Asian Journal of Atmospheric Environment最新文献

Brown carbon (BrC) has a substantial impact on the earth’s radiative stability and is considered a climate-forcing agent. The concentration and optical characteristics of BrC were analyzed in ambient particulate matter (PM) of Dhaka, Bangladesh, during November–December 2019. PM samples were collected on quartz filters using a low-volume air sampler during day and night. BrC was extracted from the filters utilizing two distinct solvents: deionized water and methanol. Mass concentration and density of BrC was calculated using an aethalometer, whereas optical properties were investigated by UV–Visible spectroscopy. At night, the average concentration of BrC was 71 ± 17 µg/m³, 1.6 times higher than the daytime concentration of 44 ± 12 µg/m³. Absorbance of methanol-soluble BrC (MeS-BrC) was higher than the water-soluble BrC (WS-BrC), since BrC was extracted more efficiently in the organic solvent. This resulted in greater values for optical parameters of MeS-BrC, compared to WS-BrC. Absorption coefficient, b_abs of BrC extracted in methanol, was 1.19 to 1.51 times higher than BrC extracted in water. MeS-BrC had more scattering capacity than WS-BrC, evident from the mass absorption efficiency (MAE) values. Absorption Angstrom exponent (AAE) of BrC in both the solvents was > 1, which indicated the presence of UV absorbing BrC in aerosols, that may be emitted from biomass burning. Higher absorbance was noticed at a greater pH and shorter wavelength for WS-BrC, indicating the deprotonation of phenolic -OH group in BrC.

Air pollution is a major problem, including harmful elements such as particulate matter (PM) and heavy metals (HMs). These pollutants are among the leading causes of premature death. This study assesses the health effects of PM₁₀, PM_2.5, and HMs between 2018 and 2019 in two areas of Dakar, Senegal: Hlm (industrial site) and Yoff (coastal and urban site). Numerous PM samples were collected, and 69 samples from each size fraction were selected for this study. Energy-dispersive X-ray fluorescence (ED-XRF) spectroscopy was used to analyse the PM and identify the HMs present in the samples. The relative risk (RR) and attributable fraction (AF) of exposure to PM₁₀ and PM_2.5 were estimated to assess mortality and morbidity. The average PM₁₀ concentrations were 232.318 μg/m³ at Hlm and 209.854 μg/m³ at Yoff, while the highest PM_2.5 concentrations reached 309.355 μg/m³ at Hlm and 319.172 μg/m³ at Yoff. For short-term exposure to PM₁₀, the RR for all-cause mortality across all age groups was 1.195% at Hlm and 1.174% at Yoff. The RR for respiratory mortality in children under five was 1.428% at Hlm and 1.377% at Yoff. For long-term exposure to PM_2.5, the RR for cardiopulmonary mortality showed slight variations between the two sites (1.964% at Hlm and 1.973% at Yoff). Similarly, the RR for lung cancer in individuals aged over 30 years was 2.746% at Hlm and 2.766% at Yoff. Additionally, the assessment of HM exposure through three routes (inhalation, ingestion, and dermal contact) allowed for the determination of the hazard index (HI) and carcinogenic risk (CR). At both sites, none of the HI values for Cr, Cu, Mn, Ni, V, and Zn exceeded the threshold of 1. This study also revealed that the CR values for Cr, Ni, and Pb were outside the regulatory range of 10^–6 and 10^–4.

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Rapid industrialization has intensified air pollution, particularly in areas where industrial and residential zones overlap. This study analyzed emissions from the Yeosu Industrial Complex, South Korea, a major source of volatile organic compounds (VOCs), methane (CH₄), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM). Advanced remote and in situ optical techniques—solar occultation flux (SOF), sky differential optical absorption spectroscopy (SkyDOAS), mobile extraction Fourier transform infrared spectrometry (MeFTIR), sniffer 4D, and LiDAR—were employed to assess spatial pollutant distribution across five zones. Zones A and B exhibited the highest emissions (8,622,468 kg/year and 21,826,416 kg/year), largely due to petrochemical and rubber manufacturing activities. Pollutants, particularly alkanes, NO₂, and SO₂, were highest during southeasterly winds, which transported emissions to nearby residential areas, increasing health risks. A comparison with the Clean Air Policy Support System (CAPSS) inventory highlighted underestimations of VOC emissions in national records. Discrepancies in PM₁₀ measurements by Sniffer 4D (2–6 µg/m³) and LiDAR (14–15 µg/m³) in zone A emphasized the importance of integrating measurement methods to improve emission accuracy. This study demonstrates the potential of combining mobile and remote sensing techniques to enhance emission inventories and provides critical insights for targeted air quality management in industrial-residential interfaces.

