Managing In-home Environments through Sensing, Annotating, and Visualizing Air Quality Data

Abstract

Air quality is important, varies across time and space, and is largely invisible. Pioneering past work deploying air quality monitors in residential environments found that study participants improved their awareness of and engagement with air quality. However, these systems fielded a single monitor and did not support user-specified annotations, inhibiting their utility. We developed MAAV-a system to Measure Air quality, Annotate data streams, and Visualize real-time PM 2.5 levelsto explore how participants engage with an air quality system addressing these challenges. MAAV supports collecting data from multiple air quality monitors, annotating that data through multiple modalities, and sending text message prompts when it detects a PM 2.5 spike. MAAV also features an interactive tablet interface for displaying measurement data and annotations. Through six long-term field deployments (20-47 weeks, mean 37.7 weeks), participants found these system features important for understanding the air quality in and around their homes. Participants gained new insights from between-monitor comparisons, reflected on past PM 2.5 spikes with the help of their annotations, and adapted their system usage as they familiarized themselves with their air quality data and MAAV. These results yield important insights for designing residential sensing systems that integrate into users' everyday lives.

Performance of low-cost indoor air quality monitors for PM2.5 and PM10 from residential sources

“…Indoor air quality (IAQ) monitoring can provide feedback on efforts to protect occupants from infiltrating (high) outdoor PM2.5, help to identify indoor activities that generate PM 2,3 , or automatically activate ventilation or filtration when readings exceed a designated threshold 4 . The potential to incorporate IAQ monitoring into smart home management systems is receiving increasing attention 5,6 .…”

Wetland Dynamics Inferred from Spectral Analyses of Hydro-Meteorological Signals and Landsat Derived Vegetation Indices

“…Components of CPSD analysis (i.e., percentage of amplitude with the major peak, phase lag, and corresponding time-lag) are shown in Table 1. Major peaks in the amplitude spectra were identified by using a threshold quantified using a smoothed z-score algorithm [52][53][54]. The algorithm is based on the principle of dispersion and is robust as it builds a separate moving mean and deviation so that the signals themselves do not pollute the threshold [53].…”

Mapping and Taking Stock of the Personal Informatics Literature

“…The relatively minor differences between deployment rates suggests that artifacts types tend to be developed at similar levels of maturity. Study durations have ranged from a single day for deployments around a special event the researchers organized (e.g., a race, a hike, a presentation [80,90,188]) to nearly a year [62,198], with a mean of 40.7 days and a median of 21 days.…”