D6.1 One-pager: Knowledge base/wiki

This deliverable presents the architecture, operational deployment, and development plan for the open-access EDIAQI Knowledge Base Wiki and online IAQ Simulation Tool. Led by Tallinn University of Technology (TalTech) under Work Package 6 (Task 6.1), its primary purpose is to provide accessible, evidence-based technical knowledge and practical guidance on indoor air quality for both non-expert citizens and building professionals. The deliverable establishes a structured Decision Tree workflow that helps users identify pollution sources, evaluate low-cost sensor options, and select targeted ventilation or filtration interventions. In parallel, it introduces an interactive machine-learning simulation tool developed to assess residential exposure risks without requiring physical monitoring hardware.

Why is this topic important?

People in developed countries spend up to 90% of their daily lives indoors, where exposure to chemical, physical, and biological contaminants poses severe risks to human health. Despite these risks, European air quality legislation has historically focused on ambient outdoor air, leaving indoor environments largely unregulated.

Furthermore, European building decarbonisation strategies, such as the Renovation Wave and the recast Energy Performance of Buildings Directive (EPBD), demand stricter indoor environmental quality monitoring during energy renovations to avoid airtight spaces with insufficient ventilation. The EDIAQI wiki directly addresses this gap by translating complex scientific findings, sensor validation data, and building-physics principles into clear, actionable advice. It enables property owners, school leaders, and municipal managers to make informed decisions about ventilation maintenance, sensor installation, and occupant health protection.

Key messages

• Open access knowledge platform: The EDIAQI wiki provides a centralised, freely accessible repository of scientific and practical knowledge on indoor air pollutants, health risks, monitoring tools, and mitigation measures.

• Structured decision-making workflow: The EDIAQI Decision Tree guides users systematically through key diagnostic stages: understanding why IAQ matters, learning how to monitor parameters, identifying whether problems originate indoors or outdoors, and applying appropriate technical solutions.

• Targeted mitigation strategies: The platform clarifies remediation pathways, highlighting filtration systems for outdoor air pollution infiltration and enhanced ventilation or source control for indoor emissions.

• Machine learning risk screening: The integrated IAQ Simulation Tool uses CatBoost regression trained on cohort data and building registries to predict household concentrations of nitrogen dioxide and fine particulate matter without requiring physical sensors.

• Secure and scalable infrastructure: The platform is built on open-source MediaWiki software hosted on DigitalOcean servers with daily backup routines and role-based user management administered by TalTech.

• Evolving project repository: The wiki serves as a living platform that continuously integrates one-pager summaries of all consortium deliverables and empirical findings from European pilot studies throughout the project lifecycle.

What did the EDIAQI project do?

TalTech, together with consortium partners USEV, KNOW, and ANT, designed and deployed the core technical framework and content taxonomy of the EDIAQI wiki using MediaWiki. The team formulated the EDIAQI Decision Tree to translate technical methodologies into practical diagnostics for non-specialists.

In parallel, partner KNOW developed the demo version of the online IAQ Simulation Tool utilizing Python, Streamlit, and Docker containerization. The predictive engine was trained on environmental measurements, household questionnaires, and building registry parameters from the retrospective COPSAC cohort using CatBoost gradient-boosted decision trees. Finally, the deliverable established a multi-year editorial roadmap (covering project milestones M10 through M48) to systematically ingest deliverables, sensor validation protocols, pilot findings, and policy recommendations into the wiki.

What does this mean in practice?

The knowledge base bridges the gap between high-level aerosol science and practical building management. It provides building operators, public administrators, and occupants with ready-to-use information for evaluating indoor environments, diagnosing ventilation shortcomings, and planning renovations.

For non-technical users, the main practical value is that indoor air quality risks can be assessed easily using the online simulator and step-by-step guidance. For technical stakeholders, the value lies in having validated sensor methodologies, heating and ventilation guidelines, and unified deliverable summaries accessible on a single platform.

User group	Practical relevance
Homeowners and tenants	Offers straightforward guidance on identifying domestic pollution sources (such as gas cooking, dampness, and smoking) and provides a free simulation tool to estimate household pollutant levels and test the impact of habit changes.
Schools and kindergartens	Equips school heads and teachers with clear threshold guidelines, ventilation recommendations, and educational resources to ensure healthy classroom environments for vulnerable children.
Commercial property owners	Delivers technical benchmarks on HVAC operation, filter selection, and sensor deployment strategies to optimize indoor air quality alongside energy-efficient building operations.
Local municipalities	Provides municipal decision-makers with evidence-based frameworks to audit public building portfolios and incorporate indoor environmental quality standards into local procurement and renovation plans.
EDIAQI consortium partners	Serves as the central exploitation and dissemination channel where each work package publishes plain-language one-pagers summarizing technical deliverables and pilot milestones.

Recommendations

• Consult the Decision Tree first: Users experiencing indoor air quality concerns should follow the step-by-step Decision Tree to distinguish between outdoor infiltration and indoor building or behavioral sources before investing in hardware.
• Leverage the simulation tool for initial screening: Building managers and occupants should use the free IAQ Simulator to gain preliminary indications of exposure risk based on location, building age, and internal appliances.
• Prioritize ventilation and filtration interventions: Address outdoor pollution primarily through effective mechanical filtration, while using adequate outdoor air exchange rates and local extraction to dilute and remove indoor-generated pollutants.
• Utilize validated low-cost sensors: When monitoring indoor climate parameters, follow the sensor placement and data evaluation guidelines outlined in the wiki to ensure reliable measurement data.
• Follow EDIAQI editorial guidelines: Consortium task leaders should regularly translate completed technical reports into structured one-pagers using the standard MediaWiki template to maintain open-access project transparency.

Limitations

Deliverable D6.1 documents the initial baseline and structural launch of the knowledge base and simulation tool at month 10 of the project. At this early stage, empirical datasets from the field pilots (P1 to P4) and targeted measurement campaigns (C1 to C4) were still in the collection phase and not yet fully incorporated into the wiki pages.

Additionally, the initial release of the IAQ Simulation Tool is calibrated on Danish cohort data and address registries, meaning predictions outside this geographic training domain should be interpreted as general screening indications rather than precise exposure measurements. Further updates across later project milestones will integrate broader multi-city datasets, time-series ventilation models, and refined toxicological findings.

Related wiki pages

• Indoor air pollutants
• Sensors
• Recommendations and guidelines
• Guidelines for national indoor environmental quality requirements

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Source deliverable

This one-pager is based on:

• Deliverable: D6.1 – Knowledge Base/WIKI
• Work Package: WP6 – GUIDE: Policy creation, recommendations and training
• Lead partner: TalTech (Tallinna Tehnikaülikool)
• Authors: Jessica Fernández-Agüera (USEV), Kalle Kuusk (TalTech), Kristina Pavlović (KNOW), Mario Lovrić (ANT)
• Original deliverable type: DEM – Demonstrator, pilot, prototype
• Dissemination level: PU – Public
• Official submission date: 30 September 2023 (Month 10)
• Actual submission date: 29 September 2023