Distribution Of Air Pollutants Research Articles

Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants

Air pollution has become a threat to human life around the world since researchers have demonstrated several effects of air pollution to the environment, climate, and society. The proposed research was organized in terms of National Air Quality Index (NAQI) and air pollutants prediction using data mining algorithms for particular timeframe dataset (01 January 2019, to 01 June 2021) in the industrial eastern coastal state of India. Over half of the study period, concentrations of PM2.5, PM10 and CO were several times higher than the NAQI standard limit. NAQI, in terms of consistency and frequency analysis, revealed that moderate level (ranges 101–200) has the maximum frequency of occurrence (26–158 days), and consistency was 36%–73% throughout the study period. The satisfactory level NAQI (ranges 51–100) frequency occurrence was 4–43 days with a consistency of 13%–67%. Poor to very poor level of air quality was found 13–50 days of the year, with a consistency of 9%–25%. Random Forest (RF), Support Vector Machine (SVM), Bagged Multivariate Adaptive Regression Splines (MARS) and Bayesian Regularized Neural Networks (BRNN) are the data mining algorithms, that showed higher efficiency for the prediction of PM2.5, PM10, NO2 and SO2 except for CO and O3 at Talcher and CO at Brajrajnagar. The Root Mean Square Error (RMSE) between observed and predicted values of PM2.5 (ranges 12.40–17.90) and correlation coefficient (r) (ranges 0.83–0.92) for training and testing data indicate about slightly better prediction of PM2.5 by RF, SVM, bagged MARS, and BRNN models at Talcher in comparison to PM2.5 RMSE (ranges 13.06–21.66) and r (ranges 0.64–0.91) at Brajrajnagar. However, PM10 (RMSE: 25.80–43.41; r: 0.57–0.90), NO2 (RMSE: 3.00–4.95; r: 0.42–0.88) and SO2 (RMSE: 2.78–5.46; r: 0.31–0.88) at Brajrajnagar are better than PM10 (RMSE: 35.40–55.33; r: 0.68–0.91), NO2 (RMSE: 4.99–9.11; r: 0.48–0.92), and SO2 (RMSE: 4.91–9.47; r: 0.20–0.93) between observed and predicted values of training and testing data at Talcher using RF, SVM, bagged MARS and BRNN models, respectively. Taylor plots demonstrated that these algorithms showed promising accuracy for predicting air quality. The findings will help scientific community and policymakers to understand the distribution of air pollutants to strategize reduction in air pollution and enhance air quality in the study region.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Latitudinal distribution and sources analysis of greenhouse gases and air pollutants observed during the 2021 Yellow Sea Air Quality campaign aboard a research vessel

The Yellow Sea serves as a pathway for air pollution transport from the Asian continent into Korea and is annually monitored by the Korean Meteorological Administration (KMA) research vessel during the spring season. This study investigated the spatio-temporal variation of long-range transported greenhouse gases (CO2 and CH4) and air pollutants (CO, NOx, SO2, O3) in the Yellow Sea during the Yellow Sea Air Quality (YES-AQ) campaign. Our analysis reveals that GHGs and air pollutants exhibit a clear latitudinal distribution. Lower concentrations are observed in the range of 31.2–34.5°N, primarily influenced by air masses traversing the ocean and the western part of Korean Peninsula. In contrast, higher concentrations occur more frequently at 34.5–37.8°N, primarily affected by continental air masses. The regression slopes of CO and CO2 vary depending on regional characteristics, particularly in air originating from Korea and China. Regression slope analysis between CO, CO2, and CH4, except for high plume of CH4 (>2.05 ppm), indicates that fossil fuel sources play a significant role. A high plume of CH4 (>2.05 ppm) was observed on April 22nd, coinciding with the maximum O3 concentrations during the period of highest temperatures throughout the voyage. The potential source regions were identified in the west-southern coast of Korea, using footprint analysis from Stochastic Time-Inverted Lagrangian Transport (STILT) and column-averaged dry-air mole fraction of CH4 (XCH4) retrieval from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The high CH4 enhancements are most likely due to biogenic emissions triggered by favorable near-surface temperatures for microbial activities in soil. Furthermore, the high CH4 enhancements were not correlated with CO and CO2, indicating that the source was not related to fossil fuel combustion. On March 29–30, a high plume of NOx was detected and is likely attributed to the Bohai Sea, which is located just above the Yellow Sea and surrounded by highly industrialized and semi-enclosed areas.

