Environmental Monitoring and Assessment最新文献

Coal-fired power plants generate coal combustion residuals (CCRs), which are typically disposed of in landfills and surface impoundments that must be monitored to ensure that hazardous constituents such as arsenic (As) and selenium (Se) do not escape into the surrounding environment. Traditional methods for monitoring surface impoundments, such as sampling discharge of all outlets of hydraulic structures to approximate CCR concentrations, are highly resource-intensive and largely manual, posing a significant cost challenge for the stakeholders responsible for them. The use of grass species as bioindicators may offer a more efficient and sustainable method for long-term monitoring of CCR impoundments. In this study, we sought to determine whether three species of grass could serve as effective bioindicators for detecting changes in As and Se soil contamination profiles and provide an evaluation of three technologies used for evaluating plant health. Lolium perenne, Panicum virgatum, and Paspalum notatum were treated with As or Se and monitored with a spectroradiometer and multispectral camera to detect a spectral response to the chemical stress. Metal transfer analysis revealed that there was a significant change in metal concentrations in plant tissue across the grass species and different treatments, with Paspalum virgatum providing the best dose response after a week of Se treatment. Lolium perenne provided a response past the Se toxicity threshold of 5 mg/kg. We then expanded this study to a field scale to determine if our results would translate to an environmentally relevant scale. Unmanned aerial vehicle (UAV) results suggested Lolium perenne was efficient in determining whether CCR was present in soils but lacked sensitivity to differentiate between low and high loadings. Monthly sampling also revealed that metal concentration in plant tissue decreased as plants underwent senescence. Data collected with the UAV proved to be the most proficient method of determining a dose response.

This study aimed to conduct the first integrated ecological assessment of the tropical Kullursandai Reservoir, India, by evaluating the interrelationships between water quality, fish diversity, invasive species, and fishery productivity. Over a 24-month period, we monitored key physico-chemical parameters, conducted comprehensive fish sampling, and analyzed historical catch data. Multivariate statistics, including principal component analysis (PCA), were used to identify dominant environmental drivers. Results revealed distinct seasonal patterns in water quality, with the reservoir maintaining a mesotrophic status. The fish assemblage was dominated by invasive Tilapia (Oreochromis spp.), which collectively constituted nearly half of the total catch, indicating a significant shift in community structure and potential ecological risks. Despite this, fish diversity indices indicated a moderately diverse and structured community. A notable finding was the substantial discrepancy between the theoretical maximum sustainable yield (8.33–12.82 kg/ha/yr) and the actual fishery yield (mean 153 kg/ha/yr), highlighting the role of adaptive management and favorable hydrology. PCA identified three key environmental gradients regulating the ecosystem: mineral-alkalinity, transparency-oxygen, and nutrient enrichment, with phosphorus levels strongly linked to tilapia dominance. Collectively, these results demonstrate that this study provides critical thresholds for guiding nutrient monitoring, invasive species control, and sustainable harvest strategies in tropical reservoirs, underscoring the delicate trade-off between high fishery production and biodiversity conservation.

The spatial structure of aquatic communities is shaped by both environmental filtering and dispersal limitation. In watershed ecosystems, species may disperse through overland and watercourse pathways, and their dispersal abilities can influence their reliance on these pathways. In this study, we examined environmental factors, algae, macroinvertebrates, and zooplankton in a near-natural mountainous watershed. Distance-decay relationships (DDR) and linear regressions (LR) were used to evaluate communities’ similarity with geographic distance and to assess dispersal limitation. Variation partitioning analysis (VPA) quantified the relative contributions of overland and watercourse pathways and examined how macroinvertebrate dispersal ability affects dependence on these pathways. The results indicate that all three aquatic communities experienced significant dispersal limitation, which explained more variation in community structure than environmental filtering. Watercourse pathways accounted for communities’ structure more effectively than overland pathways, while macroinvertebrates with higher dispersal abilities showed lower reliance on watercourse pathways. These findings provide a scientific basis for watershed ecological management and underscore the importance of considering multiple dispersal pathways in studies of community assembly.

Volatile organic compounds (VOCs) are major atmospheric pollutants commonly derived from the fossil fuel combustion. The concentration of VOCs in the atmosphere and its dynamics have widely been used to evaluate their source, formation processes, residence time, and photochemical reactions involved in the atmosphere. However, little is known about the effect of UV degradation of VOCs during their transport from the source to the study area, which always reduces accuracy in the understanding of VOCs’ characteristics in the atmosphere. In the present study, we investigated the fractionation of carbon isotopes (¹³C/¹²C) of toluene (methyl benzene) during UV degradation (254 nm UV-C), a common VOC, in the atmosphere. The observed fractionation (α = 0.9935) confirms that UV-C photodegradation enriches ¹³C in residual toluene, but applying this result to quantify sources or degradation in the atmosphere requires further constraints and assumptions. This correlation thus can be useful for the quantitative illustration of the environmental behavior of toluene (e.g., excretion sources, transfer, UV degradation, deposition) in the atmosphere and biosphere.

