Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

BACKGROUND: Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. OBJECTIVE: This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. METHODS: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. RESULTS: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. IMPACT STATEMENT: We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations.

Evidence on the effect of long-term exposure to fine particulate matter (PM2.5) on erythrocytosis and thrombocytosis prevalence was limited. We aimed to investigate the association of PM2.5 and its constituents with the risks of erythrocytosis and thrombocytosis. The present study included a total of 33,585 participants from the Henan Rural Cohort at baseline between 2015 and 2017. A hybrid satellite-based model was employed to estimate the concentrations of PM2.5 mass and its constituents (including black carbon [BC], nitrate [NO3-], ammonium [NH4+], inorganic sulfate [SO42-], organic matter [OM], and soil particles [SOIL]). The logistic regression model was used to assess the associations of single exposure to PM2.5 and its constituents with the risks of erythrocytosis and thrombocytosis, and the quantile G-computation method was applied to evaluate their joint exposure risk. For the independent association, the odds ratios for erythrocytosis/thrombocytosis with 1 µg/m3 increase was 1.049/1.043 for PM2.5 mass, 1.596/1.610 for BC, 1.410/1.231 for NH4+, 1.205/1.139 for NO3-, 1.221/1.359 for OM, 1.300/1.143 for SO42-, and 1.197/1.313 for SOIL. Joint exposure to PM2.5 and its components was also positively associated with erythrocytosis and thrombocytosis. The estimated weight of NH4+ was found to be the largest for erythrocytosis, while OM had the largest weight for thrombocytosis. PM2.5 mass and its constituents were positively linked to prevalent erythrocytosis and thrombocytosis, both in single-exposure and joint-exposure models. Additionally, NH4+/OM was identified as a potentially responsible component for the association between PM2.5 and erythrocytosis/thrombocytosis.

Associations of PM2.5 composition and green space with metabolic syndrome in a Chinese essential hypertensive population.

BACKGROUND: Metabolic syndrome (MetS) has emerged as a significant global public health concern. While environmental factors, including PM2.5, have been identified as important risk factors for MetS in the general population, limited studies have investigated their impact on individuals with essential hypertension. Therefore, our study aims to explore the relationship between PM2.5 composition, green space, and their combined effects on MetS among a Chinese essential hypertensive population. METHOD: A total of 20,131 participants diagnosed with essential hypertension from 10 provinces in China were included in this study. Individual level exposure to various environmental factors (including PM2.5, PM2.5 composition, green space and temperature) were evaluated using spatiotemporal models based on satellites data. Participants were defined as MetS according to the definition issued by the International Diabetes Federation. Generalized additive mixed models were used to analyze the individual air pollutants, green space and their interaction on MetS. RESULT: The prevalence of MetS in this population was 44.33%. The adjusted odd ratio (OR) of MetS, with each one unit increase in SO42-, BC and NO3- were 1.077 (1.049, 1.106), 1.126 (1.077, 1.177) and 0.977 (0.958, 0.996) respectively. Additionally, each unit increase of the Normalized Difference Vegetation Index (NDVI) was associated with a decreased risk of MetS (OR: 0.988, 95% CI: 0.984-0.993). In particular, green space was found to mitigate the adverse impacts of PM2.5 on MetS (OR: 0.988, 95% CI: 0.984-0.993). CONCLUSION: Our results suggested that there was a positive association between PM2.5 and its composition (SO42-, BC) with MetS in the essential hypertensive population, while green space might play a protective role. Moreover, green space could effectively weaken the positive relationship between air pollutants and MetS, especially in males and participants younger than 60 years old.

Long-term exposure to PM2.5 and its components is associated with elevated blood pressure and hypertension prevalence: Evidence from rural adults.

