Evaluation of NO_2 predictions by the plume volume molar ratio method (PVMRM) and ozone limiting method (OLM) in AERMOD using new field observations

摘要

The U.S. Environmental Protection Agency (EPA) plume volume molar ratio method (PVMRM) and the ozone limiting method (OLM) are in the AERMOD model to predict the 1-hr average NO_2/NOx concentration ratio. These ratios are multiplied by the AERMOD predicted NOx concentration to predict the 1-hr average NO_2 concentration. This paper first briefly reviews PVMRM and OLM and points out some scientific parameterizations that could be improved (such as specification of relative dispersion coefficients) and then discusses an evaluation of the PVMRM and OLM methods as implemented in AERMOD using a new data set. While AERMOD has undergone many model evaluation studies in its default mode, PVMRM and OLM are nondefault options, and to date only three NO_2 field data sets have been used in their evaluations. Here AERMOD/PVMRM and AERMOD/OLMcodes are evaluated with a new data set from a northern Alaskan village with a small power plant. Hourly pollutant concentrations (NO, NO_2 ozone) as well as meteorological variables were measured at a single monitor 500 m from the plant. Power plant operating parameters and emissions were calculated based on hourly operator logs. Hourly observations covering 1 yr were considered, but the evaluations only used hours when the wind was in a 60° sector including the monitor and when concentrations were above a threshold. PVMRM is found to have little bias in predictions of the C(NO_2)/C(NOx) ratio, which mostly ranged from 0.2 to 0.4 at this site. OLM overpredicted the ratio. AERMOD overpredicts the maximum NOx concentration but has an underprediction bias for lower concentrations. AERMOD/PVMRM overpredicts the maximum C(NO_2) by about 50%, while AERMOD/OLM overpredicts by afactor of 2 For 381 hours evaluated, there is a relative mean bias in C (NO_2) predictions of near zero for AERMOD/PVMRM, while the relative mean bias reflects a factor of 2 overprediction for AERMOD/OLM.