Asian Journal of Atmospheric Environment最新文献

Trace elements released into the atmosphere by human activities, such as fossil fuel combustion, flow into forest ecosystems via wet and dry deposition and can flow out via mountainous stream water. The Kajikawa catchment site in Niigata Prefecture, located on the Sea of Japan side in central Japan, suffers from large amounts of acidic substance deposition due to its geographical conditions and meteorological seasonality and is affected by transboundary air pollution originating from the Asian continent due to northwesterly seasonal winds in winter. This study determined the concentrations of trace elements dissolved in precipitation and stream water to comprehensively evaluate the effects of transboundary air pollution on forest ecosystems. The results showed that the concentrations of trace elements and major inorganic ions in precipitation tended to be high in winter and low in summer. The fluxes of many trace elements also increased in winter, reflecting high precipitation amount. Except for Sr, the enrichment factors of the highly enriched trace elements did not show clear seasonality. Therefore, they were continuously influenced by anthropogenic activities. Furthermore, the stable Pb isotope ratio changed significantly during the observation period; however, this was not explained by seasonal changes. This study revealed that trace elements are transported to the Sea of Japan side in central Japan, especially in winter; however, their quantity and content change in response to changes in local and transboundary air pollution.

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Chemical accidents significantly impact environmental and human health. However, studies investigating the impacts of such accidents have primarily focused on single-route exposures, potentially underestimating the extent of damage. This study aimed to conduct an aggregate risk assessment for multi-route exposure to hazardous chemicals to ensure systematic and rational management of the health impacts on residents exposed to chemical accidents, considering the behavior of a hazardous chemical from a chemical accident within environmental media. Drawing upon a real chemical accident that occurred in Siheung, Gyeonggi-do, in 2019, leakage of 500 L of toluene over an hour was assumed. Employing a multimedia environmental dynamics model, the time-dependent concentrations across various environmental media were calculated, and the average daily dose (ADD), hazard quotient (HQ), and hazard index (HI) for each exposure route included in the multi-route exposure assessment were derived. Health risks were deemed present if the calculated HQ and HI values exceeded the threshold of 1. The results indicated the highest ADD values among the 0–9 age group, with inhalation exposure registering the highest ADD across all exposure routes. However, no significant health risks were observed, with both HQ and HI values not exceeding 1. This aggregate risk assessment approach is proposed as an effective preliminary evaluation method for health impact assessments in areas affected by chemical accidents.

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The study investigated PM_2.5 and heavy metal pollutant concentrations in Seoul and Wonju, South Korea, emphasizing the importance of considering PM_2.5 chemical constituents for health impact assessments. While PM_2.5 concentrations were similar between the two cities with slight variations, heavy metal concentrations differed significantly. Regional sources, PM_2.5 composition, and meteorological conditions influenced these variations. Exposure to Fe was highest in all areas, with some heavy metals exceeding permissible levels, stressing the need to consider regional characteristics in assessments. Different heavy metals influence health risks differently in each city. When examining the HI (the summation of the hazard quotient for heavy metals), Seoul remained below an HI value of 1, while Wonju exceeded this threshold when exposed to CTE concentrations. In Seoul, As had a significant health impact of PM_2.5, while in Wonju, Mn was more influential. Source apportionment confirmed different pollution sources in Seoul and Wonju, affecting the distribution of PM_2.5 constituents in the atmosphere. Effective PM_2.5 management requires a comprehensive approach considering chemical constituents and health impacts not just mass-based PM_2.5 management, emphasizing regional-specific assessments and policy considerations to mitigate health risks. Therefore, tailored management strategies based on regional characteristics are necessary.

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Globally, the COVID-19 outbreak has had a devastating impact on both health and economy. In contrast, the reduction in anthropogenic emissions has resulted in a major improvement in air quality. In this study, US National Aeronautics and Space Administration (NASA) satellite datasets and related reanalysis model data were used with validation using ground-based data to evaluate the effects of aviation-based emissions on aerosol optical depth (AOD) and black carbon (BC). The contributions from five airports in Bangladesh were assessed during the pre-lockdown (01 Jan to 22 March), lockdown (23 March to 30 May), and post-lockdown (31 May to 30 Aug) periods in 2019 and 2020. The study’s findings show that during the 2020 lockdown, AOD and BC concentrations significantly decreased at all five airports. The overall decline of AOD was ~ 18.5% (13.1% to 22.8%) and BC was ~ 18.1% (16.6% to 22.2%) in 2020 compared to 2019. The three international airports that were examined—Dhaka, Chattagram, and Sylhet—showed an average reduction of about ~ 9.7%, while Jashore and Barisal—two domestic airports—saw a minor increase in AOD of ~ 0.8% over the same period. However, the average BC concentration at both international and domestic airports dropped by ~ 9.8% and ~ 10.2%, respectively. This is the first study to use reanalysis datasets in Bangladesh to evaluate air pollution levels and aviation-based emissions. The results highlight the significant impact of reduced aviation activity on air quality and provide valuable insights for future air pollution management strategies.

