52084746
Managing In-home Environments through Sensing, Annotating, and Visualizing Air Quality Data
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 PM2.5 levels -- to 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 PM2.5 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 PM2.5 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.
7501449
Chronic Exposure to Fine Particles and Mortality: An Extended Follow-up of the Harvard Six Cities Study from 1974 to 2009
Background: Epidemiologic studies have reported associations between fine particles (aerodynamic diameter ≤ 2.5 µm; PM2.5) and mortality. However, concerns have been raised regarding the sensitivity of the results to model specifications, lower exposures, and averaging time. Objective: We addressed these issues using 11 additional years of follow-up of the Harvard Six Cities study, incorporating recent lower exposures. Methods: We replicated the previously applied Cox regression, and examined different time lags, the shape of the concentration–response relationship using penalized splines, and changes in the slope of the relation over time. We then conducted Poisson survival analysis with time-varying effects for smoking, sex, and education. Results: Since 2001, average PM2.5 levels, for all six cities, were < 18 µg/m3. Each increase in PM2.5 (10 µg/m3) was associated with an adjusted increased risk of all-cause mortality (PM2.5 average on previous year) of 14% [95% confidence interval (CI): 7, 22], and with 26% (95% CI: 14, 40) and 37% (95% CI: 7, 75) increases in cardiovascular and lung-cancer mortality (PM2.5 average of three previous years), respectively. The concentration–response relationship was linear down to PM2.5 concentrations of 8 µg/m3. Mortality rate ratios for PM2.5 fluctuated over time, but without clear trends despite a substantial drop in the sulfate fraction. Poisson models produced similar results. Conclusions: These results suggest that further public policy efforts that reduce fine particulate matter air pollution are likely to have continuing public health benefits.
18292092
Lung Cancer and Cardiovascular Disease Mortality Associated with Ambient Air Pollution and Cigarette Smoke: Shape of the Exposure–Response Relationships
Background: Lung cancer and cardiovascular disease (CVD) mortality risks increase with smoking, secondhand smoke (SHS), and exposure to fine particulate matter < 2.5 μm in diameter (PM2.5) from ambient air pollution. Recent research indicates that the exposure–response relationship for CVD is nonlinear, with a steep increase in risk at low exposures and flattening out at higher exposures. Comparable estimates of the exposure–response relationship for lung cancer are required for disease burden estimates and related public health policy assessments. Objectives: We compared exposure–response relationships of PM2.5 with lung cancer and cardiovascular mortality and considered the implications of the observed differences for efforts to estimate the disease burden of PM2.5. Methods: Prospective cohort data for 1.2 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II. We estimated relative risks (RRs) for increments of cigarette smoking, adjusting for various individual risk factors. RRs were plotted against estimated daily dose of PM2.5 from smoking along with comparison estimates for ambient air pollution and SHS. Results: For lung cancer mortality, excess risk rose nearly linearly, reaching maximum RRs > 40 among long-term heavy smokers. Excess risks for CVD mortality increased steeply at low exposure levels and leveled off at higher exposures, reaching RRs of approximately 2–3 for cigarette smoking. Conclusions: The exposure–response relationship associated with PM2.5 is qualitatively different for lung cancer versus cardiovascular mortality. At low exposure levels, cardiovascular deaths are projected to account for most of the burden of disease, whereas at high levels of PM2.5, lung cancer becomes proportionately more important.
12736261
Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research
Studies of the health impacts of airborne particulates’ chemical constituents typically assume spatial homogeneity and estimate exposure from ambient monitors. However, factors such as local sources may cause spatially heterogeneous pollution levels. This work examines the degree to which constituent levels vary within communities and whether exposure misclassification is introduced by spatial homogeneity assumptions. Analysis considered PM2.5 elemental carbon (EC), organic carbon matter, ammonium, sulfate, nitrate, silicon, and sodium ion (Na+) for the United States, 1999–2007. Pearson correlations and coefficients of divergence were calculated and compared to distances among monitors. Linear modeling related correlations to distance between monitors, long-term constituent levels, and population density. Spatial heterogeneity was present for all constituents, yet lower for ammonium, sulfate, and nitrate. Lower correlations were associated with higher distance between monitors, especially for nitrate and sulfate, and with lower long-term levels, especially for sulfate and Na+. Analysis of colocated monitors revealed measurement error for all constituents, especially EC and Na+. Exposure misclassification may be introduced into epidemiological studies of PM2.5 constituents due to spatial variability, and is affected by constituent type and level. When assessing health effects of PM constituents, new methods are needed for estimating exposure and accounting for exposure error induced by spatial variability.
