Asian Journal of Atmospheric Environment最新文献

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.

Microplastics are a very complex, diverse, and persistent contaminant class in aquatic ecosystems, providing significant challenges for scientists in developing analytical methodologies, fate and transport models, identification of exposure routes, and toxicological risk evaluation are all key difficulties for scientists. Despite a considerable and developing body of thought concerning the effects of microplastics on aquatic species, nothing is known about the effects of microplastics on humans. Microplastics have been found in food all across the world. As a result, human exposure to microplastics through tainted food is unavoidable, possibly creating health risks. In recent years, a major research effort has added to our understanding, but there is an urgent need to simplify and integrate the findings. This review focuses on the effects of microplastics as well as methods for decomposing plastics without creating microplastic particles. Among the various plastic breakdown methods, employing microorganisms and nanotechnology might be a long-term solution in preventing environmental microplastic contamination.

In recent years, endotoxins have received considerable attention as substances associated with allergic diseases. Endotoxins are cell wall components of gram-negative bacteria that are widely present in the living environment. Endotoxin concentrations are particularly high in environments where animals are housed. However, while the status of endotoxin concentrations in the general environment is becoming clearer, there remains a scarcity of studies in environments with potentially higher concentrations.

In this study, we measured indoor endotoxin concentrations in buildings in Japan that are strongly associated with horses. The target buildings include a “Magariya,” an old Japanese house, an accommodation facility connected to a horse stable, and a stable specifically for thoroughbreds. Air and dust samples were collected at these measurement targets and analyzed for air and dust concentrations.

Airborne concentrations were higher in buildings with horses present than in buildings without horses, and the presence/absence of horses is thought to have a significant effect on the airborne concentration of endotoxin. Additionally, as the density of horses increases, endotoxin concentrations also tend to increase. Dust concentration had different values in different rooms even in the same building. These results suggest that dust concentrations may be affected by floor materials, frequency of cleaning, and frequency of human traffic from areas of high concentrations. Endotoxin concentrations were high in the stable during the work because of the replacement of the dried straw in the stalls and the removal of horse excrement. These results are expected to be useful in controlling endotoxin concentrations in indoor environments of various building types.

The emission sources of fine particulate matter (PM_2.5) have not yet been fully identified in Ho Chi Minh City (HCMC), Vietnam, presenting difficulties to authorities in controlling air pollution efficiently. To address this issue, this study explores the source apportionment of PM_2.5 by the positive matrix factorization (PMF) model and identifies potential regional sources through the weighted concentration-weighted trajectory (WCWT) model based on the field observation data of PM_2.5 in HCMC. 24-h PM_2.5 samples were collected in central HCMC for a year (September 2019–August 2020). Herein, inductively coupled plasma mass spectroscopy was used to analyze trace elements, in addition to identifying PM_2.5 mass and other chemical species, such as water-soluble ions and carbonaceous species, reported in our former study. The PMF results showed that PM_2.5 in HCMC was dominated by anthropogenic-rich sources comprising biomass burning, coal combustion, transportation, and crustal origins (36.4% of PM_2.5 mass), followed by secondary ammonium sulfate (18.4%), sea salt (13.7%), road dust (9.6%), and coal and crude oil combustion (9.4%). WCWT results suggested that the geological sources of PM_2.5 were mainly from local areas and scattered to the northeast/southwest of HCMC. In addition, the long-range transport of PM_2.5 from surrounding countries was revealed during the assembly restriction and lockdown period in 2020.

Graphical Abstract

Paju City is located in the northwest of Gyeonggi-do, and its chemical emissions in 2020 were 1,287,917 kg, the 4th highest in Gyeonggi-do. In particular, the Munsan High-Tech Industrial Complex in Paju has LCD manufacturing plants and partner companies distributed in groups, and the volatile organic compounds used by these companies are causing many problems, such as causing bad odors, to the local community. In this sense, real-time analyzing equipment (proton-transfer-reaction time-of-flight mass spectrometry) was mounted on a vehicle for this study to look into the air quality around VOCs-using companies inside the High-tech Industrial Complex in Munsan, Paju from October 19 to October 21, 2020.

