concerted and included in the discussion, proposition, co-design and decision process of policy, in order to take their respective interests and responsibilities into account.

Biochar with enhanced performance prepared based on "graphite-structure regulation" conjecture designed to effectively control water pollution.

In this work, corn straw was used as raw material, Hummers method and activation were used to adjust the graphite structure in biochar, and preparing straw based biochar (H-BCS) with ultra-high specific surface area (3441.80 m2/g), highly total pore volume (1.9859 cm3/g), and further enhanced physicochemical properties. Compared with untreated straw biochar (BCS), the specific surface area and total pore volume of H-BCS were increased by 47.24 % and 55.85 %, respectively. H-BCS showed good removal ability in subsequent experiments by using chloramphenicol (CP), hexavalent chromium (Cr6+), and crystal violet (CV) as adsorption models. In addition, the adsorption capacities of H-BCS (CP: 1396.30 mg/g, Cr6+: 218.40 mg/g, and CV: 1246.24 mg/g) are not only higher than most adsorbents, even after undergoing 5 cycles of regeneration, its adsorption capacity remains above 80 %, indicating significant potential for practical applications. In addition, we also speculated and analyzed the conjecture about the "graphite-structure regulation" during the preparation process, and finally discussed the possible mechanism during the adsorption processes. We hope this work could provide a new strategy to solve the restriction of biochar performance by further exploring the regulation of graphite structure in carbon materials.

Uncertainty in Environmental Micropollutant Modeling.

Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

Residents' Willingness to Pay for Water Pollution Treatment and Its Influencing Factors: a Case Study of Taihu Lake Basin.

This study designs a double-bounded dichotomous questionnaire, and uses the Contingent Valuation Method (CVM) to estimate residents' willingness to pay(WTP) for water pollution control along the Taihu Lake Basin. The results of the returned questionnaire show that 82.76% of the residents are willing to pay. CVM estimation results show that the average WTP of residents for water pollution control is 138.86 yuan/year. In addition, the influencing factors of WTP are explored using a Logistic regression model, and the heterogeneity of WTP among residents of different genders is analyzed. The study found that: (1) The younger the residents, the higher their WTP; (2) The higher the income, the higher the residents' WTP for water pollution control; (3) Residents with higher educational level are more willing to pay; (4) The higher the degree of residents' understanding of water pollution control policies, the higher the WTP; (5) The higher the degree of residents' recognition of pro-environmental behavior, the higher the WTP; (6) Male residents' WTP is mainly affected by cognitive factors such as their understanding of governance policies and their approval of pro-environmental behaviors, while female residents' WTP is mainly affected by personal attributes, such as age, income, and the number of household laborers. Furthermore, this study proposes targeted measures to improve residents' WTP from three aspects: the government enriches the channels for residents to participate in water pollution control, the social media enriches the popularization of water environment knowledge, and the school strengthens the education of environmental protection knowledge, considering the differences in residents' characteristics. Therefore, this study can provide a theoretical reference and decision-making basis for encouraging residents to participate in water pollution control, promote the construction of a beautiful watershed, and provide a reference for other basins.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.