High Concentrations Of PM2 Research Articles

Effects of Urban Landscapes on Pollutant Concentrations in Chengdu Plain Urban Agglomeration

Frequent air pollution due to urbanization poses a severe threat to urban environments. In this study, the effects of urban landscapes on pollutant concentrations at different spatial and temporal scales are examined using “3S” technology, based on land-use (LU) classification maps for two time phases (2015 and 2018) in conjunction with monitoring data for air pollutants of the same periods. The results showed that: (1) A very high overall LU ratio was found for Chengdu Plain urban agglomeration (CPUA), with only 0.3% of the land being unused. Farmlands (43%), forests (36.45%), and grasslands (14.17%) were identified as the main landscape types. A decrease in the proportions of the total area occupied by farmlands and grasslands was 0.44% and 0.72%, respectively, and 0.10%, 0.04%, and 0.97% increased in the proportions of the total area occupied by forests, water bodies, and developed lands, respectively, were found from 2015 to 2018. (2) NO2, PM2.5, and PM10 were mainly distributed in the central and eastern parts of the study area, while SO2 was mainly distributed in the southwest. In 2018, compared with 2015, the maximum concentration of NO2 decreased from 60.36 μg/m3 to 49.75 μg/m3, and the distribution range of high concentration NO2 was reduced and concentrated in Chengdu; the concentrations of PM10 and PM2.5 decreased significantly, and the maximum concentration decreased by 20.53% and 23.93%, respectively, but the concentration of pollution in the northeast increased significantly. The scope of SO2 pollution had shifted from the south to the southwest, and the pollution level had decreased from south to north. (3) The effects of various landscape types on pollutant concentrations were complex. At a patch-type level, increasing the area proportions of “pollution-reducing” landscape types could reduce pollutant concentrations. Specifically, increasing the area, largest patch index, and patch cohesion of forests and grasslands, as well as reducing the area, largest patch index, and patch cohesion of farmlands and developed lands, could effectively lower pollutant concentrations. From a landscape pattern perspective, high shape regularity and low diversity of landscape patches resulted in high concentrations of NO2, PM10, and PM2.5. In contrast, high levels of dominance and aggregation of landscapes lead to low concentrations of SO2.

Use of biomass fuels predicts indoor particulate matter and carbon monoxide concentrations; evidence from an informal urban settlement in Fort Portal city, Uganda

BackgroundPoor indoor air quality (IAQ) is a leading cause of respiratory and cardiopulmonary illnesses. Particulate matter (PM2.5) and carbon monoxide (CO) are critical indicators of IAQ, yet there is limited evidence of their concentrations in informal urban settlements in low-income countries.ObjectiveThis study assessed household characteristics that predict the concentrations of PM2.5 and CO within households in an informal settlement in Fort Portal City, Uganda.MethodologyA cross-sectional study was conducted in 374 households. Concentrations of PM2.5 and CO were measured using a multi-purpose laser particle detector and a carbon monoxide IAQ meter, respectively. Data on household characteristics were collected using a structured questionnaire and an observational checklist. Data were analysed using STATA version 14.0. Linear regression was used to establish the relationship between PM2.5, CO concentrations and household cooking characteristics.ResultsThe majority (89%, 332/374) of the households used charcoal for cooking. More than half (52%, 194/374) cooked outdoors. Cooking areas had significantly higher PM2.5 and CO concentrations (t = 18.14, p ≤ 0.05) and (t = 5.77 p ≤ 0.05), respectively. Cooking outdoors was associated with a 0.112 increase in the PM2.5 concentrations in the cooking area (0.112 [95% CI: -0.069, 1.614; p = 0.033]). Cooking with moderately polluting fuel was associated with a 0.718 increase in CO concentrations (0.718 [95% CI: 0.084, 1.352; p = 0.027]) in the living area.ConclusionsThe cooking and the living areas had high concentrations of PM2.5 and CO during the cooking time. Cooking with charcoal resulted in higher CO in the living area. Furthermore, cooking outdoors did not have a protective effect against PM2.5, and ambient PM2.5 exceeded the WHO Air quality limits. Interventions to improve the indoor air quality in informal settlements should promote a switch to cleaner cooking energy and improvement in the ambient air quality.

