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Pollencatchers – An NWSP project by NUS High School
Christopher ChangJohannes Liew
Kylie GohViona Lam
Table of Contents• Literature Review• State of Current Research• Aims and Objectives • Hypothesis• Methodology • Data Analysis• Relevance of our project • Implications of Global Climate Change • Conclusion
Literature Review
• Aerobiology and Airspora• Prevalence of airspora
dependent on weather conditions
• Tropical airspora do play a part in allergic diseases
State of Current Research
• Research has been done in this
field to relate weather variables
with airspora count and
composition, however rarely in a
local context.
Pictures of Pollen
Allergenic Tree/Fern/Palm SporesScientific name Common name Spore type
Acacia auriculiformis Common acacia Tree pollen
Asplenium nidus Bird's nest fern Fern spore
Casuarina equisetifolia Rhu Tree pollen
Dicranopteris curranii Resam fern Fern spore
Dicranopteris linearis Resam fern Fern spore
Elaeis guineensis Oil palm Palm pollen
Nephrolepis auriculata Ladder fern Fern spore
Pteridium aquilinum Bracken fern Fern spore
Stenochlaena palustris Climbing swamp fern Fern spore
Hypothesis
• Pollen and Fern spore count is
directly related to temperature,
solar energy and wind speed,
and inversely related to
humidity and rainfall.
Aims and Objectives
• To identify a correlation between
airspora (pollen and fern spores)
composition and count and weather
variables
• To identify weather variables that
indicate airspora density
Materials
• Main Materials Needed
– 1 Burkard spore trap
– 1 weather station
– Light microscopes
How the Burkard Spore Trap works
General Methods• Set up Burkard Spore Trap and Davis mini-
weather station to collect data• Load with drum and change drum weekly• Dissect and mount tape from previous
drum• Prepare drum for the next week’s data
collection• Under a light microscope, scroll
horizontally through the tape, taking 5 longitudinal bands
• Determine trends between airspora count and weather variables
Methods for Preparation of Drum1. Screw the drum onto the laboratory
stand2. Clean the drum3. Adhere Melinex tape around the drum
using double-sided tape4. Clean the surface of the Melinex tape
with tissue5. Coat the tape with a thin and evenly
spread layer of Silicon grease6. Keep the drum in the drum-carrying
case
Methods for dissection and mounting the tape
1.Align the Melinex tape with the markings on the Perspex Glass Cutting Block
2.Based on the markings, divide the tape into sections with the needle and scissors
3. Mount the sections of the Melinex tape onto glass slides with water
4. Label the slides (Time, date, Week of data collection)
Data Analysis - Justification of Choice• Lack of independent variables in
our study • Use of correlation analysis (instead
of regression analysis) and non-parametric Spearman’s correlation
• Lack of line of best fit on our scatter-plot graphs
Data AnalysisAirspora Count against Outside Humidity
-2
0
2
4
6
8
10
12
14
60 65 70 75 80 85 90 95 100
Humidity/%
Air
spo
ra C
ou
nt
R = - 0.42, P < 0.01, N = 404
Data AnalysisAirspora Count against Rainfall
-2
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30
Rainfall/mm
Air
sp
ora
Co
un
t
R = - 0.190, P < 0.01, N = 404
Data AnalysisAirspora Count against Temperature
-2
0
2
4
6
8
10
12
14
22 23 24 25 26 27 28 29 30 31 32 33 34
Temperature/degrees Celsius
Air
spo
ra C
ou
nt
R = 0.434, P < 0.01, N = 404
Data AnalysisAirspora Count against Solar Energy
-2
0
2
4
6
8
10
12
14
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170
Solar Energy/Langleys
Air
sp
ora
co
un
t
R = 0.420, P < 0.01, N = 404 Note: 1 Langley = 11.622 Watt-hours per square meter
Data AnalysisAirspora count against Wind Speed
-2
0
2
4
6
8
10
12
14
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Wind Speed/ m/s
Air
sp
ora
Co
un
t
R = 0.292, P < 0.01, N = 404
Summation• All of our trends are valid (P<0.01)• Airspora count is
– Inversely proportional• Humidity (r = -0.420)• Rainfall (r = -0.190)
– Proportional• Temperature (r = 0.434)• Solar Energy (r = 0.420)• Wind speed (r = 0.292)
• Based on values of r; temperature, outside humidity and solar energy are the best indicators with which to predict airspora count.
Data Analysis
• Similar trends in other research; but with varying relative strength of trends
• HCI only identified wind speed and humidity as considerable factors influencing airspora count (Wind speed is the 2nd weakest trend in our study, and we have found other stronger trends)
“How wonderful it is that nobody need wait a single moment before starting to improve the world.” - Anne Frank
Relevance of our project
• Our research helps …
• People who are asthmatic or allergic to asthma
• The general public
• Health authorities
• People who own artwork and furniture
• Researchers
Implications on and of Global Climate Change
• Global warming caused by the greenhouse effect
• Airspora count may increase as a result
• Action must be taken immediately - reducing CO2 emissions, finding alternative sources of energy …
Conclusion of Study
• We need more data sets, and more research on similar hypothesis are needed to conclude our findings into general trends.
• Further research: Trends of specific airspora species; Relating airspora count to other variables; Investigation of seasonal and diurnal cycles
THE END!