This document discusses how TIBCO Spotfire software can be used to visualize and analyze urban air quality data collected by sensors in the Elm network in Boston. Key features of TIBCO Spotfire identified include creating visualizations for time-series trend analysis using line and map charts, implementing custom color rules to identify pollutant concentrations, and linking datasets to perform real-time analysis. The investigation demonstrated how TIBCO Spotfire can identify pollution events, meteorological impacts, and predict pollution transport using the Elm sensor data.

1. Introduction
The world we live in, and the experience of our
life in it, is directly impacted by the quality of
the surrounding air. With greater demand for
industrial activity and travel, levels of pollutants
such as traffic emissions, ground-level ozone and
particulate matter have become of increasing
concern. Elevated levels of these pollutants in the air have adverse effects on the ability of plants
to undergo photosynthesis, degradation in the appearance of leaves, and diminished ability of
these plants to defend against particular insects and disease- drastically impacting the health of
trees and other fauna in locations as small as town parks to those as large as national forests1
.
This investigation used TIBCO Spotfire®
in order to visualize urban air quality data retrieved from
the Elm air sensing network in Boston, Massachusetts. Key features of TIBCO Spotfire®
, which
may be used to enrich the understanding of air quality, were identified and discussed in depth.
The goal of this visual exploration was to understand how TIBCO Spotfire®
can use these tools in
order to connect the detection of pollution events and monitoring efforts to determine potential
correlations between urban air quality and its environmental impact.
Advances in the Visualization
ofUrbanAirQualityDataand
Environmental Monitoring
Using TIBCO Spotfire® and
the Elm Air Sensing Network
A P P L I C A T I O N N O T E
Author:
Kathryn Kuhr
PerkinElmer, Inc.
Shelton, CT
Air Quality Monitoring
PerkinElmer is the exclusive global distributor
of the TIBCO™
Spotfire®
platform for certain
scientific and clinical R&D applications.
Spotfire

2. 2
Experimental
This investigation focused on the performance of TIBCO Spotfire®
in visualizing environmental trends concerning urban air quality
data. Data was collected from 25 sensors located in the greater
Boston area as a part of the Elm sensor network; these sensors
were located in the towns of: Arlington, Belmont, Boston,
Cambridge, Chelsea, Lexington, Medford, Newton, Quincy,
Sommerville, Stoneham, Waltham, Westwood, and Winthrop.
When determining sensor placement, attention was targeted
towards the sensors’ potential proximity to high traffic areas and
nearby residential neighborhoods. Examples of these focus areas
include highways, schools, and businesses. Each sensor was
capable of detecting and recording levels of ozone (O3
), total
reducing gases (TRG), similar to volatile organic compounds
(VOC), noise, particulate matter (PM), total oxidizing gases (TOG),
similar in trend to nitrogen dioxide (NO2
), temperature, and
humidity. Upon importing the collected data from the above
sensors, key features within TIBCO Spotfire®
were used to
distinguish central characteristics within the dataset pertaining to
time-series trending, meteorological shifts, and the visualization of
government air quality standards.
Results
Air Pollution Time Series Trending
When analyzing the concentration of pollutants in a
microenvironment, simultaneous analyses are needed which
identify both pollution origin and pollution trend over time. This
can be accomplished through the use of line and map charts, as
well as the implementation of custom rules. As seen in Figure 2,
TIBCO Spotfire®
can be used to develop these visualizations for
time-series trending. The X-axis can be aggregated in a variety of
date-time options such as, but not limited to: hourly, daily, weekly,
and monthly trends. Having such an aggregation can be used to
identify the moment when a specific pollution event occurs. By
juxtaposing a map chart with this line chart, the location of a
pollution hot spot can be identified concurrently; this action is
shown in Figure 3. By either marking the desired sensor locations
on the map chart or highlighting the pollution event on the line
chart, all visualizations will simultaneously update to reflect the
marked data. This functionality can be carried across all pages of
the analysis and in any number of visualizations.
