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Help & FAQ

OpenAQ provides a REST API for programmatic access of the entire data set. Bulk data downloads are also available via Amazon Web Services S3 object storage downloads. OpenAQ also provides an interactive web application for browsing and searching the database from a graphical user interface.

OpenAQ ingests ambient air quality data measurements from ground-level stations. Data must be ‘raw’ and reported in physical concentrations on their originating site. Data cannot be shared in an ‘Air Quality Index’ or equivalent format. Data must be at the ‘station-level,’ associable with geographic coordinates, not aggregated into a higher (e.g., city) level. Data should be from measurements averaged between 10 minutes and 24 hours.

For the best support and to connect with others who might have encountered similar challenges, we highly encourage you to join our Slack community or submit issues on our GitHub page at https://github.com/openaq in the relevant repository. Slack is a fantastic place to engage in real-time discussions with our team and fellow users, allowing for quick exchanges of ideas, troubleshooting tips, and shared experiences. It’s more than just a support channel—it’s a space where you can be part of an active, collaborative community that’s eager to help each other out.

On the other hand, GitHub is perfect for more detailed issue tracking and problem-solving. By submitting issues or browsing through existing ones, you can see how others have tackled similar problems and contribute your own solutions. GitHub’s structured format makes it easy to document and follow up on bugs, feature requests, and ongoing discussions, ensuring that valuable insights aren’t lost.

Both Slack and GitHub are designed to foster community interaction and shared learning. Whether you’re asking questions, offering advice, or just exploring what others have done, these platforms are powerful tools for building knowledge together and helping each other succeed. We believe that by participating in these communities, you’ll not only get the support you need but also contribute to a collective learning environment where everyone benefits.

The OpenAQ database currently ingests the following pollutant data, with a focus on those in bold.

  • PM1 - particulate matter 1 microns (μm)
  • PM2.5 - particulate matter 2.5 microns (μm)
  • PM4 - particulate matter 4 microns (μm)
  • PM10 - particulate matter 10 microns (μm)
  • BC - black carbon particulates, part of PM2.5
  • O₃ - Ozone gas
  • CO - Carbon monoxide gas
  • NO₂ - Nitrogen dioxide gas
  • NO - Nitrogen monoxide gas
  • NOx - Nitrogen oxides
  • SO₂ - Sulfur dioxide gas
  • CH₄ - Methane gas
  • CO₂ - Carbon dioxide gas

For information on these pollutants, their sources, their health risks, and recommended guideline values, visit World Health Organization’s “Types of pollutants” web page . Britannica’s air pollution page is another good source of information on air and climate pollutants.

Units of measurement

Measurements of pollutants are reported in a variety of units depending on how the data is reported from the original data provider. Units are not normalized in the OpenAQ system, with the exception of converting ppm (parts per million) to ppb (parts per billion). Volume units are not converted to mass units, nor vice versa; they are served as originally reported.

Air quality data is factual in nature, and in some jurisdictions may not be subject to copyright or other protections limiting its use or distribution. However, in some jurisdictions, copyright and/or laws and regulations may apply to some of the data on the OpenAQ platform.

A number of our sources provide their air quality data under specific licensing, such as Creative Commons licensing or open government licenses, which require source attribution. Regardless of whether such requirements exist or not, we provide attribution to the underlying source of data ingested onto the OpenAQ Platform.

When required, OpenAQ users must acknowledge the original source(s) of any data they use, following the provider’s terms, and OpenAQ encourages users to acknowledge all original sources, even if the provider does not require it.

Read our Data Policy for more information.

When you use OpenAQ and its tools to access air quality data, we request attributions both to the original data provider and OpenAQ whenever the original data provider is known. Suggested citations:

If data provider is known (via metadata):

Data Provider. (202X). Dataset Title (if known). OpenAQ API Available from: https://api.openaq.org

General OpenAQ citation:

OpenAQ, Inc. (202X). OpenAQ API Available from: https://openaq.org

If using bibtex . For author include OpenAQ and any data providers from metadata in accordance with source license.

 @misc{OpenAQ,
   author = {{ OpenAQ }},
   title = {{Retrieved from https://api.openaq.org }},
   howpublished = "\url{ https://api.openaq.org }",
   year = {2023},
 }

Links to our training resources can be found in: linktr.ee/openaq . Contact us if you would like to host an OpenAQ training (due to limited capacity, we prioritize larger, mission-aligned projects).

OpenAQ is a data aggregator. We aggregate data from a variety of instruments that measure air pollution and share the pollutant measurements without modification (other than standardizing the format, e.g., converting disparate ppb to ppm). We also share metadata (descriptors that describe each instrument, such as data provider, type of instrument, company producing the instrument, and location). Sharing pollutant measurements without modification allows end users to apply their preferred correction methods; it also allows end users to compare the actual performance of air sensors with reference monitors.

Reference monitors (aka “government monitors,” “reference-grade monitors,” “research-grade monitors”) are the gold standard [1]. They produce very high quality, accurate data that can be used to develop and enforce regulations. Data from air sensors (aka “low-cost sensors”) vary in accuracy due to such factors as technology used, differences in validation and calibration efforts, and different weather conditions and pollution environments. Despite being less accurate than reference monitors, air sensors play an important role in advancing understanding of air quality. Because they are lower in cost, portable, and generally easier to use than reference monitors, they can be deployed more easily and can support citizen science, in particular.