Analysis The Effect of Large-Scale Social Restrictions on Air Quality in DKI Jakarta

The Covid-19 pandemic has caused all countries to implement strategies to suppress its spread, one of which is Indonesia, especially DKI Jakarta, which has implemented Large-Scale Social Restrictions (PSBB) since April 10 2020. Apart from being able to suppress the spread of the Covid-19 virus, PSBB is thought to have an impact on the environment, especially air quality in DKI Jakarta. According to research from the BMKG, Jakarta's air quality has improved over the last 5 years with the implementation of the PSBB. Besides analyzing the effect of the PSBB on air quality in DKI Jakarta, this research also aims to help governments in every region of Indonesia that do not have air quality monitoring stations. The method used in this study is to utilize Imagery from Sentinel-5P to measure concentrations of NO2, CO and SO2 gases validated using field data and utilize the NOAA Satellite acquired with Ventusky to analyze the effect of wind on the distribution of air pollution due to the PSBB. The results showed that the ratio of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta decreased respectively to 27.70% ; 10.20% ; 42.06%. This shows an increase in air quality in DKI Jakarta due to the implementation of the PSBB. Comparison of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta during the PSBB and after the PSBB increased slightly respectively to 11.92% ; 1.89% ; 35.84%. This shows that there is a decrease in air quality in DKI Jakarta which was caused after the implementation of the PSBB. Wind also affects the concentration of NO2, CO and SO2 gases. This is evidenced by the results of the correlation where the gas concentration is low when the wind speed is high, and vice versa. It was concluded that during the COVID-19 pandemic the concentrations of NO2, CO and SO2 in DKI Jakarta decreased and slightly increased after the PSBB, and wind could affect the distribution of these gases.

Assessing the impact of a waste incinerator on the environment using the MAIAC-AOD and AERMOD models

The distribution and concentration of air pollutants from infectious waste incineration between 2019 and 2021 were examined in this study using an AERMOD model, including sulfur dioxide (SO2), nitrogen dioxide (NOx), and particulate matter (PM2.5). The MAIAC-AOD value at a 1 km resolution was used to develop a regression model with meteorological information for predicting PM2.5, which was then compared with the concentration from the AERMOD simulation. The following maximum 1-h, 24-h, and annual average concentrations of all pollutants were found to have occurred in 2019. The distribution of SO2 and NOx in 1 h was largest in 2020 at 1,000 m to the northwest, with concentrations of 37.68 and 93.99 μg/m3, respectively. The 24-h concentrations of SO2 and NOx were greatest in 2021 at 3.63 and 8.90 μg/m3, respectively, 720 m northeast of the stack. The annual concentrations of SO2 and NOx were highest in 2019 at 0.56 and 1.36 μg/m3, respectively. The highest annual PM2.5 concentration was 0.033 μg/m3, 730 m to the northeast in 2019. The predicted PM2.5 using MAIAC-AOD correlated with the simulated value from AERMOD, with R2 values of 0.7630, 0.7607, and 0.6504 for 2019, 2020, and 2021, respectively, which were higher closer to the stack than outside. As a result, investigations into the distribution of air pollution should benefit from the integration of air modeling and satellite information.

Air quality analysis and modeling in urban area: A review study

Urban areas across the world are experiencing significant challenges related to air quality, with adverse effects on public health, the environment, and overall quality of life. This thesis aims to investigate and model air quality in urban environments, focusing on the identification of pollution sources and the evaluation of potential mitigation strategies. The research begins by analyzing the complex interplay of factors influencing urban air quality, including emissions from vehicular traffic, industrial activities, and meteorological conditions. Various modeling techniques, such as numerical simulations, statistical analyses, and machine learning algorithms, are employed to assess the spatiotemporal distribution of air pollutants within urban areas. The overview is carried out to focus on various parameters such as emission sources, meteorological parameters, and topographical features to predict pollutant concentrations. The study also explores the effectiveness of different mitigation strategies to improve urban air quality. These strategies may include urban planning measures, such as the design of green spaces and pedestrian-friendly zones, as well as transportation-related interventions like the promotion of electric vehicles and the enhancement of public transportation systems. The impact of these strategies on reducing pollutant concentrations is quantitatively assessed using the developed air quality models. Furthermore, the study considers the implications of climate change on urban air quality, acknowledging that changing climate patterns can exacerbate air pollution issues. Potential adaptation strategies to address these challenges are discussed. In conclusion, this thesis offers a comprehensive analysis of air quality modeling in urban areas, providing insights into the sources of pollution and the effectiveness of mitigation measures. The research aims to inform policymakers, urban planners, and environmental stakeholders in their efforts to create healthier and more sustainable urban environments.