Measurement report: The 10-year trend of PM2.5 major components and source tracers from 2008 to 2017 in an urban site of Hong Kong, China

Abstract. Fine particulate matter (PM2.5) remains a major air pollutant of significant public health concern in urban areas. Long-term monitoring data of PM2.5 chemical composition and source-specific tracers provide essential information for the identification of major sources as well as evaluation and planning of control measures. In this study, we present and analyze a 10-year dataset of PM2.5 major components and source-specific tracers (e.g., levoglucosan, hopanes, K+, Ni, V, Al, and Si) collected over the period of 2008–2017 in an urban site in Hong Kong, China. The time series of pollutants were analyzed by the seasonal and trend decomposition using the locally estimated scatter plot smoothing (LOESS) method and general least squares with the autoregressive moving average method. Bulk PM2.5 and all its major components displayed a significant decline of varying degrees over the decade. PM2.5 was reduced by 40 % at −1.5 µgm-3yr-1. PM2.5 components that are predominantly influenced by local vehicular emissions showed the steepest decline, with nitrate decreasing by −66 %, elemental carbon by −60 %, and hopanes by −75 %, confirming effective control of local vehicular emissions. For components that are significantly impacted by regional transport and secondary formation, they had a notably lower percentage reduction, with sulfate declining by −40 % and organic carbon by −23 %, reflecting complexity in their region-wide contributing sources and formation chemistry. Levoglucosan and K+, two tracers for biomass burning, differed in their reduction extent, with K+ at −60 % and levoglucosan at −47 %, indicating they likely track different biomass burning types. Dust components in PM2.5 also decreased, by −37 % for Al and −46 % for Si. The year of 2011 was an anomaly in the overall trend in having higher concentrations of PM2.5 and components than its adjacent years, and the long time series analysis attributed the anomaly to unusually lower rainfall associated with strong La Niña events. This 10-year trend analysis based on measurements exemplifies the utility of chemical composition data in support of an evidence-based approach for control policy formulation.

Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms

In this study, highly accurate particulate matter (PM10 and PM2.5) predictions were obtained using meteorological prediction data from the local data assimilation and prediction system (LDAPS) and tree-based machine learning (ML). The study area was Seoul, South Korea, and data from July 2018 to June 2021 as well as LDAPS 36-h predictions with 1-h intervals 4 times a day were used. The predicted PM values were then compared with the observed PM measurements to evaluate the prediction accuracy. The PM prediction performance of the Community Multi-Scale Air Quality (CMAQ)-based chemical transport model (CTM) was compared with that reported by this study. The experimental results report that, among tree-based ML algorithms, light gradient boosting (LGB) is the most suitable for PM prediction. The PM prediction results of the LGB algorithm for the hourly test data were: bias = −0.10 μg/m3, root mean square error (RMSE) = 13.15 μg/m3, and R2 = 0.86 for PM10 and bias = −0.02 μg/m3, RMSE = 7.48 μg/m3, and R2 = 0.83 for PM2.5, and for daily mean were: RMSE ≤1.16 μg/m3 and R2 = 0.996. The relative RMSE (%RMSE) is 21% lower than the results of the CTM model, and R2 is 0.20 higher. Even in the high PM concentration case prediction results, the algorithm showed good predictive performance with %RMSE = 8.91%–20.43% and R2 = 0.89–0.97. Therefore, in addition to the CTM, high-accuracy PM prediction results using ML can also be used for air quality monitoring and improvement.

Elucidating the Chemical Compositions and Source Apportionment of Multi-Size Atmospheric Particulate (PM10, PM2.5 and PM1) in 2019–2020 Winter in Xinxiang, North China

The pollution characteristics of multi-size atmospheric particulates in Xinxiang, which was one of the most polluted cities across China, are still unclear even through air quality in Xinxiang has been improved in recent years. PM10, PM2.5, and PM1 samples were synchronously collected from 21 December 2019 to 17 January 2020 to explore pollution levels and reveal sources of PM in Xinxiang. The average mass concentrations of PM10, PM2.5, and PM1 were as high as 155.53 μg m−3, 120.07 μg m−3, and 85.64 μg m−3 during the observation period, respectively. Almost all of the chemical compositions in PM10, PM2.5 and PM1 increased continuously and obviously with the aggravation of the pollution level. Compared with the clean period, the enhancement of sulfate (23–27%) in PM was obvious higher than nitrate (19–22%) during the pollution period, which demonstrated that sulfate was the main contributor to the high concentration of PM in this study. Similar source distributions for PM10, PM2.5, and PM1 were also found, including traffic source, combustion source, secondary aerosols, industrial source, and fugitive dust, by using the positive matrix factorization (PMF) model. Furthermore, the contributions of the combustion source and secondary aerosol were found to be higher in smaller particles (PM2.5 and PM1), while the contribution of fugitive dust was higher in PM10. Moreover, dust and sand were entrained by air masses from the northwest that increased the contribution of dust in PM at the observation site. The potential source contribution function (PSCF) analysis illustrated that regional emission sources in northern and eastern Xinxiang might be important potential contributors to PM pollution in Xinxiang.

