Investigate learning methodologies, incorporate

The gap

Evidence profile

Stated in the limitations and future work and recommendations sections of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Ensemble Learning Approaches for Air Pollution Classification and Environmental Health Risk Assessment (2026) · JURNAL KESEHATAN LINGKUNGAN · doi

  Although the ensemble models achieved an impressive accuracy rate, studies have identified some major issues that need to be resolved. First, the studies were conducted using open-source datasets with very little time and space resolution; thus, these 166 Jurnal Kesehatan Lingkungan/10.20473/jkl.v18i2.2026.159-170 Vol. 18 No.2 April 2026 (159-170) datasets do not adequately represent the potential for variability in pollutant exposure for populations living in urban areas. Second, the primary focus of the study was on classification accuracy and did not provide an explicit representation of health outcomes (i.e., hospital admission and death rates). This study did not incorporate spatio-temporal dimensions, as the primary objective was to evaluate the performance of ensemble learning models in pollutant and health-risk classification based on historical exposure records. Future research should integrate spatial information (e.g., geographic pollutant distribution) and temporal variation (e.g., hourly, daily, or seasonal fluctuations) to enable spatial risk mapping, forecasting, and more comprehensive time-series environmental health surveillance. In future studies, researchers should incorporate spatio-temporal data and population vulnerability factors (age, socioeconomic status) to enable risk stratification. The potential to increase the interpretability of the model and to support the application of this type of model to real-world policy decisions will also be enhanced through the expansion of the model into a multi-source hybrid ensemble model. In addition to enhancing the potential of this type of model to be applied to real-world decision- making, collaborative efforts between environmental agencies and public health institutions will be needed to develop and implement the infrastructure necessary to support the implementation of these types of intelligent monitoring systems nationally (39,43–44).

  generalstated in limitationsevidence 5/5
  Keywords: model health ensemble potential pollutant temporal risk models accuracy source datasets time exposure primary classification
• Explainable PSO-optimised machine learning models for multi-pollutant air quality forecasting in major African cities with transfer learning (2026) · Frontiers in Environmental Science · doi

  investigate learning methodologies, incorporate supplementary machine including deep learning, spatiotemporal models, and hybrid frameworks, so as to enhance the understanding of complex pollutant interactions and urban heterogeneity. Adding secondary and trace pollutants to the list of pollutants and using highresolution data on weather, transportation, and land use, could make predictions even more accurate. Research could also examine scalable domain adaptation and transfer learning methodologies inadequate monitoring infrastructure, encompassing real-time model updating and ensemble methods that integrate several source-city models. Finally, in operational air quality management systems would allow for an understanding of how strong, long-lasting, and useful predictive forecasting may be for policy and public health actions.

  generalstated in future workevidence 5/5
  Keywords: learning models methodologies understanding pollutants long investigate incorporate supplementary machine including deep spatiotemporal hybrid frameworks
• PM10 Concentration Forecasting: A Comparative Evaluation of Deep Learning and Time Series Methods (2026) · Academic Platform Journal of Engineering and Smart Systems · doi

  This study provides many opportunities for further study and application in air quality forecast models. First, since deep-learning-based methods (e.g., LSTM and GRU) have shown generally lower error rates than traditional statistical models in terms of predicting air pollution levels, but are limited in the accuracy of their predictions due to the lack of data and assumptions used in their development, they have the potential to be used as complementary tools to traditional statistical models in early warning systems provided that sufficient contextual information is available and proper validation techniques are employed. Second, researchers should develop spatiotemporal models (e.g., CNN-LSTM models, attention-based neural networks) that can learn both temporal patterns and spatial relationships among different air pollution monitoring sites in order to improve the robustness of predictive models in urban areas where emissions vary greatly over space and time. Third, incorporating additional variables that describe the context in which an area's air pollution level is being measured (e.g., intensity, industrial production indicators, land-use characteristics, population density, extreme weather events) has the potential to increase the explanatory power of models and better represent the various factors that influence variability in PM10 levels. traffic Lastly, providing the results of PM10 forecasts to the public through digital decision-making tools (e.g., web-based dashboards, mobile apps), could allow for timely access to air quality information and potentially support preventative action by vulnerable populations (i.e., children, older adults, individuals with chronic respiratory or cardiovascular disease). If uncertainty associated with the models is communicated evaluated continuously, the integration of PM10 forecasts into digital decision-making tools has the potential to provide significant contributions to evidence-based public health and environmental management. and models clearly are Author contributions: All authors have contributed equally to work.

  generalstated in recommendationsevidence 5/5
  Keywords: models learning based quality machine forecasting statistical pollution levels potential tools monitoring authors comparative deep

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