AirNow “Fire and Smoke”

Ripley Cleghorn

My map critique will be focusing on AirNow’s “Fire and Smoke” map interface. To provide context, AirNow is “your one-stop source for air quality data”, according to their website. The project is a partnership among 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”. While the map provides a regularly updated source of air quality data, I want to highlight general issues with its data sourcing and reliability.  Below is a screenshot of the initial view of the map:

Upon first glance, one will notice that the map uses colored squares and circles as symbols. Unfortunately, in order to understand what those symbols mean, one must click on the data layers icon, and then they are shown the legend. In reality they only see half the legend; they can see what the symbols mean, but not the colors. That is only visible when one clicks on the “FAQ” button. In the map, the symbols represent whether it’s a monitor (and if it’s temporary or permanent) which are usually placed by the government, or whether it’s a low-cost sensor, which is provided by a company called PurpleAir. The colors represent the level of air quality, ranging from ‘good’ to ‘hazardous’. Part of my first critique is that these should be shown together, on the main screen, or at the very least be shown using a “legend” button. On a positive note, the filters shown on the data layers page offer easy-to-understand and genuinely useful filters for the user.

Now I’d like to focus on the data-sourcing portion of the map. As mentioned in the first paragraph, AirNow boasts a “one-stop source”. Within the context of their “Fire and Smoke” map, they accomplish this by combining a range of data sources, including permanent monitors, temporary monitors, and low-cost sensors. According to the FAQ’s, the monitors are “generally operated by state, local or tribal air quality agencies”. The sensor data, on the other hand, is described as being crowdsourced from the company, PurpleAir’s, particle pollution sensors, which are owned by individuals across the country. PurpleAir data is less accurate than the monitors’ (according to the map’s website), but in turn it has the benefit of being a low-cost sensor. In order to normalize the data coming from this third source, “EPA and USFS apply both a scientific correction equation to mitigate bias in the sensor data, and the NowCast, the algorithm to show the data in the context of the Air Quality Index.” After searching for the definition of the scientific correction, I was able to find it farther down on the FAQ list. The NowCast algorithm, however, was defined on a completely different AirNow page. Another critique I have is that these definitions should both be linked when the site first references them.

In general, I believe it’s a positive factor that the map includes data from a mix of sources and concerns itself with normalizing this data. At the same time, I argue that too much emphasis is placed on the fact that the PurpleAir data is crowdsourced. Because of this, they seem to believe that they have the perfect mix of governmental and citizen-attributed data. Crowdsourced should not be confused with open sourced. ThePurpleAir sensor is a product just like any product, with the difference being the real-time data that is collected is sent to a public map hosted on PurpleAir’s website, and consequently also lands on the “Fire and Smoke” map on the AirNow website. Although I don’t want to downplay the importance of collecting data from multiple sources and thus probably being more representative, I also want to point out that PurpleAir is a corporate company profiting off people’s health insecurities regarding their air quality. This is in contrast with open source software, which I will mention in the application section.

To mention a final critique, according to one article explaining how PurpleAir sensors work, “[the] devices rely on a laser to count the particles in the air, and use an average density to determine air quality at the monitor’s location. The calculation is an estimate, however, especially during fire season, as wood-smoke particles have a different density from gravel dust or other pollutants.” This is quite problematic; these sensors are recognized as less accurate during fire season, yet they are integrated in a map of fire and smoke-affected air quality. This is not just unreliable, it’s potentially dangerous given that the map doesn’t mention this inaccuracy.

            On the other hand, the map does a good job of communicating how often the data is refreshed. On the bottom left corner of the map is a ‘refresh’ icon with a time stamp next to it, indicating the last time it was updated. In addition, after staying on the page for several minutes, a notification appears at the top of the page reminding the user to refresh the site. This is a clear indication that the data aren’t updated automatically, which can sometimes be confusing on maps of this nature.

            As mentioned, I’d like to apply this critique to an application. Last semester, as part of one of my courses, we were supposed to buy a temperature sensor and install it in our current environment. We learned the struggles and benefits of using real-time data. Some of the struggles were the actual installation and maintenance of the sensor, in addition to finding cost-efficient ways of storing such large amounts of data. The benefit was that we were able to constantly monitor the temperature in our environment and create a visualization to show this. This experience demonstrated that open sourced is different from crowdsourced. According to Wikipedia, “Open-source hardware consists of physical artifacts of technology designed and offered by the open-design movement.” Open source technology is thus beneficial because it is a combination of many individuals’ non-financial motivations and collaboration, rather than being a technology developed by a single for-profit company. In the case of my class, we used a sensor provided by the company Adafruit, which is an opensourced hardware company based in New York City. I think that if AirNow really wanted to have an integrated database, they would use data from opensource sensors. This would probably create less of a financial barrier than PurpleAir sensors, which roughly cost $250. Finally, the open-sourced community is often very proactive in providing help and solutions online, since anyone can offer their help (and those people are usually motivated by a passion or hobby of using the technology). This would help users work through problems in real time and not have to depend on a business answering their questions or resorting to an FAQ.

Figure 1: a screenshot from my temperature sensor project.

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