Long term trend analysis and Spatio-temporal variation of air pollutants over Uttar Pradesh, India

Air pollution poses a significant challenge for developing countries like India, particularly in cities where industrial growth and urban development have led to increased levels of air pollutants. Due to limited monitoring resources and a focus on specific variables, there is a lack of comprehensive understanding regarding air quality in Indian urban areas. This study aims to explore the spatial and temporal variations in air pollutant concentrations and identify long-term trends over a seven-year period (2016-2022) in the state of Uttar Pradesh. Data for PM10, SO2, and NO2 were collected from 78 ambient air quality monitoring stations. The study investigates how these pollutants behave in terms of their distribution on different temporal scales (seasonal, yearly) as well as across various locations. The findings reveal that the concentration of PM10 consistently exceeds national and international air quality standards. Using the Inverse Distance Weighting (IDW) technique, air pollutant distribution maps were created to identify specific hotspots. Additionally, the Mann-Kendall Test was applied to analyze trends within the available data. This research can serve as valuable information for local government authorities to formulate effective strategies for mitigating air pollution in the region.

Spatiotemporal Variation and Pattern Analysis of Air Pollution and Its Correlation with NDVI in Nanjing City, China: A Landsat-Based Study

The rapid socio-economic development and urbanization in China have led to a decline in air quality. Therefore, the spatial and temporal distribution patterns of urban air pollution, as well as its formation mechanisms and influencing factors, have become important areas of research in atmospheric environment studies. This paper focuses on nine monitoring sites in Nanjing, where concentration data for six air pollutants and vegetation index data were collected from 2013 to 2021. The objective of this study is to investigate the changes in air pollutants and vegetation index over time and space, as well as their relationship with each other, and to assess the social and environmental impacts of air pollution. The findings reveal a spatial distribution pattern of air pollution in Nanjing that exhibits significant variability, with pollutant concentrations decreasing from the city center towards the surrounding areas. Notably, the main urban area has lower air quality compared to the peripheral regions. The results obtained from best-fit linear regression models and correlation heatmaps demonstrate a strong correlation (coefficient of determination, R2 > 0.5) between the normalized difference vegetation index (NDVI) and pollutants such as SO2, NO2, PM2.5, PM10, and O3 within a radial distance of 2 km from the air pollutant monitoring sites. These findings indicate that NDVI can be an effective indicator for assessing the distribution and concentrations of air pollutants. Negative correlations between NDVI and socio-economic indicators are observed under relatively consistent natural conditions, including climate and terrain. Therefore, the spatiotemporal distribution patterns of NDVI can provide valuable insights not only into socio-economic growth but also into the levels and locations of air pollution concentrations.

Seasonal and multi-scale difference of the relationship between built-up land landscape pattern and PM2.5 concentration distribution in Nanjing

The urban landscape pattern, which can affect the air pollutants distribution in urban areas, was significantly changed due to the expansion of built-up land. Deeply understanding of the mechanisms by which urban landscape pattern influence the PM2.5 concentration distribution is fundamental for urban pollution control. However, the relationship between the built-up land landscape pattern and PM2.5 concentration distribution is still unclear at different grid scales in different seasons. In this study, four landscape metrics were calculated to quantify the distribution characteristics of built-up land in Nanjing, and the impact of built-up land landscape pattern on PM2.5 concentration distribution at different grid scales in different seasons were further analyzed. Results showed that the dominant landscape pattern metrics affecting PM2.5 concentration in different areas of Nanjing were closely related to the topography: in the main urban areas with flat terrain, the aggregation, shape, and proportion of built-up land significantly affected PM2.5 concentration, while the influence of the shape and proportion of built-up land was more noticeable in the hilly southwest suburbs. The effect of built-up land landscape pattern on PM2.5 concentration in winter was more significant than that in summer in general, and the relatively greater seasonal differences in this effect can be seen in the aggregation and proportion of built-up land. With the grid increasing, the correlation between the built-up land landscape pattern and the PM2.5 concentration distribution increased in the main urban areas but mostly decreased in the southwest suburbs, indicating that the PM2.5 concentration distribution in the main urban areas was mostly contributed by spatial transmission of pollutants, while the PM2.5 concentration distribution in the southwest suburbs was mostly dominated by local emissions. We suggest that the planning of flexible urban strategies with respect of PM2.5 pollution control in different areas should be implemented considering the influence of topography.

