Environmental Monitoring and Assessment最新文献

The Sontecomapan lagoon (Mexico) is a Ramsar site within the Los Tuxtlas Biosphere Reserve, facing the Gulf of Mexico. Although the site has a protected area status, it is vulnerable to microplastic contamination, whose long-term effects are uncertain. This study gives the first approach to the degree of contamination by microplastics in surface waters, zooplankton, and sediments in the lagoon. The samples in these three environmental compartments were collected in June 2018 and analyzed in the laboratory to extract and quantify the microplastics. The microplastics sampled were classified into fibers, fragments, and foams and identified as polyester, acrylic, and rayon, among others. In the surface waters, the mean concentration of microplastics was 7.5 ± 5.3 items/L, which is higher than the values registered in other protected coastal systems, perhaps because of differences in the methods used. Zooplankton, represented by copepods, luciferids, and chaetognaths, showed concentrations of 0.002 ± 0.005, 0.011 ± 0.011, and 0.019 ± 0.016 items/individual, respectively. These values were low compared to systems with high anthropic influence, and the differences between the three kinds of organisms were attributed to their feeding habits. In the sediments, the mean concentration was 8.5 ± 12.5 items/kg, lower than the values registered in sites of high human impact; the maximum value here found (43 items/kg) was recorded in the internal part of a lagoon arm of almost stagnant water. In general, the degree of contamination by microplastics in the lagoon was low; however, their presence indicates a potential risk to the biota.

The study was conducted in Cerritos, San Luis Potosí, México, near the Guaxcama mine, focused on environmental contamination (groundwater and agricultural soil) from antimony (Sb), arsenic (As), lead (Pb), cadmium (Cd), and mercury (Hg). In March 2022, 20 agricultural soil and 16 groundwater samples were collected near the historically cinnabar (HgS)- and arsenopyrite (FeAsS)-rich Guaxcama mine. Hydride generation atomic fluorescence spectrometry (HG-AFS) for As, cold vapor atomic fluorescence spectrometry (CV-AFS) for Hg, and inductively coupled plasma optical emission spectrometry (ICP-OES) for Cd, Pb, and Sb were used for the determinations of potentially toxic elements (PTEs). While concentrations of Cd, Hg, Pb, and Sb in groundwater were below detection limits, As levels exhibited a range from 40.9 ± 1.4 to 576.0 ± 1.0 µg/L, exceeding permissible limits for drinking water (10 µg/L). In agricultural soil, As was between 7.67 ± 0.16 and 24.1 ± 0.4 µg/g, Hg ranged from 0.203 ± 0.018 to 2.33 ± 0.19 µg/g, Cd from 2.53 ± 0.90 to 2.78 ± 0.01 µg/g, and Pb from 11.7 ± 1.2 to 34.3 ± 4.1 µg/g. Only one study area surpassed the Mexican As soil limit of 22 µg/g. Sequential extraction (four-step BCR procedure) indicated significant As bioavailability in soil (fractions 1 and 2) ranging from 3.66 to 10.36%, heightening the risk of crop transfer, in contrast to the low bioavailability of Hg, showing that fractions 1, 2, and 3 were below the limit of quantification (LOQ). Crucial physicochemical parameters in soil, including nitrate levels, pH, and organic matter, were pivotal in understanding contamination dynamics. Principal component analysis highlighted the influence of elements like Fe and Ca on phytoavailable As, while Pb and Cd likely originated from a common source. Ecological risk assessments underscored the significant impact of pollution, primarily due to the concentrations of Cd and Hg. Non-cancer and cancer risks to residents through As poisoning via contaminated water ingestion also were found. The hazard index (HI) values varied between 4.0 and 82.2 for adults and children. The total incremental lifetime cancer risk (TILCAR) values for adults ranged from 7.75E - 04 to 1.06E - 02, whereas for children, the values were from 2.47E - 04 to 3.17E - 03.

