Daily Average Concentrations Of PM2 Research Articles

Decoding temporal patterns and trends of PM10 pollution over Delhi: a multi-year analysis (2015-2022).

The current study delved into an extensive analysis of multi-year observations on PM10 to have trends at various time scales in Delhi, India. High-resolution ground observations from all 37 monitoring stations from 2015 to 2022 were used. This study used non-parametric generalized additive model (GAM) based smooth-trend and Theil-Sen slope estimator techniques to analyze temporal trends and variations. The long-term PM10 concentration, both in its ambient and de-seasonalized forms, exhibited a statistically significant decreasing trend. An average decrease of - 7.57 [95% confidence interval (CI) - 16.51, 0.18] µg m-3year-1 for ambient PM10 and - 8.45 [95% CI - 11.96, - 5.58] µg m-3year-1 for de-seasonalized PM10 mass concentration was observed. Breaking it down into seasons, we observed significant declines in PM10 concentrations during monsoon (- 10.71µgm-3year-1, p < 0.1) and post-monsoon (- 7.49µgm-3year-1, p < 0.001). On the other hand, summer and winter displayed statistically insignificant declining trends of - 5.32µgm-3year-1 and - 6.06µgm-3year-1, respectively. Remarkably, all months except March displayed declining PM10 concentrations, suggesting a gradual reduction in particle pollution across the city. Further analysis of PM10 across various wind sectors revealed a consistent decreasing trend in all wind directions. The most substantial decrease was observed from the northwest (- 10.24µgm-3year-1), while the minimum reduction occurred from the east (- 5.67µgm-3year-1). Throughout the 8-year study period, the daily average PM10 concentration remained at 228 ± 124µgm-3, ranging from 33 to 819µgm-3. Seasonal variations were apparent, with concentrations during winter, summer, monsoon, and post-monsoon seasons averaging 279 ± 133, 224 ± 117, 135 ± 95, and 323 ± 142µgm-3, respectively. November had the highest and August had the lowest concentration. Weekend PM10 concentration is slightly lower than weekdays. These findings emphasize the need for more stringent government action plans.

PM2.5-bound heavy metals in a typical industrial city of Changzhi in North China: Pollution sources and health risk assessment

Heavy metal contamination in fine particulate matter (PM2.5) emitted in industrial cities is extremely severe. Hence, determining their pollution sources apportionment is critical to inform effective pollution control strategies for reducing the risks associated with heavy metals in PM2.5. This study comprehensively investigated the characteristics, source apportionment, and health risks associated with heavy metals in PM2.5 at three monitoring stations—Jiance, Qinghua, and Shenjiju—in Changzhi, a typical industry city in China, during three seasons: autumn and winter of 2017 and summer of 2018. The daily average concentrations of PM2.5 (65.68 μg m−3) during the study period were lower than the second-grade limit value established in China (75 μg m−3). The highest mean PM2.5 concentrations were observed during winter (72.45 μg m−3) and at the Jiance station (64.51 μg m−3), respectively. At Qinghua station, vehicle emissions contributed more V to PM2.5, whereas Jiance station showed higher levels of Pb and Zn from coal combustion. In autumn, there was an increased in the Fe content in PM2.5 from vehicle emissions, whereas elevated concentrations of Cr and Ni from industry sources were observed in summer. According to the results of a positive matrix factorization, the main sources of PM2.5-bound heavy metals in Changzhi were coal combustion (34.3%), vehicle emissions (30.9%), fugitive dust (21.8%) and industry (13.0%). The major sources during autumn, winter, and summer were vehicle emissions (39.0%), coal combustion (35.0%), and industrial emissions (61.7%), respectively, showing a clear seasonal pattern. At both the Jiance station and Shenjiju station, extensive coal-based heating was the primary source of emissions, whereas vehicle emissions were the main source of emissions at QH owing to its high levels of traffic. The total hazard quotient (HQ) values for heavy metals were below the safe level (HQ = 1); but the carcinogenic risks (CR) exceed the lower limit of tolerance (1E-6). Stronger emissions from industrial sources in summer and the large amount of coal combustion for heating at the Jiance station are possible reasons for the higher health risk of heavy metals. Identification the source of PM2.5-bound heavy metals could support the targeted reduction of associated emissions, consequently helping alleviate heavy metals contamination and control associated risks to human health.

