Development of Rapid Prototyping Development of Rapid Prototyping Capability to Evaluate Potential Uses Capability to Evaluate Potential Uses of of NASA Research Products and NASA Research Products and Technologies to Estimate Distribution Technologies to Estimate Distribution of Mold Spore Levels of Mold Spore Levels over Space and Time over Space and Time UMMC UMMC Fazlay Faruque Fazlay Faruque
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Development of Rapid Prototyping Capability to Evaluate Potential Uses of NASA Research Products and Technologies to Estimate Distribution of Mold Spore.
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Development of Rapid Prototyping Development of Rapid Prototyping Capability to Evaluate Potential Uses of Capability to Evaluate Potential Uses of
NASA Research Products and NASA Research Products and Technologies to Estimate Distribution of Technologies to Estimate Distribution of
Mold Spore Levels Mold Spore Levels over Space and Timeover Space and Time
UMMCUMMC
Fazlay FaruqueFazlay Faruque
Team OrganizationsTeam Organizations
University of Mississippi Medical Center
Science Systems and Applications, Inc. (SSAI)
Mississippi State University
Jackson State University
Major Steps CompletedMajor Steps Completed
•Installation of field meteorological data monitoring stationsInstallation of field meteorological data monitoring stations
•Installation of mold spore monitoring stationsInstallation of mold spore monitoring stations
•Majority of the mold spore slide sample preparationMajority of the mold spore slide sample preparation
•Partial mold spore genera identification and countingPartial mold spore genera identification and counting
•Collection of NASA data productsCollection of NASA data products
•Preliminary analysis and model developmentPreliminary analysis and model development
PREPARING SAMPLES FORPREPARING SAMPLES FOR
MICROSCOPIC EXAMINATIONMICROSCOPIC EXAMINATION
Lift start of tape sample from 2X tape with spatula
Placing 24HR tape section onto Gelvatol bead
1. Apply tape to Gelvatol bead 2. “pull” tape between forceps & slicer blade
3. If bubbles form…
4. Lift tape with slicer blade to release them
5. Allow Gelvatol to dry/harden before applying stain
LAB SETUP
GENERAL ANALYSIS OF GENERAL ANALYSIS OF SPORE COUNTSSPORE COUNTS
WinterWinterNovember 2007 – February 2008November 2007 – February 2008
Counting Spore Bursts Counting Spore Bursts
•If the number of a specific mold spore genera within a field is 50% or more of the total spore count within that field
•Threshold of burst is 10 spores per field
•Burst frequency: •[# fields with burst / total field (24)]x100
Average Daily Total Spore Counts for Collection Sites Evaluated for Genera vs CladosporiumDominant Burst Spore Counts at Specific Sites in Winter
Frequency during the winter of the top 11 identified spore bursts varied significantly across thecollection sites, P<0.0001,Two-way ANOVA. Aspergillus/Penicillium, Stereum and Cladosporiumhad the highest frequencies, P<0.05, Kruskal-Wallis test with Dunn's Multiple Comparison Test*Cladosporium: Level associated with severity of ashma*Aspergillus/ Penicillium: Exposure in some produces respiratory involvement *Fusarium: Allergen with same potential reaction as Aspergillus/ Penicillium and toxin producer
