What can you do with air quality data once you have it? Whether you’ve collected the data yourself through community air monitoring or obtained it from an open database, there are many ways to communicate the data and make it meaningful.
On this wiki page, we’re collecting resources on understanding and communicating air quality data. Please add to these resources and help to improve the page by editing this wiki!
On this page:
Questions and activities about air quality data from the community
Understanding and preparing air quality data
Different kinds of air quality data
Units of measurement
Cleaning and organizing air quality data
Communicating with air quality data
Designing a data story
Graphs, maps and more ways to present air quality data
Tools for making visualizations and other media
Sharing and taking action with air quality data
Further reading and resources
Next step challenges
Visit the [air-quality-data tag page](https://publiclab.org/tag/air-quality-data) to see the latest community posts about this topic on Public Lab, and receive updates by following:
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## Questions about air quality data
Questions tagged with `question:air-quality-data` will appear here
## Activities about air quality data
Activity posts tagged with `activity:air-quality-data` will appear here
## Understanding and preparing air quality data
Research notes tagged with `getting-started-air-quality-data` will appear here
### Different kinds of air quality data
Becoming familiar with the kind of air quality data you have can help you on the way to figuring out what you eventually want to do with the data.
_Image: A variety of different kinds and sources of air quality data, by @renee._
Here are some questions to consider about the data:
Is the data from a sensor?
Is the data from a stationary sensor 📍 or a portable one 🚶🏽🚴🏽 that was used at different locations?
Is the data collected continuously over time ⏳ 🔁 or only at certain times and dates 🕑 📆 (intermittently)?
Is the data for a general outdoor area 🏙️ (ambient air) or a specific emission source 🏭 (e.g., fenceline monitoring)?
Is the data from a lab report ⚗️📄 for an air grab sample taken at a specific place and time?
Did you collect the data yourself 🙋🏾♀️ or is it from an existing database with publicly accessible data 🔓 💻?
If it’s from an existing database, is the data from regulatory monitors or another monitor network (e.g., community monitors, low-cost monitor networks)?
Considering the equipment used to collect the data, what are its detection limits and data resolution? (This could look like minimum and maximum levels for measurements ⬇️⬆️, and how small a change ↔️ the equipment can measure)
Is your data from indoor air 🏠 or outdoor air ☀️ (ambient)?
Is the data mostly not numerical (qualitative)?
An odor log or odor report? 👃🏽
An oral history? 💬
A visual observation? 👀 (e.g., soot, colored dust, smoke)
**More about different kinds of environmental data (not specific to air quality data):**
+ “[Kinds of environmental data you might have](https://publiclab.org/wiki/presenting-data#Kinds+of+environmental+data+you+might+have)” on the Presenting Environmental Data wiki page
+ “[Types of samples](https://publiclab.org/wiki/start-enviro-monitor-study#Types+of+Samples)” and “[Interpreting the data](https://publiclab.org/wiki/start-enviro-monitor-study#Interpreting+the+Data)” on the Start an Environmental Monitoring Study wiki page
_What other questions can help with understanding air quality data? Please [edit this page](https://publiclab.org/wiki/edit/air-quality-data) to add more!_
### Units of measurement
Looking closely at units in data can help you understand the scale of your measurements and start thinking about how to communicate that scale so it’s meaningful to other people.
_Image: Illustrating the volume of carbon dioxide emitted from burning one gallon of gasoline. [Carbon Visuals](https://www.flickr.com/photos/carbonquilt/8228690605/), [CC BY](https://creativecommons.org/licenses/by/2.0/)_
**Resources on units of measurement:**
+ [Common Units in Air, Soil, and Water Testing](https://publiclab.org/notes/kgradow1/12-17-2020/common-units-in-air-soil-and-water-testing): a workshop guide from _[Statistics for Action](https://sfa.terc.edu/materials/activities.html)_ that helps "participants discuss, read, and practice using one or more units of measurement found in environmental science."
### Cleaning and organizing air quality data
Putting your air quality data into an organized table gets it ready for making charts, graphs, and other visualizations. Below are some resources on making tables of tidy data and on "cleaning data."
_Image: Illustrations from the [Openscapes](https://www.openscapes.org/) blog “[Tidy Data for reproducibility, efficiency, and collaboration](https://www.openscapes.org/blog/2020/10/12/tidy-data/)” by Julia Lowndes and Allison Horst, [CC BY](http://creativecommons.org/licenses/by/4.0/)_
## Communicating with air quality data
### Designing a data story
Research notes with the tag `data-storytelling` will appear here
**More resources on deciding what data to share:**
+ “[Finding newsworthy data](https://sfa.terc.edu/materials/pdfs/finding_newsworthy_data.pdf)”: workshop activity guide from Statistics for Action.
