Transit Pollution Exposure Monitoring using Low-Cost Wearable Sensors
TL;DR: In this paper, the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters is analyzed by using extensive experiments carried out in the Helsinki metropolitan region, and they demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors.
Abstract: Transit activities are a significant contributor to a person’s daily exposure to pollutants. Currently obtaining accurate information about the personal exposure of a commuter is challenging as existing solutions either have a coarse monitoring resolution that omits subtle variations in pollutant concentrations or are laborious and costly to use. We contribute by systematically analysing the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters. Through extensive experiments carried out in the Helsinki metropolitan region, we demonstrate that low-cost sensors can capture the overall exposure with sufficient accuracy, while at the same time providing insights into variations within transport modalities. We also demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors. For example, we demonstrate that location within the vehicle carriage can result in up to 25 % increase in daily pollution exposure – a significant difference that existing solutions are unable to capture. Finally, we highlight the practical benefits of low-cost sensors as a pollution monitoring solution by introducing applications that are enabled by low-cost wearable sensors.
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TL;DR: Wang et al. as discussed by the authors presented a low-cost portable sensor and carried out a measurement campaign using the sensors to demonstrate the validity and benefits of citizen-based pollution measurements, which successfully classified the data into indoor and outdoor, and validated the consistency and accuracy of the outdoor-classified data to the measurements of a high-end reference monitoring station.
19 citations
19 citations
01 Jan 2016
TL;DR: The paper demonstrates the viability of using inexpensive static and mobile AirSpeck monitors for mapping trends in particulate concentrations in urban spaces and Networks of air-quality monitors using inexpensive sensors offer a cost-effective approach for recording trends in air quality at a higher spatial resolution.
Abstract: The Automatic Urban and Rural Network (AURN) [1] is a set of high quality reference monitoring sites for recording air quality in the United Kingdom. They are costly to install and expensive to run, and are therefore limited in numbers. The data from these networks are used to inform regulatory compliance with the Ambient Air Quality Directives [2]. There is also a requirement to monitor air pollution at sufficiently high spatial and temporal resolutions around people to estimate personal exposure to particulates, and gases such as Nitrogen Dioxide and Ozone for better understanding their health impacts. Such high resolution measurements can also be used for validating the air quality models' estimates of variability over space and time due to complex interactions. Networks of air-quality monitors using inexpensive sensors offer a cost-effective alternative approach for recording trends in air quality at a higher spatial resolution, albeit not as accurately as the reference monitoring sites. This paper describes the design, implementation, and deployment of a family of air quality monitors: stationary (AirSpeck-S) monitors for measuring ambient air quality, and mobile wearable AirSpeck-P for monitoring personal exposure to air borne particulates (PM10, PM2.5 and PM1), and the gases - Nitrogen Dioxide and Ozone. Results are presented for characterising the ambient air quality in public spaces gathered from people wearing the AirSpeck-P monitors who are out and about in two cities as pedestrians (Edinburgh, Scotland) and as car passengers (Delhi, India). The paper demonstrates the viability of using inexpensive static and mobile AirSpeck monitors for mapping trends in particulate concentrations in urban spaces. Results are presented for comparisons of the mobile personal exposure data from pedestrians with static AirSpeck-S monitors along the same route, and the characterization of urban spaces based on levels of particulate concentration using the AirSpeck-P monitor.
15 citations
TL;DR: A comprehensive survey of the current research on COVID-19 transmission mechanisms and how they relate to public transport is presented in this paper , where social distancing and control on passenger density are found to be the most effective mechanisms.
Abstract: The COVID-19 pandemic is posing significant challenges to public transport operators by drastically reducing demand while also requiring them to implement measures that minimize risks to the health of the passengers. While the collective scientific understanding of the SARS-CoV-2 virus and COVID-19 pandemic are rapidly increasing, currently there is a lack of understanding of how the COVID-19 relates to public transport operations. This article presents a comprehensive survey of the current research on COVID-19 transmission mechanisms and how they relate to public transport. We critically assess literature through a lens of disaster management and survey the main transmission mechanisms, forecasting, risks, mitigation, and prevention mechanisms. Social distancing and control on passenger density are found to be the most effective mechanisms. Computing and digital technology can support risk control. Based on our survey, we draw guidelines for public transport operators and highlight open research challenges to establish a research roadmap for the path forward.
9 citations
TL;DR: In this article , an intelligent sensors calibration method that facilitates correcting air quality low-cost sensors (LCSs) measurements accurately and detecting the calibrators' drift is proposed, which uses Bayesian framework to establish white-box and black-box calibrators.
Abstract: Air quality low-cost sensors (LCSs) are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular, when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting LCSs measurements accurately and detecting the calibrators’ drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators’ strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of LCSs suffered from sensing accuracy.
7 citations
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TL;DR: A large-scale research and innovation program is needed to meet the interlinked grand challenges in this gigacity and to serve as a platform for finding pathways for sustainable development of the whole globe as mentioned in this paper.
