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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09 Sep 2019
TL;DR: This work proposes a deep autoencoder framework based inference algorithm that can obtain up to 2x performance improvement over existing methods, benefited from its high robustness against different background pollution level and accidental sensor errors.
Abstract: Air pollution is a global health threat. Nowadays, with the increasing amount of air pollution monitoring data from either conventional official stations or mobile sensing systems, the role of deep learning methods in pollution map recovery becomes gradually apparent. To address the challenges including the irregular sampling from mobile sensing and the non-interpretability of deep learning models, we proposed a deep autoencoder framework based inference algorithm. By separating the process of pollution generation and data sampling, this framework is able to deal with sampling under any irregular intervals in time and space. Also, we adopt a convolutional long short-term memory (ConvLSTM) structure to model the pollution generation after revealing its internal connections with an atmospheric dispersion model. Our algorithm is evaluated over a three-month real-world data collection in Tianjin, China. Results show our method can obtain up to 2x performance improvement over existing methods, benefited from its high robustness against different background pollution level and accidental sensor errors.
7 citations
01 Oct 2019
TL;DR: In this paper, a case study in Rennes shows how a group of urban public actors reuses methods and technology from citizen sciences to raise the urban air quality issue in the public debate.
Abstract: Based on a case study in Rennes, the article presents how a group of urban public actors re-uses methods and technology from citizen sciences to raise the urban air quality issue in the public debate. The project gives a group of inhabitants the opportunity to follow air quality training and proceed PM2.5µm measurements. The authors question the impact of the ongoing hybridisation between citizen science and urban public action on participants' commitment. The authors present how the use of PM2.5-sensors during 11 weeks led to a disengagement phenomenon, even if the authors observe a strong participation to workshops. These results come from an interdisciplinary methodology using observations, interviews, and data analyses.
7 citations
Dissertation•
01 Jan 2018
TL;DR: In this paper, the authors used the cyclic design model to evaluate the functionality and accuracy of the design of the sensor node and the results from these prototypes were analyzed using data from one of the weather stations of the faculty ITC.
Abstract: The urban heat island effect is responsible for higher temperatures in urban areas than in rural areas nearby With most of the worlds population living in urban areas the high temperatures in these areas are a concern for public health The municipality of Enschede is concerned of this issue and therefore interested in the magnitude of the urban heat island in Enschede At the end of last year, the concept of monitoring the urban heat island effect with autonomous sensor nodes was proven to be effective The goal of this project was to improve the design of the sensor node, so it would be able to give a better image of the urban heat island effect of Enschede To achieve this goal the phenomenon of the urban heat island and its drivers were researched and with the help of the stakeholders, requirements were setup A system to meet these requirements was designed using an adapted version of the cyclic design model Three generations of prototypes were building to evaluate the functionality and accuracy of the design The results from these prototypes were analysed using data from one of the weather stations of the faculty ITC and used to improve the next prototype This resulted in a sensor node which measures air temperature, humidity and wind speed with an accuracy relevant for gaining information about the magnitude of the urban heat island effect in Enschede and which can withstand the climatological conditions in Enschede This node is autonomous as it generates its own power and sends data wirelessly over the LoRaWAN network This gives a huge freedom of placement as it does not need any infrastructural changes before it can be placed Next to this, the sensor node is relatively cheap and easy to manufacture Allowing for a flexible system which can be adapted and expanded with relative ease
7 citations
Dissertation•
01 May 2020
TL;DR: In this article, the accuracy and usability of aerosol sensor to urban air quality measurements were evaluated in laboratory and field studies for their error sources and particle size selectivity, specifically, diffusion charging-based sensors, which measure lung deposited surface area of particles, were evaluated for their suitability to measure combustion emitted particles, such as vehicular exhaust and residential wood combustion emissions.
Abstract: Atmospheric particles are one of the leading mortality risk factors in the global burden of disease analysis. The association between particulate mass of particles smaller than 2.5 μm in diameter (PM2.5) and cardiovascular and pulmonary diseases has been characterized by multiple epidemiological studies, and varying estimates suggest that several million premature death occur globally each year due to PM2.5 exposure. Mitigation of the adverse health effects of particulate matter requires comprehensive understanding of their sources and dynamic processes, such as spatial dispersion.
Recent emergence and development of aerosol sensors, which are typically characterized as small, relatively low cost and easy to use, have enabled new opportunities in air quality monitoring. As a result of their practical convenience, sensors can be deployed to the field in high quantities which, consequently, enables network-type, spatially comprehensive measurements. However, with more simplified and less expensive measurement approach, less accurate and reliable results may be expected. This study aimed to evaluate and characterize the accuracy and usability of aerosol sensor to urban air quality measurements. The investigation focused on two of the most prominent measurement techniques applicable to sensor type monitoring; optical and diffusion charging-based techniques. Sensors utilizing optical technique were evaluated in laboratory and field studies for their error sources and particle size-selectivity, specifically. Diffusion charging-based sensors, which measure lung deposited surface area of particles, were evaluated in the field for their suitability to measure combustion emitted particles, such as vehicular exhaust and residential wood combustion emissions.
Results of the study indicated that optical aerosol sensors are unlikely to be fit for long-term regulatory monitoring. The main issues preventing this arise from their improper calibration which poses a significant risk of data misinterpretation; none of the laboratory evaluated sensors measured particle sizes which their technical specifications implied. On the other hand, field tests showed that when the measured size fraction was targeted to match the true detection range of the sensor, highly accurate and repeatable results were obtained. This implies that, while the usability of optical sensors is limited in their current form, the concept and vision of a sensor driven air quality monitoring network remains valid and achievable. In comparison to optical sensors, diffusion charging-based sensors were found to be more mature in terms of their technological development. The evaluated sensors exhibited accurate and stable performance throughout the test campaigns and were shown to be particularly well-suited the measurement of combustion emitted particles. As optical methods cannot be used to measure ultrafine particles reliably, and particles emitted from combustion sources have a significant contribution to air quality, diffusion charger sensors would be a valuable…
7 citations
TL;DR: This paper proposes the first framework for the simulation of sensor networks enabling a systematic comparison of in situ calibration strategies with reproducibility, and scalability, and shows how this framework can be used as a tool for the design of a network of low-cost sensors.
Abstract: The drastically increasing availability of low-cost sensors for environmental monitoring has fostered a large interest in the literature. One particular challenge for such devices is the fast degradation over time of the quality of their data. Therefore, the instruments require frequent calibrations. Traditionally, this operation is carried out on each sensor in dedicated laboratories. This is not economically sustainable for dense networks of low-cost sensors. An alternative that has been investigated is in situ calibration: exploiting the properties of the sensor network, the instruments are calibrated while staying in the field and preferably without any physical intervention. The literature indicates there is wide variety of in situ calibration strategies depending on the type of sensor network deployed. However, there is a lack for a systematic benchmark of calibration algorithms. In this paper, we propose the first framework for the simulation of sensor networks enabling a systematic comparison of in situ calibration strategies with reproducibility, and scalability. We showcase it on a primary test case applied to several calibration strategies for blind and static sensor networks. The performances of calibration are shown to be tightly related to the deployment of the network itself, the parameters of the algorithm and the metrics used to evaluate the results. We study the impact of the main modelling choices and adjustments of parameters in our framework and highlight their influence on the results of the calibration algorithms. We also show how our framework can be used as a tool for the design of a network of low-cost sensors.
7 citations