A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science

• Main Authors: Balogun, A.-L., Tella, A., Baloo, L., Adebisi, N.
• Format: Article
• Published: Elsevier B.V. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116586656&doi=10.1016%2fj.uclim.2021.100989&partnerID=40&md5=e9112bf356cb636668131f2b545a3b5c; http://eprints.utp.edu.my/29653/

Air pollution is a global geo-hazard with significant implications, including deterioration of health and premature death. Climatic variables such as temperature, rainfall, wind, and humidity impact air pollution by affecting the strength, transportation, and dispersion of pollutants in the atmosphere. Emerging data science tools, particularly Machine Learning (ML) big data analytics, are being utilized to predict air pollution intensity and frequency under varying climatic conditions for effective mitigation plans. However, comprehensive documentation of these digitalization approaches and outcomes in terms of correlating future air pollution with climate change remains scant. This study addresses this gap by systematically reviewing pertinent literature on climate change and air pollution studies. We also investigated the potentials of integrated spatial data science for spatial modelling and identifying cities vulnerable to air pollution hazards. Our findings show that climatic factors and seasonal variations are critical predictors of air quality in urban areas. A strong correlation exists between climate change and air quality, and air quality in urbanized regions is projected to deteriorate with climate change in the future. Therefore, climatic variables remain essential factors for the prediction of air quality. Also, air pollutants tend to have higher concentration in the warm season, making the consideration of seasonal changes crucial in air quality management. The study also revealed that machine learning algorithms such as random forest, gradient boosting machine, and classification and regression trees (CART) accurately predict air pollution hazard when integrated with spatial models. The detailed review of literature undertaken in this study provides a strong basis for the conclusion that the integration of spatial techniques and machine learning has the potential to improve air pollution prediction outcome and aid appropriate intervention initiatives by the stakeholders. Thus, emerging geospatial intelligence technologies and digital innovations particularly Artificial intelligence, machine learning and big data analytics that underpin the fourth industrial revolution (IR 4.0) can enhance existing early warning mechanisms and support a prompt and effective response to climate-change-induced air pollution, thereby fostering sustainable cities and societies. © 2021 Elsevier B.V.