Increase in sick leave episodes from short-term fine particulate matter exposure: A case-crossover study in Stockholm, Sweden

Air pollution's short-term effects on a wide range of health outcomes have been studied extensively, primarily focused on vulnerable groups (e.g., children and the elderly). However, the air pollution effects on the adult working population through sick leave have received little attention. This study aims to 1) estimate the associations between particulate matter ≤2.5 μm3 (PM2.5) and sick leave episodes and 2) calculate the attributable number of sick leave days and the consequential productivity loss in the City of Stockholm, Sweden.Individual level daily sick leave data was obtained from Statistics Sweden for the years 2011–2019. Daily average concentrations of PM2.5 were obtained from the main urban background monitoring station in Stockholm. A case-crossover study design was applied to estimate the association between short-term PM2.5 and onset of sick leave episodes. Conditional logistic regression was used to estimate the relative increase in odds of onset per 10 μg/m3 of PM2.5, adjusting for temperature, season, and pollen. A human capital method was applied to estimate the PM2.5 attributable productivity loss.In total, 1.5 million (M) individual sick leave occurrences were studied. The measured daily mean PM2.5 concentration was 4.2 μg/m3 (IQR 3.7 μg/m3). The odds of a sick leave episode was estimated to increase by 8.5% (95% CI: 7.8–9.3) per 10 μg/m3 average exposure 2–4 days before. Sub-group analysis showed that private sector and individuals 15–24 years old had a lower increase in odds of sick leave episodes in relation to PM2.5 exposure. In Stockholm, 4% of the sick leave episodes were attributable to PM2.5 exposure, corresponding to €17 M per year in productivity loss.Our study suggests a positive association between PM2.5 and sick leave episodes in a low exposure area.

An analysis of air pollution associated with the 2023 sand and dust storms over China: Aerosol properties and PM10 variability

Every spring, a large part of China is confronted with sand and dust storms (SDS) – mainly originating in the Gobi (including Chinese and Mongolian Gobi) and Taklamakan deserts. In March-April 2023, most of northern, northwestern and northeastern China was struck by three sandstorms that affected an area with more than 500 million people. In this study, aerosol optical, microphysical and radiative properties were studied during these SDS events using an integrated approach that combines satellite, terrestrial and re-analysis data. The results showed that dusty conditions were observed in most areas north of the Yangtze River (Chang Jiang) with daily average PM10 concentrations exceeding 1000 µg/m3 in many cities. VIIRS aerosol optical depth (AOD) at 550 nm during three SDS events exceeded a value of 1 throughout nearly the entire northern part of the country. The AERONET data obtained from the AOE_Baotou site showed a significant increase in total AOD and a corresponding decrease in AE during the SDS. The single scattering albedo (SSA), asymmetry parameter (ASY), real refractive index (RRI) and imaginary refractive index (IRI) values indicate an abundance of scattering coarse-mode particles. Aerosol radiative forcing (ARF) at top of the atmosphere and at the earth's surface was nearly always negative during the period and ranged from −48.5 to +2.7 Wm−2 and from −180.8 to −66.6 Wm−2, resulting in high positive ARF values at ATM (from +63.8 to +132.3 Wm−2). Each of these affects the heating of the atmosphere and cooling on the earth's surface. The atmospheric heating rates ranged from 1.8 to 3.7 K day−1. The formation of these SDS mainly resulted from the passage of cold fronts associated with low pressure systems in the Gobi and Taklamakan deserts, creating conditions for dust to rise into the atmosphere and move further downwind.

