1. Region 4 Air Sensor Projects
Fall Air Directors Meeting
November 9, 2016

2. CitySpace Air Sensor Research Project in Memphis, Tennessee
Participants and Collaborators:
EPA Regions 4, 6, and 7; EPA Office of Research and Development (ORD); Memphis and Shelby County Health Department; Mississippi Department of Environmental Quality, Arkansas Department of Environmental Quality; Memphis Area Transit Authority; University of Memphis
Objectives:
Field-test new, lower-cost PM sensors in the Memphis area.
Understand how this emerging technology can add valuable information about air pollution patterns in neighborhoods.
Fact Sheet:

3. CitySpace Air Sensor Research Project in Memphis, Tennessee
Sensor pods were installed at 16 sites in October 2016, and will continue monitoring until February 2017
Two additional sites to be installed in November
Each sensor pod continuously measures:
PM in various size increments
Wind speed, wind direction, temperature, and humidity
Monitoring locations were selected with input from the local community, and by using mapping tools developed by EPA’s Sustainable and Healthy Communities research program
Several monitors are co-located with regulatory PM2.5 monitors
Blue dots are the sites that have been installed, grey dots are the ones that will be installed by the end of November.

4. Community Air Sensor Network (CAIRSENSE) Project Overview
Participants:
EPA Regions 4, 1, 5, 7, and 8; EPA Office of Research and Development (ORD); EPA Office of Air Quality Planning and Standards (OAQPS); and Georgia Environmental Protection Division (EPD), Colorado Department of Public Health and the Environment; Jacobs Technology (ORD contract support).
Objectives:
1. Evaluate in situ the long-term comparability of several lower cost sensors of interest against regulatory monitors.
2. Determine the capabilities and limitations of a long-term multi-node wireless sensor network applied for community air monitoring, in terms of operational stability (communications, power) and long-term data quality under ambient conditions.
Research findings available: toolbox/air-sensor-toolbox-resources-and-funding#RTF
Some of the takeaways from the CAIRSENSE results:
There was a wide range of data quality from different sensors tested – some sensors produced very useful data, and some did not.
In some cases, there was also variability between different units of the same model of sensor tested
It is important to build good QA practices (e.g. collocation with other sensors and a reference) into any future sensor applications
Sensors may potentially be useful for a wide range of applications, such as providing community- or individual-level pollution data. However, many sensors have not yet been evaluated to determine their reliability and accuracy. The Community Air Sensor Network (CAIRSENSE) project is a collaboration among multiple EPA regions, the Office of Research and Development (ORD), and Office of Air Quality Planning and Standards (OAQPS) to understand the field performance and utility of the next-generation ambient air quality measurement instrumentation, and is part of EPA’s Regional Methods Program.

5. CAIRSENSE Correlation matrix (Pearson correlation) of 12-hr average PM between sensors and co-located FEM
Moderate to high correlation between most identical units
SAFT-Egg 3 Dust
SAFT-Egg 1 Dust
SAFT-Egg 2 Dust
SAFT-Shinyei 2
SAFT-Shinyei 1
SAFT- Dylos 1 S
SAFT- Airbeam 3
SAFT- Airbeam 2
SAFT- Airbeam 1
SAFT- Dylos 3 S
SAFT- Dylos 2 S
FEM PM2.5
SAFT- MetOne 3
SAFT- MetOne 1
SAFT- MetOne 2
WSN-N4 Shinyei
SAFT-Egg 3 Dust
SAFT-Egg 1 Dust
SAFT-Egg 2 Dust
SAFT-Shinyei 2
SAFT-Shinyei 1
SAFT- Dylos 1 S
SAFT- Airbeam 3
SAFT- Airbeam 2
SAFT- Airbeam 1
SAFT- Dylos 3 S
SAFT- Dylos 2 S
FEM PM2.5
SAFT- MetOne 3
SAFT- MetOne 1
SAFT- MetOne 2
WSN-N4 Shinyei
After the sensor field deployment, Multiple sensors reporting the same pollutant of interest were compared against readings recorded by the regulatory NCore instruments. For duplicate or triplicate sensors evaluated in SAFT, readings were compared between or among sensors to understand the reproducibility of signal from different units of the same sensor type.
This presents presents selected results for ambient PM, O3 and NO2 concentration measurements, representing approximately 9 months of continuous field data collection (Aug 2014to May 2015). Correlation matrices are presented for 12-hr average PM, and hourly O3 and NO2 readings measuring the same pollutant of interest.
The correlation plots show both comparison between sensors measuring the same pollutant as well as comparing against the South Dekalb federal equivalent method (FEM) monitor. These plots represent the correlation values between all pairs of data by shape (ellipses), color and the numeric values. The ellipses are visual representations of scatter plot; for example, perfect positive correlation is shown as a red 1:1 line and has a numerical value 100 (Pearson R = 1.0) and no correlation is indicated as a yellow circle with numerical value close to zero. A negative numerical value represents a negative relationship between paired variables.
Generally, sensors from the same manufacturer demonstrate much higher between-sensor correlations (e.g., R>0.9) than when paired with sensors from other manufacturers. Correlations between individual sensor and FEM measurements are shown within the purple rectangle.
The performance of PM sensors are widely variable, with the correlation coefficient between individual sensor and co-located FEM instrument ranging from to 0.68 for 12-hr averaged concentrations.
Variable correlation with reference (r = to 0.68)

6. CAIRSENSE Correlation Matrix of Hourly Average O3 between Sensors and Co-located FEM
Strong correlation between identical units
FEM O3
WSN N4-Aeroqual
SAFT-Aeroqual 1
SAFT-Aeroqual 2
SAFT-AQMesh 1
SAFT-AQMesh 2
SAFT_CairClip 1 (-FEM NO2)
WSN N4-
CairClip (-FEM NO2)
FEM O3
WSN N4-Aeroqual
SAFT-Aeroqual 1
SAFT-Aeroqual 2
SAFT-AQMesh 1
SAFT-AQMesh 2
SAFT_CairClip 1 (-FEM NO2)
WSN N4-CairClip (-FEM NO2)
In comparison, the correlation coefficients between O3 sensor and FEM varies from 0.15 to 0.95 for hourly data. For sensors such as Cairpol Cairclip that report combined O3/NO2 readings, individual pollutant concentrations were separated from each other by subtracting the reference FEM data.
Variable correlation with reference (r = 0.15 to 0.95)