1. Weather data and soil agrochemicals' concentration data in a lake: risk assessment for the population living in the neighborhood
by Dr. Tilemachos Koliopoulos
Technological Educational Institute of Athens -
Telegeco Research Center, Greece
3-4 April 2017, University of Barcelona, Faculty of Library and Information Science, Spain, Interest Group on Agricultural Data (IGAD) pre-meeting

2. Aims
Learn from past case studies the importance to collect weather data in relation to environmental impacts on food safety and agricultural productivity – the case of lake Koronia
Support API's for open data
Identify thresholds of environmental impacts for investigation of lake topographies related to food safety; qualitative agricultural plans and collected weather data
Data management and interoperability for Agricultural Data in developing countries

3. In the 1950s, Koroneia was among the lakes of Greece with the biggest fish production
In the 1970s, its area was 45 km² and had a depth of about 5 metres

4. Since then the lake has shrunk to about one third of its original area, and its depth decreased to less than 1 metre in The lake suffered from pollution and intensive agriculture
Water resources from mean average precipitation 440 M m^3
Water resources from mean average evapotranspiration 400 M m^3

5. Pasanidou, E., 2013, A.U.T, Greece

6. In general terms, risk depends on the following 3 factors:
How much of a chemical is present in an environmental medium (e.g., soil, water, air),
How much contact (exposure) a person or ecological receptor has with the contaminated environmental medium, and
The inherent toxicity of the chemical.
Ahealth risk assessment is the process to estimate the nature and probability of adverse health effects in humans who may be exposed to chemicals in contaminated environmental media, now or in the future.

7. Integrated Risk Data Information System
Lutz model: max agrochemical conentration, Cmax (OECD, 2002; Gutsche, V. and D. Rossberg. 1999)
USEPA IRIS web link for Reference risk dose (RfD) of chemicals
Risk of cancer or chronic diseases by chemical pollutants (USEPA , 2000)
RfD (mg/kg/day) for Atrazine: 3.5*10E-2, Cmax=3.7E-4, RF = 1.06E-2
RfD for 2-4 D: 10E-2 , calculated Cmax based on a given scenario, Cmax= 5.35*10E-3 , Risk factor RF = Cmax/RfD = 0.535

8. Utilize proper data management
Develop digital models, collect on line weather data in the web or set up web weather stations for real time data
Develop digital mapping utilities; apply weather data; precipitation and evapotranspiration; data in water consumption & land uses; hydrogeological data - water volume capacity
Identify hazards of chemical data in pesticides that are used in selected case studies – apply properly Socrata Open Data API or other ones
Archive necessary weather data related to: identified hazards and associated risks; hazards that exist from agrochemicals in developing countries

9. Sustainability of data projects
Integration of data projects – set up collaboration of research partners in working packages WP's-D's
Exchange data; identify particular characteristics in data update - digital terrains for reclamation works;
Set up inventories of data – update monitoring data;
Propose Metadata for research data archives in Libraries; XML schemas for the collected data;
Develop right web portal frontends for data update with easy access by staff in developing countries;
Use on line questionnaires for any additional information ; give motivations to realise sustainable development projects in developing countries

10. Identify data categories - RDBM
Slopes; steep top soil ; erosions with high inclinations and mass transfer of agrochemical pollutants
identify critical computational parameters that will be investigated on vector data of digital maps
search proper metadata in open data to integrate future research of data between stakeholders
Register associated data of chemical indexes with varying characteristics in soil sample profiles in time

11. Motivations to realise data collection for sustainable development projects in developing countries
Propose data collection for reclamation works
Propose data monitoring plans based on the collected results
Motivations to realise new jobs in local communities for data collection & data processing in developing countries
Analyse data socioeconomic indexes that will come up from the operation of the reclamation projects and environmetal resources protection works

12. Operational tools in data management
Apply open g.i.s tools to visualise data and determine critical data that are necessary to be combined with weather data for mitigation of pollution hazards to agricultural and food resources;
-Utilise properly open source web g.i.s technologies for web on line alerts – Geonetwork, Geoserver
-Develop API's for access to open data; Visualise associated data to identify hazards - decision making; -Archive proper data and investigate changes in time of chemical solute transport and associate threats to agriculture and resources of food safety

13. Utilitities
frequent update of weather data utilising proper web ICT's gathering necessary hydological data
Upload weather data on line supporting information to open data applications for better administrative purposes to interesting local authorities
Develop proper interdisciplinary web tools for any interesting parties based on the weather data and the relational data that are necessary for decision making in any similar examining case studies

14. apply HACCP data programs on critical locations
More data should be identified after the running of a proper data monitoring scheme - right identification of data types in hazards for an investigated area
apply proper data viewer; use open source technologies; JavaScripts etc., Node.js uses an event- driven, non-blocking I/O model that makes it lightweight and efficient

15. Conclusions make use of e-infrastructures; develop metadata
Utilise repositories like git hub
Upload data utilities for processing weather data related to environmental impacts minimisation; include web links that demonstrate the use of it
e-infrastructures should be applied for collected relational weather data by interesting parties providing useful results of open data
Interesting parties should upload research results,
Publish useful open data making them accessible for other projects in future research teams

16. Identify any similar problems that have been met researchers in similar data management projects
Identify weather data and environmental impact characteristics that met in developing countries in agricultural data - IGAD
Include perspectives for extension of associated research data categories and adoption of them in flexible relational data base management schemes between weather data; water resources; socioeconomic indexes; sustainable development and chemical indexes

17. archive properly data in relational databases between soil characteristics soil flow rates; so as to develop better HACCP and monitoring programs
Provide on line information for thresholds in order to minimise hazards to populations; fisheries; food chain production activities; public health and environmental resources
due to climate change and probable extremely flood events that could exist in soil profiles and associated chemical indexes are needed frequent proper data monitoring schemes

18. Thank you
for your attention