We breathe over 10 kilograms of air every single day, far more than the food and water we consume (Pleil et al., 2021). And yet, air is invisible, intangible, and often taken for granted until it makes us cough, wheeze, or stay inside due to smoke. But the air we breathe is a personal experience as well as a measurable quantity. Those measurements are what shape public health, environmental justice, policy, and global climate action.
This blog post is about the data. What air data exists? How do we collect it? Who uses it and why? Where does it fall short? And how will improving it change lives?
What “Air Data” Means: A Map of the Measurement Landscape
“Air data” refers to all the measurements, models, and records that describe what’s in the air, and how it changes across space and time. But there is no single dataset. Instead, we have different monitoring systems that each capture a different piece of the puzzle.
- Regulatory Monitoring Networks
The backbone of public air quality reporting comes from government-operated reference monitors, the ones used by systems like EPA AirNow in the U.S., UK Defra in the United Kingdom, or India’s CPCB monitoring stations (Carotenuto et al., 2023; Ganguly et al., 2020).
- They use high-precision, calibrated reference monitors
- They determine compliance with national air quality standards
However, because these monitors are expensive to install and maintain, they are often few and far between. Many cities have only one or two monitors, meaning entire neighborhoods can be left unmeasured, even while pollution varies dramatically from block to block.
- Low-Cost Sensors & Citizen Networks
In contrast, low-cost air sensors such as PurpleAir, Clarity, or AirCasting have expanded rapidly. They offer dense networks of readings that reveal hyperlocal air variations. For example, air might be much worse near a freeway, refinery, or school drop-off zone.
- Offer high spatial coverage at low cost
- Require correction and calibration using statistical correction methods because readings drift and vary with humidity and temperature (Raheja et al., 2023).
- Satellite Remote Sensing
Satellites orbiting hundreds of kilometers above Earth collect data about atmospheric gases and aerosols.
- TROPOMI (ESA) on the Sentinel-5P satellite detects nitrogen dioxide, methane, and other trace gases
- MODIS (NASA) estimates aerosol optical depth (AOD), used as a proxy for PM2.5
However, satellites do not directly measure surface-level air; they measure what is present in the full atmospheric column. To convert satellite observations to ground-level PM2.5 estimates, scientists combine them with models and surface monitors (Qayyum et al., 2022).
- Chemical Transport Models & Reanalyses
Atmospheric models, including NASA’s GEOS-Chem and the Copernicus Atmospheric Monitoring Service (CAMS), mathematically simulate how pollutants move and transform in the atmosphere. These models merge real observations with meteorology, producing continuous global air quality fields, even in regions with no monitors. They are essential for forecasting, policy evaluation, and global burden studies (Gualtieri et al., 2025; Keller et al., 2021).
- Emissions Inventories
To understand why pollution exists, we need estimates of how much pollution is being emitted and from where. Emissions inventories like EDGAR record pollution from transportation, industry, agriculture, energy, and waste. These inventories feed directly into regulatory strategy and climate negotiation (Banja et al., 2025).
- Mobile Monitoring & Field Campaigns
For deeply local questions like which street, factory block, or neighborhood experiences the worst air, scientists use mobile monitoring vans or aircraft research campaigns such as NASA’s DISCOVER-AQ. These produce detailed snapshots that can reveal pollution “hotspots” invisible to both satellites and stationary monitors (Blanco et al., 2023).
- Health & Demographic Data
Finally, air pollution only matters because of what it does to people. To calculate health impacts, researchers combine pollution concentration maps with population density, hospital admissions, disease statistics, and mortality records. This is the basis of the Global Burden of Disease (GBD) framework that estimates how many deaths are attributed to air pollution each year.
Where to Get the Data
| Platform | What It Offers | Why It Matters |
| OpenAQ | Harmonized global air data from governments + research + citizen sensors | Enables cross-city comparisons and transparency |
| NASA EarthData / ESA Sentinel Hub | Satellite NO2, ozone, fire, AOD datasets | Critical for regions lacking monitors |
| CAMS Reanalysis | Global PM2.5, ozone, gas fields updated daily | Useful for forecasting & research |
| EPA AirNow & global national systems | Regulatory-grade daily & historical monitoring | Used for standards and compliance |
| PurpleAir Maps | Hyperlocal sensor data | Reveals neighborhood air variations |
| GBD, IHME, HEI State of Global Air | Health impacts and long-term trends | Links pollution to disease burden |
Measuring What Matters
Air is something we move through constantly, but rarely think about. It has no color, no weight in the hand, no edges to trace. Yet it enters our bodies with every breath, shaping our lungs, our hearts, our development, our life expectancy. The only way we truly come to know the air is through data.
