Remote sensing of fluorescent humification levels and its potential environmental linkages in lakes across China

Satellite Remote Sensing-Implemented Nontargeted Screening of Emerging Contaminant Fingerprints in a River-to-Ocean Continuum through Interpretable Machine Learning: The Pivotal Intermediary Role of Dissolved Organic Matter

Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satellite remote sensing is a cost-effective technology for water quality assessment, accurately measuring ECs in a river-to-ocean continuum remains a significant challenge due to their trace levels. To address this challenge, we innovate a strategy utilizing satellite remote sensing to achieve high-resolution nontargeted EC screening. By employing DOM as an intermediary variable, bridging the gap between satellite remote sensing and ECs in river-to-ocean continua. DOM, including the total sum of ECs, reflects their distribution and spectral sensitivity, enabling satellite sensing to capture their unique fingerprints. In this study, this strategy has enhanced the accuracy of nontargeted EC screening from 32.2 to 95.7% using machine learning. Interpretable machine learning causal inference and SHAP models reveal that shortwave infrared (SWIR) S2-B11 is crucial for EC screening while emphasizing the importance of avoiding multicollinearity with similar SWIR band S2-B12. Additionally, the band reflectance is influenced by the proportion of polarity-related heterogeneity in the ECs. Furthermore, we developed a real-time remote sensing surveillance system featuring interactive maps for nontargeted screening of ECs and GPT-based contamination interpretation.

Structural diversity and environmental impacts of Cladophora mats in a large plateau brackish lake

Filamentous algal blooms (or algal mats) are increasingly recognized as a growing threat to clear lakes worldwide, particularly in the context of climate change and lake eutrophication. Nevertheless, knowledge about filamentous algal mats and their environmental consequences is still limited. In this study, we investigated the structural characteristics and environmental impact of filamentous algae (Cladophora) mats in the brackish water of Qinghai Lake on the Tibetan Plateau. Our results classify the development of Cladophora blooms into three distinct stages: the attachment stage (May to July), the floating stage (August to September), and the decomposition stage (October to November), corresponding to attached, floating, and decaying mats, respectively. The attached mats consist of single layer, while the floating and decaying mats exhibit more complex structures, with two-layer and three-layer formations, respectively. Each layer displays distinct physiological states in the vertical direction, highlighting their structural diversity. The layered structure enables Cladophora mats to better adapt to environmental changes, ensuring long-term stability in the lake ecosystem through the synergistic effects of upper-layer protection, middle-layer growth, and bottom-layer decomposition. Notably, the water surrounding the decaying mats showed significantly elevated concentrations of nitrogen, phosphorus, and dissolved organic matter. Partial Least Squares Path Modeling analysis further revealed that Cladophora mats have a substantial influence on dissolved organic carbon and fluorescent dissolved organic matter, with path coefficients of 0.84 and 0.65, respectively. These findings significantly enhance our understanding of the dynamics of filamentous algal blooms and their environmental impacts, and are crucial for the conservation of lakes with high water quality.

Unraveling the dual roles of dissolved organic matter on the photodegradation of aquatic contaminants: Molecular weight- and type-dependent heterogeneities

Dissolved organic matter (DOM) in natural waters can regulate the behaviors and fates of aquatic contaminants, while the specific effects on contaminant attenuation are highly dependent on its inherent properties [e.g., molecular weights (MW) and types]. In this study, the algae-derived organic matter (AOM) and humic acid (HA) were selected as the representative autochthonous and allochthonous DOMs, which were further fractionated into low MW (LMW, <1 kDa) and high MW (HMW, <1 kDa∼0.45 μm) fractions to evaluate the MW- and type-dependent heterogeneities in the photodegradation of sulfadiazine (SDZ). Results showed that presence of bulk AOM promoted SDZ photodegradation by 2.45 folds while those of the bulk HA inhibited SDZ photodegradation by 1.70 folds due to the higher light screening effects and phenolic antioxidant concentrations. Further analysis revealed obvious MW-dependent heterogeneities that, regardless of DOM types, the HMW-fraction always inhibited SDZ photodegradation while the LMW-fraction promoted photodegradation efficiencies owing to higher carbonyl contents and electron transfer capabilities. In addition, the MW-dependent heterogeneities within DOM samples resulted in different photodegradation pathways and Ecological Structure-Activity Relationship (ECOSAR) calculation showed that most of photodegradation products in the LMW-fraction were more ecotoxicity than the parent SDZ while those in the HMW-fraction exhibited alleviated ecotoxicity. This study indicated that the dual roles of aquatic DOMs on contaminant photodegradation were MW- and type-dependent, and detailed structural composition analysis on DOM matrix was needed for a better assessment of the behaviors and fates of contaminants in aquatic ecosystems.

