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Background: The study of ambient air particulate matter (PM 2.5 )-associated health and economic burdens of cardiovascular disease are crucial for air pollution control and disease prevention strategies. Quantified evidence remains inadequate., Objectives: This study aimed to estimate the PM 2.5 associated risk in cardiovascular hospital admission as well as attributable health burdens and economic costs., Methods: A total of 2,202,244 hospital admission records of cardiovascular disease and six common clinical subtypes in Beijing were included. A time-stratified case-crossover design was applied to estimate the associations and the concentration-response curve. Then, the annual average additional hospital admissions, days of hospital stay, and hospital expenditures were evaluated from 2013 to 2017 and compared between 2017 and 2013., Results: The results showed that each 10 μg/m 3 increase in previous-day PM 2.5 concentration was associated with a risk increase of 0.44% (95%CI: 0.40%, 0.47%) for cardiovascular disease, 0.66% (95%CI: 0.58%, 0.73%) for angina pectoris, 0.53% (95%CI: 0.39%, 0.66%) for chronic ischemic heart disease, 0.48% (95%CI: 0.34%, 0.63%) for myocardial infarction, 0.44% (95%CI: 0.29%, 0.60%) for hypertensive heart disease and 0.40% (95%CI: 0.27%, 0.52%) for ischemic stroke. There were 1938 PM 2.5 attributed additional hospital admissions, resulting in 21,668 additional days in hospital, along with 5527.12 and 1947.04 ten-thousand of additional total hospital cost and self-afforded cost, respectively. Compared with 2013, the above-mentioned four burdens decreased by 18.17%, 28.80%, 18.90% and 13.72% in 2017, respectively., Conclusion: PM 2.5 exposure was significantly associated with substantial burdens of cardiovascular hospital admission and economic expenditures. The results highlight the necessity of continuous PM 2.5 control from the perspective of healthy and sustainable city development in urban China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Heavy fine particulate matter with an aerodynamic diameter ≤2.5 μm (PM 2.5 ) pollution events continue to occur frequently in developing countries., Objectives: The authors conducted a case-crossover study aimed at exploring the association between heavy PM 2.5 pollution events and hospital admission for cardiovascular diseases., Methods: Hospital admissions for cardiovascular diseases were observed by Beijing Municipal Commission of Health and Family Planning Information Center from 2013 to 2017. Air pollution data were collected from the Beijing Municipal Environmental Monitoring Center. Distinct definitions were used to identify heavy and extremely heavy fine particulate pollution events. A conditional logistic regression model was used. The hospital admission burdens for cardiovascular disease were also estimated., Results: A total of 2,202,244 hospital admissions for cardiovascular diseases and 222 days of extremely heavy PM 2.5 pollution events (PM 2.5 concentration ≥150 μg/m 3 ) were observed. The ORs associated with extremely heavy PM 2.5 pollution events lasting for 3 days or more for total cardiovascular disease, angina, myocardial infarction, ischemic stroke, and heart failure were 1.085 (95% CI: 1.077-1.093), 1.112 (95% CI: 1.095-1.130), 1.068 (95% CI: 1.037-1.100), 1.071 (95% CI: 1.053-1.090), and 1.060 (95% CI: 1.021-1.101), respectively. The numbers and days of cardiovascular disease hospital admission annually related to extremely heavy PM 2.5 pollution events lasting for 1 day or more were 3,311 (95% CI: 2,969-3,655) and 37,020 (95% CI: 33,196-40,866), respectively., Conclusions: Heavy and extremely heavy PM 2.5 pollution events resulted in substantial increased hospital admission risk for cardiovascular disease. With higher PM 2.5 concentration and longer duration of heavy PM 2.5 pollution events, a greater risk of cardiovascular hospital admission was observed., Competing Interests: Funding Support and Author Disclosures This work was funded by National Research Program for Key Issues in Air Pollution Control grant DQGG0401, the Beijing Natural Science Foundation grant 7172145, and the National Natural Science Foundation of China grant 92043301. The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Ambient ozone (O 3 ) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01 ° × 0.01 ° resolution, and estimated daily maximum 8h average O 3 (O 3 -8hmax) concentration, daily average O 3 (O 3 -mean) concentration, and daily maximum 1h O 3 (O 3 -1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R 2 of sample-based O 3 -8hmax, O 3 -mean and O 3 -1hmax models were all greater than 0.80, while the R 2 of site-based and date-based model were 0.68-0.87. From 2010 to 2017, O 3 -8hmax, O 3 -mean, and O 3 -1hmax concentrations in the BTH region increased by 4.18 μg/m 3 , 0.11 μg/m 3 , and 4.71 μg/m 3 , especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O 3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

High spatiotemporal resolution fine particulate matter (PM 2.5 ) simulations can provide important exposure data for the assessment of long-term and short-term health effects. Satellite-based aerosol optical depth (AOD) data, meteorological data, and topographic data have become key variables for PM 2.5 estimation. In this study, a random forest model was developed and used to estimate the highest resolution (0.01° × 0.01°) daily PM 2.5 concentrations in the Beijing-Tianjin-Hebei region. Our model had a suitable performance (cv-R 2  = 0.83 and test-R 2  = 0.86). The regional test-R 2 value in southern Beijing-Tianjin-Hebei was higher than that in northern Beijing-Tianjin-Hebei. The model performance was excellent at medium to high PM 2.5 concentrations. Our study considered meteorological lag effects and found that the boundary layer height of the one-day lag had the most important contribution to the model. AOD and elevation factors were also important factors in the modeling process. High spatiotemporal resolution PM 2.5 concentrations in 2010-2016 were estimated using a random forest model, which was based on PM 2.5 measurements from 2013 to 2016., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)