Environmental Science and Ecotechnology最新文献

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for COD_Mn and 0.57 for NH₃N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for COD_Mn and 21.3% for NH₃N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

Climate change and anthropogenic activities are reshaping dryland ecosystems globally at an unprecedented pace, jeopardizing their stability. The stability of these ecosystems is crucial for maintaining ecological balance and supporting local communities. Yet, the mechanisms governing their stability are poorly understood, largely due to the scarcity of comprehensive field data. Here we show the patterns of community temporal stability and its determinants across an aridity spectrum by integrating a transect survey across China's drylands with remote sensing. Our results revealed a U-shaped relationship between community temporal stability and aridity, with a pivotal shift occurring around an aridity level of 0.88. In less arid areas (aridity level below 0.88), enhanced precipitation and biodiversity were associated with increased community productivity and stability. Conversely, in more arid zones (aridity level above 0.88), elevated soil organic carbon and biodiversity were linked to greater fluctuations in community productivity and reduced stability. Our study identifies a critical aridity threshold that precipitates significant changes in community stability in China's drylands, underscoring the importance of distinct mechanisms driving ecosystem stability in varying aridity contexts. These insights are pivotal for developing informed ecosystem management and policy strategies tailored to the unique challenges of dryland conservation.

The ubiquity of refractory organic matter in aquatic environments necessitates innovative removal strategies. Sulfate radical-based advanced oxidation has emerged as an attractive solution, offering high selectivity, enduring efficacy, and anti-interference ability. Among many technologies, sulfite activation, leveraging its cost-effectiveness and lower toxicity compared to conventional persulfates, stands out. Yet, the activation process often relies on transition metals, suffering from low atom utilization. Here we introduce a series of single-atom catalysts (SACs) employing transition metals on g-C₃N₄ substrates, effectively activating sulfite for acetaminophen degradation. We highlight the superior performance of Fe/CN, which demonstrates a degradation rate constant significantly surpassing those of Ni/CN and Cu/CN. Our investigation into the electronic and spin polarization characteristics of these catalysts reveals their critical role in catalytic efficiency, with oxysulfur radical-mediated reactions predominating. Notably, under visible light, the catalytic activity is enhanced, attributed to an increased generation of oxysulfur radicals and a strengthened electron donation-back donation dynamic. The proximity of Fe/CN's d-band center to the Fermi level, alongside its high spin polarization, is shown to improve sulfite adsorption and reduce the HOMO-LUMO gap, thereby accelerating photo-assisted sulfite activation. This work advances the understanding of SACs in environmental applications and lays the groundwork for future water treatment technologies.

Evaluating environmental flow (EF) is pivotal for conserving and restoring riverine ecosystems. Yet, prevalent EF evaluations presume that a river reach's hydraulic conditions are exclusively governed by inflow discharge, presupposing a state of equilibrium in the river channel. This presumption narrows the scope of EF evaluations in expansive alluvial rivers like the Middle Yangtze River (MYR), characterized by marked channel alterations. Here we show the profound channel erosion process and its impact on EF requirements for riparian habitats within the MYR. Our research unveils that: (i) pronounced erosion has led to a mean reduction of 1.0–2.7 m in the riverbed across four sub-reaches of the MYR; (ii) notwithstanding a 37–107% increase in minimal discharges post the Three Gorges Project, the lowest river stages at some hydrometric stations diminished owing to bed erosion, signifying a notable transformation in MYR's hydraulic dynamics; (iii) a discernible rightward shift in the correlation curve between the weighted useable area and discharge from 2002 to 2020 in a specific sub-reach of the MYR, instigated by alterations in hydraulic conditions, necessitated an increase of 1500–2600 m³ s⁻¹ in the required EF for the sub-reach; (iv) it is deduced that macroinvertebrate biomass rapidly decreases as the flow entrains the riverbed substrate, with the maximum survivable velocity for macroinvertebrates being contingent on their entrainment threshold. These findings highlight the importance of incorporating channel morphological changes in devising conservation strategies for the MYR ecosystem.

