Fine Particulate Matter Concentrations Research Articles

Exploring the Burden of PM2.5-Related Deaths and Economic Health Losses in Beijing

Air pollution is one of the major global public health challenges. Using annual fine particulate matter (PM2.5) concentration data from 2016 to 2021, along with the global exposure mortality model (GEMM), we estimated the multi-year PM2.5-pollution-related deaths divided by different age groups and diseases. Then, using the VSL (value of statistical life) method, we assessed corresponding economic losses and values. The number of deaths attributed to PM2.5 in Beijing in 2021 fell by 33.74 percent from 2016, while health economic losses would increase by USD 4.4 billion as per capita disposable income increases year by year. In 2021, the average annual concentration of PM2.5 in half of Beijing’s municipal administrative districts is less than China’s secondary ambient air quality standard (35 μg/m3), but it can still cause 48,969 deaths and corresponding health and economic losses of USD 16.31 billion, equivalent to 7.9 percent of Beijing’s GDP. Therefore, it is suggested that more stringent local air quality standards should be designated to protect public health in Beijing.

Relationship between fine particulate matter concentration and winter monsoon in the tropics under the influence of climate change and topographic effects: A case study of Kaoping area, Taiwan, since 1959

Relationship between fine particulate matter concentration and winter monsoon in the tropics under the influence of climate change and topographic effects: A case study of Kaoping area, Taiwan, since 1959

Joint estimation of PM2.5 and O3 concentrations using a hybrid model in Beijing-Tianjin-Hebei, China

The distribution data of fine particulate matter (PM2.5) and ozone (O3) concentrations at high spatiotemporal resolutions play a pivotal role in monitoring and collectively combating atmospheric PM2.5 and O3 pollution, thereby enhancing the quality of human habitation environments. This study integrates reanalysis, satellite observations, and ground-level monitoring data to develop a GLMM-DF (generalized linear mixed effects model-deep forest) hybrid model for joint estimation of PM2.5 and O3 concentrations in the Beijing-Tianjin-Hebei region. The model effectively considers the spatiotemporal heterogeneity inherent in PM2.5 and O3 and their nonlinear relationships with the estimating variables. The cross-validation results indicate that the GLMM-DF model exhibits high estimation accuracy. For PM2.5 estimation, the coefficient of determination (R2), mean absolute error (MAE), and root mean square Error (RMSE) are 0.949, 5.81 μg·m-3, and 9.42 μg·m-3, respectively. Regarding O3 estimation, the R2, MAE, and RMSE are 0.966, 7.77 μg·m-3, and 11.82 μg·m-3, respectively. Furthermore, the model demonstrates favorable spatiotemporal performance, with the PM2.5-O3 interaction contributing to enhancing model performance. Utilizing this model for mapping, we analyzed the spatiotemporal trends of PM2.5 and O3 in the Beijing-Tianjin-Hebei region, uncovering several intriguing phenomena.

Spatial synergistic effect of urban green space ecosystem on air pollution and heat island effect

It is crucial for urban greening studies to comprehend the spatial synergy effect of the urban green space ecosystem on air pollution and the heat island effect. However, there are limited studies that have considered the impact of green space ecosystems on different air pollutants and urban heat island effects across different seasons from an urban space perspective. Therefore, this study took an urban space perspective and established a high-precision spatial grid with a resolution of 500 m. The spatial econometric regression and bivariate local Moran's I test were employed to analyze the impacts of urban green space ecosystems on air pollutants (including summer ozone (O3) concentration, winter fine particulate matter (PM2.5) concentration and winter inhalable particulate matter (PM10) concentration) and heat islands in summer and winter.The results showed that the spatial regression coefficients between green space and heat islands, PM2.5 concentration and PM10 concentration are significantly negative, −0.00049***, −0.00329***, and −0.00365*, respectively. The urban green space ecosystems are observed to effectively mitigate the heat island effect and help to reduce PM2.5 and PM10 pollution in winter. There are 88 high-high clustering in the spatial interactions between green space and ozone concentration in summer, that is, the grids with high O3 concentration surrounded by the high green space grids. It implies that urban green space ecosystems can significantly increase O3 pollution in summer. This study reveals that low BVOCs tree species should be widely planted in urban green spaces, while large-scale green space parks should be established in the urban region to continuously increase the proportion of green space. This study would contribute to improving the awareness of the government and relevant employees on scientific urban greening methods.

