Winter PM2 Research Articles

Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations

Air pollution continues to have a significant impact on Europeans living in urban areas, and episodes of elevated PMx are responsible for a large number of premature deaths (mostly due to heart disease and stroke) each year. According to the annual EEA reports, Poland is one of the most polluted countries in Europe, experiencing high PMx concentrations during winter that mostly result from large emissions and unfavourable weather conditions in combination with environmental features. Thus, in addition to implementing municipal mitigation strategies, alerting residents to pollution episodes through accurate PMx forecasting is necessary. This research aimed to assess the feasibility of short-term PMx forecasting via machine learning (ML) and the subsequent identification of the primary meteorological covariates. The data comprised 10 years of hourly winter PM10 and PM2.5 concentrations measured at 11 urban air quality monitoring stations, including background, traffic, and industrial sites, in four large Polish agglomerations, viz., Poznań, Kraków, Łódź, and Gdańsk, which cover areas with high population density and diverse environments that extend from the Baltic Sea coast (Tricity) through the lowlands (Poznań and Łódź) to the highlands (Kraków). We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. Employing analysis and cross-validation, we found that XGBoost performed the best, followed by random forests and neural networks, and stepwise regression performed the worst. This ranking was apparent in the threshold exceedance values of the binary forecasts obtained via regression. Overall, our results confirm the high applicability of ML to short-term air quality prediction with the perfect prog approach.

The effect of toxic components on metabolomic response of male SD rats exposed to fine particulate matter.

PM2.5 pollution was associated with numerous adverse health effects. However, PM2.5 induced toxic effects and the relationships with toxic components remain largely unknown. To evaluate the metabolic toxicity of PM2.5 at environmentally relevant doses, investigate the seasonal variation of PM2.5 induced toxicity and the relationship with toxic components, a combination of general pathophysiological tests and metabolomics analysis was conducted in this study to explore the response of SD rats to PM2.5 exposure. The result of general toxicology analysis revealed unconspicuous toxicity of PM2.5 under environmental dose, but winter PM2.5 at high dose caused severe histopathological damage to lung. Metabolomic analysis highlighted significant metabolic disorder induced by PM2.5 even at environmentally relevant doses. Lipid metabolism and GSH metabolism were primarily influenced by PM2.5 exposure due to the high levels of heavy metals. In addition, high levels of organic compounds such as PAHs, PCBs and PCDD/Fs in winter PM2.5 bring multiple overlaps on the toxic pathways, resulting in larger pulmonary toxicity and metabolic toxicity in rats than summer.

Comparison of chemical characteristics of PM2.5 during two winters in Xiangtan City in south central China

To assess the efficacy of the “Implementation Details of Air Pollution Prevention and Control Action Plan”, the chemical composition of PM2.5 and other pollutants was determined during the winters of 2013–2014 and 2016–2017 at two urban sites in Xiangtan City, Hunan. The concentrations of PM2.5, SO2, and NO2 decreased from 146.0 to 94.5 μg/m3, 75.9 to 33.5 μg/m3, and 80.6 to 55.8 μg/m3, respectively, from winter 2013–2014 to winter 2016–2017. The concentrations of almost all the major chemical components of PM2.5 decreased as well, particularly secondary inorganic aerosols (SIAs). These results indicate that the implementation of the air quality control plan was very effective in improving air quality. Analysis of the data also suggests that SIA formation is likely responsible for high winter PM2.5 pollution and that high relative humidity levels and low wind speed can promote the formation of SIA. A 72-h back trajectory analysis shows that both regional transport and the accumulation of local pollutants under stagnant meteorological conditions promote the occurrence of episodes of high wintertime pollution levels.