Prediction of PM10 Concentration in Malaysia Using K-Means Clustering and LSTM Hybrid Model

Following the rapid development of various industrial sectors, air pollution frequently occurs in every corner of the world. As a dominant pollutant in Malaysia, particulate matter PM10 can cause highly detrimental effects on human health. This study aims to predict the daily average concentration of PM10 based on the data collected from 60 air quality monitoring stations in Malaysia. Building a forecasting model for each station is time-consuming and unrealistic; therefore, a hybrid model that combines the k-means clustering technique and the long short-term memory (LSTM) model is proposed to reduce the number of models and the overall model training time. Based on the training set, the stations were clustered using the k-means algorithm and an LSTM model was built for each cluster. Then, the prediction performance of the hybrid model was compared with the univariate LSTM model built independently for each station. The results show that the hybrid model has a comparable prediction performance to the univariate LSTM model, as it gives the relative percentage difference (RPD) less than or equal to 50% based on at least two accuracy metrics for 43 stations. The hybrid model can also fit the actual data trend well with a much shorter training time. Hence, the hybrid model is more competitive and suitable for real applications to forecast air quality.

Distribution over time and space of PM2.5 in Beijing- Tianjin- Hebei region and its influence of meteorological factors

As the capital economic belt and the political center of China, Beijing-Tianjin-Hebei region has always been faced with the problem of air pollution. In this study, the emission, seasonal and geographical changes of PM2.5 in this region is analyzed with spatial-temporal distribution. It is found that the concentration of PM2.5 shows a spatial distribution of decreasing from southwest to northeast. In winter, the daily average concentration of PM2.5 is significantly higher than that in other seasons. The influence of meteorological factors on PM2.5 in each city is discussed by cluster analysis, and the correlation between these factors and PM2.5 is studied. The results indicate that the daily average precipitation and wind speed are negatively correlated with PM2.5 concentration, while the daily average relative humidity is positively correlated with it. Finally, this study also puts forward suggestions for PM2.5 control in this area according to the types of cities’ PM2.5 discharge affected by meteorological elements.

Comparison of the Application of Three Methods for the Determination of Outdoor PM2.5 Design Concentrations for Fresh Air Filtration Systems in China.

With the increasing popularity of fresh-air filtration systems, the methods of determining the outdoor PM2.5 design concentration have become more important. However, the monitoring of atmospheric fine particles in China started relatively late, and there are relatively few cities with complete data, with obvious regional differences, which led to many problems in the selection of air filters for fresh-air filtration systems. In this paper, three methods of determining outdoor PM2.5 design concentration were analyzed using the daily average concentration of PM2.5 in 31 provincial capital cities from 2016 to 2020. Six typical cities in different regions were also taken as examples. The advantages and disadvantages of the three existing statistical methods were compared and analyzed, as well as the corresponding differences in the selection of outdoor PM2.5 concentration value on the filter systems. The results showed that the method of mathematical induction was more accurate and reasonable for the calculation of outdoor PM2.5 design concentrations. The local outdoor PM2.5 design concentration could be quickly calculated using the recommended coefficient K and annual average PM2.5 concentration of the region, especially for small and medium-sized cities without monitoring data. However, the recommended coefficient K should be provided based on the specific region, and should be divided into values for strict conditions and normal conditions during use. This would provide a simple and effective way to select the correct air filters for practical engineering.