CDOM, an important ocean colour product, accounts for 90% of non-water UV absorption in the upper ocean. CDOM absorption triggers photochemical reactions resulting in the release of greenhouse gases and alters microbial bioavailability of organic matter. The three different approaches for retrieving a_dg(λ) from satellite data were validated using a_CDOM(λ) generated from OC-CCI-derived remote sensing reflectance (R_rs) and in situ measured a_CDOM(λ). The multiple linear regression (MLR) model performed better than the two exponential decay models in quantifying CDOM in the UV region. The better performance of R_rs-based algorithms indicated that absorption-based algorithms need considerable improvement when compared to algorithms based on the combined absorption by detrital matter and CDOM (a_dg(λ)). As a result, the absorption-based algorithm was modified as the ASCDOM algorithm, which demonstrated improved retrieval at 275, 355, 38 and 412 nm for a_CDOM(λ). The ASCDOM algorithm’s strong statistical performance highlights its accuracy in retrieving satellite products for water quality evaluations and ocean colour monitoring.

5G is currently under development in Greece, with operators adopting different strategies and rollout schedules. Meanwhile, 4G has reached a highly mature stage, supporting the initial deployment of 5G through NSA (Non-Standalone) architecture. Cellular service is provided by a combination of systems: the legacy 2G system operational mainly in rural areas, the advanced 4G, and the emerging 5G systems, 3G was phased out in 2023. This transitional phase renders the cellular landscape both dynamic and region-specific. Recurrent, large-scale measurement campaigns are essential to effectively track networks development. This study presents findings from a year-long EMF measurement campaign (July 2023-June 2024) in the Peloponnese Region of Greece. Using frequency-selective equipment, the campaign combined spatially distributed short-term ground measurements with long-term monitoring at fixed locations. Data was analyzed by service type (e.g. cellular, WiFi), cellular system (2G/3G/4G/5G), and network operator. All measured electric field values remained well below the stringent Greek safety limits; the highest ground-level measurement was approximately 18 times lower than the limit. The 900 MHz band was identified as the dominant contributor to EMF exposure, followed by the 1800 MHz band used by 4G and 2G networks. 4G contributed the most (53%), while 5G impact was only 3%, reflecting its early stage of deployment in the region. Long-term monitoring revealed peak exposure between 15:00 and 21:00, coinciding with increased network usage. The findings provide reassurance regarding public safety, highlight the value of combining spatial and temporal analysis, and offer baseline data for future studies as 5G networks continue to expand.

Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll-a (chl-a), a common proxy for algal biomass; however, the dynamic nature of rivers complicates approaches traditionally applied to lakes and oceans. During 2021–2023, we collected discrete samples for laboratory measurement of chl-a and measured in situ chl-a fluorescence during a series of longitudinal boat surveys along a 220-km reach of the lower Hudson River. Surveys were timed to coincide with Sentinel-2 satellite overpasses. We first investigated relations between laboratory-measured chl-a concentration and field-measured chl-a fluorescence, observing a weak correlation (r² = 0.25) that improved substantially after splitting data by day (mean r² = 0.53). Separately, to estimate chl-a fluorescence using satellite data, we developed a series of random forest models leveraging the rich fluorescence dataset collected. We tested three model types: individual day models, leave-one-out models trained on all days except a holdout test day, and a single pooled model trained on all days. Generally, individual day models exhibited lowest error (mean of mean absolute error [MAE] = 0.16 relative fluorescence units [RFU]), followed by the single pooled model (MAE = 0.22 RFU). Daily holdout models showed highest error (mean MAE = 0.40 RFU); this approach was intended to represent model performance on a day unseen in the training set, providing a more conservative estimate of performance than the more traditional pooled approach. Findings from both analyses emphasize the importance of considering temporal variability when modeling riverine systems.