Main Factors Influencing Winter Visibility at the Xinjin Flight College of the Civil Aviation Flight University of China

Utilizing routine hourly meteorological data of Xinjin Airport and daily average PM2.5 concentration data for Chengdu, winter visibility characteristics at Xinjin Airport between 2013 and 2017 and their relationship with meteorological conditions and particulate matter were analyzed. Between 2013 and 2017, the average winter visibility in Xinjin Airport was lowest in January, followed by that in December. The occurrence frequency of haze days in winter was much higher than that of nonhaze (clean) days, being 90.2% and 9.8%, respectively. These were mainly mild haze days, with an occurrence frequency of 44.4%, while severe haze days occurred the least, with a frequency of 7.7%. The linear and nonlinear relationships between winter visibility, meteorological factors, and PM2.5 were measured using daily data in winter from 2013 to 2016. The linear correlation between PM2.5 concentration and visibility was the most evident, followed by that of relative humidity. Visibility had a higher nonlinear correlation with PM2.5 concentration, relative humidity, and dew point depression. When relative humidity was between 70% and 80%, the negative correlation between visibility and PM2.5 concentration was the most significant and could be described by a power function. The multivariate linear regression equation of PM2.5 concentration and relative humidity could account for 65.9% of the variation in winter visibility, and the multivariate nonlinear regression equation of PM2.5 concentration, relative humidity, and wind speed could account for 68.1% of the variation in winter visibility. These two equations reasonably represented the variation in winter visibility in 2017.

PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach

Some environmental factors are associated with an increased risk of multiple sclerosis (MS). Air pollution could be a main one. This study was conducted to investigate the association of particulate matter 2.5 (PM2.5) concentrations with MS prevalence in the province of Pavia, Italy. The overall MS prevalence in the province of Pavia is 169.4 per 100,000 inhabitants. Spatial ground-level PM2.5 gridded data were analysed, by municipality, for the period 2010–2016. Municipalities were grouped by tertiles according to PM2.5 concentration. Ecological regression and Bayesian statistics were used to analyse the association between PM2.5 concentrations, degree of urbanization, deprivation index and MS risk. MS risk was higher among persons living in areas with an average winter PM2.5 concentration above the European annual limit value (25 μg/m3). The Bayesian map revealed sizeable MS high-risk clusters. The study found a relationship between low MS risk and lower PM2.5 levels, strengthening the suggestion that air pollution may be one of the environmental risk factors for MS.

Biotoxic effects and gene expression regulation of urban PM2.5 in southwestern China

Atmospheric fine particulate matter (PM2.5) causes severe haze in China and is regarded as a threat to human health. The health effects of PM2.5 vary location by location due to the variation in size distribution, chemical composition, and sources. In this study, the cytotoxicity effect, oxidative stress, and gene expression regulation of PM2.5 in Chengdu and Chongqing, two typical urban areas in southern China, were evaluated. Urban PM2.5 in summer and winter significantly inhibited cell viability and increased reactive oxygen species (ROS) levels in A549 cells. Notably, PM2.5 in winter exhibited higher cytotoxicity and ROS level than summer. Moreover, in this study, PM2.5 commonly induced cancer-related gene expression such as cell adhesion molecule 1 (PECAM1), interleukin 24 (IL24), and cytochrome P450 (CYP1A1); meanwhile, PM2.5 commonly acted on cancer-related biological functions such as cell-substrate junction, cell-cell junction, and focal adhesion. In particular, PM2.5 in Chengdu in summer had the highest carcinogenic potential among PM2.5 at the two sites in summer and winter. Importantly, cancer-related genes were uniquely targeted by PM2.5, such as epithelial splicing regulatory protein 1 (ESRP1) and membrane-associated ring-CH-type finger 1 (1-Mar) by Chengdu summer PM2.5; collagen type IX alpha 3 chain (COL9A3) by Chengdu winter PM2.5; SH2 domain-containing 1B (SH2D1B) by Chongqing summer PM2.5; and interleukin 1 receptor-like 1 (IL1RL1) and zinc finger protein 42 (ZNF423) by Chongqing winter PM2.5. Meanwhile, important cancer-related biological functions were specially induced by PM2.5, such as cell cycle checkpoint by Chengdu summer PM2.5; macromolecule methylation by Chengdu winter PM2.5; endoplasmic reticulum-Golgi intermediate compartment membrane by Chongqing summer PM2.5; and cellular lipid catabolic process by Chongqing winter PM2.5. Conclusively, in the typical urban areas of southern China, both summer and winter PM2.5 illustrated significant gene regulation effects. This study contributes to evaluating the adverse health effects of PM2.5 in southern China and providing public health suggestions for policymakers.

