The data feeding your "environmental" apps

The data management strategies that help get air quality data into the hands of so many decision-makers via applications - and how they can be applied to other monitoring efforts.
Digital Services
June 9, 2023
The data feeding your "environmental" apps

On the evening of June 8, 2023, a red-bellied woodpecker was getting his dinner on the tree outside my window. I was watching his hunt from the comfort of my air-filtered office, after spending an uncomfortable hour outside. My eyes were itchy, my throat was raspy, and my hair smelled like I time-travelled back to a dive bar circa 2000 (pre-indoor smoking bans). On an hourly basis, I’d been opening up my iPhone and checking the latest AirQuality index score on a super easy-to-use app, AirNow.

My home office is outside of Washington DC. Right now DC is experiencing its worst air quality in over a decade. Folks in other cities have it even worse, with residents in New York and Baltimore and Michigan (to name a few) experiencing crippling toxic air quality thanks to giant forest fires in Canada sending plumes of smoke south. Headlines, inboxes, and social media feeds are flooded with messaging around the need to stay indoors, the dangers of poor air quality, and murmurs about how climate change has finally reached the east coast. To our west coast veterans of wildfire-sourced smoke, this is a rookie air quality disaster event. 

I’ll leave the policy debates and finger pointing surrounding this latest manifestation of climate change to different circles of environmentalists and climate activists. I’m here as the voice of data. That’s right, while fires are burning, I want to talk about the backend, foundational processes and needs surrounding how all of these decision-making stakeholders get and use data to form opinions and strategies about how to respond immediately and in the longer term to these sorts of public health and environmental events. Let’s give boring data management a shout out for being the unsung workhorse of the apps keeping us informed on air quality status.

Before we dive deep into the technology strategies at play, I'd like to give thanks to the powerful network of air quality monitors that are providing data to critical public health information apps, like EPA’s AirNow, which as of last night was more visited than Facebook and came in as the 13th most downloaded app in the App Store. This sensor network has been growing steadily over time - I’ll be honest that I’m not intimately familiar with the background of how this network grew so this won't be a history lesson. At its core, this network works with states and counties to collect high quality data about air quality via sensors and feeds that data into repositories that turn the data into information that can benefit the public consistently and constantly. There are other air quality apps, too, because once you have structured data you aren’t limited to one end use case - and that means that you can use data to answer different questions.

Without further ado, let’s explore the universal data management and technology strategies that helped get air quality data into the hands of so many decision-makers via applications(and how they can be applied to other monitoring efforts).

Foundations in Building Data-Driven Environmental Applications

1. Expand the number of data providers and facilitate the adoption of standards

We have a vast network of air quality monitoring sensors to frequently collect readings on parameters of interest. The reliability and existence of this network is clutch to all of application and discourse on the air quality topics circling right now. The more sensors that can be added to the network that meet the standards required, the more data we will have, and therefore, the more accurate our analysis and understanding of air quality will be.

2. Build well-structured data models and data management systems

The applications we are using to check air quality are possible only because of the well-structured and accessible air quality monitoring sensor data being collected. How data is collected is critical to make sure that the readings are accurate. But what is equally important is how the collected data is managed so that it can be shared and used for analysis. Setting up data models, building data management systems that encourage open use and data sharing via APIs, and using standard nomenclature mean that data collectors can contribute to broader efforts.

3. Emphasize real-time data sharing

The third point I’d like to make is that responsiveness and real time data sharing is invaluable.

If you’ve been checking your EPA’s AirNow app as frequently as I have, you’ll notice that the current air quality reading keeps changing. That’s because they have set up the system to update with a high frequency. No one is manually translating or entering the data into repositories. Also, no one is obstructing the sharing of data so that it cannot reach these critical end uses. This real time translation is critical to keeping the public informed about their local conditions. You’ll also notice that the application communicates a very simple message with visual and numerical indicators. 

Screenshots that I felt compelled to save to my phone and then share broadly to my family and friends to demonstrate how bad the air quality was at our house.
Look how easy it is for any audience to interpret the data being fed to this platform.