Diffusion Characteristics of PM2.5 in Rural Dwelling under Different Daily Life Behavior: A Case Study in Rural Shenyang of China

The highest concentration of PM2.5 in cold rural dwellings of Northeast China is often generated by using mini stoves for cooking and heating, which can directly influence human health. As of yet, little is known about the impact of different daily life behavior on PM2.5 diffusion and residents’ exposure in rural dwellings. In this study, the characteristics of indoor PM2.5 variation and diffusion in rural dwellings was described by measuring some rural dwellings and establishing a multi-zone network model. The calculated results indicated that the relative errors between theoretical calculated results and experimental measured results are within 10%. PM2.5 diffusion in a rural dwelling can be predicted. Furthermore, the impacts of daily life behavior on PM2.5 diffusion and exposure assessment can be analyzed. Through discussion, heating behavior is the most important factor causing high concentrations of PM2.5 in each room, followed by cooking, smoking, and cleaning. Door opening time can lead to different interzonal airflows and PM2.5 diffusion rates. By reducing the inner door opening time to less than 1 min, PM2.5 could decrease to 300 μg/m3. Door closing behavior could decrease risk that PM2.5 diffuses to bedrooms by more than 50%, and exposure of residents in bedrooms could reduce to 100 (μg·h)/m3 effectively.

Trend in Particulate air pollutants in Visakhapatnam city for 2018-2020: A case study

Rapid industrialization aided by urbanization led to severe air pollution in Visakhapatnam city, withprominent pollutant being particulate matter (PM10). This paper aims to anlyse the trend in particulatematter over a three year period, 2018-20. The results indicate high mass concentrations of PM10 in winter,mostly exceeding NAAQS limits. Clearer air quality is observed in summer and monsoon seasons. Thecity’s bowl-shaped area promotes the accumulation of pollution in winter.

Ambient concentrations and dosimetry of inhaled size-segregated particulate matter during periods of low urban mobility in Bragança, Portugal

The restrictive measures in place during the COVID-19 pandemic provided a timely scenario to investigate the effects of human activities on air quality, and the extent to which mobility reduction strategies can impact atmospheric pollutant levels. Real-time concentrations of PM1, PM2.5 and PM10 were measured using a mobile platform in a small city of Portugal, during morning and afternoon rush hours, in two distinct phases of the pandemic: emergency phase (cold period, lockdown) and calamity phase (warm period, less restricted). The Multiple-Path Particle Dosimetry Model (MPPD) was used to calculate the PM deposition for adults. Large spatio-temporal variabilities and pronounced changes in mean PM concentrations were observed, with lower concentrations in the calamity phase: PM1 = 2.33 ± 1.61 μg m−3; PM2.5 = 5.15 ± 2.77 μg m−3; PM10 = 23.30 ± 21.53 μg m−3 than in the emergency phase: PM1 = 16.85 ± 31.80 μg m−3; PM2.5 = 30.92 ± 31.93 μg m−3; PM10 = 111.27 ± 104.53 μg m−3. These changes are explained by a combination of meteorological factors and local emissions, mainly residential firewood burning. Regarding regional deposition, PM1 was the main contributor to deposition in the tracheobronchial (5%) and pulmonary (12%) regions, and PM10 in the head region (92%). In general, total deposition doses were higher for males than for females. This work quantitatively demonstrated that even with a 38% reduction in urban mobility during the lockdown, the use of firewood for residential heating is the main contributor to the high concentrations of PM and the respective inhaled dose.

