Introduction As urbanization continues to progress around the world, the need for air quality monitoring in urban areas increases. Ground level ozone, particulate matter and nitrogen oxides are all air pollutants with connection to human municipal and commercial activities such as vehicle exhaust and industrial emission1. The presence of these pollutants can cause breathing trouble and respiratory irritation in people of

all ages, induce heart attacks, and even cause premature death in susceptible population groups2. For these reasons, and many more, the need for urban air pollution monitoring is of utmost importance. This investigation used TIBCO Spotfire® to visualize urban air quality data collected from the Elm air sensing network in Boston, Massachusetts. Strategic features of TIBCO Spotfire®, which can be used to develop the understanding of urban air quality, were identified and comprehensively discussed. The goal of this exploration into the visualization of urban air quality data was to address how the features of TIBCO Spotfire® can be utilized to promote advanced awareness of the relationship between air pollutant origin and its resulting impact on public health.

Developing Insights Between Urban Air Quality and Public Health Through the Employment of 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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The goal of this exploration was to identify the features of TIBCO Spotfire® which make it possible to meaningfully analyze urban air quality data from the perspective of public health professionals. The involved data was collected using the Elm air sensing network located around the greater Boston area; this network consists of 25 sensors placed in the towns of: Arlington, Belmont, Boston, Cambridge, Chelsea, Lexington, Medford, Newton, Quincy, Sommerville, Stoneham, Waltham, Westwood, and Winthrop and is shown in Figure 1. Sensor locations were determined with

consideration given to their proximity to major access areas and surrounding highly populated suburban neighborhoods. Examples of these focal points include office buildings, an airport, and schools. The sensors were capable of capturing 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. Individual features of TIBCO Spotfire® were identified which strengthen those analyses pertaining to real-time analysis, time-series trending, and the impact of emergency and historical events.

Results

As a result of the Clean Air Act, the United States Environmental Protection Agency (EPA) has been required to establish a set of National Ambient Air Quality Standards (NAAQS)3. These standards are outlined in Title 40 of the Code of Federal Regulations, Part 504. In an effort to mitigate the negative effects of air pollution, primary and secondary standards were created to monitor the levels of certain pollutants which have adverse effects to human and environmental health. This desire to monitor air pollution levels in accordance with national regulation thus sets a framework for the necessity of sensors located in meaningful areas in order to detect shifts in the air pollutant levels of unique microenvironments.

Real-Time Analysis

The Elm sensors, which were deployed in the greater Boston area, collect and record data every 20 seconds. With this frequency of data collection, using TIBCO Spotfire® in conjunction with Elm can produce nearly real-time results and automatic updates.

TIBCO Spotfire® is capable of establishing information links to a database which submit structured requests for particular columns of data to be brought into the analysis. These information links may contain prompts, or filters, which limit the data based on the desired request. Real-time data may also be imported into TIBCO Spotfire® directly through a connected database. This may be done using ODBC, OLE DB, OracleClient and SQLClient default drivers5.

Without using information links or a database, TIBCO Spotfire® can still analyze real-time, or nearly real-time, data through its “Reload Data” feature. This feature is much simpler than either of the two previously mentioned methods. When data is imported into the analysis, it is automatically saved with the data table “linked to source” versus the alternative of “embedded in analysis”. When data is linked to the source, the TIBCO Spotfire® file will reflect edits and additions of data to the source file each time it is opened. Note that changes, or the deletion, of data in TIBCO Spotfire® will not edit data in the source file. Enabled for both linked and embedded data, the TIBCO Spotfire® toolbar contains a button which will reload data from the source file when pressed. Any custom formatting (such as color by rules, size by rules, and the addition of horizontal limit lines, mentioned later) which has been applied by the user to the analysis, will automatically update with the new data and distinguish suspect data from expected results.

Having the ability to visualize real-time shifts in microenvironments may lead to the ability to identify the many different sources of pollution that are relevant to unique urban settings. Different regions of an urban area may be comprised of population groups that have alternative commuting patterns, a high population of contrasting age groups, or have unique trends that reflect the influence of a particular culture. Identifying these demographical characteristics about a population can help tailor a real-time alert for air pollution contaminants that may affect these clusters of residents. Identifying the different patterns that are present in diverse parts of a city may further lead to specially designed pollution treatment programs.