Air Quality Research Based on B-Spline Functional Linear Model: A Case Study of Fujian Province, China

It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) were fitted to functional curves using the B-spline basis function. Compared with discrete data, functional data can better express the dynamic characteristics of data and reduce information loss. Additionally, functional clustering based on the principal component coefficient was established to analyze the spatiotemporal dynamic characteristics of air quality, and a functional linear model was established to analyze the relationship between pollutants and anthropogenic factors. The results showed that air pollutants in Fujian Province were found to have certain temporal and spatial heterogeneity, among which the seasonal characteristics of NO2 and O3 (high in summer, low in winter) were opposite to those of the other pollutants considered. The spatial distribution of air pollution was low (high) pollution in inland (coastal) areas, and the primary air pollutants in Fujian Province were PM10 and PM2.5. The functional linear model indicated that anthropogenic factors (e.g., vehicle numbers and emissions of industrial NOX emissions) were found to have a notable impact on air pollutants. The findings of this study could act as a reference in support of air pollution control.

Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality.

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.

Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach

In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence of multiple monitoring sites and their inherent connections with meteorological factors. To address this issue, we introduce an innovative deep-learning-based multi-graph model using Beijing as the study case. This model consists of two key modules: firstly, the 'Meteorological Factor Spatio-Temporal Feature Extraction Module'. This module deeply integrates spatio-temporal features of hourly meteorological data by employing Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) for spatial and temporal encoding respectively. Subsequently, through an attention mechanism, it retrieves a feature tensor associated with air pollutants. Secondly, these features are amalgamated with PM2.5 concentration values, allowing the 'PM2.5 Concentration Prediction Module' to predict with enhanced accuracy the joint influence across multiple monitoring sites. Our model exhibits significant advantages over traditional methods in processing the joint impact of multiple sites and their associated meteorological factors. By providing new perspectives and tools for the in-depth understanding of urban air pollutant distribution and optimization of air quality management, this model propels us towards a more comprehensive approach in tackling air pollution issues.

Spatiotemporal distribution of air pollutants during a heat wave-induced forest fire event in Uttarakhand.

Prevailing dry conditions and rainfall deficit during the spring season in North India led to heat wave conditions which resulted in widespread and intense forest fire events in the Himalayan state of Uttarakhand during April 16-30, 2022. A total of 7589 active fires were detected by VIIRS during the second half of April 2022 compared to 1558 during the first half. The TROPOMI observed total column values of CO and NO2 increased by 4.4% and 11.7%, respectively during April 16-30, 2022 with respect to April 1-15, 2022. A noticeable increase in surface level concentration of trace gases was also observed at Dehradun. In situ measurements of CO, NOx, and O3 during April 16-30, 2022 show an increase of 133, 35, and 6%compared to previous year observations during the same period. Weather Research and Forecasting model with chemistry (WRF-Chem) is utilized to quantitatively estimate the contribution of this event on the distribution of air pollutants over this state. The model results were evaluated against ERA5 reanalysis, upper air soundings, and TROPOMI-retrieved total column density (TCD) of CO, NO2, and O3. Two simulations with (Fire) and without (NoFire) biomassburning emissions input were performed to quantify the contribution of forest fires to the concentration of trace gases and particulates. The CO, NO2, and O3 emitted/produced from forest fire over Uttarakhand during April 2022 contributed approximately 39.95, 35.73, and 9.97% to the surface concentration of respective gas. In the case of aerosols, it was around 71.20, 71.44, and 33.62% for PM2.5, PM10, and BCrespectively. The vertical profile analysis of pollutants revealed that extreme forest fire events can perturb the distribution of air pollutants from the surface up to 450 hPa.

