Urban Air Quality Analysis and Prediction Using Remote Sensing and Machine Learning

Dear Colleagues,

The aim of this Special Issue is to provide a deeper investigation into the field of urban air quality. Atmospheric aerosols have harmful effects on climate, human health, and even plant growth and industry. As urbanization accelerates, days with bad air quality have become more frequent in urban areas due to local aerosol sources such as industrial sites, traffic, and residential and commercial areas. In addition to these local emissions, the transport of aerosols from neighboring countries adversely affects air quality, and this can be coupled with a stagnant atmospheric situation, which leads to the exacerbation of high-concentration aerosols. As the frequency of occurrence of high-concentration aerosol events in urban areas increases, public awareness of risks and anxiety about aerosols and air quality also rise. It is necessary to provide accurate and useful information on air quality to policymakers to establish efficient plans for better air quality. Various aerosol retrieval studies have been conducted using satellite and remote sensing data by applying various machine learning and deep learning algorithms. However, this discipline still requires more accurate air quality information in near-real time on the urban scale. In this context, it is necessary to develop more improved algorithms for aerosol retrieval and forecasting from multiple satellite or remote sensing data with both conventional and state-of-the-art machine learning and deep learning methods. In recognition of this necessity, the open-access journal Atmosphere is hosting a Special Issue to bring together the most recent findings related to air quality prediction and analysis in urban areas. This topic encompasses machine learning and deep learning-based prediction and forecasting, multi-sensor remote sensing data, multivariate data analysis, including spectral information and environmental data, etc., focusing in particular on urban areas. Ultimately, this Special Issue aims to showcase the most recent studies to develop algorithms to estimate and forecast urban air quality, and to provide more detailed, accurate information on air quality in urban areas by investigating aerosol episodes under a variety of environmental conditions in urban areas.

Dr. Miae Kim
Prof. Dr. Jan Cermak
Guest Editors

Manuscript Submission Information

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• urban air quality
• aerosols
• particulate matter
• satellite
• remote sensing
• machine learning
• deep learning