Identifying high-emitting heavy-duty vehicles using remote emission sensing technology

Abstract

Vehicle emissions are major contributors to air quality issues in many areas of the world. Policymakers are actively exploring new technologies for monitoring vehicle emissions on roads, and remote emission sensing (RES) is a promising approach. However, it is mostly used to evaluate the fleet average emission characters. In this study, we evaluated the accuracy of RES for a single measurement and its ability to identify high-emitting vehicles by conducting concurrent tests with another real-world methods, i.e., using portable emissions measurement system (PEMS) in a test field, as well as city demonstration tests. It was found that the relative errors of single RES measurements decreased from an average of 212.42% to 24.68% when the $NO$ emission factors exceeded 5 g/kg fuel. The China VI high-emitting diesel vehicles identified by RES $NO$ measurements were also found to release severe $NOx$ emissions based on their on-board diagnostics (OBD) data. This study demonstrates that RES is a suitable tool for detecting high-emitting heavy-duty vehicles with acceptable uncertainty, and provides specific criteria for improving the accuracy of RES data. Additionally, it presents a method to utilize OBD data for identifying high-emitters.