Recent Air Quality Deterioration on Weekends in Seoul, South Korea: a Focus on External Contribution

This study has found that the wintertime (November–March) air quality in Seoul, Korea had been deteriorated in weekends during the period of 2016–2019. Specifically, the concentration of particulate matters (PMs) of aerodynamic diameter less than 2.5 μm (PM2.5) in weekends (Saturday–Sunday) was up to 30% higher than that in the mid-week (Wednesday–Thursday) days (probability value < 0.01). As the weekend PM concentration had been lower than the mid-week values by 10% until 2015, such a sudden change in the intra-weekly air quality is unexpected. This study finds out that the deterioration of air quality in weekends can be attributed primarily to secondary particle formations and external transports from China (Shandong and northeast provinces) and domestic provinces (southern Gyeonggi and Chungcheong in Korea) to Seoul. High concentration of PM2.5 on weekend could be partially explained by the differences in the concentrations of inorganic PM components including nitrate, ammonium, and sulfate between weekends and mid-week days. About 40% of the differences are attributed to the domestic sources located in the southern region to Seoul. However, domestic emission from power generations and industry sector in southern source region on weekends does not well explain the variations of the PM precursors in weekends. Therefore, a clear strategy for improving air quality on the weekend in Seoul requires steady efforts to accurately calculate regional emissions and to reveal missing emissions sources.

Spatio-temporal co-variability of air pollutants and meteorological variables over Haqel and Jeddah, Saudi Arabia

This study presents a first simultaneous trend and magnitude assessment of the air pollutants (CO, H2S, SO2, NO2, NO, NOx, O3 and PM10), and meteorological variables (rainfall or RF, relative humidity or RH, atmospheric pressure or PR, temperature or TC, wind speed or WS and wind direction or WD) in the city of Haqel and at four different locations in the city of Jeddah, in Saudi Arabia, for a continuous 5-year period (2008−2012). The spatio-temporal co-variations of air pollutants in terms of their diurnal, weekly, seasonal and annual cycles, and their relationship with meteorological conditions, along with the estimates of the weekend effect, are described. A decreasing annual trend was observed for most air pollutants analyzed except for O3 and PM10. The CO, NO2, NO and NOx displayed a strong weekend effect. A percentile-based change analysis displayed an increase in concentrations for O3 (PM10) in the lower (higher) percentiles from the first to second half of the study period. The study identified 12 cyclonic weather events during the 5-year time period associated with high PM10 concentrations (&gt; 500 µg m-3) relative to a mean value of 102 µg m-3, with a standard deviation value of 179 µg m-3. The study also analyzed the impacts of several mid-latitude anti-cyclonic events on air pollutant concentrations and found a significant change in air pollutant concentrations (CO, SO2, NO2, NO, NOx, O3 and PM10) and meteorological variables (RH, PR, TC, WS, and WD) associated with stagnant upper air conditions during the atmospheric blocking.

Evaluation of vertical and horizontal distribution of particulate matter near an urban roadway using an unmanned aerial vehicle.

Measurement of traffic emissions has gained a lot of interest in recent times due to its contribution to urban pollution. This paper reports the outcome from an unmanned aerial vehicle (UAV) based measurement of PM concentration near an urban roadway at Kolkata, India. A total of 54 flights were carried out for simultaneous measurements of PM1, PM2.5 and PM10 mass concentration and meteorological parameters in vertical as well as in horizontal direction. Results for the vertical flight up to 100 m showed that the PM1, PM2.5 and PM10 concentrations at higher altitudes are less (mean; 24.6, 39.9 and 103.8 μg m-3) compared to the respective ground level concentrations (mean; 26.3, 50.4 and 201.9 μg m-3). For all the three particle sizes, the majority of the cases of higher PM concentration at higher altitudes happened during the evening flight. Low mixing height and low wind speed are suggested to be the reasons for the poor dispersion of pollutants in the evening. While there was a 7-10% fall of fine particles (PM1 and PM2.5) mass concentrations up to 90 m away from the road, no trend could be seen for PM10. The random forest model to predict the UAV/Ground concentration ratio showed high accuracy (R2 = 0.82-0.95) for all three particle sizes. This is an important finding from this study, which shows how UAV measurement data can be used to generate models that can predict the higher altitude concentrations from the ground based measurements.

Long-term exposure to air pollution might decrease bone mineral density T-score and increase the prevalence of osteoporosis in Hubei province: evidence from China Osteoporosis Prevalence Study.