Valorization of food waste to biofertilizer and carbon source for denitrification with assistance of plant ash and biochar toward zero solid discharge

This study developed a novel strategy for food waste (FW) valorization through incorporating plant ash and biochar into enzymatic hydrolysis of FW. After 12-h hydrolysis of FW with fungal mash, the solid and soluble products were separated and harvested as solid biofertilizer and carbon source for denitrification respectively. Soluble COD produced from plant ash and biochar mediated FW hydrolysis could reach approximately 170 g/L on average, which showed a specific denitrification rate of 26.23-31.33 mg N/g MLVSS/h higher than that with commercial glucose (i.e. 25.92 mg N/g MLVSS/h). The applicability of solid biofertilizers produced from plant ash- or biochar-assisted hydrolysis of FW was evidenced by the higher germination rate of 138-166 % against that without exogeneous additives (122 %). It is expected that the proposed approach can offer an effective solution for upgrading FW into value-added products, while realizing a complete resource recycle with no wastes discharged.

A new insight into the straw decomposition associated with minerals: Promoting straw humification and Cd immobilization

Organic matter (OM) derived from the decomposition of crop residues plays a key role as a sorbent for cadmium (Cd) immobilization. Few studies have explored the straw decomposition processes with the presence of minerals, and the effect of newly generated organo-mineral complexes on heavy metal adsorption. In this study, we investigated the variations in structure and composition during the rice straw decomposition with or without minerals (goethite and kaolinite), as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization. The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix (EEM) fluorescence and Ultraviolet-visible spectroscopy (UV-vis), while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms. The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components, indicating that the degree of straw decomposition and humification were further intensified during incubation. Additionally, the existence of goethite (SG) accelerated the humification of OM. Sorption experiments revealed that the straw humification increased Cd adsorption capacity. Notably, SG exhibited significantly higher adsorption performance compared to the organic matter without minerals (RS) and the existence of kaolinite (SK). Further analysis using FT-IR spectroscopy and XPS verified that the primary mechanisms involved in Cd immobilization were complexion with -OH and -COOH, as well as the formation of Cd-π binds with aromatic C=C on the surface of solid OMs. These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.

Evaluating drivers of PM2.5 air pollution at urban scales using interpretable machine learning

Reducing urban fine particulate matter (PM2.5) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM2.5 will enable the development of targeted strategies to reduce PM2.5 levels. This study introduces a machine-learning model that combines CatBoost and the Tree-Structured Parzen Estimator (TPE) to analyze PM2.5 concentration across 297 cities between 2000 and 2021. SHapley Additive exPlanations (SHAP) were employed to identify the primary factors influencing urban PM2.5 concentrations. The study revealed that the proposed model has high accuracy in predicting urban PM2.5 concentrations, achieving a coefficient of determination (R2) score of 96.44%. Socioeconomic and industrial activity are key drivers of PM2.5 concentrations. This study not only quantifies the primary factors exacerbating or alleviating pollution for each city or province during the 2000-2021 period but also evaluates the influence of operational factors such as technological and public financial expenditures. In 2000, the main contributors to pollution in four heavily polluted cities included substantial nitrogen oxide emissions, inadequate technology investments, and excessive population density and liquefied gas consumption. Due to the rapid reduction in nitrogen oxide emissions, pollution levels in these cities have improved substantially. In the future, the most effective strategies for pollution reduction in these cities will focus on controlling population density and slowing down mining development. The proposed framework serves as a robust evaluation tool and can propose tailored strategies to control PM2.5 concentrations effectively in each city.

