Major PM2 Research Articles

Individual and joint exposures to PM2.5 constituents and mortality risk among the oldest-old in China.

Cohort evidence linking long-term survival of older adults with exposure to fine particulate matter (PM2.5) constituents remains scarce in China. By constructing a dynamic cohort based on the Chinese Longitudinal Healthy Longevity Study, we aimed to assess the individual and joint associations of major PM2.5 constituents with all-cause death in Chinese oldest-old (.80 years) adults. Time-dependent Cox proportional hazards models were adopted to estimate death risks of long-term exposure to PM2.5 constituents. Among 14,884 participants, totaling 56,342 person-years of follow-up, 12,346 deaths were identified. The highest mortality risk associated with an interquartile range (IQR) increase in exposure was 1.081 (95% confidence interval [CI]: 1.055-1.108) for sulfate (IQR=4.1 μg m-3), followed by 1.078 (95% CI: 1.056-1.101) for black carbon (IQR=1.6 μg m-3), 1.056 (95% CI: 1.028-1.084) for ammonium (IQR=3.2 μg m-3), 1.050 (95% CI: 1.021-1.080) for nitrate (IQR=5.8 μg m-3), and 1.049 (95% CI: 1.024-1.074) for organic matter (IQR=10.3 μg m-3). In joint exposure, each IQRequivalent rise of all five PM2.5 constituents was associated with an 8.2% (95% CI: 4.0%-12.6%) increase in mortality risk. The weight analysis indicated the predominant role of sulfate and black carbon in driving PM2.5-related mortality. Octogenarians (aged 80-89 years) and rural dwellers were at significantly greater risk of mortality from individual and joint exposures to PM2.5 constituents. This study suggests that later-life exposure to PM2.5 constituents, particularly sulfate and black carbon, may curtail long-term survival of the oldest-old in China.

The Characteristics of the Chemical Composition of PM2.5 during a Severe Haze Episode in Suzhou, China

During the past decade, the air quality has been greatly improved in China since the implementation of the “Clean Air Act”. However, haze events are still being reported in some regions of China, and the pollution mechanism remains unclear. In this study, we investigate the chemical characteristics of the pollution mechanism of the PM2.5 composition in Suzhou from October 18 to December 15, 2020. A notable declining trend in temperature was observed from 18 to 27 November, which indicates the seasonal transition from fall to the winter season. Four representative periods were identified based on meteorological parameters and the PM2.5 mass concentrations. The heavy pollution period had the typical characteristics of a relatively low temperature, a high relative humidity, and mass loadings of atmospheric pollutants; nitrate was the dominant contributor to the haze pollution during this period. The nitrate formation mechanism was driven by the planetary boundary layer dynamics. The potential source contribution function model (PSCF) showed that the major PM2.5 composition originated from the northwest direction of the sampling site. The aerosol liquid water content presented increasing trends with an increasing relative humidity. The pH was the highest during the heavy pollution period, which was influenced by the aerosol liquid water content and the mass loadings of NO3−, SO42−, NH4+, and Cl−. The comprehensive analysis in this paper could improve our understanding of the nitrate pollution mechanism and environmental effects in this region.

Modelling of atmospheric concentrations of fungal spores: a 2-year simulation over France using CHIMERE

Abstract. Fungal spore organic aerosol emissions have been recognised as a significant source of particulate matter as PM10; however, they are not widely considered in current air quality models. In this work, we have implemented the parameterisation of fungal spore organic aerosol (OA) emissions introduced by Heald and Spracklen (2009) (H&S) and further modified by Hoose et al. (2010) in the CHIMERE regional chemistry-transport model. This simple parameterisation is based on two variables, leaf area index (LAI) and specific humidity. We have validated the geographical and temporal representativeness of this parameterisation on a large scale by using yearly polyol observations and primary biogenic organic aerosol factors from positive matrix factorisation (PMF) analysis at 11 French measurement sites. For a group of sites in northern and eastern France, the seasonal variation of fungal spore emissions, displaying large summer and small winter values, is correctly depicted. However, the H&S parameterisation fails to capture fungal spore concentrations for a smaller group of Mediterranean sites with less data availability in terms of both absolute values and seasonal variability, leading to strong negative biases, especially during the autumn and winter seasons. Two years of CHIMERE simulations with the H&S parameterisation have shown a significant contribution of fungal spore OA to PM10 mass, which is lower than 10 % during winter and reaches up to 20 % during summer in high-emission zones, especially over large forested areas. In terms of contributions to organic matter (OM) concentrations, the simulated fungal spore contribution in autumn is as high as 40 % and reaches at most 30 % of the OM for the other seasons. As a conclusion, the fungal spore OA contribution to the total OM concentrations is shown to be substantial enough to be considered a major PM10 fraction and should then be included in state-of-the-art chemistry-transport models.

