Air pollution of late has been the focus of many studies due to the detrimental health risks
that it poses to individuals. University environments have several academic departments
with peculiar activities that could be affecting the indoor and outdoor air quality (AQ) of
these environments. University settings differ from other environments because of the
variety of activities and different lines of work that go on inside buildings housing academic
departments and their surroundings, which are likely to have an impact on indoor air
quality (IAQ) and outdoor air quality (OAQ) in this environment. Only a few AQ studies
have been done in university sites and surrounds worldwide and in these studies, IAQ
was given primary importance; whereas, the outdoor environment was and is often
neglected. A study comparing both IAQ and OAQ is critical to further understand the
relationship between IAQ and OAQ within a university campus. The University of Limpopo
(UL) in the Mankweng township of South Africa has been undergoing some
refurbishments with numerous construction activities going on in addition to the academic
activities of UL. These activities may be affecting the AQ in this unique environment. The
main aim of this study was to determine differences between indoor and outdoor AQ in a
university environment and to understand how AQ in this unique environment varies with
seasons and building function. The study was carried out in three buildings housing three
different academic departments in UL namely: Department of Physiology and
Environmental Health (PEH), Department of Biochemistry, Microbiology, and
Biotechnology (BMBT) and the Department of Biodiversity (BIOD). Twenty indoor and 20
outdoor measuring sites were identified per departmental building from where real-time
measurements of 11 AQ parameters (linear air velocity (LAV), dry-bulb temperature (Tdb),
relative humidity (RH), carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), sulphur
dioxide (SO2), nitrogen dioxide (NO2), hydrogen sulphide (H2S), non-methane
hydrocarbons (NMHCs) and volatile organic compounds (VOCs)) were taken over three
consecutive days per season. Thus, a total of 60 indoor and 60 outdoor measurements
were taken for each parameter in each of the three buildings of interest per season,
leading to 360 measurements per season and 1440 measurement per parameter over the
one-year period of study across the study area. A hot-wire anemometer was used to
measure LAV, whereas the Q-Trak indoor AQ monitor was used in the measurement of
Tdb, RH, CO and CO2. Aeroqual AQ monitors were employed in the measurement of O3,
SO2, NO2, H2S, NMHCs and VOCs. The Wilcoxon signed ranks test was used to determine differences between indoor and outdoor environments. Significant differences
were found between the indoor and outdoor environments for LAV (all three buildings),
Tdb (PEH and BMBT), RH (BIOD), O3 (all three buildings), NO2 (all three buildings), CO
(all three buildings), CO2 (all three buildings), NMHCs (BMBT and BIOD), and VOCs (all
three buildings) (p < 0.05). Linear air velocity, O3, SO2, CO, CO2, and H2S
values/concentrations across the indoor/outdoor environments were within the
ASHRAE/DEA/WHO guidelines/standards, whereas Tdb, RH and NO2
values/concentrations were not. Air quality in the study area varied with building, with the
best AQ across both the indoor and outdoor environments being within the BIOD building,
whilst the worst AQ across both environments was encountered in the PEH building.
Seasonal differences between buildings were also identified between indoor and outdoor
environments among the PEH, BMBT and BIOD buildings (p < 0.008). Across the indoor
environment, the winter season was found to be the season with the best AQ, since all
the pollutants were found at minimum concentrations. Factors affecting AQ in the study
area included thermal comfort, occupant densities, building function, laboratory
emissions, renovation activities, generators, vehicular emissions, among others. The best
AQ across the outdoor environment occurred during the autumn season, since all the air
pollutants were present at minimal concentrations during this time. The best predictors of
LAV, Tdb, CO, CO2, NO2, and NMHCs were seasons (R2 = 1.000, p < 0.01). For the
parameters RH, H2S, and VOCs, the best predictor was building type (R2 = 1.000, p <
0.01). The indoor and outdoor environment were the best predictors for SO2 (R2 = 0.999,
p < 0.01). Ozone had no single predictor that was found to significantly influence its
concentration in this study. In relation to an air pollution index (API), generally all pollutant
indices fell within the fair, good to very good range when using mean and maxima
concentrations, whereas, corresponding NO2 concentrations throughout the study fell
within the poor to very poor range (105.660–250.000). University management should
take into consideration ventilation in laboratories, occupant densities and location of
standby generators and car parks in the management of AQ on the university campus. All
heating, ventilation and air conditioning (HVAC) systems need to be upgraded and work
in tandem with natural ventilation when having high occupant densities within buildings.
Future studies in this sector could incorporate larger sample sizes, be designed as a
longitudinal study, and make use of questionnaires and sample more AQ parameters to
get a detailed understanding of a university site and its surrounds. / Environmental Sciences / Ph. D. (Environmental Science)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/27534 |
| Date | 23 June 2021 |
| Creators | Mundackal, Antony Jino |
| Contributors | Ngole-Jeme, Veronica |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1 online resource (xvi, 234 leaves) : color illustrations, color graphs, application/pdf |
Page generated in 0.0028 seconds