Air Quality Monitoring with SensorMap

Abstract

The mobile air quality monitoring network (MAQUMON) is presented. The system consists of a number of car-mounted sensor nodes measuring different pollutants in the air. The data points are tagged with location and time utilizing an on-board GPS. Periodically, the measurements are uploaded to a server, processed and then published on the SensorMap portal. Given a sufficient number of nodes and diverse mobility patterns, a detailed picture of the air quality in a large area will be obtained at a low cost.

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Air Quality Monitoring with SensorMap

Péter Völgyesi, András Nádas, Xenofon Koutsoukos, Ákos Lédeczi
Institute for Software Integrated Systems
Vanderbilt University
Nashville, TN, 37235, USA
{peter.volgyesi, andras.nadas, xenofon.koutsoukos, akos.ledeczi}@vanderbilt.edu


Abstract

The Mobile Air Quality Monitoring Network
(MAQUMON) is presented. The system consists of a
number of car-mounted sensor nodes measuring
different pollutants in the air. The data points are
tagged with location and time utilizing an on-board
GPS. Periodically, the measurements are uploaded to
a server, processed and then published on the
SensorMap portal. Given a sufficient number of nodes
and diverse mobility patterns, a detailed picture of the
air quality in a large area will be obtained at a low
cost.

1. Introduction

Air pollution is one of the most important factors
affecting the quality of life and the health of the
increasingly urban population of industrial societies. In
the U.S., all major cities have networks of monitoring
stations providing continuous measurements of the
most important pollutants. However, the number of
these stations is usually very small. In the Nashville
metropolitan area, for example, there are ten such
stations sparsely covering the central area of Davidson
county [1]. The situation is not much different in
others cities. Air pollution is highly location-
dependent, for example, the vicinity of traffic
chokepoints or certain industrial installations has much
worse air quality than average. Furthermore, currently
the data of the different pollutants measured at the
different stations in the city are aggregated to a single
number, the air quality index (AQI), that is published
once a day on a website. In other words, there is not
enough data gathered to evaluate air quality in a given
neighborhood and the publicly available information is
even more deficient.
In contrast to monitoring methods using stationary
stations, a detailed picture based on real-time data from
mobile sensors for the entire populated area would
offer major benefits to air quality control. We are
building a prototype Mobile Air Quality Monitoring
Network (MAQUMON) comprised of sensor nodes
mounted on cars. A sensor node consists of a
microcontroller, an on-board GPS unit and set of gas
sensors measuring ozone,
concentrations. The node is Bluetooth enabled, so it
can connect to a PDA or laptop to upload the
measurements.
When the car is in motion, the device samples the
sensors every minute and store the results tagged with
a location and time stamp. When the car is parked, the
samples are only taken a few times an hour. When a
car is within the coverage area of an available WiFi
hotspot, all data are uploaded to our server, processed
and published on the SensorMap portal. Given a
sufficient number of nodes and diverse mobility
patterns, a detailed picture of the air quality in a large
area will be obtained at a low cost.

2. Hardware Platform

The sensor platform supports autonomous data
collection, storage and off-line data retrieval or the
streaming of live sensor readings. An integrated
Bluetooth module provides a wireless interface for
laptop computers or PDAs.

CO, and NO2

This work has been supported by a gift from Microsoft Research.
2008 International Conference on Information Processing in Sensor Networks
978-0-7695-3157-1/08 $25.00 © 2008 IEEE
DOI 10.1109/IPSN.2008.50
529

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Figure 1. Architecture of the sensor node
Alternatively, the system can be accessed through a
USB cable. This wired interface can also provide
power to the board both for on-line operation and for
charging the integrated Li-ion battery. The battery life
of the device is limited to a few hours (in active data
acquisition mode), but in the car mounted scenario, it
can be constantly powered from the cigarette lighter.
Furthermore, a 2-axis MEMS accelerometer is used to
detect if the system is in motion and can automatically
turn off all the power hungry components (GPS,
Bluetooth, gas sensors) if not in use. Location and time
information is provided by an on-board 20 channel
SiRF-III-based GPS module at 1 Hz sampling rate.
Gas concentration levels are measured by three analog
sensors: O3, NO2 and CO/VOC. These readings along
with temperature and relative humidity data and GPS
information are stored in a serial flash device 2MB. A
2x16 character LCD panel provides immediate visual
feedback about the status of the system (connected
interfaces, GPS lock, time, motion detection, sensor
readings). The Intel 8051-based microcontroller
controls every aspect of the system from battery
charging to analog/digital conversions and the USB
protocol.
A photograph of the prototype board in its
enclosure is shown below.



Figure 2. Sensor node prototype

3. Demonstration

The demonstration will have two main parts. First,
the prototype hardware platform will be shown and its
operation demonstrated. While we do not plan to
release any of the gases the system can measure in the
confined area, the presence of at least one gas can be
demonstrated. The breath of humans contains different
amounts of CO depending on the lung function of the

individual. For example, smokers have typically higher
amounts of CO. We will demonstrate our simple
Smoke® Detector application that detects when
somebody breathes on the unit using the temperature
and humidity sensors and tell whether the person is a
smoker or not. The result is displayed on the integrated
LCD display of the unit.
The second part of the demonstrations will illustrate
the integration with the MSR SensorMap portal [2].
Since gathering data indoors in a small area would be
insufficient for this purpose, we will use prerecorded
data. Visualizing air quality presents two challenges.
First, the sensors are mobile, so they cannot be
associated with a single geographic location. Second, it
is not data associated with a single sensor that is
interesting, but the overall air quality of an area
determined by a set of sensors measuring pollutants for
a certain amount of time.
Sensor mobility is handled using the SensorMap
mobile proxy feature. Overall air quality will be
displayed in the form of contour maps utilizing image
overlays. The time series data for a given sensor and/or
a given geographic location will be also available. The
figure below illustrates the visualization techniques
employed by the MAQUMON system.



Figure 3. Visualization in SensorMap

4. References

[1] Annual Report, Metro Public Health Department of
Nashville/Davidson County Pollution Control Division,
2005 http://healthweb.nashville.org/env/aqi/psipoll.asp

[2] Microsoft Research
http://atom.research.microsoft.com/sensormap/

SensorMap Portal,
530