The monitors on city rooftops, sensors on school fences, satellites orbiting hundreds of kilometers overhead, and atmospheric models running on supercomputers are all part of a shared effort to make the invisible visible. Each method has strengths and gaps. Regulatory monitors can offer precision, but not coverage. Low-cost sensors offer coverage, but need correction. Satellites offer a global view, but require models to interpret. Health datasets show the cost, but only when paired with exposure estimates.
Taken together, they form a measurement ecosystem—one that allows scientists, policymakers, health researchers, environmental justice advocates, and communities to talk about the same reality.
The next step is what we do with the data. In the next blog, we’ll look at how these measurements become real-world action, from global health guidelines to school street closures, or from wildfire emergency alerts to national clean air laws. We’ll see how communities, scientists, and policymakers turn numbers into decisions, and decisions into healthier air for millions.
References
Banja, M., Crippa, M., Guizzardi, D., Muntean, M., Pagani, F., & Pisoni, E. (2025). A comparative analysis of EDGAR and UNFCCC GHG emissions inventories: insights on trends, methodology and data discrepancies. https://doi.org/10.5194/essd-2025-385
Blanco, M. N., Bi, J., Austin, E., Larson, T. V., Marshall, J. D., & Sheppard, L. (2023). Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models. Environmental Science & Technology, 57(1), 440–450. https://doi.org/10.1021/acs.est.2c05338
Carotenuto, F., Bisignano, A., Brilli, L., Gualtieri, G., & Giovannini, L. (2023). Low‐cost air quality monitoring networks for long‐term field campaigns: A review. Meteorological Applications, 30(6). https://doi.org/10.1002/met.2161
Ganguly, T., Selvaraj, K. L., & Guttikunda, S. K. (2020). National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans. Atmospheric Environment: X, 8, 100096. https://doi.org/10.1016/j.aeaoa.2020.100096
Gualtieri, G., Brilli, L., Carotenuto, F., Cavaliere, A., Gioli, B., Giordano, T., Putzolu, S., Vagnoli, C., & Zaldei, A. (2025). Assessing capability of Copernicus Atmosphere Monitoring Service to forecast PM2.5 and PM10 hourly concentrations in a European air quality hotspot. Atmospheric Pollution Research, 16(8), 102567. https://doi.org/10.1016/j.apr.2025.102567
Keller, C. A., Knowland, K. E., Duncan, B. N., Liu, J., Anderson, D. C., Das, S., Lucchesi, R. A., Lundgren, E. W., Nicely, J. M., Nielsen, E., Ott, L. E., Saunders, E., Strode, S. A., Wales, P. A., Jacob, D. J., & Pawson, S. (2021). Description of the NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0. Journal of Advances in Modeling Earth Systems, 13(4). https://doi.org/10.1029/2020MS002413
Pleil, J. D., Ariel Geer Wallace, M., Davis, M. D., & Matty, C. M. (2021). The physics of human breathing: flow, timing, volume, and pressure parameters for normal, on-demand, and ventilator respiration. Journal of Breath Research, 15(4), 042002. https://doi.org/10.1088/1752-7163/ac2589
Qayyum, F., Tariq, S., ul-Haq, Z., Mehmood, U., & Zeydan, Ö. (2022). Air pollution trends measured from MODIS and TROPOMI: AOD and CO over Pakistan. Journal of Atmospheric Chemistry, 79(3), 199–217. https://doi.org/10.1007/s10874-022-09436-1
Raheja, G., Nimo, J., Appoh, E. K.-E., Essien, B., Sunu, M., Nyante, J., Amegah, M., Quansah, R., Arku, R. E., Penn, S. L., Giordano, M. R., Zheng, Z., Jack, D., Chillrud, S., Amegah, K., Subramanian, R., Pinder, R., Appah-Sampong, E., Tetteh, E. N., … Westervelt, D. M. (2023). Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana. Environmental Science & Technology, 57(29), 10708–10720. https://doi.org/10.1021/acs.est.2c09264