Emerging Sensor Technologies

Transcription

1 Emerging Sensor Technologies Ron Williams U.S. EPA, RTP, NC WESTAR September 19, 2018

2 To cover: EPA s Diverse Array of Monitoring Research FEM/FRM Air Toxics Sensors Special purpose monitoring studies What is on the horizon for EPA/ORD Representing research from two ORD National Laboratories and one Center, EPA ORD grants and Challenges, collaborations with EPA Program Offices, Regions, other Federal agencies, state/local/tribal agencies, industry, schools, and more.

3 Air Sensor Research Diverse array of R&D at EPA, including - In-house research: - Evaluation of air sensor technology performance - Development and application of sensors for various research purposes - Grantee research Current focus on miniaturized systems for: - Criteria pollutants (PM 2.5 / PM 10, ozone, carbon monoxide, nitrogen dioxide) - Total VOCs - Black carbon FEM/FRM Air Toxics Sensors Special purpose monitoring studies

4 Technology and Information Sharing Emerging technologies represent challenges for immediate community use. Areas of U.S. EPA provided community assistance include: Discovery of new technologies Evaluation of sensors Application of new technologies in research efforts to determine their value

5 New Technologies-Where to Start?

6 Numerous Sensor Technologies Exist

7 Sensor Progression Evaluations (Past) Initial Performance Evaluations (in lab and field) Short Term Studies/Applications EPA Air Sensors Toolbox AQ Spec Networks (Present) Smart Cities Local Networks Community Engagement Near Source Monitoring Long Term Performance Characterization Integration (Future) Data Quality Data Interpretation Data Management Personal Exposure Data Fusion

8 Publicly Available Sensor Data: How Good is it?

9 The Sensor Reality Current Regulatory Technology 1. Expensive 2. Often snapshot 3. May require expertise to use 4. Often delays for lab analysis 5. Established QA protocols 6. Collected by gov, industry, researchers 7. Data stored and explained on gov websites Emerging Sensor Technology 1. Low cost 2. Often continuous 3. Sometimes easy-to-use 4. Real-time w/o lab analysis 5. QA protocol gaps 6. Collected by communities and individuals, commercial groups 7. Data crowd-sourced, shared and accessed on non-gov sites 18

10 Discovery-Summary Low cost sensors dominate the commercial market (<$2500) relative to sheer numbers Relatively few sensing elements actually exist. Many manufacturers using same elements Greater availability of different PM sensors versus gas phase sensors (brands) Gas phase sensors dominated by electrochemical and metal oxide varieties Data output often driven by ease of use concepts (cloud, android, WiFi). Output requirements often complicates use by professionals No industry standardization as to data output format, data 18processing, or calibration of response functions

11 General Research Discovery Findings Microprocessor Selection Wide variety of capable low cost components ($100-$300) Code development will be required It is not as easy as it sounds to integrate compounds in a stable processing environment Dry run of completely assembled unit a must do to ensure reliability Power Selection 50W solar cells ~ $90 and provide direct or back-up energy supply. Need hrs of daylight for small sensor pods Multi-day use pod systems need ~ 18 AHR rechargeable batteries ($40) Will need power management components to use solar cells/batteries ($60) Consider using land power if at all possible (higher data collection rates)

12 General Research Discovery Findings Selection of Complete or Component PM Sensors Cost range from $25 to $2500 for the low cost variety Component variety requires expertise in engineering (power integration/data processing/data storage) R 2 versus reference monitors widely variable (0.01 to ~ 0.8) in field evaluations Chamber tests do not replicate results under ambient conditions Light scattering particle detection from ~ 0.3 µm to 17 µm Most have no direct size fractionation options Selection of Gas Phase Sensors O 3, NO 2, SO 2, CO Component (~$50 to $300) to Complete Pod systems ($1500-$10K) exist O 3 sensors (~ $50-$1500) have shown excellent reference agreement (R 2 > 0.9); Detection limit = ~5 ppb NO 2 sensors (~$50-$1500) co-responsive with O 3 and must be resolved (R 2 > 0.8); Detection limit = ~5 ppb SO 2 sensors (~$50-$1500) have poorest limits of detection being reported (~50 ppb). Little improvement observed during 2012 to present CO sensors (~$100-$2500) have difficulty with <5 ppm measurements and temperature changes