Osteoporosis is becoming an increasingly serious public health problem with the advent of global aging. Long-term exposure to air pollution has been linked to multitudinous adverse health outcomes, but evidence is still relatively limited and inconsistent for BMD T-score and osteoporosis. This study aimed at exploring the associations between long-term exposure to air pollution and BMD T-score and osteoporosis. The Hubei part of the China Osteoporosis Prevalence Study was extracted. Data on air pollutants were collected by the national air quality real-time release platform of China Environmental Monitoring Station. Linear mixed models and multilevel logistic regression analyses were performed to assess the associations between air pollution and BMD T-score and osteoporosis, respectively. Subgroup analyses were conducted to identify vulnerable populations. A total of 1845 participants were included in this cross-section study. Per 10 ug/m3 increase in PM2.5 and SO2 were associated with 0.20 (95% CI: 0.04, 0.36) and 0.31 (95% CI: 0.11, 0.51) decrease in BMD T-score of the neck of femur, respectively. Per 10 ug/m3 increase in CO was linked with 0.03 (95% CI: 0.02, 0.05) decrease in BMD T-score of the total hip. Per 1 ug/m3 increase in PM2.5 was associated with 5% increase in the prevalence of osteoporosis in all participants. In general, the higher concentrations of PM2.5 with the more adverse effect on osteoporosis (P for trend = 0.01). The impact of PM2.5 on osteoporosis in males was higher than that in females [1.29, 95% CI (1.11, 1.50) vs 1.01, 95% CI (0.95, 1.07)]. Per 1 ug/m3 increase in PM10 corresponded with 4% elevation in the risks of osteoporosis in rural population. The ORs (95% CI) for the association of osteoporosis and NO2 in ever/current smoking and drinking population were 1.07 (1.01, 1.13) and 1.05 (1.00, 1.09), respectively. SO2 had a statistically significant positive effect on people with comorbidity [OR = 1.10, (95% CI: 1.00 to 1.21)], while none in people without comorbidity [OR = 0.96, (95% CI: 0.88 to 1.05)]. Our study provided evidence that long-term exposure to PM2.5 was linked with the decreased BMD T-score and increased risk of osteoporosis among all participants. The adverse impacts of PM2.5, PM10, and NO2 were larger in males than in females. People having comorbidity, living in rural areas, and current/ever smoking or drinking were more vulnerable to air pollution. Public health departments should consider air pollution to formulate better preventive measures for osteoporosis.

Air pollution and green spaces in relation to breast cancer risk among pre and postmenopausal women: A mega cohort from Catalonia

BackgroundThe association between air pollution and green spaces with breast cancer risk stratified by menopausal status has not been frequently investigated despite its importance given the different impact of risk factors on breast cancer risk depending on menopausal status. ObjectivesTo study the association between air pollution, green spaces and pre and postmenopausal breast cancer risk. MethodsWe conducted a population-based cohort study using electronic primary care records in Catalonia. We included women aged 17–85 years free of cancer at study entry between 2009 and 2017. Our exposures were particulate matter <2.5 μm (PM2.5) & <10 μm (PM10), nitrogen dioxide (NO2), normalized difference vegetation index (NDVI), and percentage of green spaces estimated at the census tract level. Breast cancer was identified with ICD-10 code C50. We estimated cause-specific hazard ratios (HR) for the relationship between each individual exposure and pre and postmenopausal breast cancer risk, using linear and non-linear models. ResultsOf the 1,054,180 pre and 744,658 postmenopausal women followed for a median of 10 years, 6,126 and 17,858 developed breast cancer, respectively. Among premenopausal women, only very high levels of PM10 (≥46 μg/m3) were associated with increased cancer risk (compared to lower levels) in non-linear models. Among postmenopausal women, an interquartile range increase in PM2.5 (HR:1.03; 95%CI:1.01–1.04), PM10 (1.03; 1.01–1.05), and NO2 (1.05; 1.02–1.08) were associated with higher cancer risk. NDVI was negatively associated with decreased cancer risk only among postmenopausal women who did not change residence during follow-up (0.84; 0.71–0.99) or who were followed for at least three years (0.82; 0.69–0.98). DiscussionLiving in areas with high concentrations of PM2.5, PM10, and NO2 increases breast cancer risk in postmenopausal women while long-term exposure to green spaces may decrease this risk. Only very high concentrations of PM10 increase breast cancer risk in premenopausal women.

