Public Lab Wiki documentation

Collecting Data on Particulate Matter

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Before undertaking air monitoring for Particulate Matter (PM) identify the end goals of monitoring for your community. Monitoring airborne particles can be prohibitively expensive and data that is actionable for regulators can take years to collect. To be efficient, the accuracy and precision of collected data should be appropriate for its end use -- not all data needs to be of regulatory quality in order to be useful. For example, a community may want to collect data to:

Airborne particles are clustered into three rough size ranges, or modes, of particles in the air: dust, droplets, and ultrafine particles. While droplets and ultrafines are largely combustion by-products, dust is broken off of larger materials. No single method of PM monitoring method covers all categories.

Dust is the most established particle mode to monitor. However, dust is ubiquitous, so industrial dust emissions can be difficult to trace back to their source.

Droplets are difficult to monitor. In real-time optical PM monitors, humidity and temperature effects interfere substantially with measurements. Humidity also affects filter-based PM monitors, and questions about allowable water content in droplets is actively debated. Read more on the NAAQS.

The study of ultrafine particles is fairly new. There are no regulatory categories that apply to ultrafines, and no inexpensive means to monitor them. Exposure to ultrafine particles is associated with proximity to combustion, especially of diesel and marine fuels, since most ultrafines are formed through atmospheric reactions of gases.


chart found on pg 27

Due to the varied and significant challenges of accurate monitoring, it is important to determine the data quality (accuracy and precision) needed for a specific research or advocacy end-goals.

Proposed precision categories for citizen monitoring

State and federal regulators are empowered make judgements based on visual assessments of particle pollution, but at present regulators have no statutory guidance or authority to interact with PM data collected with instruments other than their (very expensive) regulatory monitors or on timescales shorter than annually. This can lead to curt rejections of scientifically sound data. Federal regulators recognize this issue and are working fund development and evaluation of lower-cost air sensors. During an evaluation process, an EPA scientist tabulated potential categories of community-collected data based on precision, as discussed in the Air Sensor Guidebook. These categories are prospective (except for regulatory monitoring, Category V) and should only be treated as guidelines for technologies in development.


Prompting action to address airborne particles

Given that regulators are currently unlikely to make judgements based any data other than visual monitoring and regulatory monitoring, community-based PM data, in isolation, is likely to be ineffective at prompting official enforcement. Thus, community-collected PM data needs to be accompanied by strong advocacy to prompt further investigation or leverage publicity and public relations. For information about best practices for developing a community environmental monitoring study, see this wiki.

Regulatory grade PM monitoring

Regulatory monitors cost $20-60,000 to buy, ~$100/day to analyze, and require 1-3 years of data to evaluate compliance with regulatory standards. It is also important to note that a failure to demonstrate an exceedance of PM2.5 or PM10 standard limits does not necessarily indicate safe conditions. Particles that are of the respirable size-fraction, which have severe health consequences, are mostly excluded from PM2.5 measurements and are not differentiated (or acknowledged) in PM10 measurements. For more information, please read this wiki. Additionally, the composition of particles is not routinely determined, so particularly damaging substances may cause negative health impacts at permissible particle concentrations. For example, airborne silica can be dangerous at 5-10% of regulatory limits on particle concentration.

Smoke School

A visible emission is any visible airborne particle resulting from a process. Visible emissions usually include respirable particles, and can be measured by their effects on the opacity of the air. Opacity is expressed as the percentage of light that is scattered or blocked by emissions such that an observer's view through a plume is obscured. Opacity can be monitored through visual assessment with only human eyes and a stopwatch. Examples of pollutants that change opacity are smoke stack emissions and fugitive dust.

Read more about visual emissions and certification programs in the visual particulate matter wiki. Certifying community observers in EPA Method 9 can be written into a facility’s permits, though it is not always. If you have information about when and where permit fees are required to cover community certifications, please add to this wiki or write a research note! Communities may find it useful to conduct visible emission monitoring and also engage in other advocacy strategies to gain the most leverage.

