Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas

Abstract

The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months. Commercial AQY1 sensors were used, and their reported readings of O3, NO2, PM2.5, and PM10 were assessed against a regulatory monitor. We assessed how well the raw and calibrated AQY1 readings matched the regulatory monitor and whether meteorology impacted performance. We found that each sensor’s response was different. Overall, the sensors performed best for O3 (R2 = 0.36–0.97) and worst for NO2 (0.00–0.58), showing a potential impact of meteorological factors, with an effect of temperature on O3 and relative humidity on PM. Calibration seemed to improve the accuracy, but not in all cases or for all performance metrics (e.g., precision versus bias), and it was limited to a linear calibration in this study. Our data showed that it is critical for users to regularly calibrate low-cost sensors and monitor data once they are installed, as sensors may not be operating properly, which may result in the loss of large amounts of data. We also recommend that co-location should be as exact as possible, minimizing the distance between sensors and regulatory monitors, and that the sampling orientation is similar. There were important deviations between the AQY1 and regulatory monitors’ readings, which in small part depended on meteorology, hindering the ability of the low-costs sensors to present air quality accurately. However, categorizing air pollution levels, using for example the Air Quality Index framework, rather than reporting absolute readings, may be a more suitable approach. In addition, more sophisticated calibration methods, including accounting for individual sensor performance, may further improve performance. This work adds to the literature by assessing the performance of low-cost sensors over one of the longest durations reported to date.

Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia

“…Recent studies comparing low-cost methods on the market to reference methods all suggest that the readings of low-cost sensors are sensitive to environmental factors, such as the relative humidity, and performances against reference monitors change with the weather [42,43,45,46]. Light-absorbing carbon estimates using image-based methods for reflectance may be sensitive to the site, season, pollution source (e.g., fuel type), and/or reference method selected for calibration.…”

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring

“…Reference CH4 measurements were provided via a research-grade Aeris Ultra gas analyzer (Aeris Ultra, Project Canary, Denver, CO, USA). The inlet of the Aeris gas analyzer was placed adjacent to the XPOD inlet on the roof of the monitoring vehicle to minimize differences in the gas composition during deployment [ 38 ] ( Figure S2 ). The response lags for pumped gas to enter both instruments were equal for the XPOD and Aeris Ultra.…”

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

“…For measurement of air pollution, several studies revealed that meteorological parameters such as relative humidity (RH), temperature (T), pressure (P), and wind impact the performance of low-cost sensors and therefore there is a need to use standard reference-grade monitoring stations for evaluation and validation. It is further advised to not rely on low-cost air-quality sensors at higher RH value locations [ 125 ].…”

“…To build confidence in low-cost sensors for real-world monitoring, regular calibration and validation with a co-located standard instrument is critical [ 125 ]. Improved statistical methods, IoT-based platforms, and space-based sensors can all improve methodological approaches for environmental pollution.…”