Indigenized Characterization Factors for Health Damage Due to Ambient PM2.5 in Life Cycle Impact Assessment in China.

Life cycle assessment (LCA) is a broadly used method for quantifying environmental impacts, and life cycle impact assessment (LCIA) is an important step as well as a major source of uncertainties in LCA. Characterization factors (CFs) are pivotal elements in LCIA models. In China, the health loss due to ambient PM2.5 is an important aspect of LCIA results, which, however, is generally assessed by adopting CFs developed by global models and there remains a need to integrate localized considerations and the latest information for more precise applications in China. In this study, we developed indigenized CFs for LCIA of health damage due to ambient PM2.5 in China by coupling the atmospheric chemical transport model GEOS-Chem, exposure-response model GEMM containing Chinese cohort studies, and the latest local data. Results show that CFs of four major PM2.5 precursors all exhibit significant interregional variation and monthly differences in China. Our results were generally an order of magnitude higher and show disparate spatial distribution compared to CFs currently in use, suggesting that the health damage due to ambient PM2.5 was underestimated in LCIA in China, and indigenized CFs need to be adopted for more accurate results in LCIA and LCA studies.

Evaluation of a new real-time source apportionment system of PM2.5 and its implication on rapid aging of vehicle exhaust

Accurate identification and rapid analysis of PM2.5 sources and formation mechanisms are essential to mitigate PM2.5 pollution. However, studies were limited in developing a method to apportion sources to the total PM2.5 mass in real-time. In this study, we developed a real-time source apportionment method based on chemical mass balance (CMB) modeling and a mass-closure PM2.5 composition online monitoring system in Shenzhen, China. Results showed that secondary sulfate, secondary organic aerosol (SOA), vehicle emissions and secondary nitrate were the four major PM2.5 sources during autumn 2019 in Shenzhen, together contributed 76 % of PM2.5 mass. The novel method was verified by comparing with other source apportionment methods, including offline filter analysis, aerosol mass spectrometry, and carbon isotopic analysis. The comparison of these methods showed that the new real-time method obtained results generally consistent with the others, and the differences were interpretable and implicative. SOA and vehicle emissions were the major PM2.5 and OA contributors by all methods. Further investigation on the OA sources indicated that vehicle emissions were not only the main source of primary organic aerosol (POA), but also the main contributor to SOA by rapid aging of the exhaust in the atmosphere. Our results demonstrated the great potential of the new real-time source apportionment method for aerosol pollution control and deep understandings on emission sources.

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands.

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016–2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50–0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24–0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays’. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35–0.70; PM2.5 hourly R2 = 0.01–0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China.

The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.

Relationships between long-term exposure to major PM2.5 constituents and outpatient visits and hospitalizations in Guangdong, China

