TEMPORAL-RESOLUTION EFFECTS ON MULTIVARIATE CALIBRATION AND AGREEMENT OF LOW-COST AIR QUALITY SENSORS
DOI:
https://doi.org/10.69980/917v0q46Keywords:
low-cost sensors, temporal aggregation, multivariate calibration, Bland–Altman agreement, air-quality monitoringAbstract
Extension of monitoring coverage may be achieved using low-cost air quality sensors, but this may lead to systematic differences in their measurements from reference sensors and/or affect their variability, calibration accuracy, and measurement agreement; temporal aggregation may affect their variability, calibration accuracy, and measurement agreement. This study assessed how 10-, 30-, and 60-minute temporal resolutions influence multivariate calibration, cross-sensor agreement, and chronological predictive performance for NO₂ and O₃ measurements. Correspondence of paired reference and low-cost sensor observations was assessed by the time stamp, descriptive statistics, Pearson and Spearman correlations, simple and multivariate ordinary least-squares calibration, Bland–Altman agreement, and proportional-bias analysis. Environmental covariates (temperature and relative humidity) were included, and heteroscedasticity and autocorrelation were addressed by using Newey–West inference. Temporal generalization was evaluated using chronological 80:20 validation. Weak negative cross-sensor correlations were found for NO₂, while the correlation of O₃ was found to be increasing with time from 0.352 after 10 minutes to 0.446 after 60 minutes. A greater improvement was obtained for O₃ using multivariate calibration, and its highest R² value was obtained at 30 minutes, with an R² value of 0.382. Temporal aggregation generally decreased calibration errors and narrowed the agreement limits, although O₃ retained substantial systematic bias. Chronological test R² remained near zero for NO₂ and negative for O₃ across all temporal resolutions. These results show that temporal aggregation can improve statistical stability and calibration accuracy without necessarily improving temporal generalisation.
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