Projected Effects of Climate Change on Urban Ozone Air Quality by Using Artificial Neural Network Approach; Case Study: Tehran Metropolitan Area, Iran

We developed an artificial neural network as an air quality model and estimated the scope of the climate change impact on future (until ۲۰۶۴) summertime trends of hourly ozone concentrations at an urban air quality station in Tehran, Iran. Our developed scenarios assume that present-time emissions conditions of ozone precursors will remain constant in the future. Therefore, only the climate change impact on future ozone concentrations is investigated in this study. General Circulation Model (GCM) projections indicate more favorable climate conditions for ozone formation over the study area in the future: the surface temperature increases over all months of the year, solar radiation increases, and precipitation decreases in future summers, and summertime daily maximum temperature increases about ۱.۲C to ۳C until ۲۰۶۴. In the scenario based on present-time ozone conditions in the ۲۰۱۲ summer without any exceedances, the summertime exceedance days of the ۸-hr ozone standard are projected to increase in the future by about ۴.۲ days in the short term and about ۱۲.۳ days in the mid-term. Similarly, in the scenario based on present-time ozone conditions in the ۲۰۱۰ summer with ۵۸ days of exceedance from the ۸-hr ozone standard, exceedances are projected to increase by about ۴.۵ days in the short term and about ۱۴.۱ days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in the ۸-hr Air Quality Index (AQI) is also projected to increase based on pollution scenarios of both summers.

Keywords:

Ozone , climate change , air quality modeling , Artificial Neural Networks , Tehran