Seasonal variation of aerosol composition in Orange County, Southern California

Southern California is an urban metropolis bordered by mountains to the north and east and the Pacific Ocean to the west. Its unique geography and climatological features lead to annual patterns in atmospheric aerosol content from natural and anthropogenic sources. Here we sought to determine the nitrogen (N, including NO3−, NH4+, and total N) content of Southern California particulate matter (PM2.5) collected in Orange County, CA, and to identify relevant sources and processes that affect PM2.5 abundance and chemistry in different seasons. PM2.5 carbon (C) and N isotopic compositions (δ13C and δ15N, respectively) suggested that the C content of the PM2.5 derived partly from the marine inversion layer that occurs in the months of May through July. The high N content and δ15N values of PM2.5 collected in summer suggest a contribution from anthropogenic emissions, which are likely trapped in the basin by the inversion layer. In contrast, winter PM2.5 had a lower N content per volume of air, and δ13C values indicated a greater terrestrial signal, consistent with easterly winds. This trend was particularly evident in samples collected during high velocity Santa Ana wind events, which bring terrestrial material from the Great Basin and mountainous regions to the east of Southern California. Based on these observations, we suggest that atmospheric deposition of N is more likely to affect terrestrial ecosystems in the summer, when N content is high and aerosols are trapped by the inversion layer, whereas coastal marine ecosystems are more likely to be affected in the winter when westward winds are more common.

Assessing the contribution of regional sources to urban air pollution by applying 3D-PSCF modeling

A three dimensional (3D) variant of the Potential Source Contribution Function (PSCF) model was used in this study, aiming to identify the source areas and altitudinal atmospheric layers associated with PM10 exceedances of the daily EU limit value (>50 μg/m3) during the different seasons in Athens (Greece). The analysis covers the period 1 May 2015 to 31 December 2018, during which direct Saharan dust intrusions from North Africa and especially Libya and Egypt, occurred mainly within the 0-1000 m a.g.l. layer, and were identified as a significant source of PM10 exceedances during all seasons. The maximum probability of dust events was detected in spring, when the dust transport pathways were also vertically extended. Additionally, an increased probability of PM10 exceedances in winter was linked to northerly airflows within both the 0-1000 m a.g.l. and 1000-2000 m a.g.l. layers, indicating regional transport from the Balkans and Northern Greece. To delineate the emission origin of winter PM10 exceedances, the 3D-PSCF model was also applied on concentrations of Black Carbon (BC), its wood burning related fraction (BCwb) and CO, recognized as markers of incomplete combustion processes. A synoptic analysis showed that the accumulation of local emissions due to stagnant anticyclonic conditions is the primary cause of the PM10, CO, BC and BCwb episodes in Athens during winter, however 3D-PSCF model also clearly demonstrated supplementary contributions from regional combustion sources located in the Balkans and Northern Greece.