Since hyper-local pollution is subject to travel through the air, real-time analysis against wind speed and direction may be able to predict where the air pollutants are carried and their rate of travel. This can then be relayed to residents of a community in the path of the pollution so they may change their daily pattern to reflect the change in air quality. Through the use of the following visualization techniques combined with real-time monitoring, the possibility of pollutant level forecasting expands to larger urban areas than just the immediate stationary unit location6.

Time-Series Trending

With the capacity to handle large volumes of data, TIBCO Spotfire® can perform time-series trending in a variety of date-time formats including, but not limited to, hourly, daily, weekly, monthly, and yearly. In Figure 2, two line charts have been prepared to visualize quantities of traffic related emissions: TOG.

Figure 1. Map Chart displaying the location of 25 Elm units located in the greater Boston area

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On the left, the sensor data has been aggregated to display daily averages from all locations over the span of one month. On the right, the sensor data has been aggregated to display the individual daily pattern, as shown in 45-minute increments. These different display patterns can be useful when examining trends regarding the daily outlook of pollution in a particular microenvironment. As you can see in Figure 3, the same visualizations are shown however, attention is only focused on three of the Elm units and their results.

When multiple column names are chosen along the Y-axis of a line chart, multiple lines appear in the chart area. Plotting the concentration, in parts per billion, of air pollutants over a unit of time allows a better understanding of the individual trends represented in unique microenvironments. As shown in Figure 4, three different sensor readings have been plotted in the same line chart to compare TOG levels in different parts of an urban environment; the red line displays data from a sensor located near a suburban school, the black line displays data from a sensor placed near a high-traffic area, and the green line represents data collected from a sensor located on an office rooftop.

The X-axis of the line chart has been aggregated by hour of the day. In doing so, it is possible to identify individual hours where average air pollutant levels are at their peak. Focusing on the urban traffic data in Figure 4, TIBCO Spotfire® clearly displays that there are peaks in TOG at approximately 8am, 5pm and 8pm EST. These hours correspond to rush hour traffic patterns due to the start and end of typical work shifts. Focusing next on the sensors located near the suburban school and office rooftop, it can be seen that similar patterns are present during the hours when traffic and attendance are also approaching their critical points. Figure 5 shows the average TRG concentration at the same three locations. The relationship between traffic pattern and air pollution concentration becomes much more apparent from this visualization. TIBCO Spotfire® is able to take the analysis a step further by differentiating between weekend and weekdays through various filtering options. In Figure 6, data from the TOG line chart in Figure 4, has been filtered to display weekend data on the left and weekday data on the right. The relative patterns in average pollution concentration still hold true, however there is a distinct difference in the peak concentration at each location.

If data from various sensors in a network are uploaded to TIBCO Spotfire® in multiple data tables rather than a single data table, these tables may then be linked. This action connects similar data through the joining of column names which may then be visualized in the same chart. With this feature, a researcher may link tables containing levels of pollutants from specific urban locations to a table of pollutant levels and their studied side effects and have the potential to draw visual insights from sources in such a way that they were unable to before.

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

Figure 4. Average level of TOG in ppb per hour of day. Red line represents sensor 1320, green line represents sensor 1318, black line represents sensor 1322.

Figure 2. Trending of TOG shown as daily averages over the course of a month and daily pattern of all sensor locations.

Figure 5. Average level of TRG in ppm per hour of day. Red line represents sensor 1320, green line represents sensor 1318, black line represents sensor 1322.

Figure 6. Average level of TOG in ppb per hour of day differentiated between weekends and weekdays. Red line represents sensor 1320, green line represents sensor 1318, black line represents sensor 1322.

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The nature of pollutants, given standard maximum limits and quantities that can quickly reach excess amounts, lends itself to analysis through the implementation of color by rules. These rules may be used to automatically identify data above or below a given result range. An example of an implemented color by rule can be seen in Figures 2 and 3. Shown in the legend on the right-hand side of both map charts, a gradient color by rule has been applied, which distinguishes markers given a color based on the assignment of unique colors to desired points; as the concentration of TOG increases, the markers on the map display the appropriate color from the scale, ranging from green to yellow to red.

Color by rules may also be used to automatically detect which points are above or below assigned thresholds. By setting a rule to identify all points above a given value as red, all current data, and any newly imported data- as previously mentioned- will automatically be presented as red when this criterion is met. This functionality is useful in numerous circumstances, but specifically when identifying if pollutant levels have reached a concentration that is dangerous to human health. When viewing air pollutant levels against NAAQS standards, levels that comply with the federal regulation may be designated green, whereby those levels which are nearing, or surpass, their standard limit may be designated yellow, red, or any other appropriately determined colors.

