A remote sensing-based methodological approach to characterize the association between fuel prices and air pollution

Creative Commons License

Aydınlı H. O., Çabuk S. N., Meçik O.

Turkish Journal of Remote Sensing, cilt.8, 2026 (Scopus, TRDizin)

Özet

Carbon monoxide (CO) is recognized as a major source of air pollution that, depending on the degree to which it is inhaled, can potentially cause cardiovascular diseases. CO is primarily produced as a by-product of the incomplete combustion of gasoline and diesel in vehicles. The aim of the present study is to investigate the relationship between fuel prices and vehicle-based CO emissions in Türkiye in 2022. For that purpose, CO levels were monitored by remote sensing data from S5P TROPOspheric Monitoring Instrument (TROPOMI). The spatial distribution of CO levels was computed from 2019 to 2022 by employing the Google Earth Engine Code Editor. A linear regression analysis was performed to explain the relationship between fuel consumption and CO levels. The analysis revealed a negative association between fuel prices and CO emissions, with coefficients of r = –0.71 (R² = 0.4084) for gasoline and r = –0.64 (R² = 0.5047) for diesel, respectively. The study has focused on both economic aspects and remote sensing techniques in terms of the impact of fuel prices on air quality. Based on these findings, the current study highlight the effective use of satellite-based remote sensing for monitoring CO levels in the atmosphere.

Carbon monoxide (CO) is recognized as a major source of air pollution that, depending on the degree to which it is inhaled, can potentially cause cardiovascular diseases. CO is primarily produced as a by-product of the incomplete combustion of gasoline and diesel in vehicles. The aim of the present study is to investigate the relationship between fuel prices and vehicle-based CO emissions in Türkiye in 2022. For that purpose, CO levels were monitored by remote sensing data from S5P TROPOspheric Monitoring Instrument (TROPOMI). The spatial distribution of CO levels was computed from 2019 to 2022 by employing the Google Earth Engine Code Editor. A linear regression analysis was performed to explain the relationship between fuel consumption and CO levels. The analysis revealed a negative association between fuel prices and CO emissions, with coefficients of r = –0.71 (R² = 0.4084) for gasoline and r = –0.64 (R² = 0.5047) for diesel, respectively. The study has focused on both economic aspects and remote sensing techniques in terms of the impact of fuel prices on air quality. Based on these findings, the current study highlight the effective use of satellite-based remote sensing for monitoring CO levels in the atmosphere.