Journal of Atmospheric Chemistry最新文献

This study analyzed the impact of air-mass transport via the Yellow Sea (YS) pathway on PM_2.5 concentrations and chemical composition in Seoul from 2016 to 2021, focusing on heavy metals while also considering inorganic and organic species. Using backward trajectories from the HYSPLIT model, the analysis showed that YS-pathway cases consistently showed higher concentrations PM_2.5 and most components compared to non-YS-pathway cases, indicating that the influence of transboundary air pollutants transported via the Yellow Sea persisted. This difference was also influenced by local meteorological conditions, such as lower planetary boundary layer heights and weaker wind speeds during YS-pathway cases in winter, which are unfavorable for pollutant dispersion. Vanadium (V) and nickel (Ni), markers of heavy oil combustion, showed similar trends, with significant declines in 2020 and 2021, likely due to the IMO 2020 regulation and reduced shipping activity during the COVID-19 pandemic. In contrast, arsenic (As) and selenium (Se), markers of coal combustion, showed different trends, suggesting variations in their emission sources. Elevated As levels observed in late 2020 were attributed to emissions from coal-fired power plants in North Korea and Liaoning Province, China. Meanwhile, lead (Pb) and As exhibited no significant differences between YS-pathway and non-YS-pathway cases, suggesting that their source distributions differ from those of other pollutants. These findings highlight the ongoing influence of transboundary air pollutants on air quality in Seoul and emphasize the need for international collaboration, sustained monitoring, and effective domestic and regional emissions controls.

Traffic activities and road dust are major contributors to the release of metals in ambient air which further cause serious health risks by entering the body. This study examined size-segregated PM concentration trends in rural site (Iradatnagar) Risk assessement and disease estimation was done by AirQ + and USEPA Methodology. PM_2.5-1.0 levels were 294.64 µg/m³ in summer and 78.81 µg/m³ was found in monsoon, while PM_1.0-0.5 measured 204.53 µg/m³ in summer and 66.42 µg/m³ in monsoon, respectively. Al, Ba, Ca, Fe, Mg, Mn, Pb, Ni, Cr, Cd, Cu, and Zn were analyzed. Total metal concentration was found to be 32.96 µg/m³ in summer and 12.27 µg/m³ in monsoon for PM_2.5−1.0, while for PM_1.0−0.5 it was found as 30.42 µg/m³ and 10.56 µg/m³ in summer and monsson respectively. According to the findings, the concentration of metals was higher in summer. Metal BI ranged from 5.12 to 6.46% (PM2.5-1.0) and 4.56–7.05% (PM1.0-0.5). HQ outcomes Cr(2.78, 9.59E-02), Ni(2.73, 6.85E-01), Al(1.43, 4.79E-01), and Mn(0.19, 1.92E-01) were observed for PM_2.5−1.0 in the summer and monsoon seasons respectively. HQ outcomes Cr(3.16, 9.59E-02), Ni(0.68, 6.85E-01), Al(1.55, 5.56E-01), and Mn(0.76, 1.92E-01) were identified for PM_1.0−0.5 in summer, and monsoon seasons. HQ was observed higher for PM_1.0−0.5 (1.95) size fractions compared to PM_2.5−1.0 (1.30). ELCR value for Cr(VI) was found higher for adult in comparison with child whereas the trend followed as as Cr(VI) (0.0007), (0.0002) > Pb (3.52E-06)(1.05E-06) > Ni (1.31E-06)(3.94E-07). Adult values were found to be greater (0.0002) than child values (7.10E-05). The Cancerous risk mean value found two times higher than the permissible limit (1 × 10^− 6).

This study investigates the mass concentrations, morphological characteristics, elemental composition and source apportionment of PM2.5 and PM10 aerosols across different seasons collected in a mountain valley of the central Himalayan region of Uttarakhand, India. The average PM10 concentration was found to be 88.74 ± 34.12 µg m⁻³, generally below the NAAQS 24-h standard, while the mean PM2.5 concentration was found to be 67.72 ± 37.00 µg m⁻³, exceeding the NAAQS standard. Elevated PM10 levels during pre-monsoon periods were linked to windblown dust from neighbouring regions and thermodynamic conditions within the planetary boundary layer, while high PM2.5 levels were attributed to temperature inversions and stable atmospheric conditions. The study identified three major particle groups—biogenic, geogenic, and anthropogenic—using SEM–EDX analysis highlighting the significant impact of both natural and anthropogenic sources. Biogenic aerosols were prevalent in the samples. Variations in the composition of the elements are noted, with C and Si being the most predominant. A strong correlation was found between carbon and oxygen (r = 0.926) using Pearson correlation matrix. NOAA HYSPLIT-4 model was used for air mass back trajectory analysis, which suggests that the receptor site station received air mass from both local sources and long-range transport.

