article · Atmosphere
A systematic review evaluating machine learning and deep learning methods for spatiotemporal air quality prediction analysed eighty relevant studies. While computational tools are progressing, achieving necessary prediction accuracy remains a barrier to implementation. Common machine learning approaches, including random forest and decision tree classifiers, show promising performance in forecasting air quality indices. Deep learning architectures prove particularly effective at managing nonlinear spatiotemporal patterns due to their hyperparameter configurations and activation functions. Integrating machine learning with deep learning helps address data constraints and pollutant complexities. Furthermore, the synthesis highlights the growing adoption of low-cost monitoring sensors, alongside modern computational methods such as transfer learning and federated learning. Specific environmental influences, notably fires and military activities, significantly alter ozone concentrations, pointing to specialised use cases for these predictive models in heavily impacted regions.
Accurate forecasting of air pollution is essential for protecting public health and managing environmental risks. This review clarifies which computational techniques best handle complex, shifting pollution patterns, helping researchers select optimal models. It also shows how affordable monitoring devices and modern algorithms can improve prediction accuracy even in data-scarce regions or areas exposed to disruptions like fires and military operations.
The reviewed algorithms could support commercial environmental monitoring platforms, municipal air quality dashboards, and planning tools for tracking atmospheric hazards in active operational zones. Intended end users include environmental protection bodies, city planners, and sensor network operators. Because the findings derive from an academic literature review, the readiness level is early-stage research, requiring software integration and field trials alongside low-cost sensor hardware before commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O3 concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/atmos15111352
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
