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
References
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TL;DR: In this article, the authors present on-road exposure of PM2.5 for 11 transport microenvironments along a fixed 8.3km arterial route, during morning rush hour.
82 citations
TL;DR: This review focus on the effects of respiratory tract exposed to PM2.5 regarding the structural characteristics of the respiratory tract and the in vivo/vitro studies that revealed the immunotoxic effects of PM 2.5.
78 citations
26 Mar 2018
TL;DR: Two deep learning models are leveraged, Convolutional Neural Network for images and Long Short Term Memory network for weather and air-pollution data, to build an end-to-end framework for training PM2.5 inference models.
Abstract: Air pollution has raised people's public health concerns in major cities, especially for Particulate Matter under 2.5μm (PM2.5) due to its significant impact on human respiratory and circulation systems. In this paper, we present the design, implementation, and evaluation of a mobile application, Third-Eye, that can turn mobile phones into high-quality PM2.5 monitors, thereby enabling a crowdsensing way for fine-grained PM2.5 monitoring in the city. We explore two ways, crowdsensing and web crawling, to efficiently build large-scale datasets of the outdoor images taken by mobile phone, weather data, and air-pollution data. Then, we leverage two deep learning models, Convolutional Neural Network (CNN) for images and Long Short Term Memory (LSTM) network for weather and air-pollution data, to build an end-to-end framework for training PM2.5 inference models. Our App has been downloaded more than 2,000 times and runs more than 1 year. The real user data based evaluation shows that Third-Eye achieves 17.38 μg/m3 average error and 81.55% classification accuracy, which outperforms 5 state-of-the-art methods, including three scattered interpolations and two image based estimation methods. The results also demonstrate how Third-Eye offers substantial enhancements over typical portable PM2.5 monitors by simultaneously improving accessibility, portability, and accuracy.
77 citations
TL;DR: Markku Kulmala calls for continuous, comprehensive monitoring of interactions between the planet’s surface and atmosphere.
Abstract: Markku Kulmala calls for continuous, comprehensive monitoring of interactions between the planet’s surface and atmosphere. Markku Kulmala calls for continuous, comprehensive monitoring of interactions between the planet’s surface and atmosphere.
76 citations
TL;DR: In this article, the authors investigate fine particulate air pollution generated by public transport and its microenvironment, PM 2.5 measurements and particle number counts for six particle size ranges (0.3 − 0.5 µm, >0.5 − 1.0 µm and >10 µm) were obtained for four public transport modes: bus, metro-bus, car and walking.
73 citations