The National Air Emission Inventory and Research Center (NAIR) has refined emissions estimation methods to enhance the accuracy and reliability of national statistics on air pollutant emissions. The center estimated 2020 national emissions by applying 23 items identified to have been improved from the improvement research and re-estimated the national emissions from 2016 to 2019 to secure the coherence of national annual emissions. This study compares national emissions of the past years before and after the re-estimation and analyzes the major causes of changes in 2020 national emissions compared to those of 2019.

The re-estimation of national emissions from 2016 to 2019 revealed the following change rates for each substance: CO, − 5.5 to 5.8%; NOx, − 3.9 to 1.8%; SOx, − 14.7 to − 0.6%; PM_2.5, − 31.5 to − 27.0%; VOCs, − 1.3 to 1.1%; NH₃, − 15.4 to 14.3%; and BC, − 4.9% to 5.5%. National air pollutant emissions in 2020 were as follows: CO, 711,399 tons; NOx, 929,227 tons; SOx, 180,157 tons; PM_2.5, 58,558 tons; VOCs, 990,629 tons; and NH₃, 261,207 tons. It turned out that the year-on-year reduction rates of emissions were as follows: CO, − 5.5%; NOx, − 11.1%; SOx, − 23.9%; PM_2.5, − 4.9%; VOCs, − 2.0%; NH₃, − 2.9%; and BC, − 11.6%.

National statistics on air pollutant emissions serve as basic data for establishing, implementing, and evaluating national atmospheric environment policies to improve air quality. It is necessary to establish effective atmospheric environment policies based on highly accurate emission statistics to improve air quality. To this end, it is imperative to conduct ongoing research including studies on improving the national air pollutant emissions estimation method as well as on verifying emissions using air quality modeling and satellite observation data.

In the Republic of Korea, air pollutant emissions are annually estimated and published. These emissions are used to formulate and evaluate national air quality policies. In this study, the 2021 National Air Pollutant Emissions Inventory in the Republic of Korea was estimated. In addition, emission sources and primary causes affecting changes in emissions were analyzed. As a result, air pollutant emissions in the Republic of Korea were 57,317 tons of PM-2.5, 160,993 tons of SOx, 884,454 tons of NOx, 1,002,810 tons of VOCs, and 262,008 tons of NH₃. PM-2.5, SOx, and NOx emissions in 2021 were lower than those in 2020 because of the reduction policy effects, such as the shutdown of old coal-fired power plants and stricter emission standards in workplaces. However, emissions of VOCs and NH₃ in 2021 increased those in 2020 due to socioeconomic effects, particularly in everyday activity sector. Specifically, it was caused by increased use of paint for construction and shipbuilding to meet rising demands as well as a rise in cattle numbers due to increased meat consumption. Spatially, Gyeonggi-do had the highest emissions of PM-2.5, NOx, and VOCs due to its dense populations and heavy traffic, while Ulsan and Chungcheongnam-do had the highest emissions of SOx and NH₃ from production process in their large national industrial complexes.

The deposition of sulfur and nitrogen from the atmosphere to the ground surface is harmful to ecosystems. This study performed long-term air quality simulations to quantify the influences of factors, including anthropogenic emissions in Japan, meteorological fields, transboundary transport, and volcanic emissions, on the long-term trends and annual variations in sulfur and nitrogen deposition in Japan from 2000 to 2020. The air quality simulations performed well in reproducing the long-term trends and annual variations in the wet deposition amount, whereas the simulated dry deposition amount may contain larger uncertainties. The decreasing trends in sulfur deposition were statistically significant during the entire study period (2000–2020) in most of Japan. They were caused by the reduction of anthropogenic SO₂ emissions in Japan and China, which was accomplished by the implementation of stringent emission controls, as well as a gradual decrease in SO₂ emissions from the Miyakejima volcano, which erupted in 2000. No significant decreasing trends were found in nitrogen deposition in Japan during the first half of the study period (2000–2010). Decreases caused by the reduction in anthropogenic NO_x emissions in Japan were compensated for by increases caused by increasing NO_x emissions in China and changes in the gas-aerosol partitioning of nitrates instead of sulfates. The decreasing trend in nitrogen deposition in Japan became statistically significant during the second half of the study period (2010–2020) after anthropogenic NO_x emissions started to decline in China. Meteorological fields primarily influenced annual variations in the amount of nitrogen deposition. This study reveals that long-term air quality simulations are useful for quantifying the influences of various factors on long-term trends and annual variations in sulfur and nitrogen deposition.