Ozone (O₃) in ambient air acts as a greenhouse gas and has harmful effects on human health and vegetation. Short-term exposure to elevated surface O₃ is linked to increased risks of respiratory and cardiovascular mortality. The emission of volatile organic compounds (VOCs) and nitrogen oxides (NO_x) into the atmosphere can trigger chemical reactions influenced by solar radiation (SR), resulting in O₃ formation in the troposphere. This study focuses on a few locations within Delhi and Mumbai using publicly available data. O₃ concentrations peak in the afternoon and decrease subsequently. During winter, NO_x concentrations were higher, while O₃ concentrations were lower, possibly due to reduced solar radiation and altered atmospheric VOC-NO_x regimes. The HCHO/NO₂ ratios in both Delhi and Mumbai are less than 1, indicating VOC-limited conditions. The secondary fraction (SA) of PM_2.5 at select locations was estimated using the Approximate Envelope Method (AEM). SA values derived from AEM exhibited diurnal trends consistent with field studies and established knowledge. This analysis demonstrated that SA can constitute up to 85% of total PM_2.5, highlighting its significant contribution to overall particulate matter levels. An evaluation of the AVOC-NOx-O₃-SA relationship revealed that elevated O₃ concentrations predominantly occur at higher AVOC/NOx ratios, often leading to increased SA levels to some extent. To predict O₃, a multiple linear regression model was employed, incorporating various parameters. The model achieved a coefficient of correlation when compared to measured data of over 0.90, indicating its effectiveness in predicting O₃ levels. This research provides valuable insights into the dynamics of surface O₃ and its implications for urban secondary pollutants.

Though Dhaka is among the most air polluted cities, the origin and impact of respirable crystalline silica (RCS) are mostly unclear. This study examines the concentrations and sources of RCS and heavy metal in the ambient air of Dhaka city due to its health hazards. Samples were obtained from the industrial area, construction zone, hospital, and workshop over a period of three consecutive days. Each sample was collected using a polytetrafluoroethylene filter for a duration of 24 h. The quantification of RCS and heavy metal concentration was performed utilizing UV-vis and ICP-MS techniques, while SEM-EDX was employed to examine the morphology of total suspended particulate matter (TSP). The construction zone had the highest concentration of RCS, measuring 2.55 µgm^− 3 suggesting the likely source of the RCS. Additionally, this research revealed that among all locations, at least five individuals per 10,000 will be susceptible to cancer as a result of RCS. Construction zone exhibited a low level of heavy metal concentration, whereas industrial area contained the highest levels. The industrial area was found to contain the highest concentrations of Pb and Hg among the four heavy metals, while the workshop exhibited the highest concentrations of Cr and As. SEM analysis revealed a lot of soot particles that had chromium in them. Aluminum was found in the silica particle that reveal the source of it with more accuracy. This aerosol morphology investigation will assist stakeholders understand pollution source and type.

Growing ambient air pollution in Lucknow is a menace to the monuments, urban dwellers, and the ecosystem. In view of the above, air pollution tolerance potential of dominant plants against air pollutants was assessed supported by indices and statistics. The study was conducted at three sampling sites in Lucknow city: commercial, industrial, and rural in the years 2021-22. PM_2.5 concentrations were 163.4 ± 25.3, 155.1 ± 14.6 and 113.2 ± 30.8 µg/m³ at commercial, industrial, and rural locations, respectively, breaching national ambient air quality standards (60 µg/m³) by 172.3, 158.5 and 88.7%. Eleven trace elements were associated with PM_2.5 and PM₁₀ out of which Sr, Al, Fe, and Zn were predominant owing to road dust entrainment and vehicular emission. Biochemical parameters were assessed for four native floral species Azadirachta indica, Mangifera indica, Ficus religiosa and Cascabela thevetia. For these species, pH ranged between 5.3-8.4, total chlorophyll 0.4–1.2 mg/g, carotenoids 0.15–0.34 mg/g, relative water content 32.1–89.9% and ascorbic acid 0.12–1.32 mg/g. Guaiacol peroxidase (19.5 ± 2.5 U/gm protein) was highest for C. thevetia, malondialdehyde (3.6 ± 1.4 nmol/gm FW) for A. indica, superoxide dismutase (339.4 ± 11.7 U/mg protein) for C. thevetia and catalase (688.7 ± 68 U/mg protein) for A. indica. Air pollution tolerance index (APTI) was higher for F. religiosa (17.53) followed by A. indica (13.34) showing their tolerance ability in response to particulate matter and heavy metals. Aforementioned plant species can be used to further investigate how plants and pollutants interact and for enhancing potential phyto-control methods for minimizing air pollution.

