Evaluation of PM<sub>10</sub> concentration by using Mars and XGBOOST algorithms in Igdir Province of Turkiye

Tirink S., Ozturk B.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, vol.20, no.5, pp.5349-5358, 2023 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 20 Issue: 5
  • Publication Date: 2023
  • Doi Number: 10.1007/s13762-022-04511-2
  • Journal Name: INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Page Numbers: pp.5349-5358
  • Keywords: PM10, Air quality, XGBoost, MARS, Meteorological traits, AIR-POLLUTION, PARTICULATE MATTER, CARDIOVASCULAR-DISEASE, ACTINIC FLUX, LUNG-CANCER, OZONE, SO2, NO2, PHOTODISSOCIATION, REGRESSION
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Air pollution is extremely harmful to human health, ecosystems and the climate. In this context, the level of particulate matter at high concentrations is one of the most important factors in determining air quality. Numerous studies have been carried out to solve the air quality problem related to PM concentrations. It is important to note that the sources of pollution have a local character for each urban area and depend on many factors such as meteorological, transportation and other air quality criteria. In this study, particulate matter with an aerodynamic diameter smaller than 10 mu m (PM10) is mathematically modeled with MARS and XGBoost algorithms in Igdir Province of Turkey. For this purpose, in addition to meteorological data such as wind speed, wind direction, relative humidity, temperature, atmospheric pressure, precipitation, evaporation and global solar radiation, air quality criteria such as SO2, NOx and O-3 were used and modeled with the aforementioned algorithms. As a result, the XGBoost algorithm can be recommended for modeling the atmospheric PM10 concentration.