The Effect of Emission Inventory on Modelling of Seasonal Exposure Metrics of Particulate Matter and Ozone with the WRF-Chem Model for Poland

In Poland, high concentrations of particulate matter (with a diameter smaller than 2.5 or 10 μm) exceeding the WHO threshold values are often measured in winter, while ozone (O3) concentrations are high in spring. In winter high PM2.5 and PM10 concentrations are linked to high residential combustion and road transport. The main objective of this study was to assess performance of the Weather Research and Forecasting model with Chemistry (WRF-Chem) model in reproducing observations for a period of 2017-2018 covering various meteorological conditions. We compare modelled and observed exposure metrics for PM2.5, PM10 and O3 for two sets of the WRF-Chem model runs: with coarse and fine resolution emission inventory (European Monitoring and Evaluation Programme (EMEP) and Chief Inspectorate of Environmental Protection (CIEP), respectively). CIEP run reduces the negative bias of PM2.5 and PM10 and improves the model performance for number of days with exceedance of WHO (World Health Organization) threshold for PM2.5 and PM10 24-h mean concentration. High resolution emission inventory for primary aerosols helps to better distinguish polluted urban areas from non-urban ones. There are no large differences for the model performance for O3 and secondary inorganic aerosols, and high-resolution emission inventory does not improve the results in terms of 8-h rolling mean concentrations of ozone.

Comparison of Atmospheric Monocarboxylic and Dicarboxylic Acids in Xi’ an, China, for Source Apportionment of Organic Aerosols

To investigate the characteristic distributions and formation mechanism of the polar organic components in atmospheric aerosols, atmospheric particulates were simultaneously sampled at three typical sites in urban(U), suburban(S) and rural(R) of Xi’an in summer and winter. Organic compositions, including dicarboxylic acids (DCAs), monocarboxylic acids (MCAs) were emphatically analyzed to explore the seasonal, spatial, and other variations. The results showed that total concentrations of DCAs and MCAs in winter were higher than those in summer. Athough the total content of DCAs in PM10 in winter is lower than that of MCAs, the total concentration of DCAs in PM2.5 and PM10 in summer are higher than that of MCAs. The total concentration of DCAs in PM2.5 in winter and summer were 1878 ± 1425 ng/m3 and 1334 ± 493 ng/m3, 1294 ± 943 ng/m3 and 728 ± 477 ng/m3 in PM10. While the total concentration of MCAs in PM2.5 in winter and summer were 1193 ± 1142 ng/m3 and 541 ± 367 ng/m3, 1659 ± 1162 ng/m3 and 492 ± 424 ng/m3 in PM10. Urban subtotal concentrations of DCAs from PM2.5 were the highest, and mass of organic acids in sunny days were significantly lower than the hazy days. Strong correlations were observed among the DCAs with ambient oxidants and precursors. Moreover, DCAs concentrations were vulnerable to the environmental factors, such as light intensity and relative humidity. Comparisons demonstrated that MCAs mainly comes from coal combustion, vehicle exhaust and other primary emissions, while the secondary photochemistry and liquid phase oxidation are the main sources of DCAs.

Asymmetrical Linkages Between Multifrequency Atmospheric Waves and Variations in Winter PM2.5 Concentrations in Northern China During 2013–2019

AbstractAtmospheric waves have a broad spectrum of momentum at various frequencies and link to the PM2.5 concentrations in northern China. We show that the meaningful circulations depended on the wave frequency and the rank of the PM2.5 concentrations. The winter 2013–2019 PM2.5 had different correlations with the subseasonal 850 hPa height anomalies at low (>30 days), intermediate (10–30 days), and high (<10 days) frequencies. The low‐frequency waves increased PM2.5 concentrations by lowering the height over Eurasia and raising the height over the North Atlantic. The first two singular value decomposition principal patterns at the low‐frequency were alike Wavenumber 3 and 4 circulations, respectively. The two patterns accounted for the majority of the background variations of winter PM2.5 in China. The intermediate‐frequency waves increased PM2.5 concentrations via an anomalous dipole pattern over East Asia with a positive polarity over Japan and a negative polarity over Mongolia. The positive polarity is indicative of persistent haze episodes. The high‐frequency waves showed a small dipole correlation pattern with PM2.5, but they barely accounted for high PM2.5 concentrations. The high‐frequency waves traveled quickly. They raised or lowered PM2.5 concentrations in a short time, preventing particulate accumulations. The anomalous heights at the low‐ and intermediate‐frequency for the PM2.5 concentrations >90th showed a few characteristics consistent with the circulation anomalies proposed in previous climate studies with monthly mean data. Synergic effects of the waves at different frequencies result in the observed PM2.5 variations.