98603434
Models for human exposure to air pollution
Four models for human exposure to air pollution are discussed and compared. The simple microenvironment monitoring model measures pollutant concentrations at fixed locations, regarded as proxies for similar locations or microenvironments. Since this model does not require pollutant measurements on the individual level, it is easy to implement. However, the model can only be used to estimate the average exposure in a population, and it does not provide any estimate of the variability and distribution of individual exposures. The replicated microenvironment monitoring model provides some estimates of the variability and distribution. However, because of the possible discrepancy between the microenvironment concentration distribution and the individual concentration distribution, some adjustment might be necessary. Integrated personal monitoring allows direct estimation of the average exposure as well as the variability and distribution of individual exposures. Coupled with the appropriate time budget data, a regression analysis can be applied to estimate the contribution from each microenvironment type. However, possible collinearity problems might result in low precision in those estimates. Moreover, it might be difficult to adjust for a possible Hawthorne effect. Continuous personal monitoring has the advantage of recording exposure in each microenvironment type separately, allowing direct estimation of the average exposure as well as the variability and distribution of exposures in each microenvironment type. Moreover, it can also be conducted in conjunction with a two-stage sampling scheme, using information from a large data base on activity patterns, thereby making more efficient use of the monitoring data. It is also easier to adjust for a possible Hawthorne effect in this design.
21233987
Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment.
Quantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activity-patterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information.
14153864
Indoor air quality monitoring system for smart buildings
Many developing countries are suffering from air pollution, especially the Particulate Matter with diameter of 2.5 micrometers or less (PM2.5). While quite a few air quality monitoring stations have been built by governments in a city's public areas, the indoor PM2.5 has not yet been monitored and dealt with effectively. Though many office buildings have an HVAC (heating, ventilation, and air conditioning) system, PM2.5 is not considered as a factor when the system circulates fresh air from outdoors. This paper introduces a real system that we have deployed in the offices of four Microsoft campuses in China. This system instantly monitors indoor air quality on different floors of a building (including office areas, gyms, garages, and restaurants), enabling Microsoft employees to enquire the air quality of a place by using a mobile phone or checking a website. The information can guide a user's decision making, e.g., finding the right time to work out in the gym or turn on individual air filters in her own office. Through analyzing the indoor and outdoor air quality data collected over a long period, our system can even offer actionable and energy-efficient suggestion to HVAC systems, e.g., automatically turning on the system only a few hours earlier than usual if it is a heavily polluted day, or identifying the filters in HVAC system that should be renewed.
683023
inAir: measuring and visualizing indoor air quality
Good indoor air quality is a vital part of human health. Poor indoor air quality can contribute to the development of chronic respiratory diseases such as asthma, heart disease, and lung cancer. Complicating matters, poor air quality is extremely difficult for humans to detect through sight and smell alone and existing sensing equipment is designed to be used by and provide data for scientists rather than everyday citizens. We propose inAir, a tool for measuring, visualizing, and learning about indoor air quality. inAir provides historical and real-time visualizations of indoor air quality by measuring tiny hazardous airborne particles as small as 0.5 microns in size. Through user studies we demonstrate how inAir promotes greater awareness and motivates individual actions to improve indoor air quality.
207176361
Common Sense: participatory urban sensing using a network of handheld air quality monitors
Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
5852377
Participatory Air Pollution Monitoring Using Smartphones
Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
4731195
Community-Empowered Air Quality Monitoring System
Developing information technology to democratize scientific knowledge and support citizen empowerment is a challenging task. In our case, a local community suffered from air pollution caused by industrial activity. The residents lacked the technological fluency to gather and curate diverse scientific data to advocate for regulatory change. We collaborated with the community in developing an air quality monitoring system which integrated heterogeneous data over a large spatial and temporal scale. The system afforded strong scientific evidence by using animated smoke images, air quality data, crowdsourced smell reports, and wind data. In our evaluation, we report patterns of sharing smoke images among stakeholders. Our survey study shows that the scientific knowledge provided by the system encourages agonistic discussions with regulators, empowers the community to support policy making, and rebalances the power relationship between stakeholders.
