Characterizing the effect of traffic density on ambient CO, NO2, and PM2.5 in Tehran, Iran: an hourly land-use regression model

Abstract

Economic development and population growth in Tehran has increased demand for goods movement and road transportation. This trend has increased mobile source emissions and citizens’ exposure, imposing severe health risks. We used the framework of Land-Use Regression models to quantify the effect of real-time traffic, within buffer distance, on hourly exposure of CO, NO2, and PM2.5. Effect of residential, industrial, and governmental land-use in addition to the effect of pollution sources, such as construction sites and airport were assessed on air pollution. Meteorological variables were also employed to adjust between day variations. R-squared of the models were 38, 27, and 38% for CO, NO2, and PM2.5, respectively. Time-activity diaries can be combined with the developed LUR models to provide detailed exposure estimation for epidemiological studies. Furthermore, capability of simulating short-term effect of different traffic scenarios enables city administrations to evaluate policies aiming to diminish pollution levels, prior to applying them.

Keywords:

• Land-use regression (LUR)
• traffic
• air pollution
• temporal variation
• Tehran

Acknowledgment

We thank Department of Environment, Air Quality Control Company, Iran Meteorological Organization, and Municipality of Tehran for providing us with the necessary data. We are also grateful to reviewers for their incisive comments and suggestions.