James G. Acker
NASA Goddard Earth Sciences Data and Information Services Center /Adnet Inc.
james.g.acker@nasa.gov

Giovanni has great potential for the investigation of different public health issues because a large variety of Earth system data variables are available in the system.

Since its inception in 2003, the NASA Giovanni system has provided access to a wide variety of NASA remote sensing data and other Earth science data sets, allowing many different kinds of researchers to apply selected data to a broad variety of research topics. In a previous Earthzine article, “Giovanni Workshop Lets Earth Scientists Share Global Earth Science Data,” the inspiration for the creation of Giovanni was described, accompanying a cross-section of research topics presented at the first Gregory G. Leptoukh Online Giovanni Workshop. The workshop was dedicated to the memory of Greg Leptoukh, who led the development of Giovanni at the Goddard Earth Sciences’ Data and Information Services Center (GES DISC).
One of the mini-focus areas at the workshop was the use of Giovanni for research related to public health issues.  Radina Soebiyanto (Universities Space Research Association, USRA) described four example studies of malaria, dengue fever, avian influenza, and seasonal (human) influenza transmission, all of which employed Giovanni.  Michael Wemberly of South Dakota State University provided a presentation that focused on the prediction of malaria outbreaks in the country of Ethiopia, based on Midekisa et al. [1].
Giovanni has great potential for the investigation of different public health issues because a large variety of Earth system data variables are available in the system. One of the primary attributes of Giovanni is ease-of-use; researchers who are unfamiliar with remote-sensing data in general can find data that is applicable to their topic area and employ it with a relatively short investment of time and effort to become facile with the system.  Correspondence with some  users in the public health sector has demonstrated their satisfaction with access to the data it provides, and the capability of determining whether or not remote-sensing data can be used in their particular research area. It is important to note that with ease-of-use comes the possibility of easy misunderstanding or misinterpretation; the NASA Applied Remote Sensing Training ARSET program [2] provides training for professional audiences, including health specialists, in the proper use and interpretation of NASA remote-sensing data such as that found in Giovanni.  ARSET has online resources  that can supplement the use of Giovanni by non-specialists in remote sensing.
The large spectrum of data types in Giovanni may lead to candy store indecision for some eager researchers (i.e., there is so much to choose from that it may be difficult to choose the best options).  However, data in Giovanni can be loosely categorized with respect to its applicability to public health issues.  In the following, three tiers of applicability will be presented: Tier 1, data that are directly applicable and clearly influential to public health; Tier 2, data that have demonstrated relationships with some type of public health concern; and Tier 3, data that are related to weather or climate with an effect on public health and well-being.
The Tier 1 data types include:

• Precipitation
• Temperature
• Aerosol Optical Depth
• Aerosol Optical Depth
• Nitrogen Dioxide (NO₂₎
• Carbon Monoxide (CO)
• Relative Humidity
• Cloud Cover.

