Articles | Volume 19, issue 2
https://doi.org/10.5194/amt-19-603-2026
https://doi.org/10.5194/amt-19-603-2026
Research article
 | 
23 Jan 2026
Research article |  | 23 Jan 2026

Enhancing Accuracy of Indoor Air Quality Sensors via Automated Machine Learning Calibration

Juncheng Qian, Thomas Wynn, Bowen Liu, Yuli Shan, Suzanne E. Bartington, Francis D. Pope, Yuqing Dai, and Zongbo Shi

Related authors

Mid- and far-infrared spectral signatures of mineral dust from low- to high-latitude regions: significance and implications
Claudia Di Biagio, Elisa Bru, Avila Orta, Servanne Chevaillier, Clarissa Baldo, Antonin Bergé, Mathieu Cazaunau, Sandra Lafon, Sophie Nowak, Edouard Pangui, Meinrat O. Andreae, Pavla Dagsson-Waldhauserova, Kebonyethata Dintwe, Konrad Kandler, James S. King, Amelie Chaput, Gregory S. Okin, Stuart Piketh, Thuraya Saeed, David Seibert, Zongbo Shi, Earle Williams, Pasquale Sellitto, and Paola Formenti
Atmos. Chem. Phys., 26, 1079–1091, https://doi.org/10.5194/acp-26-1079-2026,https://doi.org/10.5194/acp-26-1079-2026, 2026
Short summary
Deciphering isoprene variability across dozen of Chinese and overseas cities using deep transfer learning
Song Liu, Xiaopu Lyu, Fumo Yang, Zongbo Shi, Xin Huang, Tengyu Liu, Hongli Wang, Mei Li, Jian Gao, Nan Chen, Guoliang Shi, Yu Zou, Chenglei Pei, Chengxu Tong, Xinyi Liu, Li Zhou, Alex B. Guenther, and Nan Wang
Atmos. Chem. Phys., 26, 635–646, https://doi.org/10.5194/acp-26-635-2026,https://doi.org/10.5194/acp-26-635-2026, 2026
Short summary
Divergent iron dissolution pathways controlled by sulfuric and nitric acids from the ground-level to the upper mixing layer
Guochen Wang, Xuedong Cui, Bingye Xu, Can Wu, Minkang Zhi, Keliang Li, Liang Xu, Qi Yuan, Yuntao Wang, Yele Sun, Zongbo Shi, Akinori Ito, Shixian Zhai, and Weijun Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-5423,https://doi.org/10.5194/egusphere-2025-5423, 2025
Short summary
Rethinking machine learning weather normalisation: a refined strategy for short-term air pollution policies
Yuqing Dai, Bowen Liu, Chengxu Tong, David C. Carslaw, A. Robert MacKenzie, and Zongbo Shi
Atmos. Chem. Phys., 25, 13585–13596, https://doi.org/10.5194/acp-25-13585-2025,https://doi.org/10.5194/acp-25-13585-2025, 2025
Short summary
Measurement report: Per- and polyfluoroalkyl substances (PFAS) in particulate matter (PM10) from activated sludge aeration
Jishnu Pandamkulangara Kizhakkethil, Zongbo Shi, Anna Bogush, and Ivan Kourtchev
Atmos. Chem. Phys., 25, 5947–5958, https://doi.org/10.5194/acp-25-5947-2025,https://doi.org/10.5194/acp-25-5947-2025, 2025
Short summary

Cited articles

Aix, M.-L., Schmitz, S., and Bicout, D. J.: Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter, Sci. Total Environ., 889, 164063, https://doi.org/10.1016/j.scitotenv.2023.164063, 2023. 
Cowell, N., Chapman, L., Bloss, W., Srivastava, D., Bartington, S., and Singh, A.: Particulate matter in a lockdown home: evaluation, calibration, results and health risk from an IoT enabled low-cost sensor network for residential air quality monitoring, Environ. Sci. Atmos., 3, 65–84, https://doi.org/10.1039/d2ea00124a, 2023. 
Crilley, L. R., Singh, A., Kramer, L. J., Shaw, M. D., Alam, M. S., Apte, J. S., Bloss, W. J., Hildebrandt Ruiz, L., Fu, P., Fu, W., Gani, S., Gatari, M., Ilyinskaya, E., Lewis, A. C., Ng'ang'a, D., Sun, Y., Whitty, R. C. W., Yue, S., Young, S., and Pope, F. D.: Effect of aerosol composition on the performance of low-cost optical particle counter correction factors, Atmos. Meas. Tech., 13, 1181–1193, https://doi.org/10.5194/amt-13-1181-2020, 2020. 
Hagan, D. H. and Kroll, J. H.: Assessing the accuracy of low-cost optical particle sensors using a physics-based approach, Atmos. Meas. Tech., 13, 6343–6355, https://doi.org/10.5194/amt-13-6343-2020, 2020. 
Johnson, K. K., Bergin, M. H., Russell, A. G., and Hagler, G. S. W.: Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments, Aerosol Air Qual. Res., 18, 565–578, https://doi.org/10.4209/aaqr.2017.10.0418, 2018. 
Download
Short summary
We developed a multi-stage AutoML (Automated Machine Learning) calibration framework to improve low-cost indoor PM2.5 sensor accuracy. Using chamber tests with varied emission sources, the method corrected drift, humidity effects, and non-linear responses, raising R2 above 0.9 and halving RMSE (Root Mean Square Error). The approach enables reliable, scalable indoor air quality monitoring for research and public health applications.
Share