Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India

TL;DR

This work tackles indoor air quality in developing nations by collecting multi-room, in-wild IAQ data and tying pollutant dynamics to occupant activities. It introduces DALTON, a low-cost, IP-agnostic IoT platform with over-the-air maintenance and an Android app for human-in-the-loop labeling, deployed across thirty households in four Indian cities over six months. The study reveals distinct patterns such as VOC linger and CO2 buildup tied to ventilation and floor plan, and demonstrates how targeted ventilation and activity-aware interventions can reduce exposure. The findings show DALTON's potential for scalable deployment, real-world impact on health outcomes, and guidance for design of future closed-loop, user-centered air-quality systems in resource-constrained settings.

Abstract

Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors like traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues like lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollutions extent are missing. To fill this gap, we carried out a six months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of VOCs in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative IoT air quality sensing platform, the Distributed Air QuaLiTy MONitor (DALTON ), explicitly designed to meet the needs of these nations, considering factors like cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot-spots in low and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring.

Figures (22)

• Figure 1: DALTON platform in Household scenarios. We deployed multiple instances of the platform throughout different household rooms to capture the origin, spread, and spatiotemporal diversity of the indoor pollutants, providing better actionable insights.
• Figure 2: Prototype of DALTON sensing device.
• Figure 3: IoT backbone microservices of DALTON platform. The solid arrow represents streaming data. The dotted arrow represents metadata such as data statistics, change points, etc. The dashed arrow represents error handling and control signals.
• Figure 4: Detected time segments from the change points computed using the KLCPD algorithm for two adjacent devices in the bedroom and kitchen. The event $E_2^m$ in the kitchen's device and $E_3$ in the bedroom's device are associated with significant time overlap.
• Figure 5: Developed Android application for user-friendly activity labeling. It uses Google's speech-to-text API for easy voice annotations.