Development of Rapid Prototyping Capability to Evaluate Potential Uses of NASA Research Products and Technologies to Estimate Distribution of Mold Spore Levels over Space and Time UMMC Fazlay Faruque

Team Organizations University of Mississippi Medical Center Science Systems and Applications, Inc. (SSAI) Mississippi State University Jackson State University

Major 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 FOR MICROSCOPIC 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 SPORE COUNTS

Winter November 2007 – February 2008

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

Daily Cladosporium and Aspergillus/Penicillium Spore Bursts in Winter with Monthly Average Temperature and Daily Hours of Leaf Wetness Cladosporium Aspergillus/Penicillium

Very WetVery DryDry

Summer June 2008

REGRESSION ANALYSIS

Regression Analyses Regression analyses are being performed to investigate the strength of the relationship between measurements of spores/m 3, 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/m 3 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, 6 th 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/m 3 and each of the weather variables and NDMI Select the top weather variables based on r values to include with spores/m 3 and NDMI in the regression analysis Perform regression analyses using R statistical software package

Monitoring Sites SiteLocation (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 N, W ForestedLeaf wetness; SMSB (water mark soil moisture) 9915 UMC N, W Urban; located on top of building SRD (solar radiation, W/m2)9415 Flora N, W ForestedLeaf wetness; SMSB (water mark soil moisture); 2” and 4” soil temperature 9715 Harrisville N, W Mixed; rural; suburbs Leaf wetness; SMSB (water mark soil moisture) 5721 DEQ N, W UrbanTold to use UMC weather data9727

MODIS: Moderate Resolution Imaging Spectroradiometer SWIR: shortwave infrared NIR: 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. 6/29/2015

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 Preparation 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 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