A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments

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• 作者：Gokhale ; Sharad ; Khare ; Mukesh
• 关键词：Vehicular pollution modelling ; Hybrid model ; Extreme pollutant concentrations ; Heterogeneous traffic conditions ; Statistical distribution models ; Log logistic distribution
• 刊名：Atmospheric Environment
• 出版年：2005
• 出版时间：July, 2005
• 年：2005
• 卷：39
• 期：22
• 页码：4025-4040
• 全文大小：1.04 M

文摘

Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the ‘extreme’ concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the ‘extreme’ concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only ‘extreme’ ranges but also the ‘middle’ ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the ‘entire range’ of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models (Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO)² concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous³ in nature and meteorology is ‘tropical’. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM–LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5–99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well (d