Indoor Air Quality among the Rural Community of Bhutan

Introduction: Poor indoor air quality (IAQ) in Bhutan remains a concern, as firewood and biomass combustion are the fuels generally used for cooking and heating. The adverse impact of indoor air pollution (AIP) on health is inevitable and requires further evidence to be acquainted with strategic modifications. This study aimed to determine IAQ in the rural communities of Bhutan. Method: Air sampling equipment DUSTTRA DRX Aerosol Monitor was used to collect samples from 161 rural households with randomised sampling from February to June 2021. An hourly survey included data extraction regarding particulate matter (PM1, PM2.5, and PM10 ) and common health problems among household members. Results: IAQ worsened during the cooking hours, where PM10, PM2.5, and PM1 increased by 118%, 112.2%, and 111.6%, respectively. Overall, 77% of households used firewood for cooking, 96.3% used it for room heating, and 100% used fodder for cooking. The main source of higher indoor air pollution was associated with the combustion of sawdust, firewood, and biogas. Similarly, traditional mud stoves generated more IAP than gas and modified mud stoves. It is found that there is a significant association of longer hours of window-opening (p &lt; 0.05) and more numbers of rooms (p &lt; 0.001), with IAP. Common health-related complaints comprised eye irritation (22%), headache (22%), coughing (19%), and skin irritation (11%). Conclusion: This study recorded high concentrations of PM during biomass combustion in rural houses in Bhutan. The Study demonstrated the importance of modified stoves with efficient venting systems as alternatives to counter the long-term burden of IAP.

Habitual exercise, chronic exposure to fine particulate matter and high-sensitivity C reactive protein in Asian adults

ObjectivesExercise may increase the inhaled amount of air pollutants and exacerbate the adverse health effects. We investigated the combined effects of chronic exposure to fine particulate matter with an aerodynamic...

PM2.5 Synergizes With Pseudomonas aeruginosa to Suppress Alveolar Macrophage Function in Mice Through the mTOR Pathway.

High concentrations of PM2.5 in enclosed broiler houses cause respiratory disorders in humans and animals. Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen that can induce severe respiratory disease in animals under stress or with abnormal immune functions. Alveolar macrophages are lung-resident immune cells that play important roles in lung host defence and immune balance. In this study, the mechanism by which PM2.5 synergizes with P. aeruginosa to damage alveolar macrophage function and induce inflammation was investigated. The results will provide a theoretical basis for improving the poultry breeding environment and preventing the recurrence of infection with P. aeruginosa. Alveolar macrophages were stimulated by PM2.5 collected in an enclosed broiler house and P. aeruginosa. Phagocytosis was determined by the neutral red test. The apoptosis rate and cytoskeleton changes were observed by flow cytometry assays and laser scanning confocal microscopy. Protein levels related to autophagy and the mTOR pathway were detected by Western blotting. The results indicated that PM2.5 in combination with P. aeruginosa could decrease phagocytosis, inhibit autophagy, increase apoptosis, and destroy the cytoskeleton in alveolar macrophages. In addition, alveolar macrophages had significantly increased expression of mTOR pathway-related proteins in response to the synergistic stimulation of PM2.5 and P. aeruginosa. The above results confirmed that PM2.5 in poultry houses synergized with P. aeruginosa to impede alveolar macrophage function and caused more severe respiratory system injuries through a process closely related to the activation of the mTOR signalling pathway.

Regional PM2.5 pollution confined by atmospheric internal boundaries in the North China Plain: Analysis based on surface observations

There are plenty of mesoscale meteorological discontinuities in the atmosphere, acting as atmospheric internal boundaries (AIBs). In conjunction with the atmospheric boundary layer in the vertical direction, they form confined three-dimensional structures that significantly affect air pollution. However, the role of AIBs in regional pollution has not been systematically elucidated. Based on surface observations, this study investigates PM2.5 pollution distributions under the forcing of various AIBs in the North China Plain. A total of 98 regional pollution episodes are identified during the autumn and winter of 2014–2020, and are further classified according to the impact of AIBs. In the pollution formation-maintenance stage, there are three categories. The frontal category (with a frequency of 41%), including the frontal trough type and frontal inverted trough type, displays the most polluted air masses along the mountains. The frontal AIB defines the lateral border of the pollution zone and forms a frontal inversion above, creating a closed and stable structure wherein the highest concentration of PM2.5 accumulates. The wind shear category (29%) is decided by the dynamic convergence AIB, which causes lighter PM2.5 pollution with diverse spatial patterns corresponding to west-southwest shear, southeast-east shear, and south-north shear. The topographic obstruction category (14%) presents as a narrow arc-shaped pollution belt at the foot of the windward mountains, resulting from the cold air damming AIB with dynamical obstruction and thermal stratification. Pollution diffuses in three ways: northwest, west, and northeast, respectively. The first one is the strongest and most frequent (42%), with both strong horizontal wind and vertical mixing. The second category is relatively rare (17%), characterized by foehn-induced active vertical ventilation. The last one is frequent (41%), but relatively weak, mainly relying on horizontal diffusion. Some evolution details of the AIB affecting PM2.5 pollution are also illustrated by a typical case.

