scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

RCH: robust calibration based on historical data for low-cost air quality sensor deployments

Guodong Li1, Rui Ma1, Liu Xinyu1, Yue Wang1, Lin Zhang1 
10 Sep 2020-pp 650-656
TL;DR: The proposed Robust Calibration approach using Historical data (RCH) can improve the accuracy and consistency of low-cost air quality sensors without the help of real-time and nearby reference data.
Abstract: Air pollution has become one of the major threats to human health. Conventional approaches for air pollution monitoring use precise professional devices, but cannot achieve dense deployment due to high cost. Therefore, systems consisting of low-cost sensors are applied as a supplement to obtain fine-grained pollution information. In order to maintain the accuracy of these low-cost sensors, it is essential to calibrate them to minimize the impact from sensor drifts. Existing field calibration methods utilize the real-time data from spatially-adjacent official air quality stations as reference. However, the real-time reference is not always accessible under existing station deployment. In this paper, we propose the Robust Calibration approach using Historical data (RCH) for low-cost air quality sensors. Our method corrects the sensor drift by adapting sensitivity and offset based on pollutant's concentration distribution. Experiments on NO2 data from real-world deployment in Foshan, China show that RCH has the similar performance compared with existing field calibration methods using real-time and spatially-adjacent references. It demonstrates that RCH can improve the accuracy and consistency of low-cost air quality sensors without the help of real-time and nearby reference data.
Citations
More filters
01 Feb 2015
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

136 citations

Proceedings ArticleDOI
Guodong Li1, Zhiyuan Wu1, Ning Liu1, Xinyu Liu1, Yue Wang1, Lin Zhang1 
21 Sep 2021
TL;DR: Wang et al. as discussed by the authors presented a novel generative framework for blind calibration problems without specific data correlation or drift model assumption, and extracted the most informative feature that maximizes correlation between reference data and target data using soft-HGR maximal correlation regression.
Abstract: In large-scale IoT systems, blind calibration problem becomes increasingly prominent for sensor calibration without ground truth reference. Most of the existing blind calibration methods adopt either a handcrafted spatio-temporal model or a specific drift mechanism assumption. However, these assumptions may be over-simplified or introduce inappropriate bias, and therefore lead to great performance degradation in the real-world deployment. In this paper, we present a novel generative framework for blind calibration problems without specific data correlation or drift model assumption. We extract the most informative feature that maximizes correlation between reference data and target data using soft-HGR maximal correlation regression. Therefore, our method can be used in different blind calibration tasks especially where data correlation or drift model is unknown or deviated. Besides, our method can be conveniently augmented with a reliable drift model to further improve performance on specific tasks. We conduct comprehensive evaluations over a three-month real-world air pollution sensing dataset collected in Foshan, China. Results show our method can obtain the best performance compared to previous blind calibration methods in the absence of accurate drift model knowledge.

2 citations

Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper proposed a variational Bayesian blind calibration algorithm to improve the accuracy of in-field calibration models by introducing a supplement set that only involves historical reference and synchronized reference measurement without sensor observation.
Abstract: Air pollution has become a global threat to urban environments and public health. Low-cost air quality sensor systems have been deployed to support fine-grained monitoring, and in-field calibration methods are necessary to assure the accuracy of sensor observation. Existing methods use in-field reference to train some specific calibration models. Nevertheless, collecting sufficient reference data after deployment is challenging. First, the synchronized data pair of reference observation is hard to obtain. Thus, many blind calibration approaches rely on some alternative reference like a historical reference before sensor observation. However, since the relationship model between alternative reference and sensor observation becomes more complicated, the amount of collected data is still insufficient to learn the complex model. To address the above challenge, we propose a Variational Bayesian Blind Calibration Algorithm. Our method introduces a supplement set that only involves historical reference and synchronized reference measurement without sensor observation to alleviate the few-reference pressure. Large amounts of the introduced supplement sets have been collected in different cities by accurate stations, which we adopt to conduct prediction tasks and build a Bayesian model to learn how to combine the formulated prediction task and target calibration task in a theoretically better way. Furthermore, we design a variational Bayesian framework to make good use of the easily obtained supplement set to train a better prediction model for improving calibration performance and relieving the pressure of collecting costly reference measurements after deployment. Evaluations of real-world and synthetic datasets show that the proposed approach has a better performance than previous baselines.

