A semi-empirical box modeling approach for predicting the carbon monoxide concentrations at an urban traffic intersection

摘要

Emissions generated roadside and at intersections are observed to be affected when there is a sudden change in the traffic flow pattern or increase in the vehicular population, particularly, during peak hours and during special events. The vehicles that queue up at traffic intersections spend a longer amount of time in idle driving mode generating more pollutant emissions per unit time. Other driving patterns (i.e., acceleration, deceleration and cruising) are also observed at intersections, affecting the emission pattern and therefore the resulting pollutant concentrations. The emission rate is not only affected by the increase in the vehicular population but also by the constantly changing traffic flow patterns and vehicles' driving modes. The nature of the vehicle flows also affects the rate and nature of the dispersion of pollutants in the vicinity of the road, influencing the pollutant concentration. It is, therefore, too complex to simulate the effect of such dynamics on the resulting emission rates using conventional deterministic causal models. In view of this, a simple semi-empirical box model based on the 'traffic flow rate', is demonstrated in the present study for estimating the hourly average carbon monoxide (CO) concentrations on a 1 -week data at one of the busiest traffic intersections in Delhi. The index of agreement for a whole week, was found to be 0.84, suggesting that the semi-empirical model is 84% error free. A value of 0.87 was found for weekdays and 0.75 for weekend days. The correlation coefficient for the whole week was found to be 0.75, with 0.78 for the weekdays and 0.62 for the weekend days. The RMSE and RRMSE were found to be 1.87% and 41% for a whole week, with 1.81% and 39.93% for the weekdays and 2.0% and 43.47% for the weekend days, respectively. Specific vehicle emission rates are optimized in this study for individual vehicle category, which may be useful in assessing their impacts on the air quality when there is a significant change in a specific vehicular population and the traffic pattern.