According to measurement results, toluene was detected the most at 25.7 ppb, followed by carbon tetrachloride (17.6 ppb), ethylbenzene (17.2 ppb), and xylene (8.5 ppb), which demonstrates that there is a need to control these substances to resolve the issue with VOCs in the region. In particular, benzene designated as the air quality standard was detected at 1.0 ppb in some sites, which is below the threshold (1.5 ppb). However, it was detected at 2.1 to 4.4 ppb, exceeding the threshold in most sites. Thus, continuous monitoring is expected to keep VOCs under control in Paju Industrial Complex down the road, using real-time measuring equipment.

The feasibility of a novel type of moss (Parkortanso No. 1 synthesized from Racomitrium japonicum, Dozy and Molk) to capture CO₂ in urban areas was demonstrated. The effects of light intensity (500, 1000, and 1500 µmol/m².s), ambient temperature (10 °C, 25 °C, and 35 °C), age (1-year-old and 3 years old), and leaf color (bright and dark green) on the CO₂ removal caused by the moss concerned were investigated. It was determined that stronger light intensity resulted in higher CO₂ removal by the target moss. The moss showed the best CO₂ capture at 25 °C, while the CO₂-capturing capacities declined when the ambient temperatures were 10 °C and 35 °C. Three years old bright green moss was found to have higher CO₂-capturing capacity than 1 year old. Similarly, bright green moss exhibited the best CO₂ uptake out of the mosses concerned. The highest net CO₂ emission of the moss was − 1.94 ± 0.72 kgCO₂/m².year, which was comparable to other moss and plant species. Consequently, the bright green and old Parkortanso No. 1 moss are recommended for a green roof application in terms of CO₂ capture.

Graphical Abstract

This study examines the change in rice yield due to ozone exposure in South Korea using extended air quality monitoring data from 2000 onwards. Notably, the maximum daily 8-h average O₃ (MDA8O3) showed a substantial annual increase of 1 part per billion by volume (ppbv) from 1990 to 2021. AOT40 (accumulated dose of ozone over a threshold of 40 ppb) levels exceeded set thresholds in the early 2010s, and the M7 (mean 7-h ozone mixing ratio) index exhibited a parallel pattern, with a more pronounced increase than the AOT40 during the same period. Spatial variations of AOT40 and M7 metrics have been assessed annually across South Korea since 2000. Both metrics displayed spatial disparities, with higher values in western regions and lower values in the east. In particular, Dangjin and Seosan counties in Chungnam province experienced the greatest rice yield loss due to extensive rice cultivation area and high ozone exposure metrics. The quantified yield loss due to AOT40 increased from 127,000 in 2000 to 230,000 tonnes in 2021 with an increasing rate of 6500 tonnes per year. M7 indicated a rise in yield loss of 3500 tonnes per year, with yield losses growing from 32,000 in 2000 to 92,000 tonnes in 2021. Despite M7’s lower loss, it demonstrated a higher percentage increase of 188% over two decades, which was double AOT40’s 81%. While the decline in rice production was mainly linked to shrinking cultivation areas, its productivity was improved. Taking both factors into account, there was an unexplained 3% decrease in production over the same period. This discrepancy was close to the 2.5% rice yield loss attributed to the AOT40 metrics, suggesting that the majority of the additional 3% decline in production, surpassing improvements in productivity, could be attributed to the impacts of ozone exposure. We estimated the annual economic loss due to rice yield loss up to around 0.6 billion US dollars, corresponding to an annual rice production loss of 230,000 tonnes using AOT40. It is important to note that this value is expected to steadily worsen as ozone levels increase. This underscores the urgency of taking swift measures to reduce ozone levels, aiming not only to mitigate future economic losses but also to prevent potential health implications.