1 citations

Journal ArticleDOI
TL;DR: In this article , a clustering-based segmented regression method was proposed for in-field sensor-based air pollution monitoring, where interference from relative humidity and temperature were taken into consideration in the particulate matter concentration calibration model.
Abstract: Nowadays, sensor-based air pollution sensing systems are widely deployed for fine-grained pollution monitoring. In-field calibration plays an important role in maintaining sensory data quality. Determining the model structure is challenging using existing methods of variable global fitting models for in-field calibration. This is because the mechanism of interference factors is complex and there is often insufficient prior knowledge on a specific sensor type. Although Artificial-Neuron-Net-based (ANN-based) methods ignore the complex conditions above, they also have problems regarding generalization, interpretability, and calculation cost. In this paper, we propose a clustering-based segmented regression method for particulate matter (PM) sensor in-field calibration. Interference from relative humidity and temperature are taken into consideration in the particulate matter concentration calibration model. Samples for modeling are divided into clusters and each cluster has an individual multiple linear regression equation. The final calibrated result of one sample is calculated from the regression model of the cluster the sample belongs to. The proposed method is evaluated under in-field deployment and performs better than a global multiple regression method both on PM2.5 and PM10 pollutants with, respectively, at least 16% and 9% improvement ratio on RMSE error. In addition, the proposed method is insensitive to reduction of training data and increase in cluster number. Moreover, it may bear lighter calculation cost, less overfitting problems and better interpretability. It can improve the efficiency and performance of post-deployment sensor calibration.
Proceedings ArticleDOI
06 Nov 2022
TL;DR: Wang et al. as discussed by the authors proposed a multi-task learning based blind calibraiton method for air quality sensors after deployments, which introduces not only the reference data of the target location to formulate calibration task, but also reference measurements collected from highly accurate stations already deployed by the government in other geographical locations to formulate prediction task.
Abstract: Air pollution problem has caught much attention globally. In addition to the national air quality monitoring stations deployed by the government, the number of low-cost air quality sensors increases rapidly as a supplement to support fine-grained monitoring. In-field calibration methods are necessary for these low-cost sensor nodes to assure the data quality. However, it is costly to collect enough reference data after deployment to train the in-field calibration model and many sensors even have no synchronized reference in the real application scenarios. To address the above challenge, we propose a multi-task learning based blind calibraiton method for air quality sensors after deployments. Our method introduces not only the reference data of the target location to formulate calibration task, but also reference measurements collected from highly accurate stations already deployed by the government in other geographical locations to formulate prediction task. To utilize the reference measurements which are not in the same location with our target sensors, e.g., in other cities, we combine the proposed calibration task and prediction task under a multi-task learning scheme. The introduced references in other locations alleviate our few-reference challenge. Furthermore, we elaborate on the choices of different tasks to have better effect of the target calibraiton task. Evaluations on the real-world collected datasets show that our proposed algorithm has better calibraiton effect.
References
More filters
Journal ArticleDOI
TL;DR: The drivers behind current rises in the use of low-cost sensors for air pollution management in cities are illustrated, while addressing the major challenges for their effective implementation.

591 citations


"RCH: robust calibration based on hi..." refers background in this paper

  • ...They are incapable of supporting fine-grained air pollution monitoring at 100m level because high perunit price limits large-scale and densely deployment of these stations [9]....

    [...]

Journal ArticleDOI
TL;DR: In this article, the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques are compared, and the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions.
Abstract: The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques are compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors was operated. The sensors were either of metal oxide or electrochemical type or based on miniaturized infra-red cell. For each method, a two-week calibration was carried out at a semi-rural site against reference measurements. Subsequently, the accuracy of the predicted values was evaluated for about five months using a few indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. The study assessed if the sensors were could reach the Data Quality Objective (DQOs) of the European Air Quality Directive for indicative methods (between 25 and 30% of uncertainty for O 3 and NO 2 ). In this study it appears that O 3 may be calibrated using simple regression techniques while for NO 2 a better agreement between sensors and reference measurements was reached using supervised learning techniques. The hourly O 3 DQO was met while it was unlikely that NO 2 hourly one could be met. This was likely caused by the low NO 2 levels correlated with high O 3 levels that are typical of semi-rural site where the measurements of this study took place.

335 citations


"RCH: robust calibration based on hi..." refers background in this paper

  • ...However, they suffer from measurement deviation due to sensor drift caused by the harsh environment and individual differences [7, 8, 17, 18]....

    [...]

Journal ArticleDOI
TL;DR: The performance of emerging air quality sensor technologies in a real-world setting is demonstrated; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.
Abstract: . Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, and −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.

329 citations


"RCH: robust calibration based on hi..." refers background in this paper

  • ...However, they suffer from measurement deviation due to sensor drift caused by the harsh environment and individual differences [7, 8, 17, 18]....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared and the accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques.
Abstract: In this work the performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques, are compared. A cluster of either metal oxide or electrochemical sensors for nitrogen monoxide and carbon monoxide together with miniaturized infra-red carbon dioxide sensors was operated. Calibration was carried out during the two first weeks of evaluation against reference measurements. The accuracy of each regression method was evaluated on a five months field experiment at a semi-rural site using different indicators and techniques: orthogonal regression, target diagram, measurement uncertainty and drifts over time of sensor predictions. In addition to the analyses for ozone and nitrogen oxide already published in Part A [1], this work assessed if carbon monoxide sensors can reach the Data Quality Objective (DQOs) of 25% of uncertainty set in the European Air Quality Directive for indicative methods. As for ozone and nitrogen oxide, it was found for NO, CO and CO 2 that the best agreement between sensors and reference measurements was observed for supervised learning techniques compared to linear and multilinear regression.

256 citations


"RCH: robust calibration based on hi..." refers background in this paper

  • ...However, they suffer from measurement deviation due to sensor drift caused by the harsh environment and individual differences [7, 8, 17, 18]....

    [...]

Journal ArticleDOI
12 Dec 2015-Sensors
TL;DR: This paper classifies the existing works into three categories as Static Sensor Network (SSN), Community Sensor network (CSN) and Vehicle sensor network (VSN) based on the carriers of the sensors.
Abstract: The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

255 citations


"RCH: robust calibration based on hi..." refers background in this paper

  • ...To achieve fine-grained air pollution monitoring, low-cost air quality sensors have been applied to monitor the urban air pollutants [6, 22] and these sensors can be deployed densely with an acceptable total cost [4, 21]....

    [...]