Atmospheric PM2 Research Articles

Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain

Ambient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of PM10, PM2.5, and PM1 were collected roughly every four days from January 2018 until March 2019 at a Barcelona urban background site and Montseny rural background site in northeastern Spain. We determined the chemical composition of samples, allowing us to perform source apportionment using positive matrix factorization. The OP of PM was determined by measuring reactive oxygen species using dithiothreitol and ascorbic acid assays. Finally, to link the sources with the measured OP, both a Pearson's correlation and a multiple linear regression model were applied to the dataset. The results showed that in Barcelona, the OP of PM10 was much higher than those of PM2.5 and PM1, whereas in Montseny results for all PM sizes were in the same range, but significantly lower than in Barcelona. In Barcelona, several anthropogenic sources were the main drivers of OP in PM10 (Combustion + Road Dust + Heavy Oil + OC-rich) and PM2.5 (Road Dust + Combustion). In contrast, PM1 -associated OP was driven by Industry, with a much lower contribution to PM10 and PM2.5 mass. Meanwhile, Montseny exhibited no clear drivers for OP evolution, likely explaining the lack of a significant difference in OP between PM10, PM2.5, and PM1. Overall, this study indicates that size fraction matters for OP, as a function of the environment typology. In an urban context, OP is driven by the PM10 and PM1 size fractions, whereas only the PM1 fraction is involved in rural environments.

Extinction Effect of Foliar Dust Retention on Urban Vegetation as Estimated by Atmospheric PM10 Concentration in Shenzhen, China

Foliar dust retention is a crucial source of uncertainty when monitoring the vegetation index using satellite remote sensing. As ground sampling conditions are limited by vegetation dust retention, separating the extinction effect of foliar dust retention from the normalized difference vegetation index (NDVI) poses a significant challenge. In this study, we conducted a correlation test between the relative change in NDVI (δNDVI, an indicator of extinction effect) retrieved by the Gaofen-4 satellite and the atmospheric PM10 concentration in different meteorological periods (before, during, and after rainfall) across 14 stations in Shenzhen City, China. The results showed a significant correlation between δNDVI and atmospheric PM10 concentration during the before-rainfall period and weaker correlations for the other periods (R = 0.680, p < 0.001, n = 63 when excluding the during- and after-rainfall data). The correlation was more significant for the stations with low NDVI values, and a coastal station had a distinct regression slope of δNDVI versus PM10 from the other stations, indicating that the extinction effect of foliar dust retention in high-NDVI and coastal areas may not be well predicted by the general δNDVI–PM10 relationship. This provides a new quantitative basis for estimating the extinction effect of foliar dust retention using PM10 data for future improvement of the accuracy of vegetation monitoring by remote sensing.

Isotopic imprints of aerosol ammonium over the north China plain

Atmospheric PM2.5 poses a variety of health and environmental risks to urban environments. Ammonium is one of the main components of PM2.5, and its role in PM2.5 pollution will likely increase in the coming years as NH3 emissions are still unregulated and rising in many cities worldwide. However, partitioning urban NH4+ sources remains challenging. Although the 15N natural abundance (δ15N) analysis is a promising approach for this purpose, it has seldom been applied across multiple cities within a given region. This limits our understanding of the regional patterns and controls of NH4+ sources in urban environments. Here, we collected PM2.5 samples using an active sampling technique during winter at six cities in the North China Plain to characterize the concentrations, δ15N and sources of NH4+ in PM2.5. We found substantial variations in both the concentrations and δ15N of NH4+ among the sites. The mean NH4+ concentrations across the six cities ranged from 3.6 to 12.1 μg m−3 on polluted days and from 0.9 to 10.6 μg m−3 on non-polluted days. The δ15N ranged from 6.5‰ to 13.9‰ on polluted days and from 8.7‰ to 13.5‰ on non-polluted days. The δ15N decreased with increasing NH4+ concentrations at all six sites. We found that non-agricultural sources (vehicle exhaust, ammonia slip and urban wastes) contributed 72%–94% and 56%–86% of the NH4+ on polluted and non-polluted days, respectively, and that during polluted days, combustion-related emissions (vehicle exhaust and ammonia slip) were positively associated with the proportion of urban area, population density and number of vehicles, highlighting the importance of local sources of particulate pollution. This study suggests that the analysis of 15N in aerosol NH4+ is a promising approach for apportioning atmospheric NH3 sources over a large region, and this approach has potential for mapping rapidly and precisely the sources of NH3 emissions.

