Investigating Global Disparities in Air Quality Monitoring
Introduction
The global state of air quality monitoring
Air pollution is one of the most the most serious environmental threats to human health, causing an estimated seven million premature deaths worldwide each year. However, the most dangerous of these pollutants are particulate matter with a diameter smaller than 2.5 micrometers - PM2.5 - which leads to the exacerbation of numerous health conditions and the impairment of cognitive development in children.
In 2023, just 9% of globally reporting cities and 7% of countries achieved the World Health Organization annual PM2.5 guideline value of 5 µg/m³, highlighting the need for collective and coordinated actions across the globe. In this context, the first and fundamental step in addressing air quality issues is the development of solid and comparable long-term monitoring of air conditions - basic requirement for more informed public health policies, effective climate action, and the achievement of the Sustainable Development Goals.
However, the way in which global air quality is measured is uneven, producing flawed policies, misallocation of resources and incorrect prioritization between the Global South and the Global North. Tacking this into account, this site explores the unequal spatial distribution of air quality sensors worldwide, and investigates the potential mechanisms driving gaps.
Regional Disparities
Why the gaps?
Exploring some of the factors contributing to monitoring disparitiesPolitical factors influence the monitoring of many environmental risks, especially air quality. Democratic countries are often more proactive about introducing or permitting independent air quality monitoring, while less democratic systems often restrict data monitoring and transparency to avoid social unrest.
Policy acts as a primary catalyst for air quality monitoring by establishing the legal frameworks that mandate data collection. When cities implement stringent regulatory standards (such as WHO guidelines), they create regulatory pressure that necessitates dense sensor networks to ensure compliance and track pollution disparities.
Infrastructural and economic barriers significantly limit sensor distribution. Global datasets like AQICN require data from 'reference' stations that cost up to $120,000 and require stable power for hourly measurements and data updates - something many cities lack. While low-cost sensors offer an alternative, they remain scientifically invalid in many global datasets unless they are co-located with reference stations for weeks to calibrate them.
Reflection
Understanding inequalities in monitoring
PM2.5 satellite analysis reveals that much of the Global South, particularly South America, Africa, South and West Asia have seen concentrations remain persistently high or worsen over 25 years whilst wealthier regions have broadly improved. Sensor coverage mapping shows these are precisely the places where ground level monitoring is most sparse. Meaning that populations most exposed to dangerous air quality are often the least measured.
The City Vulnerability Index brings this disparity into focus at the city scale. Cities like Hyderabad and Cairo carry a compounded burden of high pollution, dense populations, and minimal sensor infrastructure. On the other end of the scale well-monitored cities in North America and in Europe, such as Chicago and Dublin, benefit from the kind of data that enables targeted intervention. The Korean case is particularly interesting with governance, not geography, explaining why one of the world's most data-rich sensor networks ends at the border with North Korea.
Expanding low-cost sensor networks in undermonitored regions particularly across the Global South would begin to close the data gap. However sensor deployment alone is insufficient without investment in costly calibration and maintenance. Ultimately, the disparities identified reflect broader inequalities in who gets to define and respond to environmental risk. Addressing air quality monitoring gaps is not just a scientific priority, it is a matter of environmental justice.
About the page
| Owner | Dataset | Description |
|---|---|---|
| AQICN | Air Sensor Points | Point locations of air sensors globally — accessed via the AQICN API |
| GeoBoundaries | Country Boundaries | Polygons of country boundaries globally |
| European Commission Joint Research Centre | GHS Gridded Population | Gridded raster layer of global population density |
| European Commission Joint Research Centre | GHS Urban Centres Data Base | Polygons of Urban Centres (cities) globally |
| NASA Earth Data | Global Annual PM2.5 Grids | Annual PM2.5 raster data from 1998-2022 at global level |
| Chicago Health Atlas | Chicago PM2.5 per census tract | Annual average PM2.5 (µg/m³) per Census Tract in Chicago |
| US Census Bureau | Illinois Census Tract boundaries | Illinois Census Tract boundaries |
| Uber Technologies | H3 Grid System | Global hex grid used for raster data aggregation into vector cells |
| Tune Inc. | pycountry-convert | Python library for mapping ISO country codes to their respective continents |
- Open Air Chicago (website)
- ETH Zurich Air Quality Research (paper)
- Assessment of Air Quality in North Korea from Satellite Observations (paper)
- Air Sensor Performance Targets and Testing Protocols (guide)
- AirQo African Cities (website)
- Air Pollution — Our World in Data (article)
- 2023 World Air Quality Report (report)
- Why Open Data? — OpenAQ (article)
- Air Quality Funding Report (report)
