BrainMap

Daniel Dunea

Prof. Dr. Eng. habil.

Professor - UNIVERSITATEA "VALAHIA" TARGOVISTE

Researcher | Teaching staff | Scientific reviewer

Publons/ResearcherID: https://publons.com/researcher/1182105/daniel-dunea/

Curriculum Vitae (20/10/2022)

Expertise & keywords

Developing of some experimental facilities for the study of grassland and forest ecosystems in Fundata mountain area, Romania – ECOPRACT Call name:
CNFIS-FDI-2017-0638 -

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA "VALAHIA" TARGOVISTE

Project partners:

Affiliation:

Project website: https://ecopract.valahia.ro/

Abstract:

A project called ECO-PRACT was implemented in 2017 to familiarize the students with several methods and techniques that are used to identify and classify species, monitor biological efficiency of the canopy in correlation with meteorological parameters, and map the mountain grasslands in the administrative territory of Fundata Village, Romania. Based on the field surveys, three groups of zonal grassland types were identified as follows: Agrostis capillaris with mesophilic character near villages, Agrostis capillaris with meso-xerophilic species on slopes, and Agrostis capillaris and other eutrophic grasses.

Towards a better protection of children against air pollution threats in the urban areas of Romania Call name:
20SEE/30.06.2014 -

Role in this project: Partner team leader

Coordinating institution: UNIVERSITATEA "VALAHIA" TARGOVISTE

Project partners:

Affiliation:

Project website: http://www.rokidair.ro/en

Abstract:

Health and wellbeing of children has always been an issue of societal importance. ROKIDAIR project is mainly focusing on the health impacts of air pollutants on children. Focusing on one of the most vulnerable groups of society the project is a good starting point to raise awareness for the whole topic of air pollution and its impact on society.
ROKIDAIR project aims to improve urban air quality monitoring and forecasting activities, focusing on the critical areas’ (CAs) spatial delimitation based on the receptors’ vulnerability, and their detailed characterization in terms of PM2.5 effects on children’s health, in two towns of Romania, namely Targoviste and Ploiesti. These urban agglomerations will serve as pilot areas in order to develop and deploy a monitoring network system and adjacent information structure that will provide synthesized data concerning PM2.5 levels obtained from simplified, but reliable monitoring micro-stations and artificial intelligence (AI) forecasting algorithms developed within the project.

Sustainable management system of resources used for monitoring and evaluating the environmental risks in order to prevent the negative effects and to manage crises situations Call name:
D11-037/18.09.2007 -

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA "VALAHIA" TARGOVISTE

Project partners:

Affiliation:

Project website: http://memdur.ssai.valahia.ro/en/

Abstract:

Study of artificial intelligence application in environmental protection Call name:
CEEX 1533/2006 -

Role in this project: Key expert

Coordinating institution: UNIVERSITATEA PETROL GAZE PLOIESTI

Project partners:

Affiliation:

Project website: http://ac.upg-ploiesti.ro/postdoctoral/postdoc3.html

Abstract:

The purpose of the programme is to develop efficient instruments, based on artificial intelligence for the environmental protection taken into account that at present both at national and international level the two domains, environmental protection and artificial intelligence (by the knowledge based solutions they provide), are of strategic interest. The processes associated to the environment are not easy to be modelled and controled, as they involve the use of incomplete and uncertain knowledge. Therefore, new performant instruments are needed in order to solve the environment problems. Artificial intelligence could provide efficient solutions to the complex problems of the environment, that take into account both quantitative and qualitative, spatial and temporal aspects of the environmental processes. Among the artificial intelligence techniques that could be used with success in environmental protection we mention the following: knowledge based systems, machine learning, case based reasoning, decision support systems, artificial neural networks and intelligent agents. The postdoctorate programme proposes the study of artificial intelligence techniques application in environmental protection and the analyses of their efficiency.

