Environmental Monitoring and Assessment最新文献

Bisphenol analogues (BPs) are increasingly detected in various environmental media and have uncertain ecological outcomes. Despite growing industrial use, comprehensive assessment data on their distribution and behavior remain limited. This study involves assessing the concentrations of several BPs (BPB, BPE, BPF, and BPAF) in the Sitalakhya River, revealing seasonal trends and their distribution patterns. A total of 90 samples (60 water, 30 sediment) were collected during the rainy and winter seasons. Gas Chromatography-Mass Spectrometry (GC–MS) technique was used to determine the presence and concentration variation of these emerging BP analogues. In the rainy-season water samples, BPF was dominant, ranging from (0.94–5.37) μg/L, followed by BPAF (0.32–1.64) μg/L, BPE (0.46–1.49 μg/L, and BPB (0.12–2.36) μg/L, respectively. Samples obtained from water sources in winter also contained BPF as the most abundant (1.98–7.46) μg/L. In the sediment sample, the leading BP was BPAF, ranging from (64.14–224.40) μg/g dw, followed by BPB (22.07–135.16), BPF (9.52–150.62), and BPE (10.42–120.55) μg/g dw, respectively. Water samples showed strong positive correlations among BPs, while sediment samples lacked significant associations. The study provides a comprehensive insight into the presence of these emerging contaminants in the urban river system of Dhaka, highlighting the need for regular monitoring.

Pesticide use in intensive agricultural systems poses a growing concern for human and environmental health. In Costa Rica, large-scale pineapple cultivation involves frequent agrochemical applications, increasing the potential for contamination of groundwater and agricultural products. This study evaluated human health risks associated with pesticide residues detected in groundwater and pineapples from the Northern Region of Costa Rica, where intensive pineapple production takes place. Concentration data from ten groundwater sources and 30 pineapple samples collected between 2015 and 2018 were analyzed. Non-carcinogenic risk (estimated as a hazard quotient, HQ) and carcinogenic risk were determined for adults and children using U.S. EPA reference doses and cancer slope factors. Nine pesticides were detected in groundwater and nine in pineapples, with bromacil (up to 3.8 μg/L) being the most frequent compound in water and carbendazim (up to 0.027 mg/kg) in the fruit. All hazard quotients (HQ < 1) indicated no non-carcinogenic risk for either exposure route. Carcinogenic risks were only assessed for imazalil and diuron, with values for imazalil in groundwater ranging from 2.4 × 10⁻⁷ to 3.3 × 10⁻⁶, slightly exceeding the reference threshold (1 × 10⁻⁶) in children. Risk values from pineapple consumption were lower (≤ 10⁻⁷) compared to those from groundwater intake, suggesting minimal concern. Although overall risks remain low, the detection of residues in both water and fruit reveals multiple exposure pathways and highlights the need for cumulative risk assessments. Strengthening environmental monitoring and promoting integrated pesticide management are essential to safeguard public health in regions under intensive agricultural production.

Chlorination of drinking water is the most widely utilized disinfection technique; however, its reaction with naturally occurring organic matter causes the production of disinfection byproducts (DBPs), such as haloacetic acids (HAAs), which are the second most common DBP after trihalomethanes. This study investigates the occurrence and spatial variation of five HAA species (monochloroacetic acid (MCAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), monobromoacetic acid (MBAA), and dibromoacetic acid (DBAA)) in drinking water samples collected from eight locations in Jamia Nagar, New Delhi, during March–April 2023. Among these, only two species, i.e., DCAA and TCAA, were quantified, with mean concentrations of 13.97 ± 2.87 µg/L and 13.22 ± 3.47 µg/L, respectively, and total HAAs ranging from 20.81 to 34.95 µg/L. These concentrations were well below the maximum contamination limit of 60 µg/L given by USEPA, whereas no corresponding standards exist in India. Statistical analysis revealed strong positive correlations of HAA formation with residual chlorine, total organic carbon, and pH, while temperature exhibited a weak negative influence. Principal component analysis confirmed that residual chlorine, total organic carbon, and pH were the dominant contributors to HAA variability. Spatial mapping using kriging interpolation demonstrated higher HAA concentrations at sampling locations farther from the water treatment plant, indicating the effect of longer contact time between chlorine and organic precursors. The results highlight the need to consider HAAs in Indian drinking water standards and suggest that spatial monitoring can guide treatment upgrades and evidence-based policy making to ensure safe drinking water.

