Story 2


From late October 2013 to early June 2015, the Citizen Sense research project collaborated with residents of northeastern Pennsylvania to develop a citizen-led air-quality monitoring project. Residents in this area were particularly concerned about the inadequate environmental regulation and monitoring in relation to expanding hydraulic fracturing activities, and had already begun to undertake community activities for monitoring environmental pollutants.

Citizen Sense worked with local residents to develop a monitoring kit that included Speck PM2.5 sensors, BTEX badges (for monitoring benzene toluene, ethylbenzene and xylene, volatile organic compounds commonly associated with petroleum-related activities), a community platform for mapping monitoring locations and viewing real-time and historic data, and a Frackbox, which monitored nitrogen oxides, ozone, volatile organic compounds, temperature, humidity and wind direction. Residents were also provided with a logbook of instructions, which suggested several options for recording observations of environmental conditions and health effects.

The Citizen Sense kit and sensors were distributed in October 2014 during a monitoring workshop and walk. In total, 30 monitors and kits were distributed to residents, and 3 Frackboxes were placed near infrastructure. The monitoring period ran for over 7 months, until June 2015. During peak monitoring activity, there were 23 active monitoring sites, and there was consistent monitoring taking place at up to 16 sites over a period of 7 months.

Fracking sitework video, Vera Scroggins

The data stories are generated using the Citizen Sense Airsift Data Analysis Toolkits, which were developed to allow for citizen-led interpretation of datasets. The core data available for interpretation is the PM2.5 sensor data using the Airsift PM2.5 Data Analysis toolkit. The Frackbox data and Airsift Frackbox Data Toolkit are also available as part of the resources section of our website. The Airsift toolkits use and adapt the open source software, openair, developed by atmospheric scientists for the analysis of air pollution data. In order to blur the exact monitoring locations, the monitoring locations have been labeled with township locations, which can be found in relation to Pennsylvania counties.

The 5 data stories presented on this site demonstrate the different patterns that have emerged from the data. Our hope is that the stories and the Airsift toolkits will provide a method and guide for others to undertake their own analysis of this citizen-gathered dataset, and to contribute to the wider development of citizen-led environmental monitoring and data analysis tools and practices.

From October 2015 to June 2015, Rebecca Roter, resident and chairperson of community group Breathe Easy Susquehanna County (BESC), collaborated with Citizen Sense, to design and use the Citizen Sense Kit to monitor air quality, in response to concerns surrounding air pollution from natural gas infrastructure. The Brooklyn N 64_323 monitoring site selected by Roter is located in Susquehanna County, approximately 4 miles southeast of the small town of Montrose, Pennsylvania. The PM2.5 monitors were located on the porch of a residential property: monitor 64 was used by Rebecca Roter, between October 2014 and February 2015, and monitor 323 between February 2015 and June 2015. (In February 2015 monitor 64 was replaced with monitor 323 as it had malfunctioned due to an insect crawling inside the sensor. The data recorded during the malfunction was not included in this data story.)

Local sources of particulate pollution
The monitor was set back on an elevated porch above the road, approximately 15 feet (5 meters) west from a dirt track road, which could be a possible source of PM2.5 in the form of wind-blown dust. The Brooklyn N 64_323 site is situated in a low-traffic area, and the dirt track road is used by local traffic and industry-related traffic. The major highway, Interstate 81, is located approximately 35 miles to the east.

There is one small family dairy farm located one mile northeast of the Brooklyn N 64_323 monitoring site, and there are grazing beef cows in the fields surrounding the house. There is no industrial-scale farming in the immediate area. Wood or coal is not burned near the monitoring site, however, in the valley below, a neighbor ¼-mile to the east of the monitoring location burns wood as the primary source of heat.

As can be seen in Figure 1, which includes geo-location data on hydraulic fracturing infrastructure documented by resident and participant Meryl Solar, there are a number of fracking sites surrounding the Brooklyn N 64_323 monitoring site. (Note that this map is historic data to reflect conditions at the time of monitoring. The landscape is continually changing, and to see a more up-to-date map of current conditions see A compressor station is located 1 mile northwest of the site. There are over 30 compressor stations within a 20-mile radius of the monitoring location. The Procter & Gamble factory is within 20 miles southwest of the monitoring site.

There are also 3 operating well pads within 1 mile of the sensor location. The first well is approximately 2500 feet to the southwest, the second well is approximately 1800 feet to the north, and the third well is approximately 1/2 mile to the northeast. During the monitoring period, fracking was taking place in the region, with drilling and fracking specifically taking place at the third well during December 2014 to March 2015.

Regional sources
Twenty miles to the north is the city of Binghamton, New York, and 30 miles to the southeast is the city of Scranton, Pennsylvania. The cities of Pittsburgh, Pennsylvania, and Harrisburg, Pennsylvania, are located more than 200 miles to the southwest of the site.

