Large gaps in monitoring urban air pollution in low- and middle- income countries associated with economic conditions and political institutions

Ambient air pollution has highly adverse effects on public health and the environment, particularly in urban areas of low- and middle- income countries (LMIC). Systematic air quality monitoring (AQM) is considered a precondition for effective policies to mitigate this problem, and making AQM data publicly available also signals commitment to take action. Thus far, little is known about the global capacity for public AQM, and how it varies across geographic location, pollution exposure, and socio-economic and political conditions. We thus constructed a novel, geocoded dataset on AQM activity in more than ten thousand urban areas of low- to middle-income countries. In almost 90% of these urban areas, we are unable to identify any monitoring activity, and the form and extent of AQM in the remaining 10% varies greatly. When modelling the occurrence and abundance of observable AQM at the city level, income levels and characteristics of political institutions (democracy) turn out to be key drivers of variation in AQM, with urban areas in more democratic countries likely to exhibit more AQM when air pollution levels are high. The evidence provided here could motivate public authorities, international institutions, and civil society stakeholders to invest far more than hitherto the case into AQM, particularly in under-monitored, less affluent, and less democratic settings.

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Publication Details

Journal
PLoS ONE
Published
2026-01-13
DOI
https://doi.org/10.1371/journal.pone.0321358
Citations
1
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
31.09

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article

Large gaps in monitoring urban air pollution in low- and middle- income countries associated with economic conditions and political institutions

Camille Fournier de Laurière, Maja Schoch, Thomas Bernauer
1 citations
PLoS ONE
Air Quality and Health Impacts
31.09
article

Large gaps in monitoring urban air pollution in low- and middle- income countries associated with economic conditions and political institutions

Camille Fournier de Laurière, Maja Schoch, Thomas Bernauer
article en
1 citations

Abstract

Ambient air pollution has highly adverse effects on public health and the environment, particularly in urban areas of low- and middle- income countries (LMIC). Systematic air quality monitoring (AQM) is considered a precondition for effective policies to mitigate this problem, and making AQM data publicly available also signals commitment to take action. Thus far, little is known about the global capacity for public AQM, and how it varies across geographic location, pollution exposure, and socio-economic and political conditions. We thus constructed a novel, geocoded dataset on AQM activity in more than ten thousand urban areas of low- to middle-income countries. In almost 90% of these urban areas, we are unable to identify any monitoring activity, and the form and extent of AQM in the remaining 10% varies greatly. When modelling the occurrence and abundance of observable AQM at the city level, income levels and characteristics of political institutions (democracy) turn out to be key drivers of variation in AQM, with urban areas in more democratic countries likely to exhibit more AQM when air pollution levels are high. The evidence provided here could motivate public authorities, international institutions, and civil society stakeholders to invest far more than hitherto the case into AQM, particularly in under-monitored, less affluent, and less democratic settings.

PLoS ONEVol. 21(1)
ETH Zurich (CH)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 2%
Air Quality and Health Impacts
31.09
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Large gaps in monitoring urban air pollution in low- and middle- income countries associated with economic conditions and political institutions — Camille Fournier de Laurière, Maja Schoch, et al. · PLoS ONE (2026) | TGRS Research Map | TGRS