1. Development of a low-cost gas sensor network for atmospheric methane concentration monitoring Michael van den Bossche (mv7b@Virginia.edu) Nathan Rose Doug Chestnut Xinrong Ren (UMD College Park) Chris Sloop (Earth Networks) Stephan de Wekker Funding from Appalachian Stewardship Foundation (ASF) AMS Annual Meeting 2015, Phoenix ASF  Blog https://pages.shanti.virginia.edu/AirQuality/

2. Introduction Recent studies show large disagreements on methane emissions from oil and gas extraction sites. There is agreement on the need for more data. Gas analyzers and eddy covariance systems are (prohibitively) expensive. Research Goal: Develop a low-cost system to monitor atmospheric methane (CH 4 ) concentrations near hydrofracturing sites. Sensor requirements: Detection range1 – 10 ppm CH 4 in air. Low-cost< $ 1,000 / sensor assembly Required resolution< 1 ppm [CH 4 ] 2

3. The sensor assembly Gas sensor: Figaro TGS2611E00, based on semiconductor technology. Resistance of the sensor varies with [CH 4 ], but also depends on rH & T. Need to calibrate the gas sensor. 3

4. The calibration setup Control: - [CH 4 ], by combining ultra-zero air with 100 ppm CH 4, - relative humidity (rH) using bubblers with saturated salt solutions, - temperature (T) using a water bath. 4

5. Results: [CH 4 ] calibration Measured at 74-75% rH; 25-27 °C; 500 mlpm. Excellent linear correlation of [CH 4 ] with ratio of sensor resistance R s and resistance R 0 at [CH 4 ] = 0. Maximum error during calibration: -0.29 ppm [CH 4 ]. RegressionRemainder 5

6. Results: rH & T compensation Used equilibrated data at 18 different conditions of (rH&T). [CH 4 ] = 0; flow rate = 800 mlpm Multivariable regression of the sensor’s resistance Rs with T and rH: Log (R s /R 0 ) = 0.392 – 0.0140 x T [°C] - 0.0019 x rH [%]. R 0 = sensor resistance at T ~ 20°C, 60% rH. < 1 ppm error for moderate conditions (14 out of 18 conditions measured). 2.76 ppm error at more ‘extreme’ conditions: low rH & T, and high rH & T. RegressionRemainder 6

7. Calibration validation Measured lab air (~2 ppm [CH 4 ]) with the calibrated gas sensor and compared this to the Picarro gas analyzer readings for ~three weeks. Picarro Even after T & rH compensation, some influence of T, rH on gas sensor signal remained. Drift: ~3 ppm in first 6 days. rH hysteresis? 7

8. Influence of heater & sensor voltage Voltage regulators, powering: #1: the sensor circuit & aref pin (no influence), #2: the gas sensor’s heater (large influence: 0.08 ppm/mV), #3: the controller board (no influence). 8

9. Field test of stationary sensor assembly -Continuous measurements of rH, T, atmospheric pressure, and ‘raw’ gas sensor resistance. -Powered by battery and solar panel. Data collection -Xbee Series1 RF-module -Real-time, on-line data collection & storage in ‘myObservatory’ Data visualization and sharing -Basic data analysis and QA -On-demand data visualization Network of sensor assemblies 33 11 22 33 11 22

10. Conclusions Very good correlation of gas sensor signal with [CH 4 ], at constant temperature and rH. rH & T compensation is promising; – error < 1 ppm for ‘moderate’ rH and T. – Errors up to 2.8 ppm for rH and T ‘extremes’. Large (3ppm) initial (6 days) change in sensor reading. – Perhaps due to rH hysteresis and using low rH during calibration procedure (< 5% rH). Recommendations: – Improve calibration procedure (no flowing of dry air). – Improve algorithm for rH & T compensation – model hysteresis. – Or: try to control rH and T in a narrow bracket. 10

11. Influence of flow rate Gas flow rate ↑, [CH 4 ] ↓ Gas flow rate ↓, [CH 4 ] ↑ This is (partly) be due by influence on rH. 11