Indoor and ambient air pollution dataset using a multi-instrument approach and total event monitoring – Nature

Report on a Multi-Instrument Dataset for Indoor Air Quality and its Alignment with Sustainable Development Goals

Introduction: Addressing Global Health and Sustainability through IAQ Monitoring

Indoor Air Quality (IAQ) is a critical determinant of public health, directly supporting Sustainable Development Goal 3 (Good Health and Well-being). With individuals spending up to 90% of their time indoors, exposure to accumulated air pollutants poses significant health risks, contributing to an estimated 1.8 million deaths globally. This report details a comprehensive study that addresses the challenges in monitoring IAQ by providing a high-resolution dataset. The research aims to generate evidence-driven strategies for healthier indoor environments, a cornerstone for building Sustainable Cities and Communities (SDG 11). This initiative, part of the Horizon EDIAQI project, provides the foundational data needed to develop policies and technologies that protect human health and promote sustainable living.

Methodological Framework for Sustainable Innovation

The study employed a robust methodological framework in a controlled experimental room in Croatia, ensuring the generation of high-quality, reliable data. This approach fosters technological advancement in line with SDG 9 (Industry, Innovation, and Infrastructure).

Experimental Setup and Instrumentation

A multi-instrumental approach was utilized to capture a holistic view of IAQ dynamics. This innovative combination of technologies is essential for developing scalable and effective monitoring solutions.

• Low-Cost Sensors (LCS): Multiple sensor types were deployed to assess their viability for widespread, affordable IAQ monitoring, a key factor for accessible health solutions under SDG 3.
• Reference-Grade Devices: High-precision instruments, including a Palas AQ Guard and an aethalometer, provided calibrated, state-of-the-art measurements for data validation.
• Auxiliary Systems: Motion sensors, door sensors, and cameras were used to contextualize pollution events with specific human activities and ventilation changes.

The monitoring focused on pollutants with significant health implications, including:

• Particulate Matter (PM)
• Black Carbon (BC)
• Volatile Organic Compounds (VOC)
• Carbon Dioxide (CO₂)
• Ozone (O₃)

Data Acquisition and Integration

Data was systematically collected from all instruments and integrated with external environmental data from meteorological services and satellite feeds. This integration provides a comprehensive understanding of the interplay between indoor and outdoor environments, which is crucial for effective urban air quality management and contributes to the knowledge base for SDG 11.

Experimental Scenarios and Resulting Data Records

Simulating Household Activities for Responsible Consumption Insights (SDG 12)

To understand the impact of daily life on IAQ, a series of controlled experiments simulating common household activities were conducted. The data from these scenarios provides tangible evidence of how consumer choices and behaviors affect indoor pollution, thereby promoting awareness and supporting SDG 12 (Responsible Consumption and Production). The documented events include:

1. Combustion Events: Burning candles and using a gas stove to assess emissions from common household sources.
2. Consumer Product Use: Operation of air diffusers with and without essential oils.
3. Human Presence and Activity: Monitoring changes in IAQ due to occupancy and physical exercise.
4. External Pollutant Intrusion: Simulating car exhaust infiltration to measure the impact of outdoor sources on indoor air.
5. Cleaning Activities: Assessing the release of VOCs and other pollutants from cleaning agents.
6. Ventilation Scenarios: Opening and closing doors to analyze the effects of natural ventilation on pollutant levels.

Dataset Composition

The resulting dataset is composed of 19 distinct subsets, offering a rich resource for researchers and policymakers. It includes high-resolution, time-stamped data from all sensor types, detailed records of each experimental event, and corresponding outdoor environmental conditions. This comprehensive record is designed to support the development of advanced machine learning models for pollution source identification and IAQ forecasting.

Implications for Sustainable Development

This dataset directly contributes to advancing several Sustainable Development Goals:

• SDG 3 (Good Health and Well-being): By providing detailed evidence on exposure to harmful indoor pollutants, the data enables the development of strategies to reduce the burden of disease associated with poor air quality.
• SDG 11 (Sustainable Cities and Communities): The findings can inform building codes, ventilation standards, and urban planning to create healthier and more sustainable indoor environments for citizens.
• SDG 9 (Industry, Innovation, and Infrastructure): The study promotes innovation by validating low-cost sensor technologies and providing a benchmark dataset for developing new AI-driven IAQ management tools.
• SDG 12 (Responsible Consumption and Production): The data empowers consumers and manufacturers with knowledge about the environmental impact of everyday products and activities, encouraging more sustainable choices.

Usage Notes and Conclusion

The dataset is publicly available and accompanied by code notebooks in Python and R to facilitate its use. The data illustrates the responsiveness of various sensors to different pollution events, such as PM₁₀ fluctuations from opening a door and CO₂ spikes from gas burning. This research provides a critical tool for advancing scientific understanding of IAQ and developing evidence-based solutions. By bridging the gap between IAQ monitoring and public health action, this work strongly supports the global agenda for a sustainable and healthy future.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on indoor air quality (IAQ) monitoring and its health impacts connects to several Sustainable Development Goals (SDGs). The primary connections are with health, sustainable urban living, and the role of innovation and partnerships in addressing these issues.