Precipitation data finds wide application in public health research.  Precipitation occurrence has frequently been associated with waterborne diseases, insect population outbreaks, and disease transmission modes (i.e., shared water resources).  Recent studies used Tropical Rainfall Measuring Mission (TRMM) daily data products to investigate the connection between rainfall and the location of cholera outbreaks in Haiti following a devastating earthquake [3].  Research on malaria transmission using remote-sensing data frequently involves rainfall data.  Malaria is a mosquito-borne disease, and since mosquitoes have an aquatic stage of their life cycle, mosquito populations are influenced by rainfall patterns.  Kiang and Soebiyanto [4] described research on malaria transmission patterns in Thailand, examining correlations with surface temperature, vegetation cover, and rainfall. Adimi et al. [5], which includes Soebiyanto and Richard Kiang of Goddard Space Flight Center (GSFC) as co-authors, described the potential for malaria risk prediction in Afghanistan. Both of these investigations accessed rainfall data products in Giovanni.  Midekisa et al. [1] also used rainfall data to create early-warning models for malaria in Ethiopia.
Precipitation extremes also have public health effects – directly due to the danger posed by flood waters, subsequently due to damage to water utilities and freshwater sources affecting the water supply, and finally due to increased potential for disease outbreaks due to contaminated water.  With regard to floods, Cools et al. [6] described the creation of a flash flood early warning system for Egypt that used precipitation data from Giovanni.  Singh, Pandey, and Nathawat [7] used Giovanni to investigate the cause of the 2008 Kosi flood in India.  Scientists from the GES DISC and GSFC also directly aided forensic meteorologists examining the deadly Jiddah, Arabia, flood that occurred on Nov. 24-25, 2009, by providing a rapid consultation on NASA rainfall data using Giovanni and other data resources [8].
The GES DISC also provides data through Giovanni from the Global and North American Land Data Assimilation Systems (GLDAS and NLDAS, respectively), which feature many different hydrological variables, including soil moisture and runoff in addition to precipitation.  These variables can be used to study severe storms, snowmelt flooding, and drought intensity.   Cloud cover data can be correlated with changing precipitation patterns, as well as for tracking severe storms and weather fronts.
Temperature data, along with relative humidity, also can provide significant insight into public health concerns. Soebiyanto, Adimi, and Kiang [9] determined that temperature was a primary variable associated with seasonal influenza transmission. Surface temperature is a fundamental variable related to water resources, drought conditions, vegetation survival, insect overwintering survival, heat stress, and disease-vector species ranges. Giovanni has remotely-sensed temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS), model temperature data, and high-resolution temperature data for specific regions.  An example of such research was presented in Shen et al. [10], which described the pioneering data portal built for the Northern Eurasian Earth Science Partnership Initiative (NEESPI). Changes in this region, such as higher temperatures and increased fire outbreaks, were described.

Another area of public health concern is air quality.  There are several regularly accessed variables related to air quality in Giovanni. Likely the most used are Aerosol Optical Depth (AOD) data products, which are acquired by MODIS and the Ozone Measuring Instrument (OMI). AOD indicates the optical clarity of the atmospheric air column, with higher values indicating more scattering and absorption by particles and chemicals in the atmosphere. Because of the direct relationship between AOD and some kinds of air pollution, these AOD data variables have been primary resources in many different studies. Two examples are Li, Shao, and Buseck [11] on the effects of biomass burning aerosols on haze in Beijing, China, and Lu et al. [12] on sulfur dioxide emissions and trends in eastern Asia.  AOD also has been used to track the regional impact of smoke from wildfires, which can be transported hundreds of miles from its source (Fig. 1).
OMI is also an important source of other atmospheric chemistry data.  The potential health significance of stratospheric ozone depletion is well-known, and OMI data is integral to that research.  OMI also provides a useful nitrogen dioxide (NO₂) data product, which can be used to track the atmospheric impact of wildfires and air pollution from the combustion of fossil fuels.  Sergei Sitnov has been a prolific user of Giovanni, using the system to publish several papers on NO₂ and air quality in Russia.  One such study looked at the weekly pattern of air quality and its relationship to meteorology in the environs of Moscow [13].
Tier 2 health-related variables in Giovanni are:

• Chlorophyll concentration (phytoplankton)
• Euphotic Depth
• Sea Surface Temperature
• Ozone (O₃) Erythemal Ultraviolet (UV) Daily Dose
• Normalized Difference and Enhanced Vegetation Indices (NDVI/EVI)
• Soil Moisture.