Mobile Sources Are Still an Important Source of Secondary Organic Aerosol and Fine Particulate Matter in the Los Angeles Region.

Secondary organic aerosol (SOA) is a significant component of atmospheric fine particulate matter. Mobile sources have historically been a major source of SOA precursors in urban environments, but decades of regulations have reduced their emissions. Less regulated sources, such as volatile chemical products (VCPs), are of growing importance. We analyzed ambient and emissions data to assess the contribution of mobile sources to SOA formation in Los Angeles during the period of 2009-2019. During this period, air quality in the Los Angeles region has improved, but organic aerosol (OA) concentrations did not decrease as much as primary pollutants. This appears to be largely due to SOA, whose mass fraction in OA increased over this period. In 2010, about half of the freshly formed SOA measured in Pasadena, CA appears to be formed from hydrocarbon (non-oxygenated) precursors. Chemical mass balance analysis indicates that these hydrocarbon SOA precursors (including intermediate volatility organic compounds) can largely be explained by emissions from mobile sources in 2010. Our analysis indicates that continued reduction in emissions from mobile sources should lead to additional significant decreases in atmospheric SOA and PM2.5 mass in the Los Angeles region.

Atmospheric saccharide composition and its possible linkage with marine phytoplankton from North Pacific to the Antarctic regions

Saccharide compounds can distinguish the influence of both biogenic emissions and anthropogenic sources. In this study, the spatial distribution of 10 saccharide compounds was observed to determine the major factor for the production of organic aerosols in the pristine marine regions from the North Pacific Ocean to the Antarctic Ocean. The pristine marine atmospheric PM2.5 (Particle matter with an aerodynamic diameter equal to or less than nominal 2.5 μm) samples were collected using a high-volume air sampler connected to a wind sector controller installed in the Korean Ice-breaking Research Vessel (IBRV) during the cruise through the Pacific Ocean: from the Yellow Sea, Korea on October 31, 2018 to the Antarctic Ocean on December 14, 2018.In the pristine marine region, the total saccharides concentrations varied greatly from 0.16 to 16.57 ng/m3 (mean: 3.02 ± 4.76 ng/m3). The compositions of 10 saccharide compounds in each PM2.5 sample changed when the marine geographic characteristics changed. The contribution of levoglucosan in the sample collected near land was higher than the sample collected in the open ocean. In addition, saccharides concentrations had a strong positive correlation with OC (Organic carbon), indicating that the increase of saccharides concentrations strongly contributes to the increase in OC concentration in the pristine marine region. The highest concentration of saccharides was observed near the New Zealand coast, which also showed highest air mass exposure to marine biology. We also found that the spatial distribution of saccharide composition was correlated with the spatial distribution of phytoplankton. From these observations, we concluded that 1) marine phytoplankton can be a significant source of organic aerosol production and 2) the type of phytoplankton in a region affects the change of saccharides composition in PM2.5.

Quantifying international and interstate contributions to primary ambient PM2.5 and PM10 in a complex metropolitan atmosphere