1722105
A low-tech sensing system for particulate pollution
We present an ultra low-cost sensing system, which enables participants to see and reflect on the particulates in their air. Drawing on prior work in paper computing, we introduce small sensors for particulate pollution that can be easily assembled from common paper materials for less than $1 USD, and mailed by regular postal service to residents of entire neighborhoods, cities, or geographic regions. Recipients collect particulate samples using these sensors and mail them back to a central location, where the particles are viewed and analyzed via a microscope. The data, which includes rich images of actual air pollution particles, can then be broadcast to larger audiences. This paper details the design of our system and its deployment with a local air quality activist community. We conclude by highlighting the tradeoffs between high-tech and low-tech sensing, and suggest opportunities for tangible interaction to support rich, new ways of seeing our environment.
22730730
Influence of ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations: Analyses of RIOPA data
The Relationship of Indoor, Outdoor and Personal Air (RIOPA) study was designed to investigate residential indoor, outdoor and personal exposures to several classes of air pollutants, including volatile organic compounds, carbonyls and fine particles (PM2.5). Samples were collected from summer, 1999 to spring, 2001 in Houston (TX), Los Angeles (CA) and Elizabeth (NJ). Indoor, outdoor and personal PM2.5 samples were collected at 212 nonsmoking residences, 162 of which were sampled twice. Some homes were chosen due to close proximity to ambient sources of one or more target analytes, while others were farther from sources. Median indoor, outdoor and personal PM2.5 mass concentrations for these three sites were 14.4, 15.5 and 31.4 μg/m3, respectively. The contributions of ambient (outdoor) and nonambient sources to indoor and personal concentrations were quantified using a single compartment box model with measured air exchange rate and a random component superposition (RCS) statistical model. The median contribution of ambient sources to indoor PM2.5 concentrations using the mass balance approach was estimated to be 56% for all study homes (63%, 52% and 33% for California, New Jersey and Texas study homes, respectively). Reasonable variations in model assumptions alter median ambient contributions by less than 20%. The mean of the distribution of ambient contributions across study homes agreed well for the mass balance and RCS models, but the distribution was somewhat broader when calculated using the mass balance model with measured air exchange rates.
22445147
The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants
Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey ( n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.
11022911
AirSense: an intelligent home-based sensing system for indoor air quality analytics
In the U.S., people spend approximately 90 percent of their time indoors. Unfortunately, indoor air quality (IAQ) may be two to five times worse than the air outdoors, and is often overlooked. Existing IAQ monitoring technologies focus on IAQ measurements and visualization. However, the lack of information about the pollution sources as well as the seriousness of the pollution makes people feel powerless and frustrated, resulting in the ignorance of the polluted air at their homes. In this work, we fill this critical gap by presenting AirSense, an intelligent home-based IAQ sensing system that is able to automatically detect pollution events, identify pollution sources, estimate personal exposure to indoor air pollution, and provide actionable suggestions to help people improve IAQ. We have deployed AirSense at five homes to evaluate its performance and investigate how users interact with it. We demonstrate that AirSense can accurately detect pollution events, identify pollution sources, and forecast IAQ information within five minutes in both controlled and real-world settings. We further show the great potential of AirSense in increasing users' awareness of IAQ and helping them better manage IAQ at their homes.
125666
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.
15268892
InAir: sharing indoor air quality measurements and visualizations
This paper describes inAir, a tool for sharing measurements and visualizations of indoor air quality within one's social network. Poor indoor air quality is difficult for humans to detect through sight and smell alone and can contribute to the development of chronic diseases. Through a four-week long study of fourteen households as six groups, we found that inAir (1) increased awareness of, and reflection on air quality, (2) promoted behavioral changes that resulted in improved indoor air quality, and (3) demonstrated the persuasive power of sharing for furthering improvements to indoor air quality in terms of fostering new social awareness and behavior changes as well as strengthening social bonds and prompting collaborative efforts across social networks to improve human health and well being.
1464948
inAir: a longitudinal study of indoor air quality measurements and visualizations
Indoor air quality (IAQ) is important for health as people spend the majority of time indoors, and it is particularly interesting over outdoor air because it strongly ties to indoor activities. Some activities easily exacerbate IAQ, resulting in serious pollution. However, people may not notice such changes because many pollutants are colorless and odorless, while many activities are inconspicuous and routine. We implemented inAir, a system that measures and visualizes IAQ that households appropriate and integrate into everyday life. The research goals of this work include understanding the IAQ dynamics with respect to habitual behaviors and analyzing behavioral and quantitative changes towards improving IAQ by the use of inAir. From our longitudinal study for four months, we found that inAir successfully elicited the reflection upon, and the modification of habitual behaviors for healthy domestic environments, which resulted in the significant improvement of IAQ.