It was common to detect unfamiliar odours ranging from sweet to acrid smells at the monitoring site. In addition, humans and animals at the site have experienced a number of health problems affecting skin, sinuses, throats, and ears.

Fracking infrastructure diagram, Kelly Finan, 2015

II. Is there evidence of a problem?

The device used to monitor PM2.5 particles is an “indicative” monitor. This means that measurements can give an indication of pollutant concentrations, but cannot be directly compared with national and international guidelines and standards in an “official” or regulatory sense. Despite this, indicative monitors are a well-established method within atmospheric science for carrying out initial surveys of an area to establish whether a potential problem merits further investigation. Indicative monitors are also becoming increasingly available for citizen-based air-quality monitoring, similar to this study.

Indicative daily mean concentrations of PM2.5 are shown as a time-series chart in Figures 2a and 2b. The World Health Organisation (WHO) guideline of 25 µg m-3 for 24-hour daily mean concentration of PM2.5 is breached on a number of occasions, suggesting that further investigation may be merited. However, it is important to determine whether these breaches were caused by “local” sources of pollution close to the sensor (i.e., within 1000 feet (300 meters)), by sources within a few miles (or kilometers), or by regional sources affecting the whole area. Regional sources of PM2.5 typically include large urban areas, major industrial or agricultural processes, or main transport routes.

Figure 3 shows an extract of the monitoring data at the Brooklyn N 64_323 site presented as 1-minute mean concentrations of PM2.5. Measurements from the Liberty E 111 site are shown for comparison. Regional sources of pollution appear as broad “humps” of elevated pollution affecting both sites, for example, in the period between May 11 to 13, 2015 (this pattern was also demonstrated in the data at the Bridgewater N 56 site, as discussed in Data Story 1). Local sources of pollution appear as short “spikes” typically affecting only one or the other site, for example, May 14 to 15, 2015, at the Brooklyn N 64_323 site (but not at the Liberty E 111 site at the same time). In a general sense, this regional-local pattern occurs because pollution mixes in the atmosphere as it travels away from a source, smoothing the speed of changes in concentrations.

Local sources often augment regional sources, which can be revealed as a spike on top of a hump (e.g., May 11, 2015, at the Bridgewater N 56 site). Figure 3 therefore indicates that there are significant local sources of particulate pollution elevating ambient concentrations well above those caused by regional sources across the monitoring period.

Figure 2a: Time series chart of 24-hour mean concentrations of PM2.5 at Brooklyn N 64.
Figure 2b: Time series chart of 24-hour mean concentrations of PM2.5 at Brooklyn N 323.
Figure 3: Comparison of 1-minute mean concentrations of PM2.5 at Brooklyn N 323 and Liberty E 111 during May 2015, highlighting local “spikes” and regional “humps.”

There are many possible sources of pollution in the area and we have to look at the measurements more closely to see if we can deduce what activities are causing these spikes. Knowing the source of pollution is important as some activities produce more toxic particulate matter than others, and actions to mitigate sources should be targeted to the cause of the problem.

III. Characterizing the problem

When is the source most evident?
We are most interested in “spikes” in pollution relating to a possible local source. We can characterize local sources by plotting a number of charts, filtering out concentrations above the regional baseline.

We refer to Figure 3 in Data Story 1 to set the regional baseline of 15 µg m-3, which is the approximate concentration at which regional “humps” in pollution peak. Data Story 1 provides the most comprehensive and extensive monitoring data, and is a good point of comparison across all data stories.

Figure 4: Mean PM2.5 concentrations at the Brooklyn N 323 monitoring site, grouped by hour, month and weekday.

Figure 4 investigates when these “spikes” in pollution occur by grouping concentrations by hour, month and day of the week. Sources of pollution related to commuter or transit traffic typically show peaks in concentrations coincidental with peaks in traffic flow, i.e., morning and evening rush hour with notably lower levels at night and on Sundays. However, on average, the local source at the Brooklyn N 64_323 monitoring site is highest early in the morning. The plot also shows high readings on Mondays. However, as the monitoring period for the 323 sensor (in comparison to the 64 sensor) was relatively short, this is likely to be dominated by one high spike on a single Monday morning. There are smaller spikes that occur on Wednesday, Thursday, Friday and Saturday in the early morning, which are also more pronounced in Data Stories 1, 3 and 4.

It should be noted that the weather plays a large role in particulate levels. For example, dust tends to be dispersed more slowly during the hours of darkness, as vertical and horizontal wind speeds are generally lower. This phenomenon may skew charts somewhat.

Which direction is PM2.5 coming from?
Wind direction has a considerable influence on pollution measurements. A sensor will only record emissions from a particular source or activity if the wind blows it from the source towards the sensor. Therefore, we can investigate where a source of pollution is likely to be located by plotting wind direction against pollution concentrations.

Figure 5: A scatter plot showing the relationship between PM2.5 concentrations and wind direction (in degrees).

