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A Gap Analysis of Low-Cost Outdoor Air Quality Sensor In-Field Calibration

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.
Citations
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01 Jul 2018
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

Journal ArticleDOI
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.

7 citations

References
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Journal ArticleDOI
TL;DR: It is hypothesized that traffic-related air pollution exposure during exercise may inhibit the positive effect of exercise on cognition, and there is a need to investigate whether the well-known benefits of regular physical activity on the brain also apply when physical activity is performed in polluted air.
Abstract: This review introduces an emerging research field that is focused on studying the effect of exposure to air pollution during exercise on cognition, with specific attention to the impact on concentrations of brain-derived neurotrophic factor (BDNF) and inflammatory markers. It has been repeatedly demonstrated that regular physical activity enhances cognition, and evidence suggests that BDNF, a neurotrophin, plays a key role in the mechanism. Today, however, air pollution is an environmental problem worldwide and the high traffic density, especially in urban environments and cities, is a major cause of this problem. During exercise, the intake of air pollution increases considerably due to an increased ventilation rate and particle deposition fraction. Recently, air pollution exposure has been linked to adverse effects on the brain such as cognitive decline and neuropathology. Inflammation and oxidative stress seem to play an important role in inducing these health effects. We believe that there is a need to investigate whether the well-known benefits of regular physical activity on the brain also apply when physical activity is performed in polluted air. We also report our findings about exercising in an environment with ambient levels of air pollutants. Based on the latter results, we hypothesize that traffic-related air pollution exposure during exercise may inhibit the positive effect of exercise on cognition.

43 citations


"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper

  • ...affecting cognitive capabilities and the central nervous system [10, 11, 12]....

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Journal ArticleDOI
04 May 2017-Sensors
TL;DR: Experimental results indicate that mass median diameter has less effect on the photodiode at the 55° angle in comparison with photodiodes at the 40° angle and 140° angle, and weather index is defined as the ratio of scattered light fluxes collected at the40° and 55° angles, which can be used to distinguish the mass Median diameter variation caused by different meteorological parameters.
Abstract: Meteorological parameters such as relative humidity have a significant impact on the precision of PM2.5 measurement instruments based on light scattering. Instead of adding meteorological sensors or dehumidification devices used widely in commercial PM2.5 measurement instruments, a novel particle sensor based on multi-angle light scattering and data fusion is proposed to eliminate the effect of meteorological factors. Three photodiodes are employed to collect the scattered light flux at three distinct angles. Weather index is defined as the ratio of scattered light fluxes collected at the 40° and 55° angles, which can be used to distinguish the mass median diameter variation caused by different meteorological parameters. Simulations based on Lorenz-Mie theory and field experiments establish the feasibility of this scheme. Experimental results indicate that mass median diameter has less effect on the photodiode at the 55° angle in comparison with photodiodes at the 40° angle and 140° angle. After correction using the weather index, the photodiode at the 40° angle yielded the best results followed by photodiodes at the 55° angle and the 140° angle.

37 citations


"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper

  • ...TEOM and BAM sensors are rather expensive, costing over $20 000 [87]....

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  • ...More expensive LSPs can use multi-angular light scattering to reduce the impact of environmental variables [87]....

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  • ...For example, optical particle counter (OPC) are high-quality variants of LSPs whereas condensation particle counters (CPCs) use alcohol or water vapor to change the physical properties of particulates before passing them through an LSP sensor [85, 86, 87, 88]....

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Proceedings ArticleDOI
15 Feb 2016
TL;DR: This work proposes a novel method to conduct in-field testing of low-cost sensors based on multiple least-squares and shows a significantly lower calibration error with better long-time stability of the calibration parameters.
Abstract: Over the past few years, many low-cost pollution sensors have been integrated into measurement platforms for air quality monitoring. However, using these sensors is challenging: concentrations of toxic substances in ambient air often lie at sensors' sensitivity boundaries, environmental conditions affect the sensor measurements, and the sensors often suffer from poor selectivity, i.e. are cross-sensitive to multiple pollutants. Datasheet information on these effects is scarce or may not cover deployment conditions. Consequently the sensors need to undergo extensive pre-deployment testing to examine their feasibility for a given application and to find the optimal measurement setup that allows accurate data collection and calibration. In this work, we propose a novel method to conduct in-field testing of low-cost sensors. The algorithm proposed is based on multiple least-squares and leverages the physical variation of urban air pollution to quantify the amount of explained and unexplained sensor signal. We verify (i) whether a sensor is feasible for air quality monitoring in a given environment and underpin our analysis with positive and negative examples of sensors available on the market, (ii) model sensor cross-sensitivities to interfering gases and environmental effects and (iii) compute the optimal sensor array and its calibration parameters for stable and accurate sensor measurements over long time periods in a given environment. Finally, we provide an experimental evaluation of our approach using over 9 million measurements of various low-cost sensors collected in an urban area. Based on the results from our testing methodology we propose an optimized sensor array setup. Further, we show---compared to a state-of-the-art calibration technique---a significantly lower calibration error with better long-time stability of the calibration parameters.

36 citations


"A Gap Analysis of Low-Cost Outdoor ..." refers background or methods 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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  • ...[38] is the only one receiving a full mark on the Reliability score....

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  • ...In the studies surveyed for this article, the sampling rate in most of the studies is between 5 seconds and 20 seconds [38, 71, 39, 40]....

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  • ...Sensor mobility is discussed in numerous studies that we review in this survey [82, 83, 27, 84, 38, 54]....

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  • ...[38, 54], to the best of our knowledge, is the only one that uses a test dataset longer than a year....

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Journal ArticleDOI
TL;DR: The AQHI increase associated with NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic, while O3 contributes to an increase in AQHI during the spring.
Abstract: In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NOx, O3, PM10 and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NOx, O3 and PM10, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM10 is somewhat smaller than the mean value of the meta-coefficient, while for O3, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NOx as an indicator of AEDV, but compared to the mean value associated with NO2, our value of NOx is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O3 contributes to an increase in AQHI during the spring. For PM10, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM10 with 3.1%, followed by O3 with 2.0%.

35 citations


"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper

  • ...Such services include more advanced air quality index (AQI) [62] models and green path routing [63] to enhance the quality of life of citizens....

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Journal ArticleDOI
26 Mar 2018
TL;DR: This paper proposes an automatic feature selection algorithm based on AIC (Akaike information criterion) for the linear model, which helps avoid overfitting due to the inclusion of inappropriate features.
Abstract: Urban air quality information, e.g., PM2.5 concentration, is of great importance to both the government and society. Recently, there is a growing interest in developing low-cost sensors, installed on moving vehicles, for fine-grained air quality measurement. However, low-cost mobile sensors typically suffer from low accuracy and thus need careful calibration to preserve a high measurement quality. In this paper, we propose a two-phase data calibration method consisting of a linear part and a nonlinear part. We use MLS (multiple least square) to train the linear part, and use RF (random forest) to train the nonlinear part. We propose an automatic feature selection algorithm based on AIC (Akaike information criterion) for the linear model, which helps avoid overfitting due to the inclusion of inappropriate features. We evaluate our method extensively. Results show that our method outperforms existing approaches, achieving an overall accuracy improvement of 16.4% in terms of PM2.5 levels compared with state-of-the-art approach.

35 citations


"A Gap Analysis of Low-Cost Outdoor ..." refers background in this paper

  • ...[117] propose a hybrid model that combines LR and RF to simultaneously learn linear and non-linear relationships....

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