The Vaal River is a vital water source for domestic, industrial, and agricultural use in South Africa’s economic heartland, but its water quality is increasingly threatened by nutrient pollution, resulting in nuisance algal blooms and invasive aquatic plant growth. This study applies an Algal Problem Index (API) to assess biological water quality and identify risks associated with algae and cyanobacteria in the Vaal River’s upper reaches. Water samples were collected over a 12-month hydrological year from 16 sites, including sites in the main stream, major tributaries, and reservoirs. Cyanobacteria and algae were identified, and analyses of physico-chemical parameters and chlorophyll-a concentrations were conducted. Multivariate statistical analysis was used to evaluate relationships between environmental conditions and algal-related problems. Results revealed 16 taxa, including Microcystis aeruginosa and Dolichospermum circinale, responsible for bad tastes, foul odours, and potential toxin production. Tributaries such as the Riet Spruit and Klip River were major sources of nutrient loading, although low light conditions limited algal growth in certain areas. Elevated API scores increased downstream, reflecting degrading water quality from the Vaal Dam to the Vaal River barrage. This study demonstrated the innovative and practical application of the API as a visual tool integrating multiple biological indicators into a single, easily interpretable measure of water quality. By summarising complex algal-related risks, it can provide water managers with a rapid, practical way to assess ecological conditions, and it can be applied in other catchments facing similar challenges.

Disinfection is a critical process in water and wastewater treatment to pathogen control and achieve Sustainable Development Goal (SDG-6). However, the formation of intermediate compounds during disinfection, known as disinfection byproducts (DBPs), poses adverse effects to human health. Trihalomethanes (THMs) and haloacetic acids (HAAs) are the major regulated halogenated DBPs formed during chlorination. In India, chlorination remains the conventional disinfection method in water and wastewater treatment systems. This study investigated the formation of THMs and HAAs in chlorinated water and wastewater samples from selected treatment facilities in Tamil Nadu, South India. Results indicated that regulated THMs (chloroform, bromodichloromethane, dibromochloromethane, and bromoform) were consistently detected across all seasons, with chloroform being the predominant species (226 µg/L). Trace amounts of HAAs were also detected, with elevated THM formation observed during summer. Additionally, the disinfection efficiency of alternative methods—chloramination, ozonation, UV irradiation, and sequential addition of disinfectants were evaluated. Lab-scale studies on DBP formation reveal distinct trends among different treatment methods. This study comparatively evaluated the formation of total trihalomethanes (TTHMs) in water treatment plant (WTP) and sewage treatment plant (STP) effluents under different disinfectants. In WTP samples, chlorine disinfection at a 5 mg/L dosage generated the highest TTHM concentration (197 µg/L), followed by chloramine (171 µg/L). This result was consistent in lab-scale studies with STP samples, where chlorination also resulted in the maximum TTHM formation, reaching 135.2 µg/L. In contrast UV irradiation generates significantly lower THMs and HAAs levels, while ozonation results in even fewer byproducts. Notably, the combination of chloramine with UV light proved most effective, yielding the lowest TTHMs concentrations of 32.2 and 35 µg/L. The lowest concentrations are observed with UV/ozone treatment with turbidity reduction. This hierarchy (chlorine (Cl₂) > chloramine (NH₂Cl) > UV > ozone > UV/ozone) highlights THMs and HAAs mitigation in water treatment processes. These findings highlight the necessity of adopting alternative disinfection strategies to minimize DBP-related health risks while ensuring effective microbial inactivation. The study also assessed the THMs and HAAs formation potential and bromine incorporation factor to optimize chlorine disinfectant dosage and emphasize the need for bromine removal in source water.

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Plastic pollution has become one of the most significant threats to the environment and human health of the twenty-first century, with more than 300 million tons of waste generated annually, and conventional disposal methods are inadequate. To address this challenge, recent research has increasingly shifted toward biodegradation and upcycling as sustainable alternatives. Microbial degradation of synthetic plastics has shown advancement. This includes the introduction of novel strains like Aspergillus niger MG654699 for the 3.6% and 5% degradation of polyethylene terephthalate and polystyrene, respectively. Also, Streptomyces sp., Methylobacterium, Arthrobacter, and Sphingomonas have been studied to be responsible for mulch film degradation. Advances in metagenomics have further revealed the complexity of microbial consortia for driving these processes, whereas kinetic modeling has provided insights into degradation rates and conditions. Building on this foundation, artificial intelligence and machine learning are now expediting enzyme discovery, optimizing degradation pathways, and enabling intelligent waste management systems. Similarly, biosensors based on Vibrio fischeri and Escherichia coli improve monitoring by detecting plastic monomers. Beyond degradation, the integration of microbial and chemical processes has enabled the upcycling of plastic monomers into value-added products such as polyhydroxyalkanoates, vanillin, bacterial nanocellulose, fuels, and biochemicals, promoting a circular bioeconomy. These advances highlight a paradigm shift from waste accumulation to resource recovery, underscoring the potential of biotechnology and engineering innovations to transform plastic management. The review concludes by highlighting the challenges of scalability, environmental variability, and policy support while positioning biodegradation and upcycling as integrated strategies for a sustainable and resilient future.

Graphical Abstract