Artificial Neural Network Modeling on PM10, PM2.5, and NO2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020

The mutual relationship among daily averaged PM10, PM2.5, and NO2 concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus (COVID-19) pandemic period from 1 January to 31 December 2020. Daily and weekly mean concentrations of NO2 in 2020 under neither locked down cities, nor limitation of the activities of vehicles and people by the Korean Government have decreased by about 15%, and 12% in Seoul, and Busan cities, than the ones in 2019, respectively. PM 10 (PM2.5) concentration has also decreased by 15% (10%), and 12% (10%) in Seoul, and Busan, with a similar decline of NO2, causing an improvement in air quality in each city. Multilayer perception (MLP), which has a back-propagation training algorithm for a feed-forward artificial neural network technique with a sigmoid activation function was adopted to predict daily averaged PM10, PM2.5, and NO2 concentrations in two cities with their interplay. Root mean square error (RMSE) with the coefficient of determination (R2) evaluates the performance of the model between the predicted and measured values of daily mean PM10, PM2.5, and NO2, in Seoul were 2.251 with 0.882 (1.909 with 0.896; 1.913 with 0.892), 0.717 with 0.925 (0.955 with 0.930; 0.955 with 0.922), and 3.502 with 0.729 (2.808 with 0.746; 3.481 with 0.734), in 2 (5; 7) nodes in a single hidden layer. Similarly, they in Busan were 2.155 with 0.853 (1.519 with 0.896; 1.649 with 0.869), 0.692 with 0.914 (0.891 with 0.910; 1.211 with 0.883), and 2.747 with 0.667 (2.277 with 0.669; 2.137 with 0.689), respectively. The closeness of the predicted values to the observed ones shows a very high Pearson r correlation coefficient of over 0.932, except for 0.818 of NO2 in Busan. Modeling performance using IBM SPSS-v27 software on daily averaged PM10, PM2.5, and NO2 concentrations in each city were compared by scatter plots and their daily distributions between predicted and observed values.

Burden of Natural-Cause and Cause-Specific Mortality Associated with Long-Term Exposure to PM2.5: A Case Study in Attica Region, Greece

In this study, the AirQ+ software proposed by the World Health Organization (WHO) was applied in order to assess the health endpoints associated with the long-term exposure to PM2.5 in Attica Region, Greece. For this purpose, we analyzed the daily average concentrations of PM2.5 registered by the air quality monitoring stations in the region, from 1 January 2007 to 31 December 2018. Although there was a decreasing trend in PM2.5 concentrations levels, the levels of PM2.5 exceeded the AQG (Air Quality Guidelines) limit value (annual value: 5 μg/m3) established by the WHO. The findings revealed that the burden of mortality (from all-natural causes) at people above 30 years old associated with PM2.5 exposure was 4752 [3179–6152] deaths in 2007 and 2424 [1598–3179] deaths in 2018. In general, the attributable mortality from specific causes of deaths (e.g., lung cancer, IHD (ischemic heart diseases) and stroke) in people above 25 years old decreased between the years, but the mortality from COPD (chronic obstructive pulmonary diseases) was stable at 146 [79–220] deaths in 2007 and 147 [63–244] deaths in 2018. We also found differences in mortality cases from IHD and stroke among the age groups and between the years 2007 and 2018.

The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester.

Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort. A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites. Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM2.5, PM10, SO2, NO2, CO and the exposure estimates of PM2.5 and NO2, and positively associated with O3. This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.

High contribution of vehicular exhaust and coal combustion to PM2.5-bound Pb pollution in an industrial city in North China: An insight from isotope