Ambient fine particulate matter (PM2.5) has attracted considerable attention due to its crucial role in the rising global disease burden. Evidence of health risks associated with exposure to PM2.5 and its major constituents is important for advancing hazard assessments and air pollution emission policies. We investigated the relationship between exposure to major constituents of PM2.5 and outpatient visits as well as hospitalizations in Guangdong Province, China, where 127 million residents live in a severe PM2.5 pollution environment. An approach that integrates the generalized weighted quantile sum (gWQS) regression with the difference-in-differences (DID) approach was used to assess the overall mixture effects and relative contributions of each constituent. We observed significant associations between long-term exposure to the mixture of PM2.5 constituents (WQS index) and outpatient visits (IR%, percentage increases in risk per unit WQS index increase:1.73, 95%CI: 1.72, 1.74) as well as hospitalizations (IR%:5.15, 95%CI: 5.11, 5.20). Black carbon (weight: 0.34) and nitrate (weight: 0.60) respectively exhibited the highest contributions to outpatient visits and hospitalizations. The overall mixture effects on outpatient visits and hospitalizations were higher with increased summer air temperatures (IR%: 7.54, 95%CI: 7.33, 7.74 and IR%: 9.55, 95%CI: 8.36, 10.75, respectively) or decreased winter air temperatures (IR%: 1.88, 95%CI: 1.68, 2.08 and IR%: 4.87, 95%CI: 3.73, 6.02, respectively). Furthermore, the overall mixture effects on outpatient visits and hospitalizations were significantly higher in populations with higher socioeconomic status (P < 0.01). It's crucial to address the primary sources of nitrate precursor substances and black carbon (mainly traffic-related and industrial-related air pollutants) and consider the complex interaction effects between air temperature and PM2.5 in the context of climate change. Of particular concern is the need to prioritize healthcare demands in economically disadvantaged regions and to address the health inequalities stemming from the uneven distribution of healthcare resources and PM2.5 pollution.

Impacts of ship emissions and sea-land breeze on urban air quality using chemical characterization, source contribution and dispersion model simulation of PM2.5 at Asian seaport

Fine particulate matter (PM2.5) emitted from marine transportation, bulk materials handling at the docks, and dust dispersion has garnered increased attention, particularly in the interface between port and urban areas. This study explored the inter-transport of PM2.5 between Kaohsiung Harbor and neighboring Metro Kaohsiung. Chemical analyses of PM2.5 samples from four sites include water-soluble ions, metallic elements, carbons, anhydrosugars, and organic acids to establish PM2.5's chemical fingerprints. The CALPUFF air dispersion model is employed to simulate the spatiotemporal distribution of PM2.5 in Kaohsiung Harbor and adjacent urban areas. A clear seasonal and diurnal variation of PM2.5 concentrations and chemical composition was observed in both harbor and urban areas. The high correlation of nighttime PM2.5 levels between the port and urban areas suggests inter-transport phenomena. Sea salt spray, ship emissions, secondary aerosols, and heavy fuel-oil boilers exhibit higher levels in the port area than in the urban area. In Metro Kaohsiung, mobile sources, fugitive dust, and waste incinerators emerge as major PM2.5 contributors. Furthermore, sea breeze significantly influences PM2.5 dispersion from Kaohsiung Harbor to Metro Kaohsiung, particularly in the afternoon. The average contribution of PM2.5 from ships' main engines in Kaohsiung Harbor ranges from 2.9% to 5.3%, while auxiliary engines contribute 3.8%–8.3% of PM2.5 in Metro Kaohsiung.

Characteristics and impacts of fine particulates from the largest power plant plume in Taiwan

In this study, the impact of the coal-fired Taichung Power Plant (TPP) plume in Taiwan during an observation experiment from Oct. 31 to Nov. 22, 2021, is simulated. We used the WRF/CMAQ and HYSPLIT models to analyze the changes in the PM2.5 concentration and the contribution percentage of major PM2.5 species along the forward trajectory of the TPP plume. Twenty trajectories with high PM2.5 concentrations above the chimney are divided into three types: transport to inland (TI), northeast monsoon transport southward (NMTS), and transport to sea (TS). The first kind, which exhibits mostly windy cases, is more harmful to people. Moreover, the largest proportion of the plume when it first exits the stack is SO42− (sulfate), which slightly decreases over time. The proportions of OM (organic matter) and NO3− (nitrate) slightly increased, and the proportion of NH4+ (ammonium ions) increased but not significantly. With the generation of low volatility/semi-volatile SOA (secondary organic aerosol), the proportion of OM significantly increases along the trajectory. Daytime photochemical reactions increase the radical concentrations, leading to an increase in the generation of low volatility/semi-volatile SOA and thus an increase in the proportion of OM. However, the proportion of NO3− in the smoke plume is smaller than that in other major PM2.5 species. Near the chimney, ANO3 (nitrate) accounts for a larger proportion of NO3− than HNO3 (nitrate acid). Furthermore, a sensitivity test shows that the height of the TPP that is reduced from 250 m to 150 m can have less effect on the environment.