Coastal deltas are increasingly confronted with salinization and metal pollution. However, the vegetation and rhizosphere within the Yellow River Delta (YRD) are scarcely studied, and in particular, cross-seasonal studies are missing. Therefore, in the present study, root-zone and non-root-zone soil samples from four typical vegetation communities within the Yellow River Delta National Nature Reserve and the Shengli Yellow River Bridge area were collected. The soil physicochemical properties were determined, pollution levels and potential ecological hazards were assessed using the geo-accumulation index (I_geo), potential ecological risk index (PERI), and pollution index (P_i). Results revealed pronounced salinization characteristics across the study area, with soil electrical conductivity, organic carbon, total nitrogen, and available phosphorus positively correlated with most metal(loid)s. The concentrations of potentially toxic elements (PTEs) were generally higher in non-rhizosphere soils than in rhizosphere soils, exhibiting a distinct vegetation gradient (Suaeda salsa > Tamarix chinensis > Phragmites australis (HSR) > Phragmites australis (FWR)). Except for chromium, the metal(loid) concentrations and ecological risks were higher in winter than in summer. Cadmium pollution was most severe, frequently exceeding national standards; mercury and arsenic concentrations were generally low but exhibited significant seasonal and spatial variations. The I_geo results indicated that most of the study area was uncontaminated by the majority of elements, with the exception of localized mild to moderate pollution by cadmium. Mercury displayed the lowest contamination levels. The PERI model identified cadmium and mercury as primary contributors to potential ecological hazards; the P_i results further confirmed cadmium as the priority pollutant. At the rhizosphere scale, there are differential sequestration and regulation mechanisms of metal (loid)s by salt-tolerant vegetation, establishing cadmium as the primary target for management. This suggests a seasonal and vegetation-directed precision remediation approach, providing a scientific basis for ecological security and sustainable development in the YRD and the broader Yellow River basin.

According to International Agency for Research on Cancer (IARC), PM_2.5 is classified as a group 1 human carcinogen. IQAir (2023) reported that India is the third most polluted country globally, after Bangladesh and Pakistan. This study investigates the spatial and temporal variations of PM_2.5 and associated health risks across India from 2019 to 2023. The studied PM_2.5 levels in India consistently surpassed the National Ambient Air Quality Standards (NAAQS) of India, China, the USA, Canada, European Union (EU), and World Health Organization (WHO). Seasonal distribution of PM_2.5 indicates that the highest concentration occurs in winter. Among the top twenty most polluted cities, Bihar has eight, and Uttar Pradesh has four. Among 31 studied states/Union Territories (UT), 17 (54.84%) exceeded the Indian NAAQS levels, and 31 (100%) violated the WHO guidelines. Similarly, for 271 cities, 175 (63.64%) cities exceed the Indian NAAQS, and all 271 (100%) cities violate the WHO guidelines. The significant number of days in a year exceeded the national and international threshold levels. To meet the Indian NAAQS levels, Delhi must reduce PM_2.5 to 20 μg/m³, and the top nine states reduce 10 μg/m³ each from the present levels. To achieve the WHO guideline (5 μg/m³), India would need approximately 10 to 20 years with a 20 to 10% annual reduction rate. The health risk study showed that the Hazard Quotient (HQ) threshold limits (HQ < 1) significantly exceed (2 to 10 times high). The premature mortality due to PM_2.5 in India is from cardiovascular disease (CVD) at 1,945,584, followed by ischemic heart disease (IHD) at 1,252,188, chronic obstructive pulmonary disease (COPD) at 725,367, and stroke at 552,974 and high health risk observed in Indo-Gangetic Plain (IGP) region. Among the total premature deaths, CVD, IHD, COPD, and stroke contributed about 43.47%, 27.97%, 16.21%, and 12.35%, respectively. IHD is the primary cause of premature deaths in India among cardiovascular diseases. This study will help the policy makers at national and regional levels and highlight the urgent need for region-specific pollution control strategies, focusing on crucial emission sources to mitigate health risks and improve air quality across India.