Organophosphate esters (OPEs) are increasingly recognized as pervasive environmental contaminants, primarily from their extensive application in flame retardants and plasticizers. Despite their widespread presence, the intricacies of OPE bioaccumulation within aquatic ecosystems remain poorly understood, particularly the environmental determinants influencing their distribution and the bioaccumulation dynamics across aquatic food chains. Here we show that water temperature plays a crucial role in modulating the dispersion of OPE in the aquatic environment of Poyang Lake. We quantified OPE concentrations across various matrices, uncovering levels ranging from 0.198 to 912.622 ng L⁻¹ in water, 0.013–493.36 ng per g dry weight (dw) in sediment, 0.026–41.92 ng per g wet weight (ww) in plankton, 0.13–2100.72 ng per g dw in benthic invertebrates, and 0.31–3956.49 ng per g dw in wild fish, highlighting a pronounced bioaccumulation gradient. Notably, the intestines emerged as the principal site for OPE absorption, displaying the highest concentrations among the seven tissues examined. Among the various OPEs, tris(chloroethyl) phosphate was distinguished by its significant bioaccumulation potential within the aquatic food web, suggesting a need for heightened scrutiny. The propensity for OPE accumulation was markedly higher in benthic invertebrates than wild fish, indicating a differential vulnerability within aquatic biota. This study lays a foundational basis for the risk assessment of OPEs as emerging contaminants and underscores the imperative to prioritize the examination of bioaccumulation effects, particularly in benthic invertebrates, to inform future environmental safeguarding strategies.

Accurately predicting the concentration of fine particulate matter (PM_2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM_2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM_2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM_2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM_2.5 by comparing their complexity and effectiveness. We categorized PM_2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM_2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM_2.5 prediction and improve the quality of related studies.

Looking back at over a decade of research by herself and her group, the author advocates the added value of gas phase measurements and the application of mass balances, as well as the synergetic benefits obtained when combining both. The increased application of off-gas measurements for greenhouse gas emission monitoring offers a great opportunity to look at other components in the gas phase, particularly oxygen. Mass balances should not be strictly reserved for modellers but also prove useful while conducting lab experiments and studying full-scale measurement data. Combining off-gas measurements with mass balances may serve not only to quantify greenhouse gas emission factors and aeration efficiency but also to follow dynamic concentration profiles of dissolved components without dedicated sensors and/or to calculate other unmeasured variables. Mass-balance-based data reconciliation allows for obtaining reliable and accurate data, and even more when combined with off-gas analysis.

Chromate [Cr(VI)] contamination in groundwater is a global environmental challenge. Traditional elemental sulfur-based biotechnologies for Cr(VI) removal depend heavily on the synthesis of dissolved organic carbon to fuel heterotrophic Cr(VI) reduction, a bottleneck in the remediation process. Here we show an alternative approach by leveraging sulfur-disproportionating bacteria (SDB) inherent to groundwater ecosystems, offering a novel and efficient Cr(VI) removal strategy. We implemented SDB within a sulfur-packed bed reactor for treating Cr(VI)-contaminated groundwater, achieving a notable removal rate of 6.19 mg L⁻¹ h⁻¹ under oligotrophic conditions. We identified the chemical reduction of Cr(VI) via sulfide, produced through sulfur disproportionation, as a key mechanism, alongside microbial Cr(VI) reduction within the sulfur-based biosystem. Genome-centric metagenomic analysis revealed a symbiotic relationship among SDB, sulfur-oxidizing, and chromate-reducing bacteria within the reactor, suggesting that Cr(VI) detoxification by these microbial communities enhances the sulfur-disproportionation process. This research highlights the significance of sulfur disproportionation in the cryptic sulfur cycle in Cr(VI)-contaminated groundwater and proposes its practical application in groundwater remediation efforts.

Recovering extracellular polymeric substances (EPS) from waste granular sludge offers a cost-effective and sustainable approach for transforming wastewater resources into industrially valuable products. Yet, the application potential of these EPS in real-world scenarios, particularly in paper manufacturing, remains underexplored. Here we show the feasibility of EPS-based biomaterials, derived from anaerobic granular sludges, as novel coating agents in paper production. We systematically characterised the rheological properties of various EPS-based suspensions. When applied as surface sizing agents, these EPS-based biomaterials formed a distinct, ultra-thin layer on paper, as evidenced by scanning electron microscopy. A comprehensive evaluation of water and oil penetration, along with barrier properties, revealed that EPS-enhanced coatings markedly diminished water absorption while significantly bolstering oil and grease resistance. Optimal performance was observed in EPS variants with elevated protein and hydrophobic contents, correlating with their superior rheological characteristics. The enhanced water-barrier and grease resistance of EPS-coated paper can be attributed to its non-porous, fine surface structure and the functional groups in EPS, particularly the high protein content and hydrophobic humic-like substances. This research marks the first demonstration of utilizing EPS from anaerobic granular sludge as paper-coating biomaterials, bridging a critical knowledge gap in the sustainable use of biopolymers in industrial applications.