Temperature-dependent composition of summertime PM2.5 in observations and model predictions across the Eastern U.S.

Throughout the U.S., summertime fine particulate matter (PM2.5) exhibits a strong temperature (T) dependence. Reducing the PM2.5 enhancement with T could reduce the public health burden of PM2.5 now and in a warmer future. Atmospheric models are a critical tool for probing the processes and components driving observed behaviors. In this work, we describe how observed and modeled aerosol abundance and composition varies with T in the present-day Eastern U.S. with specific attention to the two major PM2.5 components: sulfate (SO4 2-) and organic carbon (OC). Observations in the Eastern U.S. show an average measured summertime PM2.5-T sensitivity of 0.67 μg/m3/K, with CMAQ v5.4 regional model predictions closely matching this value. Observed SO4 2- and OC also increase with T; however, the model has component-specific discrepancies with observations. Specifically, the model underestimates SO4 2- concentrations and their increase with T while overestimating OC concentrations and their increase with T. Here, we explore a series of model interventions aimed at correcting these deviations. We conclude that the PM2.5-T relationship is driven by inorganic and organic systems that are highly coupled, and it is possible to design model interventions to simultaneously address biases in PM2.5 component concentrations as well as their response to T.

Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities

Abstract. Exposure models for some criteria of air pollutants have been intensively developed in past research; multi-air-pollutant exposure models, especially for particulate chemical species, have been however overlooked in Asia. Lack of an integrated model framework to calculate multi-air-pollutant exposure has hindered the combined exposure assessment and the corresponding health assessment. This work applied the land-use regression (LUR) approach to develop an integrated model framework to estimate 2017 annual-average exposure of multiple air pollutants in a typical high-rise and high-density Asian city (Hong Kong, China) including four criteria of gaseous air pollutants (particulate matter with an aerodynamic diameter equal to or less than 10 µm (PM10) and 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)), as well as four major PM10 chemical species. Our integrated multi-air-pollutant exposure model framework is capable of explaining 91 %–97 % of the variability of measured gaseous air pollutant concentration, with the leave-one-out cross-validation R2 values ranging from 0.73 to 0.93. Using the model framework, the spatial distribution of the concentration of various air pollutants at a spatial resolution of 500 m was generated. The LUR model-derived spatial distribution maps revealed weak-to-moderate spatial correlations between the PM10 chemical species and the criteria of air pollutants, which may help to distinguish their independent chronic health effects. In addition, further improvements in the development of air pollution exposure models are discussed. This study proposed an integrated model framework for estimating multi-air-pollutant exposure in high-density and high-rise urban areas, serving an important tool for multi-air-pollutant exposure assessment in epidemiological studies.

Industrial agglomeration and PM2.5 pollution in Yangtze River Economic Belt in China: non-linear estimation and mechanism analysis

Introduction: The uncertainty associated with PM2.5 pollution hinders the economic high-quality development and threatens public health. Industrial agglomeration stands as a critical factor in regional economic and environmental governance, and the current studies about its impact on PM2.5 pollution are mostly limited to a specific industry or unidirectional influence.Methods: Our study constructed spatial econometric models to analyze the effect of three major industrial agglomerations on PM2.5 pollution, based on evidence from 110 prefecture-level cities of the Yangtze River Economic Belt in 2005–2019.Results: The results show that: 1) The three major industrial agglomerations and PM2.5 pollution present different spatiotemporal characteristics and show prominent positive spatial autocorrelation and agglomeration effect. 2) The primary industrial agglomeration contributes to a decrease in PM2.5 pollution and exhibits negative spatial spillover effects. A nonlinear relationship is observed between the secondary industrial agglomeration and PM2.5 pollution. The tertiary industrial agglomeration results in an increase in PM2.5 pollution. 3) The effects of secondary industrial agglomeration on PM2.5 pollution exhibit varying degrees of ‘inverted U-shape’ curves in the upstream, midstream, and downstream cities. The midstream cities are the first to reach the inflection point of agglomeration. 4) Industrial agglomeration affects PM2.5 pollution through three mechanisms, including scale expansion effect, technological spillover effect, and population scale effect.Discussion: Based on the empirical findings, this study provides scientific support and decision-making reference to improve the positive impacts of industrial agglomerations on PM2.5 pollution.