INTRODUCTION: The toxicity of fine particulate matter (PM2.5) is determined by its components, while the evidence regarding associations of PM2.5 components with blood pressure (BP) is limited, especially in rural areas. OBJECTIVES: This study aimed to explore the associations of PM2.5 and its chemical components with systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), mean artery pressure (MAP) levels and hypertension prevalence, and to identify key components in Chinese rural areas. METHODS: 39,211 adults from the Henan Rural Cohort were included during 2015-2017. Different periods of PM2.5 and chemical components were estimated by hybrid satellite model. The single-pollutant, component-PM2.5 model, component-residual model and component-proportion model were applied to explore the associations of pollutants with BP levels and hypertension prevalence. Exposure-response (E-R) relationships, stratified analyses and sensitivity analyses were used to explore these associations further. RESULTS: 12,826 (32.71%) were identified with hypertension. For each 1 µg/m3 increase of pollutants, the adjusted odds ratio (OR) for hypertension prevalence was 1.03 for PM2.5 mass, 1.40 for BC, 1.16 for NH4+, 1.08 for NO3-, 1.17 for OM, 1.12 for SO42- and 1.25 for SOIL in the single-pollutant model. BC and SOIL were statistically significant in the component-PM2.5 model, component-residual model and component-proportion model. Similarly, associations of these pollutants with elevated BP levels were also found in aforementioned four models. These pollutants produced a stronger association with SBP than DBP, PP and MAP. Most of associations were non-linear in E-R relationships. The groups of older, the men, with lower per capita monthly income, lower educational level and higher BMI were more vulnerable to these pollutants in stratified analyses. The results remained stable in sensitivity analyses. CONCLUSION: Long-term exposure to PM2.5 and its components, especially BC and SOIL, was associated with elevated BP and hypertension prevalence in rural adults, and decreasing pollutants may provide additional benefits.

Emissions of multiple metals from vehicular brake linings wear in China, 1980-2020.

Metals emitted from brake linings wear have adverse effects on air quality and human health due to their toxicity and reactivity. However, complexities of factors affecting brake like conditions of vehicles and roads hinder the accurate quantification. Here, we established a comprehensive emission inventory for multi-metals from brake linings wear in China during 1980-2020, based on metals contents in well-representative samples, the wear of brake linings before replacement, vehicle populations, fleet composition, and vehicle-kilometers travelled (VKT). We show that with the boom of vehicle population, the total emissions of studied metals have surged from 3.7 × 106 g in 1980 to 4.9 × 1010 g in 2020, which mainly concentrated in coastal and eastern urban areas while grown significantly in the central and western urban areas in recent years. Therein, Ca, Fe, Mg, Al, Cu, and Ba were the top six metals emitted, together responsible for >94 % of the mass total. Jointly determined by brake linings especially metals contents thereof, VKTs, and vehicle populations, heavy-duty trucks, light-duty passenger vehicles, and heavy-duty passenger vehicles were the top three contributors in metals emissions, together accounting about 90 % of the total. Moreover, more precise description on real-world metals emissions from brake linings wear are still urgently needed, considering the increasingly significant role it has been playing on worsening air quality and public health.

Air pollution health burden embodied in China's supply chains.

Product trade plays an increasing role in relocating production and the associated air pollution impact among sectors and regions. While a comprehensive depiction of atmospheric pollution redistribution through trade chains is missing, which may hinder targeted clean air cooperation among sectors and regions. Here, we combined five state-of-the-art models from physics, economy, and epidemiology to track the anthropogenic fine particle matters (PM2.5) related premature mortality along the supply chains within China in 2017. Our results highlight the key sectors that affect PM2.5-related mortality from both production and consumption perspectives. The consumption-based effects from food, light industry, equipment, construction, and services sectors, caused 2-22 times higher deaths than those from a production perspective and totally contributed 63% of the national total. From a cross-boundary perspective, 25.7% of China's PM2.5-related deaths were caused by interprovincial trade, with the largest transfer occurring from the central and northern regions to well-developed east coast provinces. Capital investment dominated the cross-boundary effect (56% of the total) by involving substantial equipment and construction products, which greatly rely on product exports from regions with specific resources. This supply chain-based analysis provides a comprehensive quantification and may inform more effective joint-control efforts among associated regions and sectors from a health risk perspective.

Present Knowledge and Future Perspectives of Atmospheric Emission Inventories of Toxic Trace Elements: A Critical Review.