4. Welcome partnerships

When partnerships are in place surrounding data collection, management, and application development, the strengths of each partner can be put to work. Excellent partnerships lean on standard processes, protocols, and overlapping goals. For example, AirNow is a partnership of the U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration (NOAA), National Park Service, NASA, Centers for Disease Control, and tribal, state, and local air quality agencies. When partners can recognize an overlap in goals and data uses, you can amplify the number of end data uses at play. While I admittedly am not an expert on the air quality monitoring sector, I would bet that this network of monitoring sensors is significantly larger than if one entity was trying to go it alone. 

5. Recognize standards, or conditional standards

Scientists and public health officials recognize a few universal parameters to monitor to determine the level of safety of air quality for people. For example, they look at particulate matter 2.5 (PM 2.5), ozone, and a few other parameters. Analysts and developers write code that feeds the raw data through a quick analysis that generates easily understood and universal indicators benchmarked as the Air Quality Index (AQI). What this means is that, regardless of where you are, if the AQI is below 50, you know the air quality is “good”. A nationally accepted indicator and standard unit of measure invites more opportunities to build nationally reaching applications based on the same data set. 

Using monitoring data to answer key questions

Right now I can literally see pollution in the air. So many people who never give the air they breathe a thought are asking the question, what’s the air quality? Is it safe to go outside? These community-focused apps are helping the public make critical decisions that can protect themselves and their communities. Without the monitoring data provided by the sensor network, we would be left to our own devices to gauge the quality of our air. We are unreliable decision-makers and having reliable data can help us make better decisions for our health. 

For my family, the school's annual Field Day was canceled. Swim practice was canceled. The first grade special picnic was canceled. My scheduled long runs turned into TV binge sessions. The data provided by the monitoring network is critical to informing the decision-makers, from school officials to legislators working on strategies to respond to climate change to the general public who just want to live their best life, safely. My lungs are grateful for access to these tools. 

Example of a community organization using and referencing the AirNow application to make and then defend activity closure decisions.

The power of structure and standardization to connect data to end uses

At The Commons, we work primarily on efforts that intersect with the Clean Water Act, not the Clean Air Act. The foundations listed above remain the same and have been at the heart of our development choices as we’ve built software applications to communicate water quality information and engaged with water monitoring groups. Unfortunately, although the water community has been striving for years to have a monitoring network as extensive, accessible, and informative as the air quality monitoring network, it is plagued by a variety of barriers to inclusion and expansion. In terms of building a structured and standard network, the water community faces different challenges in standardization adoption and quality assurance. By the nature of the resource - water flows, air floats - most water quality and quantity issues and the ability to adopt the foundational principles listed above are inhibited by local or regional capacities.

As much as the water monitoring community would love to have an application as streamlined and informative as the AirNow application, the movement has not yet achieved the necessary foundational capacity in terms of establishing protocols, identifying what key water parameters map to a national indicator, defining standardization, adopting data management strategies, and using data management platforms that enable data sharing. 

The Commons sits on the Steering Committee of the Water Data Collaborative. This Collaborative is elevating community-based monitoring groups’ data contributions to broader monitoring initiatives. We value this Collaboration because they recognize the value of building a network of community-based water monitoring programs that meet standards and structures that enable broader use and sharing of data. As we can move more community-based water monitoring programs into a more modernized data management system and adoption of FAIR data standards, the sector will be better positioned to share out data to inform the public and decision-makers when the need arises.

We value the work of restoration practitioners, regulatory agencies, philanthropic organizations, and all other stakeholders committed to addressing the threats to our natural resources. Data is at the heart of everything we do for addressing the challenges of climate change, natural resource destruction, and more. Building robust data sets means that we can transform that raw data into information-sharing applications and analytical components. Structured data and smart environmental technology gave us EPA’s AirNow. The paths are not parallel, but similar applications can be powered for water quality monitoring if stakeholders can adopt the foundational conditions for success.

We want a healthy, vibrant planet with universal access to clean water and fresh air for all. We cannot underscore the importance of building data management networks that amplify data end uses because we want engagement with all stakeholders, from soccer moms to federal legislators.