You don’t need a sensor to know which way the smog is blowing. In the most extreme cases, outdoor air pollution can immediately identified even without any special training. It casts a haze over cities, collects on streets and buildings, and provides dramatic fodder for the news.
But while high drama is often a prerequisite for news about air quality to be reported, the real story is the health impacts that occur even when the air isn’t thick enough to see. The health eﬀects attributed to outdoor ﬁne particulate matter (PM2.5) rank it among the risk factors with the highest health impacts in the world, accounting for over 3.2 million premature deaths annually. In October 2013, the World Health Organization announced they consider particulate matter, a major component of indoor and outdoor air pollution, as a Group 1 carcinogen along with tobacco smoke and asbestos.
With this in mind, the Earth Journalism Network is working with scientists and hardware developers to create the DustDuino—a low-cost particulate matter monitor that can help media audiences understand the quality of the air they breathe. However, trying to fill gaps in air quality data can open up new problems about the quality of the data itself.
The primary sensor component of the DustDuino is the Shinyei PPD42NS -- a $15USD optical sensorthat uses an LED and a lens to determine the concentration of dust in a partially closed chamber that draws in air from its surroundings. This January, a group of academics interested in the potential applications of low-cost sensors published a research article comparing the data quality of various PM monitors including the Shinyei. The key questions: how does this low-cost newly off-the-shelf PM monitor compare with costlier models used by governments (the BAM-1020 from Met One Instruments) researchers (OPC Model 1.108 by GRIMM and Dusttrak by TSI) and companies (the DC1700 from Dylos Corp)?
They concluded that the results of the low-cost sensor were about equivalent to much more expensive ones when analyzing data at hourly intervals. ”Performance at 1 [hour] integration times was comparable to commercially available optical instruments costing considerably more,” according to the study.
These finding suggest that there is a practical space for the application of the DustDuino by journalists and citizen scientists seeking to participate in community monitoring of air quality. This is especially true in situations where a lower number of high-quality sensors are used to model exposure over large areas as is often the case in cities where environmental monitoring infrastructure is scarce. On a neighborhood level, deploying many low-cost sensors can cope more readily with the monitoring demands of changing land use, help verify urban “hot spots,” and target the use of higher-cost monitors to confirm results with follow-up measurements.
There are some caveats, however. Low-cost sensors may not provide high-resolution information at shorter time intervals. It’s unclear whether co-locations with higher quality instruments like they did in the research study are necessary to calibrate low-cost sensors. They have a difficult time differentiating between different types of particulates, and have not yet been tested in more remote or extreme environments. Many of these limitations require further testing and study.
As journalists and citizen scientists chart new ground in the world of data collection, it’s important to remember that collaborating with trained scientists can be the difference between meaningless and meaningful results. By applying solid methodology and consulting with a scientific advisor, journalists can use sensors to get ground-breaking stories on both indoor and outdoor air pollution that are currently being under-reported.