An attempt has been made to comprehend the ground water quality and climate impacts of the Chennai River basin, which is aimed at its main socio-economic growth of the state of Tamil Nadu. The ground water samples collected from the study area were analyzed for its hydrogeochemical elements. The ground water quality and irrigation suitability were determined using several water quality assessment metrics. Ground water is extensively utilized for irrigation in the entire basin area for the past two decades, especially in the 38 over-exploited Firkas out of the 109 Firkas of the basin. It is inferred that the phreatic aquifer ground water quality is fresh in about 20%, as indicated by the EC value (< 750 µs/cm) at 25 °C. In about 63% of the ground water indicating the moderately fresh showing the EC varies between 751 and 2250 µs/cm at 25 °C, 11% of ground water exerted an EC ranging between 2251 and 3000 µs/cm at 25 °C indicating that the ground water is slightly mineralized, and in about 6% of groundwater, the EC is > 3000 µs/cm at 25 °C indicating that the ground water is highly mineralized. There were no water samples that exceeded the permissible limit of chloride either in phreatic aquifer or in fracture aquifer. The changes in rainfall frequency and atmospheric temperature affect the ground water movement and storage directly and indirectly. Similarly, the temperature data shows a positive relationship with the concentration of fluoride and nitrate ions in the water.

Assam, located in the Northeast of India, is highly flood-prone, and the erosional and depositional processes highly influence the landforms. The formation and development of landforms are directly related to the geology, geomorphology, drainage basin characteristics, and soil types of the region. In the present study, a remote sensing and GIS-based geomorphodiversity index (GMI) assessment of Assam is performed using three sub-indices: geodiversity, morphometric diversity, and drainage diversity index. Sixty-six potential geomorphosites are identified with their geological, geomorphological, and GMI classes. With the help of a flood inundation map, the inundated area of each GMI class is calculated. According to the result, 27.02%, 10.76%, and 3.7% of the total area of Assam fall under moderate, high, and very high GMI classes, respectively. Barak Valley and Central Assam region exhibit high to very high GMI values. Geology and geomorphology have a strong influence on GMI values. About 22.32%, 28.33%, 37.18%, 38.25%, and 35.37% of areas with low, moderate, high, and very high GMI are inundated, respectively. This study determined that areas having high GMI can increase the geomorphological heritage value of the region and can play a significant role in promoting geotourism with an increase in the scientific, educational, and aesthetic value of geomorphosites. This study can also help the local governing authorities to conduct and implement better management and conservation policies for vulnerable locations.

Geomorphometric analysis using geomorphic indices is essential to comprehend the evolution of a river basin including denudation, surface runoff, subsurface infiltration, differential erosion, lithological variations, possible surface tilting, landslides, and the influence of geological formations and structure. Research in morphometric measurements continues to face many challenges and difficulties despite all the effort carried out. These include the inaccuracy of morphometric measurements and the time it takes to obtain the expected results in large basins. Under such condition, the purpose of the study is to conduct an analysis for the group of indices which includes SL index, transverse topographic symmetry factor, and hypsometry curve along with its integral value in the Mandakini Catchment. Examining the spatial distribution of knickzones has not been well documented, particularly in the Mandakini Catchment; hence, we further analyzed the spatial distribution of knickpoints, channel steepness index, and chi-index along with the longitudinal river profile. Through this analysis, we aim to determine how these indices collectively contribute to the comprehensive characterization of the landscape evolution within the study area and to find the landscape signatures of the uplift by comparing different river profiles. Various knickpoints were found mainly in the upper reaches at higher elevation, validated through aerial imagery and then through detailed field observation. During the field investigation, various geomorphic indicators such as fluvial terraces, entrenched river meandering, active landslides, extensive toe erosion, and waterfalls associated were observed. The study also found out that the places near the Kedarnath, Sonprayag, and Kalimath-Kotma, show high SL index and high steepness index that may correlate with the presence of active thrust and faults.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM_2.5 and PM₁₀-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM_2.5 levels peaked at 36.57 µg/m³ and PM₁₀ at 39.58 µg/m³ in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Limited research has been conducted on microplastic (MP) contamination in the rivers of Goa. To address this gap, this study examines the levels of MP contamination in the surface water of the Zuari River, Goa. We investigate the abundance, characteristics (size, shape, colour, and polymer composition), and risk assessment of MPs. MPs were detected at all sampling stations in the Zuari River, with concentrations varying from 0.01 particles/L (S3) to 1.38 particles/L (S13). The average abundance of MPs in the water samples was 0.28 ± 0.35 particles/L. MPs were more common in the 0.3-1 mm size range (51.70%) than in the 1-5 mm range (48.30%). The most common MP shapes observed were fibers (37.88%) and fragments (29.66%). FTIR analysis confirmed the presence of polyethylene terephthalate, high-density polyethylene, polypropylene, and polyacrylamide carboxyl-modified MPs. The Pollution Load Index (PLI) showed an average value of 3.8, indicating significant contamination (PLI > 1). Scanning electron microscopy (SEM) revealed various degradation features such as pits, scratches, grooves, and cracks on the MPs surfaces, while energy dispersive X-ray spectroscopy (EDS) detected metals on the MP's surfaces. This study provides key insights into MP pollution in the Zuari River's surface water and lays the groundwork for future research and management strategies in the region.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

