Ambient PM2.5 and its chemical constituents on lifetime-ever pneumonia in Chinese children: A multi-center study

Shi, Wenming; Liu, Cong; Annesi‐Maesano, Isabella; Norbäck, Dan; Deng, Qihong; Huang, Chen; Qian, Hua; Zhang, Xin; Sun, Yuexia; Wang, Tingting; Donkelaar, Aaron van; Martin, Randall V.; Zhang, Yinping; Li, Baizhan; Kan, Haidong; Zhao, Zhuohui

doi:10.1016/j.envint.2020.106176

Cited by 41 publications

(40 citation statements)

References 61 publications

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“…According to their findings, NO 2 levels decreased by 47.9, 36.7, and 23.9%, PM 2.5 levels decreased by 18.1, 17.3, and 2.6%, and O 3 levels increased by 34.1, 7.4, and 0.1% at urban traffic, urban background, and rural sites, respectively. Furthermore, several other papers reported similar results, for example, [ 31 , 32 , 33 ] analysed the effect of the COVID-19 lockdown on air quality and reported that the concentrations of NO 2 and PM 2.5 had decreased, whereas the concentration of O 3 had significantly increased.…”

Section: Discussionmentioning

confidence: 60%

Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach

Habeebullah

Munir

Zeb

et al. 2022

Toxics

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To reduce the spread of COVID-19, lockdowns were implemented in almost every single country in the world including Saudi Arabia. In this paper, the effect of COVID-19 lockdown on O3, NO2, and PM10 in Makkah was analysed using air quality and meteorology data from five sites. Two approaches were employed: (a) comparing raw measured concentrations for the lockdown period in 2019 and 2020; and (b) comparing weather-corrected concentrations estimated by the machine learning approach with observed concentrations during the lockdown period. According to the first approach, the average levels of PM10 and NO2 decreased by 12% and 58.66%, respectively, whereas the levels of O3 increased by 68.67%. According to the second approach, O3 levels increased by 21.96%, while the levels of NO2 and PM10 decreased by 13.40% and 9.66%, respectively. The machine learning approach after removing the effect of changes in weather conditions demonstrated relatively less reductions in the levels of NO2 and PM10 and a smaller increase in the levels of O3. This showed the importance of adjusting air pollutant levels for meteorological conditions. O3 levels increased due to its inverse correlation with NO2, which decreased during the lockdown period.

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Section: Discussionmentioning

confidence: 60%

Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach

Habeebullah

Munir

Zeb

et al. 2022

Toxics

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“…After particulate matter is inhaled, the particles could result in cardiovascular and respiratory illness. For children, high levels of PM 2.5 exposure could lead to acute low respiratory infections include pneumonia which is one of the determinant cause of mortality among children ( Liu et al, 2017 , Ruan, 2021 , Shi et al, 2021 ). One study found that each 10 μg/m 3 increase in PM 2.5 could result in an increase in pneumonia hospitalization by 1.21% ( Wang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Empirical dynamic modeling of the association between ambient PM2.5 and under-five mortality across 2851 counties in Mainland China, 1999–2012

Alnwisi

Chai

Acharya

et al. 2022

Ecotoxicology and Environmental Safety

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“… 53 In early childhood, exposure to TSP increases the risk of pneumonia, especially in PM 2.5 form and its constituents. 54 A study found a non-carcinogenic risk of dust exposure through hand-mouth intake to children in industrial areas in north China. 9 If non-carcinogenic and carcinogenic risks occur from an early age, higher frequency and longer exposures will have more harmful effects.…”

Section: Discussionmentioning

confidence: 99%

Community Health Risk Assessment of Total Suspended Particulates near a Cement Plant in Maros Regency, Indonesia

Rauf

Mallongi

Daud

et al. 2021

Journal of Health and Pollution

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Background. Cement plants generate particulate matter (PM) across processes from raw material preparation to packaging. The presence of total suspended particulates (TSP) coming out of the stack causes a high accumulation of dust in residential areas. Human exposure to TSP could affect human health and wellbeing. Objectives. The present study aims to evaluate concentrations of TSP and to estimate the health risks of TSP exposure through the inhalation pathway in communities surrounding a private cement industry in Maros regency, Indonesia. Methods. Total suspended particulates were collected using a high-volume air sampler (HVAS) at five locations. Samples were taken by grab sampling for 24 hours. The SCREEN3 program was used to view the maximum range and distribution of pollutants based on the geographical, stack profiles and meteorological factors in the study area. Hazard quotient (HQ) was used to estimate non-carcinogenic risks of TSP in surrounding communities. Results. Total suspended particulate concentrations were measured with a maximum value of 133.24 μg/m3 and a minimum value of 18.48 μg/m3. This maximum value exceeds the minimum acceptable level from Canadian National Ambient Air Quality Objectives (C-NAAQOs). The non-carcinogenic risks from the inhalation pathway were low except for location 3 (HQ>1) across all locations. Conclusions. The cement plant may significantly contribute to total TSP concentrations in air and may potentially have adverse effects on human health. Communities near the cement plant are vulnerable to TSP exposure and measures are needed to reduce TSP in Maros regency, Indonesia. Participant Consent. Obtained Ethics Approval. This study was approved by the Health Research Ethics Committee of Hasanuddin University with protocol number 28920093022. Competing Interests. The authors declare no competing financial interests.

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Ambient PM2.5 and its chemical constituents on lifetime-ever pneumonia in Chinese children: A multi-center study

Cited by 41 publications

References 61 publications

Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach

Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach

Empirical dynamic modeling of the association between ambient PM2.5 and under-five mortality across 2851 counties in Mainland China, 1999–2012

Community Health Risk Assessment of Total Suspended Particulates near a Cement Plant in Maros Regency, Indonesia

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