Rajni Dhyani 1, Niraj Sharma2

  • 1 Academy of Scientific and Innovative Research, CSIR-Central Road Research Institute, New Delhi-110025, India
  • 2 Environmental Science Division, CSIR-Central Road Research Institute, New Delhi-110025, India

Received: January 12, 2016
Revised: June 7, 2016
Accepted: June 12, 2016
Download Citation: ||https://doi.org/10.4209/aaqr.2016.01.0012  

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Cite this article:
Dhyani, R. and Sharma, N. (2017). Sensitivity Analysis of CALINE4 Model under Mix Traffic Conditions. Aerosol Air Qual. Res. 17: 314-329. https://doi.org/10.4209/aaqr.2016.01.0012


HIGHLIGHTS

  • Vehicular pollution dispersion modeling in mixed traffic conditions.
  • Performance evaluation of CALINE4 model under mixed traffic conditions.
  • Parameters influencing vehicular pollution dispersion.
  • Sensitivity analysis of CALINE4 model under mixed traffic conditions.
  • Identification of input parameters influencing CALINE4 model prediction capability.

 

ABSTRACT


Highway dispersion models are routinely used for prediction of air quality along road and highway corridors. Various input parameters used by these models for prediction of air quality along the road corridors. In the present study, sensitivity analysis of CALINE4 model was carried out under mixed traffic conditions to identify the input parameters and the extent to which they influence the output (i.e., predicted concentration) of the model. The road corridor selected in Delhi city (India) was a stretch of National Highway-2 (NH-2) passing through the city. The selected corridor caters to both inter-city and intra-city traffic and had mixed traffic conditions. Carbon monoxide was selected as indicator pollutant for prediction and sensitivity analysis exercise. Sensitivity analysis of the CALINE4 model was carried out for three hours input datasets representing different meteorological (wind speed, wind direction, mixing height, stability class), traffic (traffic volume and emission factors) and road characteristic (roadway width) along with terrain (surface roughness) characteristic. Input parameters corresponding to selected datasets were varied separately and systematically and their impact on predicted CO concentrations was observed and quantified. It was observed that apart from source strength (traffic volume and emission factors), meteorological parameters viz., wind speed and wind direction influenced the prediction capabilities of model considerably. Whereas, surface roughness, mixing height had relatively less contribution in model's output.


Keywords: CALINE4 model; Mix traffic conditions; Sensitivity analysis; Performance evaluation; Meteorological parameters


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