RESUMEN
Reducing the differences between real-world and certificated NOx emission levels is an important element of in-use emission surveillance programs. Therefore, investigating the characteristics of the vehicles which have much higher NOx emissions (i.e., high-emitters) and determining a reasonable cut-off point to identify high-emitters with a low false detection rate is important. In this study, six diesel trucks were tested under different aftertreatment conditions. The results showed that the discrepancies of fuel-specific NOx emissions between vehicles with functioning and tampered selective catalytic reduction (SCR) systems occur mainly from medium- to high-speed modes. This is because the SCR systems were at low conversion efficiencies when the exhaust temperature was low, including cold-start and urban creep conditions. By using binary classification, we selected fuel-specific NOx cut-off points for high-emitters from China V and China VI diesel trucks. The false detection rate of high-emitters can decrease by 33 % and 95 %, if only NOx emissions from medium- to high-speed modes were used for the chosen cut-off points, respectively. This work highlights the importance of in-use emission compliance programs. It also suggests that high-emitters can be more accurately identified at medium- to high-speed modes if using instantaneous emission data.
RESUMEN
High-emission vehicles (high emitters) likely have significantly higher nitrogen oxide and particle number (PN) emission factors compared to other vehicles. Effective identification of these vehicles in road traffic requires efficient and cost-effective instruments. In this study, a compact, cost-effective sensor platform was developed and evaluated in a field experiment. The platform was deployed on a roadside, and we measured pollutant concentrations in the exhaust plumes of four diesel trucks with various aftertreatment systems, cargo loads, and driving speeds. The sensor platform successfully measured carbon dioxide, PN, and nitric oxide (NO) concentrations, and the data were used to derive the plume-based emission factors of these pollutants. By considering both NO and PN emission factors, three diesel trucks with failed or outdated aftertreatment systems were successfully identified as potential high emitters. The NO emission factor obtained by the sensor platform was consistent with that of the benchmark portable emission measurement system. The sensor platform also effectively elucidated the differential influences of aftertreatment systems and driving conditions on emission factors. This pilot test demonstrates the feasibility of a sensor-based system for high emitter identification. Owing to its cost-effective and compact design, the proposed sensor platform has greater potential for mass networked deployment than regular-size instruments, thereby effectively supporting regulatory protocols for screening high emitters on public roads.