Air Pollution and Cardiac Arrest: A More Significant Intermediate Role of COPD than Cardiac Events.

No prior studies have linked long-term air pollution exposure to incident sudden cardiac arrest (SCA) or its possible development trajectories. We aimed to investigate the association between long-term exposure to air pollution and SCA, as well as possible intermediate diseases. Based on the UK Biobank cohort, Cox proportional hazard model was applied to explore associations between air pollutants and SCA. Chronic obstructive pulmonary disease (COPD) and major adverse cardiovascular events (MACE) were selected as intermediate conditions, and multistate model was fitted for trajectory analysis. During a median follow-up of 13.7 years, 2884 participants developed SCA among 458 237 individuals. The hazard ratios (HRs) for SCA were 1.04-1.12 per interquartile range increment in concentrations of fine particulate matter, inhalable particulate matter, nitrogen dioxide, and nitrogen oxides. Most prominently, air pollutants could induce SCA through promoting transitions from baseline health to COPD (HRs: 1.06-1.24) and then to SCA (HRs: 1.16-1.27). Less importantly, SCA could be developed through transitions from baseline health to MACE (HRs: 1.02-1.07) and further to SCA (HRs: 1.12-1.16). This study provides novel and compelling evidence that long-term exposure to air pollution could promote the development of SCA, with COPD serving as a more important intermediate condition than MACE.

Effects of short- and long-term exposures to multiple air pollutants on depression among the labor force: A nationwide longitudinal study in China

BackgroundDepression prevalence has surged within the labor force population in recent years. While links between air pollutants and depression were explored, there was a notable scarcity of research focusing on the workforce. MethodsThis nationwide longitudinal study analyzed 27,457 workers aged 15–64. We estimated monthly mean concentrations of fine particulate matter (PM2.5), its primary components, and Ozone (O3) at participants' residences using spatiotemporal models. To assess the relationship between short- (1 to 3 months) and long-term (1 to 2 years) exposure to various air pollutants and depressive levels and occurrences, we employed linear mixed–effects models and mixed–effects logistic regression. We considered potential occupational moderators, such as labor contracts, overtime compensation, and total annual income. ResultsWe found significant increases in depression risks within the workforce linked to both short- and long-term air pollution exposure. A 10 μg/m3 rise in 2-year average PM2.5, black carbon (BC), and O3 concentrations correlated with increments in depressive scores of 0.009, 0.173, and 0.010, and a higher likelihood of depression prevalence by 0.5 %, 12.6 %, and 0.7 %. The impacts of air pollutants and depression were more prominent in people without labor contracts, overtime compensation, and lower total incomes. ConclusionExposures to air pollutants could increase the risk of depression in the labor force population. The mitigating effects of higher income, benefits, and job security against depression underscore the need for focused mental health interventions.

Benefits of air quality for human health resulting from climate change mitigation through dietary change and food loss prevention policy

AbstractFood production, particularly cattle husbandry, contributes significantly to air pollution and its associated health hazards. However, making changes in dietary habits, such as reducing red meat consumption and minimizing food waste, can lead to substantial improvements in both air quality and human health. In this study, we explored the impact of dietary changes on future air quality and human wellbeing. We also assessed the influence of dietary transformation policies in the context of climate change mitigation, with the objective of understanding how policies can effectively complement each other. We used a chemical transport model and an integrated assessment model to determine changes in fine particulate matter (PM2.5) and ozone (O3) concentrations. Then, an exposure model was applied to estimate premature deaths as a consequence of air pollution. Our results showed that dietary changes could play a crucial role in mitigating air pollution, particularly in regions where agricultural activities emit significant quantities of ammonia. In the European Union, for example, dietary changes could lead to a reduction of 5.34% in PM2.5 by 2050. Similarly, in Asia, the models projected a reduction of 6.23% in PM2.5 by 2100. Ground surface O3 levels in Southeast Asia were projected to drop by as much as 12.93% by 2100. Our results further showed that dietary changes could lead to significant reductions in global mortality associated with PM2.5 and O3, with 187,500 and 131,110 avoided deaths per year expected by 2100. A combined approach that integrates dietary changes with climate change mitigation measures could lead to more comprehensive air quality improvements in specific regions. However, careful consideration is needed to address any potential adverse effects on O3 concentrations in some areas.