Types of monitoring equipment

Most monitors give a mass-based particle concentration for all particles in a size category, meaning they do not differentiate between the relative mass contribution of different sizes of particles within that category. Only systems that capture and save particulate matter can identify, or ‘speciate’ particles by size or elemental composition.

The sections below briefly describe different approaches to PM monitoring and show what the Public Lab community is asking and saying about each approach.

Filter-based systems

Used for: regulatory monitoring, supplementary monitoring

Filter-based systems can collect particles for laboratory methods of speciation, and are the basis of Federal Reference Methods. Data can only be analyzed after collection, not in real-time. Usually samples are collected over a 24-hour period and the weighted average concentration (by mass) for that 24-hours is produced. Filter-based gravimetric systems are usually the most precise measurements of PM.

Optical systems

Used for: personal exposure monitoring, supplementary monitoring, hotspot identification, hotspot characterization, education

Optical electronic systems offer the possibility of real-time particle counts which are valuable for hotspot identification, recording short-term high emissions events, and identifying when air may pose a health threat. Their data is significantly affected by humidity though. More precise monitors usually include a filter-based system to correct data after collection, such as what Public Lab plans to do by collocating optical systems with passive monitors.

Passive systems

Used for: personal exposure monitoring, supplementary monitoring, education, hotspot characterization, education

Passive systems have no moving parts and are easy to deploy for long-term monitoring without electricity. They can approach the precision of regulatory monitoring and are within the accuracy and precision ranges necessary for supplementary monitoring. Passive monitors generally require longer sample collection periods (3-10 days) than active filter-based monitoring, and are better used to characterize hotspots than to identify them.

Passive monitors collect particles onto filters or slides, so there is the opportunity to do some limited speciation analyses of particles.

Title Author Created | Updated Likes Views Type
Choosing a method for Particulate Matter Monitoring @stevie over 3 years ago 22
SEM stub monitor for particulate matter @mathew over 6 years ago 17
What is the scenario under which you would use an optical vs. a passive monitor to measure dust? @kgradow1 almost 7 years ago 1
Can a passive dust monitoring housing be made from a cheaper/easier material? @warren over 7 years ago 1
Sizing particles in microscope images at Portland Science Hackday @mathew almost 8 years ago 1
Calibrating a Microscope @mathew almost 8 years ago 1
OpenFlexure Microscope: high-resolution assembly @mathew about 8 years ago 1
Sample prep for Passive Particle Monitors @mathew about 8 years ago 1
Passive Particle Monitors @mlamadrid about 8 years ago 1
Automating ImageJ for particle image analysis @SimonPyle about 8 years ago 1
Making an OpenFlexure Microscope @mathew over 8 years ago 1
Mapping dust hotspots with low-cost monitors @mathew over 8 years ago 1
Automating Passive Particle Monitor Analysis @mathew over 8 years ago 1
Passive Particle Monitor Deployments: feedback @mathew over 8 years ago 1
ImageJ exporting data into Excel: Used for Analysing Images of particulate matter @pagyebo over 8 years ago 1
Systematic Imaging of Passive particle monitors on an SEM @mathew over 8 years ago 1
Deploying Passive Particle Monitors @mathew over 8 years ago 1
Using imageJ to adjust threshold using mode Entropy @damarquis almost 9 years ago 1
Coal Ash and Citizen Monitoring @gretchengehrke almost 9 years ago 1
When $100,000 is not enough: how citizen data (could) relate to government regulation @liz almost 9 years ago 1
Deploying UNC passive sampler on South Side of Chicago @AmberWise almost 9 years ago 1
Using ImageJ to process passive particle monitor samples @mathew almost 9 years ago 1
analyzing passive monitors @mathew almost 9 years ago 1
Optical Imaging of Passive Particle Monitors @mathew almost 9 years ago 1

Further reading and resources