Although industrial emissions are the major pollution source of PM2.5-bound Pb, which affects air quality in cities, their influence has gradually decreased with the enhancement of industrial control in recent years. Thus, at present, the source of PM2.5-bound Pb is unclear, especially in an industrial city in North China. In this study, by incorporating the stable Pb isotopic compositions into a Bayesian isotope mixing model (MixSIAR), source apportionment of PM2.5-bound Pb was conducted during autumn and winter in 2017, and summer in 2018 in Changzhi. The daily average concentrations of PM2.5 and PM2.5-bound Pb during the study period were lower than the second-grade limit value of China (75 μg m−3) and the Chinese National Ambient Air Quality Standard (1 μg m−3), respectively. The pollution sources of PM2.5-bound Pb were fully distinguishable using Pb isotopes. In Changzhi, the 206Pb/207Pb ratio in soil (average: 1.0891) and exhaust dust from gasoline (average: 1.0832) and diesel (average: 1.0814) showed low levels of Pb isotope signature in Changzhi, whereas the rest of the source exhibited Pb isotope signature levels. The source apportionment results revealed that in an industrial city with many steel and coking plants, the primary source of PM2.5-bound Pb were motor vehicle emission (29.7%) and coal combustion (26.9%), rather than industrial emission (26.5%). Moreover, seasonal variations in PM2.5 of Pb sub-sources were observed: soil dust (21.7% and 18.4%) and diesel motor vehicles emission (16.4% and 15.6%) in summer and autumn; domestic coal combustion (17.1%) and coal-fired power plant emission (15.5%) in winter. The carcinogenic risk from motor vehicle emission in autumn was higher than that from industrial and coal sources in winter, and higher than that from motor vehicle sources in summer. This finding is a critical evidence of an anomalistic enrichment of PM2.5-bound Pb in an industrial city. These results obtained through Pb isotopic composition and MixSIAR model also improved the accuracy of source apportionment of atmospheric PM2.5-bound Pb, representing a high scientific significance and reference value for Pb pollution control in North China.

Spatial-temporal Distribution and Evolution Characteristics of Air Pollution in Beijing-Tianjin-Hebei Region Based on Long-term "Ground-Satellite" Data

This study aimed to promote the coordinated development of regional social economy and ecological environment, build a better living environment, accurately prevent and control pollution, and carry out in-depth surveys and general surveys of air pollution in Beijing, Tianjin, and Hebei. Based on 6 years (June 2014 to December 2019) of ground environmental observation data and satellite data from 2000 to 2019, the distribution characteristics and evolution trend of air pollution in different time and spatial scales were analyzed. The results showed that:① according to the daily average concentration of PM2.5 at the sites, the pollution in the Beijing-Tianjin-Hebei region showed the characteristics of more days, heavy levels, and overall improvement. Pollution mainly occurred from October to April of the following year, accounting for nearly half a year. The pollution level of PM2.5 was the best at Zhangjiakou, followed by Qinhuangdao. ② Based on the 20-year average PM2.5 annual average concentration data retrieved from satellites, the PM2.5 concentration presented a spatial distribution characteristic in which that in the plains was higher than that in mountain area, and PM2.5 concentration in the city was higher than that in the suburbs. PM2.5 concentration changed with time, showing a four-stage bimodal structure of "M"-type evolution characteristics, which gradually increased starting in 2000; the first peak appeared in 2006 and gradually decreased from 2007 to 2012. It rose sharply to the second peak in 2013 and then decreased yearly until 2017. ③ The monthly average AOT data based on satellites every 10 years indicated that the value of AOT in the first time period (2000-2009) was larger than that in the same month of the second time period (2010-2019). The maximum value was in July, and the minimum value was in December. The monthly average AOT in Zhangjiakou and Chengde changed slightly over the past 20 years, and the seasonal and spatial differences were significant in the plain area. ④ Judging from the daily average value of O3-8h observed at the stations, good levels of O3-8h concentrations in the Beijing-Tianjin-Hebei area occurred frequently and widely from March to October. There were at least seven instances of light pollution levels, and the moderate pollution levels and above were not observed. ⑤ The daily average value of SO2 observed on the ground showed that there was no light pollution or above; the good pollution level occurred in winter, and most appeared in the form of pollution for several consecutive days. ⑥ The analysis of AQI data revealed that from 2015 to 2019, the proportion of AQI excellent grades in Beijing increased from 27% to 38%, and the proportion of Tianjin AQI good grades increased from 44% to 64%. The highest proportion of Handan AQI superior grades appeared in 2016, accounting for only 9%. ⑦ The 20-year monthly average concentration of SO2 data based on satellites showed that high-value areas were in Handan, Xingtai, and Shijiazhuang, and low-value areas were in Zhangjiakou and Chengde. The 20-year average NO2 data showed that the high-value centers were in Beijing, Tianjin, Tangshan, Handan, Xingtai, and Shijiazhuang.