97593657
Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone
Abstract This review describes databases of small-scale spatial variations and indoor, outdoor and personal measurements of air pollutants with the main focus on suspended particulate matter, and to a lesser extent, nitrogen dioxide and photochemical pollutants. The basic definitions and concepts of an exposure measurement are introduced as well as some study design considerations and implications of imprecise exposure measurements. Suspended particulate matter is complex with respect to particle size distributions, the chemical composition and its sources. With respect to small-scale spatial variations in urban areas, largest variations occur in the ultrafine ( 10–2.5 , resuspended dust). Secondary aerosols which contribute to the accumulation mode (0.1–2 μm) show quite homogenous spatial distribution. In general, small-scale spatial variations of PM 2.5 were described to be smaller than the spatial variations of PM 10 . Recent studies in outdoor air show that ultrafine particle number counts have large spatial variations and that they are not well correlated to mass data. Sources of indoor particles are from outdoors and some specific indoor sources such as smoking and cooking for fine particles or moving of people (resuspension of dust) for coarse particles. The relationships between indoor, outdoor and personal levels are complex. The finer the particle size, the better becomes the correlation between indoor, outdoor and personal levels. Furthermore, correlations between these parameters are better in longitudinal analyses than in cross-sectional analyses. For NO 2 and O 3 , the air chemistry is important. Both have considerable small-scale spatial variations within urban areas. In the absence of indoor sources such as gas appliances, NO 2 indoor/outdoor relationships are strong. For ozone, indoor levels are quite small. The study hypothesis largely determines the choice of a specific concept in exposure assessment, i.e. whether personal sampling is needed or if ambient monitoring is sufficient. Careful evaluation of the validity and improvements in precision of an exposure measure reduce error in the measurements and bias in the exposure–effect relationship.
96953550
Physical properties, chemical composition, sources, spatial distribution and sinks of indoor aerosol particles in a university lecture hall
Abstract PM10 mass, particle number (N) and CO2 concentrations, particle number size distributions and meteorological parameters were determined with high time resolution, and daily aerosol samples were collected in the PM10–2.0 and PM2.0 size fractions for chemical analysis in the middle of a university lecture hall for one week. Median concentrations for the PM10 mass and N of 15.3 μg m−3 and 3.7 × 103 cm−3, respectively were derived. The data are substantially smaller than the related outdoor levels or typical values for residences. There were considerable concentration differences for workdays, weekends and various lectures. Main sources of PM10 mass include the usage of chalk sticks for writing, wiping the blackboard, ordinary movements and actions of students and cleaning. High PM10 mass concentration levels up to 100 μg m−3 were realised for short time intervals after wiping the blackboard. The mass concentrations decreased rapidly after the emission source ceased to be active. Two classes of coarse particles were identified. General indoor dust particles exhibited a residence time of approximately 35 min, while the residence time for the chalk dust particles was approximately 20 min as lower estimates. Emission source rate for wiping the blackboard was estimated to be between 8 and 14 mg min−1. This represents a substantial emission rate but the source is active only up to 1 min. Suspension of the chalk (made mainly of gypsum) dust particles was confirmed by enrichment of Ca and S in the hall with respect to ambient urban aerosol. Contribution of ambient aerosol via the heating, ventilation and air conditioning (HVAC) facility was considerable for time intervals when the indoor sources of PM10 mass were not intensive. The HVAC facility introduces, however, the major amount of aerosol particles from the outdoors as far as their number concentration is regarded. Mean contribution of ultrafine particles to the total particle number was (69 ± 7)%, which is smaller than for the related outdoor urban environment. This can indicate aged ultrafine aerosol. The major amounts of CO2 arrive from the corridors through open doors by infiltration. Spatial distribution of the PM10 mass concentration within the hall was derived by CFD modelling, and spatial inhomogeneities were obtained.
129383366
Relative contribution of outdoor and indoor particle sources to indoor concentrations
The effect of indoor particle sources on indoor particle size distributions and concentrations was previously investigated using real-time indoor and outdoor particle size distribution data collected in four homes in Boston in 1996. These data demonstrated the importance of indoor sources (i.e., cooking, cleaning, and movement of people) and air exchange rates on observed indoor concentrations. As part of the continued analyses of these data, a simple physical model was used to determine the source emission and infiltration rates for specific particle sizes. Decay rates were also estimated. Cooking, cleaning, and indoor work (characterized by movement of people) significantly increased PM(0.7-10) concentrations by 0.27, 0.27, and 0.25 μm3 cm-3 min-1, respectively. Cooking was the only variable significantly associated with generation of particles less than 0.5 μm in diameter. Outdoor particles (0.02−0.5 and 0.7−10 μm) were found to contribute significantly to indoor particle levels. Effective penetration ...