Role of low-density lipoprotein in mediating the effect of air pollution on coronary heart disease: a two-step multivariate Mendelian randomization study.

Air pollution is affecting the health of millions of people all over the world. The causal correlations of PM2.5, PM10, and nitrogen dioxide (NOx), as the main fine particulate matter, and coronary heart disease (CHD) are yet to be explored. Low-density lipoprotein (LDL) has been a principal factor in the pathogenesis of CHD. It is an interesting issue to consider whether LDL mediates the effect of air pollutants in CHD pathogenesis. A genome-wide association study (GWAS) on the European population, followed up from 2010 to 2018, involving over 400,000 participants, was based on a land-use regression model. The annual mean concentrations of major air pollutant particles, PM2.5 (n=423,796), PM10 (n=423,796), and NOx (n=456,380), were recorded. The large GWAS database of CHD covered over ten million SNPs with independent single nucleotide polymorphisms (SNPs). LDL database collected major biochemical blood parameters from over 400,000 patients (n=440,546). Taken together, we conducted independent two-sample Mendelian randomization (MR) analyses for the causality between air pollutants (PM2.5, PM10, and NOx) and CHD. Multivariate MR analysis was conducted using causal relationships to determine the direct effects of exposure on outcome. The fixed-effect inverse variance weighted (IVW2) method was mainly employed to assess this relationship, with a confidence interval of 95% for the odds ratio (OR). Also, MR-Egger, weighted median, maximum likelihood ratio method, and random-effects inverse variance-weighted (IVW1) method were adopted as supplementary methods. Two-sample MR results based on the IVW2 method suggested positive correlations between PM2.5 and CHD [OR 1.875 (1.279-2.748), p=0.001], PM10 and CHD [OR 2.586 (1.479-4.523), p=0.001], and NOx and CHD [OR 2.991 (2.021-4.427), p=4.37E-08]. The direct effect and mediating proportion were calculated using multivariable Mendelian randomization (MVMR). Lastly, the mediating proportions of LDL in the regulatory roles of PM2.5, PM10, and NOx in CHD were 2.82%, 4.73%, and 9.54%, respectively. PM2.5, PM10, and NOx share direct causal associations with CHD, and LDL performs a mediating role in this pathogenic process. Early prevention against air pollution (such as increasing green areas and reducing large-scale industrial dust emissions) and early lipid-lowering treatment can effectively prevent the occurrence of CHD.

Quantification of ambient PM2.5 concentrations adjacent to informal brick kilns in the Vhembe District using low-cost sensors

The widespread exposure to ambient PM2.5 poses a substantial health risk globally, with a more pronounced impact on low- to medium-income nations. This study investigates the spatiotemporal distribution of PM2.5 in the communities hosting informal brickmaking industries in Vhembe District. Utilizing Dylos DC1700, continuous monitoring of PM2.5 was conducted at nine stations adjacent to informal brick kilns from March 2021 to February 2022. The study determined the correction factor for PM2.5 measurements obtained from the Dylos DC1700 when it was collocated with the GRIMM Environmental Dust Monitor 180. Additionally, the diurnal and seasonal variations across monitoring stations were assessed, and potential PM2.5 sources were identified. The study also evaluated the compliance of ambient PM2.5 concentrations across the stations with the South African National Ambient Air Quality Standard (NAAQS) limits. Annual PM2.5 concentrations for the stations ranged from 22.6 to 36.2 μgm−3. Diurnal patterns exhibited peak concentrations in the morning and evening, while seasonal variations showed higher concentrations in winter and lower concentrations in summer and spring. All monitoring stations reported the highest daily exceedance with respect to the daily NAAQS limit in the winter. Major PM2.5 sources included domestic biomass combustion, vehicular emissions, industrial emissions, and construction sites. Well-calibrated low-cost sensors could be employed in suburb regions with scarce air quality data. Findings from the study could be used for developing mitigation strategies to reduce health risks associated with PM2.5 exposure in the area.

