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 of Urban Air Quality Data and 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 distributorof the TIBCO™ Spotfire® platform for certainscientific and clinical R&D applications.

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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 TrendingWhen 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

Figure 1. Map chart displaying the locations of 25 sensors in the Elm sensor network

Figure 3. Trending of TOG shown as 30-minnute averages over the course of a month and daily pattern of three selected sensor locations. Results have been filtered from Figure 2.

Figure 2. 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

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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 FluctuationsThe 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

Figure 4. 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 MotionOn the opposing end of events of environmental emergency, everyday weather events, which cause sudden shifts in pollution

Figure 5. Eight Elm unit locations spread throughout the greater Boston area with distance noted in miles.

Figure 6. Elm units from Figure 5 grouped into pairs with the westerly direction of wind noted below.

Daily Average TOG All Sensors

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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

Figure 7. Raw (top) and normalized (bottom) dust data from all 8 Elm units shown in Figures 5 and 6.

Figure 8. 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.

Figure 9. 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.