Challenges and perspectives of air pollution control in China

Air pollution is one of the most challenging environmental issues in the world. China has achieved remarkable success in improving air quality in last decade as a result of aggressive air pollution control policies. However, the average fine particulate matter (PM2.5) concentration in China is still about six times of the World Health Organization (WHO) Global Air Quality Guidelines (AQG) and causing significant human health risks. Extreme emission reductions of multiple air pollutants are required for China to achieve the AQG. Here we identify the major challenges in future air quality improvement and propose corresponding control strategies. The main challenges include the persistently high health risk attributed to PM2.5 pollution, the excessively loose air quality standards, and coordinated control of air pollution, greenhouse gases (GHGs) emissions and emerging pollutants. To further improve air quality and protect human health, a health-oriented air pollution control strategy shall be implemented by tightening the air quality standards as well as optimizing emission reduction pathways based on the health risks of various sources. In the meantime, an “one-atmosphere” concept shall be adopted to strengthen the synergistic control of air pollutants and GHGs and the control of non-combustion sources and emerging pollutants shall be enhanced.

The Relationship between Indoor and Outdoor Fine Particulate Matter in a High-Rise Building in Chicago Monitored by PurpleAir Sensors.

In urban areas like Chicago, daily life extends above ground level due to the prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between 8 April and 7 May 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 m away. The results show that outdoor PM2.5 concentrations peak at 14 m height, and then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 a.m. under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Associations Between Early-Life Adversity, Ambient Air Pollution, and Telomere Length in Children.

Examine the independent associations and interaction between early-life adversity and residential ambient air pollution exposure on relative buccal telomere length (rBTL). Experiences of abuse, neglect, household challenges, and related life events were identified in a cross-sectional sample of children ages 1-11 years (n = 197) using the 17-item Pediatric ACEs and Related Life Event Screener (PEARLS) tool. The PEARLS tool was analyzed both as a total score and across established domains (Maltreatment, Household Challenges, and Social Context). Ground-level fine particulate matter (PM 2.5 ) concentrations were matched to residential locations for the one and twelve months prior to biospecimen collection. We used multivariable linear regression models to examine for independent associations between continuous PM 2.5 exposure and PEARLS score/domains with rBTL. Additionally, effect modification by PEARLS scores and domains on associations between PM 2.5 exposure and rBTL was examined. Study participants were 47% girls, with mean age = 5.9 years [standard deviation: 3.4] median reported PEARLS score of 2 [interquartile range (IQR): 4], median 12-month prior PM 2.5 concentrations of 11.8 μg/m 3 [IQR: 2.7], median 1-month prior PM 2.5 concentrations of 10.9 μg/m 3 [IQR: 5.8], and rBTL of 0.1 [IQR: 0.03]. Mean 12-month prior PM 2.5 exposure was inversely associated with rBTL (ß = -0.02, 95% CI: -0.04, -0.01). While reported PEARLS scores and domains were not independently associated with rBTL, we observed a greater decrement in rBTL with increment of average annual PM 2.5 as reported Social Context domain items increased (p-interaction<0.05). Our results suggest that adverse Social Context factors may accelerate the association between chronic PM 2.5 exposure on telomere shortening during childhood.

Characteristics and health implications of fine particulate matter near urban road site in Islamabad, Pakistan

The escalating issue of air pollution has become a significant concern in urban regions, including Islamabad, Pakistan, due to the rise in air pollutant emissions driven by economic and industrial expansion. To gain a deeper understanding of air pollution, a study was conducted during winter 2022–2023, assessing physical, chemical, and biological factors in Islamabad. The findings revealed that the average concentration of fine particulate matter (PM2.5) was notably greater than the World Health Organization (WHO) guidelines, reaching 133.39 μg/m³. Additionally, the average concentration of bacteria (308.64 CFU/m³) was notably greater than that of fungi (203.55 CFU/m³) throughout the study. Analytical analyses, including SEM-EDS and FTIR, showed that the PM2.5 in Islamabad is composed of various particles such as soot aggregates, coal fly ash, minerals, bio-particles, and some unidentified particles. EF analysis distinguished PM2.5 sources, enhancing understanding of pollutants origin, whereas Spearman's correlation analysis elucidated constituent interactions, further explaining air quality impact. The results from the Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES) indicated a gradual increase in the total elemental composition of PM2.5 from autumn to winter, maintaining high levels throughout the winter season. Furthermore, a significant variation was found in the mass concentration of PM2.5 when comparing samples collected in the morning and evening. The study also identified the presence of semi-volatile organic compounds (SVOCs) in PM2.5 samples, including polycyclic aromatic hydrocarbons (PAHs) and phenolic compounds, with notable variations in their concentrations. Utilizing health risk assessment models developed by the US EPA, we estimated the potential health risks associated with PM2.5 exposure, highlighting the urgency of addressing air quality issues. These findings provide valuable insights into the sources and composition of PM2.5 in Islamabad, contributing to a comprehensive understanding of air quality and its potential environmental and health implications.

