D6.1 One-pager: Knowledge base/wiki: Difference between revisions

Revision as of 11:20, 3 September 2026

Link

EDIAQI Wiki
IAQ Simulation Tool

WP: WP6 (GUIDE) Lead: TalTech Type: DEM Level: PU Submitted: 29/09/2023 (M10)

Authors: J. Fernández-Agüera (USEV), K. Kuusk (TalTech), K. Pavlović (KNOW), M. Lovrić (ANT)

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.

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 and facility managers	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.
Maintain regular one-pager contributions: 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[cite: 20]. 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[cite: 20, 21].

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[cite: 20]. Further updates across later project milestones will integrate broader multi-city datasets, time-series ventilation models, and refined toxicological findings[cite: 20].

Related wiki pages

[+] View technical source and page metadata

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

@@ Line 1: / Line 1: @@
-__NOTOC__
+{{Side box
-= D6.1 One-pager: Knowledge Base/WIKI =
+| bodystyle = width: 250px; float:right; clear:right;
+| title = EDIAQI Project
+| image = [[File:onepager-image_250x250.jpg|200px|link=]]
+| below =
+'''Link'''
+* '''[http://206.189.52.199/index.php/Main_Page EDIAQI Wiki]'''
+* '''[https://iaq-simulator.know-center.at/ IAQ Simulation Tool]'''
-{{Side box | bodystyle = width: 280px; float:right; clear:right; margin: 0 0 1em 1.5em; border: 1.5px solid #00A896; border-radius: 12px; background-color: #ffffff; padding: 15px; box-shadow: 0 4px 12px rgba(0, 168, 150, 0.08); | title = <span style="color: #0E6251; font-weight: bold; font-size: 16px;">EDIAQI Project Deliverable</span> | image = [[File:EDIAQI_Logo.png|220px|link=]] | below = '''Reference Details'''<br/>
+'''WP:''' WP6 (GUIDE)
-* '''Deliverable:''' D6.1 (WP6)
+'''Lead:''' TalTech
-* '''Lead Beneficiary:''' [[Tallinna Tehnikaülikool|TalTech]]
+'''Type:''' DEM
-* '''Dissemination:''' Public (PU)
+'''Level:''' PU
-* '''Project Duration:''' 2022–2026
+'''Submitted:''' 29/09/2023 (M10)
-* '''Grant Agreement:''' 101057497
+'''Authors:''' J. Fernández-Agüera (USEV), K. Kuusk (TalTech), K. Pavlović (KNOW), M. Lovrić (ANT)
-* '''Full Report / Tool:''' [https://iaq-simulator.know-center.at/ IAQ Simulator]
 }}
-Deliverable D6.1 establishes the architectural, technical, and conceptual framework of the centralized EDIAQI Knowledge Base/WIKI alongside the open-access public IAQ Simulation Tool. Developed under the scientific leadership of Tallinn University of Technology (TalTech) within Work Package 6 (Task 6.1), this open platform translates cutting-edge indoor environmental science, European building guidelines, and consortium findings into clear, actionable knowledge for non-specialist audiences. It addresses critical gaps in how indoor air pollution (IAP) is identified, monitored, and mitigated across European residential and public buildings[cite: 18, 19].
+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 modern urban environments spend up to 90% of their daily lives indoors, where the concentration of pollutants can frequently surpass outdoor levels[cite: 18, 19]. While ambient outdoor air has been strictly governed by European Union directives for decades, statutory indoor environmental quality standards remain fragmented across Member States[cite: 1, 18]. Poor indoor air quality, coupled with inadequate ventilation, moisture accumulation, and internal chemical emissions, substantially increases the incidence of chronic respiratory illnesses, childhood asthma, and sick building syndromes[cite: 18, 19].
+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.
-This knowledge platform directly supports major European policy initiatives, including the recast Energy Performance of Buildings Directive (EPBD), the Renovation Wave for Europe, and the Zero Pollution Action Plan (ZPAP) under the European Green Deal[cite: 1, 18]. As deep energy renovations accelerate across the continent, building owners and facility managers must be equipped with sound building-physics principles to prevent building hermetization without sufficient mechanical air exchange or filtration.
+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.
-== Who is this information for? ==
-This platform and deliverable provide tailored guidance for:
-* '''Homeowners and Residents:''' Seeking practical steps to identify indoor pollution sources (such as cooking, cleaning, or mold) and optimize domestic ventilation[cite: 18].
-* '''School and Kindergarten Heads:''' Tasked with safeguarding classroom air hygiene and maintaining optimal CO2 thresholds for cognitive performance and child health[cite: 18].
-* '''Building Owners and Facility Managers:''' Designing HVAC maintenance protocols, selecting air filters, and conducting continuous indoor environmental monitoring[cite: 18].
-* '''Municipal Authorities and Policymakers:''' Establishing municipal air quality action plans, benchmarking public buildings, and framing future indoor air quality standards[cite: 18].
 == Key messages ==
-* '''The EDIAQI Decision Tree:''' Navigates users from basic awareness ("Why?") and sensor selection ("How?") to specific diagnosis ("Building vs. Inhabitant vs. Outdoor") and remediation ("What now?")[cite: 18].
+* '''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.
-* '''Ventilation and Filtration as Key Levers:''' Outdoor pollution penetration demands certified mechanical filtration (e.g., ISO 16890 standards), whereas indoor-generated contaminants require adequate ventilation rates (tracer gas CO2 decay validation) and local source extraction[cite: 12, 18, 19].