Toxic trace elements (TEs) can pose serious risks to ecosystems and human health. However, a comprehensive understanding of atmospheric emission inventories for several concerning TEs has not yet been developed. In this study, we systematically reviewed the status and progress of existing research in developing atmospheric emission inventories of TEs focusing on global, regional, and sectoral scales. Multiple studies have strengthened our understanding of the global emission of TEs, despite attention being mainly focused on Hg and source classification in different studies showing large discrepancies. In contrast to those of developed countries and regions, the officially published emission inventory is still lacking in developing countries, despite the fact that studies on evaluating the emissions of TEs on a national scale or one specific source category have been numerous in recent years. Additionally, emissions of TEs emitted from waste incineration and traffic-related sources have produced growing concern with worldwide rapid urbanization. Although several studies attempt to estimate the emissions of TEs based on PM emissions and its source-specific chemical profiles, the emission factor approach is still the universal method. We call for more extensive and in-depth studies to establish a precise localization national emission inventory of TEs based on adequate field measurements and comprehensive investigation to reduce uncertainty.

Association of long-term exposure to PM2.5 constituents with glucose metabolism in Chinese rural population.

BACKGROUND: Evidence on the associations of fine particulate matter (PM2.5) constituents and glucose metabolism is limited in resource-limited areas. This study aimed to explore the associations of PM2.5 constituents with glucose metabolism in rural areas, and to further specify the most responsible constituent. METHODS: A total of 38,442 adults were recruited from the Henan Rural Cohort Study during 2015-2017. Three-year averaged concentrations of PM2.5 mass and its constituents (black carbon (BC), ammonium (NH4+), nitrate (NO3-), organic matter (OM), inorganic sulfate (SO42-), soil particles (SOIL) and sea salt (SS)) were estimated by a hybrid satellite-based model. Generalized linear model was applied to explore the associations of PM2.5 mass and its constituents with type 2 diabetes mellitus (T2DM), fasting blood glucose (FBG), insulin, and HOMA-ß. Proportion and residual analyses were employed to specify the most responsible constituent. RESULTS: The adjusted odds ratio (OR) for T2DM associated with 1 µg/m3 increase was 1.02 for PM2.5 mass, 1.28 for BC, 1.15 for NH4+, 1.08 for NO3-, 1.10 for OM, 1.11 for SO42-, and 1.12 for SOIL. Significant associations of PM2.5 mass and its constituents with elevated FBG, decreased insulin and HOMA-ß were also observed. Proportion and residual analyses indicated that BC was the most responsible constituent, in which 1 percentage increment in the proportion of BC in PM2.5 corresponded with 1.51-fold risk for T2DM, 0.17 mmol/L increase in FBG, 2.18 µU/mL decrease in insulin, and 38.26 % decrease in HOMA-ß; and 1 µg/m3 increment in the PM2.5-adjusted BC corresponded with 1.59-fold risk for T2DM, 0.53 mmol/L increase in FBG, 4.79 µU/mL decrease in insulin, and 91.32 % decrease in HOMA-ß. CONCLUSIONS: PM2.5 mass and its constituents (BC, NH4+, NO3-, OM, SO42-, SOIL) were associated with T2DM, increased FBG, decreased insulin and HOMA-ß, of which BC was most responsible for these associations. TRIAL REGISTRATION: The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 06 July 2015. http://www.chictr.org.cn/showproj.aspx?proj=11375.

Analysis of VOC emissions and O3 control strategies in the Fenhe Plain cities, China.

Long-term continuous hourly measurements of ambient volatile organic compounds (VOCs) are scarce at the regional scale. In this study, a one-year hourly measurement campaign of VOCs was performed in Lvliang, Linfen, and Yuncheng in the heavily polluted Fenhe Plain region in China. The VOC average (±standard deviation, std) concentrations in Lvliang, Linfen, and Yuncheng were 44.4 ± 24.9, 45.7 ± 24.9, and 37.5 ± 25.0 ppbv, respectively. Compared to published data from the past two decades in China, the observed VOCs were at high concentration levels. VOCs in the Fenhe Plain cities were significantly impacted by industrial sources according to calculated emission ratios but were less affected by liquefied petroleum gas and natural gas (LPG/NG) and traffic emissions than those in megacities abroad. The emission inventories and observation data were combined for verification and identification of the key VOC species and sources controlling ozone (O3). Industrial emissions were the largest source of VOCs, accounting for 65%-79% of the total VOC emissions, while the coking industry accounted for 45.2%-66.0%. The emission inventories significantly underestimated oxygenated VOC (OVOC) emissions through the verification of VOC emission ratios. O3 control scenarios were analyzed by changing VOC/NOX reduction ratios through a photochemical box model. O3 control strategies were formulated considering local pollution control plans, emission inventories, and O3 formation regimes. The O3 reduction of reactivity-control measures was comparable with emission-control measures, ranging from 16% to 41%, which was contrary to the general perception that ozone formation potential (OFP)-based measures were more efficient for O3 reduction. Sources with high VOC emissions are accompanied by high OFP on the Fenhe Plain, indicating that the control of high-emission sources can effectively mitigate O3 pollution on this region.