For every visualization, distinct color by rules can be implemented.
In the legend panel of the map chart in Figures 2 and 3, the
parameters of a gradient color by rule are shown. In these charts,
all markers will be displayed a unique shade of a particular color
based on their average concentration of TOG. Based upon
assigned concentration indicators, the markers will be displayed
green when safe levels of TOG are detected, slowly change to
yellow when levels are nearing hazardous concentrations, and
finally become red when they represent levels which have
surpassed the standard upper limit. When new data is imported
into this file, the color by rule will remain and the new data will
automatically be visualized using these custom assignments.
Rules such as this can be directly applied to National Ambient Air
Quality Standards (NAAQS). The NAAQS were implemented by the
EPA as directed by the Clean Air Act; a set of primary and secondary
standards were developed to limit the concentrations of hazardous
pollutants in air. These quality standards can be found as outlined in
40 CFR part 502
. NAAQS can be implemented as color by rules to
compare industrial emissions in an urban area to these standard
levels of pollutants. This information can provide insight regarding
the potential of individual hyper-local areas in contributing to overall
environmental health through air quality. These standards are
averages of data collected over long periods of time which should
also be considered during data analysis. Analyzing data collected
over similar periods of time may provide a more accurate
comparison than analysis of real-time results.
Many other formats can be chosen for the implementation of
custom color by rules such as assigning segments with clear upper
and lower values, or by identifying all data above or below a
particular limit value as either passing or failing. The implementation
Figure1.Map chart displaying the locations of 25 sensors in the Elm sensor network
Figure3.TrendingofTOGshownas30-minnuteaveragesoverthecourseofamonthand
dailypatternofthreeselectedsensorlocations.ResultshavebeenfilteredfromFigure2.
Figure2.Trending of TOG shown as 30-minute daily averages over the course of a
month and daily pattern of all sensor locations.
30 Minute TOG Average Data ppb Daily Pattern TOG ppb
30 Minute TOG Average Data ppb Daily Pattern TOG ppb

3. 3
of color by rules to analyses can help to quickly identify data of
concern, directing action towards critical hotspots sooner and with
more appropriate measures.
Another application of time-series visualizations, which can be
performed by TIBCO Spotfire®
, is the comparison of select sensors
from specific locations though interactive filtering. In Figure 4,
filters were applied which limited the line chart to show TOG
results, measured in parts per billion (ppb), for three Elm units;
one unit each located near a suburban school, urban traffic, and
on an office rooftop. The presence of TOG is an indicator of traffic
patterns and positively correlates with the presence of vehicle
emissions3
. Figure 4 displays this trend in all three investigated
sensor locations. The black line, representing the sensor near
urban traffic, distinctly displays relative peak values at
approximately 8:00am, 5:00pm, and 8:00pm EST corresponding
to the start and end times of typical work shifts. As seen from the
green line, representing the location on the office rooftop, the
peak TOG values occurred just before the peak traffic events.
Similarly, expected trends transpired for the location near the
suburban school. The capability to visualize such trends while
being able to corroborate their findings leads to the prospective
future of such wireless sensor networks in locations like national
forests and other protected lands.
Detection of Meteorological Fluctuations
The collaboration of data analysis through the employment of Elm
and TIBCO Spotfire®
can also be used to detect fluctuations in
relevant meteorological conditions such as temperature and
humidity. Combination charts may be used to plot both lines and
bars in the same chart area. This is useful when analyzing how
pollution levels correlate to the weather patterns of a given day or
time period. With the potential to make such connections
between temperature, humidity and pollutants, comes the
possibility of forecasting how the levels of pollutants will change
over time. Such an ability may be investigated and visualized
through the analysis of ground-level ozone. The manifestation of
ground-level ozone, resulting from chemical reactions between
existing pollutants, is also dependent upon temperature; levels are
more likely to increase, and near hazardous concentrations, on
days where the temperature is higher4
. This data can also be
visualized on a 2D scatterplot with a linear regression or other
lines indicative of predictive trends. Along with the addition of
color by rules, horizontal lines can be added to charts at upper
Figure4.Average level of TOG in ppb per hour of day. Black line represents sensor near
urban traffic, green line represents sensor on an office rooftop, red line represents
sensor near a suburban school.
and/or lower limit bounds when more substantial visual
representation is necessary.