Response of warm season secondary pollutants to emissions and meteorology in the North China Plain region during 2018–2022

Air quality in China has continued to improve since 2013, although severe pollution events still occur over the North China Plain (NCP). It is noticeable that contributions from secondary pollutants have increased, but understanding of their formation and variations with changing emissions and meteorological conditions remains limited. In this study, the warm season of May to September from 2018 to 2022 was selected to explore the response of secondary pollutants to meteorology and emissions using the Community Multi-scale Air Quality model (CMAQ). Fine particulate matter (PM2.5) concentrations over the NCP decreased significantly by 31%–37% from 2018 to 2022, while ozone (O3) and nitrogen dioxide (NO2) generally showed decreasing trends by 1% and 0.5% per year, respectively. Secondary inorganic aerosol (including sulfate, nitrate, and ammonium) and secondary organic aerosol (SOA) also decreased significantly, by 9% and 6% per year, respectively. The results showed that emissions contributed 96% to the decreases in PM2.5 concentrations, while O3 fluctuated due to meteorological changes. Although the decreases in sulfate and ammonium were mainly associated with emission reductions, that of nitrate was more sensitive to meteorological changes. Meteorological and emission changes were similarly important for the overall decrease in SOA, with anthropogenic SOA being more sensitive to emissions control, while biogenic SOA was more easily attributed to meteorological changes. This research emphasizes the importance of controlling anthropogenic emissions in relieving summer secondary pollution in the NCP region.摘要自2013年起, 中国空气质量虽改善, 但华北平原(NCP)重污染仍存在且二次污染加剧, 而 人们对其成因和变化了解有限. 本研究利用2018–2022年数据, 借助CMAQ模型探讨此污染响应. 结果显示, 在2018–2022年间, PM2.5浓度显著下降31%–37%, O3和NO2的年下降速率分别为1%和0.5%. SIA和SOA也显著减少, 每年分别减少9%和6%. PM2.5主要因排放减少而下降, 而O3则受气象影响而波动. 硫酸盐和铵下降的主因是减排, 而硝酸盐对气象变化敏感. 排放和气象变化对SOA的总体减少同样重要, 但人为SOA对排放控制敏感, 生物SOA易受气象变化影响. 研究强调了控制人为排放对缓解NCP地区夏季二次污染的重要性.

Exploring role-playing as a tool for involving citizens in air pollution mitigation urban policies

The design of air pollution control policies is a complex decision-making process. Public participation can enhance the legitimacy and effectiveness of environmental urban regulations but does not always occur naturally. This article presents the experience of the use of role-playing in six mid-sized cities in southern Chile to evaluate how residents affected by high concentrations of fine particulate matter perceive the problem and debate possible solutions. For this, the participants were asked to assume the role of advisors. On that premise, they had to prioritize between a series of mitigation measures and reach a consensus with other advisors. The results show the potential of role-playing games, on the one hand, to promote critical thinking and argument skills among the general public and, on the other, to help policymakers in the design of more operative and fair plans of action.

Chronic exposure to ambient air pollution and the risk of non-alcoholic fatty liver disease: A cross-sectional study in Taiwan and Hong Kong

