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The Tangled Web of Air Quality Monitoring and Air Dispersion Modeling

On Friday, April 9, the White House issued President Joe Biden’s request for fiscal year 2022 discretionary funding. The full 58-page document can be found here: https://www.whitehouse.gov/wp-content/uploads/2021/04/FY2022-Discretionary-Request.pdf


The request is broken down into a series of sections. Under the section entitled “Tackling The Climate Crisis” there is a paragraph that addresses making the “largest investment in (the history of) environmental justice.” As part of that initiative the request calls for “$100 million to develop a new community air quality monitoring and notification program, which would provide real-time data in the places with the highest levels of exposure to pollution.”


No more details are given as to what this monitoring and notification program might involve, but with the Biden Administration’s focus on air quality monitoring I thought it would be timely to chat with you all on the use of air quality monitoring data as it relates to air dispersion modeling. As we’ll see, each have their place in air quality management.


Monitoring and modeling both have their pros and cons. Monitoring data very frequently are more accurate than model predictions as they reflect what the air quality truly was at that specific location and time. However, monitors are expensive to purchase, install, maintain, and operate, and only provide pollutant information for a specific location and time. Modeling, on the other hand, can provide pollutant information over a much larger area and time period and at much less cost—but of course, the model results are driven by how good the model is and the accuracy of the model inputs.


Modeling can also help answer a question that often is difficult to address with monitoring alone—what sources are actually responsible for air quality issues? My career has taken me to many “developing” countries from an air quality standpoint—from Africa to Asia to eastern Europe. Too often in my travels I have seen countries with poor air quality who are fixated on just installing more monitors. I find this approach to be misguided; if the country already knows it has poor air quality, deploying more monitors will simply confirm what they already know--that they have poor air quality. So why not take the hundreds of thousands, or in some cases millions, of dollars allocated toward additional monitors and put it toward a comprehensive air dispersion modeling analysis that can identify which sources are causing the poor air quality? This in turn can inform control strategies to reduce air pollution which ultimately will lead to better air quality.

Ideally monitoring and modeling should work hand in hand to address air quality. For example, when AERMOD was being developed its performance was validated against datasets of monitored concentrations for different source/dispersion situations. Only when EPA was satisfied with AERMOD’s performance was it released as an approved model. But those sorts of model performance evaluations against monitored concentrations still come up today; techniques such as Q-Q plots (shown to the right) can be used to assess how well a model performs against monitored data.


Much of the air quality world recognizes that a modeling analysis does not account for all pollutant emissions within the modeling domain. Therefore, in many instances a “background” concentration will be added to the model-predicted concentration to develop a total pollutant concentration which is then compared to an air quality standard. In the United States this is done when demonstrating compliance with the National Ambient Air Quality Standards (NAAQS).


EPA has published its “Guideline on Air Quality Models”[1] (GAQM) to establish procedures for how to conduct air dispersion modeling in the United States. Section 8.3 of the GAQM speaks to background concentrations and addresses considerations such as how the background concentration is supposed to include impacts from nearby industrial facilities, natural sources, and pollution that has been transported in from the surrounding region. The choice of the monitor to use for the background concentration must be representative of the area being modeled, similar to the choice of the source of meteorological data in a modeling analysis. In this section, EPA also provides a procedure to calculate a background concentration that excludes values when the monitor being used is impacted by a nearby source or sources.


In the United States, the choice of a background concentration when conducting a NAAQS analysis can sometimes be very important and other times not so much. When demonstrating compliance with some of the older NAAQS (CO, for instance) it often doesn’t make much of a difference how careful you are in determining what the background concentration is—most likely you’ll be able to meet the NAAQS even using the worst-case CO background concentration.


On the other hand, when modeling for the newer NAAQS (1-hr SO2, 1-hr NO2, and PM2.5) it is often far more difficult to comply with the standard and, as a result, justifying as low a background concentration as possible becomes critical. EPA has published guidance for modeling these pollutants and included in that guidance are suggested procedures for how to calculate background concentrations. Those documents can be found on EPA’s SCRAM website: https://www.epa.gov/scram/clean-air-act-permit-modeling-guidance#otherguide. Of course, since these documents are merely guidance you will need to obtain approval from your regulatory agency for your proposed approach before using it in an analysis.


In closing, I offer one final thought on background concentrations. Some regulatory agencies offer to provide an applicant with a background concentration; while it may be tempting to take them up on their offer to save some time/money, I always recommend to my clients that we develop background concentrations ourselves before submitting them as part of the modeling analysis. There are a multitude of approaches to calculating a background concentration and you want to be sure that what you end up using in your analysis is supportive of your goals—as opposed to having on record the regulator recommending the use of a “high” background concentration. After all, to paraphrase the old trial law saying, “you never ask a question you don’t already know the answer to.”


While it remains to be seen what exactly will come of President Biden’s interest in air quality monitoring with notification, it may prove to be another nexus of monitoring and modeling. Facilities requiring more substantive review for environmental justice should, in the near future, consider conducting some internal air quality modeling using both worst-case/permitted emission rates and actual historical emissions to best characterize any potential risk they would incur from the potential installation of air quality monitors nearby. If the modeling does show some hot spots now would be the time to develop strategies to mitigate those impacts—before EPA implements a monitoring program that could begin raising air quality alarms in the area.

[1] 40 CFR 51, Appendix W

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