Asian Journal of Atmospheric Environment最新文献

This study aimed to identify the factors causing NH₃ emissions in the South Korean Peninsula and West Sea region. To analyze the trends of NH₃ and other air pollutants, such as NO_x, CO, and NR-PM₁, we collected samples from six supersites across the peninsula, a roadside in Seoul, and the West Sea over different sampling periods, ranging from 1 month to 1 year. The highest NH₃ concentrations were found at rural areas, ascribed to agricultural activities, particularly NH₄NO₃ decomposition at high summer temperatures. Areas with low population densities recorded the lowest NH₃ concentrations, attributed to the lack of anthropogenic activities. A roadside field experiment confirmed the close link between ambient NH₃ and vehicle emissions in urban regions by showing a strong correlation between CO and NO_x concentrations and that of NH₃. Moreover, we examined oceanic emissions near the eastern coast of South Korea in the West Sea. Long-range transportation studies confirmed that most of the pollutants (NH₃, CO, and PM₁) were transported by wind from the northeastern region of China. A maritime origin study showed that oceanic emissions and NH₄NO₃ decomposition in the atmosphere owing to high temperatures were the causing NH₃ pollution. These findings provided valuable insights into the emission sources of NH₃ in primary air pollutants in South Korea, highlighting the contributions of land-based and oceanic sources. Our study can help inform policymakers and stakeholders for developing effective regional air pollution control strategies.

Records of stable isotopes (²H and ¹⁸O), deuterium excess (d-excess) and tritium (³H) values in precipitation (P) during 1990–2022, together with long-term time series (1919–2022) of air temperature (T) and P-amount values at the Damascus station, were analysed to explore the seasonal and annual variability patterns of those parameters in P and assess the vulnerability to climate change in this area. Variation of the annual average air T values over the period 1919–1969 shows an increase gradient of ≈ + 0.1 °C/decade. However, a remarkable much higher gradient (+ 0.64 °C/decade) is calculated for the period 1990–2022. The average P-amount value calculated for the last three decades (≈185 mm) was lower by ≈28 mm, compared to the value (≈213 mm), relative to the reference period (1919–1969). This significant decline in the annual P-amount value by ≈11–13%, accompanied by an annual heating of 0.2–0.6 °C/decade in the annual air T, is likely the result of the climate change affecting this area. The linear relationships between annual average δ¹⁸O and annual average δ²H values versus time over the period 1990–2019 indicate increased gradients in both stable isotopes (≈0.3–0.5‰ and ≈1.1–3.2‰ per decade for δ¹⁸O and δ²H, respectively), accompanied with a decrease gradient of ≈0.9–1.1‰ per decade in d-excess values. Variability of annual ³H concentrations towards low levels (< 6 TU) during the later years strongly suggests the return back towards the cosmogenic production of this radioisotope in the upper atmosphere. Information gained from this work would offer new insights to improve the understanding of the temporal variability of P isotopes and assess the risks associated with climate change on the natural water resources in the Eastern Mediterranean region.

The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.

This study aims to analyze the sensitivity of volatile organic compounds (VOCs) to ambient concentrations of fine particles (PM_2.5) in the representative industrial city of Ulsan, Korea. For the calculation of sensitivity coefficients between VOCs and PM_2.5 (SVOCs-PM_2.5), PM_2.5 data were obtained from an air quality monitoring station, and their corresponding 6-h average concentrations of VOCs (alkanes, alkenes, aromatics, and total VOCs) were measured at the Yeongnam intensive air monitoring station. The air monitoring period was divided into the warm-hot season (May–October 2020) and the cold season (November 2020–January 2021). The sensitivity coefficients in the low pollution period of PM_2.5 (5 < PM_2.5 ≤ 15 μg/m³) were higher and much higher than those in the medium pollution period (15 < PM_2.5 ≤ 35 μg/m³) and high pollution period (35 < PM_2.5 ≤ 50 μg/m³), respectively. This result indicates that the change ratios of PM_2.5 concentrations to the background (PM_2.5 ≤ 5 μg/m³) per unit concentration change of VOCs (particularly alkenes) in the high PM_2.5 pollution period were much higher than those in the low pollution period. This also indicates that PM_2.5 concentrations above 35 μg/m³ were more easily affected by the unit concentration change of VOCs (particularly alkenes) than those below 15 μg/m³. The average sensitivity coefficients during the cold season increased in a range of 23–125% as compared to those during the warm-hot season, except the alkenes-PM_2.5 sensitivity with a decrease of 7%. It means that the impact of VOCs (except alkenes) on PM_2.5 concentrations was relatively low in the cold season. However, in the cold season, the alkenes might contribute more to PM_2.5 formation, particularly over the high pollution period, having PM_2.5 > 35 μg/m³, than other VOC groups. The result of this study can be a basis for establishing PM_2.5 management plans in industrial cities with large VOC emissions.

