Distribution Of PM2 Research Articles

Decoupling Analysis between Socio-Economic Growth and Air Pollution in Key Regions of China

The coordinated development of atmospheric pollution and socio-economic growth plays a core role in the sustainable development of cities and regions. The relationship between socio-economic growth and air pollution can be described using decoupling analysis. The seven key regions of China (168 cities), including Beijing–Tianjin–Hebei and its surrounding areas (BTHSR), the Yangtze River Delta region (YRDR), the Fen-Wei Plain (FWP), the Chengdu–Chongqing region (CCR), the urban agglomeration of the middle reaches of the Yangtze River (MLRYR), the Pearl River Delta region (PRDR), and other provincial capitals and municipalities with specialized plans (OPCCSP) were taken as targets to investigate the spatiotemporal evolution characteristics of AQI values and PM2.5 concentrations from 2014 to 2022. Then, the decoupling relationship between the AQI/PM2.5 and the socio-economic growth index (SEGI) in these key regions was deeply researched by the Tapio decoupling model. The main results were as follows: (1) Although the continuous improvement in air quality was observed in these seven key regions in China, the PM2.5 concentration in the BTHSR and FWP was still higher than 35 μg·m−3. The AQI showed a spatial pattern of high in the north and low in the south, and the distribution of PM2.5 in China was high in the east and low in the west. (2) The decoupling degree between air pollution and socio-economic growth was relatively high in the PRDR and YRDR. In contrast, the degree of decoupling was poor in the FWP and OPCCSP. The decoupling states were primarily influenced by industrial structure, energy consumption, and urbanization. (3) The decoupling of air pollution from socio-economic growth was in a strong decoupling state throughout the majority of the study period, achieving a comparatively ideal decoupling state in 2018. However, the overall decoupling states of the seven regions were not sustainable, and the decoupling stability was relatively poor. During the research period, the decoupling state between socio-economic growth and air pollution changed and was unstable.

Analyzing the impact of planning schemes on the neighborhood-scale PM2.5 distribution through Pix2PixGAN and crowd-source data

The distribution of PM2.5 concentrations at the neighborhood scale is interfered with not only by spatial morphology, but also by spatial functions, and this effect shows heterogeneity in different urban locations due to differences in spatial dynamics. Taking the area within the Fourth Ring Road of Beijing in 2020 as an example, this paper provides urban planners with an evaluation framework for fine-grained prediction of neighborhood-scale PM2.5 distribution based on the Pix2PixGAN model and the multi-source data with crowd-source data at its core. Through the crowd-source data, the spatial functions and corresponding spatial dynamics in the neighborhood scale can be characterized in detail. Then the framework utilizes Geographically weighted poisson regression (GWPR) to screen spatial functions with significant geographic correlation with PM2.5 distribution and their heterogeneity coefficients. Based on the filtered functions, heterogeneity coefficients, and PM2.5 distribution raster, the dataset of Pix2PixGAN model is composed, and the optimal model with the most accurate PM2.5 distribution prediction is trained and selected. Finally, the paper illustrates how to analyze the qualitative and quantitative impacts of planning proposals on the distribution of PM2.5 through the optimal model, using six planning scenarios as examples.

Characterization of Spatial and Temporal Variations in Air Pollutants and Identification of Health Risks in Xi’an, a Heavily Polluted City in China

The study of the temporal and spatial characteristics of air pollutants in heavily polluted cities is extremely important for analyzing the causes of pollution and achieving a viable means of control. Such characteristics in the case of Xi’an, a typical heavily polluted city in Fenwei Plain, China, have remained unclear due to limitations in data accuracy and research methods. The monthly, daily, and hourly patterns of O3 and particulate matter (PM2.5 and PM10) are analyzed in this study using on-site data provided by an urban air quality monitoring network. The analysis of variance (ANOVA) method was used to compare differences in pollutant concentrations during different seasons and time periods. The spatial distributions of O3, PM2.5, and PM10 at different time points following interpolation of the air quality monitoring sites have been analyzed. The results show that the O3 concentration from 12 p.m. to 3 p.m. was significantly higher than that in the morning and evening, and the concentrations of PM2.5 and PM10 from 7 p.m. to 10 p.m. were significantly higher than those in the morning and afternoon. The number of qualified days for PM2.5 was less than 30 and unqualified days for O3 was more than 100 in 2019. There is a potential risk of exposure to pollution with associated health risks. Even on the same day, the spatial pollutant distributions at different time points can differ significantly. This study provides a scientific basis for reducing O3 and particulate matter exposure. Outdoor activities in the morning in summer are more beneficial to reduce O3 exposure, and outdoor activities should be curtailed in the evening in winter to reduce particulate exposure. This study provides a scientific basis for the government to formulate public health policies to reduce pollution exposure from outdoor activities.