We quantify the contributions of long-range and regionally transported aerosols to ambient primary PM2.5 and PM10 in a representative United States industrialized/urban atmosphere via detailed elemental analysis and chemical mass balance (CMB) modeling after identifying their presence using a variety of publicly available satellite data/information, software products, and synoptic-scale aerosol models. A year-long study in Houston, Texas identified North African dust as the principal long-range global source of primary particulate matter (PM). CMB estimated transatlantic dust from the Sahara-Sahel region to be dominant in the summer months contributing an average of 3.5 μg m−3 to PM2.5 and 7.9 μg m−3 to PM10 during May–August, i.e., the active Saharan dust season. Biomass burning was the chief source of regionally transported PM impacting air quality on different occasions throughout the year depending on the fire location. Four major biomass combustion events affecting air quality in Texas were calculated to contribute an average of 1.3 μg m−3 to PM2.5 and 1.4 μg m−3 to PM10 in corresponding samples whose origins were tracked to Canada, southeastern states of USA, and Central America using fire maps, HYSPLIT back trajectories, and the Navy Aerosol Analysis and Prediction System global aerosol model. Elemental concentrations and signature ratios revealed significant mixing of potassium, rare earth metals, and vanadium from proximal and distal crustal (natural) sources with anthropogenically emitted PM. This demonstrates the need to isolate the non-mineral components of these metals to employ them as tracers for primary PM emitted by biomass burning, petroleum refineries, and oil combustion. Transboundary contributions to primary PM2.5 were 1.5 μg m−3 and 3.1 μg m−3 to PM10 adding 16% to annual average mass concentration of both size fractions demonstrating that local sources were primarily responsible for ambient air quality with non-trivial contributions from international and interstate sources. Rigorously identifying and quantifying aerosol sources assists in improving air quality management policies designed to protect public health and comply with ever-decreasing federal PM standards that allow state agencies to exclude contributions that are not reasonably controllable or preventable from regulatory decisions and actions.

Compositional Constraints are Vital for Atmospheric PM2.5 Source Attribution over India.

India experiences some of the highest levels of ambient PM2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMBModified: 0.19; NMBBase: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMBModified: 0.08; NMBBase: 1.64) and ammonium simulation (NMBModified: 0.49; NMBBase: 0.90). We use this validated simulation to estimate a population-weighted annual PM2.5 exposure of 61.4 μg m–3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM2.5 aerosol source attribution for health assessments and air quality management in India.

Impacts of condensable particulate matter on atmospheric organic aerosols and fine particulate matter (PM2.5) in China

Abstract. Condensable particulate matter (CPM) emitted from stationary combustion and mobile sources exhibits high emissions and a large proportion of organic components. However, CPM is not generally measured when conducting emission surveys of PM in most countries, including China. Consequently, previous emission inventories have not included emission rates for CPM. Here, we construct an emission inventory of CPM in China with a focus on organic aerosols (OAs) based on collected CPM emission information. Results show that OA emissions are enhanced twofold after the inclusion of CPM in a new inventory for China for the years 2014 and 2017. Considering organic CPM emissions and model representations of secondary OA (SOA) formation from CPM, a series of sensitivity cases have been simulated here using the three-dimensional Community Multiscale Air Quality (CMAQ) model to estimate the contributions of CPM emissions to atmospheric OA and fine PM (PM2.5, particulate matter with aerodynamic diameter not exceeding 2.5 µm) concentrations in China. Compared with observations at a Beijing site during a haze episode from 14 October to 14 November 2014, estimates of the temporal average primary OA (POA) and SOA concentrations were greatly improved after including the CPM effects. These scenarios demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to the POA (51 %–85 %​​​​​​​), SOA (42 %–58 %), and total OA concentrations (45 %–75 %). Furthermore, the contributions of CPM emissions to total OA concentrations were demonstrated over the 2 major cities and 26 other cities of the Beijing–Tianjin–Hebei region (hereafter referred to as the “BTH2 + 26 cities”) in December 2018, with average contributions of up to 49 %, 53 %, 54 %, and 50 % for Handan, Shijiazhuang, Xingtai, and Dezhou, respectively. Correspondingly, the inclusion of CPM emissions also narrowed the gap between simulated and observed PM2.5 concentrations over the BTH2 + 26 cities. These results improve the simulation performance of atmospheric OA and PM2.5 and may also provide important implications for the sources of OA.

Characteristics of Carbonaceous Species in PM2.5 in Chengdu Under the Background of Emission Reduction

