Environmental Monitoring and Assessment最新文献

Coal mining has resulted in pollution of groundwater in mining areas, which poses a risk to human health. However, understanding of groundwater evolution in mining areas and the associated implications remains insufficient. This study collected 13 groundwater samples from an abandoned coal mine in southwestern China. Ionic ratio analysis, hydrochemical simulation, health risk assessment, and entropy-weighted water quality index (EWQI) were applied to characterize the groundwater quality and the associated risks to human health. Monte Carlo analysis was used to quantify the uncertainty in the health risk assessment. The results indicated that the groundwater samples are of the HCO₃-Ca and SO₄-Ca water chemistry types. Water–rock interaction and mining activities were the main processes regulating groundwater hydrochemistry. Acid mine drainage was mainly responsible for elevated groundwater sulfate in the study area. EWQI ranged from 13 to 515 (mean of 111), and 75% of the samples fell within classes 1 and 2, meeting the World Health Organization (WHO) drinking water standards. Health risk assessment indicated adults to be more at risk from groundwater ingestion than children, with this result confirmed by uncertainty analysis. This study comprehensively examined groundwater evolution and its potential impacts through the example of a typical mining area. The results provides valuable insights into the identification of factors affecting groundwater, the evolution of hydrochemical processes, and the sustainable development of groundwater resources in mining areas globally. 

Due to extensive anthropogenic activities, soil fertility may degrade severely, threatening the food security. Traditional soil sampling from fields, and their subsequent chemical analysis, is invasive, time-consuming, and not economical at the regional or global level. Remote sensing techniques, specifically the hyperspectral imaging due to their sensitive narrow wavelength bands, offer a non-invasive, fast, reliable, and economical technique to map soil health parameters at the regional scale. New generation hyperspectral satellites with better signal to noise ratio (SNR), may open up a new domain of rapid soil health digital mapping to meet the sustainable precision agriculture targets under the UN SDGs. Despite the high potential, because of the non-availability of satellite sensors (beyond EO-1 Hyperion with low SNR), and limited expertise in high dimensional data handling beyond the scientific community, hyperspectral imaging has restricted applications in agriculture. This review summarises the applications of satellite hyperspectral imaging for soil health assessment, and the developed models. It identifies the research gaps for wide-scale soil and agricultural applications using new-generation hyperspectral satellites. It also examines the upper hand of hyperspectral over multispectral images for assessment of soil health, and critically analyses the various satellite hyperspectral sensors for assessment of soil health parameters, as an efficient alternative to traditional field-based methods. Finally, the review identifies the challenges in the large-scale application of the technology and the way forward for popularisation of hyperspectral imaging for ushering in environmental sustainability. This extensive compilation of reports for assessment of soil attributes through satellite hyperspectral imaging would eventually help researchers to focus on the grey areas, with possibilities to integrate cutting-edge AI/ML models with latest hyperspectral satellites’ datasets for a wide range of soil and agricultural applications.

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R² = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting.

Wetland ecosystems are vulnerable to various environmental pollutants. In southwest Iran, the presence of significant wetlands alongside multiple oil facilities has led to serious biological issues for these ecosystems. This study was conducted in 2022 to investigate biological indicators of heavy metal pollution in the sediments and plants of the Hoor-al-Azim wetland. A total of 10 samples of sediments and the plant species Typha latifolia were collected. Heavy metals consist of Pb, Hg, Ni, Cu, and Cd were measured. The contamination level of sediments was assessed using contamination factor (CF) and ecological risk (ER) indices, while the transfer of pollution to native plant species was evaluated through transfer factor (TF) and bioaccumulation factor (BCF) indices. Results indicated that in sediment samples, Cd had the lowest average concentration (0.052 mg/kg) and Ni had the highest (147 mg/kg). In plant samples, Cd also showed the lowest concentration (0.086 mg/kg), while Hg had the highest (43.6 mg/kg). Pb and Ni levels were significantly elevated compared to other metals. The CF and ER indices revealed that Ni and Pb posed the greatest pollution levels and ecological risks. The TF index indicated that lead had the highest biological pollution potential (1.06). The average BCF values for lead, nickel, and copper were 0.05, 0.053, and 0.12, respectively. Overall, sediment pollution levels in the wetlands near oil facilities are concerning. Therefore, implementing environmental management strategies, including bioremediation, is crucial to mitigate pollution impacts.

