Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks
Bibliographic record
Abstract
Air quality is a critical determinant of human health, with severe consequences resulting from air pollution. The growing necessity for air quality monitoring has led to the adoption of IoT sensor networks, which provide real-time data for forecasting, issuing warnings, and informing public health interventions. In this context, machine learning (ML) algorithms have proven to be powerful tools for enhancing air quality prediction and addressing monitoring challenges. However, a comprehensive review compiling the research space of ML for air quality is seldom available. This review analyzes over 70 recent studies that apply ML techniques to air quality monitoring, categorizing them based on the type of learning approach employed, with a focus on identifying the most effective algorithms in each category. The findings demonstrate that ensemble models such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost) consistently achieve high accuracy in structured datasets, while deep learning (DL) approaches like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) excel in capturing temporal dependencies and spatial patterns in pollution forecasting. Unsupervised approaches like clustering and anomaly detection effectively enhance data quality and sensor calibration, whereas reinforcement learning shows promise in adaptive control scenarios, despite challenges related to computational intensity and interpretability. This review is highly significant, offering valuable insights for policymakers and researchers in developing strategies to mitigate air pollution and improve public health using advanced ML techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".