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 work discusses possibilities and lays out several criteria for using smart technologies for external exposome studies, and improves the collection of long term exposure factor data, which can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.
Abstract: The advent of the exposome concept, the advancement of mobile technology, sensors, and the "internet of things" bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more "traditional" measurement data to model exposures to numerous environmental factors.
69 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: Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, the authors demonstrates how smart technologies and indivi...
Abstract: Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and indivi...
68 citations
TL;DR: In this paper, the influence of the implementation of electromobility assumptions on reduction of air pollution in cities in terms of sustainable urban transport systems was taken up, where the authors showed that public transport, apart from the obvious effect of decreased number of private vehicles and decreased congestion, can contribute to reduced air pollutant emissions and become a significant driver for the implementation in cities.
Abstract: This article has taken up an issue concerning the influence of the implementation of electromobility assumptions on reduction of air pollution in cities in terms of sustainable urban transport systems. The essential nature of the subject is confirmed by the following figures: transport is responsible for almost 25% of greenhouse gas emissions in Europe and is the main cause of air pollution in cities, almost 90% of city dwellers in the EU are exposed to levels of air pollutants deemed harmful by the WHO, and approximately 95% of vehicles on European roads still use fossil fuels. Therefore, the implemented transport policy, both in the international and domestic aspect of particular countries, is facing the need to significantly reduce the negative influence of transport on people and the environment. Electromobility has become one of the concepts that makes it possible to achieve this goal. Its main objective is to reduce emissions of harmful substances into the environment by deploying electric vehicles (EVs). Research conducted by the authors showed that public transport, apart from the obvious effect of decreased number of private vehicles and decreased congestion, can contribute to reduced air pollutant emissions and become a significant driver for the implementation of electromobility in cities. However, the achievement of this goal depends on taking appropriate actions not only in the transport branch but also in other sectors of economy. The following research methods were applied in the article: literature analysis, documentary method, case study, and mathematical methods. The research area was the city of Szczecin, Poland.
68 citations
01 Apr 2019
TL;DR: In this paper, the authors measured NO2, ultrafine particle concentration (UFP), and PM1 with both stationary and mobile platforms in Pittsburgh, PA in 2016 and 2017.
Abstract: Long- and short-term exposure to airborne pollutants results in adverse health effects. Regulatory monitors can be used to determine if regional concentrations meet regulatory standards of air pollution. As assessments of air pollutant exposure become more spatially resolved, evaluation is needed to assess the spatial representativeness of monitors in different environments. We measured NO2, ultrafine particle concentration (UFP), and PM1 with both stationary and mobile platforms in Pittsburgh, PA in 2016 and 2017. We sampled in eight ∼1 km2 neighborhoods representing different land use and exposure regimes (e.g., urban and suburban, high and low traffic). Mobile sampling was conducted on up to 25 days in each neighborhood to study fine-scale spatial variation in pollutant concentrations. NO2 exhibited within-neighborhood spatial variation, with hotspots elevated by up to a factor of 5 above the regional background. Spatial differences in UFP within the same 1 km2 neighborhoods could be a factor of 2.4 times regional background. PM1 was more regional and less spatially variable. Most neighborhoods exhibited less than 1 μg m−3 spatial variability in PM1. Spatial variability of NO2 and UFP showed moderate correlation (R2 > 0.5) with traditional land use covariates such as traffic volume and restaurant density. We used the Wilcoxon rank-sum test to calculate the fraction of each neighborhood represented by the same underlying concentration distribution. PM1 was the most spatially homogeneous, with 80–100% of each 1 km2 area being statistically similar to a reference location. Quantifying pollutant spatial patterns with high fidelity (e.g.,
67 citations