Effects of low exposure to traffic related air pollution on childhood asthma onset by age 10 years

Lau, Nelson; Smith, Mary Jane; Sarkar, Achyut; Gao, Zhiwei

doi:10.1016/j.envres.2020.110174

Cited by 11 publications

(4 citation statements)

References 47 publications

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“… 43 Another recent study investigated the association between NO 2 and early- or late-onset childhood asthma phenotypes in Canada and reported that children exposed to high levels of NO 2 were more than twice as likely to have early-onset asthma, suggesting that the impact of NO 2 on the onset of asthma may vary depending on the childhood developmental stage. 44 The current findings on the association between air pollution and adult-onset asthma are inconsistent. Exposure to high levels of NO 2 has been linked to an increased prevalence of adult asthma in Taiwan, China.…”

Section: Recent Investigations On Air Pollution and Onset Of Asthmamentioning

confidence: 99%

“…This study linked the exposure of children to higher levels of PM 2.5 to onset of asthma, with weak evidence for PM 10 and NO 2 43 . Another recent study investigated the association between NO 2 and early‐ or late‐onset childhood asthma phenotypes in Canada and reported that children exposed to high levels of NO 2 were more than twice as likely to have early‐onset asthma, suggesting that the impact of NO 2 on the onset of asthma may vary depending on the childhood developmental stage 44 . The current findings on the association between air pollution and adult‐onset asthma are inconsistent.…”

Section: Recent Investigations On Air Pollution and Onset Of Asthmamentioning

confidence: 99%

“…46,47 In general, the recent findings on the association between air pollution and childhood-and adult-onset asthma vary across studies, and this may be related to the differences in genetic factors, indoor/household exposure to air pollutants, lifestyle, and diet. In addition, the susceptibility of children to the impact of air pollution may vary with the developmental stages, 44 and further investigations on the susceptible window should be conducted.…”

Section: Recent Investigations On Air Pollution and Onset Of Asthmamentioning

confidence: 99%

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Outdoor air pollution and the onset and exacerbation of asthma

Madaniyazi

Seposo

2021

Chronic Diseases and Translational Medicine

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Exposure to outdoor air pollution has been consistently associated with asthma. In this study, we reviewed the epidemiological studies published within the last 5 years on the association between outdoor air pollution and exacerbation and onset of asthma. A large number of studies have been published within the last 5 years. Short-term exposure to outdoor air pollution is associated with exacerbation of pre-existing asthma, manifested as worsening of symptoms and increasing of asthma-related emergency room visits and hospital admissions. Furthermore, increasing evidence suggests that long-term exposure to outdoor air pollution can result in onset of asthma. Children are more susceptible to outdoor air pollution. Future studies should be conducted to explore the mechanisms underlying the association between air pollutants and onset of asthma, including gene involvement. In addition, disentangling the effect of a mixture of air pollutants and identifying the key components of air pollution will complete the existing evidence. More importantly, a better understanding is required on the future impact of air pollution on asthma under a changing climate.

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Section: Recent Investigations On Air Pollution and Onset Of Asthmamentioning

confidence: 99%

Section: Recent Investigations On Air Pollution and Onset Of Asthmamentioning

confidence: 99%

Section: Recent Investigations On Air Pollution and Onset Of Asthmamentioning

confidence: 99%

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Outdoor air pollution and the onset and exacerbation of asthma

Madaniyazi

Seposo

2021

Chronic Diseases and Translational Medicine

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“…The effects of air pollution on asthma were examined considering its harmful effects (Pfeffer et al, 2020). Exposure to air pollution caused by car traffic and asthma in children ten years and older was suggested and the results showed that NO2 pollutants play a very bad role in asthma (Lau et al, 2020). Ghanbari and Isazadeh the concentrations of ozone and nitrogen oxide pollutants were modeled annually for Tehran using Sentinel-5 product in the Google Earth system (Ghanbari and Isazadeh, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Air Pollutant Concentrations Using GWR and Anfis Models in Tehran City

Qasimi¹,

Seraj²,

Isazade³

2022

Env.cont.Reviews

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Today, air quality is a major subject in city regions that have affected human health, the environment, and the city ecosystem. Therefore, government officials, environmental organizations, health organizations, and city managers often need to model the concentration of air contaminants. This study aimed to compare geographically weighted regression (GWR) modeling and neural network (ANFIS) using Segno and Mamdani rules to spatially predict the concentration density of fNO2, CO, and SO2 pollutant indices. And PM 2.5 for the year 2021 in Tehran. The results of the statistical analysis of Sugeno and Mamdani rules revealed that the (RMSE) in evaluating the ANFIS model with the Mamdani method was 0.895 ppm, and with the Sugno method it was 1.004 ppm, whereas the RMSE in terms of Spatial weighted regression model was obtained on digital model with a height of (12.5 m) and a value of 692.0 ppm. The evaluation results showed that Mamdani and Sugno laws do not have the same and desirable accuracy. For Mamdani law, the RMSE level of PM 2.5 pollutant was (0.71 ppm) and according to Sugno law, this level was obtained for CO pollutant (0.81 ppm). While evaluating the geographically weighted regression model for the four air pollution indices the digital altitude model of (12.5 m) had similar results, which statistically for the digital altitude model of (12.5 m) obtained the RMSE for PM 2.5 (0.82 ppm). The findings of this study demonstrated that the weighted geographic regression model and the ANFI neural network have acceptable functionalities for spatial prediction of air pollutants.

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