Recent changes in winter PM2.5 contributions from wood smoke, motor vehicles, and other sources in the Northwest U.S.

In the Northwest U.S. elevated measurements of PM2.5 from anthropogenic sources occur most often in winter. Major contributors to winter PM2.5 are direct primary emissions of wood smoke from residential wood combustion, primary emissions from motor vehicles, gaseous NOx emissions leading to particulate nitrate, and primary and secondary sources of particulate sulfate. A number of communities in the Northwest U.S. now have long data records of chemically speciated PM2.5 from which receptor-based source apportionment can be performed. This work uses receptor-based source apportionment on data from these monitoring sites to evaluate changes in the major contributors to winter PM2.5 over the available monitoring time span. Data from 9 sites are analyzed in this work using the Positive Matrix Factorization (PMF) source apportionment model. Each site was modeled individually rather than grouping the data from multiple sites. All sites had data through the summer of 2018, with most sites having 11 years of data and one site having 9 years of data. The number of PMF factors identified was between 5 to 10, depending on the site. Associations were made between PMF factors and PM2.5 sources based on comparison of PMF factor chemical profiles with published source test data and source profiles identified in other published studies. The most common factors found were: fresh wood smoke, aged wood smoke, soil dust, gas engines, mixed - gas engines and nitrate, ammonium sulfate, and ammonium nitrate. In this work, total wood smoke was identified as the combined contribution of fresh and aged wood smoke, and winter season data was defined as encompassing the last two months of a year and the first two months of the next year. To evaluate changes over time, average winter season PM2.5 measurements, major PM2.5 chemical components, and PMF factor results for the winter seasons of 2007 - 2009 were compared with the winter seasons of 2015 - 2017. The result for total 3-year average winter season PM2.5 was a decrease between 2% and 29% at the 9 sites, and the decreases were statistically significant at 3 sites. However, total winter season wood smoke contributions to PM2.5 decreased at every site between the two 3-year periods and the decreases were statistically significant at 8 of 9 sites, with decreases from 48% to 74% at those 8 sites. All PMF factors associated with ammonium nitrate (identified at 5 of 9 sites) decreased a statistically significant 11% to 54% between the two 3-year winter season periods. All PMF factors associated with ammonium sulfate (identified at 7 of 9 sites) decreased a statistically significant 27% to 81% between the two 3-year winter season periods. In contrast to the significant reductions in PM2.5 from PMF factors related to wood smoke, ammonium nitrate and ammonium sulfate, PMF factors associated with gas engines increased from 6% to 226% between the two 3-year winter season periods. Increases in PM2.5 contributions from gas engine related factors explain why overall average winter season PM2.5 had more modest percent reductions compared to the percent reductions for wood smoke, ammonium nitrate, and ammonium sulfate factors between the two 3-year winter season periods.

Identification of wintertime carbonaceous fine particulate matter (PM2.5) sources in Kaunas, Lithuania using polycyclic aromatic hydrocarbons and stable carbon isotope analysis