Emission characteristics and inventory of volatile organic compounds from the Chinese cement industry based on field measurements.

Volatile organic compounds (VOCs) are major precursors of ozone (O3) and secondary organic aerosols (SOA), which degrade air quality and pose a serious risk to human health and ecological systems. Previous studies on the emission characteristics of VOCs have predominantly focused on petrochemical and solvent-using sources, while localized studies on the cement industry are scarce in China. Field measurements for four cement plants were carried out in this study to investigate the emission levels, source profiles, and secondary pollutant generation potential of 98 VOCs species emitted from rotary and shaft kilns in China. Furthermore, a species-differentiated VOCs emission inventory was compiled for the Chinese cement industry in 2019. The results demonstrated that the mass concentration of VOCs emitted from shaft kiln was more than 20-fold higher than that emitted from rotary kilns, and the alkanes was the dominant species (56%) in shaft kilns, while oxygenated VOCs (OVOCs) and halocarbons were the main species in rotary kilns. Moreover, alkenes & alkyne were the dominant contributors to ozone formation potential (OFP) in shaft kilns, whereas alkenes & alkyne and OVOCs were comparable and prominent contributors in rotary kilns. In contrast, secondary organic aerosol potential (SOAP) for the two types of kilns was dominated by aromatics. In 2019, approximately 18.18 kt VOCs were emitted from cement production and were found to be largely concentrated in the southeast and central provinces of China. Considering the influence on environmental conditions, high OFP-contributing species in cement kilns are suggested to be a priority in the pollution mitigation of O3. This study provides a new, comprehensive, and reasonable cognition of the current VOCs emissions from both rotary and shaft kilns in China, which will aid in a better understanding of VOCs emission characteristics and guide future policy-making.

Exploring the driving factors of haze events in Beijing during Chinese New Year holidays in 2020 and 2021 under the influence of COVID-19 pandemic.

Unexpected outbreak of the 2019 novel coronavirus (COVID-19) has profoundly altered the way of human life and production activity, which posed visible impacts on PM2.5 and its chemical species. The abruptly emergency reduction in human activities provided an opportunity to explore the synergetic impacts of multi-factors on shaping PM2.5 pollution. Here, we conducted two comprehensive observation measurements of PM2.5 and its chemical species from 1 January to 16 February in Beijing 2020 and the same lunar date in 2021, to investigate temporal variations and reveal the driving factors of haze before and after Chinese New Year (CNY). Results show that mean PM2.5 concentrations during the whole observation were 63.83 and 66.86 µg/m3 in 2020 and 2021, respectively. Higher secondary inorganic species were observed after CNY, and K+, Cl- showed three prominent peaks which associated closely with fireworks burnings from suburb Beijing and surroundings, verifying that they could be used as two representative tracers of fireworks. Further, we explored the impacts of meteorological conditions, regional transportation as well as chemical reactions on PM2.5. We found that unfavorable meteorological conditions accounted for 11.0 % and 16.9 % of PM2.5 during CNY holidays in 2020 and 2021, respectively. Regional transport from southwest and southeast (south) played an important role on PM2.5 during the two observation periods. Higher ratio of NO3-/SO42- were observed under high OX and low RH conditions, suggesting the major pathway of NO3- and SO42- formation could be photochemical process and aqueous-phase reaction. Additionally, nocturnal chemistry facilitated the formation of secondary components of both inorganic and organic. This study promotes understandings of PM2.5 pollution in winter under the influence of COVID-19 pandemic and provides a well reference for haze and PM2.5 control in future.

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China.

Ambient PM2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R2 of 0.84 and an RMSE of 4.9 µg/m3, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.

Quantification of enhanced VOC emissions from fireworks.