1. SDG 3: Good Health and Well-being: This is the most directly addressed SDG. The article’s entire premise is based on the significant influence of indoor air quality on human health. It explicitly states that “low indoor air quality (IAQ) was associated with 1.8 million deaths and more than 60 million disability-adjusted life years globally,” directly linking air pollution to mortality and health burdens.
2. SDG 11: Sustainable Cities and Communities: The study is set in a residential home within a community, examining the interplay between indoor and outdoor environments. It mentions the proximity to a highway and monitors traffic, acknowledging that urban planning and outdoor pollution sources directly impact the living conditions and air quality inside homes. The focus on household activities (cooking, cleaning) and building characteristics (ventilation) is central to creating sustainable and healthy living spaces in cities.
3. SDG 9: Industry, Innovation, and Infrastructure: The article details a scientific study that leverages technological innovation to solve a problem. It describes a “multi-instrumental approach,” the use and validation of “low-cost sensors” against “reference-grade devices,” and the goal of applying “machine learning (ML) or artificial intelligence (AI)” to the collected data. This focus on enhancing scientific research and developing new technologies for monitoring and analysis aligns perfectly with SDG 9.
4. SDG 17: Partnerships for the Goals: The research presented is part of the “Horizon EDIAQI project (Evidence Driven Indoor Air Quality Improvement),” which is a collaborative, multi-stakeholder initiative. The extensive list of authors and affiliations from various institutions across different countries (Croatia, Belgium, Spain, Germany, etc.) demonstrates a strong partnership to share knowledge, expertise, and technology. The public sharing of the dataset on Zenodo further supports the goal of collaborative progress.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s focus, the following specific SDG targets can be identified:

• Target 3.9: “By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.” The article directly supports this target by investigating the sources and levels of indoor air pollutants (PM, VOCs, BC) that are known to cause death and illness. The study aims to provide “evidence-driven strategies to improve IAQ,” which is a direct action towards achieving this target.
• Target 11.6: “By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality…” The research contributes to this target by providing detailed data on air quality in a residential urban setting. It examines how both indoor activities and outdoor urban sources (like car exhaust from a nearby road) affect the air people breathe, which is crucial for developing policies to mitigate the environmental impact of cities on their inhabitants.
• Target 9.5: “Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries…encouraging innovation…” The study is a clear example of enhancing scientific research. It introduces an innovative “multi-instrumental approach,” validates new monitoring technologies (low-cost sensors), and creates a large-scale dataset intended for the application of novel AI/ML algorithms, thereby upgrading technological capabilities in the field of environmental monitoring.
• Target 17.16: “Enhance the Global Partnership for Sustainable Development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology…” The article is an output of the Horizon EDIAQI project, a multi-stakeholder partnership involving numerous research institutions and companies across Europe. The project’s methodology and the public availability of its data are acts of mobilizing and sharing knowledge and technology to address a global health and environmental issue.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions and implies several indicators that can be used to measure progress.

• For Target 3.9:
  • Official Indicator (Implied): The article’s motivation is based on Indicator 3.9.1 (Mortality rate attributed to household and ambient air pollution). While the study does not calculate this rate, it measures the pollutants that are the primary cause.
  • Specific Indicators Measured in the Study: The progress towards reducing illnesses from air pollution can be measured by monitoring the concentrations of the specific pollutants the article focuses on. These serve as direct indicators of air quality and exposure risk:
    • Concentrations of Particulate Matter (PM), including PM₁₀, PM_2.5, and PM₁.
    • Concentrations of Black Carbon (BC).
    • Concentrations of Total Volatile Organic Compounds (TVOC) and specific VOCs (e.g., benzene, toluene).
    • Concentrations of Polycyclic Aromatic Hydrocarbons (PAHs).
    • Concentrations of other gases like CO₂, O₃, CO, and NO₂.
• For Target 11.6:
  • Official Indicator (Directly Relevant): The study’s measurements of PM are directly relevant to Indicator 11.6.2 (Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities). The dataset provides high-resolution PM data in a residential area, contributing to the body of knowledge needed to track this indicator.
• For Target 9.5:
  • Implied Indicators: Progress is demonstrated by the outputs of the scientific research itself. The article implies indicators such as:
    • The development and validation of new monitoring technologies (e.g., performance of low-cost sensors).
    • The creation and public dissemination of large-scale, high-resolution scientific datasets (the 19-subset dataset on Zenodo).
    • The number of scientific publications and collaborative research projects (like EDIAQI) focused on environmental innovation.
• For Target 17.16:
  • Implied Indicators: The existence and activities of the partnership serve as indicators.
    • The number of cross-border and multi-stakeholder research collaborations (the EDIAQI project itself).
    • The public availability of data and research tools on platforms like Zenodo and GitHub, which measures the extent of knowledge sharing.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com