Enhanced Air Pollution Monitoring: Low-Cost Monitors, Mobile Monitors, and Satellite Remote Sensing

bartc

New Capabilities
Low-cost, real-time PM monitors can greatly extend the range of expensive (but more accurate) networks run by regulatory agencies. Both types of networks are useful for determining areas with unhealthy pollution levels and tracking long-term trends, but lack the ability to distinguish sources or the effectiveness of source-specific control efforts , which are usually done with resource-intensive special studies -- either air quality modeling (dependent on accurate meteorological and emission input data) or source tracer measurements (dependent on unique chemical signatures for each source type).

Mobile monitors (vehicles instrumented with high accuracy monitoring equipment) collect air quality data with high temporal and spatial resolution to study near-roadway exposures in highly impacted communities and measure pollutant gradients under a variety of conditions. This research tool is an important supplement to regional air quality monitoring networks and new U.S. requirements for near-road monitoring, and can help identify contributing sources.

Satellite-based aerosol optical depth readings (interpreted through comparison with ground-level PM monitors) can provide detailed PM2.5 maps throughout a region, although limited to daylight, cloud-free conditions. The GOES-16 satellite has readings every 15 minutes at a 1-2 km resolution, and my former group is collaborating with Emory University on a NASA-funded project to develop a "decoder" for California to interpret and display the PM2.5 maps in near real-time. Research is needed to apply this tool to other regions and combine with other satellite data streams to distinguish sources and track trends. The Multi-angle Imaging SpectroRadiometer (MISR) provides information on aerosol shape, size, and extinction globally for a continuous period that can be used to estimate PM2.5 speciation concentrations since 2000.

All these approaches are complementary with one another in determining current air quality, pollution hotspots, compliance with regulatory air quality standards, contributing sources, and overall/source-specific trends, as well as useful for health, exposure, and environmental justice studies. We are currently working on a paper to describe a multi-scale framework for community-level air quality analysis in California that takes advantage of these new capabilities.

Further Reading
World's Air Pollution: Real-time Air Quality Index -- Compendium of real-time air quality for more than 10,000 stations around the world.

Kozawa et al. (2013) Mobile Measurement Platform: An Innovative Tool for Studying the Impacts of Traffic Emissions -- Overview of the California Air Resources Board's mobile monitoring research program.

Apte et al. (2017) High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data -- Google Street View vehicles equipped with a fast-response pollution measurement platform used to repeatedly sample every street in a 30-km2 area of Oakland, CA. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks.

Lee and Son (2016) Spatial Variability of AERONET Aerosol Optical Properties and Satellite Data in South Korea during NASA DRAGON-Asia Campaign -- Both transported dust and local combustion contribute to PM2.5 levels in South Korea, although the study is limited to the spring of 2012. This shows the promise of satellite-based aerosol optical depth readings (in combination with strategically placed ground-level PM monitors) to provide detailed air quality maps throughout a region.

Meng et al. (2018) Estimating PM2.5 speciation concentrations using prototype 4.4 km-resolution MISR aerosol properties over Southern California -- PM2.5 sulfate, nitrate, OC and EC trends from 2001 to 2015.

CARB (2018) Use of Satellite Remote Sensing Data to Support Air Quality Decision-making -- Overview of the California Air Resources Board's remote sensing research program.

Shashi

Thanks @bartc for sharing these resourceful articles. We are thrilled to have you in our community.

akb

That's a great contribution @bartc
Do you know of any technologies that could be used to provide extensive monitoring of the chemical composition of PM?

hopkepk

They do require extensive and repeated calibration. However, they can provide critical input data for the development of land use regression models to provide better exposure assessments in health effects studies. See:
PM2.5
Zikova, N., Masiol, M., Chalupa, D.C., Rich, D.Q., Ferro, A.R., Hopke, P.K., 2017. Estimating hourly concentrations of PM2.5 across a metropolitan area using low-cost particle monitors. Sensors 17 (8)

Masiol, M., Zíková, N., Chalupa, D.C., Rich, D.Q., Ferro, A.R., Hopke, P.K., 2018d. Hourly land-use regression models based on low-cost PM monitor data. Environ. Res. 137, 7–14.

O3
Masiol,M., Squizzato, S., Chalupa, D.C., Rich, D.Q., Hopke, P.K., 2018b. Evaluation and field calibration of a low-cost ozone monitor at a regulatory urban monitoring station.
Aerosol Air Qual. Res. 18, 2029–2037.

Masiol M, Squizzato S, Chalupa D, Rich DQ, Hopke PK. Spatiotemporal variations of ozone concentrations across a metropolitan area using a network of low-cost monitors and an hourly land-use regression model. Sci Total Environ. 2019;654:1167–78.

Ananya_Roy

Thanks for starting this discussion @bartc!
I second this "All these approaches are complementary with one another in determining current air quality, pollution hotspots, compliance with regulatory air quality standards, contributing sources, and overall/source-specific trends, as well as useful for health, exposure, and environmental justice studies".

EDF and others have been working to really demonstrate what can be done using
sensors to:
1) Detect air pollution and health impacts at a hyperlocal scale:
Apte et al. (2017) High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data -- Google Street View vehicles equipped with a fast-response pollution measurement platform used to repeatedly sample every street in a 30-km2 area of Oakland, CA. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks.

Alexeeff SE, Roy A, et al. High-resolution mapping of traffic related air pollution with Google street view cars and incidence of cardiovascular events within neighborhoods in Oakland, CA. Environmental Health. 2018 Dec;17(1):38.

https://www.edf.org/media/study-web-sensors-shows-how-pollution-varies-over-space-and-time

2) Increase awareness:
https://www.edf.org/airqualitymaps/oakland
https://www.breathelondon.org/

3) Scale this approach to different cities:
https://www.edf.org/airqualitymaps

http://business.edf.org/blog/2018/06/25/how-hyperlocal-air-pollution-monitoring-will-create-smarter-healthier-cities

4) increase enforcement :
https://www.edf.org/media/multi-day-measurements-indicate-sustained-exposure-high-levels-benzene
( work with @ET_Tony )

https://www.houstonchronicle.com/news/houston-texas/houston/article/Houston-develops-tool-to-better-monitor-levels-of-14383089.php
We have been using higher grade sensors than traditional low cost sensors and there is a need for good quality low cost sensors. The XPRIZE could be instrumental in making a difference in designing low cost sensors that can give better quality data.

Ananya_Roy

Additionally with regards to satellite data for air pollution monitoring.
this is a really useful group to track.
https://haqast.org/

they have been doing some cutting edge work on applying remote sensing data for air quality & health applications and have active conversations on how this data can be applied for regulatory decision making.

One thing to note is that Remote sensing allows estimation of air pollution on the ground through models and they still require ground level monitoring to validate and calibrate their models. in many places in the global south there are many countries that are data poor and require good quality low cost solutions to air pollution monitoring.

akb

That's a great contribution @Ananya_Roy

Years ago I did emissions modelling of traffic emissions, and it indicated that emission rates would be different along each street (and junction), depending on the pollutant (CO, HC, NOx). So I welcome your suggestion for better and cheaper sensors to monitor these characteristics.

A number of scenarios were modelled in Athens and the most effective one was the introduction of low/zero emission zones, perhaps not surprisingly.

Alternatively, adjusting traffic light timings was able to reduce some pollutants on some streets, but at the expense of increases in other pollutants and/or increased emissions on other streets.

I've posted more on this topic in this XPRIZE community: Analysing: Traffic Pollution.

akb

@Ananya_Roy Those examples that were able to identify the source of the pollution are very useful.

(Allowing us to take action to resolve the problem.)