CHARACTERIZATION AND DEVELOPMENT OF EMISSION QUALITY INDEX FOR PASSENGER CARS

Abstract

Most of the developing countries still have various makes of petrol-driven cars dominating the overall passenger vehicle fleet. In such countries, the emission certification policy for in-use vehicles remains an area of concern, making the I/M (Inspection and Maintenance) program less effective. Thorough investigation of the exhaust emission from such cars is required to explore and address this concern. In this connection, the present thesis provides an insight into the effect of vehicle variables on tailpipe emission parameters from an exclusively larger and heterogeneous dataset of inuse cars (n = 1580). reiterated that not only the vehicle variables such as age, mileage, emission norm and maintenance category, but also two engine variables, i.e., aspiration type and fuel mixing conditions had a more significant and direct influence on tailpipe parameters, namely, CO, HC, CO2, O2,  and AFR (Carbon Monoxide, Hydrocarbon, Carbon Dioxide, Oxygen, Lambda and Air-Fuel Ratio respectively). Stronger correlations were found with the relatively larger (considering age, R2 for COidle = 0.88, HCidle = 0.73, f.idle = 0.74, AFRf.idle = 0.73 and considering mileage, R2 for COidle = 0.75, HCidle = 0.67, f.idle = 0.62, AFRf.idle = 0.61 for whole dataset) and diverse make-wise (R2 values fared even better, 0.87 – 0.93 for CO and 0.69 – 0.77 for HC) data collected during the study. The present research provides a first-hand comprehensive analysis of the effect of stringency of the emission norms and maintenance category on the exhaust emission from in-use cars. The polynomial emission equations generated by the study can reliably predict the emission levels for CO and HC basis the age and / or mileage of the cars. Further, the results recommend the policy measures to be taken up, to upgrade the existing emission certification infrastructure and phasing-out policy of cars. Keeping in view the significant number of diesel-driven passenger cars in the existing light motor vehicle fleet in Delhi, India, a case study on smoke emission measurement from 460 number of such cars was conducted. Smoke exhaust data was collected from the diesel cars while the vehicles presented themselves for periodic renewal of pollution under control (PUC) certification at authorized emission testing centres across Delhi, India. Along with the smoke emission, various vehicle and engine-related aspects, supposed to affect tailpipe smoke emission, were also recorded aiming at data analysis for two datasets, namely whole and top 5 makes. The smoke density under no-loading condition in the free acceleration test IX mode was measured. The research study reported a strong correlation between vehicle parameters, such as, age, mileage, maintenance category, emission norm and engine aspiration; and the smoke emission (R2 values for vehicle age and mileage vs. smoke emission for whole dataset = 0.872 and 0.873, respectively). Top 5 make-wise correlations fared even better (R2 for age and mileage vs. emission in the range of 0.85 – 0.92 and 0.86 – 0.93, respectively). Further, the predictive emission equations using best-fit trendlines were also developed for both datasets. Such equations may be used by the car manufacturers to adopt a suitable strategy for tuning of engine or vehicle as such, to retain their cars in the longer state of compliance to the extant emission norms, Further, the research recommends including vehicle mileage as an important factor in upgrading the existing inspection and maintenance programs, especially in the developing countries. The I/M (Inspection/Maintenance) programs exist in most countries, aiming at vehicular emission reduction through exhaust emission monitoring and compliance policy to the extant norms. However, considering the absence of an intra-vehicle approach, the higher success rate of vehicles towards compliance policy, remains a grey area. The present research work attempts to examine this issue through the application of an Exhaust Emission Index (EEI) for petrol-driven cars. The study observed two different scales finding that the BS (Bharat Stage) emission norm scale method reports lower ranges of EEI compared to LS (Linear Scale) method (EEImin-BSNS = 1.12 and EEImin-LS = 1.25; EEImax-BSNS = 20.70 and EEImax-LS = 29.54). The LS method and the maximum operator form (MOF) of aggregation are recommended as these can find the highest number of non-compliant cars (21.81 % and 12.03 % of the ‘poor’ class respectively) in the whole fleet tested. The EEI gives more scientific approach to the vehicular emission evaluation like what AQI (Air Quality Index) does in case of the ambient air quality. It helps the vehicle owners know their car’s emission status as a quick reference index (EEI). The accurate status of such emission further helps the policymakers affect the better phasing-out norms.