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!