{"id":12950,"date":"2026-10-09T14:30:01","date_gmt":"2026-10-09T06:30:01","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12950"},"modified":"2026-10-09T21:50:49","modified_gmt":"2026-10-09T13:50:49","slug":"jase-202701-36-010","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-010","title":{"rendered":"Seasonal Monitoring of Indoor Air Pollutants Using a Low- Cost Sensor System Based on Internet of Things Technology"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Yuanyuan Xiao<\/i><\/a>, Tingting Liu, and Mengmeng Li<\/p>\n\n\n\n

Shandong Huayu University of Technology, Dezhou, Shandong, 253000, China<\/p>\n<\/div>\n\n\n\n

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Received: August 3, 2025
Accepted: August 20, 2026
Publication Date: October 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Test-set comparison of predictive accuracy and uncertainty performance across the six UQ-enabled models<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.010<\/a> <\/p>\n\n\n\n

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The concentration of Fine Particulate Matter (PM2.5) and Indoor Air Quality (IAQ) has a great influence on human health and cognitive performance. Making PM2.5 predictions with meaningful Uncertainty Quantification (UQ) is essential to proactive IAQ management. This paper is an in-depth assessment of deep learning (LSTMAttn-UQ, GRUAttn-UQ, WNN-UQ) and machine learning (CatBoost-UQ, LightGBM-UQ, HGBoost-UQ) to predict indoor PM2.5 concentrations and related uncertainties, based on a publicly available time-series dataset. The measurement of performance was conducted through standard regression measurements and UQ measurements, including Prediction Interval Coverage Probability (PICP) and Mean Prediction Interval Width (MPIW), with a target PICP of 0.90. Test set: It was found that LSTMAttn-UQ and GRUAttn-UQ showed better UQ calibration (PICP 0.895-0.899), which means that uncertainty estimates are good. On the other hand, WNN-UQ exhibited the highest point-prediction accuracy (e.g., the lowest Root Mean Squared Error (RMSE) of 2.7495) and computation efficiency, but with a relatively lower PICP (0.8285). The other ensemble models demonstrated bad generalization of UQ. These results indicate a critical trade-off, which is understandable when modeling selection is concerned with a preference in the achievement of either UQ reliability or point prediction accuracy and efficiency for IAQ applications.<\/p>\n\n\n\n

Keywords: IAQ, UQ, Machine Learning (ML), Deep Learning (DL), PM2.5 forecasting<\/em><\/p>\n\n\n\n

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