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A Gap Analysis of Low-Cost Outdoor Air Quality Sensor In-Field Calibration
13 Dec 2019-
TL;DR: This article presents low-cost sensor technologies, and it survey and assess machine learning-based calibration techniques for their calibration, and presents open questions and directions for future research.
Abstract: In recent years, interest in monitoring air quality has been growing. Traditional environmental monitoring stations are very expensive, both to acquire and to maintain, therefore their deployment is generally very sparse. This is a problem when trying to generate air quality maps with a fine spatial resolution. Given the general interest in air quality monitoring, low-cost air quality sensors have become an active area of research and development. Low-cost air quality sensors can be deployed at a finer level of granularity than traditional monitoring stations. Furthermore, they can be portable and mobile. Low-cost air quality sensors, however, present some challenges: they suffer from crosssensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Some promising machine learning approaches can help us obtain highly accurate measurements with low-cost air quality sensors. In this article, we present low-cost sensor technologies, and we survey and assess machine learning-based calibration techniques for their calibration. We conclude by presenting open questions and directions for future research.
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Queensland University of Technology1, Norwegian Institute for Air Research2, United States Environmental Protection Agency3, University of Surrey4, Hong Kong University of Science and Technology5, Hong Kong Environmental Protection Department6, City University of Hong Kong7, Curtin University8, Southern Cross University9
TL;DR: In this article, the authors conducted a comprehensive literature search including both the scientific and grey literature, and concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use.
Abstract: Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.
138 citations
TL;DR: In this paper, low-cost air quality sensors (LCAQS) can be deployed in dense monitoring networks to provide timely and comprehensive snapshots of pollutant concentrations and their spatial and temporal variability at various scales with relatively less cost and labor.
Abstract: As a potential complement to traditional regulatory instruments, low-cost air quality sensors (LCAQS) can be deployed in dense monitoring networks to provide timely and comprehensive snapshots of pollutant concentrations and their spatial and temporal variability at various scales with relatively less cost and labor. However, a lack of practical guidance and a limited understanding of sensor data quality hinder the widespread application of this emerging technology. We leveraged air quality data collected from state and local monitoring agencies in metropolitan areas of the United States to evaluate how low-cost sensors could be deployed across the U.S. We found that ozone, as a secondary pollutant, is more homogeneous than other pollutants at various scales. PM2.5, CO, and NO2 displayed homogeneities that varied by city, making it challenging to design a uniform network that was suitable across geographies. Our low-cost sensor data in New York City indicated that PM2.5 sensors track well with light-scattering reference methods, particularly at low concentrations. The same phenomenon was also found after thoroughly evaluating sensor evaluation reports from the Air Quality Sensor Performance Evaluation Center (AQ-SPEC). Furthermore, LCAQS data collected during wildfire episodes in Portland, OR show that a real-time (i.e. in situ) machine learning calibration process is a promising approach to address the data quality challenges persisting in LCAQS applications. Our research highlights the urgency and importance of practical guidance for deploying LCAQS.
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References
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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
TL;DR: In this paper, a review of the non-dispersive infrared (NDIR) gas sensors applied in an environmental field is presented, where the authors consider the advantages and disadvantages of these sensors, such as spectral interference and high detection limit.
Abstract: Non-dispersive infrared (NDIR) gas sensors applied in an environmental field are considered. Disadvantages of the non-dispersive infrared (NDIR) gas sensors include spectral interference and high detection limit. Efforts to improve these disadvantages are reviewed in this paper. Interference caused by water vapor and gas matrix has been partially solved using optical filters and interference correction factors. Limitations such as accuracy and sensitivity of the sensor were overcome by the improvements of inlet gas concentrations, infrared sources, optical designs (including optical filter and gas chamber) and detectors. These improvements are limited to a few gases, in particular, carbon dioxide. Drawbacks related to water vapor still remain and need to be addressed.
255 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...NDIR sensors are also subject to drift [51] and they cost considerably more than MOS or EC sensors (up to a 10-fold increase in price)....
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...Sensors Gas sensor technologies [48, 29, 49, 33] MOS sensors [50] NDIR sensors [51] Portable sensors [52, 53] Wearable sensors [54] Commercial sensors [55, 26] Low-cost sensors quality [56, 57] Usability of low-cost air quality sensors (AQSs) for atmospheric measurements [58] Deployment Cities and projects [33] Calibration Adaptation to drift [42] Optical PM sensors [43, 44] Error sources in calibration [45]...
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TL;DR: In this article, a quantification system was developed to convert the metal oxide semiconductor (MOx) sensor signals into concentrations, and two types of sensors were used to measure CO, O3, NO2, and total VOCs.
