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 (r = 0.83) and O₃ (r = 0.79), while the PM_2.5 sensor tended to overestimate PM_2.5 under high RH conditions. When the data at RH >95% were removed, a strong non-linear relationship could be well fitted for PM_2.5 between the sensor and reference data (r = 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’ 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.

China has been experiencing fast-paced urbanization and industrialization as well as stringent air pollution control in the past decades, which are expected to cause drastic changes in the anthropogenic emissions of primary air pollutants (e.g., SO2, NOx and VOCs). Long-term observations are fundamental to the assessment of response of atmospheric composition to the changing anthropogenic emissions, which are, however, very limited in China. Here we integrated the observational data of trace gases and aerosol obtained during 2003-2023 at Mount Tai – the peak of the North China Plain (1534 m above sea level), a highly polluted region of China. The data were analyzed to understand the long-term changes of a variety of trace gases and aerosol properties such as ozone (O3), PM2.5 composition, particle number and size distribution, new particle formation and growth parameters, and O3 depleting substances (ODS). Surface O3 concentrations showed a significant increasing trend in summertime with a rate of ~2 ppbv yr-1, despite the persistent decrease in NOx emissions since 2012, and can be attributed to the increasing VOCs and O3 production efficiency. Sharp reduction in SO2 emissions have resulted in significant decrease of sulfate in PM2.5, whilst nitrate showed a strong increasing trend. A multi-phase chemical box model illustrated that the reduced SO2 and sulfate enhanced nitrate formation by lessening the aerosol acidity and facilitating the partitioning of HNO3 to the particle phase. The apparent formation rate of new particles in spring has increased at Mt. Tai, while the particle growth rate significantly decreased. The contributions of new particles to the cloud condensation nuclei (CCN) were also decreasing. The ODS regulated by the Montreal Protocol (MP) showed a significant downward trend, but the MP-controlled and unregulated halocarbon species showed overall upward trends. We will also present the results about the impacts of COVID lockdown on the regional air quality as observed at Mt. Tai.