Air Quality Data

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! Visit the [air-quality-data tag page]( to see the latest community posts about this topic on Public Lab, and receive updates by following: Follow air quality data _Lead image: Infographic on air quality data by @renee. See full image below._ 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 ## Questions about air quality data Questions tagged with `question:air-quality-data` will appear here [questions:air-quality-data] ## Activities about air quality data Activity posts tagged with `activity:air-quality-data` will appear here [notes:activity:air-quality-data] ## Understanding and preparing air quality data Research notes tagged with `getting-started-air-quality-data` will appear here [notes:getting-started-air-quality-data] ### 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](” on the Presenting Environmental Data wiki page + “[Types of samples](” and “[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]( 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](, [CC BY]( **Resources on units of measurement:** + [Common Units in Air, Soil, and Water Testing]( a workshop guide from _[Statistics for Action]( 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]( blog “[Tidy Data for reproducibility, efficiency, and collaboration](” by Julia Lowndes and Allison Horst, [CC BY]( [wikis:data-cleaning] ## Communicating with air quality data ### Designing a data story Research notes with the tag `data-storytelling` will appear here [notes:grid:data-storytelling] **More resources on deciding what data to share:** + “[Finding newsworthy data](”: 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]( to add more examples and improve this wiki!_ _Image: “Which visualizations should I use?” infographic by @renee._ + See **"[Ways to present environmental data](''** for more examples of ways to show environmental data, both visual and non-visual. + **[Data Viz Project](**: 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 [notes:grid:data-visualization-tool] ### 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](, 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 [nodes:data-advocacy] ## Further reading and resources ### More on data advocacy + **[Statistics for Action activities on “Communicating”](**: excellent activity guides in English and Spanish on communicating environmental data, including [Memorable Messages]( and [Memorable Graphs]( + **[Guidebook for Developing a Community Air Monitoring Network: Steps, Lessons, and Recommendations from the Imperial County Community Air Monitoring Project](**: Chapter 15: Disseminating the air monitoring data, and Ch. 16: Communicating air monitoring data on the web. + **[Cities & Air Pollution Challenges](**: 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](**: a comprehensive online tool cataloging examples of data visualizations from the design firm Ferdio. + **[Data Carpentry](**: “Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research.” [Example workshop here](, including a syllabus and lesson plans. + **[Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code](**. Open-access web edition, by Jack Dougherty and Ilya Ilyankou. + **[Toolkit: Data Activist Co-op Sessions](** from Greenpeace ### Literature + Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK. 2017. **[Good enough practices in scientific computing](**. _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]( _Environmental Modelling & Software_, 85: 299-318. ## Next step challenges + Document examples and stories of community scientists presenting air quality data and enacting change on a local issue + Test more data visualization tools and collaboratively grow the [table comparing tools here]( And possibly add another table with tools for other kinds of data communications (e.g., nonvisual, using physical media, other creative means!) + Create a choose-your-data-viz catalog, similar to the [Data Viz Project](, but open source and for environmental data ...

Author Comment Last activity Moderation
bhamster "Thanks for sharing your experience and these resources, @julia_e_masters, they're super helpful! " | Read more » 11 months ago
julia_e_masters "In my experience, well-thought out data visualization is a huge benefit to making content digestible, approachable, and meaningful. EDF pulls in..." | Read more » 11 months ago
Ag8n "For the most part, my experience was on the manufacturing end. And the answer would be very similar, but stated quite differently " the cost bene..." | Read more » 12 months ago
denissebn_06 "This might be some useful information for you @sarage Check out the report linked " Making most of monitoring". " | Read more » 12 months ago
bhamster "@LESBreathe, this post and the report linked in it might be helpful to check out " | Read more » 12 months ago
bhamster "@WendyBrawer @LESBreathe This flyer is incredible! ❤️ Thanks so much for sharing! As another way to get it on a screen, would you be interested in ..." | Read more » about 1 year ago
WendyBrawer "At @LESBreathe, we made a Cleaner Air at Home flyer this season with many no-cost suggestions. We're developing more ways to get it into peoples' h..." | Read more » about 1 year ago
sarasage "This is really, really cool @seankmcginnis " | Read more » about 1 year ago
seankmcginnis "My first barnstar!!!!!!!!! Thanks @liz " | Read more » about 1 year ago
liz "@liz awards a barnstar to seankmcginnis for their awesome contribution! " | Read more » about 1 year ago
bhamster "This is absolutely wonderful, @seankmcginnis! Thanks for responding to needs brought up on the Open Calls by creating this custom code and sharing ..." | Read more » about 1 year ago
amocorro "@eustatic just in case you didn't see this! " | Read more » about 1 year ago
keshavgarg234156 "Whenever we have such large numbers of columns then we first perform correlation analysis before plotting the graphs. In correlation analysis, we c..." | Read more » over 2 years ago
guolivar "Normalising is your friend when you have things with very different ranges. Depending on what you're looking for you can divide each column by its ..." | Read more » over 2 years ago
eustatic "Pivot tables? Lookup or index command? Conditional formatting? all of these things can help. i had to have 10 years and 14 plant plot growth s..." | Read more » almost 3 years ago
warren "I also noticed that some very high values make others basically unreadable. So having 2 y-axis scales can sometimes help, i guess, although that's ..." | Read more » almost 3 years ago
warren "Thanks! Yes, i started by removing things like firmware #, for sure. It's interesting to see how some values track each other roughly, while others..." | Read more » almost 3 years ago
guolivar "This is very much dataset, project and software dependent. Sometimes I remove columns, other times I combine them (averaging, adding, etc), other ..." | Read more » almost 3 years ago
warren "Hi @stevie, @Aleah, @Cbarnes9, and @crispinpierce, I posted this in follow up after the purple air datalogs ended up so dense and tough to read! Th..." | Read more » almost 3 years ago
warren "OK, i got the units to show. I trimmed back one file to just a few of the columns, just to make it more readable, but it doesn't have all the diffe..." | Read more » almost 3 years ago
warren "If you'd like, I can change it to just links for download! But it was interesting to see the data a bit in a graph. There are just so many fields, ..." | Read more » almost 3 years ago
warren "Hi, all, drag-and-drop for big CSVs is working again now. The graph that's generated is a bit wild because there are so many fields. But there is a..." | Read more » almost 3 years ago
stevie "Hi Jeff I have them, I also tried to get them up. I'll send them to you. " | Read more » almost 3 years ago
warren "Crispin, could you email me the CSVs you're working with at so we can debug a bit? And, you had tried uploading them here in the..." | Read more » almost 3 years ago