Wind speed can also be visualized against pollution data to
predict where it will travel and the rate at which it will take to get
there. While the implementation of air quality sensors in urban
networks is an important role of monitoring trends in hyper-local
areas, detected pollutants are not limited to remain within these
bounds. Extreme examples of such phenomenon exist when
major events of environmental concern take place. In the case of
wildfires, pollutants can travel hundreds of miles, affecting the
health of countless numbers of susceptible fauna species and
weaken the defenses of many more1,5
. Smoke from wildfires may
travel into an urban area and dramatically increase the
concentration of dust and TRGs. The transportation of smoke
related pollutants into an urban area has the potential to impact
the health of plants in town parks and other central locations in
addition to the appearance of trees and flowers planted alongside
city streets and sidewalks.
If more than one data table is imported into TIBCO Spotfire®
, the
two charts can be linked in order to view the data within the
same visualization. This is executed through the connection
between similar columns of data from each table such as the
date/time descriptor. This feature can be used to visualize such
things as the concentration of a pollutant on a given day and the
count of compromised species detected, or other unique
environmental markers resulting from air pollution side effects.
Real-Time Analysis: Putting the Techniques in Motion
On the opposing end of events of environmental emergency,
everyday weather events, which cause sudden shifts in pollution
Figure5.Eight Elm unit locations spread throughout the greater Boston area with
distance noted in miles.
Figure6.Elm units from Figure 5 grouped into pairs with the westerly direction of wind
noted below.
Daily Average TOG All Sensors

4. 4
concentration and direction of travel, can be monitored for their
potential to cause hazardous conditions or bring pollution to areas
already on the verge of environmental distress. Figure 5 displays a
group of 8 Elm sensors spread across the greater Boston area with
their relative distances from one another noted in miles. Sensors
1311 and 1321 are located furthest east and sensors 1312 and
1313 are located furthest west. Figure 6 shows the assigned
pairings of sensors and the wind direction during the time of the
investigated air quality event. On June 13, 2014, the Boston Elm
network was able to capture elevated levels of particulate matter
(PM) throughout the city. With such close proximity to the Atlantic
shoreline, it is possible that these elevated levels could be due to
sea salt in addition to sand and urban dust. Figure 7 presents all
gathered data around the onset of the dust event at approximately
15:00 with raw data on top and the normalized data shown below.
In order to minimize the impact of temperature and other factors
on results, attention was directed to the event at these eight
locations alone.
Since the wind was recorded traveling in a westerly direction,
peak dust concentrations were expected to be seen in senor pair
1 first, traveling toward pair 4. This hypothesis was confirmed
through the visualization in Figure 8. As expected, dust
concentrations in pair 1 begin to increase around 14:45 and end
around 15:15. By this point, concentrations begin to rise in pair 2
and 3, and finally pair 4, all within the span of one hour. A similar
pattern can be seen in the recorded PM concentration, Figure 9,
of the four sensor pairs at approximately the same time with a 71
minute travel time between the onset of the major peak from pair
1 to 4.
Applying urban air quality monitoring in such a way allows for the
analysis and prediction of pollution transportation in future events.
In the occasion that wind is responsible for carrying a more
dangerous pollutant or any pollutant in levels of great concern,
this previous knowledge can be leveraged to gain multifaceted
insight. With the known delay in transportation time from one
sensor to another at a given wind speed, this advanced warning
can be adjusted to give environmental protection officials time to
remedy the concern as best they can. Mapping pollution travel
with wind speed and direction can also be used to extrapolate the
origin of an event. If severe pollution is affecting a region of
environmental concern, this fundamental combination of
technologies may be utilized in order to predict the hotspot origin
and subsequently implement a program for the remediation of
the surrounding air, which will help protect the environment from
further degradation.