Fireworks are widely used in celebrations worldwide. The effects of fireworks on the physicochemical characteristics of atmospheric particles are well documented. However, the influence of firework burning on ambient volatile organic compound (VOC) emissions remains unclear. To determine the impact of firework-burning events on VOC emissions, ambient VOCs were measured at a receptor site on the Fenwei Plain during the Chinese Spring Festival period. Firework-burning plumes were identified by using potassium ions (K+) as tracers, and twenty VOC species were obtained as firework tracers. The emission ratios of the VOC species relative to K+ were in a range of 5.40 × 10-3-1.41 µg m-3/µg m-3 and were first estimated through the linear fitting method and source-tracer-ratio method. The VOC contributions of firework burning during the Lantern Festival (31.7 ± 8.3%) were higher than the levels during the Chinese New Year (28.6 ± 7.5%). The daytime net ozone (O3) formation rates during the Spring Festival and Lantern Festival increased by 11.4% and 15.2%, respectively, on average due to firework emissions. Secondary organic aerosol formation potential (SOAP) increased by 18.2% and 34.1% on average, respectively. These results can provide the source tracers of fireworks, and can subsequently help assess their impact on regional air quality and public health.

Heterogeneous Variations on Historical and Future Trends of CO2 and Multiple Air Pollutants from the Cement Production Process in China: Emission Inventory, Spatial-Temporal Characteristics, and Scenario Projections.

Cement production is a major contributor to carbon dioxide (CO2) and multiple hazardous air pollutant (HAP) emissions, threatening climate mitigation and urban/regional air quality improvement. In this study, we established a comprehensive emission inventory by coupling the unit-based bottom-up and mass balance methods, revealing that emissions of most HAPs have been remarkably controlled. However, an increasing 6.0% of atmospheric mercury emissions, as well as 14.1 and 23.7% of fuel-related and process-related CO2 emission growth were witnessed unexpectedly. Industrial adjustment policies have imposed a great impact on the spatiotemporal changes in emission characteristics. Monthly emissions of CO2 and multiple HAPs decreased from December to February due to the "staggered peak production," especially in northern China after implementing the intensified action plan for air pollution control in winter. Upgrading environmental technologies and adjusting capacity structures are identified as dominant driving forces for reducing HAP emissions. Besides, energy intensity improvement can help offset some of the impact caused by the increase in clinker/cement production. Furthermore, scenario analysis results show that ultra-low emission and low-carbon technology transformation constitute the keys to achieve the synergic reduction of CO2 and multiple HAP emissions in the future.

Chemical constituents of ambient fine particulate matter and obesity among school-aged children: A representative national study in China.

BACKGROUND: Studies show that fine particulate matter (PM2.5) contributes to childhood obesity. However, evidence on the effects of its constituents on obesity has not been explored. METHODS: Using multistage stratified cluster sampling, we enrolled 41,439 school-age children (aged 6-17 years) from a representative nationwide survey of 30 provinces in China (mean age ± standard deviation: 12.0 ± 3.3 years). Weight and height were measured using a physician beam scale with a height rod, and covariates were determined using a standard questionnaire. The concentration of PM2.5 chemical constituents was estimated by a chemical transport (GEOS-Chem) model using input satellite data and ground-based observations. The constituents included black carbon, ammonium, nitrate, organic matter, sulfate, and soil dust. Generalized linear models were used to estimate the association between the chemical constituents of PM2.5 and obesity. RESULTS: A positive association between the constituents of PM2.5 and obesity were observed. Children were more susceptible to black carbon than other species. A 1-µg/m3 increase in black carbon led to a 0.079 (95 % confidence interval [CI]:0.028, 0.130)-kg/m2 increase in body mass index (BMI). This also increased the odds of being obese and overweight to 1.174 (95 % CI: 1.111, 1.240) and 1.165 (95 % CI: 1.116, 1.216), respectively. Stratified analyses showed that the effects were stronger in girls and older children, as well as in urban and Northeast regions. The effect of the PM2.5 constituents on obese and overweight children from urban areas significantly interacted with that of rural areas. CONCLUSIONS: The PM2.5 constituents were associated with an increased BMI and childhood obesity. Further studies are warranted to validate these results and clarify their potential mechanisms. We suggest focusing on black carbon and Northeast regions.

