Toxic Schools ↗ Open map | Center for Global Development
CGD Working Paper  ·  June 2026

Schools in the Shadow of Toxic Sites: Pollution Proximity in Low- and Middle-Income Countries

Lee Crawfurd  ·  Center for Global Development

How many children in low- and middle-income countries attend school near sources of pollution? We match the locations of about 2.6 million schools across 17 countries to 11,301 documented toxic sites — combining the Pure Earth Toxic Sites Identification Programme with national government contaminated-site registers in India, Brazil (São Paulo), Mexico and Peru — and find that 9.7% of schools lie within 5 km of a documented site. Weighting by enrolment, 12.7% of students in the seven countries with enrolment data attend a school within this distance. These remain lower bounds: the available data capture only a fraction of actual contaminated sites. Proximity is overwhelmingly an urban phenomenon — urban schools are 4 to 28 times more likely than rural schools to be near a site, depending on the country. Within countries, schools in the wealthiest neighbourhood quintile are about 14 times more likely to be near a site than schools in the poorest (34.5% versus 2.4%), reflecting the spatial concentration of industry in wealthier urban areas of LMICs — the opposite of the US environmental-justice pattern. Where data on school management are available, private schools are also more likely than public schools to be near sites in all eight such countries.

↗ Open interactive map Replication data (Zenodo) CGD Working Paper

Key findings

11,301
Documented contaminated sites across 17 countries (TSIP + 4 govt registers)
2.6M
Geocoded schools analysed
252,809
Schools within 5 km of a documented site (9.7%)
12.7%
Students within 5 km (7 countries with enrolment data)
14×
Wealthiest neighbourhood quintile vs poorest (within-country, enrolment-weighted)
8 / 8
Countries where private schools are more proximate to polluted sites than public schools

Schools near polluted sites are located in wealthier neighbourhoods in 16 of 17 countries, reflecting the concentration of private schools, industry, and economic activity in urban cores. Within urban areas, private schools near polluted sites are in wealthier neighbourhoods than unexposed urban private schools — inconsistent with a land-market sorting explanation and consistent with all three (schools, industry, wealth) clustering together in the urban core.

Caveat: these figures measure proximity to documented sites, not confirmed exposure or health outcomes. The TSIP also includes inactive and legacy sites. Proximity is a necessary but not sufficient condition for exposure, and these estimates are best understood as indicators of potential risk that identify schools warranting further environmental investigation.

About the interactive map

The interactive map displays all 4,596 TSIP sites globally — coloured by industry type and sized by number of nearby schools — alongside school locations for the 14 countries covered by Overture Maps in our dataset. Click any site to see its name, industry category, country, and a list of the nearest schools with distances.

Map coverage note: the map shows schools for 14 countries derived from Overture Maps / OpenStreetMap. Five additional paper countries (Argentina, Bangladesh, Mexico, Pakistan, Uruguay) use national EMIS data not yet included in the map layer. The paper's 6.3% proximity figure covers all 17 countries including these five.

Data sources

Contaminated sites — Pure Earth TSIP

Polluted site locations come from the Toxic Sites Identification Program (TSIP) maintained by Pure Earth, downloaded in March 2026 via the contaminatedsites.org API. The TSIP is the most comprehensive global inventory of contaminated sites in lower-income settings, drawing on government requests, community reports, academic research, and media coverage. It covers a range of pollutants: lead (1,582 sites globally), mercury (620), arsenic (464), chromium (404), and others, including both active and inactive legacy sites.

The TSIP substantially undercounts actual contaminated sites. Estimates suggest it captures one-seventh to one-ninth of real sites in Ghana (Dowling et al., 2016). All proximity figures in the paper are therefore lower bounds on true school–pollution co-location.

School locations

The paper uses national school census (EMIS) data for 11 countries and Overture Maps data for 6 additional countries where public geocoded EMIS data are not available. The map displays schools for the 14 countries where Overture Maps school data were available.

Country Source Schools Year In map
IndiaUDISE+ (Ministry of Education)1,372,1952022/23✓ map
IndonesiaDapodik (Ministry of Education)396,2802024✓ map
BrazilINEP Censo Escolar150,1312023✓ map
MexicoSEP Catálogo de Centros de Trabajo270,2282025—
ArgentinaMapa Educativo Nacional (MEN)64,6382023—
PhilippinesDepEd school master list16,0222023✓ map
PeruMINEDU Padrón de IE175,7542024✓ map
GhanaESRAG EMIS (geocoded via GhanaPostGPS)22,5812020/21✓ map
KenyaNEMIS (Ministry of Education)26,1972023✓ map
ColombiaSISE (DANE)56,6502024✓ map
BangladeshLGED education facilities (HDX)78,1292023—
PakistanPunjab EMIS———
UruguayANEP———
VietnamOverture Maps (Feb 2026)67,0872026✓ map
CambodiaOverture Maps (Feb 2026)17,1482026✓ map
KazakhstanOverture Maps (Feb 2026)6,3652026✓ map
KyrgyzstanOverture Maps (Feb 2026)1,9352026✓ map
ArmeniaOverture Maps (Feb 2026)1,3792026✓ map
MongoliaOverture Maps (Feb 2026)7952026✓ map

Overture Maps data are available under the CDLA Permissive 2.0 licence. National EMIS datasets are obtained from government open-data portals; access conditions for each country are documented in the replication package.

Neighbourhood wealth — Meta Relative Wealth Index

The paper uses the Meta Relative Wealth Index (RWI) (Chi et al., 2022), providing gridded estimates of relative household wealth at 2.4 km resolution for all LMICs, derived from machine learning on satellite imagery and connectivity data. RWI data are available from the Humanitarian Data Exchange.

Replication

All Python analysis code, raw school census data, TSIP pollution site data, government contaminated site registers, and generated tables and figures are archived at:

https://doi.org/10.5281/zenodo.19359187

Citation

Crawfurd, Lee (2026). "Schools in the Shadow of Toxic Sites: Pollution Proximity in Low- and Middle-Income Countries." Center for Global Development Working Paper. https://doi.org/10.5281/zenodo.19359187