The Air Pollution Prevention and Control Action Plan and Three-year Plan on Defending the Blue Sky promulgated by the State Council of the People's Republic of China have played an important role in the overall improvement of air quality in China. However, few studies have evaluated the implementation effects of these two policies in Sichuan Basin and the new characteristics of PM2.5 chemical components after the implementation of these policies. The key periods for evaluating the implementation effects of these two pollution reduction policies are 2017 and 2020, respectively. In order to study the atmospheric PM2.5 and carbonaceous species in Chengdu during these two periods, this study sampled the PM2.5 in Chengdu from October 2016 to July 2017 and December 2020, respectively, and the organic carbon (OC) and elemental carbon (EC) were analyzed. The results showed that the annual ρ(PM2.5) from 2016-2017 in Chengdu was (114.0±76.4) μg·m-3. The maximum value of the ρ(PM2.5) appeared in winter[(193.3±98.5) μg·m-3], and the minimum value appeared in spring[(73.8±32.3) μg·m-3]. By contrast, the ρ(PM2.5) in winter decreased significantly in 2020, with a value of (96.0±39.3) μg·m-3. The annual ρ(OC) and ρ(EC) from 2016-2017 were (21.1±16.4) μg·m-3 and (1.9±1.3) μg·m-3, which accounted for 18.5% and 1.7% of the PM2.5 mass, respectively. The seasonal variation characteristic of ρ(OC) was:winter[(40.6±21.5) μg·m-3]>autumn[(17.0±7.0) μg·m-3]>summer[(14.4±3.9) μg·m-3]>spring[(12.6±6.0) μg·m-3], whereas the ρ(EC) in the four seasons were close, ranging from 1.3 to 2.4 μg·m-3. The annual ρ(SOC) was (9.4±9.1) μg·m-3, which accounted for 44.5% of the OC mass. Compared with that in winter 2016, the ρ(OC) decreased by 52.7% in winter 2020, whereas the ρ(EC) increased by 26.1%. With the aggravation of pollution, the change trends of carbon species and their contributions were different. Compared with that in winter 2016, the variation in the contribution of OC with the aggravation of pollution in winter 2020 was more stable, whereas the proportion of SOC increased more obviously. There were obvious differences in the direction of air masses and the potential source area of pollutants in each season. Although there was no significant change in the direction of air masses in winter 2020 compared with those in winter 2016, the pollutant concentrations corresponding to each cluster decreased significantly, and the potential source area of pollutants expanded significantly to the eastern area.

Lockdown-induced Urban Aerosol Change over Changchun, China During COVID-19 Outbreak with Polarization LiDAR.

Depending on various government policies, COVID-19 (Corona Virus Disease-19) lockdowns have had diverse impacts on global aerosol concentrations. In 2022, Changchun, a provincial capital city in Northeast China, suffered a severe COVID-19 outbreak and implemented a very strict lockdown that lasted for nearly two months. Using ground-based polarization Light Detection and Ranging (LiDAR), we detected real-time aerosol profile parameters (EC, extinction coefficient; DR, depolarization ratio; AOD, aerosol optical depth), as well as air-quality and meteorological indexes from 1 March to 30 April in 2021 and 2022 to quantify the effects of lockdown on aerosol concentrations. The period in 2022 was divided into three stages: pre-lockdown (1–10 March), strict lockdown (11 March to 10 April), and partial lockdown (11–30 April). The results showed that, during the strict lockdown period, compared with the pre-lockdown period, there were substantial reductions in aerosol parameters (EC and AOD), and this was consistent with the concentrations of the atmospheric pollutants PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) and PM10 (particulate matter with an aerodynamic diameter ≤ 10 µm), and the O3 concentration increased by 8.3%. During the strict lockdown, the values of EC within 0–1 km and AOD decreased by 16.0% and 11.2%, respectively, as compared to the corresponding period in 2021. Lockdown reduced the conventional and organized emissions of air pollutants, and it clearly delayed the time of seasonal emissions from agricultural burning; however, it did not decrease the number of farmland fire points. Considering meteorological factors and eliminating the influence of wind-blown dust events, the results showed that reductions from conventional organized emission sources during the strict lockdown contributed to a 30% air-quality improvement and a 22% reduction in near-surface extinction (0–2 km). Aerosols produced by urban epidemic prevention and disinfection can also be identified using the EC. Regarding seasonal sources of agricultural straw burning, the concentrated burning induced by the epidemic led to the occurrence of heavy pollution from increased amounts of atmospheric aerosols, with a contribution rate of 62%. These results indicate that there is great potential to further improve air quality in the local area, and suggest that the comprehensive use of straw accompanied by reasonable planned burning is the best way to achieve this.