Chemical explosion, COVID-19, and environmental justice: Insights from low-cost air quality sensors.

OBJECTIVES: To examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors. METHODS: We compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems. FINDINGS: We found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index. IMPLICATION: Our study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies. SYNOPSIS: We used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters.

Effects of long-term exposure to PM2.5 and chemical constituents on blood lipids in an essential hypertensive population: A multi-city study in China.

Previous studies on the effects of fine particulate matter (PM2.5) and chemical constituents on lipid disorder among hypertension populations, particularly in China, are very limited. We aimed to examine the effects of long-term exposure to PM2.5 and chemical constituents on dyslipidemias in China. Finally, we included 34,841 participants with essential hypertension from 19 regions in China during 2010-2011. Data were modeled using the generalized additive mixed model. We found that PM2.5 and chemical constituents exposure were positively associated with the increased risk of dyslipidemias and increased levels of total cholesterol (TC) and triglyceride (TG). The odds ratio for hypercholesterolemia was 1.356 [95% confidence interval (CI): 1.246, 1.477] for PM2.5, and the strongest association with PM2.5 constituents was found for nitrate. Each 10 µg/m3 increase in PM2.5 showed a significant increase of TC by 2.60% (95% CI: 2.03, 3.17) and TG by 2.91% (95% CI: 1.60, 4.24), respectively. Meanwhile, an interquartile range increase in nitrate, ammonium and organic matter had stronger associations with TC and TG parameters than black carbon, sulfate, and mineral dust. Our findings may contribute to a better understanding of the chronic effects of PM2.5 and chemical constituents on lipid disorder in an essential hypertensive population.

Fine particulate matter (PM2.5/PM1.0) in Beijing, China: Variations and chemical compositions as well as sources.

Particulate matter (i.e., PM1.0 and PM2.5), considered as the key atmospheric pollutants, exerts negative effects on visibility, global climate, and human health by associated chemical compositions. However, our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013-2017. Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing. Here, we conducted simultaneous measurements on PM in Beijing during 2018-2019. Results indicate that annual mean PM1.0 and PM2.5 concentrations were 35.49 ± 18.61 µg/m3 and 66.58 ± 60.17 µg/m3, showing a positive response to emission controls. The contribution of sulfate, nitrate, and ammonium (SNA) played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes. Discrepancies observed among chemical species between PM1.0 and PM2.5 in spring suggest that sand particles trend to accumulate in the range of 1-2.5 µm. Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter, respectively. Results from positive matrix factorization (PMF) combined with potential source contribution function (PSCF) models showed that potential areas were seasonal dependent, secondary and vehicular sources became much more important compared with previous studies in Beijing. Our study presented a continuous investigation on PM and sources origins in Beijing, which provides a better understanding for further emission control as well as a reference for other cities in developing countries.

Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in air quality. Here, we applied a machine learning algorithm (random forest model) to eliminate meteorological effects and characterize the high-resolution variation characteristics of air quality induced by COVID-19 in Beijing, Wuhan, and Urumqi. Our RF model estimates showed that the highest decrease in deweathered PM2.5 in Wuhan (-43.6%) and Beijing (-14.0%) was at traffic stations during lockdown period (February 1- March 15, 2020), while it was at industry stations in Urumqi (-54.2%). Deweathered NO2 decreased significantly in each city (∼30%-50%), whereas accompanied by a notable increase in O3. The diurnal patterns show that the morning peaks of traffic-related NO2 and CO almost disappeared. Additionally, our results suggested that meteorological effects offset some of the reduction in pollutant concentrations. Adverse meteorological conditions played a leading role in the variation in PM2.5 concentration in Beijing, which contributed to +33.5%. The true effect of lockdown reduced the PM2.5 concentrations in Wuhan, Beijing, and Urumqi by approximately 14.6%, 17.0%, and 34.0%, respectively. In summary, lockdown is the most important driver of the decline in pollutant concentrations, but the reduction of SO2 and CO is limited and they are mainly influenced by changing trends. This study provides insights into quantifying variations in air quality due to the lockdown by considering meteorological variability, which varies greatly from city to city, and provides a reference for changes in city scale pollutant concentrations during the lockdown.