Atmospheric aerosols, including primary aerosols emitted directly into the atmosphere and secondary aerosols generated in the atmosphere from various chemically complex particles, cause a variety of environmental problems such as climate change, photochemical smog formation, and a decrease in incoming solar radiation. Therefore, it is important to understand the causes of aerosol particles and their impact on human society. In particular, particle size is an important indicator of lung penetration depth, aerosol transport, and optical properties. Hence, we mathematically estimated the airborne particle size distributions of each chemical component by collecting aerosol samples from the atmosphere using two types of cyclone samplers, large and small cyclone samplers. This study’s findings also suggest that calculated changes in particle size distribution can reflect changes in particle sources. The higher resolution of the continuous functions will enable the detection of the subtle changes in particle size distributions of each chemical component, which is helpful to understand the temporal changes in the chemical properties of the airborne aerosol particles.

Exhausts or emission from industries/automobiles/indoor appliances is one of the most prominent sources of air pollution. Innumerable noxious gases have been identified and been recurrently treated through various technologies from past many decades. Cumulative studies suggest that air pollutants affect the respiratory and cardiovascular systems along with the central nervous system, may it be directly or indirectly. In particular, acquaintances to such air pollutants in early life can lead to developmental delays and may stunt neurological development. This review presents the recent technologies that have been tested at the laboratory level as well as in situ utilizing one of the abundantly available industrial wastes, i.e. red mud. Unlike the conventional expensive catalysts, red mud provides a cheaper alternative in the treatment of toxic exhaust gases from various sources. Furthermore, the review identifies the gap through which experts from other disciplines can explore the employment of red mud in the comprehensive spectrum of pollution control.

The chemical characteristics of particulate matters collected from 53 schools in 2019 through 2022 were closely investigated to determine the main sources of classroom PM_2.5. On average, indoor PM_2.5 measured during class hours distributed from 3.3 μg/m³ to 45.97 μg/m³, and it consisted of 45% of ions, 33% of carbons, 17% of metals and others. The average indoor-to-outdoor ratio (I/O) of PM_2.5 was 0.73. Values for I/O ranged from 0.6 to 0.91 for inorganic elements; 0.3 to 0.8 for ions; 0.50 to 2.69 for elemental carbons (EC), and 0.52 to 8.50 for organic carbons (OC). The linear correlation of indoor EC with concentrations of K⁺ and NO₃⁻ indicates that the contribution of combustion-related sources to classroom PM_2.5 is significant in roadside schools. The findings from this study should help establish construction guidelines for urban schools near high-traffic areas.

The Observation-Constrained Atmospheric BOX model (OCABOX) was used to analyze the formation of secondary inorganic PM species in the Seoul Metropolitan Area (SMA), South Korea. The measurement data of the ionic components of PM_2.5 and their gaseous precursors made at the Olympic Park ground site (37.53°N, 127.12°E) during the Korea-United States Air Quality field campaign were used to run OCABOX in observation-based mode and compare the simulation results. The use of the HNO₃ concentrations measured at a marine background site as the boundary conditions appeared to increase the accuracy of the model prediction of HNO₃ and particulate NO₃⁻ concentrations. For the primary precursors emitted considerably throughout the SMA, such as NO_x and NH₃, using the data measured inside the SMA as the boundary conditions could lead to more accurate predictions. OCABOX was shown to be a reliable tool to analyze the formation of secondary inorganic aerosol in the SMA if used with appropriate regional background concentrations and observation-based constraints

A study on monitoring PM_2.5 concentrations at an underground subway station using light scattering and beta attenuation methods was conducted. Six optical particle counters (OPCs), which were representative of the light scattering method and had 16 size channels, were installed at different sampling sites in the waiting room and platforms to compare PM_2.5 concentrations, determine PM_2.5 /PM₁₀ ratios, and characterize the size distribution based on particle number. In terms of the beta attenuation method, a beta-ray attenuation monitor (BAM) was set up at the platform to compare it with OPCs for one month. It was found that 1h-average PM_2.5 concentrations varied at different sampling sites depending on PM composition, while 24h-average PM_2.5 concentrations were independent of the sampling sites. There was a significant difference between the means of 1h-average PM_2.5 concentrations observed by the BAM and OPC when PM_2.5 contributed to less than 50% or higher than 80% of PM₁₀. In contrast, the 24h-average PM_2.5 concentrations observed by the BAM and OPC showed the same patterns as each other with an insignificant difference. Therefore, an OPC can be used to monitor the 24h-average PM_2.5 concentration in an underground subway station. However, the use of OPC to measure the 1h-average PM_2.5 concentration should consider PM composition and other factors. In addition, more frequent calibration is needed on a regular basis.

The preparation of eco-friendly carbon-rich (biochar) materials by thermal pyrolysis of waste biomass has been recognized as one of the most economical and effective strategies for gas purification in recent years. Through control of synthesis and activation methods, the surface features and catalytic sites in biochar can be engineered for diverse heterogeneous catalytic reactions. Nonetheless, its commercial utilization in air pollution control has yet been limited to a large extent because of (i) the shortage of databases related to the actual catalytic performance of biochar and (ii) the complexity involved in industrial upscaling. Herein, the merits and demerits of biomass-to-biochar catalyst conversion are discussed, along with the factors to consider in the synthesis stage for enhancing catalytic activities toward air purification applications. This paper also offers an in-depth evaluation of the techno-economic and environmental aspects of biochar-based catalysts and their catalytic reactions for air pollution control and energy production. Lastly, a contemporary perspective is offered to help develop novel biochar-based catalysts for real-world applications in air purification fields.