Assessing the Nonlinear Relationship between Land Cover Change and PM10 Concentration Change in China

Inhalable particulate matter (PM10) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM10, which poses a threat to human health. Therefore, exploring the relationship between PM10 concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM10 monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM10 concentrations across China. The coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m3, and 8.826 μg/m3, respectively. Based on this, we analyzed the spatio-temporal distribution of PM10 concentrations in China from 2015 to 2021. High PM10 values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM10 concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM10 concentration change to land-cover change. The PM10 concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.

Associations between atmospheric PM2.5 exposure and carcinogenic health risks: Surveillance data from the year of lowest recorded levels in Beijing, China

Scant research has pinpointed the year of minimum PM2.5 concentration through extensive, uninterrupted monitoring, nor has it thoroughly assessed carcinogenic risks associated with analyzing numerous components during this nadir in Beijing. This study endeavored to delineate the atmospheric PM2.5 pollution in Beijing from 2015 to 2022 and to undertake comprehensive evaluation of carcinogenic risks associated with the composition of atmospheric PM2.5 during the year exhibiting the lowest concentration. PM2.5 concentrations were monitored gradually in 9 districts of Beijing for 7 consecutive days per month from 2015 to 2022, and 32 kinds of PM2.5 components collected in the lowest PM2.5 concentration year were analyzed. This comprehensive dataset served as the basis for carcinogenic risk assessment using Monte Carlo simulation. And we applied the Positive Matrix Factorization (PMF) method to identity the sources of atmospheric PM2.5. Furthermore, we integrated this source appointment model with risk assessment model to discern the origins of these risks. The findings revealed that the annual average PM2.5 concentration in 2022 stood at 43.1 μg/m3, marking the lowest level recorded. The mean carcinogenic risks of atmospheric PM2.5 exposure calculated at 6.30E-6 (empirical 95% CI 1.09E-6 to 2.25E-5) in 2022. The PMF model suggested that secondary sources (35.4%), coal combustion (25.6%), resuspended dust (15.1%), biomass combustion (14.1%), vehicle emissions (7.1%), industrial emissions (2.0%) and others (0.7%) were the main sources of atmospheric PM2.5 in Beijing. The mixed model revealed that coal combustion (2.41E-6), vehicle emissions (1.90E-6) and industrial emissions (1.32E-6) were the main sources of carcinogenic risks with caution. Despite a continual decrease in atmospheric PM2.5 concentration in recent years, the lowest concentration levels still pose non-negligible carcinogenic risks. Notably, the carcinogenic risks associated with metals and metalloids exceeded that of PAHs. And the distribution of risk sources did not align proportionally with the distribution of PM2.5 mass concentration.

Impact of Seasonal Heating on PM10 and PM2.5 Concentrations in Sučany, Slovakia: A Temporal and Spatial Analysis

Complying with strict PM10 and PM2.5 limit values poses challenges in many European regions, influenced by diverse factors such as natural, regional, and local anthropogenic sources. Urban air pollution, exacerbated by road transport, local industry, and dust resuspension, contrasts with rural areas affected by solid fuel-based local heating and increasing wood burning. This study focuses on village of Sučany, located in Slovakia, analysing PM concentrations during non-heating and heating seasons. The method of analysis relies on the use of the MP101M air quality analyser that utilises beta radiation absorption method. One set of measurements was conducted at five distinct locations during the heating season (18/01/2019 to 28/02/2019) and non-heating season (14/08/2018 to 1/10/2018). Significant differences emerged during the non-heating season with corresponding PM10 averages of 23.0 µg/m3 and PM2.5 at 19.3 µg/m3. In contrast, the PM10 averaged 53.9 µg/m3 and 52.8 µg/m3 during the heating season. The heating season shows PM2.5 contributing up to 98% of total PM10. The distribution of PM10 and PM2.5 pollution and the location of the potential source obtained using polar plots differed during the heating and non-heating seasons. This research underscores the impact of local heating on air quality in a typical Slovak village. The key recommendation for targeted interventions is supporting up-to-date air quality data, education, and financial incentives for citizens in order to implement cleaner and modern heating solutions.

Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2.5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2.5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16–0.94) observed in the wet and inter-monsoon seasons is due to fewer number of data used in model calibration. Integration of two AOD products from the Advanced Himawari Imager (AHI) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2.5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2.5 prediction accuracy.

A Comparison of Health Risks from PM2.5 and Heavy Metal Exposure in Industrial Complexes in Dangjin and Yeosu·Gwangyang.

Particulate matter (PM) can cause illness, including respiratory diseases, and PM2.5 compositions are likely to vary according to the emission profiles of industrial complexes. This study analyzed and compared the concentrations and distributions of PM2.5 and heavy metals in two regions of Republic of Korea: Yeosu·Gwangyang, which houses a massive national industrial complex, and Dangjin, which houses power plants. Further, we conducted a health risk assessment on the residents of the areas near these industrial complexes. Measurements were taken at five different points in each setting over a two-year period from August 2020 to August 2022. We found differences in PM2.5 concentrations and heavy metal composition ratios across the sites. Specifically, PM2.5 concentrations exceeded the standard of 1 at all measurement sites, while the specific heavy metals exceeding the standard varied across the sites. Ultimately, we observed regional differences in PM2.5 composition across measurement sites across and within the two regions and variations in health risks and according health effects due to the absence of PM2.5 toxicity values, and compared the health risks of two industrial complexes with different characteristics. These findings underscore the importance of considering not only PM2.5 but also its composition in exposure and health risk assessments.

Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

Addition of Facilities to Bottle Capping Machine

Particulate pollution increases with population and activity. Likewise, the city of Surabaya is the 2nd largest metropolitan city in Indonesia based on its population. PM2.5 is a fine particulate that can pose a greater health risk than PM10. This research aims to identify PM2.5 ambient air quality in the city of Surabaya by analyzing daily fluctuations in PM2.5 and calculating an index to be able to categorize ambient air quality in the city of Surabaya. The analytical method used is a quantitative observation method that involves analyzing fluctuations in the distribution of PM2.5 over 24 hours and 7 days and calculating ISPU and NAAQS. The results of the observations showed an increase in PM2.5 particulates at certain times, and the highest spike in PM2.5 levels occurred on Saturday. Based on ISPU and NAAQS calculations, there are differences: according to ISPU, the ambient air quality in Surabaya is classified as moderate, while according to US-EPA standards, the ambient air quality in Surabaya is classified as unhealthy for sensitive groups.

Analisa Kualitas Lingkungan Udara Ambien (PM2.5) di Kota Surabaya

Particulate pollution increases with population and activity. Likewise, the city of Surabaya is the 2nd largest metropolitan city in Indonesia based on its population. PM2.5 is a fine particulate that can pose a greater health risk than PM10. This research aims to identify PM2.5 ambient air quality in the city of Surabaya by analyzing daily fluctuations in PM2.5 and calculating an index to be able to categorize ambient air quality in the city of Surabaya. The analytical method used is a quantitative observation method that involves analyzing fluctuations in the distribution of PM2.5 over 24 hours and 7 days and calculating ISPU and NAAQS. The results of the observations showed an increase in PM2.5 particulates at certain times, and the highest spike in PM2.5 levels occurred on Saturday. Based on ISPU and NAAQS calculations, there are differences: according to ISPU, the ambient air quality in Surabaya is classified as moderate, while according to US-EPA standards, the ambient air quality in Surabaya is classified as unhealthy for sensitive groups.

Uptake and distribution of the inorganic components NH4+ and NO3− in PM2.5 by two Chinese conifers