Abstract: . The eastern part of China as a whole is practically a gigacity; it is a conglomeration of megacities with circa 650 million inhabitants. The gigacity, with its emissions, processes in pollution cocktail, numerous feedbacks and interactions, has a crucial and big impact on regional air quality within itself and on global climate. A large-scale research and innovation program is needed to meet the interlinked grand challenges in this gigacity and to serve as a platform for finding pathways for sustainable development of the whole globe.
11 citations
01 Nov 2017
TL;DR: In this paper, the alpha prototype for a wearable air quality sensor system is presented, which is used to create precise, high-resolution maps of the environment to help individuals with respiratory disease track their response to pollutants, determine when to pre-medicate, or avoid areas with poor air quality altogether.
Abstract: In this work, we demonstrate the alpha prototype for a wearable air quality sensor system. This system will be used to create precise, high-resolution maps of the environment to help individuals with respiratory disease track their response to pollutants, determine when to pre-medicate, or avoid areas with poor air quality altogether. The data from such a map will provide improved accuracy over the single air quality index value provided for large metropolitan areas. We provide data from continuous monitoring over several locations to demonstrate the difference that can be observed within a small geographic area.
9 citations
23 Oct 2016
TL;DR: In this paper, the authors describe the design, implementation, and deployment of a family of air quality monitors: stationary (AirSpeck-S) monitors for measuring ambient air quality, and mobile wearable AirSpeckP for monitoring personal exposure to air borne particulates (PM10, PM25 and PM1), and the gases - Nitrogen Dioxide and Ozone.
Abstract: The Automatic Urban and Rural Network (AURN) [1] is a set of high quality reference monitoring sites for recording air quality in the United Kingdom They are costly to install and expensive to run, and are therefore limited in numbers The data from these networks are used to inform regulatory compliance with the Ambient Air Quality Directives [2] There is also a requirement to monitor air pollution at sufficiently high spatial and temporal resolutions around people to estimate personal exposure to particulates, and gases such as Nitrogen Dioxide and Ozone for better understanding their health impacts Such high resolution measurements can also be used for validating the air quality models' estimates of variability over space and time due to complex interactions Networks of air-quality monitors using inexpensive sensors offer a cost-effective alternative approach for recording trends in air quality at a higher spatial resolution, albeit not as accurately as the reference monitoring sites This paper describes the design, implementation, and deployment of a family of air quality monitors: stationary (AirSpeck-S) monitors for measuring ambient air quality, and mobile wearable AirSpeck-P for monitoring personal exposure to air borne particulates (PM10, PM25 and PM1), and the gases - Nitrogen Dioxide and Ozone Results are presented for characterising the ambient air quality in public spaces gathered from people wearing the AirSpeck-P monitors who are out and about in two cities as pedestrians (Edinburgh, Scotland) and as car passengers (Delhi, India) The paper demonstrates the viability of using inexpensive static and mobile AirSpeck monitors for mapping trends in particulate concentrations in urban spaces Results are presented for comparisons of the mobile personal exposure data from pedestrians with static AirSpeck-S monitors along the same route, and the characterization of urban spaces based on levels of particulate concentration using the AirSpeck-P monitor
8 citations
01 Jun 2017
TL;DR: In this article, a system calibration model for mobile PM25 sensing systems using COTS low-cost particle sensors is presented, which can be used for other mobile sensing applications that facilitate micro-scale environmental sensing on the move.
Abstract: In this paper, we present a system calibration model (SCM) for mobile PM25 sensing systems using COTS low-cost particle sensors To implement such systems, we first assess the accuracy of low-cost dust sensors and identify the most reliable sensor through a comprehensive set of evaluations We also investigate the inner working principle of the selected sensor By conducting a set of lab-scale controlled experiments, we obtained a logarithmic regression model that models the impacts of mobility and ambient wind velocity on PM25 sensing results Moreover, using a low-cost water flow sensor, we design a customized micro anemometer and apply a linear regression model to convert the flow rate readings from the sensor to wind velocity values Finally, we conduct a field experiment to evaluate the proposed calibration model in a real-world setting The results show that the accuracy of the PM25 measurement results improves significantly when the model is utilized The calibration model is simple and effective, and it can be utilized by other mobile sensing applications that facilitate micro-scale environmental sensing on the move
6 citations
01 Jan 2016
TL;DR: In this paper, a mathematical model for the determination of human exposure to ambient air pollution in an urban area, called EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes).
Abstract: Most epidemiological studies have been conducted based on relations between pollution concentrations measured at fixed ambient air quality monitoring sites, or modelled values using land-use regression models, and various health indicators. However, such simplistic modelling ignores several crucial factors, such as, (i) the activity patterns of individuals, i.e. people’s day-to-day movements, and (ii) the differences between indoor and outdoor air. We have developed a mathematical model for the determination of human exposure to ambient air pollution in an urban area, called EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines (i) predicted concentrations, and (ii) information on people’s activities and location of the population, to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments. In particular, the model takes into account the movements of the population and the infiltration from outdoor to indoor air. We present fine-resolution numerical results on annual spatial concentration, time activity and population exposures to PM2.5 in London and in the Helsinki Metropolitan Area, for 2008 and 2009. We have shown that the effect of neglecting the movements of the population, which is the currently commonly applied procedure, can result in an underprediction of exposure by several tens of per cent.
5 citations