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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: Suggestive evidence was found that exposure to PM2.5 is positively associated with mortality from coronary heart diseases and exposure to SO2 increases mortality from lung cancer and for the other pollutants and health outcomes, the data were insufficient to make solid conclusions.
Abstract: We conducted a systematic review of all studies published between 1950 and 2007 of associations between long-term exposure to ambient air pollution and the risks in adults of nonaccidental mortality and the incidence and mortality from cancer and cardiovascular and respiratory diseases. We searched bibliographic databases for cohort and case-control studies, abstracted characteristics of their design and conduct, and synthesized the quantitative findings in tabular and graphic form. We assessed heterogeneity, estimated pooled effects for specific pollutants, and conducted sensitivity analyses according to selected characteristics of the studies. Our analysis showed that long-term exposure to PM2.5 increases the risk of nonaccidental mortality by 6% per a 10 microg/m3 increase, independent of age, gender, and geographic region. Exposure to PM2.5 was also associated with an increased risk of mortality from lung cancer (range: 15% to 21% per a 10 microg/m3 increase) and total cardiovascular mortality (range: 12% to 14% per a 10 microg/m3 increase). In addition, living close to busy traffic appears to be associated with elevated risks of these three outcomes. Suggestive evidence was found that exposure to PM2.5 is positively associated with mortality from coronary heart diseases and exposure to SO2 increases mortality from lung cancer. For the other pollutants and health outcomes, the data were insufficient data to make solid conclusions.
353 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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Argonne National Laboratory1, University of Maryland, College Park2, University of Iowa3, Saint Louis University4, Goddard Space Flight Center5, National Center for Atmospheric Research6, University of Colorado Boulder7, United States Environmental Protection Agency8, Harvard University9, Emory University10, Dalhousie University11
TL;DR: In this article, a comprehensive literature review and comprising input by both satellite experts and emission inventory specialists, the review identifies several targets that seem promising: large point sources of NOx and SO2, species that are difficult to measure by other means (NH3 and CH4, for example), area sources that cannot easily be quantified by traditional bottom-up methods (such as unconventional oil and gas extraction, shipping, biomass burning, and biogenic sources), and the temporal variation of emissions (seasonal, diurnal, episodic).
338 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background or methods in this paper
...Integration Air quality sensor networks [59] Land-use regression models [46, 47] Satellite-based estimations [60, 61]...
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...In this survey, our focus is on per-device operations, as several existing surveys have focused on application domains that are related to the integration operations [59, 46, 47, 60, 61]....
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TL;DR: In this paper, the authors observed the atmospheric mixing layer height (MLH) in Beijing from July-2009 to December-2012 using a ceilometer and found that the estimated MLH is low in autumn and winter and high in spring and summer in Beijing.
Abstract: . The mixing layer is an important meteorological factor that affects air pollution. In this study, the atmospheric mixing layer height (MLH) was observed in Beijing from July 2009 to December 2012 using a ceilometer. By comparison with radiosonde data, we found that the ceilometer underestimates the MLH under conditions of neutral stratification caused by strong winds, whereas it overestimates the MLH when sand-dust is crossing. Using meteorological, PM2.5, and PM10 observational data, we screened the observed MLH automatically; the ceilometer observations were fairly consistent with the radiosondes, with a correlation coefficient greater than 0.9. Further analysis indicated that the MLH is low in autumn and winter and high in spring and summer in Beijing. There is a significant correlation between the sensible heat flux and MLH, and the diurnal cycle of the MLH in summer is also affected by the circulation of mountainous plain winds. Using visibility as an index to classify the degree of air pollution, we found that the variation in the sensible heat and buoyancy term in turbulent kinetic energy (TKE) is insignificant when visibility decreases from 10 to 5 km, but the reduction of shear term in TKE is near 70 %. When visibility decreases from 5 to 1 km, the variation of the shear term in TKE is insignificant, but the decrease in the sensible heat and buoyancy term in TKE is approximately 60 %. Although the correlation between the daily variation of the MLH and visibility is very poor, the correlation between them is significantly enhanced when the relative humidity increases beyond 80 %. This indicates that humidity-related physicochemical processes is the primary source of atmospheric particles under heavy pollution and that the dissipation of atmospheric particles mainly depends on the MLH. The presented results of the atmospheric mixing layer provide useful empirical information for improving meteorological and atmospheric chemistry models and the forecasting and warning of air pollution.
336 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...For example, seasonality influences the elevation of the atmospheric mixing layer [106], which in turn affects the extent of pollutants that can be captured [107]....
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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
TL;DR: In this article, three low-cost particle sensors based on light scattering (Shinyei PPD42NS, Samyoung DSM501A, and Sharp GP2Y1010AU0F) were evaluated by calibration methods adapted from the US EPA 2013 Air Sensor Workshop recommendations.
Abstract: Particle sensors offer significant advantages of compact size and low cost, and have recently drawn great attention for usage as portable monitors measuring particulate matter mass concentrations. However, most sensor systems have not been thoroughly evaluated with standardized calibration protocols, and their data quality is not well documented. In this work, three low-cost particle sensors based on light scattering (Shinyei PPD42NS, Samyoung DSM501A, and Sharp GP2Y1010AU0F) were evaluated by calibration methods adapted from the US EPA 2013 Air Sensor Workshop recommendations. With a SidePak (TSI Inc., St. Paul, MN, USA), a scanning mobility particle sizer (TSI Inc.), and an AirAssure™ PM2.5 Indoor Air Quality Monitor (TSI Inc.), which itself relies on a GP2Y1010AU0F sensor as reference instruments, six performance aspects were examined: linearity of response, precision of measurement, limit of detection, dependence on particle composition, dependence on particle size, and relative humidity and temperatu...
329 citations
"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper
...[97] studied the effects of temperature and humidity on low-cost PM sensors and found relative humidity to affect the accuracy of sensor technology....
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