Asian Journal of Atmospheric Environment最新文献

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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Biological contamination of fresh produce by fungi in storage is becoming a serious problem. Gaseous chlorine dioxide (ClO₂) has been used to prevent fungal growth on fresh produce; however, the specific effects of gaseous ClO₂ at concentrations low enough to be safe for the human body on fungal growth remain unknown. Therefore, in this study, we aimed to investigate the effect of low-concentration gaseous ClO₂ on fungal growth in sweet potatoes over 1 month. Here, a mechanochemical reaction involving the collision of two types of powders was used to produce low concentrations of gaseous ClO₂. The experiment was conducted in a container and chlorine dioxide gas was diffused by a circulator to verify its microbiostatic effect in a large space. A clear microbiostatic effect was observed in potatoes without skin when exposed to low-concentration ClO₂ for 3 days. Notably, low concentrations (< 1.0 ppm) of ClO₂ reduced Rhizopus stolonifer growth in sweet potatoes with skin over 1 month. Therefore, low concentrations of gaseous ClO₂ are sufficient to inhibit fungal growth via gas diffusion.

This study investigates the impact of the long-term use of inlet-heated tubes on the performance of mid-cost optical particle counters (OPCs) for ambient air monitoring of fine particles (PM_2.5). Two OPCs, equipped with inlet-heated tubes, were deployed over a 6-month period, with a beta attenuation monitor (BAM) serving as the reference device. The performance of the OPCs using the same inlet tubes for the first 3 months was compared to their performance after the frequent replacement of clean tubes during the final 3 months. The correlation coefficients (r²) for the 1 h and 24 h average PM_2.5 concentrations between the OPCs and the BAM were lower with long-term contaminated tubes (0.82 < r² < 0.93) compared to clean tubes (r² > 0.93). The relative mean errors and biases significantly increased over time with contaminated tubes. Temperature, humidity, precipitation, and wind speed were found to have an insignificant effect (r² < 0.1) on the performance of the two OPCs with inlet-heated tubes over the 6-month period. The relative average PM_2.5 error when using clean tubes was less than 4%. These findings highlight the importance of inlet-heated tubes in improving OPC performance, particularly for mitigating humidity effects.

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Seosan is a concentrated industrial complex in the midwestern region of Korea. A study was conducted from December 2020 to January 2021, measuring PM2.5 and chemical components in Seosan using online instruments every hour. The concentration of PM2.5 during the winter season was 31.4±17.8 μg/m³, exceeding the national ambient air quality standard of Korea. The mass fraction of organic matter, elemental carbon, three major ions, five minor ions, crustal elements, and trace elements in PM2.5 accounted for 24.5%, 4.36%, 32.0%, 2.82%, 4.11%, and 5.17% of the total PM2.5 mass concentration, respectively. Source identification was conducted using positive matrix factorization modeling, revealing eight sources of PM2.5: Secondary inorganic aerosol (SIA), vehicle exhaust, industry, coal combustion, biomass burning/incinerator, oil combustion, soil, and aged sea salt. Source contributions varied during high pollution episodes (HPE), with SIA dominating in HPE1 and soil and aged sea salt in HPE2. The potential source contribution function and conditional probability function were utilized to estimate the potential local and regional emission areas for the identified sources. In Seosan, vehicle exhaust and biomass burning/incinerator were primarily influenced by local sources. SIA, industry, and oil combustion sources were significantly affected by short-range transport from eastern China. Soil and aged sea salt, which exhibited high contributions during HPE2, were associated with long-range transport from Inner Mongolia. Coal combustion was attributed to both local sources, particularly large industrial complexes near Seosan, and long-range transport from Northeast China and Inner Mongolia.