Figure 5 shows how pollutant concentrations at Brooklyn N 64_323 are influenced by wind direction. It is clear that the highest peaks are recorded when the wind blows from the southwest (220O and 240O) and northwest (300O to 320O). Figures 6a and 6b display rose plots, which allow us to look at the relationship between PM2.5 pollution concentrations, wind direction and wind speed. The fourth panel on the far right of the rose plot shows that the highest levels of PM2.5 at low wind speeds are from the north and northwest, followed by the southwest. The fourth panel also shows that there are high levels from the southwest at high wind speeds. A similar pattern, though slightly less pronounced, is shown in the monitoring data from October 2014 to February 2015.

Figure 6a: A rose plot showing the relationship between PM2.5 concentrations, wind direction and wind speed.
Figure 6b: A rose plot showing the relationship between PM2.5 concentrations, wind direction and wind speed.

A polar plot is a more intuitive way of looking at this relationship. As indicated in Figure 7, these plots show color contours of pollutant concentrations in relation to wind direction and wind speed (zero wind in the center, increasing to 35 ms-1 at the outer ring). The highest mean concentrations of PM2.5 are shown in red, lowest in blue.

Figure 7: A polar plot of PM2.5 concentrations higher than 15 µg m-3 at Brooklyn N 323 (left panel) and Liberty E 111 (right panel). The color scale shows mean PM2.5 concentration, wind direction around the polar axis, wind speed from the center outward. Note that the mean concentrations shown are relative (e.g., highest mean concentration is >32 µg m-3 at Brooklyn N 323, and >100 µg m-3 at Liberty E 111)

In common with Figures 6a and b, when we look at Figure 7 both sites show higher concentrations when the wind is from the southwest, and Figure 7 shows that the highest concentrations are recorded at Brooklyn N 64_323 when the wind blows from the southwest and northwest.

In contrast, there are clearly source(s) of pollution to the north and southeast of Liberty E 111, which are not evident at Brooklyn N 64_323. As both sites show a source to the southwest, there may be a regional source of air pollution in that direction, which is detected by most sensors in the area.

Under which weather conditions are PM2.5 levels most evident?
Different sources of pollution will behave in distinct ways according to the weather. For example, windblown dust will primarily occur during dry, windy conditions. Sometimes, you can learn about a source by characterizing this weather-related behavior.

Figure 8: Relationship between PM2.5 and wind speed, indicating that higher concentrations tend to occur at lower wind speeds.

The relationship between particulate pollution concentrations and wind speed is shown in Figure 8. This suggests that the main source is not wind-blown dust, as peak concentrations are recorded during lower wind speeds. This conclusion is supported to some degree by Figure 9, which shows that the highest 1-minute mean concentrations of PM2.5 were recorded during relatively humid conditions (around 45% to 60% humidity), and during high humidity there would be fewer occurrences of wind-blown dust.

Figure 9: Relationship between PM2.5 and humidity at Brooklyn N 323.

IV. Drawing the evidence together

Using the tools provided through the Citizen Sense Airsift Data Analysis Toolkits, we have characterized sources of particulate pollution detected by the Brooklyn N 64_323 sensor as follows:

  • While regional sources of pollution were detected, there was clear evidence of an additional local source or sources.
  • The strongest local source(s) appear(s) to be to the southwest and northwest of the sensor, and are therefore not related to Highway 1-81 or to the dirt track road to the east. Looking at all other local sites and industries capable of generating PM2.5 as documented in Figure 1, the local source(s) to the southwest could include the first well located 2500 feet to the southwest of the monitor.
  • Looking at all other local sites and industries capable of generating PM2.5 as documented in Figure 1, the local source(s) to the northwest may include a combination of the nearby compressor station, located 1 mile northwest of the monitoring site and the second well located 1800 feet north of the monitor.
  • The regional source(s) could be a combination of emissions from the Procter & Gamble factory combined with emissions from the cities located to the southwest.
Compressor station video, Frank Finan


The Citizen Sense project is led by Dr Jennifer Gabrys, working in collaboration with researcher Helen Pritchard.

Dr Benjamin Barratt at the Environmental Research Group, King’s College, University of London contributed to the co-authoring and analysis of the data stories.

Lau Thiam Kok contributed to the co-development of the Citizen Sense Airsift Data Analysis Toolkits, using and adapting openair software developed by Dr David Carslaw.

Raphael Faeh contributed to the digital design and layout of the data stories.

Special thanks are due to the participants and residents in northeastern Pennsylvania who contributed to the design, development, and testing of the monitoring kit, as well as to the collection and analysis of data, and communication of results to wider publics and regulators. The Brooklyn data story received additional contributions from Rebecca Roter. For more information on project contributors, see Citizen Sense People.

This data story is prepared under the assumption that all pollutant, cartographic and meteorological measurements are valid and not sufficiently biased to cause misrepresentation of results. Please refer to the Airsift Data Analysis Toolkits and Terms of Use for further information.

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 313347, “Citizen Sensing and Environmental Practice: Assessing Participatory Engagements with Environments through Sensor Technologies.”

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