Mapping lake water quality & ALS risk factors across northern New EnglandNathan Torbick: ntorbick@appliedgeosolutions.com, Megan Corbiere

Applied Geosolutions, LLC, Newmarket, New Hampshire, USAElijah Stommel, Dartmouth Hitchcock Medical Center

OverviewConcern over water quality and human health is rising as anthropogenic

eutrophication and climate change are increasing the frequency, magnitude, and intensity of harmful algal blooms. The cyanotoxin, Beta-Methylamino L-Alanine (BMAA), has been identified as a risk factor for Amyotrophic Lateral Sclerosis (ALS). ALS is a progressive motor neuron disease with unknown etiology. Amyotrophic lateral sclerosis (ALS) is the most common adult onset motor neuron disease. The pathologic hallmark of ALS is the selective death of motor neurons in the brain and spinal cord that innervate skeletal muscles, producing debilitating symptoms of progressive weakness, muscle wasting and spasticity. A lack of water quality information has limited our ability to understand linkages among cyanobacteria toxicity and human health.

The goal of this project is the development of operational water quality and cyanobacteria risk maps that utilize multiscale satellite remote sensing platforms to drive an eco-epidemiological model for evaluating linkages between water quality and Amyotrophic Lateral Sclerosis.

Figure: NASA EOS Terra MODIS image. Lake Erie western basin, Microcystis aeruginosa bloom

Approach & Results Operationalize Landsat maps of lakes >8 hectares for for wall-to-wall

mapping of Chl-a, TN, and SD Band ratio regression using MODTRAN corrected water leaving radiances Operationalize cyanobacteria metrics for lakes >100 hectares with MERIS

using spectra derivative shape filters

Figure: Example chlorophyll-a n-fold validation (left) used to help determine optimal algorithm. Scatterplot (center) between out of sample predictions and observations for chlorophyll-a concentrations were relatively strong (rho:0.80, p-vale<0.000) indicating a rigorous and scalable index. Example map product

(right) using Landsat for one time slice across northern New England.

Clustering routines for examining ALS hot spots across northern NE

Figure: Local Indicator of Spatial Autocorrelation Map with a tract-level aggregation of ALS cases represented as normalized excess case counts. Map of

the levels of statistical significance where clusters are present.

Spatial multi-logistic regression, Receiver Operating Characteristic , Area Under the Curve used to generate eco-epidemiological modeling framework

Figure: Risk of belonging to a localized cluster of higher than expected ALS counts based on eco-epidemiological model at census tract scale.

Results & Conclusions

A large portion of the spatial autocorrelation in hot spot membership is explained by use of spatially varying risk factors, as seen in the variograms above

Summary modeling results indicating role of water quality metrics influencing ALS cluster membership; lake water quality is an important risk factor

Minimum lake distance, number of lakes within 10km (NL10km), secchi depth of lakes within 30 km (SD30km), chlorophyll-a of lakes within 10 km (CHLA10km), and area weighted total nitrogen for lakes within 30 km (TN30km) were all significant predictors of ALS hot spots at a 5% level of significance

The odds of belonging to an ALS hot spot are approximately 2.7 times higher with an increase in total nitrogen and 59% lower for each meter increase in secchi depth

Figure: Currently assessing role of climate change / lake temperature trends as driver of lake status by using archives of thermal imagery and NOAA buoys for calibration

Operational Landsat metrics for Chl-a, TN, SD for all lakes >8 ha feasible

R2 range from 0.65-0.88; tradeoff between research vs. products

Operational MERIS metrics for Chl-a, PFT/cyanos, PC for large lakes feasible with shape based techniques

Next Steps Refining model and scaling procedures and integration methods

underway

Adding California, Finland, and Florida ALS case studies

Thanks to:

Map of risk hot spots: WQ & Geo Model by Tract

Data Approach relies on multiple satellites:o Landsat 5 TM & 7ETM+, and LDCM scale imagery 8-day intervals, 30m spatial, 180km swath, broad vis-nir channels

o MERIS ESA platform, ~weekly Full Resolution, ~240m spatial, 15 narrow bands

across vis-nir domain o MODIS high temporal frequency, 250m -1km spatial, large area, 2001- present Human health ALS databaseo Dartmouth Hitchcock Medical Center is compiling an ALS database with more than

800 cases across northern New England. Records from DHMC, the Muscular Dystrophy Association of Northern New England, and surveys were searched to identify cases of ALS diagnosed between January 1997 and October 2009. When possible, we confirmed accuracy of diagnosis, year of diagnosis, and demographic history of patients identified by review of medical records, the Social Security Death Index, obituaries, and data supplemented from questionnaires.

o Strict adherence to privacy and high standards for handling human health data

Coeffi cients:Estimate Std. Error z value Pr(>|z|) OR 2.50% 97.50%

(Intercept) -0.89885 1.01519 -0.885 0.375943 (Intercept) 0.4070379 0.05702247 3.0859086NL10km 0.02048 0.01885 1.086 0.277377 NL10km 1.0206885 0.98226504 1.0581664CHLA10km 0.04047 0.02408 1.681 0.092794 CHLA10km 1.0413043 0.99217194 1.0919685SD30km -0.93155 0.23429 -3.976 7.01E-05 SD30km 0.3939408 0.24391784 0.6106643TN30km 0.97772 0.26089 3.748 0.000179 TN30km 2.6583829 1.6014012 4.526794

Null deviance: 507.90 on 708 degrees of freedomResidual deviance: 436.72 on 703 degrees of freedomAIC: 448.72Number of Fisher Scoring iterations: 6