Better monitoring of particulate matter

For the purposes of an XPRIZE an opportunity arises to address a fundamental issue associated with the measurement of particulate matter (and the consequences for better epidemiological studies, and health, in the future)…

Particulate matter varies widely in its chemical composition - particles from different emission sources can have radically different chemicals in them. This means the actual health impact for a given exposure to a specific mass of particles will probably be different, depending on the chemical composition of the particles. Some chemicals might be biologically inert while others (e.g. diesel emissions from vehicles) contain toxic carcinogenic compounds!

So not only is there a need to measure particle size (e.g. PM2.5, PM10) there is a need to detect the specific chemicals in (and on) those particles. We need a technology to detect chemical compounds. To date no widely available, low cost, technology has been developed and deployed.

Currently, such chemical analysis is typically conducted in a relatively slow and expensive manner in laboratories - meaning that we have no idea what the actual chemical compositions are for most urban and rural areas. [Modelling alone has a limited accuracy.]

Monitoring of chemical composition across a majority of urban streets with high traffic flows (and ideally in rural areas too) has the potential to greatly enhance the effectiveness of epidemiological studies and to increase our understanding of the specific health impacts for each type of particulate matter.

A real-time monitoring system also provides the opportunity to implement real-time control systems and strategies - to respond to pollution incidents and mitigate their impact.

It is also possible to pro-actively prevent some pollution incidents by using sophisticated modelling and prediction systems (based on accurate data and patterns of behaviour).

Hi Akb, @akb
I moved your above post from the recommended reading section to market trends, as it tries to provide a probable solution. Thanks for your amazing input.

@akb this idea of real-time particulate matter speciation has definitely struck a chord within our team and has also been voiced by several of the experts we’ve spoken with.

We have been exploring this idea for a prize direction, but came across a crossroads in our thinking that while a lower-cost speciation tool might provide great value to researchers and long-term health studies, we are not confident that the wider market and the average consumer will be able to interpret or appreciate the provided data enough to make a real difference in their behavior and health outcomes.

However, we were thinking of this as a personal solution (a device+app sort of thing) and what you speak about above seems to be a more city-wide implementation, so we’d certainly be interested to pick your brain further about your thoughts on this! For more context we are also cognizant of many companies that are already working on influencing behavior change by connecting air quality data to health recommendations, so there is a possibility that the market may deliver a individual-focused solution without an XPRIZE intervention.

@TerryMulligan @jamesburbridge feel free to chime in on where our headspace has been and currently is on all this, and thank you again @akb for all your valuable contributions to this community!

If the goal of the competition is to reduce the amount of particulate levels in the atmosphere, then speciation will not be much help. Further, we have yet to “close the loop” on the actual impacts of the 6 criteria pollutants, never mind the 189 hazardous air pollutants (HAPs). All we have, at the present time, are estimates, most of which are on the high side (to be conservative). Speciation can be of great help in a local area. For example, if a local emitter puts out particulate matter that happens to be high in mercury (whatever high is), then the local population can be warned about the dangers of mercury in the blood stream. However, the main impact of particulate matter on health has been considered to be that small particles can be carried deep into the lungs (thoracic particles) and remain there (hence the interest in PM2.5 vs PM10). The goal of reducing ambient concentrations of particulates is to minimize the opportunity for such small particles to enter the lungs. The current ambient annual PM2.5 standard is 12 micrograms/Nm3. The 24 hour standard is 35 micrograms/Nm3 to account for short term exposures. These standards are intended to provide protection for children, older adults, persons with pre-existing heart and lung diseases, and other at risk populations from both short and long term exposures. Most of the US is already at or below this level. The problem is to find the best way to get there for other parts of the world.

@carlbozzuto I agree, today, we need to deal with the known/accepted science, and do the best we can to protect people at risk. Tomorrow, with better data regrading PM2.5 speciation, we can treat different PM differently.

Hi @JessicaYoon
Thanks for your consideration and interesting comments.

As you point out, a low cost device that can identify the chemical composition of particulate matter would be of great value to researchers and health studies.

It also has the potential to identify the types and locations of sources are contributing to the ambient pollution levels - when combined with readings from the devices of others (in conjunction with GPS location data, wind data, and time-stamps). This could be useful for real-time interventions, predictions, and tracking down specific pollution sources. This presents new opportunities for managing and preventing pollution. Each particulate emissions source might have a unique signature (particle size, internal chemistry, and adsorbed chemicals). Using clever software (or AI) it should be possible to identify these unique sources on a map.

The average consumer will not be able to appreciate the finer details of this raw data. However, with the use of an app and an online database, information can be provided that will make a real difference to their behavior and health outcomes. For example, one type of “pollutant” that might show up in particulate measurements are allegens from plant pollen grains. An intelligent system would be able to notify users that suffer from allergies that it is time to take action. Similarly, the system could notify users that suffer from asthma when they should take action. It is also possible that this could work with devices that monitor heart beat and exercise, to warn people when they should avoid doing exercise because of specific pollutants at that location. This ability to distinguish between particulate types and their chemistry presents a range of health improvement opportunities.

The personal solution (device+app) could link up with an online (Cloud) database to provide city-wide coverage too.

As you point out there are some personal devices emerging. However, it would take an XPRIZE to deliver the breakthrough that can distinguish between different types of particulate: their size, their internal composition, and chemicals adsorbed on their surface. Such a breakthrough could help to deliver all of the above benefits.