Ulsan City, South Korea, hosts various industries, including metal processing, petrochemicals, machinery, and electronics. The complex interplay of industrial activities, traffic emissions, and marine influences contributes to the formation of fine particulate matter (PM2.5). This study investigated the chemical composition and sources of PM2.5 in Ulsan using samples collected periodically from five air monitoring stations between August 2020 and December 2023. The highest PM2.5 concentrations were observed in areas with dense industrial facilities, such as the Onsan National and Mipo General Industrial Complexes. These areas were characterized by large-scale industrial operations, including metal processing facilities and petrochemical plants, which were identified as major pollution sources. Coefficient of divergence (COD) analysis revealed minimal differences in pollution sources between the monitoring sites, suggesting that the primary emission sources were relatively uniform across the study area. Furthermore, principal component analysis (PCA) identified various contributors, including metal processing and smelting industries, soil dust, and sea salt particles. Elements such as Fe and Mn were strongly associated with metal processing and machining activities, whereas V and Ni, which originated from petrochemical processes, were indicators of petroleum combustion. PM2.5 exhibited seasonal variations, with higher levels in winter due to increased heating emissions and in spring due to dust storms. The primary sources of pollution included vehicle exhaust, metal processing and smelting, and sea salt particles, with a more pronounced distribution in regions of intense industrial activity. This study provides essential information on PM2.5 sources and emissions within Ulsan’s industrial complexes, serving as a foundational resource for air quality management and policy development in the city.Graphical

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 NOx emissions in Japan were compensated for by increases caused by increasing NOx 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 NOx 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.Graphical

The study investigated PM2.5 and heavy metal pollutant concentrations in Seoul and Wonju, South Korea, emphasizing the importance of considering PM2.5 chemical constituents for health impact assessments. While PM2.5 concentrations were similar between the two cities with slight variations, heavy metal concentrations differed significantly. Regional sources, PM2.5 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 PM2.5, while in Wonju, Mn was more influential. Source apportionment confirmed different pollution sources in Seoul and Wonju, affecting the distribution of PM2.5 constituents in the atmosphere. Effective PM2.5 management requires a comprehensive approach considering chemical constituents and health impacts not just mass-based PM2.5 management, emphasizing regional-specific assessments and policy considerations to mitigate health risks. Therefore, tailored management strategies based on regional characteristics are necessary.Graphical

In this study, we developed a prediction model for heavy metal concentrations using PM2.5 concentrations and meteorological variables. Data was collected from five sites, encompassing meteorological factors, PM2.5, 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.

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 (PVEsrm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{srm}$$\\end{document}) and the reference database (PVEref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{ref}$$\\end{document}) 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 PVEsrm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{srm}$$\\end{document} and PVEref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PVE_{ref}$$\\end{document} 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.

In the region where heat stress has become evident, the elevation of air temperature could reduce yield of heat stress-susceptible crops, such as rice (Oryza sativa L.), which is a major food staple in Asia. In addition to air temperature, atmospheric CO2 is projected to be elevated in the future. To project rice yield in the future, it is necessary to clarify the responses of rice to concurrent elevations of air temperature and atmospheric CO2. In the present study, two japonica rice cultivars with different heat tolerance, Hinohikari (sensitive) and Nikomaru (tolerant), were grown in pots inside open-top chambers and exposed to elevated air temperature and/or CO2. The degrees of increase in the air temperature and CO2 concentration by the treatments were approximately 1 °C and 120 µmol mol−1 (ppm). The study was conducted in Nagasaki, Japan, where heat stress on rice has become evident. Elevated air temperature significantly decreased both whole-plant growth and grain yield. Elevated CO2 significantly increased the growth but significantly decreased the yield. The effects of elevated air temperature and elevated CO2 on growth and yield did not significantly differ between two cultivars. In both cultivars, the main cause of yield reduction by both treatments was reduction in spikelet fertility, which is typical heat stress on rice. The elevated CO2-induced reduction in spikelet fertility could be explained partially by high-temperature regime during flowering due to acceleration of heading and by increase in canopy temperature via stomatal closure in flag leaves. Because elevated air temperature and elevated CO2 treatments additively reduced spikelet fertility in both cultivars, concurrent elevations of air temperature and CO2 caused considerable reduction in grain yield.

This study presents an analysis of the atmospheric footprint sensitivities and CO2 enhancements measured at three in situ stations in South Korea (Anmyeondo (AMY), Gosan (JGS), Ulleungdo (ULD)) using the KIM-STILT and WRF-STILT atmospheric transport models. Monthly aggregated footprints for each station were compared between the models for July and December 2020. The footprints revealed major source regions and the sensitivity of atmospheric mole fractions at the receptor to upstream surface fluxes. In July, both models showed similar major source regions for the AMY station, including Korea, the Yellow Sea, and Japan. However, a discrepancy was observed in the Eastern Pacific Ocean, with KIM-STILT showing larger sensitivity compared to WRF-STILT. In December, both models indicated strong sensitivity over Northeast and Eastern China, but KIM-STILT exhibited smaller sensitivities towards Northwestern China and Mongolia compared to WRF-STILT. At station ULD in July, both models exhibited comparable source regions, but a notable difference was found in Southeast China, where KIM-STILT showed stronger sensitivity. For the JGS station, both models agreed on major sources, but WRF-STILT demonstrated stronger sensitivity over North and Northeastern China. Regarding CO2 enhancements, both models generally underestimated the amplitude of CO2 enhancements, especially in July. However, in December, there was better agreement with observed data. The models were able to reproduce the phase of measured ΔCO2 reasonably well despite the underestimation of CO2 amplitudes. The contribution of biospheric CO2 to the observed enhancements, along with fossil-fuel emissions, was highlighted. In specific cases with significant CO2 enhancements, the models provided varying estimates of CO2ff values, particularly in the source regions of Eastern China. The differences in sensitivity estimations emphasize the need for further investigation to understand the underlying factors causing disparities. Overall, this study provides valuable insights into the potential advantages of each model in capturing dispersion patterns in specific regions, highlighting the importance of understanding these differences to improve the accuracy of atmospheric transport models. Further work is necessary to address the observed disparities and enhance our understanding of the transport models in the studied regions.

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 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.