Size-segregated chemical compositions of particulate matter including PM0.1 in northern Vietnam, a highly polluted area where notable seasonal episodes occur

In October 2017 and June 2018, measurements of size-segregated atmospheric particles containing ultrafine particles (PM0.1) were carried out using a Nanosampler in northern Vietnam (urban, peri-urban and rural areas). This study shows that the variations of PM concentration at the three sites were very similar and that air pollution is widespread in northern Vietnam. However, a concentration difference between cities and a change of composition at high PM concentration were confirmed, and there were some characteristic regional, particle-size, and component trends. From the carbon-fraction distribution, the carbon components of atmospheric particles in Vietnam were affected by vehicles (OC3) and biomass combustion (OC3, EC1), and PM0.1 was affected strongly by vehicles (EC2). The water-soluble ionic component of fine particles in Vietnam is characterized by the highest amount of nitrate next to sulfate. The nitrate formation path is analyzed from the degree of neutralization with ammonium. In coarse and fine particles, particle formation of nitrate due to heterogeneous reaction is dominant, and the contribution of HNO3 adsorption by soil particles and N2O5 hydrolysis is high. For PM0.1, the formation of ammonium nitrate by a homogeneous reaction is the main production path. High-concentration events with increased nitrate were observed in late October, characterized by the effects of long-distance transportation and the promotion of nocturnal N2O5 hydrolysis with increased humidity. In June, when there was an increase in PM concentration derived from biomass combustion, PM0.1 indicated characteristic behavior, suggesting that it is possible to estimate local sources from PM0.1 components and variations.

School Children Exposure to Low Indoor Air Quality in Classrooms During Covid-19 Pandemic: Results of a Pilot Study

Background: Indoor air quality (IAQ) in classrooms affects children s health and academic performance. The aim of this pilot study was to determine IAQ in elementary schools different in their internal and external characteristics, in settings of COVID-19 epidemics. Methods: IAQ parameters: fine particulate matter (PM2,5) mass concentration, CO2 concentration, temperature and relative humidity were measured in parallel in four elementary schools/classrooms during October (non-heating season) and four months (including holiday in January) of heating season. IAQ parameters were measured in settings of anti-epidemic restrictions (≤13 students in classroom, frequent ventilation). Results: During October, except in one school, PM2,5 concentrations were below the upper recommended value (25μg/m ), but started rising in all schools in the heating season. The highest concentrations of PM2,5 were registered in two schools with closed or shortly opened windows. CO2 concentrations were mostly in the recommended range (up to 1000ppm) except in the school with constantly closed windows and in three schools in February when concentrations were higher. Except in one, the same school, and in January, both temperature and relative humidity were out of the recommended range (24,0 27,0°C in non-heating;20,0 24,0°C in heating season;and 45 55%), with temperature mainly above and relative humidity mainly below it in three schools. The largest deviation in temperature and relative humidity were registered in urban schools. Registered differences may be explained by different internal and external characteristics. Original paper Conclusion: Despite anti-epidemic restrictions, most of the measured IAQ parameters were out of the recommended values in heating season. In addition, further deterioration of IAQ could be expected if all students had been presented in the classroom. Finally, to assure a healthy school environment in heating season, further optimisation of both indoor and outdoor conditions is needed in both pandemic and non-pandemic settings.

SPATIAL OVERFLOW EFFECT OF INTERCITY PARTICULATE POLLUTION: A CASE STUDY IN HUBEI, CHINA

Abstract. Complex interaction between emissions, meteorology and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can dig out potential laws from massive data, providing new possibilities for understanding the evolution and causes of particulate pollution. Based on the Granger method and block modeling analysis, this study explored the spillover effects of hourly PM2.5 to determine the specific role (i.e., overflow, bilateral, inflow and limited inflow) of each city in Hubei, China. The results suggest that the northern and central cities with high level PM concentration in Hubei have a significant spillover effect, while the eastern and southern cities generally play a role as the sink of pollutant.