HomeMy WebLinkAbout01. Excellence in Water Research Awards Student Winner presentation Page 1 of 2
Item 1.
CENTRALSAN
Jdf A- hom
CENTRAL CONTRA COSTA SANITARY DISTRICT
May 20, 2021
TO: HONORABLE BOARD OF DIRECTORS
FROM: EMILY BARNETT, COMMUNICATIONS AND INTERGOVERNMENTAL
RELATIONS MANAGER
REVIEWED BY: PHILIP LEIBER, DIRECTOR OF FINANCE AND ADMINISTRATION
ROGER S. BAILEY, GENERAL MANAGER
SUBJECT: PRESENTATION BYMELANIE QUAN, 12TH GRADERAT LAS LOMAS HIGH
SCHOOL IN WALNUT CREEK, WINNER OF THE CONTRA COSTA COUNTY
SCIENCE AND ENGINEERING FAIR - EXCELLENCE IN WATER,
WASTEWATER AND RECYCLED WATER RESEARCH AWARD:
FIRST PLACE, SENIOR DIVISION: "ANALYSIS OF PLASTIC PELLET
DISTRIBUTION USING CITIZEN SCIENCE NURDLE PATROL DATA
AND BATCH IDENTIFICATION TO DIFFERENTIATE SPILLS WITHIN
SAMPLES"
The 2021 Contra Costa County Science and Engineering Fair took place March 12th, 2021 virtually. This
was the eighth year that eleven Contra Costa County water and wastewater agencies combined efforts to
create the Regional Excellence in Water, Wastewater and Recycled Water Research Awards. Central
San staff members helped coordinate the event and judge the technical exhibit submissions.
Within Central San's service area, the following award winner was named:
Senior Division
First Place
Melanie Quan
Las Lomas High School, Walnut Creek.
Project Title: "Analysis of Plastic Pellet Distribution Using Citizen Science Nurdle Patrol Data and Batch
Identification to Differentiate Spills Within Samples"
Prize: $800
Ms. Quan, who is in 12th grade at Las Lomas High School in Walnut Creek, will give a presentation on her
project at the meeting.
Ms. Maria Laws, Miss Quan's sponsor teacher, will also receive a prize of$100.
May 20, 2021 Regular Board Meeting Agenda Packet- Page 7 of 457
Page 2 of 2
Strategic Plan Tie-In
GOAL ONE: Customer and Community
Strategy 1—Deliver high-quality customer service, Strategy 2—Maintain a positive reputation
May 20, 2021 Regular Board Meeting Agenda Packet- Page 8 of 457
Item 01.
(Handout)
ANALYSIS OF PLASTIC PELLET
DISTRIBUTION USING CITIZEN
SCIENCE NURDLE PATROL DATA AND
BATCH IDENTIFICATION TO
DIFFERENTIATE SPILLS WITHIN SAMPLES
Melanie Quan
Research Science Institute 2020
Under the direction of
Colleen Johnson,EarthCon Consulting Inc.
Jace Tunnell,University of Texas Marine Science Institute,Founder of Nurdle Patrol
1
INTRODUCTION- PLASTIC POLLUTION
AL
Figure 1:Macro-and Micro- plastics in the marine environment
2
1
INTRODUCTION- NURDLES
r'
Making oWW SPILLS IN MICS
R
yY� nurdles aem
ea
...�a RBCyCIEd FAe���m e0 •r��IR76LNSR�
intonurdles mea m 0e
Melted
- together
SMIISArSEA
a m e m a m Shipped around m�@ SPILLS INTRANSIT
eQ-Z LOST A7 SEA the world a
Figure 2:Nurdles(left)and nurdle production cycle(right)
3
INTRODUCTION- NURDLES IN MARINE FOOD WEB
Y r
fit, a
Figure 3:Fish eggs(left) and nurdles(right)
4
2
INTRODUCTION- NURDLE SPILLS IN TRANSIT
Figure 4:2018 Durban Harbour,South Africa(left) and nurdles(top)and Nurdle Spills Along Railroads(right)
5
PURPOSE- CITIZEN SCIENCE WITH NURDLE PATROL
Goal 1: Determine relationship between nurdle manufacturing sites and nurdle
concentrations using Nurdle Patrol data.
