Ricardo Piedrahita

MAQS: a personalized mobile sensing system for indoor air quality monitoring

Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO2 sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via user study. Detailed evaluation results demonstrate that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency.

Collaborative calibration and sensor placement for mobile sensor networks

Mobile sensing systems carried by individuals or machines make it possible to measure position- and time-dependent environmental conditions, such as air quality and radiation. The low-cost, miniature sensors commonly used in these systems are prone to measurement drift, requiring occasional re-calibration to provide accurate data. Requiring end users to periodically do manual calibration work would make many mobile sensing systems impractical. We therefore argue for the use of collaborative, automatic calibration among nearby mobile sensors, and provide solutions to the drift estimation and placement problems posed by such a system. Collaborative calibration opportunistically uses interactions among sensors to adjust their calibration functions and error estimates. We use measured sensor drift data to determine properties of time-varying drift error. We then develop (1) both optimal and heuristic algorithms that use information from multiple collaborative calibration events for error compensation and (2) algorithms for stationary sensor placement, which can further decrease system-wide drift error in a mobile, personal sensing system. We evaluated the proposed techniques using real-world and synthesized human motion traces. The most advanced existing work has 23.2% average sensing error, while our collaborative calibration technique reduces the error to 2.2%. The appropriate placement of accurate stationary sensors can further reduce this error.

Characterization and Nonparametric Regression of Rural and Urban Coarse Particulate Matter Mass Concentrations in Northeastern Colorado

The Colorado Coarse Rural Urban Sources and Health study (CCRUSH) is an ongoing study of the relationship between coarse particulate mass concentrations (PM10–2.5, particulate matter with diameter between 2.5 and 10 μm) and selected health effects. For two urban monitoring sites in Denver, CO, and two comparatively rural sites in Greeley, CO, hourly mass concentrations of PM10–2.5 and fine particulate matter (PM2.5, diameter less than 2.5 μm) have been measured by using dichotomous tapered element oscillating microbalances (TEOMs) with Filter Dynamics Measurement Systems (FDMS). This paper presents air quality results from just over a year of PM2.5 and PM10–2.5 measurements. Average PM2.5 concentrations ranged from 7.7 to 9.2 μg m−3 across the four sites with higher concentrations in Denver than Greeley. Average PM10–2.5 concentrations ranged from 9.0 to 15.5 μg m−3 with the highest values at the site in northeast Denver. Temporal variability in PM10–2.5 was higher than that in PM2.5 concentrations at all four sites. The two Greeley sites displayed moderate spatial correlation for PM2.5 and high correlation for PM10–2.5, whereas the two Denver sites showed lower spatial correlation for both PM sizes. PM10–2.5 concentrations in Denver were highest with winds from the direction of the citys urban core. PM10–2.5 concentrations in Greeley were moderately elevated with winds from the southwest to the northwest, coming from Denver and other large Front Range communities. Wind speed regressions for PM10–2.5 at the Denver sites primarily exhibited resuspension effects, while PM10–2.5 concentrations in Greeley showed relatively complex wind speed dependence. Copyright 2012 American Association for Aerosol Research

Quantification Method for Electrolytic Sensors in Long-Term Monitoring of Ambient Air Quality

Traditional air quality monitoring relies on point measurements from a small number of high-end devices. The recent growth in low-cost air sensing technology stands to revolutionize the way in which air quality data are collected and utilized. While several technologies have emerged in the field of low-cost monitoring, all suffer from similar challenges in data quality. One technology that shows particular promise is that of electrolytic (also known as amperometric) sensors. These sensors produce an electric current in response to target pollutants. This work addresses the development of practical models for understanding and quantifying the signal response of electrolytic sensors. Such models compensate for confounding effects on the sensor response, such as ambient temperature and humidity, and address other issues that affect the usability of low-cost sensors, such as sensor drift and inter-sensor variability.