‘Bushfire Season’ in Australia: Determinants of Increases in Risk of Acute Coronary Syndromes and Takotsubo Syndrome

BackgroundClimate change has resulted in an increase in ambient temperatures during the summer months as well as an increase in risk of associated air pollution and of potentially disastrous bushfires throughout much of the world. The increasingly frequent combination of elevated summer temperatures and bushfires may be associated with acute increases in risks of cardiovascular events, but this relationship remains unstudied. We evaluated the individual and cumulative impacts of daily fluctuations in temperature, fine particulate matter of less than 2.5 µm (PM2.5) pollution and presence of bushfires on incidence of acute coronary syndromes and Takotsubo syndrome. MethodsFrom November 1, 2019, to February 28, 2020, all admissions with acute coronary syndromes or Takotsubo syndrome to South Australian tertiary public hospitals were evaluated. Univariate and combined associations were sought among each of 1) maximal daily temperature, 2) PM2.5 concentrations, and 3) presence of active bushfires within 200 km of the hospitals concerned. ResultsA total of 504 patients with acute coronary syndromes and 35 with Takotsubo syndrome were studied. In isolation, increasing temperature was associated (rs = 0.26, P = .005) with increased incidence of acute coronary syndromes, while there were similar, but nonsignificant correlations for PM2.5 and presence of bushfires. Combinations of all these risk factors were also associated with a doubling of risk of acute coronary syndromes. No significant associations were found for Takotsubo syndrome. ConclusionThe combination of high temperatures, presence of bushfires and associated elevation of atmospheric PM2.5 concentrations represents a substantially increased risk for precipitation of acute coronary syndromes; this risk should be factored into health care planning including public education and acute hospital preparedness.

Characteristics and health impacts of PM2.5-bound PCDD/Fs in three Asian countries.

Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) were ubiquitous, persistent chemical compounds attached to particulate matter in the atmosphere. We aimed to study the characteristics of these pollutants in atmospheric PM2.5 of three Asian countries, including Taiwan (Taipei), Thailand (Chiang Mai), and Vietnam (Hanoi). We carried out a source apportionment analysis to determine significant PCDD/F contributors in these areas. Multiple media model was conducted to access the health impact assessment. The PM2.5 concentration in Taipei (n=7), Chiang Mai (n=20), and Hanoi (n=10) were 18.4±6.21μg/m3, 133±49.5μg/m3, and 88.1±12.6µg/m3, respectively. The PCDD/Fs level in Hanoi was 92.4±67.3fg I-TEQ/m3, and in Taipei and Chiang Mai was 5.01±2.39fg I-TEQ/m3 and 14.4±13.1fg I-TEQ/m3, respectively, which showed that the higher PM2.5 concentration was not necessary to follow with higher PCDD/Fs level. In all three cities, the effect of traffic on ambient PCDD/F level was significant (23-25%). However, we also observed the specific sources of PCDD/Fs in each city during the sampling periods, namely long-range transport (Taipei, 55%), Biomass/open burning (Chiang Mai, 77%), and industrial activities (Hanoi, 34%). In the carcinogenic risk estimation, the highest median total carcinogenic risk was in Hanoi (5.87×10-6), followed by Chiang Mai (1.06x10-6), and Taipei (2.95×10-7). Although diet was the major absorption pathway, the food contributor of exposure differed among the three areas due to the difference in food consumption composition.

Dynamics of pollutant emissions from wildfires in Mainland China

We analyzed the dynamics of pollutant emissions from wildfires in mainland China from 2001 to 2019 using MODIS fire products combined with the measurements of emission factors of different vegetation types. The biomass distribution in Mainland China has heterogeneous temporal and spatial pattern, with inter-year variations and a decreasing trend from east to west. Overall, from 2001 to 2019, biomass combustion in Mainland China reached 479.59Tg (25.24Tg·a-1), in which northeast, north, east, south, central, northwest, and southwest regions accounted for 20.95%, 31.14%, 8.89%, 9.06%, 3.98%, 0.33% and 25.64% of total biomass combustion, respectively. The emissions of CO, CO2, CxHy, NOx, PM2.5, TC, OC and EC were 47.30, 288.05, 12.90, 0.40, 1.43, 0.83, 0.70, and 0.12Tg (1Tg=1012g), respectively. PM2.5, TC and OC emissions increased in the southwest, while all pollutant emissions declined significantly in the southern region. For particulate matter from wildfires, both the ratio of its emissions to total dust and the ratio of its concentration to atmospheric PM2.5 showed an increasing trend, implying that the relative environmental impacts of particulate emissions from wildfires may be rising. In addition, our results show that the current Chinese wildfire management has successfully reduced on average more than 80% of pollutant emissions from wildfire from 2001 to 2019 compared to the natural wildfire regime (no strict wildfire management). This research on the temporal-spatial changes of pollutant emissions from wildfires in Mainland China provides support for further exploration of wildfire impacts on regional environments, and indicates the effectiveness of Chinese current wildfire policy on the pollutant emission mitigation.