We urge anyone analyzing air sensor data to review available information on the performance of the air sensors producing the data, as well as environmental conditions that could impact measurements. Of particular note, recent studies highlight limitations in air sensors’ ability to measure greater than PM2.5 (notated as PM 2.5-10 and PM10) [2–9].

If using air sensor data to educate, inform, advocate, or evaluate, limitations must be understood and corrections must be applied whenever possible.

References:

  1. EPA scientists develop and evaluate Federal Reference & Equivalent Methods for measuring key air pollutants. Retrieved from https://www.epa.gov/air-research/epa-scientists-develop-and-evaluate-federal-reference-equivalent-methods-measuring-key . Accessed 21 February 2023.
  2. Molina Rueda, E., Carter, E., L’Orange, C., Quinn, C., & Volckens, J. (2023). Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution. Environmental Science & Technology Letters. https://doi.org/10.1021/acs.estlett.3c00030
  3. Hagan, D. and Kroll, J.H. (2020). Assessing the accuracy of low-cost optical particle sensors using a physics-based approach. Atmospheric Measurement Techniques, 13, 6343-6355. https://doi.org/10.5194/amt-13-6343-2020
  4. Kuula, J., Mäkelä, T., Aurela, M., Teinilä, K., Varjonen, S., González, Ó., & Timonen, H. (2020). Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors. Atmospheric Measurement Techniques, 13(5), 2413-2423. https://doi.org/10.5194/amt-13-2413-2020
  5. Ouimette, J. R., Malm, W. C., Schichtel, B. A., Sheridan, P. J., Andrews, E., Ogren, J. A., & Arnott, W. P. (2022). Evaluating the PurpleAir monitor as an aerosol light scattering instrument. Atmospheric Measurement Techniques, 15(3), 655-676. https://doi.org/10.5194/amt-15-655-2022 .
  6. Levy Zamora, M., Xiong, F., Gentner, D., Kerkez, B., Kohrman-Glaser, J., & Koehler, K. (2018). Field and laboratory evaluations of the low-cost plantower particulate matter Sensor. Environmental science & technology, 53(2), 838-849. https://doi.org/10.1021/acs.est.8b05174
  7. Demanega, I., Mujan, I., Singer, B. C., Anđelković, A. S., Babich, F., & Licina, D. (2021). Performance assessment of low-cost environmental monitors and single sensors under variable indoor air quality and thermal conditions. Building and Environment, 187, 107415. https://doi.org/10.1016/j.buildenv.2020.107415
  8. Manikonda, A., Zíková, N., Hopke, P. K., & Ferro, A. R. (2016). Laboratory assessment of low-cost PM monitors. Journal of Aerosol Science, 102, 29-40. https://doi.org/10.1016/j.jaerosci.2016.08.010
  9. Clements, A., Duvall, R. (2019). ORD SPEAR Program: Air Quality Sensors. https://www.epa.gov/sites/default/files/2020-01/documents/airsensor_evaluation_duvall_0.pdf

Additional Resources on Correction Algorithms for Air Sensor Data:

Barkjohn, K.K.; Holder, A.L.; Frederick, S.G.; Clements, A.L. Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke. Sensors 2022, 22, 9669. https://doi.org/10.3390/s22249669

Jaffe, D., Miller, C., Thompson, K., Nelson, M., Finley, B., Ouimette, J., and Andrews, E.: An evaluation of the U.S. EPA’s correction equation for Purple Air Sensor data in smoke, dust and wintertime urban pollution events, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-265 , in review, 2022.

Hagan, D. and Kroll, J.H. (2020). Assessing the accuracy of low-cost optical particle sensors using a physics-based approach. Atmospheric Measurement Techniques, 13, 6343-6355. https://doi.org/10.5194/amt-13-6343-2020

McFarlane, C., Raheja, G., Malings, C., Appoh, E. K., Hughes, A. F., & Westervelt, D. M. (2021). Application of Gaussian mixture regression for the correction of low cost PM2. 5 monitoring data in Accra, Ghana. ACS Earth and Space Chemistry, 5(9), 2268-2279. https://doi.org/10.1021/acsearthspacechem.1c00217

Venkatraman Jagatha, J.; Klausnitzer, A.; Chacón-Mateos, M.; Laquai, B.; Nieuwkoop, E.; van der Mark, P.; Vogt, U.; Schneider, C. Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research. Sensors 2021, 21, 3960. https://doi.org/10.3390/s21123960

Kosmopoulos, G., Salamalikis, V., Pandis, S. N., Yannopoulos, P., Bloutsos, A. A., & Kazantzidis, A. (2020). Low-cost sensors for measuring airborne particulate matter: Field evaluation and calibration at a South-Eastern European site. Science of The Total Environment, 748, 141396. https://doi.org/10.1016/j.scitotenv.2020.141396

Diez, S., Lacy, S. E., Bannan, T. J., Flynn, M., Gardiner, T., Harrison, D., Marsden, N., Martin, N. A., Read, K., and Edwards, P. M.: Air pollution measurement errors: is your data fit for purpose?, Atmos. Meas. Tech., 15, 4091–4105, https://doi.org/10.5194/amt-15-4091-2022 , 2022.

US EPA (2022). How to Use Air Sensors: The Enhanced Air Sensor Guidebook. https://www.epa.gov/air-sensor-toolbox/how-use-air-sensors-air-sensor-guidebook

Tools, Datasets, and Repositories

Resources for Developing an Air Sensor Project