Asian Journal of Atmospheric Environment最新文献

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.

High-resolution regional model simulation of CO₂ may be more beneficial to reduce the uncertainty in estimation of CO₂ source and sink via inverse modeling. However, the study of atmospheric CO₂ transport with regional models is rare over India. Here, weather research and forecasting chemistry model adjusted for CO₂ (WRF-CO₂) is used for simulating vertical profile of CO₂ and its assessment is performed over Delhi, India (27.4–28.6° N and 77–96° E) by comparing aircraft observations (CONTRAIL) and a global model (ACTM) data. During August and September, the positive vertical gradient (~ 13.4 ppm) within ~ 2.5 km height is observed due to strong CO₂ uptake by newly growing vegetation. A similar pattern (~ 4 ppm) is noticed in February due to photosynthesis by newly growing winter crops. The WRF-CO₂ does not show such steep increasing slope (capture up to 5%) during August and September but same for February is estimated ~ 1.7 ppm. Generally, CO₂ is quite well mixed between ~ 2.5 and ~ 8 km height above ground which is well simulated by the WRF-CO₂ model. During stubble burning period of 2010, the highest gradient within 2.5 km height above ground was recorded in October (− 9.3 ppm), followed by November (− 7.6 ppm). The WRF-CO₂ and ACTM models partially capture these gradients (October − 3.3 and − 2.7 ppm and November − 3.8 and − 4.3 ppm respectively). A study of the seasonal variability of CO₂ indicates seasonal amplitudes decrease with increasing height (amplitude is ~ 21 ppm at the near ground and ~ 6 ppm at 6–8 km altitude bin). Correlation coefficients (CC) between the WRF-CO₂ model and observation are noted to be greater than 0.59 for all the altitude bins. In contrast to simulated fossil CO₂, the biospheric CO₂ is in phase with observed seasonality, having about 80% at the lowest level and gradually declines with height due to mixing processes, reaching around 60% at the highest level. The model simulation reveals that meteorology plays a significant role of the horizontal and vertical gradient of CO₂ over the region.

In this study, we developed a prediction model for heavy metal concentrations using PM_2.5 concentrations and meteorological variables. Data was collected from five sites, encompassing meteorological factors, PM_2.5, and 18 metals over 2 years. The study employed four analytical methods: multiple linear regression (MLR), random forest regression (RFR), gradient boosting, and artificial neural networks (ANN). RFR was the best predictor for most metals, and gradient boosting and ANN were optimal for certain metals like Al, Cu, As, Mo, Zn, and Cd. Upon evaluating the final model’s predicted values against the actual measurements, differences in the concentration distribution between measurement locations were observed for Mn, Fe, Cu, Ba, and Pb, indicating varying prediction performances among sites. Additionally, Al, As, Cd, and Ba showed significant differences in prediction performance across seasons. The developed model is expected to overcome the technical limitations involved in measuring and analyzing heavy metal concentrations. It could further be utilized to obtain fundamental data for studying the health effects of exposure to hazardous substances such as heavy metals.

The presence of polycyclic aromatic hydrocarbons (PAHs) in the atmosphere has been linked to health concerns, including cancer. Automobile workshops are significant contributors to PAH emissions due to their operations. Hence, this investigation aimed to identify and quantify the sources of PM2.5-bound PAHs in the ambient air of automobile workshops in Benin City, Nigeria, using molecular diagnostic ratios. PM2.5 samples were collected from 60 automobiles over 1 year, during the rainy (April to November) and dry (December to March) seasons of 2019. Sample collection utilized a low-volume air sampler with quartz filter paper, and extraction was performed using a 1:1 mixture of acetone and dichloromethane. The analysis involved an HP Agilent Technology 6890 Gas Chromatography (GC) system with a flame ionization detector. The annual average concentrations of PM2.5-bound PAHs in Benin City were 269.87 ± 249.32 ng/m³ (dry season) and 216.30 ± 204.89 ng/m³ (wet season). Molecular diagnostic ratios, such as Fl/(Fl + Py), An/(An + Phe), BaP/(BaP + Chry), BbF/BkF, InP/(InP + BghiP), and BaA/(BaA + Chr), aided in identifying PAH sources. Gasoline combustion, diesel combustion, traffic emissions, and emissions from automobile panel welders were found to be the primary sources of PAHs near vehicle workshops. These findings provide crucial insights for developing effective strategies to reduce emissions and protect public health in the air surrounding automobile workshops in Benin City.

This study evaluates the impact of the stratospheric aerosol injection (SAI) technique for solar radiation management (SRM) on the potential of photovoltaic energy in four climatic regions throughout Nigeria. The photovoltaic energy potential for the SRM scenario ((PVE_{srm})) and the reference database ((PVE_{ref})) were evaluated using solar radiation and temperature data from the ARISE-SAI-1.5 model and from the MERRA-2 climate data repository, respectively. Before projecting the impact of the SAI approach on photovoltaic energy generation, the agreement between (PVE_{srm}) and (PVE_{ref}) was evaluated using the index of agreement metric. The analysis showed that the index of agreement had values of 0.90 in the Sahel, 0.98 in the Guinea Savannah, 0.97 in the rainforest, and 0.82 in the coastal regions. Other validation metrics used also showed similar trends across the climatic regions in Nigeria. The projected analysis of the impact on photovoltaic energy generation between 2035 and 2069 indicated potential gains of + 5.20 in the Sahel, + 3.60 in the Guinea Savannah, and + 3.40 in the rainforest, but a decline of − 3.20 in the coastal region, all values in watts per square meters. In conclusion, this study reveals that the implementation of the SAI approach for solar radiation management would have a relatively gainful influence on solar power generation in the Sahel, the Guinea Savannah, the rainforest but declined effect in the coastal region. The results of this study provide valuable insights into the influence of solar radiation management and renewable energy generation in different climatic zones across Nigeria.