Abstract The study aimed at demonstrating the application of PAHs and δ13C indoor and outdoor measurements towards resolving airborne carbonaceous particle sources in wintertime. Concurrent outdoor and indoor measurements of PM2.5 and size-resolved (PM0.056-2.5) particulate matter were conducted at 6 one-family houses in Kaunas, Lithuania during January–March 2013. PM2.5 and size-resolved filter samples were analyzed for total carbon (TC) and stable carbon isotopic composition by elemental analyzer connected to isotope ratio mass spectrometer. Moreover, the indoor-outdoor concentrations of 10 selected polycyclic aromatic hydrocarbons (PAHs) in PM2.5 were quantified. The simultaneous stable carbon isotope ratio and PAHs diagnostic ratio analysis enabled to identify main anthropogenic sources (traffic, biomass burning, and coal combustion) of PM2.5. Our study indicated that during winter period outdoor PM2.5 in Kaunas were mainly derived from biomass burning (solid fuel stoves) and vehicular emissions. Whereas indoor particulate matter had different stable carbon isotope composition of TC indicating the existence of pollution sources which was likely cooking emissions. The differences of indoor/outdoor stable carbon isotope composition in PM2.5 and size-resolved aerosol particles supported the existence of distinct TC sources and suggested that cooking emissions inside the house could strongly affect δ13C values.

Nitrated monoaromatic hydrocarbons (nitrophenols, nitrocatechols, nitrosalicylic acids) in ambient air: levels, mass size distributions and inhalation bioaccessibility

Nitrated monoaromatic hydrocarbons (NMAHs) are ubiquitous in the environment and an important part of atmospheric humic-like substances (HULIS) and brown carbon. They are ecotoxic and with underresearched toxic potential for humans. NMAHs were determined in size-segregated ambient particulate matter collected at two urban sites in central Europe, Ostrava and Kladno, Czech Republic. The average sums of 12 NMAHs (Σ12NMAH) measured in winter PM10 samples from Ostrava and Kladno were 102 and 93 ng m−3, respectively, and 8.8 ng m−3 in summer PM10 samples from Ostrava. The concentrations in winter corresponded to 6.3–7.3% and 2.6–3.1% of HULIS-C and water-soluble organic carbon (WSOC), respectively. Nitrocatechols represented 67–93%, 61–73% and 28–96% of NMAHs in PM10 samples collected in winter and summer at Ostrava and in winter at Kladno, respectively. The mass size distribution of the targeted substance classes peaked in the submicrometre size fractions (PM1), often in the PM0.5 size fraction especially in summer. The bioaccessible fraction of NMAHs was determined by leaching PM3 samples in two simulated lung fluids, Gamble’s solution and artificial lysosomal fluid (ALF). More than half of NMAH mass is found bioaccessible, almost complete for nitrosalicylic acids. The bioaccessible fraction was generally higher when using ALF (mimics the chemical environment created by macrophage activity, pH 4.5) than Gamble’s solution (pH 7.4). Bioaccessibility may be negligible for lipophilic substances (i.e. log KOW > 4.5).

Pollution characteristics of bioaerosols in PM2.5 during the winter heating season in a coastal city of northern China

Frequent heavy air pollution occurred during the winter heating season of northern China. In this study, PM2.5 (particles with an aerodynamic diameter less than 2.5 μm) was collected from a coastal city of China during the winter heating season from January 1 to March 31, 2018, and the soluble ions, organic carbon (OC), elemental carbon (EC), bacterial, endotoxin, and fungal concentration in PM2.5 were analyzed. During the winter heating season, PM2.5 and bioaerosols increased on polluted days, and the secondary inorganic ions, including NO3-, NH4+, and SO42-, increased significantly. Meteorological factors, such as wind direction and wind speed, had major impacts on the distributions of PM2.5 and bioaerosols. Pollutant concentration was high when there was a westerly wind with the speed of 3-6 m/s from inland area. Using the air mass backward trajectories and principal component analysis, we elucidate the potential origins of bioaerosol in PM2.5. The backward trajectory suggested that air mass for polluted samples (PM2.5 > 75 μg/m3) commonly originated from continent (9.62%), whereas air masses for clean samples (PM2.5 < 35 μg/m3) were mainly from marine (56.73%). The interregional transport of pollutants from continental area contributed most to PM2.5. Principal component analysis of the water-soluble ions and bioaerosol indicated that air pollution of the coastal city was greatly affected by coal combustion, biomass burning, and regional transmission of high-intensity pollutants from continent. Among that, interregional transport, biomass burning, and dust from soil and plants were main sources of bioaerosol. Our findings provide important insights into the origins and characteristics of bioaerosol in PM2.5 during the winter heating season of the coastal city in northern China.

