Chemical Composition and Sources of Indoor and Outdoor PM10 in Primary Schools

2013 ◽  
pp. 379-383
Author(s):  
Noorlin Mohamad ◽  
Mohd Talib Latif ◽  
Md Firoz Khan
2021 ◽  
Vol 782 ◽  
pp. 146820
Author(s):  
Estela D. Vicente ◽  
Daniela Figueiredo ◽  
Cátia Gonçalves ◽  
Isabel Lopes ◽  
Helena Oliveira ◽  
...  

2020 ◽  
Vol 183 ◽  
pp. 109203 ◽  
Author(s):  
Vânia Martins ◽  
Tiago Faria ◽  
Evangelia Diapouli ◽  
Manousos Ioannis Manousakas ◽  
Konstantinos Eleftheriadis ◽  
...  

Author(s):  
Barend L. van Drooge ◽  
Ioar Rivas ◽  
Xavier Querol ◽  
Jordi Sunyer ◽  
Joan O. Grimalt

Airborne particulate matter with an aerodynamic diameter smaller than 2.5 µg, PM2.5 was regularly sampled in classrooms (indoor) and playgrounds (outdoor) of primary schools from Barcelona. Three of these schools were located downtown and three in the periphery, representing areas with high and low traffic intensities. These aerosols were analyzed for organic molecular tracers and polycyclic aromatic hydrocarbons (PAHs) to identify the main sources of these airborne particles and evaluate the air quality in the urban location of the schools. Traffic emissions were the main contributors of PAHs to the atmospheres in all schools, with higher average concentrations in those located downtown (1800–2700 pg/m3) than in the periphery (760–1000 pg/m3). The similarity of the indoor and outdoor concentrations of the PAH is consistent with a transfer of outdoor traffic emissions to the indoor classrooms. This observation was supported by the hopane and elemental carbon concentrations in PM2.5, markers of motorized vehicles, that were correlated with PAHs. The concentrations of food-related markers, such as glucoses, sucrose, malic, azelaic and fatty acids, were correlated and were higher in the indoor atmospheres. These compounds were also correlated with plastic additives, such as phthalic acid and diisobutyl, dibutyl and dicyclohexyl phthalates. Clothing constituents, e.g., adipic acid, and fragrances, galaxolide and methyl dihydrojasmonate were also correlated with these indoor air compounds. All these organic tracers were correlated with the organic carbon of PM2.5, which was present in higher concentrations in the indoor than in the outdoor atmospheres.


2011 ◽  
Vol 20 (6) ◽  
pp. 607-617 ◽  
Author(s):  
Nor Husna Mat Hussin ◽  
Lye Munn Sann ◽  
Mariana Nor Shamsudin ◽  
Zailina Hashim

This study reports the types and concentrations of bacterial and fungal bioaerosols found in five randomly selected primary schools in Malaysia. Normal flora bacteria was the most frequently isolated bacteria including Staphylococcus spp., Pseudomonas spp. and Bacillus spp. Terribacillus spp. found in this study had never been reported before. The most frequently isolated fungal genera were Aspergillus, Penicillium, Fusarium, Rhizopus and Zygomycetes. The average concentration of bacteria in indoor and outdoor air were 1025 ± 612 CFU/m3 and 1473 ± 1261 CFU/m3, respectively, while the average concentration of fungal bioaerosol in indoor and outdoor air were 292 ± 83 CFU/m3 and 401 ± 235 CFU/m3, respectively. The percentages of bacterial and fungal samples that were within the American Conference of Industrial Hygenists (ACGIH) recommended levels were 44% and 33.8%, respectively. The ratio of indoor to outdoor fungi concentration was below 1.0, suggesting minimal indoor generative source for fungal bioaerosols. However, the ratio of indoor to outdoor bacteria concentration was approaching 1.0, suggesting the presence of potential internal generative source and inadequate ventilation. Building occupants might be one of the potential sources of bacteria in the indoor air as the bacteria concentrations without occupants were significantly lower than with occupants (p < 0.05).


2000 ◽  
Vol 12 (sup1) ◽  
pp. 139-144 ◽  
Author(s):  
M. D. Lebowitz ◽  
M. K. O𠅩Rourke ◽  
S. Rogan ◽  
J. Reses ◽  
P. Van de Water ◽  
...  

2014 ◽  
Vol 77 (14-16) ◽  
pp. 900-915 ◽  
Author(s):  
N. Canha ◽  
S. M. Almeida ◽  
M. C. Freitas ◽  
H. T. Wolterbeek

Author(s):  
Jun Xia ◽  
Pei-Jie Chen ◽  
Ji-Hong Wang ◽  
Jie Zhuang ◽  
Zhen-Bo Cao ◽  
...  

The aim of this study is (a) to develop, test, and employ a combined method of unsupervised machine learning to objectively assess the condition of sports facility in primary schools (PSSFC) and (b) examine the examine the geographical and typological association with PSSFC. Based on the Sixth National Sports Facility Census (NSFC), six PSSFC indicators (indoor and outdoor facility included) were selected as the measurements and decomposed by using the t-stochastic neighbor embedding (t-SNE). Thereafter, the Fuzzy C-mean (FCM) algorithm was used to cluster the same type of PSSFC with selecting the optimum numbers of evaluation level. Overall 845 primary schools in Shanghai, China were recruited and tested by this combined approach of unsupervised machine learning. In addition, the two-way analysis of covariance was used to examine the location and types of school associated with PSSFC variables in each level. The combined method was found to have acceptable reliability and good interpretability, differentiating PSSFC into five gradient levels. The characteristics of PSSFC differ by the location and school type of individual school. Our findings are conducive to the regionalized and personalized intervention and promotion on the children&rsquo;s physical activity (PA) upon the practical situation of particular schools.


2000 ◽  
Vol 12 (s1) ◽  
pp. 139-144
Author(s):  
M. D. Lebowitz, M. K. O'Rourke, S. Rog

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