Chemodiversity of dissolved organic matter and its association with the bacterial community at a zinc smelting slag site after 10 years of direct revegetation

Dissolved organic matter (DOM) plays a critical role in driving the development of biogeochemical functions in revegetated metal smelting slag sites, laying a fundamental basis for their sustainable rehabilitation. However, the DOM composition at the molecular level and its interaction with the microbial community in such sites undergoing long-term direct revegetation remain poorly understood. This study investigated the chemodiversity of DOM and its association with the bacterial community in the rhizosphere and non-rhizosphere slags of four plant species (Arundo donax, Broussonetia papyrifera, Cryptomeria fortunei, and Robinia pseudoacacia) planted at a zinc smelting slag site for 10 years. The results indicated that the relative abundance of lipids decreased from 18 % to 5 %, while the relative abundance of tannins and lignins/CRAM-like substances increased from 4 % to 10 % and from 44 % to 64 % in the revegetated slags, respectively. The chemical stability of the organic matter in the rhizosphere slag increased due to the retention of recalcitrant DOM components, such as lignins, aromatics, and tannins. As the diversity and relative abundance of the bacterial community increased, particularly within the Proteobacteria, there was better utilization of recalcitrant components (e.g., lignins/CRAM-like compounds), but this utilization was not invariable. In addition, potential preference associations between specific bacterial OTUs and DOM molecules were observed, possibly stimulated by heavy metal bioavailability. Network analysis revealed complex connectivity and strong interactions between the bacterial community and DOM molecules. These specific interactions between DOM molecules and the bacterial community enable adaptation to the harsh conditions of the slag environment. Overall, these findings provide novel insights into the transformation of DOM chemodiversity at the molecular level at a zinc smelting slag sites undergoing long-term revegetation. This knowledge could serve as a crucial foundation for developing direct revegetation strategies for the sustainable rehabilitation of metal smelting slag sites.

A machine learning based framework to tailor properties of nanofiltration and reverse osmosis membranes for targeted removal of organic micropollutants

Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which puts higher demands on the customization of membranes suitable for OMPs removal based on the rejection mechanisms. Here, the pathways of OMPs-targeted optimization for membranes were constructed by using machine learning (ML) to capture the correlations between OMPs removal efficiency with properties of membranes and OMPs. Through expertise assistance and rigorous modeling methodology, an accurate and robust Extreme Gradient Boosting (XGBoost) model was established, which could well recognize the dominant rejection mechanisms of OMPs (i.e., the size exclusion effect and electrostatic interactions). An exemplary application to another dataset of several high-risk OMPs showed how the optimized model could be used to estimate the overall efficiency of OMPs risk control and, more importantly, to provide quantitative guidance on membrane properties for specific removal targets. The satisfying prediction results demonstrated the good generalization of the ML model and consequently its ability to sensitively define the ideal membrane properties for the targeted removal of these (and any other concerned) OMPs. This study provides a feasible and universal ML-based framework to achieve the tailored selection and design of NF/RO membranes for OMPs risk control.

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm

High-precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm-random forest (ChOA-RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: "The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water." Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3-N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3-N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA-RF model is compared with the traditional empirical value model and the particle swarm optimization-random forest (PSO-RF) model. The results show that the ChOA-RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO-RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high-precision evaluation of regional water environment quality but also for other machine learning fields.

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

Chemical characteristics and health risk evaluation of natural waters in the Du River Source National Nature Reserve: A case study in Zhushan County, Hubei Province, China

To study the distribution of trace elements in natural water of the Du River Source National Nature Reserve and to assess the water quality and health risks, Zhushan County in Hubei Province was selected as the study area. Element content in 361 natural water samples collected from Zhushan County were measured by ICP-MS, ICP-OES, and HG-AFS. The main anions and cations present in water samples from Zhushan County are Ca2+ and HCO3-. The water chemistry is predominantly influenced by the weathering of carbonate rocks. The water samples with high content of selenium (Se) (0∼82.9 μg/L, mean 4.6 μg/L) in natural water in Zhushan County are mainly distributed in the northern part of Zhushan. The strontium (Sr) content of 49.6% of the water samples (0.001-2.177 mg/L, mean 0.234 mg/L) reached the criteria of natural mineral water for drinking in China (Sr ≥ 0.2 mg/L), which is distributed throughout the county. The high content of metasilicic acid (H2SiO3) (0.026-35.910 mg/L, mean 12.598 mg/L) and zinc (Zn) (0∼407.218 μg/L, mean 12.406 μg/L) are concentrated in northern Zhushan County. 99.7% water samples were freshwater and 98.9% meet the criteria of "good" water quality. All of the natural water samples have low health risk and low heavy metal pollution. 6.1% water samples meet the criteria of Se-type mineral water, while 45.4% meet the criteria of Sr-type mineral water, and 4.4% water samples meet the criteria of "low sodium, high Se, and high Sr" mineral water. Zhushan County has the potential for Se-type mineral water and Sr-type mineral water development. The findings of this study hold immense significance for the public health implications of drinking water in Du River Source, thereby offering valuable insights for effective water resources management.

