scholarly journals Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data

2019 ◽  
Vol 16 (22) ◽  
pp. 4522 ◽  
Author(s):  
Mingxiao Li ◽  
Song Gao ◽  
Feng Lu ◽  
Huan Tong ◽  
Hengcai Zhang
Keyword(s):  
Air Pollution ◽  
Mobile Phone ◽  
Pollution Monitoring ◽  
Spatiotemporal Variability ◽  
Mobile Phone Data ◽  
Potential Health ◽  
Individual Exposure ◽  
Air Pollution Exposure ◽  
Exposure Levels ◽  
Pollution Exposure

The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

Population dynamics based on mobile phone data to improve air pollution exposure assessments

2018 ◽  
Vol 29 (2) ◽  
pp. 278-291 ◽  
Author(s):  
Miguel Picornell ◽  
Tomás Ruiz ◽  
Rafael Borge ◽  
Pedro García-Albertos ◽  
David de la Paz ◽  
...  
Keyword(s):  
Air Pollution ◽  
Population Dynamics ◽  
Mobile Phone ◽  
Mobile Phone Data ◽  
Air Pollution Exposure ◽  
Pollution Exposure

scholarly journals Individual exposure level following indoor and outdoor air pollution exposure in Dakar (Senegal)

2019 ◽  
Vol 248 ◽  
pp. 397-407 ◽  
Author(s):  
A. Ndong Ba ◽  
A. Verdin ◽  
F. Cazier ◽  
G. Garcon ◽  
J. Thomas ◽  
...  
Keyword(s):  
Air Pollution ◽  
Exposure Level ◽  
Outdoor Air ◽  
Individual Exposure ◽  
Outdoor Air Pollution ◽  
Air Pollution Exposure ◽  
Pollution Exposure ◽  
Indoor And Outdoor

scholarly journals Air pollution interventions and respiratory health: a systematic review

2020 ◽  
Vol 24 (2) ◽  
pp. 150-164 ◽  
Author(s):  
S. Saleh ◽  
W. Shepherd ◽  
C. Jewell ◽  
N. L. Lam ◽  
J. Balmes ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Respiratory Health ◽  
Grey Literature ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Potential Health ◽  
Exposure Reduction ◽  
Air Pollution Exposure ◽  
Pollution Exposure

BACKGROUND: Indoor and ambient air pollution exposure is a major risk to respiratory health worldwide, particularly in low- and middle-income countries (LMICs). Interventional trials have mainly focused on alternatives to cooking stoves, with mixed results. Beyond cooking, additional sources of particulate matter also contribute to the burden of air pollution exposure. This review explores evidence from current randomised controlled trials (RCTs) on the clinical effectiveness of interventions to reduce particulate matter in LMICs.METHODS: Twelve databases and the grey literature (e.g., Government reports and policy papers) were searched. Eligible studies were RCTs conducted in LMICs aiming to reduce particulate exposure from any source and reporting on at least one clinical respiratory outcome (respiratory symptoms, lung function or clinical diagnoses). Data from relevant studies were systematically extracted, the risk of bias assessed and narrative synthesis provided.RESULTS: Of the 14 included studies, 12 tested ‘improved' cookstoves, most using biomass, but solar and bioethanol cookers were also included. One trial used solar lamps and another was an integrated intervention incorporating behavioural and environmental components for the treatment and prevention of chronic obstructive pulmonary disease. Of the six studies reporting child pneumonia outcomes, none demonstrated significant benefit in intention-to-treat analysis. Ten studies reported respiratory symptom outcomes with some improvements seen, but self-reporting made these outcomes highly vulnerable to bias. Substantial inter-study clinical and methodological heterogeneity precluded calculation of pooled effect estimates.CONCLUSION: Evidence from the RCTs performed to date suggests that individual household-level interventions for air pollution exposure reduction have limited benefits for respiratory health. More comprehensive approaches to air pollution exposure reduction must be developed so their potential health benefits can be assessed.

scholarly journals Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data

2020 ◽  
Vol 141 ◽  
pp. 105772 ◽  
Author(s):  
Xiaonan Yu ◽  
Cesunica Ivey ◽  
Zhijiong Huang ◽  
Sashikanth Gurram ◽  
Vijayaraghavan Sivaraman ◽  
...  
Keyword(s):  
Air Pollution ◽  
Mobile Phone ◽  
Location Data ◽  
Exposure Estimation ◽  
Daily Mobility ◽  
Air Pollution Exposure ◽  
Pollution Exposure ◽  
The Impact

Assessing air pollution exposure and health impacts using mobile phone activity

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Audrey de Nazelle* ◽  
Juan Pablo Orjuela ◽  
Markus Schläpfer
Keyword(s):  
Air Pollution ◽  
Mobile Phone ◽  
Health Impacts ◽  
Air Pollution Exposure ◽  
Pollution Exposure

scholarly journals Air pollution exposure in relation to guard duty at Tidworth Camp: A cross-sectional study