Plants can effectively purify PM2.5 in the air, thereby improving air quality. Understanding the mechanisms of the uptake and distribution of PM2.5 in plants is crucial for enhancing their ecological benefits. In this study, the uptake and distribution of the water-soluble inorganic compounds ammonium (NH4+) and nitrate (NO3−) ions in PM2.5 by the two native Chinese conifers Manchurian red pine (Pinus tabuliformis) and Bunge's pine (P. bungeana) were investigated using a one-time aerosol treatment method combined with 15N tracing. The results showed the following: (1) Plants can efficiently uptake NH4+ (0.08–0.21 μg/g) and NO3− (0.03–0.68 μg/g) from PM2.5. Manchurian red pine uptakes these compounds more effectively with increases of 2.01-fold for NH4+ and 1.02-fold for NO3− compared with Bunge's pine. (2) The aboveground organs of the plants uptake and distribute more 15N than the belowground organs. The branches had the highest unit mass uptake (0.08–1.60 μg/g) and rate of distribution (16.91–53.60 %) for NH4+, while the leaves had the highest unit mass uptake (0.15–1.18 μg/g) and rate of distribution (50.78–84.88 %) for NO3−. (3) The ability of the aboveground organs to uptake 15N is influenced by the concentration of PM2.5, which showed an overall increase with increasing concentrations with some fluctuations in specific organs. However, the belowground organs were not affected by the concentration of PM2.5. (4) A larger specific leaf area, root-shoot ratio, branch biomass ratio, coarse root biomass ratio, and lower trunk biomass ratio favors the uptake of NH4+ from PM2.5, whereas these traits had a minimal influence on the uptake of NO3−. Manchurian red pine uptaked significantly more NH4+ compared with Bunge's pine, which benefited from the traits described above. These findings further revealed the mechanism of PM2.5 uptake by plants and its relationship with PM2.5 concentration and plant traits, and provided a scientific basis for how to effectively utilize plants to reduce PM2.5 pollution and purify the environment in areas with different pollution concentrations.

The relevancy-analysis of air quality index to Acute Respiratory Infection (ARI) in A landfill site of Medan

The existence of open dumping in Medan City’s final waste processing since 1993 has certainly had a negative impact in the form of a decrease in air quality which has a further impact on public health problems. This study aims to analyse and maps the distribution of PM10 and PM2.5 concentrations in the Terjun Landfill and to examine the relationship between the air quality index and the prevalence of acute respiratory infection (ARI) in the nearby landfill site. The sampling method for this research uses the manual active and is mapped using the GIS application. The results of ambient air concentrations were converted into an air quality index which subsequently was tested in a statistical correlative method to investigate the prevalence of ARI. The results demonstrated the PM2.5 concentrations ranged from 38.94 – 163.1 µ/m3, the PM10 was between 46.84 – 184.2 µ/m3; indicating an excessive level of national ambient threshold. The distribution of the highest PM2.5 and PM10 is in the active zone of the landfill. The prevalence of ARI in the nearby landfill site showed a strong relationship; 0.86 for PM2.5 and 0.62 for PM10. These findings illustrate a poor-quality air would increase the number of inhabitants with ARI.

Aerial monitoring of atmospheric particulate matter produced by open-pit mining using low-cost airborne sensors

Mining is an economic activity that entails the production and displacement of significant amounts of atmospheric particulate matter (PM) during operations involving intense earthcrushing or earthmoving. As high concentrations of PM may have adverse effects on human health, it is necessary to monitor and control the fugitive emissions of this pollutant. This paper presents an innovative methodology for the online monitoring of PM10 concentrations in air using a low-cost sensor (LCS, <300 USD) onboard an unmanned aerial vehicle. After comprehensive calibration, the LCS was horizontally flown over seven different areas of the large Riotinto copper mine (Huelva, Spain) at different heights to study the PM10 distribution at different longitudes and altitudes. The flights covered areas of zero activity, intense mining, drilling, ore loading, waste discharge, open stockpiling, and mineral processing. In the zero-activity area, the resuspension of PM10 was very low, with a weak wind speed (3.6 m/s). In the intense-mining area, unhealthy concentrations of PM10 (>51 μgPM10/m3) could be released, and the PM10 can reach surrounding populations through long-distance transport driven by several processes being performed simultaneously. Strong dilution was also observed at high altitudes (> 50 m). Mean concentrations were found to be 22–89 μgPM10/m3, with peaks ranging from 86 to 284 μgPM10/m3. This study demonstrates the potential applicability of airborne LCSs in the high-resolution online monitoring of PM in mining, thus supporting environmental managers during decision-making against fugitive emissions in a cost-effective manner.

