DATA DESCRIPTION
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Estimates of annual average fine particulate matter (PM2.5) are available between 2010 and 2019 using the Data Integration Model for Air Quality (DIMAQ), which was originally developed globally for the World Health Organization at a 100km2 resolution and later refined to a 1 km2 resolution for the Gateway to Global Aging Data project. We have supplemented these estimates with monthly predictions between 1998 and 2022 from a Geographically Weighted Regression (GWR) model developed for the Institute for Health Metrics and Evaluation Global Burden of Disease Project at a 1 km2 resolution. Both models leverage satellite data, a chemical transport model, meteorological information, ground-based monitoring data, and local characteristics of place to generate estimates.
| Years | 1998-2022 |
| 0.0083⁰ (~1km2) | |
| Units | Micrograms per cubic meter of air (μg/m3) |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | G. Shaddick et al. (2018). Data integration model for air quality: A hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society Series C: Applied Statistics. van Donkelaar A. et al. (2021). Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty. Environmental Science & Technology. |
| Source | Atmospheric Composition Analysis Group |
Distribution of PM2.5 by country in 2019
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.
Estimates of fine particulate matter (PM2.5) from different emission sources were derived through repeated simulations using chemical transport models for the Global Burden of Disease from Major Pollution Sources (GBD MAPS) project. Sources include agriculture, energy production, industry, commercial combustion, residential combustion, transportation, international shipping, wildfires, and windblown dust.
| Years | 2017 |
Resolution | 0.5⁰ x 0.625⁰ (~55km x ~70km) in North America, Europe, and East Asia; 2⁰ x 2.5⁰ (~225km x ~275km) elsewhere |
| Units | Proportion |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | E. E. McDuffie et al. (2021). Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales. Nature Communications. |
| Source | European Organization for Nuclear Research (CERN) |
Distribution of PM2.5 from Road Transportation by country in 2017
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.
Spatial estimates of nitrogen dioxide (NO2) concentrations have been predicted at a resolution of 0.0083⁰ (~1km2) every 5-years from 1990 to 2010 and then annually from 2010 to 2019. These estimates were generated by extending an existing spatial prediction model derived with ground measurements, land use information and satellite data to later years and refining this model for better performance in rural areas.
| Years | 1990,1995,2000,2005-2020 |
| 0.0083⁰ (~1km2) | |
| Units | Parts per billion (ppb) |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | S. C. Anenberg et al. (2022). Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets. The Lancet Planetary Health. |
| Source | George Washington University |
Distribution of NO2 by country in 2020
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.
Estimates of ground-level ozone (O3) concentrations were originally derived for the Global Burden of Disease (GBD) Study, which quantifies ozone exposure as the annual maximum of the 6-month running mean of the monthly average daily maximum 8-hour mixing ratio (OSDMA8). These estimates integrate information from ground-level measurements and simulations from numerous chemical transport models. They also use local and flexible bias corrections to optimize their predictions.
| Years | 1990-2020 |
| 0.1⁰ (~11km2) | |
| Units | Parts per billion (ppb) |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | J. S. Becker et al. (2023). Using regionalized air quality model performance and bayesian maximum entropy data fusion to map global surface ozone concentration. Elementa: Science of the Anthropocene. |
| Source | Jason West at University of North Carolina |
Distribution of O3 by country in 2020
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.
Estimates of annual average nighttime light radiance were derived using satellite measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS). Annual estimates are calculated by taking the median of monthly cloud-free averages and zeroing out background noise, biomass burning, and aurora.
| Years | 2012-2021 |
| 0.0042⁰ (~500m2) | |
| Units | Nanowatts per square centimeter per steradian (nW/cm2/sr) |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | C. D. Elvidge et al. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing. |
| Source | Earth Observation Group |
Distribution of Nighttime Light by country in 2021
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.
Estimates of greenspace were derived from the Normalized Difference Vegetation Index (NDVI) generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA’s Terra satellite. We have calculated the maximum, minimum, and mean NDVI for each year to capture seasonal changes in vegetation.
| Years | 2000-2021 |
| 250 meters (0.06 km2) | |
| Units | Indexed (-1 to +1) |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | Didan, K. et al. (2015). MODIS vegetation index user’s guide (MOD13 series v006). |
| Source | U.S. Geological Survey |
Distribution of NDVI by country in 2021
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.
Estimates of surface water exposure were derived from a static global map distinguishing water bodies from land. This binary dataset was generated by integrating multiple satellite datasets spanning 2000-2012 at a 150-meter resolution.
| Years | 2000-2012 |
| 150 meters (0.02 km2) | |
| Units | Unitless |
| Countries | Brazil, Chile, England, India, Ireland, Mexico, Northern Ireland, United States |
| Reference | Lamarche, C. et al. (2017). Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing. |
| Source | ESA Climate Change Initiative |
Distribution of Blue Space by country in 2000
These graphics were generated using a random, population-weighted sample of 10,000 people from each country.