BackgroundInformation on the relation of air pollution with non-alcoholic fatty liver disease (NAFLD) is scarce. We thus conducted a large cross-sectional study in Asia to investigate the role of air pollution in NAFLD. MethodsWe recruited 329,048 adults (mean age: 41.0 years) without other liver disease (hepatitis and cirrhosis) or excessive alcohol consumption in Taiwan and Hong Kong from 2001 to 2018. The concentrations of nitrogen dioxide (NO2) and ozone (O3) were estimated using a space–time regression model, and the concentrations of fine particulate matter (PM2.5) was evaluated using a satellite-based spatio-temporal model. NAFLD was determined using either the fatty liver index (FLI) or the hepatic steatosis index (HSI). The NAFLD-related advanced fibrosis was defined according to BARD score or the fibrosis-4 (FIB-4). A logistic regression model was adopted to explore the relationships of ambient air pollution with the odds of NAFLD and NAFLD-related advanced fibrosis. ResultsWe found positive relationships between PM2.5 and the odds of NAFLD and advanced fibrosis, with every standard deviation (SD, 7.5 µg/m3) increases in PM2.5 exposure being associated with a 10% (95% confidence interval [CI]: 9%–11%) increment in the prevalence of NAFLD and an 8% (95% CI: 7%–9%) increment in the prevalence of advanced fibrosis. Similarly, the prevalence of NAFLD and advanced fibrosis increased by 8% (95% CI: 7%–9%) and 7% (95% CI: 6%–8%) with per SD (18.9 µg/m3) increasement in NO2 concentration, respectively. Additionally, for every SD (9.9 µg/m3) increasement in O3 concentration, the prevalence of NAFLD and advanced fibrosis decreased by 12% (95% CI: 11%–13%) and 11% (95% CI: 9%–12%), respectively. ConclusionHigher ambient PM2.5 and NO2 are linked with higher odds of NAFLD and advanced fibrosis. Our findings indicate that reducing PM2.5 and NO2 concentrations may be an effective way for preventing NAFLD. Further studies on O3 are warranted.

Assessing the 2023 Canadian wildfire smoke impact in Northeastern US: Air quality, exposure and environmental justice

The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM2.5 exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM2.5) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM2.5 measurements, providing a more accurate estimation of PM2.5 distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM2.5 levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM2.5 levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM2.5 effects from wildfire smoke. Furthermore, we observed mixed correlations between PM2.5 concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

Long-Term Exposure to Air Pollution and Risk of Acute Lower Respiratory Infections in the Danish Nurse Cohort.

Air pollution is a major risk factor for chronic cardiorespiratory diseases, affecting both the immune and respiratory systems' functionality, while the epidemiological evidence on respiratory infections remains sparse. We aimed to assess the association of long-term exposure to ambient air pollution with risk of developing new and recurrent ALRIs that characterized by persistently severe symptoms necessitating hospital contact, and identify the potential susceptible populations by socio-economic status (SES), smoking, physical activity status, overweight, and co-morbidity with chronic lung disease. We followed 23,912 female nurses from the Danish Nurse Cohort (> 44 years) from baseline (1993 or 1999) until 2018 for the incident and recurrent ALRIs defined by hospital contact (in-, outpatient, and emergency room) data from the National Patient Register. Residential annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) were modelled using Danish DEHM/UBM/AirGIS system. We used marginal Cox models with time-varying exposures to assess the association of 3-year running-mean air pollution with incident and recurrent ALRIs and examine effect modification by age, socio-economic status (SES), smoking, physical activity, body mass index, and comorbidity with asthma or chronic obstructive pulmonary disease (COPD). During a 21.3 years mean follow-up, 4,746 ALRIs were observed, of which 2,553 were incident. We observed strong positive associations of all three pollutants with incident ALRIs, with hazard ratios and 95% confidence intervals of 1.19 (1.08-1.31) per 2.5 µg/m3 for PM2.5, 1.17 (1.11-1.24) per 8.0 µg/m3 for NO2, and 1.09 (1.05-1.12) per 0.3 µg/m3 for BC, and slightly stronger associations with recurrent ALRIs. Associations were strongest in COPD patients and nurses with low physical activity. Long-term exposure to air pollution at low levels was associated with risk of new and recurrent ALRIs, with COPD patients and physically inactive subjects most vulnerable. Primary Source of Funding: This study was supported by the Novo Nordisk Foundation Challenge Programme (NNF17OC0027812).

Time-series analysis of the association between air pollution exposure and outpatient visits for dry eye disease: a case study in Zhengzhou, China.