The new WHO air quality guidelines for PM2.5: predicament for small/medium cities.

The global burden of disease estimated that approximately 7.1 million deaths worldwide were related to air pollution in 2016. However, only a limited number of small- and middle-sized cities have air quality monitoring networks. To date, air quality in terms of particulate matter is still mainly focused on mass concentration, with limited compositional monitoring even in mega cities, despite evidence indicating differential toxicity of particulate matter. As this evidence is far from conclusive, we conducted PM2.5 bioaccessibility studies of potentially harmful elements in a medium-sized city, Londrina, Brazil. The data was interpreted in terms of source apportionment, the health risk evaluation and the bioaccessibility of inorganic contents in an artificial lysosomal fluid. The daily average concentration of PM2.5 was below the WHO guideline, however, the chemical health assessment indicated a considerable health risk. The in vitro evaluation showed different potential mobility when compared to previous studies in large-sized cities, those with 1 million inhabitants or more (Curitiba and Manaus). The new WHO guideline for PM2.5 mass concentration puts additional pressure on cities where air pollution monitoring is limited and/or neglected, because decision making is mainly revenue-driven and not socioeconomic-driven. Given the further emerging evidence that PM chemical composition is as, or even more, important than mass concentration levels, the research reported in the paper could pave the way for the necessary inter- and intra-city collaborations that are needed to address this global health challenge.

Progressively narrow the gap of PM2.5 pollution characteristics at urban and suburban sites in a megacity of Sichuan Basin, China

Nowadays, the fine particle pollution is still severe in some megacities of China, especially in the Sichuan Basin, southwestern China. In order to understand the causes, sources, and impacts of fine particles, we collected PM2.5 samples and analyzed their chemical composition in typical months from July 2018 to May 2019 at an urban and a suburban (background) site of Chengdu, a megacity in this region. The daily average concentrations of PM2.5 ranged from 5.6-102.3 µg/m3 and 4.3-110.4 µg/m3 at each site. Secondary inorganics and organic matters were the major components in PM2.5 at both sites. The proportion of nitrate in PM2.5 has exceeded sulfate and become the primary inorganic component. SO2 was easier to transform into sulfate in urban areas because of Mn-catalytic heterogeneous reactions. In contrast, NO2 was easily converted in suburbs with high aerosol water content. Furthermore, organic carbon in urban was much greater than that in rural, other than elemental carbon. Element Cr and As were the key cancer risk drivers. The main sources of PM2.5 in urban and suburban areas were all secondary aerosols (42.9%, 32.1%), combustion (16.0%, 25.2%) and vehicle emission (15.2%, 19.2%). From clean period to pollution period, the contributions from combustion and secondary aerosols increased markedly. In addition to tightening vehicle controls, urban areas need to restrict emissions from steel smelters, and suburbs need to minimize coal and biomass combustion in autumn and winter.

The Effects of Surface Lignite Mines Closure on the Particulates Concentrations in the Vicinity of Large-Scale Extraction Activities

The European green deal and energy transition policies and the competition primarily shaped by the high price of carbon dioxide emission allowances and the consistently reduced cost of renewable energy technologies directly affect the coal and lignite extraction industry. Lignite production in Western Macedonia Lignite Centre reduced from 43.2 million tons in 2010 to 10.3 million tons in 2020. This development affects the ambient air quality of the large lignite mine area, as evidenced by the records of 10 monitoring stations incorporating the laser light scattering method. All stations measure reduced particulate matter (PM10 and PM2.5) concentrations compared to the period before 2010, while the number of annual exceedances of the limit value for the daily average PM10 concentrations has been decreased. Moreover, differences in air quality measurements of monitoring stations related to their distance from mining activities now tend to be minimized. Based on these facts, it can be predicted that, after the closure of lignite extraction and the electricity generation activities, the concentration of particulates in the atmosphere will reach the typical levels for rural areas of Southern Europe, no matter what the mines’ land reforestation and repurposing program will include.

