Real-time information on air pollution and avoidance behavior: evidence from South Korea

Geunsik, Yoo

doi:10.1007/s11111-020-00368-0

Cited by 25 publications

(13 citation statements)

References 18 publications

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“…Likely, in the CCZ after 2016 when the new mayor Sadiq Khan took office and started to stress the risks of air quality in central London high SES parents might have started to take actions to protect their children keeping 20 Data on distance from school and other metrics is present in the UK Datastore: https://data.london.gov.uk/dataset/london-schools-atlas them at home during polluted days. For example, previous studies have shown individuals buying extra medical insurance or taking precautionary behaviours when exposed to high levels of pollution (Chen & Chen, 2020;Yoo, 2021). However, precautionary behaviours showed to not coincide with hospitalization for respiratory diseases possibly explaining the decrease in air pollution for low SES students that are more likely to suffer from respiratory disease and to not undertake compensatory behaviours (Janke, 2014).…”

Section: Discussionmentioning

confidence: 98%

“…For example, individuals suffering from chronic obstructive pulmonary disease, allergic symptoms, asthma or rhinitis are ill prepared to affront high levels of air pollutants (Alotaibi et al, 2019;Jiang, Mei, Feng, 2016;Mudway et al, 2019;Wood et al, 2015). A competing explanation is based on behaviorial responses to air pollution, whereby individuals prefer to avoid exposure to air pollution by staying at home when air quality is low (Yoo, 2021).…”

Section: Air Pollution Socioeconomic Status and Educational Inequalitiesmentioning

confidence: 99%

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Congestion charge, air pollution and children’s school attendance: benefitting the poor

Keivabu¹,

Rüttenauer²

2021

Preprint

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Air pollution negatively affects individuals’ health and human capital accumulation. For example, students school performance is hampered by air pollution as it decreases cognitive abilities and increases absences. Moreover, low-income students are the most exposed and vulnerable to the negative effects of air pollution lacking protective resources and suffering from pre-existing health conditions. Here, we inquire how more stringent traffic regulations implemented in Central London from late 2015 affected pollution levels and school absences. First, we observe a substantive decrease in pollution in the area affected by the regulations from 2016. Secondly, we use a Difference in Difference approach to estimate the causal effect of the policy on air pollution and school absences. For all schools combined, findings do not show any substantive improvement in attendance. However, when looking at the heterogeneous effects of the policy, we observe a larger decrease in absences for Low socioeconomic status (SES) students. Moreover, we find a positive spillover effect for schools just outside the area targeted by the policy. Consequently, the findings highlight the efficacy of environmental policy in diminishing pollution levels and to benefit the poor.

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Section: Discussionmentioning

confidence: 98%

Section: Air Pollution Socioeconomic Status and Educational Inequalitiesmentioning

confidence: 99%

Congestion charge, air pollution and children’s school attendance: benefitting the poor

Keivabu¹,

Rüttenauer²

2021

Preprint

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“…Due to the fact that urban heat islands are threatening human health and comfort [3,4], individual temperature feedback also deserves more attention in the future. While heat alerts and air pollution alerts are spread via media such as newspapers, TV or the radio [18][19][20][21][22][23][24] we see great potential for personal sensors in this field. Not only can wearable sensors provide information on the temporal and spatial distribution of pollutants; feedback from wearable sensors can also give more precise information about individual exposure patterns, which can inform behaviour change such as choosing different routes or travel times in everyday outdoor mobility.…”

Section: There Is a Lack Of Feedback Studies On Outdoor Exposurementioning

confidence: 99%

“…This review focuses on personal feedback. Apart from personal feedback, there is some literature on the effects of regional air pollution or heat alerts [18][19][20][21][22][23][24]. Some alerts are bound to specific events such as smoke alerts due to wildfire [25,26].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Personalized Sensor Feedback Regarding Exposure to Environmental Stressors

et al. 2021

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Feedback on personal exposure to air pollution, noise or extreme temperatures through wearable sensors or sensors installed at home or in the workplace can offer information that might motivate behaviours to mitigate exposure. As personal measurement devices are becoming increasingly accessible, it is important to evaluate the effects of such sensors on human perception and behaviour. We conducted a systematic literature research and identified 33 studies, analysing the effects of personal feedback on air pollution, noise and temperatures. Feedback was given through reports including different forms of visualization, in-person or over the telephone, or directly on the sensor or through a phone app. The exposure feedback led to behaviour changes particularly for noise and temperature feedback while findings on behaviour adaptation to avoid air pollution were mixed. Most studies reported increased awareness and knowledge from receiving exposure feedback. Many participants in studies on air pollution reported low levels of self-efficacy regarding exposure mitigation. For a better understanding of the effects of personal exposure feedback, more studies are required, particularly providing feedback from wearable sensors measuring outdoor air pollution, noise and temperature.

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“…The subject of air quality study and prediction is a very important research area [1][2][3]. As reported by the authors [4], air pollution prediction methods can be divided into statistical, numerical, neural network and hybrid models.…”

Section: Introductionmentioning

confidence: 99%

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

et al. 2022

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Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).

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Real-time information on air pollution and avoidance behavior: evidence from South Korea

Cited by 25 publications

References 18 publications

Congestion charge, air pollution and children’s school attendance: benefitting the poor

Congestion charge, air pollution and children’s school attendance: benefitting the poor

Impacts of Personalized Sensor Feedback Regarding Exposure to Environmental Stressors

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

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