_This is a draft wiki page that will change frequently!_
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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:

<a class="btn btn-primary btn-lg" href="https://publiclab.org/subscribe/tag/air-quality-data">Follow air quality data</a>

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## Understanding air quality data

[notes:grid:getting-started-air-quality-data]

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### 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.

<img style="width: 80%; height: 80%" src="https://publiclab.org/i/45016?s=o" alt="Illustration of people collecting various kinds of air quality data around a city, an industrial facility, and a recreational park">

_Image: A variety of different kinds and sources of air quality data, by @renee._

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<br>

<div class="alert alert-secondary" role="alert">
<b>Here are some questions to consider about the data:</b>
<ul>
<p>
<li>Is the data from a <b>sensor</b>?</li>
<ul>
<li>Is the data from a stationary sensor 📍 or a portable one 🚶🏽🚴🏽 that was used at different locations?</li>
<li>Is the data collected continuously over time ⏳ 🔁 or only at certain times and dates 🕑 📆 (intermittently)?</li>
</ul>
</p>
<p>
<li>Is the data for a <b>general outdoor area</b> 🏙️ (ambient air) or a <b>specific emission source</b> 🏭 (e.g., fenceline monitoring)?</li>
</p>
<p>
<li>Is the data from a <b>lab report</b> ⚗️📄 for an <b>air grab sample</b> taken at a specific place and time?</li>
</p>
<p>
<li>Did you <b>collect the data yourself</b> 🙋🏾‍♀️ or is it from an existing <b>database with publicly accessible data</b> 🔓 💻?</li>
<ul>
<li>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)?</li>
</ul>
</p>
<p>
<li>Considering the equipment used to collect the data, what are its <b>detection limits</b> and <b>data resolution</b>? (This could look like minimum and maximum levels for measurements ⬇️⬆️, and how small a change ↔️ the equipment can measure) </li>
</p>
<p>
<li>Is your data from <b>indoor</b> air 🏠 or <b>outdoor</b> air ☀️ (ambient)?</li>
</p>
<p>
<li>Is the data mostly <b>not numerical (qualitative)</b>?</li>
<ul>     
<li>An odor log or odor report? 👃🏽</li>
<li>An oral history? 💬</li>
<li>A visual observation? 👀 (e.g., soot, colored dust, smoke)</li> 
</ul>
</p>
</ul>     
</div>

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**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 

<br>

_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!_

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### Initial analysis & visualizations to understand data

#### 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. 


<img style="width: 50%; height: 50%" src="https://publiclab.org/i/45022?s=o" alt="a cube representing the volume of carbon dioxide emitted from burning a gallon of gasoline appears next to the silhouette of an adult and child">

_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/)_

<br>

**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."

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#### 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."  

[wikis:data-cleaning]

<img style="width: 70%; height: 70%" src="https://publiclab.org/i/45058?s=o" alt="Stylized text providing an overview of Tidy Data. The top reads “Tidy data is a standard way of mapping the meaning of a dataset to its structure. - Hadley Wickham.” On the left reads “In tidy data: each variable forms a column; each observation forms a row; each cell is a single measurement.” There is an example table on the lower right with columns ‘id’, ‘name’ and ‘color’ with observations for different cats, illustrating tidy data structure.">

<br>

_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/)_ 

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#### Making visualizations to see trends and potential problems

_more to come here_

<br>

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## Communicating with air quality data

### Designing a data story

Research notes with the tag `data-storytelling` will appear here

[notes:grid:data-storytelling]

<br>

_more to come here_

<br>
<hr>

### Ways to present air quality data

The info on this wiki is organized into sections to make it easier to digest, but it’s often helpful to determine your data advocacy goals (see section below) before choosing a type of data visualization and tools. In reality, you might be jumping back and forth between these sections as you define your advocacy goals, determine what kind of data viz you’d like to make, and what tools suit your needs and constraints.


* **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.

_more to come here_

<br>
<hr>

### Tools for making visualizations and other media

[notes:grid:data-visualization-tool]

<br>
<hr>

### Communicating the data

_more to come here_

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## Questions about air quality data

Questions tagged with `question:air-quality-data` will appear here

[questions:air-quality-data]

<br>

## Activities about air quality data data

Activity posts tagged with `activity:air-quality-data` will appear here

[notes:activity:air-quality-data]

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## Further reading and resources

+ **[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.” 
+ **[Guidebook for Developing a Community Air Monitoring Network](https://trackingcalifornia.org/cms/file/imperial-air-project/guidebook)**: Steps, Lessons, and Recommendations from the Imperial County Community Air Monitoring Project
+ 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. [LINK to paper](https://doi.org/10.1371/journal.pcbi.1005510).