Reduction of Outdoor and Indoor PM2.5 Source Contributions via Portable Air Filtration Systems in a Senior Residential Facility in Detroit, Michigan.

Background: The Reducing Air Pollution in Detroit Intervention Study (RAPIDS) was designed to evaluate cardiovascular health benefits and personal fine particulate matter (particulate matter < 2.5 μm in diameter, PM2.5) exposure reductions via portable air filtration units (PAFs) among older adults in Detroit, Michigan. This double-blind randomized crossover intervention study has shown that, compared to sham, air filtration for 3 days decreased 3-day average brachial systolic blood pressure by 3.2 mmHg. The results also showed that commercially available HEPA-type and true HEPA PAFs mitigated median indoor PM2.5 concentrations by 58% and 65%, respectively. However, to our knowledge, no health intervention study in which a significant positive health effect was observed has also evaluated how outdoor and indoor PM2.5 sources impacted the subjects. With that in mind, detailed characterization of outdoor and indoor PM2.5 samples collected during this study and a source apportionment analysis of those samples using a positive matrix factorization model were completed. The aims of this most recent work were to characterize the indoor and outdoor sources of the PM2.5 this community was exposed to and to assess how effectively commercially available HEPA-type and true HEPA PAFs were able to reduce indoor and outdoor PM2.5 source contributions. Methods: Approximately 24 h daily indoor and outdoor PM2.5 samples were collected on Teflon and Quartz filters from the apartments of 40 study subjects during each 3-day intervention period. These filters were analyzed for mass, carbon, and trace elements. Environmental Protection Agency Positive Matrix Factorization (PMF) 5.0 was utilized to determine major emission sources that contributed to the outdoor and indoor PM2.5 levels during this study. Results: The major sources of outdoor PM2.5 were secondary aerosols (28%), traffic/urban dust (24%), iron/steel industries (15%), sewage/municipal incineration (10%), and oil combustion/refinery (6%). The major sources of indoor PM2.5 were organic compounds (45%), traffic + sewage/municipal incineration (14%), secondary aerosols (13%), smoking (7%), and urban dust (2%). Infiltration of outdoor PM2.5 for sham, HEPA-type, and true HEPA air filtration was 79 ± 24%, 61 ± 32%, and 51 ± 34%, respectively. Conclusions: The results from our study showed that intervention with PAFs was able to significantly decrease indoor PM2.5 derived from outdoor and indoor PM2.5 sources. The PAFs were also able to significantly reduce the infiltration of outdoor PM2.5. The results of this study provide insights into what types of major PM2.5 sources this community is exposed to and what degree of air quality and systolic blood pressure improvements are possible through the use of commercially available PAFs in a real-world setting.

Impact of the COVID-19 lockdown to a port-city area: A two-year comparative PMF analysis of PM10 of polluting sources

The objective of this study was to evaluate the effects that the COVID-19 lockdown had in the PM10 levels and sources in a city adjacent port where the transport of bulk materials is one of the main activities. For this, the 2020 PM10 chemistry and sources will be compared to an annual baseline period of 2017-18 in the same area.A total of 337 samples were chemically analyzed (30 chemical species) and a positive matrix factorization model was used to identify major PM10 sources. A Mann-Whitney test was used to find statistically relevant differences between periods.The model identified 8 factors two of them directly related to port activities (Shipping emissions and bulk materials), one associated to road traffic, three linked secondary sources (aged sea salt, secondary nitrate, ammonium sulfate) and two natural sources including fresh sea salt and long-range transport from Saharan intrusions.Road traffic and Shipping sources show a significant reduction in the lockdown period. However, after this period, the road traffic source quickly recovered showing no differences on a yearly basis with the reference year. Only the Shipping emissions source were greatly reduced in 2020 compared to 2017-18. On the other hand, the influence of natural sources kept PM10 from reducing drastically during the lockdown year (2020).