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Ammonia (NH₃) has received considerable attention as a major reduced nitrogen. However, accurate estimates of the deposition amount are difficult due to its complex behavior characterized by bidirectional exchange between the atmosphere and the surface. We observed the vertical profile of NH₃ concentration in a deciduous forest in Japan for 1 year to further advance the studies on NH₃ bidirectional exchange in Asia, especially focusing on the process near the forest floor. The observation period lasted from September 29, 2020, to September 28, 2021, including leafy and leafless periods. Using the denuder sampling technique, we measured NH₃ concentration in the forest at three heights (above the forest canopy, 30 m, and near the forest floor, 2 m and 0.2 m). NH₃ concentrations tended to be highest at the top of the canopy (30 m). Focusing on the concentration near the forest floor, the concentrations at 0.2 m were frequently higher than those at 2 m regardless of the leafy and leafless period, thus suggesting NH₃ emissions from the forest floor. NH₃ concentration near the forest floor showed strong positive correlations with air temperature during the leafy period. The NH₃ emissions from the forest floor during the leafy period were possibly due to the decomposition of leaf litter with increased air temperature. The decrease in leaf area index might induced the increase in NH₃ concentration and emission. NH₃ emission during the leafless period was also possibly dependent on the state of the deposition surface, apart from air temperature, relative humidity, and leaf area index.

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In megacities, road traffic is a major source of particulate matter (PM), requiring a critical understanding of effective air pollution control. Despite existing methods to determine PM emission factors (EFs) of vehicles, accurate estimation of PM emissions under real driving conditions remains challenging. We aimed to assess the EFs of organic aerosol (OA) and equivalent black carbon (eBC) from vehicles through on-road measurements in Seoul, Korea, to understand real-world PM emissions. We used a mobile laboratory equipped with an aerosol mass spectrometer and an aethalometer to measure the composition of PM. On-road measurements were conducted in vehicle tunnels, urban roadways, and residential areas, and the characteristics of measurement points were compared and analyzed. Our results showed that concentrations of OA increased proportionally with the influence of vehicle exhaust, while oxidation states of the OA decreased. Mobile measurements revealed spatial heterogeneities in aerosols, highlighting distinct characteristics of fresh OA on vehicle roads and elevated oxidation state values in residential areas. Active nitrate formation near vehicles led to elevated NO₃ concentrations on roads compared to residential areas. Our study shows that mobile PM measurements, including OA and eBC, are valuable for the direct evaluation of emission inventories. However, given that the calculated EFs may not be applicable to other cities due to differences in vehicle composition and traffic conditions, the development of city-specific EFs will be necessary in the future. Furthermore, it is recommended to integrate this methodology with conventional emission inventories to identify vehicle-type-specific emissions.

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The study examines the spatiotemporal and statistical characteristics of the daily ultraviolet index (UVI) over the Johor state of Malaysia. The datasets utilized in this study are the Ozone Monitoring Instrument (OMI)/Aura satellite’s daily UVI observations between October 2004 and March 2023. The innovative trend analysis (ITA) method is employed to identify the statistical trend of daily UVI. Basic statistics of daily, monthly, and seasonal UVI are also carried out to better characterize UVI in the study area. The daily UVI data collected for the analysis exhibit wide variability, with more than 80% of the daily UVI data falling above UVI 8, indicating the severity of UV radiation (UVR) reaching the study area. The monthly and seasonal UVI also display highly varying characteristics, as observed in the daily data. The pixel-based analysis of ITA results indicates a significant increasing and decreasing trend of daily UVI in the region, with values varying between − 9.42 × 10⁻⁶ and 3.79 × 10⁻⁵. However, for the study area as a whole, UVI shows a significant increasing trend of 4.50 × 10⁻⁵. The influence of parameters such as cloud optical thickness (COT), solar zenith angle (SZA), and O3 column on the strength of UVR reaching the study area is also investigated. Based on the results, the study area is characterized by low thickness, broken clouds, and sometimes cloudless conditions. Overall, the results show that UVI in the study area is increasing, and therefore, proper health guidelines and direct UVR exposure regulations need to be implemented to reduce serious health risks associated with UVR exposure.

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