Ecological patterns of phytoplankton across lake cross-section: insights into co-evolution of physicochemical conditions in Chashma Lake on Indus River

Physicochemical properties of water influence planktonic diversity and distribution, which is essential in obtaining basic knowledge of aquatic biodiversity. Thus current study aims to investigate the spatiotemporal diversity, abundance ratio, and distribution of phytoplankton species and their association with water quality parameters of Chashma Lake, Pakistan. During the study period from 2018 to 2019, we measured 13 physicochemical parameters across three selected sampling sites (S1, S2, and S3) in Chashma Lake, revealing both spatial and temporal variability. Dissolved oxygen (DO) was higher in S3, while S1 exhibited higher alkalinity levels, carbon dioxide, phosphorus, and chloride levels. The study identified 77 phytoplankton species grouped into five taxonomic categories, with Cyanobacteria dominating (39.90%), followed by Chlorophyta (33.4%) and Bacillariophyta (24.88%). Euglenozoa and Ochrophyta were less abundant (1.3% and 0.41%, respectively). Spatial variations in phytoplankton distribution were noted, with Chlorophyta being more abundant at S2, Bacillariophyta and Cyanobacteria at S1, and Euglenozoa dominating at S3. Canonical Correspondence Analysis (CCA) revealed the influence of various physicochemical parameters on phytoplankton distribution. This comprehensive study provides valuable insights for the ecological assessment and monitoring of water bodies. It is recommended that continuous monitoring is required to capture long-term trends, further explore the specific environmental drivers impacting phytoplankton dynamics, and consider management strategies for maintaining water quality and biodiversity in Chashma Lake.

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

Enhancing nutrient absorption through the influence of mangrove ecosystem on flow rate and retention time in salt marshes

This study investigates the impact of pneumatophores (the aerial roots of Avicenna marina) on water flow rate, retention time, contact time, and consequently on nutrient absorption through the sediment in sub-tropical salt marshes. The goal is to realize how the density of mangroves in salt marshes influences the kinematic factors of streamflow at estuaries. To this end, a field experiment was carried out to assess nutrient and organic compound levels in the sediment and water samples, spanning six sampling stations along the Chabahar River discharging to the Chabahar Bay, Iran. Then, we delved into the influence of altering environmental parameters, such as density and geometry, on the kinematic features of the flow through statistical analysis and hydraulic modeling. The results showed that the aerial roots reduce the flow rate and increase both retention and contact times. The longest retention time was observed at station #5 due to increased vegetation density and decreased instream velocity. In addition, measurements of total organic matter, total organic carbon, and total nitrogen indicated that an extended contact time resulted in increased absorption flux to the stream by sediments. As a result, pneumatophores can serve as an effective sink for organic matter in ecotones in salt marshes.

Seasonal and interannual changes (2005-2021) of lake water quality and the implications for sustainable management in a rapidly growing metropolitan region, central China

A series of restoration measures such as municipal wastewater treatment and aquaculture closures have been implemented in Wuhan City during recent years. In order to explore the impact of restoration measures and climate change on lake water quality, long-term (2005-2021) water quality data of 47 lakes were explored to reveal spatiotemporal changes in lake water quality. Percentages of polluted lakes were calculated according to six water-quality parameters, including total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), biological oxygen demand (BOD), chemical oxygen demand using potassium permanganate as oxidant (CODMn) and petroleum contamination (PET), at interannual and monthly timescales. At the interannual timescale, percentages of COD, BOD, CODMn and PET pollution decreased significantly, suggestive of water quality improvement during recent years. At the monthly timescale, low percentages of NH3-N and BOD pollution in March 2020 probably resulted from the sharp reduction in human activities during the COVID-19 lockdown. At the monthly timescale, temperature was positively correlated with percentage of CODMn pollution, but negatively correlated with percentage of NH3-N pollution; precipitation was negatively correlated with percentage of BOD pollution. The similarity of water-quality parameters generally decreased with an increase in geographical distance between each pair of lakes. However, the regression coefficients between the similarity of lake water quality and the geographical distance decreased with time, probably resulting from enhanced similarity of water quality parameters among all lakes with rapid urbanization. Our results highlight the importance of active restoration measures for sustainable management of lakes in Wuhan City, as well as in similar developing regions.

