AI Article Synopsis
Article Abstract
Exposure to ambient fine particulate matter (PM) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM to interpret globally dispersed PM mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM composition varies substantially for secondary inorganic aerosols (2.4-19.7 μg/m), mineral dust (1.9-14.7 μg/m), residual/organic matter (2.1-40.2 μg/m), and black carbon (1.0-7.3 μg/m). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 μg/m), industry (6.5 μg/m), and power generation (5.6 μg/m) are leading sources of outdoor global population-weighted PM concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM provides insight into sources and processes that influence the global spatial variation in PM composition.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acs.est.8b01658 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
PM and hospitalizations through the emergency department in people with disabilities: a nationwide case-crossover study in South Korea.
Lancet Reg Health West Pac
December 2024
School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Yangsan, South Korea.
Background: Little is known about the impact of PM on people with disabilities. We aimed to explore the association between PM and hospitalization via the emergency department (ED admission) among people with disabilities, together with the attributable ED admission cases and costs.
Methods: We applied a time-stratified case-crossover design adjusting ozone, holiday, and temperature using seven years (2015-2021) of claim-based data on ED admissions from the Korean National Health Insurance Database.
Collaborative control path of pollution and carbon reduction in industrial field in Hohhot-Baotou-Ordos region of Inner Mongolia.
Heliyon
December 2024
Center for Pollution and Carbon Reduction, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
The collaborative control of pollution and carbon emission reduction in industrial fields is crucial for improving regional air quality and mitigating climate change. However, to our knowledge, there is limited research regarding the collaborative control paths for reducing industrial pollution and carbon emissions. The present study assesses the potential for reducing emissions of air pollutants and carbon dioxide (CO) in the industries operating in the Hohhot-Baotou-Ordos (HBO) region of Inner Mongolia.
View Article and Find Full Text PDFLong-term associations between ambient air pollution and self-perceived health status: Results from the population-based KORA-Fit study.
Int J Hyg Environ Health
December 2024
Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Background: Little is known about the association between air pollution and self-perceived health (including both health-related quality of life [HRQoL] and self-rated health [SRH]). The aim of this study was therefore to explore whether long-term air pollution exposure is associated with worse self-perceived health, as measured by different tools.
Methods: We used a land-use regression model to determine the annual average levels of particulate matter with a diameter <10 μm (PM), coarse particles (PM), fine particles (PM), fine particle absorbances (PM), particle number concentration (PNC), ozone (O), nitrogen dioxide (NO), and nitrogen oxide (NO) for geocoded residential addresses (2014-2015).
Effects of PM components on hypertension and diabetes: Assessing the mitigating influence of green spaces.
Sci Total Environ
December 2024
Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China; Tianjin Key Laboratory of Environment, Nutrition, and Public Health, Tianjin Medical University, Tianjin 300070, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China; Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin 300070, China.
Background: Particulate matter with diameters ≤2.5 μm (PM) is a significant air pollutant associated with hypertension and diabetes. However, the specific contributions of its components and their joint exposure with green spaces remain poorly understood, especially in developing regions.
View Article and Find Full Text PDFImproving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.
Environ Int
December 2024
School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2.5 μm) predictions. This study compared the optimization effects of these two methods and developed a new scheme that combines DA and BC simultaneously.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!