A Hybrid Sensor System for Indoor Air Quality Monitoring

Indoor air quality is important. It influences human productivity and health. Personal pollution exposure can be measured using stationary or mobile sensor networks, but each of these approaches has drawbacks. Stationary sensor network accuracy suffers because it is difficult to place a sensor in every location people might visit. In mobile sensor networks, accuracy and drift resistance are generally sacrificed for the sake of mobility and economy. We propose a hybrid sensor network architecture, which contains both stationary sensors (for accurate readings and calibration) and mobile sensors (for coverage). Our technique uses indoor pollutant concentration prediction models to determine the structure of the hybrid sensor network. In this work, we have (1) developed a predictive model for pollutant concentration that minimizes prediction error; (2) developed algorithms for hybrid sensor network construction; and (3) deployed a sensor network to gather data on the airflow in a building, which are later used to evaluate the prediction model and hybrid sensor network synthesis algorithm. Our modeling technique reduces sensor network error by 40.4% on average relative to a technique that does not explicitly consider the inaccuracies of individual sensors. Our hybrid sensor network synthesis technique improves personal exposure measurement accuracy by 35.8% on average compared with a stationary sensor network architecture.

Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana (REACCTING): study rationale and protocol

BackgroundCooking over open fires using solid fuels is both common practice throughout much of the world and widely recognized to contribute to human health, environmental, and social problems. The public health burden of household air pollution includes an estimated four million premature deaths each year. To be effective and generate useful insight into potential solutions, cookstove intervention studies must select cooking technologies that are appropriate for local socioeconomic conditions and cooking culture, and include interdisciplinary measurement strategies along a continuum of outcomes.Methods/DesignREACCTING (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana) is an ongoing interdisciplinary randomized cookstove intervention study in the Kassena-Nankana District of Northern Ghana. The study tests two types of biomass burning stoves that have the potential to meet local cooking needs and represent different “rungs” in the cookstove technology ladder: a locally-made low-tech rocket stove and the imported, highly efficient Philips gasifier stove. Intervention households were randomized into four different groups, three of which received different combinations of two improved stoves, while the fourth group serves as a control for the duration of the study. Diverse measurements assess different points along the causal chain linking the intervention to final outcomes of interest. We assess stove use and cooking behavior, cooking emissions, household air pollution and personal exposure, health burden, and local to regional air quality. Integrated analysis and modeling will tackle a range of interdisciplinary science questions, including examining ambient exposures among the regional population, assessing how those exposures might change with different technologies and behaviors, and estimating the comparative impact of local behavior and technological changes versus regional climate variability and change on local air quality and health outcomes.DiscussionREACCTING is well-poised to generate useful data on the impact of a cookstove intervention on a wide range of outcomes. By comparing different technologies side by side and employing an interdisciplinary approach to study this issue from multiple perspectives, this study may help to inform future efforts to improve health and quality of life for populations currently relying on open fires for their cooking needs.

MAQS: a mobile sensing system for indoor air quality

Most people spend more than 90% of their time indoors. Indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This demo introduces MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method; (2) an air exchange rate based IAQ sensing method; and (3) a zone-based proximity detection method for collaborative sensing.

User-centric indoor air-quality monitoring on mobile devices

Since people spend a majority of their time indoors, indoor air quality (IAQ) can have a significant impact on human health, safety, productivity, and comfort. Due to the diversity and dynamics of peoples indoor activities, it is important to monitor IAQ for each individual. Most existing air quality sensing systems are stationary or focus on outdoor air quality. In contrast, we propose MAQS, a user-centric mobile sensing system for IAQ monitoring. MAQS users carry portable, indoor location tracking and IAQ sensing devices that provide personalized IAQ information in real time. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO

Exposures to and origins of carbonaceous PM2.5 in a cookstove intervention in Northern Ghana

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The next generation of low-cost personal air quality sensors for quantitative exposure monitoring

sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via a real-world user study. This evaluation demonstrates that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency. We also found that study participants frequently experienced poor IAQ.