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

Abstract

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (∆CO:∆NOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx 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 NOx 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:∆NOx 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:∆NOx but not cross-road gradient ∆CO:∆NOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NOx 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.

Keywords

• near-road measurements
• mobile source emissions
• CO
• NO_x
• linear regression
• top-down constraints