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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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TL;DR: Long-term exposure to traffic-related air pollution increases the risk for asthma hospitalisation in older people, and people with previous asthma or COPD hospitalisations are most susceptible.
Abstract: Background Exposure to air pollution in early life contributes to the burden of childhood asthma, but it is not clear whether long-term exposure to air pollution can lead to asthma onset or progression in adulthood. Objectives The authors studied the effect of exposure to traffic-related air pollution over 35 years on the risk for asthma hospitalisation in older people. Methods 57053 participants in the Danish Diet, Cancer and Health cohort, aged 50e65 years at baseline (1993e1997), were followed up for first hospital admission for asthma until 2006, and the annual nitrogen dioxide (NO2) levels were estimated as a proxy of the exposure to traffic-related air pollution at the residential addresses of the participants since 1971. The association between NO2 and hospitalisation for asthma was modelled using Cox regression, for the full cohort and in people with and without previous hospitalisations for asthma, and the effect modification by comorbid conditions was assessed. Results During 10.2 years’ median follow-up, 977 (1.9%) of 53695 eligible people were admitted to hospital for asthma: 821 were first-ever admissions and 176 were readmissions. NO2 levels were associated with risk for asthma hospitalisation in the full cohort (HR and 95% CI per IQR, 5.8 mg/m 3 : 1.12; 1.04e1.22), and for first-ever admissions (1.10; 1.01e1.20), with the highest risk in people with a history of asthma (1.41; 1.15e2.07) or chronic obstructive pulmonary disease (COPD) (1.30; 1.07e1.52) hospitalisation. Conclusions Long-term exposure to traffic-related air pollution increases the risk for asthma hospitalisation in older people. People with previous asthma or COPD hospitalisations are most susceptible.
132 citations
15 Feb 2012
TL;DR: This paper studies three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly and validates all three algorithms with real ozone pollution measurements carried out in an urban setting.
Abstract: Air quality monitoring is extremely important as air pollution has a direct impact on human health. Low-cost gas sensors are used to effectively perceive the environment by mounting them on top of mobile vehicles, for example, using a public transport network. Thus, these sensors are part of a mobile network and perform from time to time measurements in each others vicinity. In this paper, we study three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly. Forward calibration, based on a traditional approach widely used in the literature, is used as performance benchmark for two novel algorithms: backward and instant calibration. We validate all three algorithms with real ozone pollution measurements carried out in an urban setting by comparing gas sensor output to high-quality measurements from analytical instruments. We find that both backward and instant calibration reduce the average measurement error by a factor of two compared to forward calibration. Furthermore, we unveil the arising difficulties if sensor calibration is not based on reliable reference measurements but on sensor readings of low-cost gas sensors which is inevitable in a mobile scenario with only a few reliable sensors. We propose a solution and evaluate its effect on the measurement accuracy in experiments and simulation.
128 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...These studies are Maag et al. (2016) [38], Spinelle et al. (2015, 2017) [72, 25], Cheng et al. [82], Esposito et al. [78], Liu et al. [43], Saukh et al. [71], Hasenfratz et al. (2012) [27], and Gao et al. [83]....
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...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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22 Jul 2012
TL;DR: The air pollution modeling problem is surveyed, a new dataset of mobile air quality measurements in Zurich is introduced, and the challenges of making sense of these data are discussed.
Abstract: Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem, introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.
126 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...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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...The most common approaches consist in using fixed infrastructures, such as street lights, or sensors mounted onto specific types of vehicles, such as trams [29], garbage trucks [99], or even Google Street View...
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...For example, low-cost sensors have been deployed onto light poles and public transport vehicles [28, 29]....
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TL;DR: Long-term exposure to traffic-related air pollution may contribute to the development of isChemic but not hemorrhagic stroke, especially severe ischemic strokes leading to death within 30 days.
Abstract: Background and Purpose—Years of exposure to tobacco smoke substantially increase the risk for stroke. Whether long-term exposure to outdoor air pollution can lead to stroke is not yet established. We examined the association between long-term exposure to traffic-related air pollution and incident and fatal stroke in a prospective cohort study. Methods—We followed 57 053 participants of the Danish Diet, Cancer and Health cohort in the Hospital Discharge Register for the first-ever hospital admission for stroke (incident stroke) between baseline (1993–1997) and 2006 and defined fatal strokes as death within 30 days of admission. We associated the estimated mean levels of nitrogen dioxide at residential addresses since 1971 to incident and fatal stroke by Cox regression analyses and examined the effects by stroke subtypes: ischemic, hemorrhagic, and nonspecified stroke. Results—Over a mean follow-up of 9.8 years of 52 215 eligible subjects, there were 1984 (3.8%) first-ever (incident) hospital admissions for...
121 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...Besides having a direct effect on mortality, air pollution is strongly associated with a broad spectrum of acute and chronic diseases, including cardiovascular diseases [2, 3, 4], lung diseases [5, 6, 7], several types of cancer [3, 8, 9], and even conditions ar X iv :1 91 2....
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Journal Article•
TL;DR: This work reviews the literature on commercial sensors for ambient gas measurements over a hundred commercial sensors and compares their performance with the specifications of the European Directive on air quality 2008/50/EC.
Abstract: The traditional ambient gases monitor stations are expensive, big and of complex operation. So they are not suitable for a network of sensors that cover large areas. To cover large areas these traditional systems algorithms usually interpolates the measurements to calculate the gas concentrations in points far away of the physical sensors. Small commercial sensors represent a big opportunity for making sensor networks that monitor the ambient gases within large areas without the necessity of interpolation. There have been some successful previous works on these sensor networks with custom sensors and with commercial sensors but the information and characteristics of these sensors is difficult to compile and compare. In this work we review the literature on commercial sensors for ambient gas measurements over a hundred commercial sensors and compare their performance with the specifications of the European Directive on air quality 2008/50/EC.
117 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...Disadvantages: PIDs affect all molecules that have less ionization energy than the UV light affecting them, which means PIDs are not specific to a particular pollutant [55]....
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...They have a small size, a small weight and low energy requirements [55, 81]....
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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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