Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 15732
Special Issue Editors
Interests: air quality monitoring; wireless sensor networks; low-cost sensors; nanostructured gas sensors
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; pattern recognition; financial engineering; text classification; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Clean air is a key issue for guaranteeing human rights such as the right to life, health, and well-being and to a safe, clean, healthy, and sustainable environment. For this reason, the evaluation and prediction of air quality (AQ) are essential for decision-making and the development and regulation of AQ policy. As such, future strategies for AQ assessment and forecasting face challenges such as building a multiscale approach from the global to the sub-urban level and predicting AQ on a subseasonal basis. Therefore, the monitoring, analysis, and modelling of AQ have become strategic research areas, which are currently facing a major revolution that is driven by the availability of wireless sensor networks (WSN) coupled with the internet of things (IoT) (which provide massive real-time measurements of pollutant concentrations in the air) and the rapid advancement of high-performance computational and analytical capabilities such as supercomputing, cloud-computing, and analytical big data (BD).
This Special Issue (SI) aims to discuss the role and applicability of top BD technologies in the evaluation and prediction of AQ based on massive air pollution measurement data provided by sensor networks. Scientists and researchers are invited to contribute to this SI by submitting manuscripts (research papers, communications, review articles) describing the fundamentals, underlying models and algorithms, and practical cases of analytical BD and AI technologies for the assessment and forecasting of AQ in real scenarios.
This SI is targeted mainly towards low-cost gas and particulate matter (PM) sensors, data mining (DM), fusion (DF), machine learning (ML), and deep learning (DL) techniques, but other BD- and AI-related technologies may also be considered. Additionally, the SI covers both ambient (outdoor) and indoor air scenarios.
Dr. Esther Hontañón
Prof. Bernardete Ribeiro
Guest Editors
Manuscript Submission Information
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Keywords
- air quality assessment and forecast
- low-cost sensors
- analytical big data
- artificial intelligence
- machine learning
- deep learning
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