  Identifying and managing high-emitters among heavy-duty diesel vehicles is a
key to mitigating urban air pollution, as a small number of such vehicles could
contribute a significant amount of total transport emissions. On-board
monitoring (OBM) systems can directly monitor the real-time emission
performance of heavy-duty vehicles on road and have become part of the future
emissions compliance framework. The challenge, however, lies in the frequent
unavailability of OBM data, affecting the effective screening of high-emitting
vehicles. This work proposes to bridge the gap by integrating OBM data with
remote sensing data to create a comprehensive monitoring system. OBM data is
used to characterize the detailed real-world NOx emission performance of both
normally-behaving vehicles and high-emitters at various vehicle operating
conditions. Remote sensing data is employed to screen out candidate
high-emitting vehicles based on thresholds determined by OBM data. Finally, the
dynamic NOx emission reduction potential across all roads is mapped by
combining the trajectory data for each vehicle with the emission data. A case
study in Chengdu, China, utilizing emission and traffic data from heavy-duty
vehicles for transporting construction waste (a.k.a. slag trucks), reveals the
national threshold for identifying high-emitters via remote sensing might be
too lenient, particularly in the medium speed range. An emission reduction of
18.8% in the China V slag truck fleet could be achieved by implementing this
novel method in practice in Chengdu. This approach establishes a reliable and
ongoing scheme for pinpointing high-emitters through multi-source data, which
allows local authorities to develop more robust and targeted strategies to
mitigate urban air pollution from heavy-duty diesel vehicles.
