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: A multi-task learning scheme, which combines the physical model and the data-driven model with both merits, enhances the data learning of a neural network with the aid of prior knowledge on atmospheric dispersion, and also controls the impact of the knowledge with a tunable weighting coefficient.
Abstract: Fine-grained air pollution monitoring has attracted increasing attention worldwide. Even with an increasing amount of both static and mobile sensing systems, an inference algorithm is still essential to achieve a comprehensive understanding of the urban atmospheric environment. Conventional physical model-based methods are unable to involve all the influencing factors with limited prior knowledge, and data-driven methods lacking physical interpretation may result in bad generalization ability. This paper presents a multi-task learning scheme, which combines the physical model and the data-driven model with both merits. It enhances the data learning of a neural network with the aid of prior knowledge on atmospheric dispersion, and also controls the impact of the knowledge with a tunable weighting coefficient. Evaluations over a real-world deployment in Foshan, China show that, with the resolution of 500m $\times 500\text{m}\times 15$ min, the proposed method outperforms the state-of-the-art ones with 7.9% error reduction and 6.2% correlation increase. Benefited from the physical knowledge, the neural network obtains stable performance with lower variance, as well as higher robustness against negative background conditions.
5 citations
TL;DR: This work highlights that sensor evaluation should be conducted under application-specific conditions, whether that be for ambient air monitoring or source characterization, and confidence in emission factor quantification and high-emitter identification improves with larger integrated peak areas of CO2 and especially BC.
Abstract: The exhaust plume capture method is a commonly used approach to measure pollutants emitted by in-use heavy-duty diesel trucks. Lower cost sensors, if used in place of traditional research-grade analyzers, could enable wider application of this method, including use as a monitoring tool to identify high-emitting trucks that may warrant inspection and maintenance. However, low-cost sensors have for the most part only been evaluated under ambient conditions as opposed to source-influenced environments with rapidly changing pollutant concentrations. This study compared black carbon (BC) emission factors determined using different BC and carbon dioxide (CO2) sensors that range in cost from $200 to $20,000. Controlled laboratory experiments show that traditional zero and span steady-state calibration checks are not robust indicators of sensor performance when sampling short duration concentration peaks. Fleet BC emission factor distributions measured at two locations at the Port of Oakland in California with 16 BC/CO2 sensor pairs were similar, but unique sensor pairs identified different high-emitting trucks. At one location, the low-cost PP Systems SBA-5 agreed on the classification of 90% of the high emitters identified by the LI-COR LI-7000 when both were paired with the Magee Scientific AE33. Conversely, lower cost BC sensors when paired with the LI-7000 misclassified more than 50% of high emitters when compared to the AE33/LI-7000. Confidence in emission factor quantification and high-emitter identification improves with larger integrated peak areas of CO2 and especially BC. This work highlights that sensor evaluation should be conducted under application-specific conditions, whether that be for ambient air monitoring or source characterization.
5 citations
TL;DR: In this paper, trois associations californiennes co-construisent des protocoles de mesures avec l’autorite de regulation environnementale (EPA) and des universitaires.
Abstract: Pour de nombreux activistes a travers le monde, le deploiement de capteurs numeriques, a l’echelle du quartier ou de l’habitation, apparait comme un moyen de combler les failles de la surveillance officielle de la qualite de l’air. Mais a quelles conditions les « donnees citoyennes » ainsi produites sont-elles credibles aux yeux des autorites officielles ? Cet article etudie trois associations californiennes, dont les membres co-construisent des protocoles de mesures avec l’autorite de regulation environnementale (EPA) et des universitaires. Nous soutenons que la credibilite des mesures produites par les capteurs citoyens du point de vue des autorites depend a minima des deux conditions suivantes : (1) que les instances scientifiques et reglementaires en viennent a considerer les capteurs citoyens comme des dispositifs de connaissance et de gouvernement ; (2) que militants, scientifiques et regulateurs s’engagent dans la mise en place d’une infrastructure partagee qui soit susceptible de reduire les tensions entre les differents acteurs engages.
5 citations
Dissertation•
01 Jul 2016
TL;DR: In this article, a large-eddy simulation (LES) coupled with a reduced chemical scheme (the LES-chemistry model) is used to investigate the processing, dispersion and transport of reactive pollutants in a deep street canyon.
Abstract: A street canyon is a typical urban configuration with surrounding buildings along the street, where emissions from vehicles are normally released. Buildings are the artificial obstacles to the urban atmospheric flow and give rise to limited ventilation, especially for deep street canyons. This study implements a large-eddy simulation (LES) coupled with a reduced chemical scheme (the LES-chemistry model) to investigate the processing, dispersion and transport of reactive pollutants in a deep street canyon. Spatial variation of reactive pollutants are significant due to the existence of unsteady multiple vortices and pollutant concentrations exhibit significant contrasts within each vortex. In practical applications of using one-box model, the hypothesis of a well-mixed deep street canyon is shown to be inappropriate. A simplified two-box model (vertically segregated) is developed and evaluated against the LES-chemistry model to represent key photochemical processes with timescales similar to and smaller than the turbulent mixing timescale. The two-box model provides the capability of efficiently running a series of emission scenarios under a set of meteorological conditions. In addition, a box model with grid-averaged emissions of street canyons is compared with a two-box model considering each street canyon independently (horizontally segregated) to evaluate uncertainties when grid-averaged emissions are adopted in a grid-based urban air quality model. This study could potentially support traffic management, urban planning strategies and personal exposure assessment.
4 citations
01 Jan 2019
TL;DR: The aim of this paper is to propose a Continuous Air Pollution Data Analytics Framework for a Pollution-Free Smart-Township thereby bringing forth an Asthma-Free generation.
Abstract: With the ever-growing infrastructure developments, social and economic standards, the rate of emergency department visits with respiratory illnesses has been on an all-time high. A wide range of factors are the cause to the debilitating ailments like Asthma. Among them atmospheric pollution is the most uncontrollable and dangerous contributor to this alarming development which inevitably comes along with the comforts we enjoy in this modern era. Unsurprisingly, children are the most affected, being the most vulnerable category of any community. This paper portrays an up-close picture of the wide range of pollutants, its hazardous impact on the pediatric population and the various prevalent techniques to monitor the pollution. The aim of this paper is to propose a Continuous Air Pollution Data Analytics Framework for a Pollution-Free Smart-Township thereby bringing forth an Asthma-Free generation.
4 citations