Dry eye disease (DED) is a prevalent ocular surface disease that significantly impacts patients' quality of life. The association between air pollution and the risk of dry eye disease remains uncertain. Data on outdoor air pollutants, meteorological information, and outpatient visits for DED were collected from July 1, 2014, to December 31, 2019. The relationship between ambient air pollutants and DED outpatient visits was analyzed using a generalized additive model with a Poisson distribution. Among the 5,204 DED patients included in the study, 63.76% were female and 36.24% were male. The single-pollutant model revealed a significant association between a 10 μg/m3 increase in concentrations of fine-particulate matter with a median aerometric diameter of less than 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) and outpatient visits for DED. Fine-particulate matter with a median aerometric diameter of less than 2.5 μm (PM2.5) showed a significant association with DED outpatient visits in males and the 19-59 years age group. The strongest associations between air pollutants and outpatient visits were observed in male patients and during the cold season. The noteworthy correlation between air pollutants and DED outpatient visits can offer evidence for policy makers and underscore the significance of reinforcing environmental protection.

Gravimetric, morphological, and chemical characterization of fine and ultrafine particulate matter inside fire stations

Poor air quality inside buildings has direct implications for human health. The activity of firefighters causes cancer and promotes the development/aggravation of cardio-respiratory diseases. The indoor air quality inside fire stations remains poorly characterized. This work assesses firefighters’ daily exposure to fine (PM2.5, PM156nm) and ultrafine (PM50.4nm, PM30.8nm) particulate matter (PM) inside three microenvironments of fire stations, and, for the first time, characterizes the morphology and elemental content of collected PM. Fine PM fractions were predominant in all microenvironments. Fine and ultrafine PM concentrations were increased in the personal protective equipment (PPE) storage room than in the common area and garage [11.84–103.5 μg/m3versus 18.67–51.89 μg/m3 and 22.57–70.69 μg/m3; p > 0.05 and 4.78–46.02 μg/m3versus 3.03–40.99 μg/m3 and 5.55–23.30 μg/m3; p > 0.05]; values were below the guideline proposed for occupational exposure to respirable dust (5.0 mg/m3). The morphological analysis revealed the presence of organic matter, microorganisms, crystals, minerals, metals, and other unidentified substances in fine/ultrafine PM. Exposure to total PM-bound metals was increased in the garage comparatively to the common area and the PPE storage room (2765.0–46586.0 pg/m3versus 2414.2–4568.6 pg/m3; p > 0.5). Cu, Zn, and Cr were the predominant PM-bound metals while Sb, Cd, and Tl were the less abundant. Exposure to PM-bound possible/probable carcinogenic metals (Ni, Cd, Sb, and Pb) accounted for 7.81–10.72% and 0.342–13.07% of fine and ultrafine PM, respectively. The data generated can support regulatory agencies in proposing air quality guidelines that promote occupational safety and health.

High-resolution estimation of PM2.5 concentrations across China using multiple machine learning approaches and model fusion

Accurate estimation of fine particulate matter (PM2.5) concentrations can provide necessary data sources for epidemiological studies and environmental management decisions. However, the different sources and resolutions of satellite data and the limited number of monitoring stations decrease the accuracy of PM2.5 concentration estimation, which subsequently complicates the continuous estimation of high-resolution PM2.5. In this study, we evaluate and optimize five different machine learning methods, including cubist, random forest (RF), support vector machine (SVM), the generalized additive model (GAM), and Extreme Gradient Boosting (XGBoost), to perform spatial high-resolution (1 km) estimations of PM2.5 concentrations. Based on the optimization, two model fusion methods, i.e., the Bayesian model averaging and Granger–Ramanathan averaging algorithms, are applied and compared for estimating PM2.5 concentrations. Among the five single machine learning models, the RF model performed the best, with a coefficient of determination (R2) of 0.84, root mean square error (RMSE) of 5.09 μg/m3, and ratio of performance to interquartile distance (RPIQ) of 3.40. The cubist model ranks second in terms of performance (R2 = 0.82, RMSE = 5.37 μg/m3, and RPIQ = 3.23). The estimation accuracies of the five models decrease in the following order: RF > cubist > XGBoost > SVM > GAM. The fusion model performed better than the single model and reliably estimated the PM2.5 concentration (R2 = 0.86, RMSE = 5.04 μg/m3, and RPIQ = 3.44). The results show that the model fusion algorithms improved the overall performance of fine-level and long-term PM2.5 concentration estimation, and that the accuracies of the fusion models are significantly higher than those of the five machine learning models used individually. Fusion model is used to generate an annual PM2.5 concentration distribution map of China from 2017 to 2021. This study provides a reference for estimating PM2.5 concentrations and contributes to the formulation of targeted policies.

Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights

Abstract. Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88, normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.