Graphical Abstract

A large-eddy simulation was implemented for the flow around a cylindrical observation tower to investigate the effects of the tower structure on wind speed and drag coefficient. The mean wind velocity accelerates above the tower because flow separation occurs at the leading edge of the top of the tower. The drag coefficient is strongly linked to the Reynolds shear stress. Above the tower, the Reynolds shear stresses change from negative to positive within the recirculation zone and return to a negative value in the latter half of the tower because of the steep velocity gradients near the top of the tower. The change in the Reynolds shear stress results in an inaccurate drag coefficient. When one anemometer is used, a location at over 10 m above the top of the tower is suitable for measuring the drag coefficient accurately. When two anemometers are used, the Reynolds shear stress can be measured more accurately. Although the effects of the tower on the drag coefficient are not entirely removed, the use of two anemometers is a promising approach to estimate the drag coefficient in a tower.

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This study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.

Graphical Abstract

The COVID-19 pandemic brought significant consequences on healthcare systems, economy, and politics. Nowadays, we know that the pathogen responsible for COVID-19 is transmitted mainly by aerosol droplets exhaled by infected individuals, which remain suspended in indoor air. There has been widespread interest in monitoring the (CO_2) levels in indoor spaces since an infected patient exhales (CO_2) and infectious aerosols when breathing. So, we designed and built an Air Quality Monitoring Device (AQMD) that measures and analyzes the levels of (CO_2) and particulate matter in the classrooms of a university with the aim of mitigating the spread of COVID-19. We divided the AQMD design into 2 phases: (i) data measurement and (ii) estimation of infection risk. Specifically, we measured the air quality in 3 classrooms of a university during different types of activities. Using these data, we calculated the recommended (CO_2) threshold for our classroom setting and estimated the probability of COVID-19 infection of a susceptible person. Our research shows that indoor (CO_2) concentrations and the probability of COVID-19 infection are influenced mainly by the type of activity and the number of windows open; besides, the number of students does not significantly impact the indoor (CO_2) concentrations levels because the range of students in the test scenario (18 to 31) was relatively small.

Reactive nitrogen negatively affects terrestrial ecosystems by excessive deposition. Nitric acid gas (HNO₃), a component of reactive nitrogen, is readily deposited on ground surfaces due to its high reactivity. However, there have been recent cases in which suppressed deposition fluxes, including upward fluxes, were observed above forests. As the mechanisms of HNO₃ dry deposition on forest surfaces are not fully understood, the accuracy of dry deposition estimates remains uncertain. To reduce uncertainties in the estimation, we investigated dry deposition of HNO₃ by 1-year measurement in a forest. We measured the vertical profiles of HNO₃, nitrate, and sulfate in PM_2.5 in a deciduous forest in suburban Tokyo (FM Tama). We observed their concentrations above the forest canopy (30 m) and near the forest floor (2 and 0.2 m) using the denuder/filter pack from October 2020 to September 2021. The HNO₃ concentration decreased significantly from 30 to 2 m. However, the decrease in HNO₃ was not as significant, and occasionally, emission profiles were produced between 2 and 0.2 m. This was likely caused by HNO₃ generated by the volatilization of NH₄NO₃ near the forest floor, which was warmed by sunlight during daytime in both leafy and leafless periods. Conversely, HNO₃ concentrations at 30 m were much higher than those at 2 m and 0.2 m, indicating that the forest acted as a sink for HNO₃ from a long-term perspective. It is presumed that HNO₃, generated just above the forest canopy, could cause an upward flux if a temperature difference of several degrees occurs between 25 and 20 m.

Gaseous and particulate 21 PAHs were monitored at a residential site in Ulsan, South Korea, over three seasons (December 2013–August 2014). The mean concentrations of Σ₂₁ PAHs were highest in winter (16.2 ± 8.2 ng/m³), followed by spring (8.37 ± 4.53 ng/m³) and summer (6.23 ± 2.53 ng/m³). The mean gaseous concentration of Σ₂₁ PAHs (7.39 ± 4.39 ng/m³) was 2.7 times higher than that of particulate PAHs (2.70 ± 3.38 ng/m³). To identify the sources of PAHs (both types of sources and their areas), diagnostic ratios, principal component analysis, and concentration-weighted trajectory (CWT) were used. The results showed that pyrogenic sources (e.g., coal combustion) were the primary emission sources of PAHs in winter and spring. In summer, the influence of both coal and heavy oil combustion was dominant, suggesting that PAHs could be transported from industrial areas of Ulsan (e.g., petrochemical and nonferrous industrial complexes) by seasonal winds. Regarding emission source areas, the CWT analysis revealed that in winter and spring, PAHs in Ulsan could be attributed to emissions from regional areas, e.g., China and North Korea. The PAH concentrations were also used to assess the health risks associated with the inhalation of these compounds for adults aged 18–70. The results showed that the cancer risks from Σ₁₉ PAHs and Σ₁₃ PAHs did not exceed the guideline set by the US EPA (10⁻⁶), indicating no cancer risks for this target group. However, it is worth noting that certain PAHs, which are not listed as priority PAHs by the US EPA, make significant contributions to the benzo[a]pyrene equivalent and the associated cancer risks. Therefore, it is necessary to investigate not only the priority PAHs but also other PAH species to fully evaluate their effect on human health.

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.