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

12/

6/07

12/

7/07

12/

8/07

12/

9/07

12/

10/0

7 1

2/11

/07

12/1

3/20

0712

/14/

2007

12/1

5/20

0712

/16/

2007

12/1

7/07

12/1

8/20

0712

/20/

2007

12/2

1/20

0712

/22/

2007

12/2

3/20

0712

/24/

2007

12/2

5/20

0712

/27/

2007

12/2

8/20

0712

/29/

2007

12/3

0/20

0712

/31/

2007

1/1/

2007

1/3/

2008

1/4/

2008

1/5/

2008

1/6/

2008

1/7/

2008

1/8/

2008

1/10

/200

81/

11/2

008

1/12

/200

81/

13/2

008

1/14

/200

81/

15/2

008

1/17

/200

81/

18/2

008

1/19

/200

81/

20/2

008

1/21

/200

81/

22/2

008

1/24

/200

81/

25/2

008

1/26

/200

81/

27/2

008

1/28

/200

81/

29/2

008

1/31

/200

82/

1/20

082/

2/20

082/

3/20

082/

4/20

082/

5/20

08

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

AllSpores12/6/07-2/5/08Cladosporium Dominant Burst

Terry

UMCDWFP

DWFP

Terry

Terry

Flora

Flora

Flora

UMC

DWFP, DEQ

UMC

DWFPFlora

UMC

UMCUMC

Date

Sp

ore

s/m

3 ai

r

Average Daily Total Spore Counts at All Collection Sites vsCladosporium Dominant Burst Spore Counts at Specific Sites in Winter

12/

6/07

12/

7/07

12/

8/07

12/

9/07

12/

10/0

7 1

2/11

/07

12/1

3/20

0712

/14/

2007

12/1

5/20

0712

/16/

2007

12/1

7/07

12/1

8/20

0712

/20/

2007

12/2

1/20

0712

/22/

2007

12/2

3/20

0712

/24/

2007

12/2

5/20

0712

/27/

2007

12/2

8/20

0712

/29/

2007

12/3

0/20

0712

/31/

2007

1/1/

2007

1/3/

2008

1/4/

2008

1/5/

2008

1/6/

2008

1/7/

2008

1/8/

2008

1/10

/200

81/

11/2

008

1/12

/200

81/

13/2

008

1/14

/200

81/

15/2

008

1/17

/200

81/

18/2

008

1/19

/200

81/

20/2

008

1/21

/200

81/

22/2

008

1/24

/200

81/

25/2

008

1/26

/200

81/

27/2

008

1/28

/200

81/

29/2

008

1/31

/200

82/

1/20

082/

2/20

082/

3/20

082/

4/20

082/

5/20

08

05000

100001500020000250003000035000400004500050000550006000065000700007500080000850009000095000

100000105000

AvgTotalSpores12/6/07-2/5/08Cladosporium Dominant Burst

Terry

UMC

UMC

UMC

DWFP

DWFP

Terry

TerryFlora

Flora

UMC

UMC

DWFP, DEQ

UMC

DWFPFlora

Date

Sp

ore

s/m

3 ai

r

Average Total Spore Counts at All Collection Sites vsAspergillus/Penicillium Dominant Burst Spore Counts at Specific Sites in Winter2

/7/0

8

2/8

/20

08

2/9

/20

08

2/1

0/2

00

8

2/1

1/2

00

8

2/1

2/2

00

8

2/1

4/2

00

8

2/1

5/2

00

8

2/1

6/2

00

8

2/1

7/2

00

8

2/1

8/2

00

8

2/1

9/2

00

8

2/2

1/2

00

8

2/2

2/2

00

8

2/2

3/2

00

8

2/2

4/2

00

8

2/2

5/2

00

8

2/2

6/2

00

8

2/2

8/2

00

8

0

1.0×105

2.0×105

3.0×105

4.0×105

5.0×105

6.0×105

7.0×105

8.0×105

9.0×105

1.0×106

1.1×106

1.2×106

1.3×106

1.4×106

1.5×106

1.6×106

AvgTotalSpores 2/7/07-2/28/08Asp/Pen Dominant Burst

H'villeH'ville,Terry

H'ville,TerryH'ville

DWFP,DEQ

DWFP

DWFP,Flora,DEQ

Flora

UMC,Terry,H'ville

UMC,H'ville

Flora,Terry,UMC

Flora,Terry,UMC,H'ville

Terry,UMC,H'ville

DWFP,DEQ

Date

Sp

ore

s/m

3 ai

r

Top 11 Winter Fungal Spore Burst by Site

Harrisville Terry UMC DEQ DWF&P Flora0

102030405060708090

100110120130140150160170180190200210

Cladosporium/Deuteromycete

Aspergillus/Pen/Deutero/AscospStereum/Basidiospore

Geotrichum/DeuteromyceteDiatrypaceae/AscosporeSmut/Myxomycete/BasidiosporeChlorophyllum/BasidiosporeFusarium/DeuteromyceteOther/MyxomycetePericonia/DeuteromyceteFuligo/Myxomycete

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

So

il M

ois

ture

(cb

ars)

Very Wet Very DryDry

SummerSummerJune 2008June 2008

Top 6 Frequent Fungal Spore Bursts by Sitein June

Harrisville Terry UMC DEQ DWF&P Flora0

20

40

60

80

100

120

140

160

180

200

220

240

260

Aspergillus/Pen/Deuteromycete/

Cladosporium/DeuteromyceteStereum/Basidiospore

Diatrypaceae/AscosporeLeptosphaeria/AscosporeFusarium/Deuteromycete

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.

Sp

ores/m

3 a

ir

Average Daily Temperature (Celsius) June 2008

21.022.023.024.025.026.027.028.029.030.0

Daily Hours RH>=80% June 2008

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00

Daily Rainfall (mm) June 2008

0.001.002.003.004.005.006.007.008.009.00

10.00

Daily Fusarium Spore Bursts in Junewith Rainfall

0500

100015002000250030003500400045005000550060006500700075008000850090009500

1000010500

Sp

ore

s/m

3 ai

r

Daily Rainfall (mm) June 2008

0.001.002.003.004.005.006.007.008.009.00

10.00

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

97 15

Harrisville 31.972190 N,

-90.079762 W

Mixed; rural; suburbs

Leaf wetness; SMSB (water mark soil moisture)

57 21

DEQ 32.385430 N,

-90.140559 W

Urban Told to use UMC weather data 97 27

MODIS: Moderate Resolution Imaging SpectroradiometerSWIR: shortwave infraredNIR: near-infrared

MODIS NDMI Values

• 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.

04/21/23

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