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0258070
Author(s):  
Ngwa Niba Rawlings ◽  
Akwah Emmanuela Ambe ◽  
Lem Ngongalah
Keyword(s):  
Air Pollution ◽  
Linear Regression Analysis ◽  
Strong Positive Correlation ◽  
Cross Sectional ◽  
Positive Correlation ◽  
Pollution Levels ◽  
Significant Difference ◽  
Air Pollution Exposure ◽  
Exposure Levels ◽  
Pollution Exposure

Background Air pollution is the largest environmental health risk in the United Kingdom, and an issue of concern amongst outdoor workers. Road transport is a major source producing the largest amount of nitrogen dioxide (NO2) and ozone (O3) (as a secondary pollutant). Hundreds of vehicles enter and exit the Tidworth Camp’s main gate daily, potentially producing these pollutants. However, the air pollution exposure experienced by personnel on guard duty is unknown. This study aimed to determine and compare background NO2 and O3 levels experienced by personnel on guard duty. Methods Cross-sectional data was collected using a static sampling technic on randomly selected days of the week. Data analysis was done using IBM-SPSS-26 and a p-value of <0.05 was considered statistically significant. Results The background concentration of NO2 and O3 pollutants were within recommended limits. There was no significant difference between mean morning and afternoon exposure levels for both pollutants. However, NO2 and O3 levels were significantly higher during weekdays compared to weekends (M = -0.022, SD = 0.007, t(6) = -8.672, p <0.0001 and M = -0.016, SD = 0.008, t(6) = -5.040, p = 0.002 respectively). Both pollutants showed no significant differences in exposure levels when only weekdays were compared. NO2 levels showed a weak positive correlation during weekdays (r = 0.04) and a strong positive correlation during weekends (r = 0.96). O3 levels had a positive correlation on both weekdays and weekends; however, levels on Monday showed a negative correlation (r = -0.55). Linear regression analysis showed that outside temperature was a significant predictor of O3 levels (p = 0.026). Conclusion Personnel on guard duty experienced higher pollution levels during weekdays compared to weekends; however, air pollution levels for both pollutants were within recommended limits. Further studies are recommended over hotter months using a personal sampling technic to measure personal air pollution exposure levels in order to minimise any health and safety risks.

scholarly journals Opportunities for mobile-phone based air pollution exposure assessment

2019 ◽  
Vol 3 ◽  
pp. 91-92
Author(s):  
de Nazelle A ◽  
Orjuela J ◽  
Avila-Palencia I ◽  
Dons E ◽  
Int Panis L ◽  
...  
Keyword(s):  
Air Pollution ◽  
Mobile Phone ◽  
Exposure Assessment ◽  
Air Pollution Exposure ◽  
Pollution Exposure

scholarly journals The Impact of Route Choice on Active Commuters' Exposure to Air Pollution: A Systematic Review

2021 ◽  
Vol 2 ◽  
Author(s):  
Mehrdad Rafiepourgatabi ◽  
Alistair Woodward ◽  
Jennifer A. Salmond ◽  
Kim N. Dirks
Keyword(s):  
Air Pollution ◽  
Route Choice ◽  
Pollution Monitoring ◽  
World Health ◽  
High Exposure ◽  
Active Mode ◽  
Exposure Route ◽  
Air Pollution Exposure ◽  
Low Exposure ◽  
Pollution Exposure

As air pollution varies significantly in both space and time, commuter exposure may differ considerably depending on the route taken between home to work. This is especially the case for active mode commuters who often have a wider range of route choices available to them compared with those traveling by private motor vehicle or by public transport. The aim of this study was to investigate the effect of route choice on air pollution exposure among active commuters, and to estimate, based on modeling, the health benefits able to be achieved from air pollution exposure reductions, modeled across a population, through route optimization. We searched for studies that used portable personal air pollution monitoring equipment during active mode commuting, and reported measurements of air quality on at least two routes, either as a journey to work or to school. The World Health Organization (WHO) model AirQ+ was then exploited to estimate the premature deaths attributable to air pollution according to route choice. Ten publications were identified that met the inclusion criteria. Ultrafine particle counts (UFP), black carbon (BC), and carbon monoxide (CO) were the most commonly measured pollutants in the studies identified. The exposures associated with “high exposure” and “low exposure” routes (categorized based on differences in traffic counts on the roads along the commute route or walking on opposite sides of the road with different levels of traffic traveling in each direction) were found to vary on average by 30 ± 8%, 42 ± 35%, and 55 ± 17% for BC, CO and UFP, respectively. On the basis of modeling, and on the estimated exposures to BC, up to 36 out of 10,000 deaths could be prevented by choosing a low exposure route compared with a high exposure route during active commuting. The results of this study may be useful for both individuals in their commute planning, and also for urban transport planners as impetus for investing in infrastructure to support healthy active mode commuting.

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