It may not be immediately apparent why oceanic phytoplankton chlorophyll concentrations are useful for health-related research.  However, this data type actually has one of the longest associations with public health of any that has been provided by the GES DISC.  This is due to the fact that Vibrio cholerae, the bacterial species responsible for cholera, has a stage in its life cycle when it infests copepods, a zooplankton species that feeds on phytoplankton.  Thus, flood-related blooms of phytoplankton can provide a fertile ground for the proliferation of copepods and V. cholerae. Coastal Zone Color Scanner (CZCS) data was used in the 1980s to examine a cholera outbreak related to a phytoplankton bloom in the Bay of Bengal.  These data in Giovanni can be used for cholera research, and to examine vectors of seafood contamination (“red tides” and other Harmful Algal Blooms, HABS), fish mortality, and severe storm effects (Fig. 2).
Phytoplankton patterns also are related to fishery success or failure. Because fish constitute the major protein source for many coastal populations, these data too can have public health ramifications.  Blooms also can indicate where someone should not  fish; research presented by Tracy Van Holt at the Giovanni Online Workshop showed that shellfish in areas with consistently elevated chlorophyll concentrations have more undesirable organisms clinging to their shells than in lower-chlorophyll zones [14].  Euphotic depth, a measure of water clarity, has been used for water quality studies and reports, and can indicate offshore flood effects. Sea surface temperature (SST) is directly related to water quality and phytoplankton growth, but indirectly it is related to coastal precipitation, storms, flooding, and the health of coral reefs.  The Caribbean SERVIR (Sistema Regional de Visualización y  Monitoreo) program used MODIS SST from Giovanni extensively in their research report “Sea Surface Temperature Trends in the Caribbean Sea and eastern Pacific Ocean” [15], published in 2011 to provide a baseline study for the impacts of these events on the population of countries in Central America, northern South America, and the Caribbean Sea.

As noted above, OMI data is an obvious choice to examine stratospheric ozone depletion and the Antarctic “ozone hole” depth and extent. But the Erythemal Daily Dose data product, which describes the impact of ultraviolet radiation exposure on humans, has been used in some unique ways.  Serrano, Cañada , and Moreno [16] used this data product to quantify the dangers to youth skiers of significant exposure to ultraviolet radiation.
NDVI and EVI, both indices of vegetation greenness and ground cover, are also potentially useful data types for health research, as is soil moisture.  These indices indicate the extent and intensity of drought, and thus are related to water resources and agricultural success.  Kiang et al. in 2006 employed the vegetation indices in modeling malaria occurrence in Thailand, as they are relevant to land use and mosquito breeding environments.  High resolution (5.6 kilometer) NDVI and EVI data are currently available in the Monsoon Asia Integrated Regional Study (MAIRS) high resolution monthly data portal.
Tier 3 data types may be related to weather and climate, with effects on public health and well-being.  Many of these data types measure quantities that are important to water resources:

• Snow Depth
• Snow Mass
• Snowfall Rate
• Snowmelt
• Fractional Snow Cover
• Snow/Ice Frequency
• Wind Speed
• Runoff.

The current drought that is besetting western states of the United States is having observable effects on snow in the mountain ranges, particularly those of California.   As this will have ramifications for the management of water resources, and also impact wetland areas, the use of Giovanni to monitor such changes may be warranted. Furthermore, heavy snows can lead to floods, which may be predictable from snow depth data and observable with runoff data. Trends in snow parameters also may be indicators of climate change impacts and shifts in freeze and melt timing. The NASA Data Investigations for Climate Change Education (DICCE) project has partnered with teachers in several different states, including New Mexico. To demonstrate how Giovanni could be used by teachers and students in New Mexico, several plots were prepared.  Figure 3 shows a 1979-2010 monthly snow mass time-series for the mountainous area of northern New Mexico, a major source area for the Rio Grande river.  Reduced snow mass from 1995-2005 is clearly visible.

