Can Air Pollution Negate the Health Benefits of Cycling and Walking?

Tainio, Marko; Nazelle, Audrey de; Götschi, Thomas; Rojas-Rueda, David; Kahlmeier, Sonja; Nieuwenhuijsen, Mark; de, Thiago Hérick; Kelly, Padraig; Woodcock, Jim

doi:10.1016/j.jth.2016.05.113

Cited by 56 publications

(68 citation statements)

References 9 publications

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“…Such inferred information from the predicted results, therefore, can help advance PA and transportation research. In addition, this study provides insights into how PA and transport‐mode classification can be applied to various research domains involving human mobility analysis (e.g., air quality; Tainio et al, ), especially with regard to how the algorithm works on sensor datasets collected in free‐living conditions and how the predicted results are represented.…”

Section: Discussionmentioning

confidence: 99%

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Lee

Kwan

2018

Transactions in GIS

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Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals' daily travels into account. PA research, however, needs to widen its focus from the intensity of PA to types of PA, which may provide useful clues for understanding specific health behaviors in particular geographic contexts. This study proposes and develops an algorithm to automatically classify PA types and in-vehicle status using GPS and accelerometer data. Walking, standing, jogging, biking and sedentary/in-vehicle statuses are identified through hierarchical classification processes based on machine learning and geospatial techniques. The proposed algorithm achieved high predictive accuracy on real-world GPS and accelerometer data. It can greatly reduce participants' and researchers' burdens by automatically identifying PA types and in-vehicle status for human mobility research, which is also known as travel mode imputation in transportation research. | 1523 LEE and KWAN

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Section: Discussionmentioning

confidence: 99%

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Lee

Kwan

2018

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“…For this purpose further studies are needed in order to balance the benefits and risks resulting from recreational physical activity in the conditions of exposure to different air pollution levels. An example of such analysis is study of Tainio et al Authors assessed that, by PM 2,5 concentrations of 100 μg/m 3 , losses outweigh benefits after exercise equal to one and half hour of cycling a day (41).…”

Section: Recreational Physical Activity and The Problem Of Air Pollutionmentioning

confidence: 99%

Recreational physical activity and the problem of particulate matter air pollution

Kocot

2019

Przegl Epidemiol

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Studies reveal the negative impact of exposure to particulate air pollution on human health, especially on respiratory and circulatory systems. At the same time the beneficial effects of maintaining adequate level of physical activity are well known. In this review author presents analysis of chosen current articles about health effects of recreational physical activity in the environment of high level of ambient particulate matter air pollution. Available data suggests that there are complex relations between physical activity and exposure to air pollution. The direct positive effects of recreational physical activity include decrease of diastolic blood pressure and bronchial dilatation. It is proven that particulate air pollution show contrary impact on respiratory and cardiovascular systems. The mechanisms of this influence consist of induction of inflammation in respiratory epithelial cells, increase of oxidative stress and alterations in autonomic nervous system regulation. Changes induced by air pollution, expressed by the decrease of spirometry parameters (most commonly FEV1 decrease) were observed even in young and healthy adults doing recreational physical activity. Nevertheless, several studies suggest modulatory impact of physical exercises, which may limit the pro-inflammatory influence of particulate air pollution. Less expressed short-term negative effects of exposure have been observed in people doing physical exercise in comparison to those being inactive at the same time. In addition, apart from direct health effects, air pollution may also discourage exposed people from doing physical activity. Therefore, balanced choices regarding physical activity are needed, in order to decrease the negative impact of exposure to air pollution, without limiting the positive impact of physical activity. Still, new research is needed in order to fully explain relations between physical activity and exposure to air pollution. This will allow to elaborate physical activity guidelines including current air pollution level, better risk communication and promotion of safe ways of exercise.

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“…In recent years, traffic-induced air pollution issues have been recognised as one of the major threats for human health in cities [1], [2], [3], [4]. Air contaminants, such as CO, NO x , and particulate matter (PM), emitted from tailpipes of conventional vehicles (i.e.…”

Section: Introductionmentioning

confidence: 99%

On the Design of an Intelligent Speed Advisory System for Cyclists

Liu

Souza

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Traffic-related pollution is becoming a major societal problem globally. Cyclists are particularly exposed to this form of pollution due to their proximity to vehicles' tailpipes. In a number of recent studies, it is been shown that exposure to this form of pollution eventually outweighs the cardio-vascular benefits associated with cycling. Hence during cycling there are conflicting effects that affect the cyclist. On the one hand, cycling effort gives rise to health benefits, whereas exposure to pollution clearly does not. Mathematically speaking, these conflicting effects give rise to convex utility functions that describe the health threats accrued to cyclists. More particularly, and roughly speaking, for a given level of background pollution, there is an optimal length of journey time that minimises the health risks to a cyclist. In this paper, we consider a group of cyclists that share a common route. This may be recreational cyclists, or cyclists that travel together from an origin to destination. Given this context, we ask the following question. What is the common speed at which the cyclists should travel, so that the overall health risks can be minimised? We formulate this as an optimisation problem with consensus constraints. More specifically, we design an intelligent speed advisory system that recommends a common speed to a group of cyclists taking into account different levels of fitness of the cycling group, or different levels of electric assist in the case that some or all cyclists use e-bikes (electric bikes). To do this, we extend a recently derived consensus result to the case of quasi-convex utility functions. Simulation studies in different scenarios demonstrate the efficacy of our proposed system.

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Can Air Pollution Negate the Health Benefits of Cycling and Walking?

Cited by 56 publications

References 9 publications

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Recreational physical activity and the problem of particulate matter air pollution

On the Design of an Intelligent Speed Advisory System for Cyclists

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