TL;DR — The US EPA’s Enhanced Air Sensor Guidebook recommends a wide range of potential use cases for low-cost air quality sensors, in alignment with the goals of non-regulatory supplemental and informational monitoring (NSIM). Deploying low-cost air pollution sensors alongside reference-grade monitors helps to fill in the gaps left behind by traditional monitoring equipment and provides additional information to characterize air quality — whether concerning spatiotemporal variability,  air pollution hotspots that can only be captured with a high-resolution network, or long-term air pollution trends.

The US EPA Enhanced Air Sensor Guidebook

This blog is part of a series that highlights key themes from the US EPA Enhanced Air Sensor Guidebook, an updated version published in 2022. The guidebook covers a variety of topics involving low-cost sensor use.

Today we’ll be tackling the theme: What are the recommended use cases for non-regulatory supplemental and information (NSIM) air monitoring equipment?

What is non-regulatory supplemental and informational monitoring?

Non-regulatory supplemental and informational monitoring (NSIM) describes when air quality monitoring devices are deployed alongside traditional reference monitoring networks to fill in the gaps left behind. 

They provide additional information about air pollution composition and trends to create a more complete picture of the air quality in a given location.

The US EPA’s recommended use cases for NSIM

The US EPA recommends a variety of potential use cases for low-cost sensors deployed as part of a non-regulatory supplemental and informational monitoring initiative. 

Assessing spatiotemporal air quality trends

One use case involves looking at spatiotemporal variability in air pollution, which involves studying the way that pollution concentrations change over geographic space and over time.

An air quality monitoring project focusing on spatiotemporal variability may look at daily trends in air pollutant concentrations, such as how fine particulate matter levels vary from day to day.

The image above shows an example of air quality data capturing temporal variability across the month of November in San Francisco, where frequently collected air quality data helps paint a picture of the variation in air quality. (Image source: Wikimedia Commons)

Low-cost sensors can also be used in gradient studies, which measure gradients of different pollutants under a variety of conditions to compare the results. 

NSIM networks can also be used to measure spatial and temporal changes in air quality and support air quality forecasting — predicting possible changes in air quality under certain conditions, or should certain policies be put into place. Air quality forecasting can help drive appropriate action to reduce air pollution exposure.

Participatory science and education-related projects can also use low-cost sensors in an NSIM context to measure spatiotemporal variability and use these findings to drive action.

Groundwork Richmond, an organization in Richmond, California, has established a low-cost sensor network that supports participatory science. By involving the community in air quality monitoring, the network can collect data on spatiotemporal pollution variability and validate residents’ long-time air quality concerns.

Groundwork decided to take a stand on the poor air quality in Richmond. With Groundwork’s support, Holmes undertook a campaign to raise awareness about the importance of air quality in Richmond, with the end goal of deploying a high-resolution, citizen air quality monitoring network that would provide the community with vital information about real-time air quality.”

— Groundwork Richmond Customer Story

Detecting air pollution hotspots

NSIM can also be used to compare air quality in different locations. This may include comparing the air quality between different monitoring networks, in different regions, or against a threshold value.

Monitoring networks focused on air quality in different locations can be used to detect pollution hotspots, or areas where air pollution is elevated and can contribute to concentrated negative health impacts.

Data fusion can also be used to combine different air quality measurements, such as those between different monitors or networks, or those in different locations, to arrive at a more nuanced understanding of air quality. 

Measuring air quality during air pollution emergencies

Low-cost sensors can be used as part of emergency responses, such as by measuring air pollution spikes during wildfire events, to help officials and residents alike understand exposure and act accordingly. 

Low-cost sensors can be deployed in locations that are missed by reference-grade monitors in order to supplement the number of data points that can be collected.

Brightline Defense, a non-profit organization in San Francisco, uses its low-cost sensor network to detect air pollution hotspots in the city, especially in the SoMa neighborhood that previously lacked ample air quality monitoring. This network helps residents understand and work to reduce their air pollution exposure — in addition to supporting efforts for larger-scale policy changes to support cleaner air.

NSIM also supports efforts to characterize long-term trends or changes in air pollution concentrations. These projects can both characterize long-term changes in air quality itself as well as the ways that this impacts health outcomes in the long run through epidemiological studies.

Providing air pollution measurement inputs for advanced air quality modeling

Networks aimed at measuring long-term trends can also help to verify air quality models, which are highly important in air quality agencies’ work to control air pollution, identify contributing pollution sources, and design strategies to reduce air pollution. 

At the Institute for Atmospheric and Earth System Research at the University of Helsinki, low-cost sensors are used in air quality modeling to support research. 

Using the existing calibrated readings from the Clarity sensing units, Zaidan and the research team also enhanced the sensing capability by estimating some unmeasured air pollutants through the use of AI-based modeling. The methodology is known as virtual sensors.” 

— Institute for Atmospheric and Earth System Research Customer Story

Check out our blog here for more information on the different possible use cases of low-cost sensors, from increasing public awareness of air quality issues to environmental justice applications to emergency deployment.

Strategically using low-cost sensors for cleaner air

A variety of different NSIM-related use cases for low-cost sensors exist, using air quality sensors to help fill in additional information regarding the air quality in a given place or concerning a specific phenomenon. 

If you’re interested in exploring any of these use cases with Clarity’s air quality sensors, learn more about the Clarity Node-S device here.