This study evaluates the accuracy and robustness of a statistical approach utilizing Box-Cox transformations combined with linear stepwise regression to estimate four water quality parameters, chlorophyll-a (Chl-a), total organic carbon (TOC), total dissolved solids (TDS), and surface water temperature (SWT), using data from four satellite sensors: Landsat-8, Sentinel-2, MODIS, and ASTER. The accuracy of this methodology was assessed using the coefficient of determination (R²) and Root Mean Square Error (RMSE). Results indicate that all four sensors produced accurate water quality models, each exhibiting high R² values (≥ 0.84). MODIS recorded the highest R² for Chl-a (0.99) and demonstrated good performance in estimating TOC and TDS. ASTER provided the most accurate estimates for TOC (R² = 0.9752, RMSE = 2.33) and SWT (R² = 0.9435). Landsat-8 also shows good performance for all water quality parameters, with maximum R² values reaching 0.9621. Although Sentinel-2 exhibited greater variability, a high R² for SWT (0.9271) was achieved. The methodology demonstrated robustness since it effectively worked across water quality parameters and sensors despite differing spatial and temporal resolutions. Consequently, this approach enhances its suitability for routine water quality monitoring in developing countries, where accurate water quality estimation across different sensors is especially valuable given the high costs of sampling and monitoring.

Pollen, produced during the flowering period of plants, especially anemogamous plants that produce high volumes of pollen, poses a risk to individuals with pollen allergies when it is present in the atmosphere. Meteorological factors are known to affect the duration, distribution, and amount of pollen in the air. The remarkable increase in allergic cases in recent years has led to many studies investigating the relationship between pollen and spores that cause allergies and meteorological factors in Türkiye as well as in the world. In this study, meteorological factors and their influence on pollen concentrations in the air were examined for the Sinop region in northern Türkiye. First, descriptive statistics for pollen obtained from plant taxa were obtained and interpreted. Precipitation, humidity, temperature, and wind speed were considered as meteorological parameters, and the effects of these variables on pollen counts and their annual changes were modelled using Poisson, negative binomial, and zero-inflated negative binomial (ZINB) regression models. The estimation results for all pollen taxa were then discussed. In the models obtained for each pollen type, the statistical significance of the independent variables such as temperature, precipitation, relative humidity, wind speed, time, and lag 1 was found to be different according to the pollen type.

This study analyzed the effects of atmospheric pollutants and meteorological factors on ozone (O₃) concentrations in Jinan using 2017–2023 daily data. Statistical analysis showed that Jinan’s annual average O₃ concentration has an upward trend, with pronounced seasonal variations (higher in spring/summer) coinciding with more O₃ exceedance days. Agglomerative hierarchical clustering, employing Euclidean distance as the similarity measure, was applied to classify 2556 daily sea-level pressure fields into 6 types. Marked differences in O₃ concentrations were observed among these pressure field categories, with lower-pressure systems consistently associated with elevated O₃ levels. To identify the key drivers of O₃ exceedance events, the eXtreme Gradient Boosting (XGBoost) model was constructed, and its classification results were interpreted using the SHapley Additive exPlanations (SHAP) method. This analysis identified the top three meteorological determinants of O₃ exceedance as follows: 2-m air temperature, ultraviolet albedo for direct radiation, and relative humidity. Furthermore, to elucidate the chemical mechanisms governing O₃ formation, threshold regression analysis was performed for the period from May to September—recognized as the critical window for O₃ pollution in Jinan. The results demonstrated that nitrogen dioxide (NO₂) and formaldehyde (HCHO) promote O₃ formation through photochemical reactions. Notably, O₃ formation was found to be significantly intensified when the formaldehyde-to-nitrogen dioxide ratio (FNR) exceeded a threshold value of 0.893. By innovatively applying threshold regression to dissect the influence of FNR on O₃ formation, this study provides a robust scientific foundation for the formulation of targeted O₃ pollution control strategies in Jinan.

