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Page history last edited by Ricardo Piedrahita 10 years, 7 months ago


Welcome to MAQS (Mobile Air Quality Sensing System)    




Project Overview

Technology and User Resources


Deployment of MAQS







          Our team is developing a Mobile Air Quality Sensing system, MAQS for short, that allows individuals to continuously and inexpensively measure the air quality they are exposed to, as well as giving them a framework to understand what the measurements indicate, and a place to discuss and explore their finding with fellow users.  The MAQS system consists of wearable personal monitors, M-Pods, an Android-based mobile phone app (MAQS App) , and an online social network (MAQS Network).  The M-Pods send readings over Bluetooth to the user's smart phone running the MAQS app.  The app then allows the user to view his or her personal air quality measurements in real time, offers some simple analysis tools, and uploads the data to a server whenever the smart phone is connected to a WiFi network.  The server's database can then be accessed via a web interface, which takes the form of an online social network, so a user can query measurement data and communicate with other users. 


Importance of the Project


          New ways of assessing air quality are very important as the problem of air pollution continues to grow in many places in the developed and developing world.  In the United States alone, poor air quality is responsible for approximately 50,000 premature deaths a year and $150 billion in medical costs (as determined by the National Oceanic and Atmospheric Administration, http://www.noaawatch.gov/themes/air_quality.php). 

     Precise air quality information is difficult to ascertain on an individual level; there are many different pollutants posing different threats, and these pollutants vary by location and time.  Since monitoring equipment is expensive,  measurements of air pollutants are taken at relatively few locations, and while this is important, more individualized information is also necessary.  This is particularly true of indoor air quality (IAQ), considering so much time is spent indoors and the factors that affect IAQ vary from building to building.  Through the use of new technologies, our team hopes to address these issues. 



Photo Credits: 1 2 3 4


Project Goals


          The projects aims to develop cost-effective, reliable air quality measurement devices and a network that allows for data collection and analysis, as well as, encouraging social interaction to discuss data and explore ways of improving air quality and protecting the environment.  All of the components will be designed for ease of use and comprehensibility for users both within the scientific community and outside of it.  Together these technologies will facilitate a large-scale environmental social study, which will provide detailed data that might allow for correlations between the temporal, spatial and behavioral relations of pollutants to be examined.  This information would benefit both scientific researchers and the users of the technology, possibly even raising awareness regarding our worsening air quality and prompting action to protect it.  



                               M-Pod and MAQS Mobile App                                                                    Project Overview




The MAQS project will monitor an individual's air quality throughout their day both indoors and outdoors.


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An Explanation of the Technology

Getting Started with the MAQS System (User Info)

Interpreting MAQS Data

          Indoor Air Quality

          General Pollutant Information



Send a ticket if you are having a problem with any part of the MAQS system




Li Shang (project PI)

Qin Lv

Mike Hannigan

Rob Dick

Kun Li

Yifei Jiang

Xin Pan

Lei Tian

Ricardo Piedrahita

Berkeley Hippel

Xiang Yun

Lan Bai

Ashley Collier

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Deployment of MAQS


Rural Ghana (in collaboration with NCAR), Winter 2011

CU-Boulder and University of Michigan Engineering Students in our team, Spring 2011

Dine Environmental Institute Science Pathway Program Interns, Dine College, Summer 2011

Dine Environmental Institute Science Pathway Program Interns, Dine College, January 2012

Dine Environmental Institute Science Pathway Program Interns, Dine College, Summer 2012


Follow this link to learn about the Project Status







1. Y. Jiang, K. Li, R. Piedrahita, X. Yun, L. Tian, O. M. Mansata, Q. Lv,R. P. Dick, M. Hannigan, L. Shang (2013). User-CentricIndoor Air Quality Monitoring on Mobile Devices. AI Magazine. 34 (2), 11. 


Status = PUBLISHED; Link: pdf


2. Y. Jiang, D. Li, Q. Lv (2013). Thinking Fast and Slow: An Approach toEnergy-Efficient Human Activity Recognition on MobileDevices. AI Magazine. 34 (2), 48.


Status = PUBLISHED; Link: pdf




3. Yifei Jiang. Crowdsourcing Based Room Localization on Smartphones.(2013). Ph.D. Thesis, University of Colorado Boulder.




