Francesco D’Amore

Francesco D’Amore

CNR-Institute of Atmospheric Pollution Research

Title: A reliable data collection and control system provided as a service within the global mercury observation network


Francesco D'Amore is PhD on Computer Science. He has a post-doc position at CNR - Institute of Atmospheric Pollution Research (CNR-IIA), working on e-Science, data harmonization and earth observation. His important works are related to the design and development of innovative Spatial Data Infrastructure, which are oriented to data quality assurance and monitoring.


The overall goal of the on-going Global Mercury Observation System (GMOS) project is to develop a coordinated observation network on global scale for mercury, by integrating historical measurements across the globe with newly established GMOS land-based sites in the Northern and Southern Hemisphere, at high altitude and sea level locations. The measurement programme includes tropospheric measurements and oceanographic campaigns to provide a comprehensive assessment of mercury concentrations in ambient air and deposition. To facilitate data sharing with major stakeholders, policymakers and the public, an ad-hoc Cyber(e)-Infrastructure (CI) was developed. The CI was designed to make data acquisition in near-real time, by apply advanced Information and Comunication Technology (ICT) systems. To provide a common framework of analysis as well as a real-time control of instruments, a dedicated GMOS-Data Quality Management system (G-DQM) was developed as a web-based service. G-DQM is able to automatically screen and quality-control the incoming atmospheric mercury raw data allowing near-real time adaptive monitoring procedures. Dealing with site-specific conditions and their related instrumental settings, the G-DQM system may provide suitable feedback to all GMOS site operators. The faithful application of the G-DQM system allows to identify the main frequent issues to be overcome for a reliable atmospheric mercury data collection. The use of this system is of greatest usefulness in preventing the production of poorquality data and assuring comparability of different datasets recorded within the GMOS network.