An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM2.5 Forecasting

Agibayeva A., Khalikhan R., Guney M., Karaca F., Torezhan A., AVCU E.

SUSTAINABILITY, cilt.14, sa.24, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 24
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/su142416641
  • Dergi Adı: SUSTAINABILITY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, CAB Abstracts, Communication Abstracts, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: air pollution, Astana, human health risk assessment, multiple linear regression, Kazakhstan, particulate matter, public health, random forest, POLLUTION, PM10, CHINA, URBAN, POLLUTANTS, PREDICTION, EMISSIONS, CITIES
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Despite Central and Northern Asia having several cities sharing a similar harsh climate and grave air quality concerns, studies on air pollution modeling in these regions are limited. For the first time, the present study uses multiple linear regression (MLR) and a random forest (RF) algorithm to predict PM2.5 concentrations in Astana, Kazakhstan during heating and non-heating periods (predictive variables: air pollutant concentrations, meteorological parameters). Estimated PM2.5 was then used for Disability-Adjusted Life Years (DALY) risk assessment. The RF model showed higher accuracy than the MLR model (R-2 from 0.79 to 0.98 in RF). MLR yielded more conservative predictions, making it more suitable for use with a lower number of predictor variables. PM10 and carbon monoxide concentrations contributed most to the PM2.5 prediction (both models), whereas meteorological parameters showed lower association. Estimated DALY for Astana's population (2019) ranged from 2160 to 7531 years. The developed methodology is applicable to locations with comparable air pollution and climate characteristics. Its output would be helpful to policymakers and health professionals in developing effective air pollution mitigation strategies aiming to mitigate human exposure to ambient air pollutants.