Abstract
The Pollution Control Department (PCD) has long been responsible for an hourly measurement of Nitrogen Dioxide (NO2) concentrations at its twelve stations located within the 430 square kilometer area of Inner Bangkok. In the past, to estimate NO2 concentrations at any unmeasured location, the proximity model, interpolation model, or dispersion model were employed. These models used distance from a measured location as a sole determinant of any estimation. Toward the end of the 1990's, the more sophisticated Land Use Regression (LUR) model was introduced. This model with its built-in Geographic Information System (GIS) and multiple regression analysis enabled the inclusion of other important determining variables such as land use types, traffic volume and selected meteorological variables. This research aims to apply the LUR model for the estimation of NO2 concentrations over the study area covering the Inner Bangkok. Monthly average NO2 concentrations, traffic count, land use types, road area together with humidity, temperature, wind speed, and rainfall data, measured at or within the vicinities of the twelve PCD stations were input into the model. Only humidity, temperature, wind speed, residential land use, industrial land use, and rainfall are found to have influenced the NO2 concentrations in the Inner Bangkok. The resulting coefficient of determination (R square) of 0.759 implies that seventy-six percent of the variations in NO2 concentrations in the Inner Bangkok can be explained by this model. However, the research will continue to obtain more precise traffic volume data in terms of time scale to improve the model.
Author Information
Pannee Cheewinsiriwat, Chulalongkorn University, Thailand
Paper Information
Conference: ACSEE2013
Stream: Sustainability
This paper is part of the ACSEE2013 Conference Proceedings (View)
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To cite this article:
Cheewinsiriwat P. (2013) Estimation of Nitrogen Dioxide Concentrations in the Inner Bangkok by Land Use Regression Model ISSN: 2186-2311 – The Asian Conference on Sustainability, Energy and the Environment 2013 – Official Conference Proceedings https://doi.org/10.22492/2186-2311.20130143
To link to this article: https://doi.org/10.22492/2186-2311.20130143
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