Environmental quality assessment of Assuit governorate using data observation and geospatial techniques

The present paper aims to assess the quality characteristics of the available water bodies, air, and agricultural lands of Assuit governorate using spatial and laboratory analyses integrated with remote sensing techniques. To fulfill this aim, a field survey was conducted for identifying the dominant anthropogenic activities, collecting water and soil for further lab analyses of physical and chemical characteristics. Contamination indices and ecological risk of heavy metals in soil were calculated and assessed. Further, calibrated space-borne multispectral Landsat images dated 1999, 2009, 2019 and 2023 were processed to retrieve the Particulate Matter (PM10) to assess air quality over the governorate. A similar spatial distribution of PM10 over Assuit governorate was recorded during the four investigated years; 1999, 2009, 2019 and 2023. PM10 exceeded the allowable Egyptian threshold limits over the desert lands (150 µg/m3). Results reported the discharge of domestic wastewater into some irrigation canals as the main reason for elevating turbidity and Organic matter levels. Reasonable Electric Conductivity (EC) levels were recorded (277 to 1094.00 µs/cm) which showed good to permissible levels for irrigation. The investigated water parameters of the River Nile are coinciding with the Egyptian threshold limits. Remarkable fluctuations were observed in the levels of most of the soil characteristics in Assuit and the spatial analyses showed that levels of soil pollution are increasing southward which is confirmed by the positive correlation between the indicators of pollution. However, soil contamination and ecological indices were low in the whole governorate.

Effect of Taebaek Mountains on the Spatial Distribution of PM10 and PM2.5 in Gangwon Province

The purpose of this study was to empirically confirm that the Taebaek Mountains are one of the primary causes of the spatial difference in the concentration of particulate matter between the west (Yeongseo) and east (Yeongdong) sides of the mountain range in Gangwon Province in the Korean peninsula. We investigated the effects of the Taebaek Mountains by analyzing data on PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations, wind direction, and wind speed, collected over seven years from 2015 to 2021 in the Yeongseo and Yeongdong regions. Our results demonstrated that wind direction and speed could be considered significant features in the aerial transport of PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;. When the concentrations were higher than normal, the reduction effect of the Taebaek mountain range on PM&lt;sub&gt;10&lt;/sub&gt; was clear, while the effect on PM&lt;sub&gt;2.5&lt;/sub&gt; was not as obvious. When wind blows from west to east across the Taebaek Mountains, the wind speed may increase on the Yeongdong; in these cases, the concentrations of PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; were much lower than at the Yeongseo. Moreover, with these westerly winds, when the wind speed at Yeongseo exceeded 2 m/s, the distribution range of high concentrations of PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; was narrower at Yeongseo and Yeongdong than when the wind speed was weaker.

Heavy vehicles’ non-exhaust exhibits competitive contribution to PM2.5 compared with exhaust in port and nearby areas

Heavy port transportation networks are increasingly considered as significant contributors of PM2.5 pollution compared to vessels in recent decades. In addition, evidence points to the non-exhaust emission of port traffic as the real driver. This study linked PM2.5 concentrations to varied locations and traffic fleet characteristics in port area through filter sampling. The coupled emission ratio-positive matrix factorisation (ER-PMF) method resolves source factors by avoiding direct overlap from collinear sources. In the port central and entrance areas, freight delivery activity emissions including vehicle exhaust and non-exhaust particles, as well as induced road dust resuspension, accounted for nearly half of the total contribution (42.5%–49.9%). In particular, the contribution of non-exhaust from denser traffic with high proportion of trucks was competitive and equivalent to 52.3% of that from exhaust. Backward trajectory statistical models further interpreted the notably larger-scale coverage of non-exhaust emissions in the port's central area. The distribution of PM2.5 were interpolated within the scope of the port and nearby urban areas, displaying the potential contribution of non-exhaust within 1.15 μg/m3–4.68 μg/m3, slightly higher than the urban detections reported nearby. This study may provide useful insights into the increasing percentage of non-exhaust from trucks in ports and nearby urban areas and facilitate supplementary data collection on Euro-VII type-approval limit settings.

ქ. რუსთავის და მიმდებარე ტერიტორიის ატმოსფეროში PM2.5-ის გავრცელების გამოკვლევა

The distribution of PM2.5 in the atmosphere of the city of Rustavi and its environs was studied under eastern background light air, gentle and fresh breeze. It has been established that the maximum concentrations of PM2.5 in the atmosphere of Rustavi are almost always higher than the corresponding maximum allowable concentrations. The trend of hourly changes in concentrations showed that during the day the concentration of microparticles reaches its maximum in the various period of the day. Numerical modeling of the local distribution of microparticles carried out in the city of Rustavi during background easterly light air, gentle and fresh breeze showed us that the change in the daily regime of relief and temperature with eastern light air in Rustavi for 12 hours leads to a complex and significant change in the wind speed directed to land. It forms on the territory of Kvemo Kartli the opposite current of the background wind, which corresponds to the horizontal circular wind circulation. The value of circulation gradually decreases with increasing height and in the boundary layer of the atmosphere (600 m above the earth's surface) has the form of a wave disturbance. the picture obtained with an average background east wind is qualitatively similar to the field obtained with a background light air, and with a fresh breeze, in contrast to the case of a light air and gentle breeze, the pollution zone covers most of the simulation zone.

