A new calibration system for low-cost Sensor Network in air pollution monitoring

Cui, Houxin; Zhang, Ling; Li, Wanxin; Yuan, Ziyang; Wu, Mengxian; Wang, Qianqian; Ma, Jie; Li, Yang

doi:10.1016/j.apr.2021.03.012

Cited by 31 publications

(22 citation statements)

References 15 publications

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“…SailHero performed internal screening procedures to identify and discard sensors with abnormal responses . As an additional precaution measurement prior to field deployment, all monitors were first tested in Portland, OR (Site ID 41-051-00804) by colocating with FEM/FRM instruments for at least one week as a part of sensor QA/QC and then transported to and deployed in each city.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…SailHero performed internal screening procedures to identify and discard sensors with abnormal responses. 55 As an additional precaution measurement prior to field deployment, all monitors were first tested in Portland, OR (Site ID 41-051-00804) by colocating with FEM/FRM instruments for at least one week as a part of sensor QA/QC and then transported to and deployed in each city. All onsite reference data (EPA FEM/FRM), including temperature and relative humidity in this research, were obtained through the EPA Air Quality System (AQS) application programming interface (API) ( ).…”

Section: Experimental Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Hasan

Ivey

et al. 2023

ACS Omega

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Low-cost air quality (LCAQ) sensors are increasingly being used for community air quality monitoring. However, data collected by low-cost sensors contain significant noise, and proper calibration of these sensors remains a widely discussed, but not yet fully addressed, area of concern. In this study, several LCAQ sensors measuring nitrogen dioxide (NO 2 ) and ozone (O 3 ) were deployed in six cities in the United States

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Section: Experimental Methodsmentioning

confidence: 99%

Section: Experimental Methodsmentioning

confidence: 99%

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Hasan

Ivey

et al. 2023

ACS Omega

Self Cite

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“…Such approaches have been used previously, for example by [23,57], or [58]. Another possibility is a lab-calibration in a climate chamber, where all potential cross-sensitivites and environmental effects could be simulated relatively quickly for a single sensor [11,59], and the resulting calibration could subsequently be applied to the remaining sensors, assuming that they all behave identically, which is mostly, but not entirely, the case considering the results shown in Figure 9. If this is carried out in a very comprehensive manner, it has the potential for resulting in sensor calibrations that are robust in the majority of environmental conditions that might be encountered in the field.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems

et al. 2023

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Low-cost air quality sensors have the potential to complement the regulatory network of air quality monitoring stations, with respect to increased spatial density of observations, however, their data quality continues to be of concern. Here we report on our experience with a small network of open low-cost sensor systems for air quality, which was deployed in the region of Stavanger, Norway, under Nordic winter conditions. The network consisted of AirSensEUR sensor systems, equipped with sensors for, among others, nitrogen dioxide and fine particulate matter. The systems were co-located at an air quality monitoring station, for a period of approximately six weeks. A subset of the systems was subsequently deployed at various roadside locations for half a year, and finally co-located at the same air quality monitoring station again, for a post-deployment evaluation. For fine particulate matter, the co-location results indicate a good inter-unit consistency, but poor average out-of-the-box performance (R2 = 0.25, RMSE = 9.6 μg m−3). While Köhler correction did not significantly improve the accuracy in our study, filtering for high relative humidity conditions improved the results (R2 = 0.63, RMSE = 7.09 μg m−3). For nitrogen dioxide, the inter-unit consistency was found to be excellent, and calibration models were developed which showed good performance during the testing period (on average R2 = 0.98, RMSE = 5.73 μg m−3), however, due to the short training period, the calibration models are likely not able to capture the full annual variability in environmental conditions. A post-deployment co-location showed, respectively, a slight and significant decrease in inter-sensor consistency for fine particulate matter and nitrogen dioxide. We further demonstrate, how observations from even such a small network can be exploited by assimilation in a high-resolution air quality model, thus adding value to both the observations and the model, and ultimately providing a more comprehensive perspective of air quality than is possible from either of the two input datasets alone. Our study provides valuable insights on the operation and performance of an open sensor system for air quality, particularly under challenging Nordic environmental conditions.

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“…Several studies have used linear models such as multiple linear regression to calibrate sensors of different gases [ 14 , 29 ]. Besides, non-linear techniques, such as k-nearest neighbors, support vector regression, random forest or neural networks, have also been used to calibrate sensors [ 8 , 9 , 10 , 14 , 25 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

Ferrer-Cid

Garcia-Calvete²,

Aina

et al. 2022

Sensors

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The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.

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A new calibration system for low-cost Sensor Network in air pollution monitoring

Cited by 31 publications

References 15 publications

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Unexpected Performance Improvements of Nitrogen Dioxide and Ozone Sensors by Including Carbon Monoxide Sensor Signal

Deployment and Evaluation of a Network of Open Low-Cost Air Quality Sensor Systems

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

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