Case Studies

An AI-powered tool to predict and classify the health and economic outcomes of non-communicable diseases based on lifestyle, socio-economic, environmental and health data  in populations exposed to steel industry pollution and related factors.

About the MISTRAL Project

A toolkit for dynaMic health Impact analysiS to predicT disability-Related costs in the Aging population based on three case studies of steeL-industry exposed areas in europe.

Air Pollution

Our main ambition is to reduce air pollution due to industry in Europe


There is a direct connection between air pollution and health, that we will analyse

Socio-economic cost

Global quality of life and health of citizens affect socio economic conditions in European cities


3 representative cities in Europe selected for our studies about air pollution, health and socio-economic cost

Infrastructure Cloud

The Data Provider Layer

The Data Provider Layer is a virtual layer that connects third-party software with the Federated Data Management and Processing layer. It allows the processing layer to access real-time data from various sources, such as EHRs with FHIR standards, administrative datasets for socio-economic status, and Copernicus S-5p mission. It uses custom connectors (e.g., JDBC) to retrieve structured and unstructured data and metadata from the healthcare facilities, the public administrations, and the regional information systems.

The Federated Data Management and Processing Layer

The Federated Data Management and Processing Layer takes data from the Data Provider Layer and performs operations such as manipulation, integration, and aggregation. This layer is the computational core of the architecture and it uses federated learning to combine the results of machine/deep learning models trained in different healthcare facilities. It balances and optimizes the methods to achieve a robust and reliable risk assessment system. It also handles local access, pseudonymization, data quality assessment, training-test-validation phases of distributed machine learning models, and GDPR compliance. It uses domain ontologies to harmonize the data syntactically and semantically, representing the global model of the data space and enabling access to it.

The Big Data Analytics and Data Visualization Layer

The Big Data Analytics and Data Visualization Layer receives the output of ML/AI processing and provides statistical analysis, dashboarding, data visualization and decision support services to the end-user. It has an analytics module that enables data exploration and system performance monitoring through appropriate metrics and KPIs. It also implements and exposes all business intelligence and data visualization functionalities. This layer includes the “Security Control” module is a cloud-based module that ensures the security of the entire infrastructure. It monitors cloud configurations, detects suspicious activity, prevents insecure deployments and limits excessive privileges for Identity and Access Management (IAM) roles.

Case Studies

Socio-economic conditions in European cities are shaped by the health and quality of life of their citizens

We have selected three European cities with significant steel industry as our case studies, using data from these cities to model the impact of air pollution on citizen health. We will use this data to develop AI tools which will facilitate better decision making around the current and future risks posed by industrial pollution in urban areas.

Taranto in southern Italy

Rybnik in Poland

Flanders in Belgium

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Funded by
the European Union



Mauro Grigioni
Project Coordinator



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