The interconnected disturbances induced by coal mining activities significantly alter the groundwater flow system, triggering multi-scale hydrodynamic–hydrochemical co-evolution processes. Based on hydrochemistry, deuterium and oxygen-18 isotopes, Pearson correlation analysis, and Bayesian mixture model methods, this study systematically evaluated the evolution of the groundwater flow system and the hydrochemical processes driven by coal mining in the Nalinhe mining area in the northern Ordos Basin, China. The results showed that the main ions in the groundwater of Quaternary and Cretaceous aquifers are Ca²⁺, Na⁺, HCO₃⁻, and SO₄²⁻, while the main ions in the Jurassic aquifer are SO₄²⁻ and Na⁺. The hydrochemical types vary with depth, transitioning from HCO₃⁻-Ca²⁺ in the Quaternary to HCO₃⁻-Na⁺·Ca²⁺ in the Cretaceous, and finally changing to SO₄²⁻-Na⁺ in the Jurassic. The variance explained by ion composition (52.07%) strongly correlates with rock weathering processes. The Cretaceous aquifer is the primary source of water inflow into mining areas, accounting for 64.25%, while the Quaternary and Jurassic aquifers contribute 18.32% and 17.43%, respectively. In this study, the hydrogeochemical evolution method and Bayesian mixing model were combined to reveal the impact of coal mining activities on groundwater circulation patterns. These findings provide valuable insights for constructing groundwater flow models and effectively managing groundwater inflow in mining regions.

Graphical Abstract

Low-cost particulate matter (PM) sensors are increasingly used for personal and environmental air quality monitoring due to their affordability and accessibility. Recent advancements make these sensors suitable for occupational settings, but their accuracy in such settings remains uncertain. This study calibrated the AirBeam 2 and AirBeam 3 against the Thermo Scientific Personal DataRAM PDR-1500 to assess their efficacy at measuring high PM concentrations, such as those in occupational exposure settings, using engine exhaust and biomass smoke as PM sources. Laboratory calibrations were conducted using a sealed chamber. Linear and polynomial regressions assessed agreement with the PDR-1500, while breakpoint analyses identified thresholds where sensor performance shifted. Field calibrations using the AirBeam 2s evaluated real-world performance and user preferences. The AirBeam 2 exhibited a novel issue where PM₁ readings exceeded PM₂.₅ at concentrations > 50 µg/m³, which was corrected through reprogramming. Polynomial models outperformed linear ones for both devices and the AirBeam 3 performed better with engine exhaust than biomass smoke (linear calibration coefficients 0.192 vs 0.102, respectively), while the AirBeam 2 performed better with biomass smoke than engine exhaust (coefficients 0.323 vs 0.274, respectively). Breakpoints suggested the AirBeam 2s may be better for high concentrations, while the AirBeam 3s were more sensitive at lower concentrations. In the field, the AirBeam 2s recorded lower mean PM concentrations than the PDR-1500 and were more influenced by environmental conditions, yet participants (n = 9) who were recruited to perform field calibrations with both devices preferred the AirBeam. While sensor performance can vary by PM source, concentration, and environmental factors, these findings suggest AirBeams can be a useful option for preliminary occupational exposure assessments after careful calibration and validation prior to use.