+* '''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.
-* '''Democratizing IAQ Monitoring:''' Combines commercial low-cost sensor (LCS) networks with high-precision reference instruments, clarifying sensor accuracy, cross-sensitivities, and placement rules[cite: 1, 18, 19].
+* '''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.
-* '''Predictive Risk Assessment:''' The integrated machine learning IAQ Simulator allows users without dedicated hardware to estimate indoor NO2 and PM2.5 concentrations based on household metadata and outdoor GIS proximity data[cite: 18].
+* '''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.
-* '''Open and FAIR Repository:''' The MediaWiki platform acts as a permanent, living knowledge base hosted on DigitalOcean, ensuring continuous updates and long-term sustainability beyond the project timeline[cite: 18].
+* '''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? ==
-Under Task 6.1, TalTech coordinated consortium input to build a dual knowledge transfer infrastructure[cite: 18, 19]:
+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.
-# '''MediaWiki Knowledge Base:''' Deployed a modular MediaWiki server featuring structured taxonomies covering pollutants (PM, VOCs, radon, bioaerosols, PAHs), monitoring sensors, ventilation engineering, and policy roadmaps[cite: 18]. A visual user guide was produced to empower consortium researchers to contribute deliverable one-pagers continuously[cite: 18].
-# '''EDIAQI IAQ Simulation Tool:''' Collaborating with Know-Center (KNOW), developed and launched a free web-based risk evaluation simulator ([https://iaq-simulator.know-center.at/])[cite: 18]. Built using Streamlit, Docker, and CatBoost gradient boosting regression, the tool utilizes retrospective cohort datasets (such as COPSAC2000) and OpenStreetMap spatial features to calculate indoor pollutant exposures[cite: 18].
+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.
+{| class="wikitable sortable" style="width:100%;"
+! style="width:25%;" | User group
+! style="width:75%;" | 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 and facility managers'''
+| 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.
+* '''Maintain regular one-pager contributions:''' 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[cite: 20]. 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[cite: 20, 21].
-== Main findings ==
+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[cite: 20]. Further updates across later project milestones will integrate broader multi-city datasets, time-series ventilation models, and refined toxicological findings[cite: 20].
-=== Finding 1: Structured Problem Diagnosis through the Decision Tree ===
+<div style="color:#202122; font-size:1.5em; font-weight:normal; border-bottom:1px solid #a2a9b1; margin-top:1.2em; margin-bottom:0.4em; padding-bottom:0.2em;">
-Deliverable D6.1 introduces a hierarchical decision tree to resolve indoor environmental complaints methodically[cite: 18]. It distinguishes whether elevated contaminants originate from outdoor air (which necessitates mechanical filtration) or indoor sources[cite: 18]. Indoor issues are further categorized into occupant behavior (e.g., smoking, unvented gas stoves) or building physical defects (e.g., structural moisture, poor insulation, or insufficient air change rates), ensuring remediation targets the true root cause[cite: 18].
+'''Related wiki pages'''
+</div>
+* [[Indoor air pollutants]]
+* [[Sensors]]
+* [[Recommendations and guidelines]]
+* [[Guidelines for national indoor environmental quality requirements]]
-=== Finding 2: Integration of Machine Learning for Exposure Prediction ===
+<div class="mw-collapsible mw-collapsed" style="border: 1px solid #ccc; padding: 10px; background-color: #f9f9f9;">
-The CatBoost regression model implemented in the IAQ Simulator demonstrated that household physical properties (construction year, floor level, total area) combined with user activity patterns (cooking hood usage, gas stove frequency, fireplace operation) and localized outdoor environmental density (surrounding road networks, industrial areas, vegetation buffers) provide reliable baseline estimates for indoor NO2 and PM2.5 burdens when physical sensor nodes are absent[cite: 18].
+'''[+] View technical source and page metadata'''
+<div class="mw-collapsible-content">
-=== Finding 3: Bridge to Real-World Interventions and Building Physics ===
+<div style="color:#202122; font-size:1.5em; font-weight:normal; border-bottom:1px solid #a2a9b1; margin-top:1.2em; margin-bottom:0.4em; padding-bottom:0.2em;">
-The deliverable establishes clear operational linkages with the ongoing field studies across European pilots (Ferrara P1, Estonia P2, Zagreb P3, and Filtration P4)[cite: 1, 18, 19]. The knowledge base emphasizes that portable air cleaners and HVAC filtration systems cannot compensate for inadequate fresh air supply; successful indoor air hygiene relies on combining continuous sensor feedback with calculated ventilation rates and proper filter maintenance[cite: 13, 18, 19].
+Source deliverable
+</div>
+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
-== Links to Official Deliverables and Resources ==
+</div>
-* '''Official Deliverable Report:''' Deliverable D6.1 Final Version (Ares(2023)6614459)[cite: 18]
+</div>
-* '''Interactive Web Tool:''' [https://iaq-simulator.know-center.at/ EDIAQI IAQ Public Simulator][cite: 18]
-* '''Project Repository:''' [[Project Deliverables|EDIAQI Official Deliverables and One-Pagers]]
 [[Category:Project Deliverables and One-Pagers]]
-[[Category:Decision Support and Tools]]
-[[Category:Ventilation and Filtration]]