Street-scale Air Pollution Modelling in İstanbul – The Case of Beşiktaş District

Air pollution has become one of the most critical global challenges, exacerbated by climate change and the growing human population. It poses a significant threat to public health, particularly in urban areas where high population density and increased vehicle numbers contribute to poor air quality. The primary motivation of this study is to estimate street-scale air pollution in Beşiktaş, İstanbul, Türkiye, with the Operational Street Pollution Model (OSPM®). Beşiktaş district is a hotspot for traffic density with approximately 170,000 residents, and it hosts critical roadways connecting the European and Asian sides of the city. Understanding air pollution at this scale in Beşiktaş is crucial due to the area’s high population density and traffic volume, significantly impacting public health and urban air quality. The road connecting the Beşiktaş district to the İstanbul Bosphorus was selected as a Case Study and our Area of ​​interest (AOI). This study provides an overview of the data collected, including air quality measurements (PM10, PM2.5, NO2) from a nearby Air Quality Monitoring Station (AQMS), meteorological data from Turkish State Meteorological Service (TSMS), and geographic and traffic data from İstanbul Metropolitan Municipality. A representative 800-meter-long road segment was selected for modeling, focusing on traffic-related air pollution using hourly vehicle data. In this study, air pollution measurement data such as PM10, PM2.5, and NOX are evaluated on the street in the Beşiktaş region with AirGIS and OSPM® modeling to be subsequently analyzed. Air quality data (PM10, PM2.5, NO2) were obtained from a nearby AQMS, while meteorological data were obtained from the TSMS, 3.5 km from the street. Geographic data and traffic data were obtained from İstanbul Metropolitan Municipality.  According to the World Health Organization (WHO), PM2.5 and NO2 concentration values ​​exceeded the limit value every day, while PM10 exceeded it for 13 days during the study period. The highest traffic density occurred at 10:00 a.m, and the average number of vehicles was found to be 1,942. In terms of traffic emissions, gasoline vehicles in total 31,829, which has a much larger share compared to diesel vehicles (7,234). The temporal changes (hourly and daily) in air pollution will be analyzed by this model. At the same time, a correlation analysis will be made between the model's concentration values and those measured by AQMS. This model enables both short- and long-term assessments of air pollution exposure, contributing to studies on human health impact.