A Survey and Comparison the Amount of Important Pollutants in the Exhaust of Light Gasoline Vehicles Referring to the Technical Inspection Centers in Urban Areas: A Case Study in Iran

Abstract

Listen

Translate article

Abstract Aims: Traffic congestion and the increase in the number of vehicles lead to an increase in vehicle emissions, consequently causing, a decrease in ambient air quality. The objective of this study is to investigate and compare the concentrations of exhaust gases, namely carbon dioxide (CO2), carbon monoxide (CO), hydrocarbon (HC), and oxygen (O2), emitted by cars tested at the Technical Inspection Centers in Shahrekord and Rasht. Methods: In this cross-sectional study, primary data from 82,316 cars examined at the technical centers in Shahrekord and Rasht were collected and subjected to statistical analysis. The car frequencies considered were 1000 and above. Overall, 16 types of Iranian and foreign cars were assessed, with 51,919 cars from Rasht and 30,397 cars from Shahrekord. The statistical analysis employed a six-way multivariate analysis of variance. Results: The proportion of carbureted cars that were rejected exceeded that of injection cars, while the percentage of cars rejected due to CO2 emissions was lower than that of authorized cars (P < 0.05). Furthermore, the likelihood of these associations occurring in Shahrekord city was higher than in Rasht city, with a chance ratio and confidence interval (CI) of 95. The odds ratios (ORs) (95% CI) were 1.936 (1.829–2.050) and 3.036 (2.924–3.151), respectively. Moreover, the rejection rates were higher (P < 0.05) for cars manufactured in Iran compared to foreign-made cars, private vehicles compared to public vehicles, and cars with CO pollution compared to licensed cars. The likelihood of these associations transpiring in Shahrekord city, in comparison to Rasht city, were as follows: OR (95% CI) of 0.793 (0.755–0.834), 0.667 (0.743–0.598), and 0.018 (0.016–0.020), respectively. The audit analysis, which involved a linear combination of O2, CO, CO2, HC, and vehicle age, successfully differentiated between cars in Shahrekord and Rasht, yielding a correct prediction rate of 81.3% (Wilks’ Lambda statistic = 0.687, Eigen value = 0.455, Canonical correlation value = 0.60 with a P < 0.0001). Conclusion: The findings of this study offer a practical approach to address the complexities arising from the emission of polluting gases, contributing to the global warming phenomenon.