Agroforestry offers a huge potential for carbon sequestration, contributing to climate change mitigation and carbon trading. This study focuses on Bauhinia variegata and Celtis australis, two important agroforestry tree species in the Western Himalayas aiming to develop allometric models and biomass prediction ratios using empirical data collected through selective sampling and minimally destructive methods. Biomass components were categorized and weighed, and allometric equations were developed using diameter at breast height and height as independent variables. Model stability was validated using cross-validation techniques, and their predictive accuracy was assessed. Models, particularly based on diameter, has significant predictive ability for predicting the biomass components for both the species. B. variegata demonstrated a higher capacity for CO₂ absorption and carbon credit generation. The biomass expansion factor and root-to-shoot ratio for C. australis and B. variegata was estimated to be 1.39 and 1.40; and 0.24 and 0.17, respectively. The annual biomass of B. variegata and C. australis was 5.97 and 4.67 Mg ha⁻¹ yr⁻¹, respectively. The total carbon stock for both species varied from 23.80 to 30.47 Mg C ha⁻¹. In B. variegata and C. australis, carbon sequestration was 105.93 and 82.11 Mg ha⁻¹, respectively, and net oxygen release ranged from 59.72 to 77.04 Mg ha⁻¹. The carbon sequestration by B. variegata translates into generating US$ 2119 in total carbon credits, with an annual credit of US$ 193, while C. australis yields US$ 1642 in total credits and US$ 149 annually. These findings highlight the utility of B. variegata and C. australis for carbon sequestration and provide valuable allometric equations for carbon credit estimation. The study emphasizes the importance of agroforestry in meeting India’s Nationally Determined Contributions and addressing climate policy goals.

Bioassessment studies in river systems of India are rather scarce and most of the monitoring programmes still rely on the traditional physical and chemical analysis. We explored the biomonitoring potential of benthic diatoms from the Sharda (Kali) river in the Himalayas, which is due interlinking with the Yamuna River under the National River Linking Programme (NRLP) in India. Seventeen sites along the Sharda were sampled in November 2022 for the analysis of 14 physical and chemical variables and benthic diatoms. Hierarchical agglomerative cluster analysis (HACA) and principal component analysis (PCA) of the physico-chemical data set revealed two major groups of sites; the majorly unpolluted sites at higher elevations of Kumaun Himalayas (KH) and the low or moderately polluted sites of Terai Plains (TP) at lower elevations. Application of Water Quality Index (WQI) assigned a good water quality class (B) to all selected sites. A total of 31 genera including 107 species of diatoms were recorded during the present study. Achnanthes pseudoswazi, Achnanthidium minutissimum, Achnanthidium pusillum, Geissleria decussis, and Reimeria sinuata were the most abundant forms from KH whereas Gomphonema acuminatum, Cymbella excisa, Cocconeis pediculus, Nitzschia linearis, and Navicula angusta were the dominant forms recorded from TP. A decrease in diatom diversity was observed from KH to TP sites due to hydrogeomorphological changes and human interventions. Significant differences (p < 0.05 and p < 0.01) between diatom diversity index scores was observed between KH and TP sites. Diversity indices correlated significantly with important water quality variables. The results of the diatom indices such as Trophic Diatom Index (TDI), Specific Pollution Sensitivity Index (IPS), Generic Diatom Index (IDG), and Louis Leclercq Diatom Index (IDSE) corroborated well with the recorded physicochemical variables and WQI values. IPS diatom index exhibited better resolution than WQI with reference to categorization of sites and subsequent establishment of ecological status. IPS was found to be the most suitable index and could be utilized for a pre-linkage ecological status establishment for the Sharda River. However, weak correlations of diatom indices and water quality variables along with low percentage of taxa included for computation of diatom indices reiterates the importance of establishment of region specific autecological preferences of diatoms and subsequent formulation of a customized diatom index for the Sharda River system.

