A computationally efficient model for estimating background concentrations of NOx, NO2, and O3

Abstract

We formulate a Lagrangian model to supplement comprehensive Eulerian grid models such as CMAQ, to estimate concentrations of NOx, NO2, and O3 averaged over a spatial scale of the order of a kilometer over a domain extending over hundreds of kilometers. The model can be used to estimate hourly concentrations of these species over time periods of years. It achieves the required computational efficiency by separating transport and chemistry using the concept of species age. The model computes concentrations by tracing the history of an air parcel reaching a receptor through back trajectories driven by surface winds. Chemical reactions within the parcel are modeled through the Generic Reaction Set (GRS) chemistry model, which approximates the photochemical processes that lead to the production of ozone. The model is evaluated with concentrations measured over two years, 2005 and 2007. Evaluation with data measured at 21 stations distributed over the California South Coast Air Basin (SoCAB) during 2007 indicates that the model provides an adequate description of the spatial and temporal variation of the concentrations of NO2, and NOx. Estimates of maximum hourly O3 concentrations show little bias (less than 10%) compared to observations, and the scatter (sg2 ≤ 2.56–95% confidence interval of the ratio of predicted to observed concentrations) is comparable to those associated with more computationally demanding models. The model was also evaluated with data collected at monitors in the San Joaquin Valley Air Basin (SJVAB) in 2005, and it shows similar performance to that at SoCAB. The paper also illustrates the application of the model to 1) screening regions for attainment of statistically based air quality standards, such as that for the daily maximum 8-h average O3, and 2) improving methods to interpolate observations.