94956658
Indoor air measurement campaign in Helsinki, Finland 1999 – the effect of outdoor air pollution on indoor air
Abstract Wintertime indoor and outdoor particle size distributions were studied in an office building near Helsinki downtown by measuring the particle size distributions with two similar differential mobility particle sizer systems (DMPS). Measurements were made simultaneously at two places; on the rooftop of a building (30 m above the ground level) in front of a ventilation system corresponding to outdoor concentration, and an office room (first floor). The ventilation rate was also continuously monitored. Indoor particle concentrations were observed to vary from 500 to 10 4 cm −3 with a high dependence on the outdoor concentrations. This indicates that in this scenario, indoor particles are mainly of outdoor origin. Effects of ventilation rates on indoor air particle concentrations and several inorganic gases were studied. We found that ventilation had a strong influence on indoor particle and gas concentrations. I/O (indoor/outdoor) ratio in different in different particle size classes.
41116891
“This has to be the cats”: Personal Data Legibility in Networked Sensing Systems
Notions like 'Big Data' and the 'Internet of Things' turn upon anticipated harvesting of personal data through ubiquitous computing and networked sensing systems. It is largely presumed that understandings of people's everyday interactions will be relatively easy to 'read off' of such data and that this, in turn, poses a privacy threat. An ethnographic study of how people account for sensed data to third parties uncovers serious challenges to such ideas. The study reveals that the legibility of sensor data turns upon various orders of situated reasoning involved in articulating the data and making it accountable. Articulation work is indispensable to personal data sharing and raises real requirements for networked sensing systems premised on the harvesting of personal data.
14882268
The seven sins of memory. Insights from psychology and cognitive neuroscience.
Though often reliable, human memory is also fallible. This article examines how and why memory can get us into trouble. It is suggested that memory's misdeeds can be classified into 7 basic "sins": transience, absentmindedness, blocking, misattribution, suggestibility, bias, and persistence. The first three sins involve different types of forgetting, the next three refer to different types of distortions, and the final sin concerns intrusive recollections that are difficult to forget. Evidence is reviewed concerning each of the 7 sins from relevant sectors of psychology (cognitive, social, and clinical) and from cognitive neuroscience studies that include patients with focal brain damage or make use of recently developed neuroimaging techniques. Although the 7 sins may appear to reflect flaws in system design, it is argued instead that they are by-products of otherwise adaptive features of memory.
117651760
Fuzzy inference system for the assessment of indoor environmental quality in a room
The aim of the project presented in this article was to design a system which allows for assessment of indoor environmental conditions, with a special consideration regarding the comfort of occupants. The system consists of two microprocessor devices with numerous sensors, as well as a PC application which includes fuzzy inference module. Fuzzy inference algorithm allows for comfort assessment based on data gathered by sensors. It can also help to analyse the efficiency of HVAC systems. The article includes description of the system's functions and selection criteria for sensors taking into account of measurands. Also described in this paper is the construction of knowledge base, based on information from environmental standards and experts' statements. The constructed system was tested and examined, to confirm its application in practical comfort assessment and to highlight the advantages of using fuzzy logic in the process of analysing measured parameters and inference, which captures the way living beings process data.