In this study, the researchers wanted to find out if potato peels (PP) and modified potato peels (MPP) could be used to remove Cu (II) ions from water solutions. The effect of adsorption parameters such as pH, contact time, adsorbent dose, and initial concentration were systematically optimized, and the adsorption kinetics and isotherms were investigated for potential use in real sample operations. The physicochemical properties and morphological structure of the adsorbent were investigated using scanning electron microscopy, Fourier transform infrared spectroscopy, and energy dispersive X-ray spectroscopy to understand the Cu (II) ion adsorption mechanism. The studies carried out at different temperatures provided a deeper understanding of the adsorption capacities of the adsorbents. The Cu (II) ion removal capacities of PP were determined as 30.95, 32.67, and 35.33 mg/g at 25, 35, and 45 °C, respectively. Under the same conditions, the removal capacities of MPP were found to be 76.33, 80.64, and 86.20 mg/g, respectively. Further investigation of the adsorption kinetics showed that the experimental data fit the pseudo-second-order model for both PP and MPP adsorbents. Thermodynamic analyses revealed that the adsorption of Cu(II) ions onto the adsorbents was an endothermic process and spontaneous. PP and MPP could be reused for several cycles without losing their adsorption performance after successful regeneration with 0.10 M HCl. The mechanism of Cu (II) ion removal was explained by intermediary ion exchange, surface precipitation, and interaction between surface functionalities of PP and MPP. Therefore, highly functional PP and MPP could be promising adsorbents for efficient Cu (II) ion removal from aqueous solutions.

Occurrence of microplastics (MP) in natural paddy fields and its impact are less studied. This study reports the abundance of MP in two paddy fields of Kerala, India, cultivating rice crops, ‘Pokkali’ and ‘Uma’ crops, which are vital to Kerala’s food security and climate resilience. Fourier transform infrared spectroscopy (FTIR) analyses confirmed the presence of polyethylene (PE) and polypropylene (PP) fragments as major MP in the surface water of paddy fields during vegetative (transplantation) and ripening (near harvesting) phases. MP density in the vegetative phase of ‘Pokkali’ (1370 ± 468.51 fragments/m³) and ‘Uma’ (1110 ± 304.96 fragments/m³) was thrice more than the ripening phase concentrations (400 ± 196.85 and 370 ± 57.00 fragments/m³, respectively). Subsequently, ecotoxicity of MP and plastic leachates (PL) on phytoplankton that are naturally found in rice fields was examined. Microalga, Chlorococcum sp., and cyanobacterium, Synechococcus sp., were grown in modified BG11 and BG11 media, respectively, and tested with paddy field concentrations for PE-MP and PE-PL. MP bestowed a significant hormetic effect on the specific growth rate of the microalga (121% of the control) whereas the cyanobacterial growth was negatively impacted (70% of the control). Both phytoplankton exhibited a similar response when exposed to PL, but results were neither dose-dependent nor significant. Further, increased catalase activity and compromised superoxide dismutase machinery in the cyanobacterium corroborated the toxic impact on growth (p ≤ 0.05), which indicates reactive oxygen species (ROS) generation in MP-treated groups. ROS generation indicates oxidative stress following MP exposure in the studied phytoplankton perhaps through surface contact or by leaching of toxic intermediates into the medium. The distinctive responses of paddy field phytoplankton to MP and PL stress suggest that MP pollution may enrich certain resilient species over others leading to a possible change in phytoplankton community structure. Pollution load indices suggest that even environmental concentrations of MP and PL may affect the rice productivity as paddy field phytoplankton play a significant role in sustaining and enhancing crop health. Therefore, the presence of MP at alarming concentrations in the paddy fields signifies the emergence of a global environmental and food security concern.

Graphical Abstract

Coastal areas globally face mounting challenges due to increased human activities, resulting in substantial environmental impacts on beaches and marine ecosystems. This study delves into the influence of peak tourist seasons on cigarette butts (CBs) contamination along an urban beach, specifically Mar Grosso beach boardwalk in Laguna, Brazil. The study period spanned peak tourist seasons (PS) and off-seasons (OS), with data collected on CBs density and degradation levels across different areas of the boardwalk. A total of 6621 CBs were collected from the beach boardwalk along Mar Grosso beach, resulting in a density of 0.50 CBs/m². It is worth noting that higher CBs densities were observed during the PS (0.38 CBs/m²), particularly in areas with urban structures that attract human presence, despite the presence of waste collection bins. Stages II and III of CBs degradation were the most abundant along the Mar Grosso beach boardwalk. Moreover, our research indicates a severe pollution index for the Mar Grosso Beach boardwalk during the PS season in all analyzed sectors (18.25–31.60). The study underscores the urgent need for improved waste management practices, environmental education, and policy interventions to curb CBs contamination and its adverse impacts on coastal ecosystems and public health.