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Open research questions in Air Quality Monitoring and Forecasting

207 unresolved questions extracted from the limitations and future-work sections of 374 Air Quality Monitoring and Forecasting papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Sensor degradation and its impact on predictive accuracy. Temporal generalization and the need for proper time-series validation. Calibration drift and its effects on predictive reliability.

    Machine Learning for Sensor Analytics: A Comprehensive Review and Benchmark of Boosting Algorithms in Healthcare, Environmental, and Energy Applications · 2025 · DOI

  • exponential loss and sequential reweighting make AdaBoost brittle to outliers, drifting distributions, and class imbalance - lack built-in mechanisms for handling high dimensionality, sparse representations, or large n - scale poorly compared to modern gradient boosting systems

    Machine Learning for Sensor Analytics: A Comprehensive Review and Benchmark of Boosting Algorithms in Healthcare, Environmental, and Energy Applications · 2025 · DOI

  • Future research should focus on improving data quality. Future research should address the challenge of interpretability. Future research should explore the integration of heterogeneous sensing systems. The development of more advanced sensing technologies is needed.

    A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting · 2025 · DOI

  • The gap in current research is the need for effective air pollutant concentration forecasting methods. The gap is due to the complexity of the relationship between air pollutants and their contributing factors. The gap is also due to the need for integration of heterogeneous sensing systems and improvement of data quality.

    A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting · 2025 · DOI

  • high energy consumption and process costs, - uncertainty in cost estimates, - limited scalability of current DAC technologies, - sensitivity to oxygen presence or insufficient membrane selectivity in alternative technologies

    Advancements and Challenges in Direct Air Capture Technologies: Energy Intensity, Novel Methods, Economics, and Location Strategies · 2025 · DOI

  • development of new, cost-effective, and efficient sorbents, - research on alternative technologies such as electrochemical and membrane-based processes, - evaluation of the commercial potential of new sorbents

    Advancements and Challenges in Direct Air Capture Technologies: Energy Intensity, Novel Methods, Economics, and Location Strategies · 2025 · DOI

  • The use of low-cost sensors (LCS) for air quality monitoring for policy and civic engagement in sub-Saharan Africa (SSA) has become paramount, as access to traditional reference-grade instruments is still sparse.

    Low-Cost PM2.5 Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa: Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project · 2025 · DOI

  • 2-(Benzotriazol-2-yl)-4,6-bis(2-methylbutan-2-yl)phenol (UV-328), a widely utilized UV absorber in plastics and diverse products, has been frequently detected in the environment; yet, research on its photochemical degradation is scarce.

    Photodegradation Mechanism of UV-328 in Natural Organic Matter Contexts Under Simulated Solar Irradiation · 2025 · DOI

  • Direct nitrous oxide (N 2 O) emissions from fertilizer application are the largest anthropogenic source of global N 2 O, but the factors influencing these emissions remain debated.

    Reevaluating the Drivers of Fertilizer-Induced N2O Emission: Insights from Interpretable Machine Learning · 2024 · DOI

  • 5 concentrations, which holds significant potential to support future epidemiological studies to address knowledge gaps in understanding the health effects of PM 10-2.

    An Ensemble Machine Learning Model to Enhance Extrapolation Ability of Predicting Coarse Particulate Matter with High Resolutions in China · 2024 · DOI

  • Ground level air monitoring stations are sparse and thus have limited coverage due to high costs.

    Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data · 2024 · DOI

  • Future research should focus on attention mechanisms and transformer architectures. Future research should explore the use of physics-informed learning and uncertainty quantification.

    Air Quality Index Prediction Using Python · 2026 · DOI

  • There is a need for more accurate and reliable AQI prediction models. There is a need for practical deployment frameworks for AQI prediction models.

    Air Quality Index Prediction Using Python · 2026 · DOI

  • Traditional air quality prediction techniques have limitations in capturing intricate nonlinear interactions. Deep learning methods have been proposed as an alternative but require further development.