Inhibiting the formation of PM0.4 by optimizing the distribution of excess air coefficient in preheating combustion of lignite

Preheating combustion can effectively reduce NOx concentration, but the formation characteristics of PM0.4 in preheating combustion is unclear. In this study, the emission of PM0.4 in preheating combustion was reduced by optimizing the distribution of excess air coefficient. The mass-based particle size distribution (MPSD) of PM10, mass yield of PM0.4, elemental compositions and morphologies of PM, and the number-based particle size distribution (NPSD) were analyzed to discuss the effect of αp on PM formation. The results showed that in preheating combustion, with an increase of αp, the mass yield of PM0.4 first decreased and then increased. The mass yield of PM0.4 is minimum when αp is 0.6. Preheating combustion can simultaneously reduce PM0.4 mass yield and NOx concentration. The relationship between NOx concentration and αp is similar to the relationship between the mass yield of PM0.4 and αp. The MPSD of PM0.4 is multimodal and unimodal when αp is less than 1 and larger than 1, respectively. Particle volume concentration of PM0.4 indicates that the multimodal and unimodal of PM0.4 is a result of evolution of particles with different characteristics of gas-to-particle transformation. The effect of αp on the transformation of inorganic element from coal particles to PM0.4 is S > Fe ≈ Ca ≈ Mg > Na > Si. αp affects the mass yield of PM0.4 by affecting the combustion intensity of coal/char particles in preheating furnace and combustion furnace. When αp is 0.6, the combustion intensity of coal particles in preheating furnace and combustion furnace minimize the vaporization of inorganic elements.

PM10 within Indian standard is achievable by mitigating the sources of PM1: A thirteen years (2009–2021) long study and future prediction (2024) over the eastern Himalayas, India

A thirteen years-long (2009–2021) study was conducted on PM10 pollution over a high-altitude station, Darjeeling (27.1° N and 88.15° E, 2000 m amsl) in the eastern Himalayas in India. To better understand the sources and build a proper mitigation strategy for PM10, we have segregated PM10 into PM1 and PM1-10 using the Anderson cascade impactor. A total of 620 sets of samples were collected during the entire study period. PM10 was found to remain within its Indian standard (60 μg m−3) in every year of the study period (long-term average PM10: 46 ± 8 μg m−3). PM10 pollution during winter (74.0 ± 15.1 μg m−3) and premonsoon (73.1 ± 12.1 μg m−3) was highest compared to other seasons. Bi-modal mass size distribution of composite PM10 was observed for all the seasons with maximum peak intensity at the finer mode (PM1). Most of the anthropogenic water-soluble ionic components as well as carbonaceous aerosols exhibited maximum intensity at PM1 too. PM10 exhibited a steady decrease from 2009 to 2013 but since 2014, a sharp rise was observed. Further, we have observed that premonsoon and winter-time PM1 pollution was the key factor for a such high rise in PM10. PM1 increased sharply at a rate of 4.1 μg m−3 yr−1 in premonsoon and 3.3 μg m−3 yr−1 in winter since 2014. Auto regressive integrated moving average (ARIMA) model for future prediction revealed that PM10 would cross its Indian standard and PM1 alone would cross the PM2.5 standard in 2024 if the current scenario remains the same and the city would be enlisted among the “non-attainment” cities of the country under “National Clean Air Program (NCAP)” of Government of India. The Positive matrix factorization (PMF) model was run to apportion the sources of PM1 and PM10. It was observed that the vehicular emissions in premonsoon (33% in PM1; 28% in PM10; contribution of vehicular emission in PM1 to PM10 > 90%) and biomass burning in winter (27% in PM1; 23% in PM10; contribution of PM1 to PM10 > 80%) are the most influencing sources that need to be curbed to mitigate PM1 and hence PM10 pollution over Darjeeling.