Analysis of pollutants in air within the territory of Ukraine using geostatistical methods

Air quality has recently been of great concern, as it directly affects people's lives. Continuous monitoring of atmospheric air quality and forecasting the dynamics of its changes are essential steps in assessing its current state and determining the concentration of pollutants. Therefore, the development of an effective system for assessing and forecasting the quality of atmospheric air has become one of the most important tasks. The subject matter of this article is geostatistical methods for air quality analysis. The goal is to analyze pollutants in the air over Ukraine's territory from 1990 to 2021. The dataset on air pollutants was provided by the State Statistics Service of Ukraine in the form of aggregated tables, which were initially processed for subsequent modelling. Cartographic modelling of pollutants was performed using geostatistical methods. As a result, this study presents 13 cartographic models showing the spatial distribution of air pollutants for different regions of Ukraine. However, because of the lack of official information on the presence of military actions, the results of geostatistical methods cannot be interpreted in the context of the military situation in the eastern part of the country. Information about military actions can be gathered from various sources, but this would require a considerable time and effort to structure and systematize the dataset. Conclusions. The method considered in this study cannot simultaneously consider multiple parameters, such as the value of pollutant indicators and the presence of military actions. Additional methods, such as graph theory and regression analysis, are employed to obtain quantitative assessments of the modelling results considering all factors influencing the environmental condition. The chosen method is a straightforward tool for solving environmental problems. Thanks to available GIS systems like ArcGIS Pro, visualization of the applied geostatistical and mathematical methods is possible. The cartographic models presented in this study cover the entire territory of Ukraine and have administrative boundaries depending on the location of the pollutant collection station.

Real-Time Size Distributions of Air Pollutants From a Wastewater Treatment Plant in the New York State Capital District

Real-Time Size Distributions of Air Pollutants From a Wastewater Treatment Plant in the New York State Capital District

Reliability Analysis Based on Air Quality Characteristics in East Asia Using Primary Data from the Test Operation of Geostationary Environment Monitoring Spectrometer (GEMS)

Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations have been released since July 2022. The reliability of GEMS must be analyzed because it is the first payload on the geostationary Earth orbit satellite to observe trace gases. This study analyzed the initial results of GEMS observations such as the aerosol optical depth and vertical column densities (VCD) of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO), and compared them with previous studies. The correlation coefficient of O3 ranged from 0.90 (Ozone Monitoring Instrument, OMI) to 0.97 (TROPOspheric Monitoring Instrument, TROPOMI), whereas that of NO2 ranged from 0.47 (winter, OMI and OMPS) to 0.83 (summer, TROPOMI). GEMS yielded a higher VCD of NO2 than that of OMI and TROPOMI. Based on the sources of O3 and NO2, GEMS observed the maximum VCD at a different time (3–4 h) to that of the ground observations. Overall, GEMS can make observations several times a day and is a potential tool for atmospheric environmental analysis.

Vertical distribution of air pollutants in an urban street canyon during winter air pollution episodes in Shenyang, China

Understanding the vertical distribution of air pollutants in urban street canyons is crucial to improve air quality in urban areas. Previous studies on air pollutants during air pollution episodes primarily focused on their distribution in rural areas, but rarely considered urban street canyons. To investigate such episodes in urban environments, we recorded concentrations of particulate matter (PM) with diameters of less than 2.5 μm and 10 μm (PM2.5 and PM10, respectively), ozone (O3), and carbon monoxide (CO), as well as air temperature, relative humidity, wind speed, wind direction, and atmospheric pressure, every minute, at bottom, middle, and roof levels in an urban street canyon in Shenyang, China, between December 2018 and March 2019. The study period included three air pollution episodes. Results indicated that PM was the primary contributor to air pollution during all episodes. During air pollution episodes, PM concentrations were higher at roof level, with increasing PM concentration differences between the bottom, middle, and roof height levels; O3 concentrations decreased; CO concentrations did not vary significantly. Additionally, higher relative humidity and lower air temperature enhanced particle formation but slowed photochemical reactions, yielding lower O3 concentrations. Finally, we established that wind speed in the street canyon depended more on canyon morphology than on the pollution level.