Horizontal lines can be used to establish clear upper and lower bounds on a set of data points in a given visualization. These are useful when supplementing a color by rule, or when more enhanced visual reference is required. Combination charts may also be used to track fluctuations in temperature or humidity alongside air pollutant trends. The combination of bars and lines that may be plotted in this type of visualization allows one to detect slight, yet significant, variations in data. Depending on the imported data, metadata statistics, such as the number of hospital visits, mortality rate, or morbidity of health effects can be plotted on a time-series chart with pollution data to analyze potential influences of the data on resultant public health. The identification of these possible connections can be used to advise residents of certain microenvironments, or people with a particular respiratory illness, when levels of air pollutants reach unsafe concentrations for their health.

Analysis of Emergency and Historical Events

In addition to the ability to import real-time data and track time-series trends, the previously identified features of TIBCO Spotfire® can be used to analyze emergency and historical events. In Laurence G Hanscom Field, near the Boston Airport, on May 31, 2014, a plane crashed and burned for reasons that were undetermined at the time. Figure 7 shows the location of the crash and its proximity to four of the deployed Elm units. Filter settings were applied to display relevant data from only these four nearby sensors.

These sensors were able to capture noticeable noise, dust, and TRG data from the crash beginning at 21:36; according to news reports, the crash occurred at approximately 9:40pm, or 21:407. Sensors 1302, 1315, 1325, and 1327 were able to pick up dust peaks as shown in Figure 8. This data was plotted with wind direction to assess the direction and rate of travel of the dust. The onset of the major dust peaks began at approximately 22:50 and dissipated around 01:50. These visual effects from burning plastic and fuel can be used to draw conclusions about how such events are capable of impacting surrounding environments. Capturing and visualizing data at the time an event takes place gives officials the ability to determine if dust levels have the potential to reach hazardous levels. With an approximately 1 hour delay in peak dust concentration in this example, emergency personnel have the time to inform nearby residents if levels are going to become a danger to their health and provide recommendations about how best to protect themselves.

The Elm sensors can also be used to look back in time at historic events. After acquiring public health data for a given set of days, it is possible to go back in time and retrieve the corresponding data from the appropriate Elm network units. Analysis can be performed between temperature, humidity, and pollution levels with the retrieved statistics to determine if any correlation between the data exists.

Figure 7. Location of crash site, Laurence G Hanscom Field, and nearby ELM units.

Figure 8. Combined dust results from four nearby sensors before and after the time of the plane crash.

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Resources

1. “Ground-level Ozone: Basic Information”, United States Environmental Protection Agency, 26 Nov 2014, Web, 23 Feb 2015

2. “Particulate Matter (PM): Health”, United States Environmental Protection Agency, 6 May 2014, Web, 23 Feb 2015

3. “Air and Radiation: National Ambient Air Quality Standards”, United States Environmental Protection Agency, 21 Oct 2014, Web, 12 Feb 2015

4. “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

5. “Open from Database Overview”, Spotfire® Technology Network, 18 Sept 2013, Web, 24 Feb 2015

6. Kelly, Frank J., Fuller, Gary W., Walton, Heather A., Fussell, Julia C., “Monitoring air pollution: Use of early warning systems for public health”, Respirology 17 (2012) 7-19, 2015

7. Salahi, Lara and Caesar, Chris, “Officials Recover Black Box from Hanscom Plane Crash”, Boston.com 2 June 2014, Web, 23 Feb 2015

Conclusion

The goal of urban air quality monitoring is to provide information regarding different microenvironments throughout an urban area. The data collected in each area can be used to determine how various activities directly affect the surrounding air, and subsequently the health of the nearby population. It is essential that the units of a sensor network be located near areas of importance, such as the Boston Airport, to determine how unique events impact the levels of pollutants in the air. This important aspect of sensor location and hyper-local analysis is essential to gaining a big-picture perspective of urban public health. Moving towards the future, this data can then be used to forecast pollutant trends based on known events. Obtaining this knowledge may, in hope, inspire people to adjust their activities or routines in such a way that better equips themselves against the harmful side effects of air pollution6.