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The negative impacts of fine particulate matter (PM) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the datasets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and datasets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004-2011. Using the Burnett et al. (2014) integrated exposure response function, we estimate that in the United States, exposure to PM accounts for approximately 2% of total deaths compared to 14% in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 14% for the U.S. and 2% for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM burden from satellite observations, which we quantify to be on the order of 20% due to uncertainties in the AOD-to-surface-PM relationship, 10% due to the satellite observational uncertainty, and 30% or greater uncertainty associated with the application of concentration response functions to estimated exposure.
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http://dx.doi.org/10.5194/acp-16-3499-2016 | DOI Listing |
Sci Total Environ
September 2025
European Commission, Joint Research Centre (JRC), Ispra, Italy. Electronic address:
Drought stress has profound impacts on ecosystems and societies, particularly in the context of climate change. Traditional drought indicators, which often rely on integrated water budget anomalies at various time scales, provide valuable insights but often fail to deliver clear, real-time assessments of vegetation stress. This study introduces the Cooling Efficiency Factor Index (CEFI), a novel metric purely derived from geostationary satellite observations, to detect vegetation drought stress by analyzing daytime surface warming anomalies.
View Article and Find Full Text PDFPediatr Allergy Immunol
September 2025
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
Background: Residential greenness is an important environmental factor potentially influencing the development of allergic diseases in adolescents; however, its impact remains understudied in South Korea. This study aimed to examine the association between residential greenness and allergic disease prevalence using nationally representative data.
Method: We analyzed data from 1,130,598 adolescents (7-12th grade) participating in the Korean Youth Risk Behavior Web-based Survey (2007-2024).
J Air Waste Manag Assoc
September 2025
Desert Research Institute, Reno, Nevada, USA.
SmokePath Explorer is a web-based decision-support tool for California, U.S.A.
View Article and Find Full Text PDFAust Vet J
September 2025
Faculty of Agricultural and Environmental Sciences, University of Salamanca, Salamanca, Spain.
Geotechnologies, such as Global Navigation Satellite Systems (GNSS) and remote sensing, are essential for documenting topographic features and analyzing land use. Among them, the GPS (Global Position System)-based sensors have proven highly effective in monitoring livestock, providing high-resolution data on movement patterns. This study tracked two Hispano-Breton mares in the Spanish Pyrenees during summer 2023 using GPS collars.
View Article and Find Full Text PDFEnviron Sci Technol
September 2025
Sustainable Energy and Environmental Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China.
Ammonia (NH) has attracted increasing attention for its reduction potential in fine particulate matter mitigation, yet current NH emission inventories involve substantial uncertainties. Previous bottom-up NH inventories are usually constrained by satellite observations, deposition measurements, or isotopic analysis and still lack careful validation at fine regional scales. This study develops a novel diagnostic framework combining multisite NH observations across the Pearl River Delta (PRD) with the Community Multiscale Air Quality (CMAQ) model simulations and machine learning techniques to evaluate and refine a regional NH inventory.
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