Transit Pollution Exposure Monitoring using Low-Cost Wearable Sensors

The AirSpeck Family of Static and Mobile Wireless Air Quality Monitors

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.

Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors

Abstract: Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.

Human health effects of air pollution

Abstract: Hazardous chemicals escape to the environment by a number of natural and/or anthropogenic activities and may cause adverse effects on human health and the environment. Increased combustion of fossil fuels in the last century is responsible for the progressive change in the atmospheric composition. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO 2 ), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O 3 ), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. It ranges from minor upper respiratory irritation to chronic respiratory and heart disease, lung cancer, acute respiratory infections in children and chronic bronchitis in adults, aggravating pre-existing heart and lung disease, or asthmatic attacks. In addition, short- and long-term exposures have also been linked with premature mortality and reduced life expectancy. These effects of air pollutants on human health and their mechanism of action are briefly discussed.

Exposure to traffic and the onset of myocardial infarction.

Abstract: Background An association between exposure to vehicular traffic in urban areas and the exacerbation of cardiovascular disease has been suggested in previous studies. This study was designed to assess whether exposure to traffic can trigger myocardial infarction. Methods We conducted a case–crossover study in which cases of myocardial infarction were identified with the use of data from the Cooperative Health Research in the Region of Augsburg Myocardial Infarction Registry in Augsburg, in southern Germany, for the period from February 1999 to July 2001. There were 691 subjects for whom the date and time of the myocardial infarction were known who had survived for at least 24 hours after the event, completed the registry's standardized interview, and provided information on factors that may have triggered the myocardial infarction. Data on subjects' activities during the four days preceding the onset of symptoms were collected with the use of patient diaries. Results An association was found between exposu...

Accelerometer-based transportation mode detection on smartphones

Abstract: We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.

Fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK.

Abstract: In order to investigate a specific area of short-term, non-occupational, human exposure to fine particulate air pollution, measurements of personal exposure to PM2.5 in transport microenvironments were taken in two separate field studies in central London, UK. A high flow gravimetric personal sampling system was used; operating at 16 l min(-1); the sampler thus allowed for sufficient sample mass collection for accurate gravimetric analysis of short-term travel exposure levels over typical single commute times. In total, samples were taken on 465 journeys and 61 volunteers participated. In a multi-transport mode study, carried out over 3-week periods in the winter and in the summer, exposure levels were assessed along three fixed routes at peak and off-peak times of the day. Geometric means of personal exposure levels were 34.5 microg m(-3) (G.S.D.= 1.7, n(s) = 40), 39.0 microg m(-3) (G.S.D. = 1.8, n(s) = 36), 37.7 microg m(-3) (G.S.D. = 1.5, n(s) = 42), and 247.2 microg m(-3) (G.S.D. = 1.3, n(s) = 44) for bicycle, bus, car and Tube (underground rail system) modes, respectively, in the July 1999 (summer) measurement campaign. Corresponding levels in the February 2000 (winter) measurement campaign were 23.5 microg m(-3) (G.S.D. = 1.8, n(s) = 56), 38.9 microg m(-3) (G.S.D. = 2.1, n(s) = 32), 33.7 microg m(-3) (G.S.D. = 2.4, n(s) = 12), and 157.3 microg m(-3) (G.S.D. = 3.3, n(s) = 12), respectively. In a second study, exposure levels were measured for a group of 24 commuters travelling by bicycle, during August 1999, in order to assess how representative the fixed route studies were to a larger commuter population. The geometric mean exposure level was 34.2 microg m(-3) (G.S.D. = 1.9, n(s) = 105). In the fixed-route study, the cyclists had the lowest exposure levels, bus and car were slightly higher, while mean exposure levels on the London Underground rail system were 3-8 times higher than the surface transport modes. There was significant between-route variation, most notably between the central route and the other routes. The fixed-route study exposure was similar in level and in variability to the 'real' commuters study, suggesting that the routes chosen and the number of samples taken provided a reasonably good estimate of the personal exposure levels in the transport microenvironments of Central London. This first comprehensive PM2.5 multi-mode transport user exposure assessment study in the UK also showed that mean personal exposure levels in road transport modes were approximately double that of the PM2.5 concentration at an urban background fixed site monitor.

Real-time air quality monitoring through mobile sensing in metropolitan areas

Abstract: Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.