Impact of Traffic on Carbon Monoxide Concentrations Near Urban Road Mid-Blocks

摘要

An increase in population, number of vehicles and rate of urbanization have happened drastically in the past decades, resulting in deteriorating air quality and a threat to the environment. Vehicular emissions are among the primary and noted sources of air pollution in urban areas. Carbon monoxide (CO) is one of the major pollutants emitted from vehicles, affecting the environment adversely. The present study attempts to develop models to predict CO concentrations at different mid-block sections of urban roads using multiple linear regression (MLR) and artificial neural networks (ANN) methods. The proportional share of vehicles and average traffic speed are considered as inputs to the models. The traffic volume, speed and CO concentrations collected at different mid-block sections have been analyzed. A good correlation between average traffic speed, traffic volume and CO concentrations was observed. The study also shows that the classified traffic volume and average traffic speed in a mid-block could help explain the variance in CO levels significantly, with R2 value of 0.91 and 0.97 for MLR and ANN models, respectively. It has been observed that the CO level would be as high as 14 ppm at or below the average speed of 25 km/h for moving vehicles. The linear increase in CO concentration was found due to decrease in average speed of traffic stream. The model validation illustrates that the estimated CO concentrations match well the observed CO concentrations under the same set of traffic and roadway conditions with an MAPE value of 0.18% and 0.11% for MLR and ANN models, respectively. It seems interesting to see that the ANN model had better predicted the CO values than MLR model. This study considers developing cities to determine the CO levels. The results of the study would help Pollution Control Board officials and traffic control authorities to implement necessary measures for improving air quality in the cities under consideration.