### Graphs, maps, and more ways to present air quality data
Data visualizations like graphs, charts, and maps are a common way to bring numbers to life. And there are also other approaches! Art, zines, non-visual media, and other interactive media can also help you tell a story with your air quality data. We’re collecting resources and examples here of different ways to present air quality data.
_Please [edit this page](https://publiclab.org/wiki/edit/air-quality-data) to add more examples and improve this wiki!_
_Image: “Which visualizations should I use?” infographic by @renee._
+ See **"[Ways to present environmental data](https://publiclab.org/wiki/presenting-data#Ways+to+present+environmental+data)''** for more examples of ways to show environmental data, both visual and non-visual.
+ **[Data Viz Project](https://datavizproject.com/)**: this resource isn’t specific to environmental data, but it’s a neat tool that enables you to choose a data visualization by function (e.g., trend over time, comparison) or raw data input type.
Making visualizations to see trends and potential problems in the data
Even before deciding on how to communicate your air quality data more broadly, it can be helpful to make rough graphs or charts just to see what’s going on with your data.
Graphing tools built right into spreadsheet programs (like Google Sheets, Excel, or LibreOffice) are often good enough for making these initial data visualizations. Besides looking for patterns, you can also look for clues that there might be problems with the data: measurements that look out of place (outliers), measurements steadily increasing or decreasing unexpectedly, and gaps in data.
### Tools for making visualizations and other media
### Sharing and taking action with air quality data
* **Real-Time Online Charts and Maps**: Great to visualize trends over time and compare air quality to other regions. This can also allow for more data aggregation and analysis.
* **Data Download**: Publicly accessible data available in easy-to-use formats, either as a direct download or via a request form. This is particularly helpful in addition to web-based charts.
* **Notifications**: Automated text, email, or phone call alerts when environmental conditions exceed a certain threshold. In locations with [limited cell service or wifi](https://publiclab.org/questions/bhamster/10-29-2021/what-are-ways-to-alert-residents-to-air-quality-concerns-without-using-cell-phones-or-wifi), an Air Quality Flag program can be an option.
* **Partnerships**: Work with local schools, existing government sites, news stations, and other media outlets to reach a broader audience and inform the public about air quality issues.
Wikis and research notes tagged with `data-advocacy` will appear here
## Further reading and resources
### More on data advocacy
+ **[Statistics for Action activities on “Communicating”](https://sfa.terc.edu/materials/activities.html#c)**: excellent activity guides in English and Spanish on communicating environmental data, including [Memorable Messages](https://sfa.terc.edu/materials/pdfs/memorable_messages.pdf) and [Memorable Graphs](https://sfa.terc.edu/materials/pdfs/memorable_graphs.pdf)
+ **[Guidebook for Developing a Community Air Monitoring Network: Steps, Lessons, and Recommendations from the Imperial County Community Air Monitoring Project](https://trackingcalifornia.org/cms/file/imperial-air-project/guidebook)**: Chapter 15: Disseminating the air monitoring data, and Ch. 16: Communicating air monitoring data on the web.
+ **[Cities & Air Pollution Challenges](http://datadrivenlab.org/cities-air-pollution/)**: the case study from the UAE gives great insights into preferred communication methods for air quality information
### More on data visualization tools and tutorials
+ **[Data Viz Project](https://datavizproject.com/)**: a comprehensive online tool cataloging examples of data visualizations from the design firm Ferdio.
+ **[Data Carpentry](https://datacarpentry.org/)**: “Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research.” [Example workshop here](https://marwahaha.github.io/2019-05-30-nas/), including a syllabus and lesson plans.
+ **[Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code](https://handsondataviz.org/)**. Open-access web edition, by Jack Dougherty and Ilya Ilyankou.
+ **[Toolkit: Data Activist Co-op Sessions](https://www.greenpeace.org/usa/toolkits/data-activist-co-op-sessions/)** from Greenpeace
+ Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK. 2017. **[Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510)**. _PLoS Comput Biol_, 13(6): e1005510.
+ Grainger S, Mao F, Buytaert W. 2016. [Environmental data visualisation for non-scientific contexts: Literature review and design framework](https://www.sciencedirect.com/science/article/pii/S1364815216305990). _Environmental Modelling & Software_, 85: 299-318.
## Next step challenges