Analysis of public health and environmental data

This scenario has been provided by TUD and UKZN regarding the analysis of relationships between Air Quality and Health in Dresden/Saxony (Germany) and Durban (South Africa).

The goal of the scenario is to investigate the spatio-temporal correlation between health data and environmental datasets (air temperature, ozone concentration and cardio-vascular disease). All environmental and health parameters could change for correlation between other parameters, like PM10 and asthma diseases.

The datasets to be used are :

1- health data : hold by Dresden and Durban partners. Not GEOSS ressource.

2- Air temperature : IASI Level 1C (needs to define relevant granule for Dresden and Durban). Available through : EUMETSAT     OR

N.B. : Other data to be tested, depending on the temporal coverage of data, may be : AIRS/Aqua Level 3 Daily standard physical retrieval (AIRS-only) (AIRS3STD) available at :

3- Ozone concentration : OMI/Aura Ozone (O3) DOAS Total Column Daily L3 Global 0.25deg Lat/Lon Grid (OMDOAO3e) available at: (Mirador is accessible through the GES DISC - Goddard Earth Sciences Data and Information Services Center).

N.B. : Other Ozone data are available at The World Data Centre for Remote Sensing of the Atmosphere :

A first step in this scenario could be the correlation between cardio-vascular diseases and extreme temperature variations and ozone concentration. For this scenario three Use Cases have been developed. Use Case AQH-D-01 deals with discovering and selecting necessary data sets for further analyses. In a second step the time series will be extracted for a given study period. Afterwards when all is defined statistical indices will be the result of the AQH-D-03. The Use Case refers to the actual correlation analysis (AQH-D-03).

Scenario Steps

 Scenario Name
Engineering Use Cases
 Specialization of Use Cases

AQH-D-01. Scientist discovers and selects one or more environmental datasets of interest (temperature, ozone concentration, health data for cardio-vascular disease)

01.1. Scientist access a GEOSS catalogue to discover available datasets .

01.2. Scientist submit a query to the CSW (Web Catalogue Service) with the parameters of interest (temperature, ozone concentration, health data for cardio-vascular disease)

01.3. Scientist selects one or more datasets (temperature, ozone concentration and health data) from a list of available datasets returned by the query.

01.4. Scientist gets the selected dataset(s) using the CSW and gets an access to the selected datasets.

AQH-D-02. Scientist extracts time series for selected datasets for a defined study area (Dresden or Saxony) and a study period.

02.1. Scientist uses a client application and defines the study area for all datasets.

02.2. Scientist defines the study period for all datasets.

02.3. Scientist gets all the time series (one for each datasets selected in AQH-D-01) computed by a GEOSS service.


AQH-D-03 Scientist correlates time series from different datasets with the same temporal resolution and on the same location, which have already been selected in AQH-D-01 and AQH-D-02 and the scientist gets statistical indices describing the datasets and the relationship.


03.1. Scientist uses a GEOSS service to correlate time series of the processed time series of temperature, ozone concentration and health data for cardio-vascular disease.


 03.2. The Scientist gets a file with statistical indices (mean, standard deviation, maximum, minimum, correlation coefficient, determination coefficient, confidence interval) showing correlations between environmental (temperature, ozone concentration) and health dataset (cardio-vascular diseases).


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