Evaluating exhaust emissions from heterogeneous car fleet through real-time field-generated dataset

Abstract

Light-duty vehicular exhaust remains one of the key sources of ambient air pollution globally, despite concerted mitigation efforts by the countries worldwide. It is of top scientific interest to explore vehicular variables affecting such emission from passenger cars through real-time monitoring (N = 1561). The research investigated emission parameters such as CO, HC, CO₂, O₂, λ (Lambda) and Air-fuel ratio (AFR), alongside the vehicular variables, namely, age, mileage, emission norm and maintenance category. The model-oriented study found the car age (R_rangeA = 0.81–0.98 for E_COI; 0.72–0.96 for E_HCI; 0.74–0.91 for λ_FI and 0.75–0.93 for AFR_FI, respectively) and mileage (R_rangeM = 0.71–0.98 for E_COI; 0.75–0.95 for E_HCI; 0.69–0.93 for λ_FI and 0.68–0.92 for AFR_FI, respectively) to be the most significant aspects. Further, the study reported that the emissions improved with the progression of in-use norms (tighter the norm, lower the emission). Interestingly, the maintenance level of cars is found to be directly and inversely related to both CO and HC emissions in different testing modes. It further presents car model-wise emission equations for car age and mileage as which can be used to accurately predict the exhaust emission from cars. The research outlines the need to incorporate car mileage, maintenance level and applicable emission norm into the present environmental policy, particularly in the developing countries. An improved emission testing, real-time emission data and appropriate environment regulation are the three major steps towards urban air quality improvement policy.