Synthesis, MS/MS characteristics and quantification of six aromatic organosulfates in atmospheric PM2.5

In this work, six aromatic organosulfates (phenyl, 2-/3-/4-methylphenyl, benzyl and phenethyl sulfates) were synthesized, with the purpose to obtain the standards and apply them to quantify the organosulfates in ambient aerosols. The purity of the synthesized organosulfates was determined by high performance liquid chromatography/ultraviolet detector (HPLC/UV). Meanwhile, the organosulfate standards and atmospheric PM2.5 samples were measured by high performance liquid chromatography/electrospray ionization/tandem mass spectrometry (HPLC/ESI/MS/MS). The characteristic ion fragments were phenolate anion m/z 93 for phenyl sulfate, creylate anion m/z 107 for methylphenyl sulfate, sulfate radical anion ·SO4− (m/z 96) for benzyl sulfate, and hydrogen sulfate anion HSO4− (m/z 97) for phenethyl sulfate. The characteristic fragment ions used for quantification can improve the identification of target compounds in chromatograms, obtain lower detection limits, and will minimize analytical errors caused by the introduction of surrogates. The concentrations of phenyl, methylphenyl, benzyl and phenethyl sulfates in winter PM2.5 in Wenjiang District of Chengdu were 0.58 ± 0.56, 2.37 ± 3.59, 1.79 ± 3.51, 0.04 ± 0.08 ng/m3, respectively.

Saccharides in atmospheric PM2.5 in tropical forest region of southwest China: Insights into impacts of biomass burning on organic carbon aerosols

Biomass burning (BB) in South and Southeast Asia has a strong impact on regional air quality, yet its impact on atmospheric PM2.5 of tropical rainforest regions, a background region occupying a large area in South Asia, has rarely been investigated. In this work, we performed one-year PM2.5 sampling during December 2018 to October 2019 at a tropical rainforest site in southwest China. PM2.5 mass concentration, major chemical components, and ten saccharides were determined to study seasonal variations of PM2.5 chemical composition, and further to understand possible impacts of BB to organic carbon (OC) aerosols at this background region. The concentration levels of PM2.5, major PM2.5 components, and total saccharides were significantly higher in dry season than in wet season. Besides, PM2.5, OC, and total saccharides were highly correlated (R2 > 0.64, p < 0.001) during the sampling period, suggesting they might share common sources. Source apportionment of saccharides revealed that BB was the main source in both seasons. Furthermore, the contributions of BB to OC (BB/OC) were estimated using levoglucosan as a molecular tracer while levoglucosan's chemical degradation was considered. It was found that over 80% of LG was degraded in both seasons, suggesting BB sources were largely from the transport of external air mass. The estimated BB/OC were over 50%, indicating BB was an important source of OC and likely of PM2.5 in both seasons. The air-mass backward trajectory analysis and active fire spots data indicate intense BB emission sources were from South and Southeast Asia in dry season and the BB emissions in southern region of China could impact on the studied area in wet season.