Evaluation of irrigation, drinking, and risk indices for water quality parameters of alpine lakes

Alpine lakes are aquatic ecosystems that maintain and regulate water supply for the downstream streams, rivers, and other reservoirs. This study examined the water characteristics of various alpine lakes in Gilgit-Baltistan, Northern Pakistan. For this purpose, water was sampled and investigated for basic parameters, anions, and cations using the multi-parameter analyzers and atomic absorption spectrophotometer. Physicochemical parameters of alpine lakes were noted under the World Health Organization water guidelines, except for fluoride (F-) and turbidity in 4.3% and 36% of samples, respectively. Water quality index (WQI) classified samples (93%) as excellent and good quality (7%). Results showed maximum chronic daily intake values (0.14 ± 0.01 mg/kg-day) for nitrate (NO3-) and hazard quotient (0.80 ± 0.24) for F- in children via water intake from Upper Kachura and Shausar Lakes, respectively. Statistical analyses of Piper and Gibbs's plots revealed that the water quality is mainly characterized by bedrock geology.

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

Spatiotemporal variations, health risk assessment, and sources of potentially toxic elements in potamic water of the Anday Stream Basin (Türkiye), Black Sea Region

Monitoring and protecting freshwater habitats are paramount for a sustainable water management perspective. This study investigated potentially toxic elements (PTEs) in the potamic water of the Anday Stream Basin (Türkiye), Black Sea Region, for a hydrological year (from May 2020 to April 2021). Among PTEs, the highest average values were recorded for sodium (Na) at 41.3 mg/L and the lowest for mercury (Hg) at 0.009 μg/L and noted under quality guidelines. The stream was found to be at the level of "Low Heavy Metal Pollution" and "Low Contamination" based on the ecotoxicological risk indices. The highest calculated hazard quotient (HQ) value of 1.21E-02 for Cd was noted in the children via the dermal pathway and the lowest of 6.91E-06 for Fe in adults via the ingestion pathway. Results revealed a higher hazard index (HI) value of 1.50E-02 for Cd to children and the lowest of 1.98E-05 for Fe to adults. As a result of applying agricultural risk indices, the stream showed sodium adsorption ratio values less than 6 and was found to be "Excellent" for agriculture. However, the sodium percentage values were less than 20 and found "Permissible" and the magnesium hazard > 50 and noted as "Unsuitable" for agriculture. Statistical analysis revealed that natural factors mainly attributed to PTE contamination of the Anday Stream Basin.

Analysis of taiga and tundra lake browning trends from 2002 to 2021 using MODIS data

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change.

Assessment of land use change and carbon emission: A Log Mean Divisa (LMDI) approach

Changes in land use have a notable influence on carbon emissions since they can affect the levels of carbon stored in both soil and vegetation. To effectively analyze the factors influencing carbon emissions from land use change, the Log Mean Divisa (LMDI) method is commonly employed. The LMDI method is a decomposition analysis that dissects changes in carbon emissions into different factors, including shifts in land use patterns, population growth, economic activity, and energy intensity. This approach enables the identification of specific drivers of carbon emission changes and the development of targeted policy interventions to address them. To explore the relationship between land use change, carbon emissions, and the LMDI method, a case study analysis can be conducted. This involves selecting a particular region or country experiencing land use change and examining the factors driving these transformations. Subsequently, the LMDI method can be applied to decompose the changes in carbon emissions within the selected region or country, thereby pinpointing the major contributors to these changes. In our study, we observed the necessity of regulating energy consumption and greenhouse gas emissions in urban communities through sustainable practices and technologies. The research highlighted variations in energy consumption, emissions, renewable energy utilization, and public transportation usage among selected cities in China. Moreover, the study demonstrated land use patterns and their associated carbon emissions, alongside the findings of the LMDI analysis, which explored carbon emissions based on different land use patterns. The study illuminates the importance of understanding the relationship between land use change and carbon emissions, employing the LMDI method as a valuable analytical tool. It underscores the significance of sustainable practices and technologies in mitigating carbon emissions in urban areas and provides insights into the role of land use patterns in shaping carbon emission outcomes.

