Research · Indoor environmental quality
Indoor Air Quality in Low-Income Housing
A published mixed-methods field study of the community, logistical, and technical conditions that shape residential IAQ research.
- Period
- 2022–2024
- Status
- First-author conference paper published · Analytical extension in progress
- Perspectives
- Research · Engineering
30-second case brief
Question, responsibility, method, result.
- Question
- A published mixed-methods field study of the community, logistical, and technical conditions that shape residential IAQ research.
- Responsibility
- First author; field deployment, monitoring workflow, mixed-methods synthesis, visual analysis, and publication development
- Methods
- 54 Philadelphia households
- Result
- First-author conference paper · 54-household recruitment frame
Project scope and responsibility
- Context
- Drexel University with Philadelphia Energy Authority, Energy Coordinating Agency, and Esperanza
- Role
- First author; field deployment, monitoring workflow, mixed-methods synthesis, visual analysis, and publication development
- Study reach
- 54 Philadelphia households
- Field deployment
- 11 sensor installations · 5 usable datasets
- Instrumentation
- Awair Element · IQAir AirVisual Pro
- Publication
- 7th RBDCC Proceedings · pp. 471–487
Research question and field context
This first-author conference paper examines what it takes to collect dependable indoor-air-quality evidence in low-income Philadelphia households. The study treats residential sensing as socio-technical fieldwork: monitor selection, placement, connectivity, privacy, trust, scheduling, and community partnerships all shape what data can be collected and interpreted.
Interactive tool · fieldwork planner
Stress-test an occupied-housing IAQ deployment.
Adjust four field conditions. The planner identifies the weakest link and the next preparation priority before monitoring begins.
Priority before deployment
Plan for offline data continuity.
Connectivity is the current bottleneck. Add local buffering, visible device-status checks, and a recovery plan before relying on remote transfer.
The 54 → 11 → 5 sequence reports the documented study. The adjustable planning signal is illustrative and does not estimate recruitment or completion probability.
Mixed-methods field design
The project combined Awair Element and IQAir AirVisual Pro monitoring with participant communication, field observation, qualitative assessment, and collaboration among Drexel researchers, the Philadelphia Energy Authority, the Energy Coordinating Agency, Esperanza, and household members. The methodology linked technological solutions, data-collection logistics, and community collaboration from the start.
What field deployment revealed
The program began with 54 targeted households. Sensors were installed in 11 homes, and five installations yielded usable data. This progression became a central research result: more than half of the potential locations lacked stable usable Wi-Fi, while privacy concerns, participation, coordination, monitor placement, building conditions, and equipment limitations further shaped deployment and retrieval.
Four-part challenge framework
Qualitative analysis organized the field experience into four connected domains: participants, collaborators, technical challenges, and logistics. Awareness, privacy, and ownership influenced participation; stakeholder coordination shaped access; sensor and building constraints affected data quality; and communication, scheduling, infrastructure, and funding determined whether the study could operate consistently.
Recommendations for stronger field studies
The published work translates those obstacles into an implementation agenda: targeted participant education, clear privacy protocols, durable community partnerships, flexible coordination, improved sensing and connectivity strategies, adequate funding, and a comprehensive field plan that integrates technical and social requirements.
Analytical extension
A related analytical extension aligns the five usable household datasets, extracts comparable six-hour windows, applies principal component analysis, and groups recurring environmental profiles with k-means clustering. This next phase connects the field-research lessons to a reproducible workflow for interpreting irregular multivariate sensor records.