Pollution Characteristics and Influencing Factors of PM2.5 in Shanxi Province Based on Wavelet Transform

Based on the daily average concentration of PM2.5, social influencing factor data, and meteorological data of 11 cities in Shanxi Province from 2015 to 2019, the concentration period of PM2.5 was determined using wavelet transform. The correlation between PM2.5 and social influencing factors and meteorological factors was explored respectively through Spearman correlation and the wavelet coherence spectrum, and the main influencing factors of long-term and short-term management and control of PM2.5 were determined. The results showed that the concentration of PM2.5 in Shanxi Province showed an upward trend from 2015 to 2017, with an average annual increase rate of 4.3% and a downward trend from 2018 to 2019, with an average annual decrease rate of 4.2%. The average concentration of PM2.5 showed a "U" distribution, with the highest value in January (95 μg·m-3) and the lowest in August (34 μg·m-3); the average value in winter was approximately twice that in summer. The ρ(PM2.5) in southern cities such as Linfen was 62 μg·m-3, and the average value in Datong and other northern cities was 45 μg·m-3, which was high in the south and low in the north. There were significant periodic changes in PM2.5 concentration in the 11 cities, including a long period of approximately 293 d and a short period of approximately 27 d. Among them, the energy consumption level and industrial structure were the strong driving factors affecting the PM2.5 concentration in the long period of Shanxi Province. In the short period, it was greatly affected by the change in atmospheric circulation, and different cities were affected by typical meteorological factors. Linfen, Yuncheng, Datong, Shuozhou, and Xinzhou were vulnerable to wind speed; Jinzhong and Luliang were vulnerable to temperature; and Taiyuan, Jincheng, Yangquan, and Changzhi were uniquely and significantly affected by relative humidity. Therefore, industrial structure adjustment and energy structure adjustment are key to the long-term control of atmospheric PM2.5 and the long-term improvement of air quality in Shanxi Province. The differential impact of different urban meteorological factors on PM2.5 should be considered when carrying out short-term regional joint prevention and control.

Spatiotemporal Variation of Air Quality (PM and NO2) in Southern Paris during COVID-19 Lockdown Periods

In urban areas, road transportation is the main source of pollutants, and weather impacts spatiotemporal variation of air quality. In this paper, we demonstrate the spatiotemporal variabilities of particulate matter (PM10 and PM2.5) and NO2 in the south of Paris, with an emphasis on the comparison of air pollutant levels between COVID-19 lockdown and non-lockdown days according to meteorological conditions. The influence of COVID-19 lockdowns could be region-specific. Thus, it is worthwhile to conduct investigations across different regions and via different methodologies. This manuscript contains data that could be relied upon to evaluate available options for mitigation of urban air pollution. Using Airparif data and mobile survey data collected by Aeroqual 500 sensors, this study confirms that road traffic seems to be the determining factor of air quality in the southern part of Paris. The daily average concentrations of NO2, PM2.5 and PM10 calculated in 2020 show a strong spatiotemporal variability explained by the type of weather on the one hand and by the proximity of emission sources on the other hand. Mobile surveys show that during lockdowns in 2020, when the weather was stable, in 13th arrondissement of Paris, NO2 values exceeded 250 µg/m3 with PM10 values over 70 µg/m3, mainly in three locations: the area between Rue Tolbiac and Rue Nationale, along Rue de Chevaleret, and on Boulevard Périphérique.