Abstract: Advances in embedded systems and low-cost gas sensors are enabling a new wave of low-cost air quality monitoring tools Our team has been engaged in the development of low-cost, wearable, air quality monitors (M-Pods) using the Arduino platform These M-Pods house two types of sensors – commercially available metal oxide semiconductor (MOx) sensors used to measure CO, O3, NO2, and total VOCs, and NDIR sensors used to measure CO2 The MOx sensors are low in cost and show high sensitivity near ambient levels; however they display non-linear output signals and have cross-sensitivity effects Thus, a quantification system was developed to convert the MOx sensor signals into concentrations We conducted two types of validation studies – first, deployments at a regulatory monitoring station in Denver, Colorado, and second, a user study In the two deployments (at the regulatory monitoring station), M-Pod concentrations were determined using collocation calibrations and laboratory calibration techniques M-Pods were placed near regulatory monitors to derive calibration function coefficients using the regulatory monitors as the standard The form of the calibration function was derived based on laboratory experiments We discuss various techniques used to estimate measurement uncertainties The deployments revealed that collocation calibrations provide more accurate concentration estimates than laboratory calibrations During collocation calibrations, median standard errors ranged between 40–61 ppb for O3, 64–84 ppb for NO2, 028–044 ppm for CO, and 168 ppm for CO2 Median signal to noise (S / N) ratios for the M-Pod sensors were higher than the regulatory instruments: for NO2, 36 compared to 234; for O3, 14 compared to 16; for CO, 11 compared to 100; and for CO2, 422 compared to 300–500 By contrast, lab calibrations added bias and made it difficult to cover the necessary range of environmental conditions to obtain a good calibration A separate user study was also conducted to assess uncertainty estimates and sensor variability In this study, 9 M-Pods were calibrated via collocation multiple times over 4 weeks, and sensor drift was analyzed, with the result being a calibration function that included baseline drift Three pairs of M-Pods were deployed, while users individually carried the other three The user study suggested that inter-M-Pod variability between paired units was on the same order as calibration uncertainty; however, it is difficult to make conclusions about the actual personal exposure levels due to the level of user engagement The user study provided real-world sensor drift data, showing limited CO drift (under −005 ppm day−1), and higher for O3 (−26 to 20 ppb day−1), NO2 (−156 to 051 ppb day−1), and CO2 (−42 to 31 ppm day−1) Overall, the user study confirmed the utility of the M-Pod as a low-cost tool to assess personal exposure
251 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background or methods in this paper
...MOS sensors are a popular sensor technology for monitoring gas concentrations of several pollutants, such as nonmethane hydrocarbons (NMHCs), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), a combination of nitrogen oxide (NO) and NO2 (NOx) and ozone (O3) [67, 40, 68, 69, 70, 71, 72, 25, 54]....
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...NDIR sensors have been mostly used to detect CO2 concentrations [69, 72, 25, 39]....
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TL;DR: The drift of some TGS sensors for 7 years as well as the difference in the temporal behaviour of identical sensors and the consequence on the e-nose results after the sensor replacement in the sensors array are presented.
Abstract: The e-nose technology has enormous potentialities for in site monitoring of off-odours. However a number of limitations are associated with the properties of chemical sensors, the signal processing performances and the real operating conditions of the environmental field. The field experience of the research group included testing of a large amount of sensors in different sensor technologies and among those the metal oxide-based gas sensors (Figaro type) are the best gas sensors for long term application, as stated during more than 1 year of field testing. To be usable for the off-odours field measurement, the e-nose has to deal with the lack of long term stability of these sensors. The drift and the sensors replacement have to be considered. In order to appraise the time evolution of the sensors and the effect on the results of an electronic nose, experimentation has been performed during more than 3 years on two identical sensor arrays. The two arrays contain the same six Figaro sensors and are in the same sensor chamber of the e-nose system. Both arrays have worked continuously, without break. This paper presents the drift of some TGS sensors for 7 years as well as the difference in the temporal behaviour of identical sensors and the consequence on the e-nose results after the sensor replacement in the sensors array. A correction of the drift and of the replacement effect is applied and the classification results are exposed, with and without correction.
250 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...For example, an analysis of metal oxide sensors (MOS; see Section 4) has demonstrated a drift in measurements higher than 200% [103]....
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TL;DR: In this article, the diffusion size classifier (DiSC) is used for personal exposure monitoring, which can be easily used for workplace exposure monitoring and can be used for traditional workplace aerosols such as welding fumes or combustion exhaust.
Abstract: Due to the increasingly widespread use of engineered nanoparticles and the increasing number of persons handling them, there is a need to monitor the personal exposure of these persons. Current gravimetric and optic methods are rather insensitive for nanoparticles ( <∼ 100 nm), and therefore not suitable for this task. To help solve this problem, we have miniaturized an instrument capable of measuring nanoparticles developed earlier by our group; the diffusion size classifier (DiSC). The instrument is now handheld (4 × 9 × 18 cm), and can easily be used for personal exposure monitoring, opening up applications for workplace exposure monitoring (for engineered nanoparticles but also for traditional workplace aerosols such as welding fumes or combustion exhaust) and medical studies. The DiSC measures the particle number concentration and the average particle diameter of an aerosol, however, like most simple instruments, it is nonspecific, i.e., it detects all nanoparticles and cannot distinguish between bac...
242 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...Disadvantages: Light-scattering based instruments fail to detect very small particles [89]....
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...Cannot distinguish between narrow and broad particle size distributions [89]....
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...Larger particles that are not precipitated by the diffusion stage eventually reach the backup filter which produces an electrical current proportional to their concentration [89, 90]....
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