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