1060467
Sensing by Proxy : Occupancy Detection Based on Indoor CO 2 Concentration
Sensing by proxy, as described in this study, is a sensing paradigm which infers latent factors by “proxy” measurements based on constitutive models that exploit the spatial and physical features in the system. In this study, we demonstrate the efficiency of sensing by proxy for occupancy detection based on indoor CO2 concentration. We propose a link model that relates the proxy measurements with unknown human emission rates based on a data-driven model which consists of a coupled Partial Differential Equation (PDE) – Ordinary Differential Equation (ODE) system. We report on several experimental results using both a CO2 pump that emulates human breathing, as well as measurements of actual occupancy by performing controlled field experiments, in order to validate our model. Parameters of the model are datadriven, which exhibit long-term stability and robustness across all the occupants experiments. The inference of the number of occupants in the room based on CO2 measurements at the air return and air supply vents by sensing by proxy outperforms a range of machine learning algorithms, and achieves an overall mean squared error of 0.6569 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and occupancy-aware services to achieve occupancy comfort and energy efficiency. The significance of this study is the proposal of a paradigm of sensing that results in a parsimonious and accurate occupancy inference model, which holds considerable potential for energy saving and improvement of HVAC operations. The proposed framework can be also applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses. Keywords–Occupancy detection; Building energy efficiency
15230601
AirCloud: a cloud-based air-quality monitoring system for everyone
We present the design, implementation, and evaluation of AirCloud -- a novel client-cloud system for pervasive and personal air-quality monitoring at low cost. At the frontend, we create two types of Internet-connected particulate matter (PM2:5) monitors -- AQM and miniAQM, with carefully designed mechanical structures for optimal air-flow. On the cloud-side, we create an air-quality analytics engine that learn and create models of air-quality based on a fusion of sensor data. This engine is used to calibrate AQMs and mini-AQMs in real-time, and infer PM2:5 concentrations. We evaluate AirCloud using 5 months of data and 2 month of continuous deployment, and show that AirCloud is able to achieve good accuracies at much lower cost than previous solutions. We also show three real applications built on top of AirCloud by 3rd party developers to further demonstrate the value of our system.
23667348
ISSAQ: An Integrated Sensing Systems for Real-Time Indoor Air Quality Monitoring
With growing transportation and population density, increasing global warming and sudden climate change, air quality is one of the critical measures that is needed to be monitored closely on a real-time basis in today's urban ecosystems. This paper examines the issues, infrastructure, information processing, and challenges of designing and implementing an integrated sensing system for real-time indoor air quality monitoring. The system aims to detect the level of seven gases, ozone (O3), particulate matter, carbon monoxide (CO), nitrogen oxides (NO2), sulfur dioxide (SO2), volatile organic compound, and carbon dioxide (CO2), on a real-time basis and provides overall air quality alert timely. Experiments are conducted to validate and support the development of the system for real-time monitoring and alerting.
62788100
Smart Sensors Network for Air Quality Monitoring Applications
This paper presents a network for indoor and outdoor air quality monitoring. Each node is installed in a different room and includes tin dioxide sensor arrays connected to an acquisition and control system. The nodes are hardwired or wirelessly connected to a central monitoring unit. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor influence quantities, i.e., temperature and humidity, are also measured. Advanced processing based on multiple-input-single-output neural networks is implemented at the network sensing nodes to obtain temperature and humidity compensated gas concentration values. Anomalous operation of the network sensing nodes and power consumption are also discussed.
35344481
CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system
Environmental exposures are a critical component in the development of chronic conditions such as asthma and cancer. Yet, medical and public health practitioners typically must depend on sparse regional measurements of the environment that provide macro-scale summaries. Recent projects have begun to measure an individual's exposure to these factors, often utilizing body-worn sensors and mobile phones to visualize the data. Such data, collected from many individuals and analyzed across an entire geographic region, holds the potential to revolutionize the practice of public health.
 We present CitiSense, a participatory air quality sensing system that bridges the gap between personal sensing and regional measurement to provide micro-level detail at a regional scale. In a user study of 16 commuters using CitiSense, measurements were found to vary significantly from those provided by official regional pollution monitoring stations. Moreover, applying geostatistical kriging techniques to our data allows CitiSense to infer a regional map that contains considerably greater detail than official regional summaries. These results suggest that the cumulative impact of many individuals using personal sensing devices may have an important role to play in the future of environmental measurement for public health.
86691679
Air-Sense: indoor environment monitoring evaluation system based on ZigBee network
In the modern life, people spend most of their time indoors. However, indoor environmental quality problems have always been affecting people's social activities. In general, indoor environmental quality is also related to our indoor activities. Since most of the organic irritants and volatile gases are colorless, odorless and too tiny to be seen, because we have been unconsciously overlooked indoor environment quality. Consequently, our body suffer a great health problem. In this work, we propose Air-Sense system which utilizes the platform of ZigBee Network to collect and detect the real-time indoor environment quality. What's more, Air-Sense system can also provide data analysis, and visualizing the results of the indoor environment to the user.
14077641
Disaggregated End-Use Energy Sensing for the Smart Grid
This article surveys existing and emerging disaggregation techniques for energy-consumption data and highlights signal features that might be used to sense disaggregated data in an easily installed and cost-effective manner.