I
N 't
des I
.moi..
r
Figure 5:Nurdle Patrol reports
6
3
PURPOSE- NURDLE BATCH ANALYSIS
Goal 2: Determine the number of batches of nurdles in selected Nurdle Patrol samples.
I
ti
Figure 6:Collections of nurdles from various batches
7
METHODS- DETERMINING SAMPLE REGIONS
Region I
Region 2
.0
Region 3
Fig 9:Regions in Gulf of Mexico that will be analyzed for trends in nurdle distribution
8
4
METHODS- MANUFACTURERS AND RAILROAD
CROSSINGS
• i �
Data source:US EPA Toxics Release Inventory Dan source:US Deparonenz'olTnnzporeuon
Inivgo.Mclonic Quan(scl� Image:Melanie Q..(sell)
Fig 11:Locations of plastic manufacturers Fig 12:Locations of railroad crossings
9
RESULTS/DISCUSSION- DISTANCES FROM PLASTIC
MANUFACTURERS ACROSS REGIONS
Distances from Plastic Manufacturers Across Regions
Region 4 Reglan 2 Reglan 3
300 000
`oywoi zoo = `o my
4.
100
v o 0
0 i 10 is m 25 90
Distance Ikm] dslen[e�Ivn� O�slan[e(kms
Distances from Railroad Crossings Across Regions
Reglvn 1 Reolw 2 Regi-3
700 300 • 300
2001 .L]00 200
4 = I
0 o v
0 s f0 1s 20 OS 0 10 20 10 40 w 0 x w N 10 in
OisGnce ikmi D.sU ce ikm} GieNrce(km}
10
5
METHODS- SAMPLE PREPARATION
" !I► i 9 samples
,,, • • W 707 nurdles
A �C B • 40 C n
1 • t�'� JvL
d Cleaned,photographed,
catalogued samples
D E • F
Prepared 355 nurdles
G H for FTIR
► -
io
Fig 14:Nurdle collection samples
11
METHODS- POLYMERS AND ADDITIVES IDENTIFIED TO SORT
BATCHES
Additive Physical Appearance
lyethylene Zinc Oxide Hollow Center
7IMe7di.. DensFity Polyethylene Alumna Silicate Cylindrical
High Density Polyethylene Quaternary Ammonium Compound Flat Disk
Polypropylene Paraffin Wax Sphere
Ethylene Propylene Diene Stearmide Cube
Monomer Irganox 1093 White
Polystyrene Talcum Clear
Polyvinyl Chloride Calcium Stearate Black
Polyethylene+ Polypropylene Octadecanoic Acid Brown
Polycarbonate Polyolefin Green
Poly(ethylene co-acrylic acid) Methyl Tin Mercaptide Red
Polyamide Dioctadecyl 3,3-thiodipropionate Blue
Dibasic Lead Stearate Yellow
Pentaerythritol
12
6
RESULTS/DISCUSSION- BATCHES IN SAMPLES
Humber of Different Batches Olverslty of Plastic Types Atl q Sample Locattans
in 10 Minute Collection SamplesL
,
. aw
oyi 1 aU .Shvio a wopl W Deft
L 89 different belchesen .Mwi-oeemy Pd"".
