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The rapid expansion of low-cost sensor networks for air quality monitoring necessitates rigorous calibration to ensure data accuracy. Despite numerous published field calibration studies, a universal and comprehensive assessment of factors affecting sensor calibration remains elusive, leading to potential discrepancies in data quality across different networks. To address these challenges, this study deployed eight sensor-based monitors equipped with electrochemical sensors for NO2, NO, CO, and O3 measurement in strategically chosen locations within Hong Kong, Macau, and Shanghai, covering a wide range of climatic conditions: Hong Kong's subtropical climate, Macau's similar yet distinct urban environment, and Shanghai's more variable climate. This strategic deployment ensured that the sensors' performance and calibration processes were tested across diverse atmospheric conditions. Each monitor employed a patented dynamic baseline tracking method for the gas sensors, which isolates the concentration signals from temperature and humidity effects, enhancing the sensors' accuracy and reliability. The tests, which involved evaluating the validation performance by analyzing randomly selected calibration sample subsets ranging from 1 to 15 days, indicated that the length of the calibration period, pollutant concentration range, and time averaging period are pivotal for sensor calibration quality. We determined that a 5–7 days calibration period minimizes calibration coefficient errors, and a wider concentration range improves the validation R2 values for all sensors, suggesting the necessity of setting specific concentration range thresholds. Moreover, a time averaging period of at least 5 minutes for data with 1-minute resolution was recommended to enable optimal calibration in field operation. This study emphasizes the need for a comprehensive calibration assessment and the importance of considering environmental variability in sensor calibration condition. These findings offer methodological guidance for the calibration of other sensor types, providing a reference for future research in the field of sensor calibration. [ABSTRACT FROM AUTHOR]

The vehicle active suspension has been widely used as it could effectively improve the vehicle ride comfort. To solve the issues of actuator uncertainty and time delay existing in the active suspension system, a multi-objective non-fragile H ∞ control algorithm is proposed in this work. First, to effectively describe the vehicle suspension dynamics, a quarter-car suspension model is constructed first in this study. Second, aiming at improving the ride quality and guarantee the hard constraints of the suspension system, a multi-objective H 2/ H∞ performance index is proposed. Subsequently, the issues of external disturbances, actuator uncertainty and delay are considered in the design process of the suspension controller. Then, a multi-objective non-fragile control algorithm is proposed based on an effective Lyapunov functional. The control gain can be obtained by solving a convex optimization problem in terms of linear matrix inequalities. Finally, numerical simulations are implemented on MATLAB/Simulink platform to show the robustness and superiority of the proposed controller. Experiments are implemented on a quarter-car test rig to validate the practical performance of the designed controller. [ABSTRACT FROM AUTHOR]