Environment Database - Exposure to PM2.5
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November 2018
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Population exposure to fine particles

Dataset documentation

Air pollution is one of the most pressing environmental and health issues across OECD countries and beyond. 

Fine particulate matter (PM2.5) is the air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator.

The underlying PM2.5 concentration estimates are taken from the Global Burden of Disease (GBD) 2017 project. They are derived by integrating satellite observations, chemical transport models and measurements from ground monitoring station networks.

The concentration estimates are population-weighted using gridded population datasets from the Joint Research Center Global Human Settlement project. These are produced by distributing census-derived population estimates from the Gridded Population of the World, version 4 from the NASA Socioeconomic Data and Applications Center according to the density and distribution of built-up areas. 

The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available.

The share of exposure attributable to mineral dust and sea salt field should be regarded as approximate. It is calculated using the difference in exposure between the dust/no dust estimates from van Donkelaar et al. (2016). 

The accuracy of these exposure estimates varies considerably by location. Accuracy is particularly poor in areas with few monitoring stations and in areas with very high concentrations such as Africa, the Middle-East and South Asia. Accuracy is generally good in regions with dense monitoring station networks (such as most advanced economies). See Shaddick et al. (2018) for further details.

Estimates for Turkey have been excluded for the present because estimates from GBD 2017 appear irreconcilable with observations from  a recently-installed PM2.5 monitoring network that were not included in the GBD concentration estimation because of time lag between different databases. The arithmetic (non population-weighted) mean concentration recorded by  Turkey's PM2.5 monitoring network was approx. 28 µg/m3 in 2017.

References:

Shaddick, G., Thomas, M., Amini, H., Broday, D.M., Cohen, A., Frostad, J., Green, A., Gumy, S., Liu, Y., Martin, R.V., Prüss-Üstün, A., Simpson, D., van Donkelaar, A., Brauer, M. (2018) Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. doi: 10.1021/acs.est.8b02864
Note: This paper details the methodology for GBD 2015 and GBD 2016 exposure estimates, there have been minor changes for GBD 2017 (corresponding publication is forthcoming).

 

European Commission, Joint Research Centre (JRC); Columbia University, Center for International Earth Science Information Network - CIESIN (2015): GHS population grid, derived from GPW4, multitemporal (1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

 

FAO (2015), The Global Administrative Unit Layers (GAUL) 2014 dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects. Available at http://www.fao.org/geonetwork/srv/en/main.home.

 

WHO (2018) Factsheet on ambient air quality and health. Available at http://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health

 

Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., ... & Winker, D. M. (2016). Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental science & technology50(7), 3762-3772. http://dx.doi.org/10.1021/acs.est.5b05833 https://fizz.phys.dal.ca/~atmos/martin/?page_id=140

 

Mackie, A., I. Hašcicand M. Cárdenas Rodríguez (2016), "Population Exposure to Fine Particles: Methodology and Results for OECD and G20 Countries", OECD Green Growth Papers, No. 2016/02, OECD Publishing, Paris. http://dx.doi.org/10.1787/5jlsqs8g1t9r-en

Environment Database - Exposure to PM2.5Contact person/organisation
env.stat@oecd.org
Date last updated
November 2018
Key statistical concept

Population exposure to fine particles

Dataset documentation

Air pollution is one of the most pressing environmental and health issues across OECD countries and beyond. 

Fine particulate matter (PM2.5) is the air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator.

The underlying PM2.5 concentration estimates are taken from the Global Burden of Disease (GBD) 2017 project. They are derived by integrating satellite observations, chemical transport models and measurements from ground monitoring station networks.

The concentration estimates are population-weighted using gridded population datasets from the Joint Research Center Global Human Settlement project. These are produced by distributing census-derived population estimates from the Gridded Population of the World, version 4 from the NASA Socioeconomic Data and Applications Center according to the density and distribution of built-up areas. 

The underlying boundary geometries are taken from the Global Administrative Unit Layers (GAUL) developed by the FAO, and the OECD Territorial Classification, when available.

The share of exposure attributable to mineral dust and sea salt field should be regarded as approximate. It is calculated using the difference in exposure between the dust/no dust estimates from van Donkelaar et al. (2016). 

The accuracy of these exposure estimates varies considerably by location. Accuracy is particularly poor in areas with few monitoring stations and in areas with very high concentrations such as Africa, the Middle-East and South Asia. Accuracy is generally good in regions with dense monitoring station networks (such as most advanced economies). See Shaddick et al. (2018) for further details.

Estimates for Turkey have been excluded for the present because estimates from GBD 2017 appear irreconcilable with observations from  a recently-installed PM2.5 monitoring network that were not included in the GBD concentration estimation because of time lag between different databases. The arithmetic (non population-weighted) mean concentration recorded by  Turkey's PM2.5 monitoring network was approx. 28 µg/m3 in 2017.

References:

Shaddick, G., Thomas, M., Amini, H., Broday, D.M., Cohen, A., Frostad, J., Green, A., Gumy, S., Liu, Y., Martin, R.V., Prüss-Üstün, A., Simpson, D., van Donkelaar, A., Brauer, M. (2018) Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. doi: 10.1021/acs.est.8b02864
Note: This paper details the methodology for GBD 2015 and GBD 2016 exposure estimates, there have been minor changes for GBD 2017 (corresponding publication is forthcoming).

 

European Commission, Joint Research Centre (JRC); Columbia University, Center for International Earth Science Information Network - CIESIN (2015): GHS population grid, derived from GPW4, multitemporal (1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

 

FAO (2015), The Global Administrative Unit Layers (GAUL) 2014 dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects. Available at http://www.fao.org/geonetwork/srv/en/main.home.

 

WHO (2018) Factsheet on ambient air quality and health. Available at http://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health

 

Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., ... & Winker, D. M. (2016). Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental science & technology50(7), 3762-3772. http://dx.doi.org/10.1021/acs.est.5b05833 https://fizz.phys.dal.ca/~atmos/martin/?page_id=140

 

Mackie, A., I. Hašcicand M. Cárdenas Rodríguez (2016), "Population Exposure to Fine Particles: Methodology and Results for OECD and G20 Countries", OECD Green Growth Papers, No. 2016/02, OECD Publishing, Paris. http://dx.doi.org/10.1787/5jlsqs8g1t9r-en