V woss all Samples .Hen D—ty Pdyelvywm
.Pcgeaygne waaNlc eee
M 75 pdml�pmre
O $a _ Pcilvxgl Oh6Me
� iti IP Pdyemylene•PuAWeP'A�
Po1�a�hmam
25 23 ;tZ 25 14 12 11 16 2f 20 17 sn
a
A B C D E F G H I Total
■ e c o f F c x
Sample senwl
�4f 1 i� are.,ee aumm.we,��n.°im
Hann ve s°um samoi�
... t
13
Pe rce nl Batch Composition of Samples 89 Batches identified
10e LOPE 1 = PP
t LOPE 2 PP 2
!• LOPE3 PP3
>• LOPEr PP
f LOPE5 PPS 5
LOPE 8 PP 8
-. LOPE7 PP
- n~F LOPE 8 PPS
so ■ LOPED PP 9
LOPE 10 PP 10
LOPE 11 PP 11
-0 LOPE 12 PP 12
�_LOPE is �e
■ fl♦ LOPE 15
w LOPE 16 W 041
HOPE
DPE 17 Lp HOPE 3
[Lppppp �(gg1 Hl7PE 1,
'€ se ■ . =LPOPP1 2 211 >•PS 1 5
■ ■ t �3
LOPE 28 PVC3
LIE 27 m' pVCJ na LOPE 2$ MOPE 1
9!-, LOPE 29
LOPE MOPE 2
30= MOPE 3 LOPE 31
LOPE 32 MBS
°4 LOPE 33 MI7PE fi
LOPE 34 EPOM 1
LOPE 35 w EPOM 2
W
LOPE$6 EPDM$
2e = LDP 1E 37 s EPOM A
LOPE 38 EPOM 5
1111111 LOPE 39 EPOM 6
LOPE 90 EPOM 7
LOPE+EPOM 1
s. PE•PP1
PE+PP2
PE PP+EPOM 1
o s
PP EPOM 1
P
>♦ olycarbonsle 1
q �� , a Pelyamide e
Polyethylen co-acryl�c a�
Fig 20:Batches in samples
14
7
CONCLUSIONS
1.More factors need to be considered in larger model. 2.Identification of unreported spills.
distance from plastic
manufacturers and
railroad crossings
tides
geography winds Manufacturers
currents Z
weather c3
production batch
OG \L�1
shipping routes
etc. so;;� n LARGE SPILLS
�nSPortation and hay
Influence state and federal legislation to better manage and prevent nurdle pollution.
15
FUTURE WORK
Multi-variable model of , ' Hindcast and forecast
nurdle distribution models for spills
••
)00 -
L
Expansion of Nur Understanding different
�y�,�� Patrol sampling batches in distribution
•-11��"
16
8
ACKNOWLEDGEMENTS
• JaceTunnell and Colleen Johnson
• Ana Lyons
• Edward Njoo
• Fremont STEM Labs
• Research Science Institute
• Center for Excellence in Education
• Massachusetts Institute of Technology
• Department of Defense
• Sponsors of CEE
• Citizen Scientists of Nurdle Patrol
17
QUESTIONS
bo
A i b s • • c
. - A
D J'i � E r F � 1
r
0
G W H - I
I
Source:Melanie Qua,(,e10
18
9
FOURIER-TRANSFORM INFRARED SPECTROSCOPY (FTIR)
Low Density Polyethylene
110-
100
ca
vEi 90
c
R
i
i—
0
80
functional group region fingerprint region
70
4000 3000 2000 1000
Wavenumber (cm i)
19
FOURIER-TRANSFORM INFRARED SPECTROSCOPY (FTIR)
LDPE 2 LDPE1
100 100
fir
U C.]
C �
N 5()- E 5D
C �
f6 �
v
a
0 0
4000 3000 2000 1000 40DD 3D00 20D0 100D
wavenumber(cm-) wavenumber(cm-)
Sample IR spectra of LDPE from different batches
20
10
RESULTS/DISCUSSION- COMMON FTIR SPECTRA OF PLASTIC
TYPES
LOPE PPI HDPE1
100 100 foo
q u 99 u
E 30 E 98 �
96
C �
0 96 9d
4000 9000 2000 1000 4000 ]000 2000 1000 4000 3000 7000 1000
wavenumber(=1] wavenumber(cm') wavenumber{cm'
EPDM3 PSI PE+PP+EPDM 1
100 loo 100
��
90 v
rk
98G.-
0 a 99
F 98 T Y
E E gp E
g7 97
ae 98 70 96
95 80 95
4000 3000 2000 1000 4000 7000 2000 1000 d00o x000 $000 1000
wavenumber(C 1) wavenumber(e wavenumber(ar 1)
Fig 19:Sample IR spectra
21
11