Indoor and outdoor aeromicrobiology

Obayori, Oluwafemi S.

doi:10.1016/b978-0-323-96122-6.00009-8

Cited by 3 publications

(3 citation statements)

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“…(Ghosh et al, 2015). Once suspended in the air, microbes have the opportunity to travel long distance with the help of wind and precipitation increasing the occurrence of wide spread diseases by these microorganisms (Obayori, 2023). The commonest genera of fungi found in indoor air are Penicillium, Aspergillus, Cladosporium, Alternaria, and yeast while the commonest genera of bacteria found in indoor air are Staphylococci, Bacillus and Clostridium (Nevalainen, 2009;Shiva, 2009).…”

Section: Introductionmentioning

confidence: 99%

Microbiological Assessment of Indoor Air Quality of Student Hostels and Canteens in a Nigerian University Campus: Risks on Students’ Health

Obajuluwa,

Akanni,

Bege

2024

FJS

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Microbial air contaminants affect the quality of air we breathe and these microbial air contaminants can settle on body parts, clothes, food etc. which poses a serious threat to human health. Therefore, the level of bacterial contamination in various in-door air of the students’ hostels and canteens of Kaduna State University (KASU) and their antibiotics susceptibility patterns was studied. A total of 44 samples were collected from the rooms (18), toilets (18) of the students hostels and canteen (8) of KASU; using the settle plate method. Gram staining and standard biochemical methods were used to identify the bacterial isolates and antibiotic susceptibility testing was determined by disc diffusion method. The bacteria load was between 1.2 x 103 to 6.7 x 103 cfu/ml3. A total of 30 bacteria isolates were identified, Staphylococcus aureus had the highest (9, 30%) occurrence, followed by Streptococcus spp (6, 20%), Bacillus subtilis and Clostridium spp had similar (4, 13.2%) occurrence, The antibiotic susceptibility testing carried out revealed that the Gram negative and Gram positive bacteria isolates were more susceptible to vancomycin (75%, 43.5%), tetracycline (75%, 47.8%) and gentamicin (50%, 43.5%) while high resistance was observed with chloramphenicol (100%, 73.9%), ceftriaxone (100%, 100%), cefoxitin (100%, 73.9%), amoxicillin/clavulanic acid (100%, 87%), and trimethoprim/sulphamethoxazole (100%, 78%). All the indoor samples were contaminated with pathogenic bacteria and there was high level of multidrug resistance. This is very dangerous to the students’ health because it can be a source of transmission infections and antibiotic resistant bacterial strains.

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Section: Introductionmentioning

confidence: 99%

Microbiological Assessment of Indoor Air Quality of Student Hostels and Canteens in a Nigerian University Campus: Risks on Students’ Health

Obajuluwa,

Akanni,

Bege

2024

FJS

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Section: Introductionmentioning

confidence: 99%

“…22 Fortunately, there are correlations between indoor bioaerosols and other physical parameters, supplying the possibility to explore a simple, fast and low-cost bioaerosol concentration prediction method. 23 Different environmental factors can affect the distribution and survival ability of indoor microorganisms, such as the survival ability of SARS-CoV-2 and its distribution in the environment. 24 Research has shown that air pollutants, especially particulate matter, and other meteorological factors, are important carriers of infectious microorganisms and play a crucial role in the spread of diseases.…”

Section: Introductionmentioning

confidence: 99%

Prediction models of bioaerosols inside office buildings: A field study investigation

Jiang

Gong

et al. 2023

Building Services Engineering Research and Technology

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Bioaerosols formed by microorganisms in the air directly affect people’s health. The air quality in an office building in Shenzhen, China, is investigated and pollutant levels measured on 36 occasions; six times for each of six indoor spaces. A relationship between indoor bioaerosols and environmental factors was determined using both linear regression analysis and Poisson regression analysis. Our results and analysis indicate that linear regression is a poor predictor for the concentration of bioaerosols based on a single indicator. In contrast, Poisson regression can better predict the concentration of bioaerosols, and PM10 may be the indicator with the greatest impact on bioaerosols. As a result, a simple, fast, and low-cost online monitoring method for monitoring indoor bioaerosols is developed and reported. Our paper provides first-hand basic data to predict the indoor bioaerosol concentration and helps to formulate appropriate monitoring guidelines. The proposed method offers more practical values compared to existing studies as our prediction model facilitates estimation of the concentration of bioaerosols at low cost. Additionally, due to the current maturity and low cost of indoor environmental sensors, the proposed method is suitable for large-scale deployment for most buildings. Practical application Based on measurement data from a real office building, our investigation explores the relationship between indoor microorganisms and building environmental indicators through a combination of probability analysis and actual measurement. We establish a novel indoor microbial prediction model using the Poisson regression model. Our work presents an effective, low-cost, method for estimating the concentration of bioaerosols and discusses the possibility for large-scale deployment of microbial monitoring equipment inside buildings which may then support real-time monitoring of indoor microbial concentration to provide healthy indoor environments for personnel.

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