Air Sciences is always looking for ways to save clients time and money while keeping abreast of the newest methods and technologies. We recently wrote about how we are helping clients calculate theoretical evaporation a simplified way. Cue the entrance of low-cost air quality sensors (LCS). Thanks to advances in laser technology and open source computing, these instruments cost just 5% of what you’d expect for conventional reference sensors. How does the adage “you get what you pay for” hold up in a situation like this? Let’s look at the LCS pros and cons and find out:
PROs | CONs |
|---|---|
| Affordability | Over- or under-measurement, sensitive to hyperlocal conditions |
| Easy installation | Calibration needed for accurate measurements |
| Low power requirements | Poor performance with some species such as PM10 |
| Small footprint | Precision insufficient for regulatory applications |
| Quick deployment | Data literacy required |
The PROs in this list are not that surprising. They’re all part and parcel of today’s technology with Raspberry Pi/Arduino computing and miniaturization in electronics. It’s really great to see these in use adjacent to the traditional precision instruments. And precision is the key word here. LCS aren’t that exact. In fact, they often do not perform that well (in evaluations by the South Coast Air Quality Management District in CA) in comparison to the United States Environmental Protection Agency (EPA) reference methods of measuring National Ambient Air Quality Standards (NAAQS) pollutants. However, several LCS correlate very well with reference monitors for particulate matter with aerodynamic diameters of 2.5 micrometers and below (PM2.5) – precisely a category of great concern in many urban environments. Yet, for other NAAQS pollutants, such as PM10, ozone (O3) and nitrogen dioxide (NO2), affordable and reliable options are scarce at the moment.
Let’s untangle some of these CONs, such as contamination from a neighbor’s BBQ grill smoke. This sensitivity to hyperlocal conditions can be offset by deploying many sensors (thanks to the affordability aspect) and also tying in data from remote sensing. More importantly, mathematical correction factors have been developed to finetune these results. One study compared LCS data against co-located reference monitors and found good agreement with linear regression and a coefficient of determination, also called R squared (R2), which lets us see how well these results “fit” and if the LCS data can be “corrected” to agree better with reference monitors. This goodness of fit does not tell the whole story. Slope and intercept require consideration, too.
Right here in Oregon, the Oregon Department of Environmental Quality has developed their own LCS to complement their network of reference monitors. Source: Oregon Department of Environmental Quality.
LCS are definitely not in the same league as federal reference methods but they do help complete the air quality picture in areas with low levels of monitoring, forming hybrid networks with reference-grade monitors. They can also help create air pollution maps with greater resolution. And at the personal level, individuals can use them to make decisions around their own immediate air quality for a very affordable price or even for free with the EPA’s air sensor loan program.
Affordable and accessible, LCS are essential for growing public awareness of air quality, informing environmental and public policy recommendations, and supporting environmental justice work. According to the World Health Organization (WHO), poor air quality is one of the greatest environmental health risks we face. In 2019, 99% of the world population was concentrated in places where the WHO air quality guidelines levels were not met. Indoor air quality is also serious for around 2.4 billion people who cook and heat their homes with biomass, kerosene fuels, and coal (see map). Premature deaths are in the millions and 91% of those are occurring in low- and middle-income countries. With many toxins and pollutants being invisible, affordable, widely distributed, and dedicated sensors are necessary.