This research article investigates the intricate interplay between climate change, global sea level rise (SLR), and the impacts of sea level rise on the coastal regions of India. Through an interdisciplinary approach, this paper provides an overview of the global consequences of SLR on coastal communities, exploring economic, social, and environmental impacts on agriculture, communities, and coastal areas. The study examines the displacement of communities and its impact on food security, infrastructure, tourism, and ecological loss based on a comprehensive literature review. This paper emphasizes the sustainable preservation of coastal ecosystems and the development of climate-resilient infrastructure. This research aims to offer a detailed understanding of the evolving landscape of coastal livelihoods, providing valuable insights for adaptive strategies, policy formulation, and sustainable development. Ultimately, this article contributes to the scientific discourse by shedding light on the complex dynamics between climate change, SLR, and coastal communities, guiding efforts toward a resilient and sustainable future. The insights are drawn from secondary data resources, including books, scholarly journals, and reports from organizations such as the IPCC and NOAA. Based on a thorough review of the relevant literature, it critically examines the existing and potential consequences of sea level rise induced by climate change.

This work evaluates utilizing the native mussel Brachidontes pharaonis as a bioindicator and sentinel organism for monitoring heavy metals Cu, Zn, and Cd along the Red Sea coast of Egypt. Samples were collected from four coastal locations, and the concentrations of heavy metals in the mussels' tissues, shells, seawater, and sediments were analyzed. Subsequently, bioassay experiments were conducted by exposing the organisms to single, binary, and tertiary metal mixtures, and the accumulation of heavy metals was determined to elucidate the dynamics of metal-metal interactions. Field samples revealed significant variations in heavy metal concentrations in the mussels' soft tissues across different locations, with Zn ranging from 58.1 to 121.0 µg/g dw (dry weight), Cu ranging between 18.3 and 36.7 µg/g dw, and Cd ranging from 0.3 to 1.04 µg/g dw. Conversely, the shells exhibited minimal spatial variations, with much lower contents of Cu (ranging from 1.9 to 2.8 µg/g dw) and Zn (ranging from 1.8 to 1.9 µg/g dw). However, the shells accumulated Cd at higher levels (ranging from 1.4 to 2.1 µg/g dw) compared to the soft tissues. Following a 96-h bioassay experiment, the soft tissues displayed a linear accumulation of metals with increasing exposure dose, with Cd showing the highest accumulation rate (approximately threefold) followed by Zn (twofold) and Cu (1.7-fold). In binary and tertiary exposures, the metals exhibited a general antagonistic interaction, affecting each other's accumulation. On the other hand, the accumulation of heavy metals in the shells after the 96-h bioassay exposure did not follow a consistent linear pattern, suggesting that accumulation during this short experimental period occurs primarily through adsorption rather than the biological pathway.