The rise of low-cost sensing for managing air pollution in cities
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.
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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
Journal Article•
TL;DR: Forouzanfar et al. as discussed by the authors provide a review of the new air pollution sensing methods to determine indoor air quality and discuss how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide.
Abstract: Household air pollution is ranked the 9th largest Global Burden of Disease risk (Forouzanfar et al., The Lancet 2015). People, particularly urban dwellers, typically spend over 90% of their daily time indoors, where levels of air pollution often surpass those of outdoor environments. Indoor air quality (IAQ) standards and approaches for assessment and control of indoor air require measurements of pollutant concentrations and thermal comfort using conventional instruments. However, the outcomes of such measurements are usually averages over long integrated time periods, which become available after the exposure has already occurred. Moreover, conventional monitoring is generally incapable of addressing temporal and spatial heterogeneity of indoor air pollution, or providing information on peak exposures that occur when specific indoor sources are in operation. This article provides a review of the new air pollution sensing methods to determine IAQ and discusses how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide. However, we also show that besides the opportunities, challenges still remain in terms of maturing technologies, or data mining and their interpretation. Moreover, we discuss further research and essential development needed to close gaps between what is available today and needed tomorrow. In particular, we demonstrate that awareness of IAQ risks and availability of appropriate regulation are lagging behind the technologies.
68 citations
TL;DR: This article summarizes the existing studies on the state-of-the-art of LCS for AQM, and conceptualizes a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data.
Abstract: Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.
33 citations
01 Jan 2014
TL;DR: In this paper, the authors reviewed some fundamental drivers of UFP emissions and dispersion, and highlighted unresolved challenges, as well as recommendations to ensure sustainable urban development whilst minimising any possible adverse health impacts.
Abstract: Ultrafine particles (UFP; diameter less than 100 nm) are ubiquitous in urban air, and an acknowledged risk to human health. Globally, the major source for urban outdoor UFP concentrations is motor traffic. Ongoing trends towards urbanisation and expansion of road traffic are anticipated to further increase population exposure to UFPs. Numerous experimental studies have characterised UFPs in individual cities, but an integrated evaluation of emissions and population exposure is still lacking. Our analysis suggest that average exposure to outdoor UFPs in Asian cities is about four-times larger than those in European cities but impacts on human health are largely unknown. This article reviews some fundamental drivers of UFP emissions and dispersion, and highlights unresolved challenges, as well as recommendations to ensure sustainable urban development whilst minimising any possible adverse health impacts.
32 citations
01 Jun 2016
TL;DR: In this paper, a taxi fleet of over 15,000 vehicles was analyzed with the aim of predicting air pollution emissions for Singapore, and the results showed that highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions.
Abstract: Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that high-resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain.
21 citations
References
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TL;DR: This study investigated linear and random forest models to correct PM2.5 measurements from the Denver Department of Public Health and Environment (DDPHE)'s network of low-cost sensors against measurements from co-located U.S. Environmental Protection Agency Federal Equivalence Method monitors.
25 citations
TL;DR: It is found that low-cost IoT sensors provide not only real-time monitoring information but also demonstrate great potential as an effective tool to understand the PM2.5 plume movement with temporal variation and geo-specific location.
25 citations
TL;DR: In this article, hourly NOx concentrations were simulated for the city of Zurich, Switzerland, at 10"m resolution for the years 2013-2014 using the nested mesoscale meteorology and micro-scale dispersion model system GRAMM-GRAL.
Abstract: . Hourly NOx concentrations were simulated for the city of Zurich, Switzerland, at 10 m resolution for the years 2013–2014. The simulations were generated with the nested mesoscale meteorology and micro-scale dispersion model system GRAMM–GRAL (versions v15.12 and v14.8) by applying a catalogue-based approach. This approach was specifically designed to enable long-term city-wide building-resolving simulations with affordable computation costs. It relies on a discrete set of possible weather situations and corresponding steady-state flow and dispersion patterns that are pre-computed and then matched hourly with actual meteorological observations. The modelling system was comprehensively evaluated using eight sites continuously monitoring NOx concentrations and 65 passive samplers measuring NO2 concentrations on a 2-weekly basis all over the city. The system was demonstrated to fulfil the European Commission standards for air pollution modelling at nearly all sites. The average spatial distribution was very well represented, despite a general tendency to overestimate the observed concentrations, possibly due to a crude representation of traffic-induced turbulence and to underestimated dispersion in the vicinity of buildings. The temporal variability of concentrations explained by varying emissions and weather situations was accurately reproduced on different timescales. The seasonal cycle of concentrations, mostly driven by stronger vertical dispersion in summer than in winter, was very well captured in the 2-year simulation period. Short-term events, such as episodes of particularly high and low concentrations, were detected in most cases by the system, although some unrealistic pollution peaks were occasionally generated, pointing at some limitations of the steady-state approximation. The different patterns of the diurnal cycle of concentrations observed in the city were generally well captured as well. The evaluation confirmed the adequacy of the catalogue-based approach in the context of city-scale air pollution modelling. The ability to reproduce not only the spatial gradients but also the hourly temporal variability over multiple years makes the model system particularly suitable for investigating individualized air pollution exposure in the city.
25 citations
TL;DR: Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality, and no significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities was observed.
Abstract: Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) sensors were deployed from December 2016 to January 2017 to determine the spatial variability of PM2.5 in Sacramento, California. Prior to, and after, the study, the 19 sensors were deployed and collocated at a regulatory air monitoring site. The sensors demonstrated a high degree of precision during all collocated measurement periods (Pearson R2 = 0.98 - 0.99 across all sensors), with little drift. A sensor-specific correction factor was developed such that each sensor reported a comparable value. Sensors had a moderate degree of correlation with regulatory monitors during the study (R2 = 0.60 - 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM2.5 spatial variability, finding an average pairwise coefficient of divergence of 0.22 and a range of 0.14 to 0.33, indicating mostly homogeneous distributions. No significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities (p value = 0.24) was observed.
25 citations
TL;DR: A comprehensive evaluation of data correction algorithms proposed to tackle the drifting response of ECGS due to environmental factors using a large dataset from field comparisons to examine the robustness of correction algorithms in response to different training and evaluation data period configurations.
Abstract: Electrochemical gas sensors (ECGS) have gained substantial popularity in ambient measurements. Several data correction algorithms had been proposed to tackle the drifting response of ECGS due to environmental factors, but there is a lack of performance evaluation of these data correction schemes. To fill this knowledge gap, we conduct a comprehensive evaluation of these data correction algorithms using a large dataset from field comparisons. The dataset covered three commonly used gas pollutants, including CO, NO2 and O3 measured by both ECGS and reference instruments, with a time resolution of 1 min and a duration of 6 months. Taking advantage of this large dataset, the performance of 8 different data correction schemes (2 new algorithms and 6 algorithms from the literature) was benchmarked by a set of evaluation metrics using raw signals from ECGS (nA level currents from the working and auxiliary electrodes). Eight scenarios were considered to examine the robustness of correction algorithms in response to different training and evaluation data period configurations. In addition, the bias dependence on temperature, RH, target gas levels and cross-sensitivity by different correction algorithms was investigated. Recommendations on data correction scheme selection are provided based on the comparison results.
25 citations