Benefits of Air Quality Monitoring

When there is a need to estimate human exposures to air pollutants, analysts generally have two options:  directly measure the air quality or rely on computerized dispersion models to assess air quality based on the input of source(s) emission rates, meteorological data, and other factors.  Which option is the best choice?  Well, that depends.  Each has its benefits, but the option remains to rely on both.

Assuming that there is a validated protocol and experienced staff to measure air quality reliably, measured data provides the most direct data.  Why would air quality models be needed to estimate air quality if direct measurement is a viable option?  That is a good question, but there are multiple reasons why air quality modeling remains a sound option.

First, air quality monitoring can provide direct and reliable concentrations at specific locations and times, e.g., for an hour or day at a particular area (monitoring site).  What about places where there are no monitors?  Suppose there are concerns about airborne exposures within a neighborhood, for example, from a nearby industrial facility or another source. In that case, having enough monitoring points and directly covering enough periods to evaluate exposures at this scale is infeasible.  

Additionally, air quality modeling estimates air quality throughout the neighborhood (and beyond) on an hour-by-hour basis for multiple years.

There are many times when a source of air pollution is no longer in operation.  This is similar to past operational periods of interest, or the facility(s) in question have shut down. Air quality modeling is not even an option then. Air quality modeling would be the method of choice based on estimates of emission rates at the time of concern.

Does this result in air quality modeling being the preferred option?  The answer for the second case listed above is yes, but if the facility(s) of concern are still operational, the ideal approach is to rely on measured and modeled air quality.  This approach to relying on integrated air quality/monitoring initiatives was an essential component of a former EPA program called the Integrated Environmental Management Project (IEMP).  David Sullivan and Dennis Hlinka of Sullivan Environmental serve as principal investigators and lead modelers for these studies.  

The blog post “Urban-Scale Air Quality Assessment ” contains more details on the IEMP.  Integrated air quality monitoring and modeling also apply at the facility and neighborhood levels.  This applies when measured data are available as a reference point.  

Like the IEMP, we use the measured data as a checkpoint in the modeling to ensure the model selection, model options, emission inputs, etc., produce modeling results that are reasonably consistent with measured observations.  When measured air quality data are unavailable, and the facility is still operational, we can collect representative air quality data, dust fall measurements when applicable to the airborne deposition pathway, and odor samples to compare with odor threshold levels.  Using the measured data is not to calibrate the model, i.e., to force it to match the measurements mathematically, but to “learn” from the measured data the most accurate way to model the observed concentrations.

 

Sullivan Environmental has extensive air quality monitoring equipment to support our air quality programs, including the following:

• 46 solar-powered, self-contained air quality sampling systems that address air pollutants using validated methods (SKC air sampling equipment).
• 15 sonic anemometers to support multiple meteorological sampling profiles to complement air quality monitoring and support data interpretation.
• 2, 3-dimensional sonic anemometers to provide heat flux data and three-dimensional turbulence data to support air quality modeling initiatives further.
• 3 Odor sampling systems to support the collection of odor samples for presentation to odor laboratory panels.
• two soil monitoring systems to measure soil temperature and soil moisture at multiple sampling depths.