Effects of exposure to ambient fine particulate matter on the heart of diet-induced obesity mouse model

Exposure to fine particulate matter (PM2.5) is associated with decreased cardiac function, especially in high risk populations such as obese ones. In this study, impacts of PM2.5 exposure on cardiac function were investigated by using the diet-induced obesity mice model. Mice were fed with normal diet or high-fat diet (HFD) for four weeks and then exposed to phosphate-buffered solution or Taiyuan winter PM2.5 (0.25 mg/kg body/day) through intratracheal instillation for another four weeks. Among physiological indices recorded, heart rate and blood pressure were increased after PM2.5 exposure in the heart of the obese mice. Metabolomics and lipidomics were applied to explore molecular alterations in response to the co-treatment of PM2.5 and HFD. Our results demonstrated both direct impacts on cardiac function and indirect effects resulted from the injury of other organs. Inflammation of lung and hypothalamus may be responsible for the elevation of phenylalanine metabolism in serum and its downstream products: epinephrine and norepinephrine, the catecholamines involves in regulating cardiac system. In intracardiac system, the co-treatment led to imbalance of energy metabolism, in addition to oxidative stress and inflammation. In contrast to the upregulation of glucose and fatty acids uptake and CoA synthesis, levels of ATP, acetyl-CoA and the intermediates in glycolysis pathway decreased in the heart. The results indicated that energy metabolism disorder was possibly one of the important contributing factors to the more severe adverse effects of the combined treatment of HFD and PM2.5.

Airborne bacteria structure and chemical composition relationships in winter and spring PM10 samples over southeastern Italy

The Redundancy Discrimination Analysis (RDA) and Spearman correlation coefficients were used to investigate relationships between airborne bacteria at the phylum and genus level and chemical species in winter and spring PM10 samples over Southeastern Italy. The identification of main chemical species/pollution sources that were related to and likely affected the bacterial community structure was the main goal of this work. The 16S rRNA gene metabarcoding approach was used to characterize airborne bacteria. Seventeen phyla and seventy-nine genera contributing each by mean within-sample relative abundance percentage > 0.01% were identified in PM10 samples, which were chemically characterized for 33 species, including ions, metals, OC, and EC (organic and elemental carbon, respectively). Chemical species were associated with six different pollution sources. A shift from winter to spring in both bacterial community structure and chemical species mass concentrations/sources and the relationships between them was observed. RDA triplots pointed out significant correlations for all tested bacterial phyla (genera) with other phyla (genera) and/or with chemical species, in contrast to correlation coefficient results, which showed that few phyla (genera) were significantly correlated with chemical species. More specifically, in winter Bacillus and Chryseobacterium were the only genera significantly correlated with chemical species likely associated with particles from soil-dust and anthropogenic pollution source, respectively. In spring, Enterobacter and Sphingomonas were the only genera significantly correlated with chemical species likely associated with particles from the anthropogenic pollution and the marine and soil-dust sources, respectively. The results of this study also showed that the correlation coefficients were the best tool to obtain unequivocal identifications of the correlations of phyla (genera) with chemical species. The seasonal changes of the PM10 chemical composition, the microbial community structure, and their relationships suggested that the seasonal changes of atmospheric particles may have likely contributed to seasonal changes of bacterial community in the atmosphere.