Conference Papers and Presentations


4. R. Piedrahita, Y. Jiang, X. Pan, K. Li, X. Yun, N. Masson, A. Collier,M. Hannigan, R. Dick, Q. Lv, L. Shang. Validation of low-cost mobile air quality monitors and initial personal exposure results of carbon monoxide, total volatile organic compounds,and carbon dioxide. in Proc. Environmental Health. 2013. Boston, MA.


Status = PUBLISHED;  Link: pdf  


5. Y. Xiang, R. Piedrahita, R. Dick, M. Hannigan, Q. Lv, and L. Shang.(2013). A hybrid sensor system for indoor air qualitymonitoring. DCOSS 2013: in Proc. the 9th IEEE International Conference on Distributed Computing in SensorSystems. May 2013, pp. 96–104. Cambridge, MA. 


Status = PUBLISHED;  Link: pdf 


6. Y. Jiang, Y. Xiang, X. Pan, K. Li, Q. Lv, R. P. Dick, L. Shang, and M.Hannigan. Hallway based Automatic IndoorFloorplan Construction using Room Fingerprints. UbiComp 2013: in Proc. the 2013 ACM International JointConference on Pervasive and Ubiquitous Computing. Zurich, Switzerland. 


Status = PUBLISHED;  Link: pdf


7. Y. Jiang, X. Pan, K. Li, Q. Lv, R. Dick, M. Hannigan, L. Shang. ARIEL: Automatic Wi-Fi based Room Fingerprinting for Indoor Localization. in Proc. the 14th intl. conference on Ubiquitous computing. Sep. 2012.


Status = PUBLISHED;  Link:pdf


8. Y. Xiang, L. S. Bai, R. Piedrahita, R. P. Dick, Q. Lv,M. P. Hannigan, and L. Shang, “Collaborative calibration and sensor placement for mobile sensor networks,” in Proc. Int. Conf.  Information Processing in Sensor NetworksApr. 2012, pp. 73–84.  


Status = PUBLISHED;  Link: pdf


9. L. Bai, R. P. Dick, P. Chou, and P. A. Dinda, “Automated construction of fast and accurate system-level models for wireless sensor networks,” in Proc.  Design, Automation & Test in EuropeConf., Mar. 2011, pp. 1083–1088.    


Status = PUBLISHED;  Link: pdf


10. L. Bai, R. P. Dick, P. A. Dinda, and P. Chou, “Simplified programming of faulty sensor networks via code transformation and run-timeinterval computation,” in Proc. Design, Automation &Test inEurope Conf., Mar. 2011, pp.  88–93. 


Status = PUBLISHED;  Link: pdf


11. Y. Jiang, K. Li, L. Tian, R. Piedrahita, X. Yun, O. Mansata, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang, MAQS: A Personalized Mobile Sensing System for Indoor Air Quality Monitoring. In Ubicomp '11: in Proc. the 13th ACM international conference on Ubiquitous computing, 2011. ACM, New York, NY, USA.


Paper Summary: MAQS: A Mobile Sensing System for Indoor Air Quality


Status = PUBLISHED; Link: pdf


12. Y. Jiang, Du Li, G. Yang, Q. Lv, and Z. Liu, Deliberation for Intuition: A Framework for Energy-Efficient Trip Detection on Cellular Phones. in Proc. the 13th ACM international conference on Ubiquitous computing, 2011.


Status = PUBLISHED; Link: pdf


13. Michael Hannigan, Ricardo Piedrahita, Nicholas Masson, John Ortega, Yifei Jiang, Xiang Yun, Kun Li, Qin Lv, Robert Dick, Li Shang.  Personal Exposure Results for the M-Pod, a Portable Low-Cost Air Quality Monitor.  AAAR Conference.   Portland OR, Sept. 30th-Oct. 4th, 2013.


Status = Presented; Link: pdf



In Preparation


14. Ricardo Piedrahita, Yun Xiang, Nicholas Masson, John Ortega, Ashley Collier, Yifei Jiang, Li Kun, Robert Dick, Qin Lv, Michael Hannigan, Li Shang.  The next generation of low-cost personal air quality sensors for quantitative exposure monitoring.  2013.  Atmospheric Environment.





15. Nicholas Masson, Ricardo Piedrahita, Xiang Yun, Michael Hannigan, Qin Lv, Robert Dick, Li Shang.  Quantification Methods for Metal-Oxide Semiconductor Gas Sensors. AAAR Conference.   Portland OR, Sept. 30th-Oct. 4th, 2013. 









This work was supported by the NSF under awards CNS-0910995 and CNS-0910816.


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