2019
DOI: 10.5755/j01.erem.75.2.21601
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Quantification of mass concentrations aerosols PM2.5 in primary schools. Case study: Tiaret city (Algeria)

Abstract: 23 primary schools have been selected in Tiaret city, and the collection of PM2.5 was performed in the morning, afternoon and evening, in the presence and absence of the pupils. The results found have shown a gradient of concentrations of PM2.5 by site typology. Low and average concentrations are recorded in the areas close to forests: 23.32±2.77 µg/m3 and the high concentrations are observed in the sites exposed to road traffic: 33.57±3.27 µg/m3 in winter. However, concentrations recorded in the urban sites a… Show more

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Cited by 3 publications
(3 citation statements)
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“… Reddy et al [ 34 ] Durban, South Africa TEOM; (24 h average), (continuous eight months). Sampling year: nr Four Primary schools, n = 341, grades 3–6 Industrial PM 10 & PM 2.5 51 ± 25.2 & 20 ± 15.0 na na Correlation between air pollution and respiratory diseases (asthma) was observed in schoolchildren in industrial areas Naidoo et al [ 52 ] Tiaret City, Algeria Dekati® PM10 impactor, Finland), (8 h average), Sampling year: 2016–2017 23 primary schools) Roadside na na PM 2.5 33 ± 3.27 PM 2.5 was elevated in the classroom with the presence of students Khadidja et al [ 36 ] Nairobi, Kenya Particle counter Sampling (Annual average), year:2016–2017 4 Schools n = 578) students (9–14) years old, dry/wet season Industrial na na PM 10 & PM 2.5 56-59 & 11-13 Low-cost sensors can collect school data, addressing poor air quality and filling data gaps. DeSouza et al [ 53 ].…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“… Reddy et al [ 34 ] Durban, South Africa TEOM; (24 h average), (continuous eight months). Sampling year: nr Four Primary schools, n = 341, grades 3–6 Industrial PM 10 & PM 2.5 51 ± 25.2 & 20 ± 15.0 na na Correlation between air pollution and respiratory diseases (asthma) was observed in schoolchildren in industrial areas Naidoo et al [ 52 ] Tiaret City, Algeria Dekati® PM10 impactor, Finland), (8 h average), Sampling year: 2016–2017 23 primary schools) Roadside na na PM 2.5 33 ± 3.27 PM 2.5 was elevated in the classroom with the presence of students Khadidja et al [ 36 ] Nairobi, Kenya Particle counter Sampling (Annual average), year:2016–2017 4 Schools n = 578) students (9–14) years old, dry/wet season Industrial na na PM 10 & PM 2.5 56-59 & 11-13 Low-cost sensors can collect school data, addressing poor air quality and filling data gaps. DeSouza et al [ 53 ].…”
Section: Resultsmentioning
confidence: 99%
“…Indoor PM 10 concentrations in schools in Kenya and Nigeria ranged between 43 and 269 μg m −3 [ 30 , 31 ], and indoor concentrations of PM 2.5 ranged between 20 and 76 μg m −3 [ 30 , 32 ], respectively. The outdoor concentrations of PM 10 in schools in South Africa ranged from 16 to 86 μg m −3 [ 33 , 34 ], while the outdoor exposure levels for PM 2.5 ranged between 11 and 33 μg m −3 [ 35 , 36 ]. ( Table 1 ).…”
Section: Resultsmentioning
confidence: 99%
“…The main research methods include health risk assessment model for risk analysis (Huang et al, 2018), enrichment factor (EF; Zhang et al, 2018), principal component analysis (PCA; Terrouche et al, 2016), correlation analysis (Soleimani et al, 2018), and positive matrix factorization (Amato and Hopke, 2012) for determining the element origin. The main research directions include the spatiotemporal change characteristics of PM 2.5 and its relevance with meteorological factors (Huang et al, 2015), the levels of PM 2.5 in city (Zhang and Cao, 2015) and school (Khadidja et al, 2019), the latent risk of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in PM 2.5 (Gao et al, 2016), and features, origins, and health risk assessment of heavy metals (Li et al, 2016). However, there are still some deficiencies in the current understanding of the spatiotemporal changes of PM 2.5 .…”
Section: Introductionmentioning
confidence: 99%