1529717
The design and evaluation of prototype eco-feedback displays for fixture-level water usage data
Few means currently exist for home occupants to learn about their water consumption: e.g., where water use occurs, whether such use is excessive and what steps can be taken to conserve. Emerging water sensing systems, however, can provide detailed usage data at the level of individual water fixtures (i.e., disaggregated usage data). In this paper, we perform formative evaluations of two sets of novel eco-feedback displays that take advantage of this disaggregated data. The first display set isolates and examines specific elements of an eco-feedback design space such as data and time granularity. Displays in the second set act as design probes to elicit reactions about competition, privacy, and integration into domestic space. The displays were evaluated via an online survey of 651 North American respondents and in-home, semi-structured interviews with 10 families (20 adults). Our findings are relevant not only to the design of future water eco-feedback systems but also for other types of consumption (e.g., electricity and gas).
18804506
Feedback on household electricity consumption: a tool for saving energy?
Improved feedback on electricity consumption may provide a tool for customers to better control their consumption and ultimately save energy. This paper asks which kind of feedback is most successful. For this purpose, a psychological model is presented that illustrates how and why feedback works. Relevant features of feedback are identified that may determine its effectiveness: frequency, duration, content, breakdown, medium and way of presentation, comparisons, and combination with other instruments. The paper continues with an analysis of international experience in order to find empirical evidence for which kinds of feedback work best. In spite of considerable data restraints and research gaps, there is some indication that the most successful feedback combines the following features: it is given frequently and over a long time, provides an appliance-specific breakdown, is presented in a clear and appealing way, and uses computerized and interactive tools.
24786623
Laboratory evaluation of a low-cost, real-time, aerosol multi-sensor
ABSTRACT Exposure to occupational aerosols are a known hazard in many industry sectors and can be a risk factor for several respiratory diseases. In this study, a laboratory evaluation of low-cost aerosol sensors, the Dylos DC1700 and a modified Dylos known as the Utah Modified Dylos Sensor (UMDS), was performed to assess the sensors’ efficiency in sampling respirable and inhalable dust at high concentrations, which are most common in occupational settings. Dust concentrations were measured in a low-speed wind tunnel with 3 UMDSs, collocated with an aerosol spectrometer (Grimm 1.109) and gravimetric respirable and inhalable samplers. A total of 10 tests consisting of 5 different concentrations and 2 test aerosols, Arizona road dust and aluminum oxide, were conducted. For the Arizona road dust, total particle count was strongly related between the spectrometer and the UMDS with a coefficient of determination (R2) between 0.86–0.92. Particle count concentrations measured with the UMDS were converted to mass and also were related with gravimetrically collected inhalable and respirable dust. The UMDS small bin (i.e., all particles) compared to the inhalable sampler yielded an R2 of 0.86–0.92, and the large bin subtracted from the small bin (i.e., only the smallest particles) compared to the respirable sampler yielded an R2 of 0.93–0.997. Tests with the aluminum oxide demonstrated a substantially lower relationship across all comparisons. Furthermore, assessment of intra-instrument variability was consistent for all instruments, but inter-instrument variability indicated that each instrument requires its own calibration equation to yield accurate exposure estimates. Overall, it appears that the UMDS can be used as a low-cost tool to estimate respirable and inhalable concentrations found in many workplaces. Future studies will focus on deployment of a UMDS network in an occupational setting.
14970263
D 3 : Data-Driven Documents
Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations.
9193529
EpiFi: An In-Home Sensor Network Architecture for Epidemiological Studies
We design and build a system called EpiFi, which allows epidemiologists to easily design and deploy experiments in homes. The focus of EpiFi is reducing the barrier to entry for deploying and using an in-home sensor network. We present a novel architecture for in-home sensor networks configured using a single configuration file and provide: a fast and reliable method for device discovery when installed in the home, a new mechanism for sensors to authenticate over the air using a subject's home WiFi router, and data reliability mechanisms to minimize loss in the network through a long-term deployment. We work collaboratively with pediatric asthma researchers to design three studies and deploy EpiFi in homes.
