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.
December 2022MDPI

Sustainability (Switzerland)
2022#14Issue 24

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 (R2 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.

air pollution , Astana , human health risk assessment , Kazakhstan , multiple linear regression , particulate matter , public health , random forest

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Environmental Science & Technology Group (ESTg), Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Environmental & Land Planning Engineering, Department of Civil, Environmental and Land Management Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy
The Environment & Resource Efficiency Cluster (EREC), Nazarbayev University, Astana, 010000, Kazakhstan
Department of Mechanical Engineering, Kocaeli University, Izmit, 41001, Turkey
Ford Otosan Ihsaniye Automotive Vocational School, Kocaeli University, Izmit, 41001, Turkey

Environmental Science & Technology Group (ESTg)
Environmental & Land Planning Engineering
The Environment & Resource Efficiency Cluster (EREC)
Department of Mechanical Engineering
Ford Otosan Ihsaniye Automotive Vocational School

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