Abstract

This study aims to be the first phase of a comprehensive assessment of street canyon air quality in Beşiktaş, Istanbul. The main objective of this initial phase is to develop and systematically evaluate a high-resolution, integrated data infrastructure to support subsequent street-scale air quality modeling (Part B). The study period covers 2016–2023, based on long-term hourly observations. Air quality data (PM10, PM2.5, O3, and nitrogen oxides reported separately as NO2 and total NOx) were obtained from the Beşiktaş air quality monitoring station. Missing values were filled using seasonal decomposition combined with linear interpolation for NO2, NOX, and PM10; for PM2.5, a multiple linear regression model incorporating PM10, temperature, relative humidity, wind speed, and cyclical time variables was applied. Meteorological variables (temperature, rainfall, relative humidity, wind speed, and direction) were primarily sourced from the Şişli station and gap-filled using linear interpolation. Both datasets were cross-validated for temporal consistency with ground-based observations. Hourly vehicle counts were obtained from the Istanbul Metropolitan Municipality (IMM), while vehicle-type and fuel-category composition were derived from camera-based observations along the corridor. High missing rates in PM2.5 data and uncertainties regarding vehicle composition are among the main limitations of this study. This study presents a coherent dataset to support street-level air quality modeling and related analyses in Türkiye. Beyond being the first long-term (2016–2023), hourly, street canyon dataset of its kind for Istanbul, this work provides a transferable framework for building model-ready air quality inputs and a practical basis for street-scale dispersion modeling, exposure assessment, and urban air quality management.

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