title: Emissions variability processor (EMVAP): design, evaluation, and application.
abstract: Emissions of pollutants such as SO2 and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO2 and NO2, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO2 modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO2 and NO2), a "critical value" can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer-term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO2 emissions. Implications: Emissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO2 and NO2, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.
authors.affiliation: AECOM, Chelmsford, MA, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Paine
authors.forename: Carlos
authors.initials: C
authors.lastname: Szembek
authors.forename: David
authors.initials: D
authors.lastname: Heinold
authors.forename: Eladio
authors.initials: E
authors.lastname: Knipping
authors.forename: Naresh
authors.initials: N
authors.lastname: Kumar
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000295:instrumentation*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009585:Nitrogen Dioxide / Q000032:analysis*; D013458:Sulfur Dioxide / Q000032:analysis*; D013997:Time Factors
chemical_list: D000393:Air Pollutants; D013458:Sulfur Dioxide; D009585:Nitrogen Dioxide