Giovanni is currently transitioning from the current system called Giovanni-3 to a new, faster and more flexible system called Giovanni-4.  One of the main features of Giovanni-4 will be the capability to plot many different variables from many different missions and projects at the same time.  This expanded capability will augment multi-disciplinary research, and is very likely to be particularly useful to public health research, allowing insight into primary and secondary environmental factors influencing a specific public health area of inquiry.
References
1. Midekisa, A., Senay, G., Henebry, G.M., Semuniguse, P., and Wimberly, M.C. (2012) Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malaria J., 11, 165-181, doi:10.1186/1475-2875-11-165, 2012.
2. ARSET Web site, http://airquality.gsfc.nasa.gov, https://earthzine.org/2012/10/03/nasas-arset-training-program-from-the-classroom-to-real-world-satellite-applications/.
3. Rebaudet, S., Gazin, P., Barrais, R., Moore, S., Rossignol, E., Barthelemy, N., Gaudart, J., Boncy, J., Magloire, R., and Piarroux, R. The dry season in Haiti: a window of opportunity to eliminate cholera. PLOS Currents Outbreaks, last modified: 2013 Jul 24, Edition 1, doi: 10.1371/currents.outbreaks.2193a0ec4401d9526203af12e5024ddc, 2013.
4. Kiang, R., Adimi, F., Soika, V., Nigro, J., Singhasivanon, P., Sirichaisinthop, J., Leemingsawat, S., Apiwathnasorn, C., and Looareesuwan, S. Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand. Geospatial Health 1(1), 71-84, 2006.
5. Adimi, F., Soebiyanto, R.P., Safi, N., and Kiang, R. (2010) Towards malaria risk prediction in Afghanistan using remote sensing. Malaria J., 9:125, doi:10.1186/1475-2875-9-125, 2010.
6. Cools, J., Vanderkimpen, P., El Afandi, G., Abdelkhalek, A., Fockedey, S., El Sammany, M., Abdallah, G., El Bihery, M., Bauwens, W., and Huygens, M. An early warning system for flash floods in hyper-arid Egypt. Natural Hazards and Earth Sys. Sci., 12, 443-457, 2012.
7. Singh, S.K., Pandey, A.C., and Nathawat, M.S. (2011) Rainfall variability and spatio-temporal dynamics of flood inundation during the 2008 Kosi flood in Bihar State, India. Asian J. Earth Sci., 4(1), 9-19, 2011.
8. NASA GES DISC ,“GES DISC and Goddard scientists assist Accuweather investigation of flooding in Saudi Arabia”, http://disc.sci.gsfc.nasa.gov/gesNews/jiddah_november_rainfall_floods, 2009.
9. Soebiyanto, R.P., Adimi, F., and Kiang, R.K. Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One, 5(3):e9450, doi: 10.1371/journal.pone.0009450, 2010.
10. Shen, S., Leptoukh, G., Loboda, T., Csiszar, I., Romanov, P., and Gerasimov, I., “The NASA NEESPI Data Portal to Support Studies of Climate and Environmental Changes in Non-boreal Europe”. In: Regional Aspects of Climate-Terrestrial-Hydrologic Interactions in Non-boreal Eastern Europe, Groisman, Pavel Ya.; Ivanov, Sergiy (Eds.), Springer, 255 p., pp 9-16, 2009.
11. Li, W.J., Shao, L.Y., and Buseck, P.R. Haze types in Beijing and the influence of agricultural biomass burning. Atmos. Chem. Phys., 10 (8), 8119–8130, doi:10.5194/acp-10-8119-2010, 2010.
12. Lu, Z., Streets, D.G., Zhang, Q., Wang,S., Carmichael, G.R., Cheng, Y.F., Wei, C., Chin, M., Diehl, T. and Tan, Q. Sulfur dioxide emissions in China and sulfur trends in East Asia since 2000. Atmos. Chem. and Phys., 10, 6311–6331, www.atmos-chem-phys.net/10/6311/2010/ doi:10.5194/acp-10-6311-2010, 2010.
13. Sitnov, S. A. Weekly cycle of meteorological parameters over Moscow region. Doklady Earth Sciences, 431(2), 507-513, doi:10.1134/S1028334X10040203, 2010.
14. Van Holt, T. “Fisher success and adaptation to plantation systems in Chile.” in Proceedings of the 2012 Gregory G. Leptoukh Online Giovanni Workshop, http://disc.sci.gsfc.nasa.gov/giovanni/additional/newsletters/vanholt_presentation_part_1_pdf, http://disc.sci.gsfc.nasa.gov/giovanni/additional/newsletters/vanholt_presentation_part_2_pdf, 2012.
15. Cherrington, E.A, Hernandez, B.E., Garcia, B.C., Clemente, A.H., and Oynela, M.O. “Sea Surface Trends in the Caribbean Sea and eastern Pacific Ocean”, 12 pages, http://issuu.com/cathalac/docs/servir_oceans_eng, 2011.
16. Serrano, M.A., Cañada, J. and Moreno, J.C. Erythemal ultraviolet solar radiation doses received by young skiers. Photochem. Photobio. Sci., doi:10.1039/C3PP50154J, published online July 24, 2013.