Air Pollution and Health Impacts of Atmospheric PM: Application of AirQ+ Model to Jiangsu Province in China

Air Pollution and Health Impacts of Atmospheric PM: Application of AirQ+ Model to Jiangsu Province in China

Associations of air PM2.5 level with gut microbiota in Chinese Han preschoolers and effect modification by oxytocin receptor gene polymorphism

Variations in the single nucleotide polymorphisms (SNPs) of the oxytocin receptors (OXTR) indicate individual differences in stress response, social behavior, and psychopathology, but very few paper mentioned OXTR as part of the mechanism linking exposure to air pollution and poor social interactions. The authors investigated the moderating role of Oxytocin receptor (OXTR) rs53576 polymorphism in the relationship between PM2.5 level and gut microbiota in children, in an attempt to provide some reference for the evidence linking biological and environmental factors to children brain development. The study included 86 healthy Chinese preschoolers (50 males, 36 females) from two campuses of a kindergarten with different air PM2.5 levels. Atmospheric PM2.5 values released by air quality monitoring stations where the two campuses are located were collected for 30 days. The genotypes of OXTR rs53576 were determined by PCR and restriction fragment length polymorphism. The gut microbiota situation was evaluated by determining urinary concentrations of short-chain fatty acids. Urinary levels of cortisone and cortisol were determined to assess the impact of air pollution on the HPA axis. Urinary 2′-Deoxy-7,8-dihydro-8-oxoguanosine and 8-oxo-7,8-dihydroguanosine were measured to evaluate the oxidative stress state. The genotype distribution frequency of rs53576 polymorphism was consistent with Hardy-Weinberg equilibrium. The average urinary concentrations of cortisone, cortisol and 8-OHdG in high pollution campus preschoolers were significantly higher than those in low pollution campus preschoolers, while situations were opposite for acetic acid, propionic acid, isobutyric acid, butyric acid and valeric acid. The interaction between OXTR rs53576 and air pollution had a significant effect on urinary acetic acid. Allele G of rs53576 may be a risk factor for gut microbiota disorder caused by air pollution, and children with GA/GG genotype may be more susceptible than those with AA genotype.

Understanding vegetation structures in green spaces to regulate atmospheric particulate matter and negative air ions

Green spaces can effectively reduce atmospheric particulate matter (PM) pollution and regulate negative air ions (NAI), this ecological function of green spaces is directly determined by their types and structures. However, the mechanism of the interactions among the types and structures of green spaces, PM, and NAI are still unclear. In the present study, we selected Dongling Park in Shenyang, China as an example of a typical green space. We ran field experiments to analyze the influences of various green space types and structures on PM10, PM2.5, and NAI. The results showed that green spaces had significantly lower PM10 and PM2.5 and higher NAI concentrations than non-green spaces. Among the four vegetation types (tree-shrub-herb, tree-herb, shrub-herb, and tree-shrub), the tree-shrub-herb type most effectively decreased atmospheric PM10 and PM2.5 concentrations (32.54 ± 8.14% and 21.26 ± 4.07%, respectively) and increased NAI concentrations (115.43 ± 10.00%) to a significantly greater extent than the other vegetation types (p < 0.05). Canopy density was strongly and significantly positively correlated with PM2.5 and PM10 concentrations (r = 0.84* and r = 0.86**, respectively). By contrast, shrub layer richness was significantly negatively correlated with PM2.5 and PM10 concentrations (r = −0.68* and r = −0.74*, respectively). Furthermore, significant positive correlations with NAI concentrations were detected in the canopy density and proportion of evergreen tree species significantly (r = 0.40* and r = 0.46*, respectively). The results illustrate that the green space vegetation types and structures, especially the density of tree canopy, the richness of shrub layer, and the proportion of evergreen tree species, can influence the airborne PM10, PM2.5 and NAI concentrations. Therefore, vegetation types and structures should be considered in the process of planning urban green spaces that perform the ecological function of improving air quality.

Assessment of atmospheric air quality in urbanised areas of the Southern Urals

ABSTRACT The paper reports a study of the concentration of fine particles in the atmospheric air with a diameter of < 2.5 microns (PM2.5) and solid particles with a diameter of < 10 microns (PM10) of urbanised territories. An environmental monitoring device (DT-9881 M) determined the concentration of dust particles, and the amount of adsorbed dust on the leaves of woody plants was determined by the flushing method. The research established that the concentration of solid particles in the atmospheric air PM2.5/PM10 depended on the intensity of motor traffic. A decrease in the content of PM2.5/PM10 was revealed in plantings where the influence of forest air on human beings was recorded. Measurements on the content of formaldehyde and carbon monoxide at all measurement points gave negative indicators. Green spaces are important to human well-being.

Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model

The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model.