| Yazarlar (1) |
Dr. Öğr. Üyesi Hakan ÇELİKTEN
Kafkas Üniversitesi, Türkiye |
| Özet |
| This study aims to evaluate air quality in Ardahan, a province located in the northeastern part of Türkiye, based on the pollutants PM₁₀, SO₂, and O₃ measured throughout 2024. Air Quality Index (AQI) values were calculated on an hourly, daily, and monthly scale, and the percentage contribution of each pollutant to the AQI was analyzed. According to the data, a total of 123 risky hours were identified where the AQI exceeded 100, a level considered unhealthy for sensitive groups. In all of these hours, the dominant pollutant was determined to be PM₁₀. The results indicate that PM₁₀ was the most influential pollutant on AQI throughout the year, particularly during winter months when high humidity and emissions from heating contributed to increased concentrations. However, from the perspective of daily average AQI values, only 3 days exceeded the threshold of 100. This suggests that while high pollution levels occurred during certain hours of some days, these peaks were not widespread enough to elevate the daily average beyond the threshold. O₃ became more prominent during summer months due to increased photochemical reactions, although it occasionally appeared as the dominant pollutant in certain periods due to data unavailability. The contribution of SO₂ to the AQI remained at a relatively low level. These findings provide important insights for air quality management and environmental policy development. They highlight how critical the issue of missing data is in AQI prediction. Therefore, it is suggested that artificial intelligence and machine learning-based models, which can produce reliable predictions even with incomplete data, are essential tools for improving air pollution early warning systems. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale |
| Dergi Adı | Frontiers in Life Sciences and Related Technologies (Online) |
| Dergi ISSN | 2718-062X |
| Dergi Tarandığı Indeksler | TR DİZİN |
| Makale Dili | İngilizce |
| Basım Tarihi | 08-2025 |
| Doi Numarası | 10.51753/flsrt.1682025 |
| Atıf Sayıları |
