Does real-time and perceived environmental exposure to air pollution and noise affect travel satisfaction? evidence from Beijing, China

2021 ◽  
Vol 24 ◽  
pp. 313-324
Author(s):  
Jing Ma ◽  
Guanqiu Liu ◽  
Mei-Po Kwan ◽  
Yanwei Chai
2021 ◽  
Vol 55 (17) ◽  
pp. 12106-12115
Author(s):  
Guannan Geng ◽  
Qingyang Xiao ◽  
Shigan Liu ◽  
Xiaodong Liu ◽  
Jing Cheng ◽  
...  
Keyword(s):  

Author(s):  
Eirini Dimakakou ◽  
Helinor J. Johnston ◽  
George Streftaris ◽  
John W. Cherrie

Human exposure to particulate air pollution (e.g., PM2.5) can lead to adverse health effects, with compelling evidence that it can increase morbidity and mortality from respiratory and cardiovascular disease. More recently, there has also been evidence that long-term environmental exposure to particulate air pollution is associated with type-2 diabetes mellitus (T2DM) and dementia. There are many occupations that may expose workers to airborne particles and that some exposures in the workplace are very similar to environmental particulate pollution. We conducted a cross-sectional analysis of the UK Biobank cohort to verify the association between environmental particulate air pollution (PM2.5) exposure and T2DM and dementia, and to investigate if occupational exposure to particulates that are similar to those found in environmental air pollution could increase the odds of developing these diseases. The UK Biobank dataset comprises of over 500,000 participants from all over the UK. Environmental exposure variables were used from the UK Biobank. To estimate occupational exposure both the UK Biobank’s data and information from a job exposure matrix, specifically developed for UK Biobank (Airborne Chemical Exposure–Job Exposure Matrix (ACE JEM)), were used. The outcome measures were participants with T2DM and dementia. In appropriately adjusted models, environmental exposure to PM2.5 was associated with an odds ratio (OR) of 1.02 (95% CI 1.00 to 1.03) per unit exposure for developing T2DM, while PM2.5 was associated with an odds ratio of 1.06 (95% CI 0.96 to 1.16) per unit exposure for developing dementia. These environmental results align with existing findings in the published literature. Five occupational exposures (dust, fumes, diesel, mineral, and biological dust in the most recent job estimated with the ACE JEM) were investigated and the risks for most exposures for T2DM and for all the exposures for dementia were not significantly increased in the adjusted models. This was confirmed in a subgroup of participants where a full occupational history was available allowed an estimate of workplace exposures. However, when not adjusting for gender, some of the associations become significant, which suggests that there might be a bias between the occupational assessments for men and women. The results of the present study do not provide clear evidence of an association between occupational exposure to particulate matter and T2DM or dementia.


2017 ◽  
Vol 230 ◽  
pp. 974-980 ◽  
Author(s):  
Yuxia Ma ◽  
Yuxin Zhao ◽  
Sixu Yang ◽  
Jianding Zhou ◽  
Jinyuan Xin ◽  
...  

2018 ◽  
Vol 24 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Chiara Ravetti ◽  
Tim Swanson ◽  
Yana Jin ◽  
Quan Mu ◽  
Shiqiu Zhang

AbstractThis paper analyses the implications of government control over public information about air pollution. First, we model the incentives for a local government with control over the media to affect popular perception concerning pollution. We argue that biased announcements can influence the inflows of labour force in a municipality beyond economic factors. Then, we examine some evidence on information misreporting in the context of Beijing, China. We show that official air pollution announcements diverge systematically from an alternative source of information, provided by the US Embassy. The results point at a manipulation of popular perception consistent with the motives indicated in our model. Furthermore, using an original household survey, we examine whether the distorted public signal affects agents' behaviour. We find that households that depend upon government-controlled media are significantly less responsive to pollution peaks.


2018 ◽  
Vol 210 ◽  
pp. 03008
Author(s):  
Aparajita Das ◽  
Manash Pratim Sarma ◽  
Kandarpa Kumar Sarma ◽  
Nikos Mastorakis

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.


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