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<p>This study evaluates the robustness of a previously developed calibration model for low-cost ozone sensors, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). The model was deployed at a different site without retraining. It was tested in Putrajaya, Malaysia, over a 2-month period in 2018 and compared with conventional laboratory-calibrated models for CO and NO₂ sensors. The ANFIS model demonstrated consistently strong performance for the OX-A431 sensor, with <i>R</i><sup>2</sup> values approaching 0.9 and lower RMSE, confirming its transferability. However, deviations of up to 70&#xa0;ppb were recorded during high ozone episodes. This may be due to the limited input variables used—namely O₃, NO₂, temperature, and relative humidity—while other influential factors such as co-pollutants or atmospheric pressure were not included. In contrast, laboratory-calibrated models for the CO-A4 and NO₂-A43F sensors exhibited poorer performance (<i>R</i><sup>2</sup> = 0.13–0.73), indicating low adaptability under field conditions. These findings underscore the importance of field calibration for low-cost sensors and suggest that incorporating additional environmental and chemical parameters may further improve calibration accuracy. This study contributes to the advancement of generalized machine learning calibration frameworks to enable scalable low-cost sensor networks for ambient air quality monitoring.</p>

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Evaluating the transferability of low-cost sensor calibration using ANFIS: a field study in Putrajaya, Malaysia

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Kemal Maulana Alhasa,
  • Mohd Shahrul Mohd Nadzir,
  • Sawal Hamid Md Ali,
  • Wahyu Sasongko Putro

摘要

This study evaluates the robustness of a previously developed calibration model for low-cost ozone sensors, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). The model was deployed at a different site without retraining. It was tested in Putrajaya, Malaysia, over a 2-month period in 2018 and compared with conventional laboratory-calibrated models for CO and NO₂ sensors. The ANFIS model demonstrated consistently strong performance for the OX-A431 sensor, with R2 values approaching 0.9 and lower RMSE, confirming its transferability. However, deviations of up to 70 ppb were recorded during high ozone episodes. This may be due to the limited input variables used—namely O₃, NO₂, temperature, and relative humidity—while other influential factors such as co-pollutants or atmospheric pressure were not included. In contrast, laboratory-calibrated models for the CO-A4 and NO₂-A43F sensors exhibited poorer performance (R2 = 0.13–0.73), indicating low adaptability under field conditions. These findings underscore the importance of field calibration for low-cost sensors and suggest that incorporating additional environmental and chemical parameters may further improve calibration accuracy. This study contributes to the advancement of generalized machine learning calibration frameworks to enable scalable low-cost sensor networks for ambient air quality monitoring.