Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions

Abstract

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 (PM2.5) 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 PM2.5 sensor tended to overestimate PM2.5 under high RH conditions. When the data at RH >95% were removed, a strong non-linear relationship could be well fitted for PM2.5 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 PM2.5. 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.

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

“…Similar effects are also reported in the literature for the same sensor: both [35] and [37] also shown an increased discrepancy between the SDS011 and reference sensor for PM 2.5 when RH increased above 80%. Reduced correlations between reference and low-cost sensor at high humidity were also seen by [41] on PM 2.5 during the deployment of a similar low-cost sensor (SDS019, Nova Fitness, Jinan, China) on a high-elevation mountain.…”

Increased new particle yields with largely decreased probability of survival to CCN size at the summit of Mt. Tai under reduced SO<sub>2</sub> emissions

“…In general, the NPF-derived CCNs were seasonally dependent, i.e., the highest number concentrations occurred in summer, followed by spring, fall, and winter. With an increase in the threshold diameters, roughly corresponding to a decrease in supersaturation from > 0.6 % to < 0.1 % (Li et al, 2015), the estimated contributions decreased because new particles were continuously removed either by coagulation or atmospheric deposition during the particle growth. During the three summer campaigns in 2009, 2014, and 2015, larger NPF-derived CCNs were estimated with average N CCN50 , N CCN80 , and N CCN100 values of 4.4 ± 2.5 × 10 3 , 1.9 ± 1.5 × 10 3 , and 1.0 ± 0.9 × 10 3 cm −3 , respectively.…”

Source Apportionment of Regional Ozone Pollution Observed at Mount Tai, North China: Application of Lagrangian Photochemical Trajectory Model and Implications for Control Policy

“…Real‐time measurements of trace gases, including O 3 , NO, NO 2 , NOy (NOy = NO + NO 2 + NO 3 + N 2 O 5 + HONO + HNO 3 + NO 3 ‐ + PANs + RONO 2 + etc. ), and CO, were implemented using standard commercial techniques (Li et al., 2020). O 3 was measured by a model T400 UV photometric ozone analyzer (Teledyne Advanced Pollution Instrumentation (T‐API)) with a detection limit of 0.4 ppbv and precision of 0.5%.…”

“…The calibration system and procedures have been described in detail by Li et al. (2020). Meteorological parameters including ambient temperature and relative humidity (RH) were obtained from the Taishan National Reference Climatological Station.…”

“…For CO, multi-point calibrations were performed monthly and the baseline was determined every 8 h by passing ambient air through the internal CO scrubber for 15 min. The calibration system and procedures have been described in detail by Li et al (2020). Meteorological parameters including ambient temperature and relative humidity (RH) were obtained from the Taishan National Reference Climatological Station.…”