Fre
qu
ency
Daily Cladosporium and Aspergillus/Penicillium Spore Bursts in Winter with Monthly Average Temperature and
Daily Hours of Leaf Wetness
Monthly Rainfall
0
50
100
150
200
Nov 07 Dec 07 Jan 08 Feb 08 Mar 08 Apr 08 May 08 Jun 08
Cu
mu
lati
ve R
ain
Fa
ll (m
m)
Daily Rainfall (mm) Dec-Feb 2007-2008
010203040506070
Daily Hours Leaf Wetness >= 6 Dec-Feb 2007-2008
0
5
10
15
20
25
0
2.5×10 3
5.0×10 3
7.5×10 3
1.0×10 41.0×10 4
1.1×10 5
2.1×10 5
3.1×10 5
4.1×10 5
5.1×10 5
6.1×10 5
7.1×10 5
Sp
ore
s/m
3a
ir
Cladosporium Aspergillus/Penicillium
Stereum Spore Bursts in Winterand Daily Average Temperature
12/2
1/07
12/2
3/07
1/11
/08
1/12
/08
1/17
/08
1/18
/08
1/21
/08
1/22
/08
1/24
/08
1/25
/08
1/28
/08
1/29
/08
2/2/
08
2/3/
08
2/4/
08
2/8/
08
2/15
/08
2/16
/08
0
1500
3000
4500
6000
7500
9000
10500
12000
13500
Sp
ore
s/m
3 ai
r
Average Daily Temperature (Celsius) Dec21-Feb16 2007-2008
-5.00.05.0
10.015.020.025.0
Daily Fusarium Spore Bursts in Winterand Daily Rainfall (mm)
12/2
4/07
1/5/
08
1/26
/08
1/26
/08
1/31
/08
2/3/
08
2/12
/08
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
Sp
ore
s/m
3 ai
r
Daily Rainfall (mm) Dec24-Feb12 2007-2008
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Alternaria Weekly Spore Counts FromNovember through February and Soil Moisture
11/1
0/20
07
11/1
7/20
07
11/2
4/20
07
12/1
/07
12/8
/200
7
12/1
5/20
07
12/2
2/20
07
12/2
9/20
07
1/5/
2008
1/12
/200
8
1/19
/200
8
1/26
/200
8
2/2/
2008
2/9/
2008
2/16
/200
8
2/23
/200
80
10
20
30
40
Sp
ore
s/m
3 ai
r
Average Daily Soil Moisture (cbars) Nov10-Feb23 2007-2008
0.0
10.0
20.0
30.0
40.0
50.0
Average Monthly Soil Moisture (cbars)
010203040
50607080
Nov 07 Dec 07 Jan 08 Feb 08 Mar 08 Apr 08 May 08 Jun 08
Of the top 6 fungal spore bursts Cladosporium and Stereum had the highest frequencyof bursts during June, P<0.01, One-way ANOVA and Tukey's Multiple Comparison Test.Fungal spore bursts were highly variable in frequency at all the collection sites,P<0.0001, Two-way ANOVA.*Cladosporium: Level associated with severity of ashma*Aspergillus/ Penicillium: Exposure in some produces respiratory involvement Fusarium: Allergen with same potential reaction as Aspergillus/ Penicillium and toxinproducer
Fre
qu
ency
Daily Rainfall (mm) June 2008
0.001.002.003.004.005.006.007.008.009.00
10.00
Daily Hours RH>=80% June 2008
0.002.004.006.008.00
10.0012.0014.0016.0018.0020.00
Daily Hours Leaf Wetness >=6 June 2008
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Daily Cladosporium Spores Bursts in Junewith Rainfall, RH>=80% and Leaf Wetness>=6
0
2500
5000
7500
10000
12500
15000
17500
20000
22500
25000
27500
30000
32500
35000
37500
Sp
ore
s/m
3 a
ir
Daily Aspergillus/Penicillium Spore Bursts in Junewith Daily Temperature, Rainfall and Wind Run
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
Sp
ore
s/m
3 a
ir
Average Daily Temperature (Celsius) June 2008
21.022.023.024.025.026.027.028.029.030.0
Daily Rainfall (mm) June 2008
0.001.002.003.004.005.006.007.008.009.00
10.00
Wind Run (km/day) June 2008
0.020.040.060.080.0
100.0120.0140.0160.0
Daily Stereum Spores Bursts in Junewith Daily Temperature, RH>=80% and Rainfall
0
10000
20000
30000
40000
50000
60000
Daily median Stereum spore bursts vary significantly over the month of June,P<0.001, Kruskal-Wallis test.