The detrimental effects of heavy metals on tributaries exert pressure on water quality, Crossocheilus aplocheilus, and the well-being of human health

The escalating presence of heavy metals (HMs) in the Panjkora River water and their impact on fish pose a significant challenge to both the ecological community and human health. Consequently, a study was conducted with the primary aim of elucidating their influence on human health-related issues. To address this, the concentrations of heavy metals, including arsenic (As), cadmium (Cd), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn), in both water and the fish species Crossocheilus diplocheilus were investigated across various locations within the study area. The quantification of HMs concentration was carried out utilizing an atomic absorption spectrophotometer. The highest concentration in water was found as 0.060 mg/L for Pb and lowest for Fe, whereas the highest concentration in fish was 2.028 mg/kg for Pb and lowest for As. Human health risk associated with fish eating was evaluated by using health risk indices (HRI) for non-carcinogenic health risks and targeted cancer risk (TR) for carcinogenic health risks. The values of the health risk index (HRI) were found greater than 1 except Fe (0.0792), Zn (0.782), and Mn (0.541). The highest mean HRI > 1 was recorded for As (62.99), Cd (26.85), and Pb (10.56). This implies that fish consumption from river Panjkora is not safe up to some extent. Similarly, the TR value for As, Cd, and Pb was found 2.8 [Formula: see text], 1.6 [Formula: see text], 2.8 ×[Formula: see text] which showed cancer risk. There is a detected risk to human health associated with the consumption of fish from the Panjkora River. The government must implement adaptive measures to address this significant issue of water pollution in the study area. Additionally, there is a need for further extensive and prolonged research studies in this context.

Temporal variation of water quality parameters in the lacustrine of the Thrace Region, Northwest Türkiye

Thrace is a part of the Marmara Region northwest of Türkiye. This region hosts several lentic ecosystems used for irrigation and drinking water supply. The present study was conducted to analyze the temporal distributions of water quality parameters (WQPs) of lentic ecosystems (lacustrine habitats), including lakes (L1-L2), reservoirs (R1-R12), and ponds (P1-P19) of the Thrace Region. Thirty-three lacustrine habitats were identified in the region. Freshwaters were collected in the wet (end of winter) and dry (end of summer) seasons of 2021-2022 and tested for 12 WQPs. Data was evaluated for the water quality index (WQI) and nutrient pollution index (NPI) and their overall quality level. For the evaluation of non-carcinogenic health risk indices of WQPs, the chronic daily index (CDI), hazard quotient (HQ), and hazard index (HI) were applied. Cluster analysis (CA), Pearson correlation index (PCI), and principal component analysis (PCA) were used to classify the lacustrine habitats and identify the source of WQPs. The average values were as follows: 9.28 mg/L for dissolved oxygen (DO), 94.6% for oxygen (O2) saturation, 9.29 for pH, 613 μS/cm for electrical conductivity (EC), 3.96 NTU for turbidity, 358 mg/L for total dissolved solids (TDS), 3.17 mg/L for nitrate (NO3), 0.05 mg/L for nitrite (NO2), 1.01 mg/L for phosphate (PO4), 78.5 mg/L for sulfate (SO4), and 102 mg/L for chloride (Cl). Results showed a significant increase in WQPs, including NO3, NO2, and PO4, in the wet season, while the salinity decreased from the dry to wet season. Results revealed that HI values of water contaminants in lacustrine habitats were noted to be less than one. Based on determined WQPs, the present study recommends using lacustrine water habitats for irrigation, drinking, and other domestic and industrial purposes.

Increased dominance of terrestrial component in dissolved organic matter in Chinese lakes

The source composition of chromophoric dissolved organic matter (CDOM) in lakes is closely related to regional environmental changes, human activities, and the carbon cycle. The spectral slope ratio (SR) is an important parameter of CDOM optical components, and combined with remote sensing technology, the source composition of CDOM can be tracked comprehensively and efficiently in large regions. Here, we proposed a CDOM source tracking remote sensing model (CDOM-SR) based on the hue angle (α) to assess the spatial pattern and long-term trend of the CDOM source composition in Chinese lakes (surface area ≥ 1 km2) from 1986 to 2021. Validation results show that the CDOM-SR model has a good SR estimation performance with a median absolute percentage difference, root mean square deviation, median ratio, and median deviation of 17.91 %, 0.23, 1.02, and 0.03, respectively. We found that the average SR of Chinese lakes presents an obvious spatial pattern of high in the west and low in the east due to the difference in human activity intensity and the natural geographical environment. Additionally, we found that the average SR of Chinese lakes from 1986 to 2021 decreased at a rate of - 0.06/10 years, of which 64.37 % of lakes decreased significantly, 15.42 % of lakes had no significant change, and only 20.20 % of lakes increased. The widespread decrease in the average SR indicates that the increasing human activity discharge of terrestrial organic matter has had an important impact on the source composition of the CDOM in Chinese lakes. Our results provide a new resource for remote sensing monitoring of CDOM sources and important insights into lake carbon cycling under the influence of ongoing human activities.

