Real-time air quality monitoring through mobile sensing in metropolitan areas
11 Aug 2013-pp 15
TL;DR: A vehicular-based mobile approach for measuring fine-grained air quality in real-time and two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device are proposed.
Abstract: Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.
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TL;DR: The concept of urban computing is introduced, discussing its general framework and key challenges from the perspective of computer sciences, and the typical technologies that are needed in urban computing are summarized into four folds.
Abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.
1,290 citations
Cites background from "Real-time air quality monitoring th..."
...Likewise, Devarakonda et al. [2013] presented a vehicularbased approach for measuring fine-grained air quality in real time....
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Queensland University of Technology1, Norwegian Institute for Air Research2, United States Environmental Protection Agency3, University of Surrey4, Hong Kong University of Science and Technology5, Hong Kong Environmental Protection Department6, City University of Hong Kong7, Curtin University8, Southern Cross University9
TL;DR: Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment, and it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure.
418 citations
TL;DR: If appropriate validation and quality control procedures are adopted and implemented, crowdsourcing has much potential to provide a valuable source of high temporal and spatial resolution, real-time data, especially in regions where few observations currently exist, thereby adding value to science, technology and society.
Abstract: Crowdsourcing is traditionally defined as obtaining data or information by enlisting the services of a (potentially
large) number of people. However, due to recent innovations, this definition can now be expanded to include ‘and/or from a
range of public sensors, typically connected via the Internet.’ A large and increasing amount of data is now being obtained
from a huge variety of non-traditional sources – from smart phone sensors to amateur weather stations to canvassing members
of the public. Some disciplines (e.g. astrophysics, ecology) are already utilizing crowdsourcing techniques (e.g. citizen science
initiatives, web 2.0 technology, low-cost sensors), and while its value within the climate and atmospheric science disciplines
is still relatively unexplored, it is beginning to show promise. However, important questions remain; this paper introduces
and explores the wide-range of current and prospective methods to crowdsource atmospheric data, investigates the quality of
such data and examines its potential applications in the context of weather, climate and society. It is clear that crowdsourcing
is already a valuable tool for engaging the public, and if appropriate validation and quality control procedures are adopted
and implemented, it has much potential to provide a valuable source of high temporal and spatial resolution, real-time data,
especially in regions where few observations currently exist, thereby adding value to science, technology and society.
271 citations
TL;DR: This paper classifies the existing works into three categories as Static Sensor Network (SSN), Community Sensor network (CSN) and Vehicle sensor network (VSN) based on the carriers of the sensors.
Abstract: The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.
255 citations
Cites background or methods from "Real-time air quality monitoring th..."
...In [117] Car Cellular network Solid-state (CO), Optical analyzer (PM) Bus battery GPS module Arduino (16 MHz/8 KB/128 KB)...
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...In [117], a fine-grained vehicular-based mobile air pollution measuring approach was presented....
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...In [98] Outdoor roadside 3 s 16 National Chiao-Tung University campus Web App In [117] Outdoor roadside 5 s 2 Citywide Web App...
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TL;DR: This paper analyzes one of the largest spatially resolved UFP data set publicly available today containing over 50 million measurements and achieves a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution.
222 citations
References
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11 Aug 2013
TL;DR: This paper infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real- time) air quality data reported by existing monitor stations and a variety of data sources the authors observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs).
Abstract: Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.
829 citations
TL;DR: An online GPRS-Sensors Array for air pollution monitoring has been designed, implemented, and tested as mentioned in this paper, which consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-enabled Pollution Monitoring Server (Pollution-Server).
Abstract: An online GPRS-Sensors Array for air pollution monitoring has been designed, implemented, and tested. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution-Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies. The Pollution-Server is interfaced to Google Maps to display real-time pollutants levels and locations in large metropolitan areas. The system was successfully tested in the city of Sharjah, UAE. The system reports real-time pollutants level and their location on a 24-h/7-day basis.
309 citations
TL;DR: There is an association between change in short-term air pollution levels, as indexed by PM and CO, and the occurrence of asthma symptoms among children in Seattle.
Abstract: We observed a panel of 133 children (5-13 years of age) with asthma residing in the greater Seattle, Washington, area for an average of 58 days (range 28-112 days) during screening for enrollment in the Childhood Asthma Management Program (CAMP) study. Daily self-reports of asthma symptoms were obtained from study diaries and compared with ambient air pollution levels in marginal repeated measures logistic regression models. We defined days with asthma symptoms as any day a child reported at least one mild asthma episode. All analyses were controlled for subject-specific variables [age, race, sex, baseline height, and FEV(1) PC(20) concentration (methacholine provocative concentration required to produce a 20% decrease in forced expiratory volume in 1 sec)] and potential time-dependent confounders (day of week, season, and temperature). Because of variable observation periods for participants, we estimated both between- and within-subject air pollutant effects. Our primary interest was in the within-subject effects: the effect of air pollutant excursions from typical levels in each child's observation period on the odds of asthma symptoms. In single-pollutant models, the population average estimates indicated a 30% [95% confidence interval (CI), 11-52%] increase for a 1-ppm increment in carbon monoxide lagged 1 day, an 18% (95% CI, 5-33%) increase for a 10-microg/m(3) increment in same-day particulate matter < 1.0 microm (PM(1.0)), and an 11% (95% CI, 3-20%) increase for a 10-microg/m(3) increment in particulate matter < 10 microm (PM(10)) lagged 1 day. Conditional on the previous day's asthma symptoms, we estimated 25% (95% CI, 10-42%), 14% (95% CI, 4-26%), and 10% (95% CI, 3-16%) increases in the odds of asthma symptoms associated with increases in CO, PM(1.0), and PM(10), respectively. We did not find any association between sulfur dioxide (SO(2)) and the odds of asthma symptoms. In multipollutant models, the separate pollutant effects were smaller. The overall effect of an increase in both CO and PM(1. 0) was a 31% (95% CI, 11-55%) increase in the odds of symptoms of asthma. We conclude that there is an association between change in short-term air pollution levels, as indexed by PM and CO, and the occurrence of asthma symptoms among children in Seattle. Although PM effects on asthma have been found in other studies, it is likely that CO is a marker for vehicle exhaust and other combustion by-products that aggravate asthma.
206 citations
11 May 2009
TL;DR: This paper explores privacy concerns about personal sensing through interviews with participants who took part in a three month study that used personal sensing to detect their physical activities and suggests ways in which personal sensing can be made more privacy-sensitive to address these concerns.
Abstract: More and more personal devices such as mobile phones and multimedia players use embedded sensing. This means that people are wearing and carrying devices capable of sensing details about them such as their activity, location, and environment. In this paper, we explore privacy concerns about such personal sensing through interviews with 24 participants who took part in a three month study that used personal sensing to detect their physical activities. Our results show that concerns often depended on what was being recorded, the context in which participants worked and lived and thus would be sensed, and the value they perceived would be provided. We suggest ways in which personal sensing can be made more privacy-sensitive to address these concerns.
183 citations
18 Aug 2008
TL;DR: A hardware and software platform for exploring algorithms and data gathered from pollution sensors integrated into cell phones, and discusses the main research agenda going forward.
Abstract: By attaching sensors to GPS-enabled cell phones, we can gather the raw data necessary to begin understand how urban air pollution impacts both individuals and communities. In this paper we introduce a hardware and software platform for exploring algorithms and data gathered from pollution sensors integrated into cell phones, and discuss our main research agenda going forward.
173 citations