In this paper, the study focuses on the forecasting of the Air Quality Index (AQI) using linear regression, random forest, and decision tree regression models in Delhi City. The AQI is a crucial metric for monitoring air quality and provides information on the level of air pollution and its potential health risks. The main research aims to develop forecasting of AQI in three scenarios based on the air pollutants data. Monthly average Nitrogen dioxide (NO₂₎, Sulfur dioxide (SO₂), Oxygen (O₃), and Particle matter (PM_2.5) data from 1987 to 2020 were included. The research was executed in two steps: preprocessing datasets, plotting the datasets, and analyzing them in the first step, and training and testing the model's accuracy in the second step. The datasets were divided into training and testing sets also we forecasted the AQI in three scenarios based on the different input variables. Feature importance was used for the selection of model input variables. Results of the study area compared the Machine Learning (ML) models in three scenarios best performance models such as Decision Tree Regression (DT) (R² = 0.99, RMSE = 0.81), Random Forest (RF) (R² = 0.98, RMSE = 16.64), and RF (R² = 0.99, RMSE = 0.27), respectively. The results of DT and RF models showed high prediction performance compared to other models in the first, second, and third scenarios, respectively. The results of 10-fold cross-validation models are cross-validated to all models, which is the RF model is best other than the models in three scenarios. Hence, the cross-validation of all ML models so important for the selection of the best model for forecasting AQI in Delhi City. The results can be helpful to urban policy makers in the Delhi city.

Organic carbon (OC) and elemental carbon (EC) play a significant role in aerosol mass and atmospheric processes. This study is focused on the eastern part of the Great Northern Plains of India, namely the Brahmaputra Plains, to understand the influence of regional and local contribution on the carbonaceous fraction of PM_2.5. Mean annual PM_2.5 concentrations exceeded the National Ambient Air Quality Standards (NAAQS), with values of 46.6 ± 30.0 μg/m³ in the rural area and 50.4 ± 34.4 μg/m³ in the semi-urban area. The range in monsoon-winter was found to be 22.7–71.9 μg/m³. OC and EC contribute 44–50% of the PM_2.5 mass concentration. The OC/EC ratios ranged from 3.3 to 9.3 in the rural area and from 4.3 to 6.9 in the semi-urban area, indicating significant secondary organic aerosol (SOA) formation, especially during the high photochemical period of the pre-monsoon season. Lower δ13C values were observed during winter (-27.5‰ rural, -27.3‰ semi-urban), pre-monsoon (-28.1‰ rural, -27.6‰ semi-urban), and post-monsoon (-28.2‰ rural, -28.1‰ semi-urban) periods, suggesting influences from biomass burning, fossil fuel combustion, and aged aerosols. The study employs cluster analysis of air mass trajectory, and Moderate Resolution Imaging Spectroradiometer (MODIS) fire data to determine the influence of the hotspot Indo-Gangetic Plain (IGP) and long-range transport on aerosol carbonaceous content during most seasons except the monsoon period June–September in the Brahmaputra Plains.

PM_2.5 samples were collected in Suncheon during the summer (June 2–11, 2023) and winter (January 15–21, 2024). The chemical composition analysis included carbonaceous components (OC, EC), secondary ionic components (NH₄⁺, NO₃⁻, SO₄²⁻), dithiothreitol - oxidative potential (QDTT-OP), and volatile organic compounds. Results showed higher summer PM_2.5 concentrations due to photochemical reactions and higher winter concentrations from heating and stable atmospheric conditions. The OC/EC ratio indicated greater secondary organic aerosol formation in summer. Oxidative potential (QDTT-OP_v) was higher in summer (0.12 µM/m³) than winter (0.09 µM/m³), correlating strongly with OC in summer. Health risk assessment of BTEX revealed higher concentrations in winter, with benzene as the primary contributor to lifetime cancer risk (LTCR). The cumulative hazard quotient (HQ) was higher in winter, indicating increased non-carcinogenic risk. The study highlighted that oxidative potential is more influenced by chemical composition than physical characteristics, suggesting that regulating PM_2.5 concentration alone may be insufficient. VOCs, as precursors of SOA, showed a positive correlation with QDTT-OP_v, with benzene exhibiting the strongest correlation in winter. These findings emphasize the need for targeted management of specific PM_2.5 components to mitigate health risks effectively.

Graphical Abstract

High levels of particulate matters in the air are a major health issue in Middle East leading to adverse health effects. In this study, we have simultaneously investigated (i) the spatio-temporal distribution of ambient particulate matters in a city located in the Middle East (Khorramabad) over the time period 2021–2022; and (ii) PM_2.5 and PM₁₀-related carcinogenic and non-carcinogenic risk assessment to exposure. For the risk assessment, hourly PM_2.5 and PM₁₀ data were obtained from three monitoring stations located in the city. A methodology for risk assessment recommended by the United State Environmental Protection Agency was used for all age groups. The excess lifetime cancer risk (ELCR) and the hazard quotient (HQ) were estimated, and the backward trajectories were assessed by the Hybrid Single-Particle Lagrangian Integrated Trajectory model. The Aerosol Optical Depth from 0 to 1000 nm was applied to observe the variations of atmospheric aerosols. The results showed that the annual PM_2.5 and PM₁₀ mean concentrations during 2021 and 2022 were exceeded the World Health Organization limit value for human health protection. In 2021 and 2022, 2.2-148.3 and 1.3-134.4 cancers per 1,000,000 inhabitants can be related to ambient PM_2.5 exposure. The HQ values for PM_2.5 and PM₁₀ were 4.7 and 1.3 in 2021, and 3.8 and 1.1 in 2022, i.e., the risk for human health is expected. To reduce the adverse health effects related to particulate matters, air emissions control strategies are required.