Identification of Airborne Particle Types and Sources at a California School Using Electron Microscopy

We conducted a pilot study to investigate air quality indoors in two classrooms and outdoors on the school grounds in a California community with historically high PM2.5 (fine particulate matter, diameter &lt; 2.5 μm). We used computer-controlled scanning electron microscopy of passive samples to identify major PM types, which were used to help interpret continuous PM2.5 and black carbon sensor data. The five major PM types were sodium salt particles with sulfur, calcium, or chlorine; aluminosilicate dusts; carbonaceous combustion agglomerates; biogenic particles; and metal-rich particles. Based on morphological evidence of water droplets, the salt particles are hypothesized to be secondary aerosols formed via the reaction of sodium chloride fog droplets with sulfur from regional sources. The carbonaceous agglomerates had unusual morphologies consistent with low-temperature combustion and smoke from open-burning activities observed nearby. The passive PM sampler and continuous sensor results indicated lower concentrations in the classroom equipped with an air cleaner. Passive samples collected in one classroom exhibited enhanced PM10–2.5 crustal particles and PM2.5 metal particles, suggesting a potential local PM source in that room. Future study designs that enable longer passive sampling times would reduce detection limits and sample contamination concerns. The determination of major airborne particle types in a given environment makes this technique a useful and unique community exposure assessment tool, even in these limited-duration (48 h) deployments.

High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85–0.96 and 0.62–0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.

Magnitude and origins of severe urban air contaminants in China during the COVID-19 lockdown: A comprehensive analysis

In an attempt to mitigate the spread of COVID-19, China implemented a series of strict lockdown measures in 2020, such as restricted road usage and reduced population flow, to safeguard public health. The resulting reduction in anthropogenic pollution emissions caused by the lockdown is informative for researchers seeking to explore the relationship between anthropogenic factors and atmospheric pollutants. Therefore, this study explored severe urban air pollutants and their driving factors before and during the lockdown in China. Across 367 Chinese cities, the implementation of strict lockdown measures led to substantial reductions of 22%–55% in major air pollutants, including PM10, PM2.5, NO2, SO2, and CO. However, O3 levels, which increased by approximately 58.0% during the lockdown, exhibited the opposite trend. This was potentially attributed to the consumption of NOx in the photooxidation process. To more thoroughly understand the relationship between air pollutants and their sources, four representative areas exhibiting considerable variations in air pollutant levels were selected: the Beijing–Tianjin–Hebei region (BTH), the Fenwei Plain (FWP), the Yangtze River Delta (YRD), and Southern central China (SCC). The land-use regression (LUR) model was employed for this purpose. Before the lockdown, various factors such as road-related emissions, rural residential areas, and grassland areas accounted for 13.2%–21.7% of PM2.5 and PM10 concentrations in the FWP region. Conversely, in BTH, YRD, and SCC, land-use-related sources such as plowland areas played an increasingly crucial role, contributing 12.9%–33.6% to PM concentrations. Moreover, human spatial activities and land-use types contributed up to 21.0%, 31.7%, and 20.8% to the elevated levels of SO2, NO2, and CO over the selected regions. During the lockdown, the LUR models revealed that changes in PM2.5 and PM10 concentrations were strongly associated with the influences of plowland and grassland. However, for NO2, SO2, and CO, anthropogenic sources such as trucks on the road, industrial emissions, and construction areas were identified as the main driving factors. Additionally, analysis using the generalized additive model revealed that before the lockdown, high humidity levels exceeding 50% were associated with increases in PM2.5 concentrations in the BTH and FWP regions. By contrast, during the lockdown, gas pollutants such as NO2 and SO2 emitted from livelihood-related activities had an effect on PM2.5 levels, contributing 10%–15% across all four regions. Overall, this study provides crucial evidence to inform the formulation of well-informed air quality strategies by considering key influencing factors in the future.