    Enhancing Air Quality Prediction Accuracy Using Hybrid Deep Learning · 2026 · DOI

  • There is a need for monitoring devices that can be set up in greater numbers and measure air quality in smaller areas. The MQ135 sensor-based IoT solutions can potentially address this need.

    A Review of MQ135 Sensor-Based IoT Solutions for Real-Time Air Pollution Tracking · 2026 · DOI

  • Sensor drift and aging occur over time as internal components break down and become contaminated, affecting baseline resistance and sensitivity, requiring recalibration or replacement.

    A Review of MQ135 Sensor-Based IoT Solutions for Real-Time Air Pollution Tracking · 2026 · DOI

  • The mechanistic relationship between air pollution and disease transmission dynamics remains incompletely understood. There is a need to develop a framework to explore this relationship.

    Discovering Mechanistic Correlations Among Respiratory Diseases and Air Quality via Dynamic Modelling Combined with Deep Learning and Symbolic Regression · 2026 · DOI

  • Extension of the framework to incorporate additional respiratory diseases beyond ILI and other environmental factors beyond air quality could improve mechanistic understanding.

    Discovering Mechanistic Correlations Among Respiratory Diseases and Air Quality via Dynamic Modelling Combined with Deep Learning and Symbolic Regression · 2026 · DOI

  • Existing approaches for Air Quality Index forecasting have shown notable progress but still face critical limitations. Statistical models fail under nonlinear pollutant-meteorology interactions. Machine-learning ensembles do not exploit temporal dependencies.

    Residual Stacking with Temporal Convolutional Network and Light Gradient Boosting for Air Quality Index Prediction · 2026 · DOI

  • The model achieves ~3% higher accuracy during festival-driven pollution surges compared to non-event periods, but the mechanism by which event flags correct TCN under-responsiveness—whether through feature gating, threshold adaptation, or residual magnitude scaling—is not mechanistically characterized.

    Residual Stacking with Temporal Convolutional Network and Light Gradient Boosting for Air Quality Index Prediction · 2026 · DOI

  • Limited understanding of the interpretability, generalization, and infrastructure requirements of deep learning models - Lack of structured synthesis of spatiotemporal modeling strategies, multi-source data fusion techniques, and uncertainty-aware forecasting approaches

    A Comprehensive Review of Deep Learning Approaches for Air Quality Pollution Analysis and Prediction · 2026 · DOI

  • The paper identifies data scarcity and quality issues as limiting factors for deep learning air quality systems but does not specify concrete data augmentation techniques, synthetic data generation methods, or handling strategies for missing values and sensor drift in spatio-temporal air quality datasets.

    A Comprehensive Review of Deep Learning Approaches for Air Quality Pollution Analysis and Prediction · 2026 · DOI

  • To apply FuXi-Air to other regions and cities. To integrate additional data sources, such as satellite imagery and social media data. To develop more advanced machine learning models that can capture complex atmospheric pollution processes.

    FuXi-Air: air quality forecasting based on emission-meteorology-pollutant multimodal machine learning · 2026 · DOI

  • The limitations of numerical simulations and single-site machine-learning approaches in air quality forecasting. The need for a multimodal machine learning model that integrates meteorological, emission, and observational data. The lack of a scientific reference and a practical example for applying deep machine learning to support rapid air pollution risk warning.

    FuXi-Air: air quality forecasting based on emission-meteorology-pollutant multimodal machine learning · 2026 · DOI

  • Missing historical observations - Irregularly sampled data - Recursive error accumulation in forecasting - The need to preserve observed measurements and restrict imputation to the historical look-back window

    Task-Aligned Transformer Imputation for Long-Horizon Air Quality Forecasting · 2026 · DOI

Most-cited papers in Air Quality Monitoring and Forecasting

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207 open questions have been extracted from the limitations and future-work passages of 374 Air Quality Monitoring and Forecasting papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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