Variability in Observation-Based Onroad Emission Constraints from a Near-Road Environment

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NO_x) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NO_x ratios (∆CO:∆NO_x) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NO_x ratios are used to infer NO_x emission adjustments for a priori emissions estimates from EPA’s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NO_x scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25 m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ∆CO:∆NO_x from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115 m and 300 m) increases OLS and orthogonal regression estimates of ∆CO:∆NO_x but not cross-road gradient ∆CO:∆NO_x. The inferred NO_x emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NO_x emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NO_x estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.