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Chronic obstructive pulmonary disease (COPD) is induced by inhalation of toxic substances such as cigarettes and air pollution. Dry powder inhalers (DPIs) are the primary treatment for these diseases. However, they have some problems, such as residuals in a capsule caused by electrostatic force before reaching the human lungs. This study investigated the particle tribocharging mechanism in a DPI using a tandem differential mobility analyzer (TDMA) and a combined discrete element method and computational fluid dynamics (DEM-CFD) approach. In the TDMA experiment, the charging state of the particles changed from negative to positive charge in the DPI device fabricated by the 3D printer. This is because tribocharging is caused by particle–particle collisions and particle–wall collisions. In the numerical simulation, particle–wall collisions occurred more frequently than particle–particle collisions. Therefore, the particle–wall collisions change the charging state of the particle in the DPI device. These results suggest that collisions between particles and walls of the device cause the particles to become charged, leading to a decrease in their deposition in the deeper regions of the lungs. Moreover, the large turbulence kinetic energy of the airflow in the DPI device caused particle–wall collisions because the particles were widely dispersed in the DPI device. These results suggest that optimum turbulence kinetic energy is necessary to reduce particle aggregation and improve the delivery efficiency of DPIs to the human lungs.

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Assessing the effects of air pollutants, including aerosols, on trees is important for protecting forests in the future. This study determined the adsorption of particles on trees after 1- or 2-year long-term exposure (for 1 or 2 h/day) to submicron-scale ammonium sulfate (AS) particles using a field-emission scanning electron microscope (FE-SEM). Energy-dispersive X-ray spectroscopy (EDX) was also used to distinguish particles resulting from exposure from those present on the leaves under natural conditions prior to the 1- or 2-year exposure. We found submicron-sized AS particles were deposited on the leaf surfaces of four tree species after long-term exposure in a growth chamber < 70% humidity. These particles occurred as individual deposits without aggregation on the abaxial and adaxial surfaces. The particle shape deposited on the leaf surface in short-term (3–30 min) exposures in a growth chamber < 70% humidity was spherical with no corners, whereas that in long-term exposures was nonspherical flattened, angular, or irregular. Few micrometers was also observed, differing from 300 to 600 nm in diameter at exposure. These differences could be caused by the possibility that the particles have been deposited for a long time or that the humidity on the leaf surface has caused them to deliquescence and change shape after deposition. We hypothesized that these particle changes facilitate the uptake of AS into the leaf interior.

Accurate air pollution predictions in urban areas facilitate the implementation of efficient actions to control air pollution and the formulation of strategies to mitigate contamination. This includes establishing an early warning system to notify the public. Creating precise estimates for PM2.5 air pollutants in large cities is a challenging task because of the numerous relevant factors and quick fluctuations. This study introduces a novel hybrid model named STL-CNN-BILSTM-AM. It combines the seasonal-trend decomposition method with LOESS (STL) to simplify learning tasks and increase prediction accuracy for complex, nonlinear time-series data. Convolutional neural networks (CNNs) extract features from decomposed components of PM2.5 and other feature variables, such as pollutants and meteorological variables. Bidirectional long-short-term memory (BILSTM) uses these features to extract temporal relationships, enabling the forecasting of daily PM2.5 levels at four locations in Delhi. This hybrid model uses attention mechanisms to extract the most significant information, as well as Bayesian optimization to tune the hyperparameters. The suggested model greatly improved performance in all four regions used in this study, as evidenced by the findings. We compared it with the CNN-BILSTM, BILSTM, LSTM, and CNN models, and the suggested model outperformed the state-of-the-art models by utilizing STL decomposition components and other features. The overall results show that the STL-CNN-BILSTM-AM is better at predicting air quality, especially the concentration of PM2.5 in cities when the data has a high seasonal trend and is complex.

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