Sullivan

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Sullivan Environmental has three meteorologists with experience in providing expert testimony involving air quality and meteorology.  There are two Certified Consulting Meteorologists with over 45 years of experience each, and a meteorologist with 17 years of experience. Much of this work involves compiling representative emissions data and other model inputs, and running dispersion models to evaluate exposures to air pollutants, ranging from toxic air pollutants to heavy metals and odor-causing emissions.  In some cases, measured data also are collected to provide direct points of reference with modeled concentrations.  The analyses have ranged from small-scale analyses near major facilities to broader scale, including regional scale involving hundreds of square miles in scope.  Different models and different options are required on a case-by-case basis.   We do not specifically describe these matters in our web page or resumes.  There is one exception, however, that can serve as a case study.

 

The Air & Waste Management Association (AWMA) presented a legal case study at its annual meeting in Chicago (June 2013).   AWMA had a rather unique session where they invited the opposing attorneys on an air quality litigation matter to discuss the matter, and also invited David Sullivan as the plaintiff’s expert on the case to describe a case involving a secondary lead smelter in the Detroit, Michigan area.  The facility operated from the early 1970s into the 1990s.  There were emissions data for the stacks but no emissions data inventoried for the fugitive emission rates. 

 

Some modeling had been performed by EPA for this facility.  We started there, and then made incremental refinements to the modeling, adding in background contributions, and refining various model inputs.  Much of the effort, however,  was on estimating the emission rates for the fugitive emissions, which were not included in the earlier EPA modeling. There was a major disagreement among the competing experts regarding the magnitude of the fugitive emissions.  Mr. Sullivan based his estimate on a peer-reviewed technical article.  The opposing expert disagreed with the use of this reference and opined that it substantially overstated the significance of the fugitive emission rates, which were estimated to comprise more than 90 percent of the modeled impacts.   By estimating the fraction of deposited lead within the 0-3 inch profile where extensive measured lead contamination data were available, the modeled deposition and measured soil were of similar magnitude.  There was not much support in terms of measured air quality data, however, to resolve the difference of opinion.

 

There also were some anecdotal measured air quality data dating to the period of interest that were collected within the near vicinity of the secondary lead smelter.  Sullivan Environmental had tried for months to obtain these data, which could be most definitively compared with the modeling of the airborne lead.  Within a week of the jury trial, the report we had been seeking was found.  There were 12 days with 24-hour integrated samples at three sites.   It showed that the modeling based on the directly inventoried stack data plus the larger fugitive component was in very close agreement at all three monitoring sites.  In fact, it showed that the modeled data were ~ 25 percent lower than the measured data, with the differences across the three sites to be 22 %, 7 %, and 44 % (for the site with the lowest impacts).  The case proceeded to trial with solid backup support for the modeling in terms of both measured air quality  and measured soil contamination data.

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Urban areas generally have the highest population density and the most pollution caused by motor vehicles, heating, and other sources of air pollutants.  Many times in the outlying areas of cities there are industrial zones that further contribute to the airborne environment.   The control of air pollutants by EPA and local and state air quality agencies is through issuing permits to reduce the emission of air pollutants.  For major sources, this includes air quality dispersion modeling to determine if the air quality standards are expected to be met.  Ultimately, State Implementation Plans (SIPs) are written with the goal of reaching lower levels of air pollutants to ensure that attainment will be met. SIPS are based on air quality models. The attainment status for metropolitan areas relative to air quality standards, however, is generally based on directly measured air quality data.  How consistent are these two pathways (SIP modeling in comparison to the directly measured data that are achieved?  As an example, the phasing-in of controls theoretically should lead to a commensurate reduction in air quality concentrations if proper consideration is made for background source contributions.  What if the actual trends based on measured data are diverging from the projected trends?  This likely will mean that the SIP will not achieve the goals it was designed to achieve.  Some mid-course corrections should be in order.  Whether it be on the urban scale or the planned reduction of air quality levels around a major facility, divergence of the planned versus achieved air quality benefits needs to be minimized.