Pillars of Solution for the Problem of Winter PM2.5 Variability in Fresno—Effects of Local Meteorology and Emissions

The mass composition of Particulate Matter (PM) with an aerodynamic diameter of 2.5 microns (PM2.5) in San Joaquin Valley (SJV) is dominated by ammonium nitrate (NH4NO3), a secondary pollutant. The goal of this research was the investigation of the relationship between emissions, meteorology and PM2.5 concentrations in Fresno for the winter season. It was found that location of sites near emission sources such as freeways compared with residential sites strongly affected measured PM2.5 concentrations. It was found that although long-term trends showed declines in both emissions and PM2.5 concentrations, there was substantial variability between the years in the PM2.5–emissions relationship. Much of the yearly variation in the relationship between emissions and PM2.5 concentrations can be attributed to yearly variations in weather, such as atmospheric stability, precipitation frequency and average wind speed. There are moderate correlations between PM2.5 concentrations and temperature differences between nearby surface stations at varying elevations which explains some of the daily and seasonal variation in PM2.5. Occurrence of precipitation was related to low PM 2.5, although the higher wind speeds and lower atmospheric stability associated with precipitation likely explain some of the low PM2.5 as well as washout of PM.

Cardiac dysfunction and metabolic remodeling due to seasonally ambient fine particles exposure

Increasing epidemiological evidences have revealed the association between ambient fine particulate matter (PM2.5) pollution and cardiovascular disease's morbidity and mortality. However, how seasonal PM2.5 exposure influence cardiac function and the underlying mechanism converged in energy metabolic remodeling remain to be elucidated. This study focused on seasonal PM2.5-induced cardiac dysfunction and metabolic remodeling, and the toxicity differences of PM2.5 samples from different sampling seasons and different exposure dosages were discussed. The results showed that seasonal haze caused cardiac dysfunctions, including decreases in heart rate (HR) and heart rate variability (HRV), abnormal changes in hemodynamic and echocardiographic parameters. Concurrently, the energy production in myocardial tissues was evidently disturbed. In particular, low dose of PM2.5 exposure notably induced the elevation of beta oxidation (β-oxidation) and tricarboxylic acid cycle (TCA cycle) as the compensation for the disturbed energy metabolism in animals, whereas high dose of PM2.5 exposure attenuated this process and the glycolysis levels were strikingly promoted, thus causing the reduced energy production and cardiac dysfunction. Comparatively, winter PM2.5 exposure caused more severe cardiac toxicity than did summer haze samples, possibly due to the existence of different components and pollutant levels in seasonal hazes. The findings on seasonal PM2.5 induced cardiac dysfunction and myocardial metabolic remodeling provided new insights into cardiovascular disease risks from haze exposure.

PM2.5-bound potentially toxic elements (PTEs) fractions, bioavailability and health risks before and after coal limiting.

Fractions, bioavailability, health risks of fine particulate maters (PM2.5)-bound potentially toxic elements (PTEs) (Pb, Cd, Cr, Cu and Zn) were investigated before and after coal limiting in Baoding city. The winter PM2.5 samples were collected at different functional areas such as residential area (RA), industrial area (IA), suburb (SB), street (ST) and Botanical Garden Park (BG) in 2016 (coal dominated year) and 2017 (gas dominated year). The fractions and bioavailability of PTEs were determined and evaluated based on BCR sequential extraction. Health risks through inhalation exposure were evaluated by US EPA health risk assessment model. The results from different years and functional areas were compared and discussed. The fractions and bioavailability of PM2.5-bound PTEs varied with functional areas. The percentages of cadmium (Cd) and zinc (Zn) in acid-soluble fraction (F1-Cd and F1-Zn) to the total amount of Cd and Zn were low in BG samples (p<0.05). Bioavailability of Cd were high in SB samples (p<0.05). Total contents of PM-bound PTEs in 2017 generally decreased compared with 2016. The differences of fraction and bioavailability between 2016 and 2017 depended on the elements and areas. Higher proportions of copper (Cu) in acid-soluble fraction (F1-Cu) and bioavailability of Cu (p<0.05) were found in 2017 samples. Significant differences were found just at IA and RA for Pb, Cd and Zn. Our results indicated that the health risks from inhalation exposure for PTEs in PM2.5 declined about 11%-52% after the coal limiting in this city.