Estimation of Air Pollutant Emissions by Tractor Utilization in Korea

The utilization of tractors is essential because of both the shortage of agricultural labor forces and the rapid aging of farmers in Korean agriculture. Tractors can be classified into two types: a walking tractor called as a power tiller, and a riding tractor. In this study, diesel emission inventories of tractors were established and variations of air pollutants were analyzed with each other using 2011 and 2019 survey data in Korea. Agricultural tractors including walking and riding types, which were categorized into three levels via the rated output power, were the main sources of diesel emissions. The emission inventory including CO, NOx, SOx, TSP(PM10), PM2.5, VOCs, and NH3 were established using a Tier 3 methodology. The total amount of emissions using agricultural tractors was decreased by about 13% from 2011 to 2019. The number of walking tractors were decreased by about 19% in 8 years; on the other hand, that of riding tractors were increased by about 12%. However, the emission reduction is about 48% for walking tractors, and the emission increment is about 5% for riding tractors. Thus, the total emission from agricultural tractors was decreased by about 16% in those periods. It is due to the decrease of 21% and 15% in the hours of use of walking and riding tractors, respectively, in 2019. Walking tractors mainly emit air pollutants from spraying and transporting. Riding tractors emit about 61% of the total air pollutants mainly from soil preparation and transporting operations. The geographic information system (GIS) was used to visualize the distribution of air pollutants in Korea. High-emission generating regions and the changes of emissions over 8 years were clearly seen in the GIS analysis. High air-pollutant emitting regions are mainly located in the western and southern regions of Korea, which have plenty of arable areas compared to other regions in Korea.

The development and application of a novel helicopter-based airborne platform for near-surface monitoring and sampling of atmospheric pollutants

This study presents a novel approach utilising helicopters as platforms for real-time monitoring of air pollutants and routine canister sampling to enhance our understanding of air pollution in the Greater Bay Area (GBA). By employing this method, local spatial and vertical distribution of various air pollutants (including CO, NO, NO2, O3, PM2.5 and VOCs) in Hong Kong could be obtained within hours for fast snapshots of air pollution.The investigation involved hovering ascent and descent operations above Tap Mun Island, revealing no indication of self-contamination from engine exhaust during the hovering process. The analysis of air pollutant spatial distribution in and around Hong Kong through multiple trips showed higher concentrations of O3 and PM2.5 in the northern region on days with episodic pollution, while urban areas exhibited elevated levels of NO2 and CO concentration. In contrast, Tap Mun Island as the background area demonstrated relatively low pollutant concentrations. The vertical distribution of concentrations of VOC species with high ozone formation potential revealed minor variations at sampling altitudes of 500 ft and 1000 ft over the waters around Hong Kong. The novel monitoring protocol using helicopters as platforms proves to be an effective approach for pollution episode investigations, offering valuable insights in the formation mechanisms and transport pathways of O3. The findings of the heterogenous spatial distribution of O3 and its precursors underscore the significance of ongoing monitoring efforts and policy development to combat air pollution in the dynamic GBA and similar regions worldwide.

Detecting Air Pollution Clusters in Japan: A Spatial Analysis Approach

Abstract We rely on satellite data to study the spatial distribution of air pollutants and economic activity for 1,650 municipalities of all four main islands of Japan: Honshu, Kyushu, Hokkaido, and Shikoku. Specifically, we analyze atmospheric particulate matter and ozone concentrations, as well as population density, accessibility to cities, and night lights for the above islands. We then make use of principal component analysis, spatial dependence analysis, and methods of regionalization to endogenously classify the municipalities based on their similarity in attributes and geographic location. The spatial dependence analysis provides results which show us the specific sites where the high-value clusters (hot spots) and low-value clusters (cold spots) are located. These show a high positive correlation between economic activity and air pollution. Additionally, we perform a regionalization analysis of the variables under consideration, which specifies how the four main islands can be regionalized into six to nine geographical regions or structures, each. The regionalization takes into consideration both pollution levels and economic activity. We then conclude by discussing how these different analyses can complement each other, and how they contribute in finding the locations where policies related to air quality can help in improving the quality of life of the population.