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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01 Jan 2018
TL;DR: In this article, the authors examined the impacts of aerosol particles and precursor gases on visibility in the cities of Beijing, China and New Delhi, India, as well as the performance of particulate matter sensors in the Cuyama Valley of California and Sacramento, California.
Abstract: Aerosol particles and their precursor gases have a range of impacts to radiative transfer, the hydrologic cycle, and human health. Understanding aerosol properties and their associated impacts is requisite to knowing what role they play in public health and regional climate. This dissertation examines the impacts of aerosol particles and precursor gases on visibility in the cities of Beijing, China, and New Delhi, India, as well as the performance of particulate matter sensors in the Cuyama Valley of California and Sacramento, California. Two studies quantify the surface level impact of aerosols on the transfer of visible radiation using publicly available observations of particulate matter, visibility and meteorology in the cities of Beijing and New Delhi. A methodology is presented to empirically estimate the hygroscopic growth rate of particles. The relative impact of dry particulate matter and aerosol bound water on visibility deterioration on seasonal and inter-annual timescales is presented. Long term visibility records are used to reconstruct particulate matter levels, and estimate the long term trends of particulate pollution. Characterizing these aspects of the environment is relevant to policy makers who are interested in improving visibility.
3 citations
TL;DR: A new mobile-wireless air pollution system composed of UAV equipped with low-cost sensors using LoRa transmission and the results showed that the system is capable of measuring CO, LPG, H2, and smoke in the vertical mode in both hovering and deploying scenarios.
Abstract: Air pollution has a severe impact on human beings and one of the top risks facing human health. The data collection near pollution sources is difficult to obtain due to obstacles such as industrial and rural areas, where sensing usually fails to give enough information about the air quality. Unmanned Aerial Vehicles (UAVs) equipped with different sensors offer new approaches and opportunity to air pollution and atmospheric studies. Despite that, there are new challenges that emerged with using UAVs, in particular, the effect of wind-generated from UAVs propellers rotation on the efficiency and ability to sense and measure gas concentrations. The results of gas measurement are affected by the propellers rotation and the wind resistance. Thus, the effect of changing UAV speed and altitude on the gas measurement both vertically and horizontally need to be performed. The aims of this paper is to propose a new mobile-wireless air pollution system composed of UAV equipped with low-cost sensors using LoRa transmission. The proposed system is evaluated by studying the effect of changing altitude and speed on the measured gas concentrations CO, LPG, H2, and smoke when flying in horizontally and vertically directions. The results showed that our system is capable of measuring CO, LPG, H2, and smoke in the vertical mode in both hovering and deploying scenarios. While in horizontal mode the results showed that system can detect and measure gas concentrations at speeds less than or equal to 6 m/s. While at high speed of 8 and 10 m/s there will be an impact on its performance and accuracy to detect the targeted gases. Also, the results showed that the LoRa shield and Radio transmitter AT9S can successfully transmit up to 800 m horizontally and 400 feet vertically.
3 citations
Posted Content•
19 Apr 2020
TL;DR: This work proposes RelSen: an optimization-based framework for simultaneous sensor reliability monitoring and process state estimation and shows that the framework can timely identify unreliable sensors and achieve higher accuracy than several baseline methods in processState estimation under three types of commonly observed sensor faults.
Abstract: Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life. Unfortunately, in most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. In this work, we propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them. Furthermore, RelSen is not application-specific as its implementation assumes a minimal prior knowledge of the process dynamics under monitoring. This significantly improves its generality and applicability in practice. In our experiments, we apply RelSen on an outdoor air pollution monitoring system and a condition monitoring system for a cement rotary kiln. Experimental results show that our framework can timely identify unreliable sensors and remove sensor measurement errors caused by three types of most commonly observed sensor faults.
3 citations
TL;DR: In this article, the spatial and temporal variability of air quality (CO, NO2, O3, and PM2.5) with a high spatial resolution in various adjacent micro-environments, 30 sets of sensor-nodes were deployed within an 800 × 800 m monitoring domain.
Abstract: To investigate the spatial and temporal variability of air quality (CO, NO2, O3, and PM2.5) with a high spatial resolution in various adjacent micro-environments, 30 sets of sensor-nodes were deployed within an 800 × 800 m monitoring domain in the center of the largest megacity (Seoul) in South Korea. The sensor network was operated in summer and winter. The daily variation in air pollutant concentrations revealed a similar trend, with discernible concentration differences among monitoring sub-sites and a government-operated air quality monitoring station. These differences in pollutant levels (except PM2.5) among the sub-sites were pronounced in the daytime with high volumes of traffic. The coefficient of divergence and Pearson correlation coefficient showed that spatial and temporal variability was more significant in summer than winter. Ozone displayed the greatest spatial variability, with little temporal variability among the sub-sites and a negative correlation with NO2, implying that ozone concentrations were primarily determined by vehicular NOX emissions due to NO titration effects under the urban canopy. The PM2.5 concentration displayed homogeneous spatial and temporal distributions over the entire monitoring period, implying that PM2.5 monitoring with at least a 1 × 1 km resolution is sufficient to examine the spatial and temporal heterogeneity in urban areas.
3 citations
01 Jan 2011
TL;DR: In this paper, the authors provide an overview of the Newcastle University Integrated Database and Assessment Platform of monitored and modelled traffic, meteorological conditions and pollution (air and noise) data and present results from its application in a demand responsive control area in the United Kingdom (UK).
Abstract: This paper provides an overview of the Newcastle University Integrated Database and Assessment Platform of monitored and modelled traffic, meteorological conditions and pollution (air and noise) data and presents results from its application in a demand responsive control area in the United Kingdom (UK). The data platform operates in real-time and has an archive capability which allows the current status to be compared with the ‘typical’ or ‘expected’ profile and thus to quantify the differences in congestion and emissions (carbon dioxide, oxides of nitrogen, particulates and carbon monoxide) of events, intelligent transportation system (ITS) technology implementation and interventions for air quality management and carbon emissions reduction. Given the launch of the real-time aspect of this integrated data platform is anticipated in 2011, the assessment capability is demonstrated, in this paper, by retrospective analysis of a decade of similar historic data available to Newcastle University captured from the Leicester City network. The results suggest that the relative impacts of air pollution and carbon emissions of policy interventions are different and the particular results presented here demonstrated that policy interventions during the decade of implementation, created up to 10% benefits in terms of the reduction in carbon dioxide, but over the decade studied increase in traffic and associated congestion has eroded all the benefits achieved by the vehicle emissions technologies.
3 citations