Analysis and Sources Identification of Atmospheric PM10 and Its Cation and Anion Contents in Makkah, Saudi Arabia

In this paper, atmospheric water-soluble cation and anion contents of PM10 are analysed in Makkah, Saudi Arabia. PM10 samples were collected at five sites for a whole year. PM10 concentrations (µg/m3) ranged from 82.11 to 739.61 at Aziziyah, 65.37 to 421.71 at Sanaiyah, 25.20 to 466.60 at Misfalah, 52.56 to 507.23 at Abdeyah, and 40.91 to 471.99 at Askan. Both daily and annual averaged PM10 concentrations exceeded WHO and Saudi Arabia national air quality limits. Daily averaged PM10 concentration exceeded the national air quality limits of 340 µg/m3, 32% of the time at Aziziyah, 8% of the time at Sanaiyah, and 6% of the time at the other three sites. On average, the cations and anions made a 37.81% contribution to the PM10 concentrations. SO42−, NO3−, Ca2+, Na+, and Cl− contributed 50.25%, 16.43%, 12.11%, 11.12%, and 8.70% to the total ion concentrations, respectively. The minor ions (F−, Br−, Mg2+, NO2−, and PO43−) contributed just over 1% to the ion mass. Four principal components explained 89% variations in PM10 concentrations. Four major emission sources were identified: (a) Road traffic, including emission from the exhaust, wear-and-tear, and the resuspension of dust particles (F−, SO42−, NO3−, Ca2+, Na+, Mg+, Br−, Cl−, NO2−, PO43−); (b) Mineral dust (Cl−, F−, Na+, Ca2+, Mg2+, PO43−); (c) Industries and construction–demolition work (F−, SO42−, Ca2+, Mg2+); and (d) Seaspray and marine aerosols (Cl−, Br−, Mg2+, Na+). Future work would include an analysis of the metal contents of PM10 and their spatiotemporal variability in Makkah.

Does the "Blue Sky Defense War Policy" Paint the Sky Blue?-A Case Study of Beijing-Tianjin-Hebei Region, China.

Improving air quality is an urgent task for the Beijing–Tianjin–Hebei (BTH) region in China. In 2018, utilizing 365 days’ daily concentration data of six air pollutants (including PM2.5, PM10, SO2, NO2, CO and O3) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM2.5, PM10, SO2, O3 by −1.951 μg/m3, −3.872 μg/m3, −1.902 μg/m3, −7.882 μg/m3 and CO by −0.014 mg/m3, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO2 increases significantly (1.865 μg/m3). In winter heating seasons, the concentration of SO2, CO and O3 decrease but PM2.5, PM10 and NO2 increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.

Frailty Related to the Exposure to Particulate Matter and Ozone: The Korean Frailty and Aging Cohort Study.

This study aims to identify the association between the concentration of particulate matter <2.5 μm (PM2.5), <10 μm (PM10), and ozone (O3) and frailty. The Korean Frailty Scale (KFS, 0–6 points) assessing physical, psychological, and social frailty, was applied to 2912 community-dwelling older adults between April 2016 and December 2017. Daily average concentrations of PM2.5, PM10, and O3 (2015–2017) were obtained and matched with the residential areas. The frailty risk associated with exposure to PM2.5, PM10, and O3 was evaluated using multiple logistic regression after adjusting for age, sex, BMI, lifestyle, socioeconomic status, and comorbidity. Participants were categorized into robust (0 points, 28.7%), pre-frail (1–2 points, 50.1%), and frail (≥3 points, 21.2%) groups. Each 1 μg/m3 increase of PM2.5 and PM10 increased the odds ratios (ORs) and 95% confidence intervals (CIs) of the frail group compared to the robust group: 1.055 (1.002, 1.112) and 1.095 (1.060, 1.131), and the pre-frail group: 1.053 (1.017, 1.090) and 1.062 (1.037, 1.087), respectively. Each 1-ppb increase of O3 increased the OR (95% CI) of the frail group: 1.041 (1.023, 1.059) and the pre-frail group: 1.005 (0.985, 1.025). PM2.5, PM10, and O3 may be associated dose-dependently with the frailty.