16813142
Understanding domestic energy consumption through interactive visualisation: a field study
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy "in the wild" -- 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
3330986
Getting closer: an empirical investigation of the proximity of user to their smart phones
Much research in ubiquitous computing assumes that a user's phone will be always on and at-hand, for collecting user context and for communicating with a user. Previous work with the previous generation of mobile phones has shown that such an assumption is false. Here, we investigate whether this assumption about users' proximity to their mobile phones holds for a new generation of mobile phones, smart phones. We conduct a data collection field study of 28 smart phone owners over a period of 4 weeks. We show that in fact this assumption is still false, with the within arm's reach proximity being true close to 50% of the time, similar to the earlier work. However, we also show that smart phone proximity within the same room (arm+room) as the user is true almost 90% of the time. We discuss the reasons for these phone proximities and the implications of this on the development of mobile phone applications, particularly those that collect user and environmental context, and delivering notification to users. We also show that we can accurately predict the proximity at the arm level and arm+room level with 75 and 83% accuracy, respectively, with features simple to collect and model on a mobile phone. Further we show that for several individuals who are almost always within the arm+room level, we can predict this level with over 90% accuracy.
25343324
The TEAM (Total Exposure Assessment Methodology) Study: personal exposures to toxic substances in air, drinking water, and breath of 400 residents of New Jersey, North Carolina, and North Dakota.
EPA's TEAM Study has measured exposures to 20 volatile organic compounds in personal air, outdoor air, drinking water, and breath of approximately 400 residents of New Jersey, North Carolina, and North Dakota. All residents were selected by a probability sampling scheme to represent 128,000 inhabitants of Elizabeth and Bayonne, New Jersey, 131,000 residents of Greensboro, North Carolina, and 7000 residents of Devils Lake, North Dakota. Participants carried a personal monitor to collect two 12-hr air samples and gave a breath sample at the end of the day. Two consecutive 12-hr outdoor air samples were also collected on identical Tenax cartridges in the backyards of some of the participants. About 5000 samples were collected, of which 1500 were quality control samples. Ten compounds were often present in personal air and breath samples at all locations. Personal exposures were consistently higher than outdoor concentrations for these chemicals and were sometimes 10 times the outdoor concentrations. Indoor sources appeared to be responsible for much of the difference. Breath concentrations also often exceeded outdoor concentrations and correlated more strongly with personal exposures than with outdoor concentrations. Some activities (smoking, visiting dry cleaners or service stations) and occupations (chemical, paint, and plastics plants) were associated with significantly elevated exposures and breath levels for certain toxic chemicals. Homes with smokers had significantly increased benzene and styrene levels in indoor air. Residence near major point sources did not affect exposure.
6038614
Assessing the availability of users to engage in just-in-time intervention in the natural environment
Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.
12449176
A lived informatics model of personal informatics
Current models of how people use personal informatics systems are largely based in behavior change goals. They do not adequately characterize the integration of self-tracking into everyday life by people with varying goals. We build upon prior work by embracing the perspective of lived informatics to propose a new model of personal informatics. We examine how lived informatics manifests in the habits of self-trackers across a variety of domains, first by surveying 105, 99, and 83 past and present trackers of physical activity, finances, and location and then by interviewing 22 trackers regarding their lived informatics experiences. We develop a model characterizing tracker processes of deciding to track and selecting a tool, elaborate on tool usage during collection, integration, and reflection as components of tracking and acting, and discuss the lapsing and potential resuming of tracking. We use our model to surface underexplored challenges in lived informatics, thus identifying future directions for personal informatics design and research.
6730223
A stage-based model of personal informatics systems
People strive to obtain self-knowledge. A class of systems called personal informatics is appearing that help people collect and reflect on personal information. However, there is no comprehensive list of problems that users experience using these systems, and no guidance for making these systems more effective. To address this, we conducted surveys and interviews with people who collect and reflect on personal information. We derived a stage-based model of personal informatics systems composed of five stages (preparation, collection, integration, reflection, and action) and identified barriers in each of the stages. These stages have four essential properties: barriers cascade to later stages; they are iterative; they are user-driven and/or system-driven; and they are uni-faceted or multi-faceted. From these properties, we recommend that personal informatics systems should 1) be designed in a holistic manner across the stages; 2) allow iteration between stages; 3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and 4) explore support for associating multiple facets of people's lives to enrich the value of systems.
18080584
Assessing asthma management practices through in-home technology probes
Asthma is a complex disorder and a leading chronic disease among children in USA. Families and pediatric patients with asthma need to manage different triggers and symptoms. We investigate existing asthma management practices and deploy technology probes to understand how they use technologies to manage asthma. The findings suggest that severity of asthma impacts the way that technologies are utilized. Thus, pervasive computing application can assist families and pediatric patients by bridging gaps between user's needs and their practices based on the severity of asthma and other contextual factors.