 

The most efficient way to evaluate divergence in projected versus actual trends in air quality in metropolitan areas is through urban-scale studies.   The staff of Sullivan Environmental has conducted numerous urban-scale studies within the U.S., Eastern Europe, and Indonesia.  The approach is to compile available point source emissions data, expand as necessary the coverage including for area sources such as gridded mobile sources, heating, etc., and model a wide range of toxic air pollutants or specific criteria pollutants (e.g., fine particulate matter, sulfur dioxide, etc.) based on the composite data set.  Such data can be compiled at the grid level, linked to population counts, and total exposures and maximum exposure evaluated.  Through the connection and reality check provided by linking the available measured air quality data to the corresponding modeled concentrations, the measurements can be used to identify the weaknesses in the analyses (by area and by pollutant) and provide guidance based on preferred options to improve model performance.  Such steps could be based on sensitivity testing of alternative models, model options, emission factors, etc.  Once the most accurate assessment is identified based on such sensitivity testing, then modeled concentrations at the current time and projections into the future can be made more confidently.

 

As an example, Sullivan Environmental developed the AIMS modeling system that linked emissions, modeling, monitoring, population data, and model performance trend testing into one integrated system.   Reviews such as this allow for projected versus actual trends in air quality progress to be objectively tracked.

 

Examples of urban-scale studies managed by our staff include the following metropolitan areas:

 

  • EPA Philadelphia Integrated Environmental Management Project (IEMP) air toxics
  • EPA Baltimore IEMP air toxics
  • EPA Kanawha Valley. West Virginia cancer / non-cancer study IEMP air toxics
  • EPA Ostrava, Czech Republic air toxics
  • EPA Katowice, Poland air toxics
  • Jakarta, Indonesia (U.S. Trade & Development Agency) criteria pollutants
  • EPA St. Louis SO2
  • Atlanta ozone precursor study
  • EPA Silicon Valley IEMP study air toxics

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When there is a need to estimate human exposures to air pollutants there are generally two options available to analysts:  directly measure the air quality or rely on computerized dispersion models to estimate 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 of course, the option remains to rely on both.

 

Assuming that there is a validated protocol and experienced staff to reliably measure air quality, 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 for specific times, e.g., for an hour or day at a specific location (monitoring site).  What about locations where there are not any monitors?  What about time periods when monitoring is not being done?  If there are concerns about airborne exposures within a neighborhood, for example, from a nearby industrial facility or another source, it is infeasible to have enough monitoring points and directly cover enough time periods to evaluate exposures at this scale.  Air quality modeling, on the other hand, could provide estimates of air quality throughout the neighborhood (and beyond) on an hour-by-hour basis for multiple years.

 

Second, there are many times when a source of air pollution is no longer in operation similar to past operational periods of interest, or the facility(s) in question have shut down.  Air quality modeling then is not even an option.  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?  For the second case listed above, the answer is yes, but if the facility(s) of concern are still operational, the ideal approach is to rely on both measured and modeled air quality.  This approach to rely on integrated air quality/monitoring initiatives was an important component of a former EPA program called the Integrated Environmental Management Project (IEMP).  David Sullivan and Dennis Hlinka of Sullivan Environmental serve as Principal Investigator and lead modeler, respectively, on these studies.  Air quality monitoring results were used as points of reference to confirm model options and emission treatments and then modeling was used to evaluate exposures sometimes to hundreds of air pollutants.  More details on the IEMP are contained in the blog “Urban-Scale Air Quality Assessment.”  Integrated air quality monitoring and modeling also apply at the facility and neighborhood level when measured data are available as a point of reference.  Similar to the IEMP, we use the measured data as a checkpoint in the modeling with the goal of ensuring the model selection, model options, emission inputs, etc. are producing modeling results that are reasonably consistent with measured observations.  When measured air quality data are not available and the facility is still operational, we can collect representative measured air quality data, dust fall measurements when applicable to the airborne deposition pathway, and odor samples to compare with odor threshold levels.  The use of the measured data is not to calibrate the model, i.e., to force it to mathematically match the measurements, 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:

 

  • 40 solar-powered, self-contained air quality sampling systems that can address a wide array of air pollutants based on validated methods.
  • 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 further support air quality modeling initiatives.
  • 3 Odor sampling systems to support the collection of odor samples for presentation to odor laboratory panels.
  • 2 soil monitoring systems to measure soil temperature and soil moisture at multiple sampling depths.
  • 5 noise monitors and one field calibrator are maintained in NIST calibration.

 

The above equipment can be used to support the modeling of exposures as well as for computing emission rates from fugitive emissions, which cannot be practically evaluated except on a composite basis, such as airborne exposures for pesticide applications, complex piping system leaks at large industrial facilities, and so forth.