Daily Alternaria Spore Bursts in Junewith Daily Temperature
0
25
50
75
100
125
150
175
200
225
250
Sp
ore
s/m
3 ai
r
Average Daily Temperature (Celsius) June 2008
21.022.023.024.025.026.027.028.029.030.0
REGRESSION ANALYSISREGRESSION ANALYSIS
Regression Analyses
• Regression analyses are being performed to investigate the strength of the relationship between measurements of spores/m3, NDMI (Normalized Difference Moisture Index), and various weather-related variables
• The ultimate goals are to identify which variables play the largest role in predicting spores/m3 and to develop a model
• The preliminary regression analysis involved the following: – Datasets for analysis
• Weather data including temperature, rainfall and humidity
• Mold spore count data available for most days in time periods
• NDMI values from MODIS time series analysis
– Focus on 5 of 6 sites, 6th site was removed because of problems for NDMI
– Collection 11/2007 – 11/2008, current available:• 11/1/2007– 2/29/2008
• 6/1/2008 – 7/1/2008
– “Global” analysis, in which data from all 5 sites were included, as well as site-specific analysis
Regression Analysis
Regression Analysis Methodology
• Prepare weather data– Compute daily average, maximum, and minimum for each variable, as well
as 2-day average max temperature, 7-day cumulative rainfall, and hours of relative humidity greater than 80%
– Use only those variables that are common to all sites
• Remove data from days for which there was no mold count data
• Generate Pearson’s correlation coefficient, r, to show strength of the relationship between spores/m3 and each of the weather variables and NDMI
• Select the top weather variables based on r values to include with spores/m3 and NDMI in the regression analysis
• Perform regression analyses using R statistical software package
Monitoring Sites
Site Location(Latitude,
Longitude, in decimal degrees)
Land Cover of Site
Weather Data(All sites have air temp, relative humidity, dew point, rain, wind
speed and direction. Additional measurements are listed below.)
No. of Daily Observations or Mold Spore Counts
Winter 07 | Summer 08
Terry 32.086042 N,
-90.371737 W
Forested Leaf wetness; SMSB (water mark soil moisture)
99 15
UMC 32.333251 N,
-90.170742 W
Urban; located on top of building
SRD (solar radiation, W/m2) 94 15
Flora 32.581822 N,
-90.335887 W
Forested Leaf wetness; SMSB (water mark soil moisture); 2” and 4” soil temperature
• NDMI (Normalized Difference Moisture Index)– Indicator of moisture content in vegetation foliage
– NDMI = (NIR–SWIR) / (NIR+SWIR)
• Time series of daily NDMI values – Generated using the NASA SSC-developed TSPT
(Time Series Product Tool) and 500 m daily MODIS reflectance data (MOD09GA)
– Extracted for each monitoring site and used in the regression analysis
TSPT – Output Example
• Example time series: – MODIS NDVI time series for Mobile Bay area with filtering
and cloud removal applied using the TSPT.
Sample NDMI Time Series
Preliminary Results of Regression Analysis
• Multiple linear regression was used to determine a model that best predicted the mold spore counts
• Based on their individual r value, the variables were systematically removed from the multiple linear regression and the suitability of the model was assessed
• The model developed using all 6 variables had the highest overall r value, but most of the variance seems to be captured by rainfall and solar irradiance.
04/18/23
Observations about Preliminary Regression Results
• Single site results are consistent with the literature – modeling multiple sites more challenging
• Benefits of NDMI are in question• Solar irradiance related variables performed
surprisingly well – may be able to add rapid assessment of this variable as possible remote sensing input
Status of Additional Earth Observations
• MODIS Aerosol MOD04 Products– Downloaded for entire time frame– Data being assessed for usability
• TRMM Rainfall data– Communicating with Goddard DAAC– Expect to receive and incorporate data shortly
Status of Mold spore Sample Status of Mold spore Sample Preparation and CountingPreparation and Counting
• Number of slides prepared for counting: 2515• Number of slides counted: 658• Number of slides remained to be counted: 1857
Additional Counting Helps Now Available:• Dr. John Coleman, Associate Professor, Microbiology • Alicia Epps, PhD student
Next StepsNext Steps• Additional data sets
– Include NOAA temperature
– Obtain and incorporate TRMM satellite rainfall product into the regression analysis
– Determine whether the MODIS aerosol product is suitable for incorporation into the regression analysis
– Consider the possibility of CERES solar irradiance products
• Calculate short (1- to 5-day) time lags for the most key weather variables and perform regressions to see effect
• Investigate differences between monitoring sites and their models