Functional biodiversity in crop protection Call name:
HPMT-CT-2000-00199 -

Role in this project: Key expert

Coordinating institution: Wageningen University

Project partners:

Affiliation:

Project website: http://cordis.europa.eu/project/rcn/62428_en.html

Abstract:

Towards a better protection of children against air pollution threats in the urban areas of Romania Call name: EEA Research Programme under EEA Financial Mechanism 2009-2014
EEA-JRP-RO-NO-2013-1-0096 2013 -

Role in this project:

Coordinating institution: UNIVERSITATEA "VALAHIA" TARGOVISTE

Project partners: UNIVERSITATEA "VALAHIA" TARGOVISTE (RO); Stiftelsen Norsk Institutt for Luftforskning (Norwegian Institute for Air Research) (NO); UNIVERSITATEA PETROL GAZE PLOIESTI (RO); UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation: UNIVERSITATEA "VALAHIA" TARGOVISTE (RO)

Project website:

Abstract:

“ROKIDAIR: Towards a better protection of children against air pollution threats in the urban areas of Romania” aims to improve urban air quality monitoring and forecasting activities, focusing on the critical areas’ (CAs) spatial delimitation based on the receptors’ vulnerability, and their detailed characterization in terms of PM2.5 effects on children’s health, in two towns of Romania, namely Targoviste and Ploiesti. These urban agglomerations will serve as pilot areas in order to develop and deploy a monitoring network system and adjacent information structure that will provide synthesized data concerning PM2.5 levels obtained from simplified, but reliable monitoring micro-stations and artificial intelligence (AI) forecasting algorithms developed within the project.
The ROKIDAIR project’s timeline is designed in the following four phases: 1) Preliminary studies of pollutant loads in the studied areas, ranking of critical areas and system’s prototyping and design; 2) Development of the monitoring infrastructure including micro-station’s sensors/hardware, the management system of the database, and the tools for geospatial analysis, pollutants’ correlation and forecasting; 3) Testing and validation of the PM2.5 monitoring system including adjacent data communication and processing infrastructure; and 4) Promoting the ROKIDAIR system to be used by relevant stakeholders. All these phases will be carried out through six interdependent Work Packages (WPs).
A database with existent data for the envisaged urban areas will be primarily developed. Based on the database (e.g., pollutant emissions, number of children with respiratory illnesses, meteorological database, etc.) and scenarios modelling, critical areas will be delimited. In these CAs, monitoring campaigns will be performed using a standardized and calibrated portable system to determine PM2.5 levels.
The research regarding the determination of differences in the health effects of Particulate Matters (PM) with various chemical compositions needs to be addressed. The sampled discs, collected during the measurements, will be analysed using laboratory methods to detect the quantity of heavy metals, Volatile organic compounds (VOCs) and other compounds.
CAs will be prioritized in order to facilitate the determination of the most relevant monitoring points. In each town, three monitoring points will be selected for each CA in order to deploy three monitoring micro-stations and one station will be located indoor at a selected area (e.g., maternity, kindergarten, school, etc.). In this way, the correlation of outdoor-indoor PM2.5 levels will provide comprehensive insights regarding the children’s exposure.
The monitoring network prototype will have eight stations in total (four for each town). These micro-stations will be gradually moved to other CAs to perform new measurements and after testing, calibration and referencing, to provide reliable information concerning PM2.5 levels and their impact on children’s health.
Collected time series will be pre-processed and adapted to feed the forecasting tool based on Artificial Intelligent (AI) algorithms. All data will be presented on a dedicated geo-portal adapted to be used by smartphones and other portable equipment. The main stream of information will be transmitted as much directly as possible towards the affected persons. The expert advises and recommendations will be transmitted via e-mails and SMS to the registered users providing support for children’s health management under impacts of air quality stressors and pressures. Early warnings will be developed in co-operation with paediatric specialists, which will synthesize the most relevant information concerning the protection of children’s health against air pollution threats. The early warning data packages will also be redirected to the authorities (e.g., EPA - Environmental Protection Agencies and DPH - Public Health Protection Directions) for informational purposes.

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects	Download (557.91 kb) 21/03/2020