A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China

Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.

Effect of the molecular weight of DOM on the indirect photodegradation of fluoroquinolone antibiotics

Dissolved organic matter (DOM) is ubiquitous and widespread in natural water and influences the transformation and removal of antibiotics. Nevertheless, the influence of DOM molecular weight (MW) on the indirect photodegradation of antibiotics has rarely been reported. This study attempted to explore the influence of the molecular weight of DOM on the indirect photodegradation of two fluoroquinolone antibiotics (FQs), ofloxacin (OFL) and norfloxacin (NOR), by using UV-vis absorption and fluorescence spectroscopy. The results showed that indirect photodegradation was considered the main photodegradation pathway of FQs in DOM fractions. Triplet-state excited organic matter (3DOM*) and singlet oxygen (1O2) were the main reactive intermediates (RIs) that affected the indirect photodegradation of FQs. The indirect photodegradation rate of FQs was significantly promoted in DOM fractions, especially in the low molecular weight DOM fractions (L-MW DOM, MW < 10 kDa). The results of excitation-emission matrix spectroscopy combined with parallel factor analysis (EEM-PARAFAC) showed that terrestrial humic-like substances had a higher humification degree and fluorophore content in L- MW DOM fractions, which could produce more 3DOM* and 1O2 to promote the indirect photodegradation of FQs. This study provided new insight into the effects of DOM at the molecular weight level on the indirect photodegradation of antibiotics in natural water.

Effect of microplastics on the degradation of tetracycline in a soil microbial electric field

The degradation of organic pollutants and the adsorption of organic pollutants onto microplastics (MPs) in the environment have recently been intensively studied, but the effects of biocurrents, which are widespread in various soil environments, on the environmental behavior of MPs and antibiotic pollutants have not been reported. In this study, it was found that polylactic acid (PLA) and polyvinyl chloride (PVC) MPs accelerated the mineralization of humic substances in microbial electrochemical systems (MESs). After tetracycline (TC) was introduced into the MESs, the internal resistance of the soil MESs decreased. Additionally, the presence of MPs enhanced the charge output of the soil MESs by 40% (PLA+TC) and 18% (PVC+TC) compared with a control group without MPs (424 C). The loss in MP mass decreased after TC was added, suggesting a promotion of TC degradation rather than MP degradation for charge output. MPs altered the distribution of the highest occupied molecular orbitals and lowest unoccupied molecular orbitals of TC molecules and reduced the energy barrier for the TC hydrolysis reaction. The microbial community of the plastisphere exhibited a greater ability to degrade xenobiotics than the soil microbial community, indicating that MPs were hotspots for TC degradation. This study provides the first glimpse into the influence of MPs on the degradation of TC in MESs, laying a theoretical and methodological foundation for the systematic evaluation of the potential risks of environmental pollutants in the future.

Carbon dots/TiO2 enhanced visible light-assisted photocatalytic of leachate: Simultaneous effects and Mechanism insights

The persistence and potential fouling risks associated with humic substances and bacteria present in leachate have gained increasing attention. Therefore, developing efficient and environmentally compatible technologies for their removal is essential. This study presented the hydrothermal synthesis of a photocatalyst by coupling carbon dots (CDs) and bulk TiO2 (P25). The incorporation of CDs increased the photocatalytic performance by enhancing visible light absorption and facilitating the separation of electrons/holes. Compared to P25, the CDs/P25 exhibited optimal photocatalytic activity for humic acid (HA), fulvic acid (FA), and leachate, with 1.64, 1.02, and 1.12 times higher activity, respectively. Remarkedly, the CDs/P25 accelerated the conversion of large HA molecules into small molecules at a faster rate and higher amount than the bulk P25, due to the increase of hydroxyl radicals, monoclinic oxygen radicals, and superoxide radicals. Additionally, the CDs/P25 demonstrated better bacterial-deactivation ability than the P25, with dead bacteria percentages of 83.3% and 34.6%, respectively. This study provides a promising strategy for efficiently applying CDs/P25 photocatalysis to leachate treatment.