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Low-cost sensors have become an increasingly important supplement to air quality monitoring networks at the ground level, yet their performances have not been evaluated at high-elevation areas, where the weather conditions are complex and characterized by low air pressure, low temperatures, and high wind speed. To address this research gap, a seven-month-long inter-comparison campaign was carried out at Mt. Tai (1534 m a.s.l.) from 20 April to 30 November 2018, covering a wide range of air temperatures, relative humidities (RHs), and wind speeds. The performance of three commonly used sensors for carbon monoxide (CO), ozone (O₃), and particulate matter (PM_2.5) was evaluated against the reference instruments. Strong positive linear relationships between sensors and the reference data were found for CO (<i>r</i> = 0.83) and O₃ (<i>r</i> = 0.79), while the PM_2.5 sensor tended to overestimate PM_2.5 under high RH conditions. When the data at RH &gt;95% were removed, a strong non-linear relationship could be well fitted for PM_2.5 between the sensor and reference data (<i>r</i> = 0.91). The impacts of temperature, RH, wind speed, and pressure on the sensor measurements were comprehensively assessed. Temperature showed a positive effect on the CO and O₃ sensors, RH showed a positive effect on the PM sensor, and the influence of wind speed and air pressure on all three sensors was relatively minor. Two methods, namely a multiple linear regression model and a random forest model, were adopted to minimize the influence of meteorological factors on the sensor data. The multi-linear regression (MLR) model showed a better performance than the random forest (RF) model in correcting the sensors&#8217; data, especially for O₃ and PM_2.5. Our results demonstrate the capability and potential of the low-cost sensors for the measurement of trace gases and aerosols at high mountain sites with complex weather conditions.