A Monte Carlo method for summing modeled and background pollutant concentrations.

Air quality analyses for permitting new pollution sources often involve modeling dispersion of pollutants using models such as AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model). Representative background pollutant concentrations must be added to modeled concentrations to determine compliance with air quality standards. Summing 98th (or 99th) percentiles of two independent distributions that are unpaired in time overestimates air quality impacts and could needlessly burden sources with restrictive permit conditions. This problem is exacerbated when emissions and background concentrations peak during different seasons. Existing methods addressing this matter either require much input data, or disregard source and background seasonality, or disregard the variability of the background by utilizing a single concentration for each season, month, hour-of-day, day-of-week, or wind direction. Availability of representative background concentrations are another limitation. Here the authors report on work to improve permitting analyses, with the development of (1) daily gridded, background concentrations interpolated from 12-km CMAQ (Community Multiscale Air Quality Model) forecasts and monitored data. A two-step interpolation reproduced measured background concentrations to within 6.2%; and (2) a Monte Carlo (MC) method to combine AERMOD output and background concentrations while respecting their seasonality. The MC method randomly combines, with replacement, data from the same months and calculates 1000 estimates of the 98th or 99th percentiles. The design concentration of background + new source is the median of these 1000 estimates. It was found that the AERMOD design value (DV) + background DV lay at the upper end of the distribution of these one thousand 99th percentiles, whereas measured DVs were at the lower end. This MC method sits between these two metrics and is sufficiently protective of public health in that it overestimates design concentrations somewhat. The authors also calculated probabilities of exceeding specified thresholds at each receptor, better informing decision makers of new source air quality impacts. The MC method is executed with an R script, which is available freely upon request.

IMPLICATIONS: Summing representative background pollutant concentrations with air dispersion model output using a Monte Carlo method that respects the seasonality of each provides for more robust and scientifically defensible air quality analyses in support of permit applications. This work provides applicants a method to demonstrate compliance with National Ambient Air Quality Standards and avoid emission controls that might be based on overly conservative analyses. It also calculates the probability of exceeding the standard, allowing regulators to make more informed permitting decisions.