Elm units collect and record data every 20 seconds. To assist in the
process of real-time, or near real-time, visual analysis, TIBCO
Spotfire®
is able to connect to a variety of databases through
ODBC, OLE DB, OracleClient and SQLClient default drivers. Pulling
data from a database can occur in the form of a direct pull or
from an established information link. Information links submit a
structured request to the database and retrieve desired columns,
or rows, of filtered data. These structured requests can include
prompts or filters to limit data before it is brought into the file.
This can be done as often as necessary to incorporate up to date
data in the analysis.
Another method of importing data, without the use of
information links or a database, involves the “Reload Data”
feature. Upon importing new data into the analysis, it is
automatically saved as “linked to source”, which can later be
manually changed to “embedded in analysis”. The fundamentality
of linking data to its source involves being able to reload refreshed
data each time the file is opened. A connection will be built
between the data and TIBCO Spotfire®
where anytime new data is
Figure7.Raw (top) and normalized (bottom) dust data from all 8 Elm units shown in
Figures 5 and 6.
Figure8.Recorded dust data split into pairs as shown in Figure 6. Difference in the
detection time of dust between pairs 1 and 4 was 1 hour.
Figure9.Recorded PM data split into pairs as shown in Figure 6. Difference in the
detection time of PM between pairs 1 and 4 was 71 minutes.

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Resources
1. “Ground-Level Ozone: Ecosystem Effects”, United States
Environmental Protection Agency, 1 Nov 2012. Web. 9 Feb 2014
2. “Title 40, Chapter I, Subchapter C, Part 50”, Electronic Code of
Federal Regulations, U.S. Government Publishing Office, 10 Feb
2015, Web, 12 Feb 2015
3. Moltchanov, Sharon et al., “On the feasibility of measuring urban
air pollution by wireless distributed sensor networks”, Science of
the Total Environment, 502 (2015) 537-547, 2015
4. “Ground-level Ozone: Basic Information”, United States
Environmental Protection Agency, 26 Nov 2014, Web, 23
Feb 2015
5. Kelly, Frank J. et al., “Monitoring air pollution: Use of early
warning systems for public health”, Respirology 17 (2012)
7-19, 2015
added to or edited in the source, the software will automatically
reflect these changes. Note that the reverse situation is not true; any
data edited or deleted from TIBCO Spotfire®
will not be reflected in
the source file. Embedding data directly in the analysis cuts this link.
With either option however, the data can be reloaded at any time
through the “Reload Data” feature, providing near real-time analysis
of data when decision making is crucial. The previously discussed
color by rules will remain in the visualization and newly imported
data will automatically be visualized based on the set parameters.
Conclusion
The Elm sensor network is located in the greater Boston area-
collecting urban air quality data central to the analysis of pollution
concentration in urban microenvironments. The compilation of
hyper-local datasets into one analysis builds a view of how individual
pollution trends culminate to affect a larger area. The strategic
deployment of sensors throughout an environment acts as a tool to
gain advanced awareness into the unique trends and habits of
certain communities and population groups. This information can
then be used to build environmental remediation programs based
on the improvement of surrounding air quality. Through collaborative
analysis between Elm and TIBCO Spotfire®
, potential connections can
be drawn between pollution events and environmental effect
monitoring which can be used for such measures as locating
pollution hotspots and locations of origin, as well as tracking the
path of pollution transportation. With a larger sensor network,
pollutant fluctuations can be forecasted with greater accuracy; early
warning signs can be monitored to more precisely predict where
resulting danger lies and when to expect the occurrence of its
greatest impact. Despite the analysis methodology, utilizing these
two technologies to achieve a common goal has been able to
synchronize techniques to further advance the understanding of the
relationship between urban air quality and its environmental effect.