Journal Article

Methods for Urban Air Pollution Measurement and Forecasting: Challenges, Opportunities, and Solutions

Trajkovik, Vladimir, Lameski, Petre, Herzog, Michael A., Zdravevski, Eftim, Mitreska Jovanovska, Elena, Batz, Victoria

2023

Overview

In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia’s capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution.

• Publisher: MDPI AG
• Subject: Air pollution / Air pollution forecasting / Air pollution measurements / air pollution prediction / Air quality / Algorithms / Big Data / Chronic illnesses / Cities / COVID-19 / Deep learning / Disease prevention / Disease transmission / Environmental monitoring / Forecasting / Geospatial data / Literature reviews / Machine learning / Mathematical models / Neural networks / Nitrogen dioxide / Outdoor air quality / Ozone / Perceptions / Performance evaluation / Pollutants / Pollution effects / Pollution levels / Predictions / R&D / Research & development / review / Statistical analysis / Time series / Trends / Urban air / Urban areas / Urban environments