13 General Research Discovery Findings Selection of Meteorological Sensors Components (~$30 to $1500) Ultrasonic, vane and cup designs are options RH and temp are must have data collections Ensure RH and temp sensors collect ambient conditions Low cost varieties often highly agree with reference monitors (R 2 >0.9) Air Toxics and Other Sensors of Interest Cost range from $50->$2000 IH-type offer good general performance as survey devices Most VOC sensors are of the total VOC variety (Photoionization Detection) Limits of detection in the range of 5-20 ppb have been reported Low cost sensors reporting VOC specificity have not been realized Awaiting nano-technology and other emerging sensing elements to reach the market

14 Understanding sensor performance Collocation of sensors with reference monitors in laboratory controlled and field environments to assess performance FEM/FRM Air Toxics Sensors Special purpose monitoring studies What is on the horizon for EPA/ORD

15 Ad-Hoc Testing AQMesh: NO 2, NO, O 3, SO 2, CO MetOne 831 particle sensor Dylos particle sensor Air Quality Egg (CO, NO2, PM, VOCs) Aeroqual SM50 O 3 sensor Airbeam particle sensor Shinyei particle sensor Not shown: Cairpol NO 2 /O 3 sensor

16 Example: Sensor Evaluation Reports Laboratory and field evaluations of select sensors on the market Evaluated performance characteristics: R 2 (coefficient of determination) sensor response compared to FEM/FRM Effect of relative humidity and temperature on sensor response Uptime Ease of operation/installation Mobility 16

17 An Example of In-Depth PM Sensor Evaluation

18 A Typical Ozone Sensor Daily Average Time Series Hourly Average Scatterplot r 1 = 0.93 r 2 = 0.92 r 3 = 0.96 Initial lab audit had 1:1 ratio Underreports regulatory monitor O 3 Consistent across seasons Strong correlation to regulatory monitor

19 Example of Air Sensor Toolbox Performance Reports Pearson correlation between and among low cost sensors in Denver-CAIRSENSE 19

20 Sensor Collocation Tools Training citizen scientists to evaluate sensor performance Now that community based science, citizen scientist, has become more popular, it is nice to have something explain how to collect more viable data using the low-cost sensors because most community members don t consider the accuracy of a sensor compared to a FRM/FEM. Katie Tiger, Eastern Band of Cherokee Indians

21 EPA-Developed Tools and Guidance Instruction guide for conducting a successful collocation evaluation of air sensors with regulatory grade instruments, provided as a PowerPoint presentation for easy reading and ample visual tools. Topics covered: Background Low-cost sensors vs reference instruments Introduction to collocation Planning collocation Making measurements Data recovery and review Data comparison Introduction of Macro Analysis Tool (MAT) Using sensors effectively Project partners provided feedback on instruction Guide and MAT, which was used by EPA to improve and finalize these products.

22 EPA-Developed Tools and Guidance Easy-to-use spreadsheet based macro analysis tool for performing data comparisons and interpreting the results. Tool tackles one of the biggest hurdles in citizen-led air monitoring projects working with the data. Example Outputs:

23 Literature Review Search Effort Combination of automated and hand-curated approach with focus on literature published after 2007 and on use of air sensors; databases searched included: Compendex, Scopus, and Web of Science for peerreviewed literature, Networked Digital Library of Theses and Dissertations, Open Grey, OpenAIRE, and Worldcat for identification of relevant information sources available in the grey literature, Catalog of US Government Publications, the Defense Technical Information Center, and the UN Digital Library for applicable US and international government documents.

24 Application Categories Air quality forecasting Air quality index (AQI) reporting Community near-source monitoring Control strategy effectiveness Data fusion Emergency response Epidemiological studies Exposure reduction (personal) Hot-spot detection Model input Model verification Process study research Public education Public outreach Source identification Supplemental monitoring

25 Accuracy/uncertainty Bias/trueness Completeness Detection limit Measurement duration Measurement frequency Measurement range Precision Response time Selectivity Performance Descriptors Variation in use of terms, units and statistical approaches made systematic categorization difficult

26 Percentage of Reports of DQOs/MQOs Pollutant Comparison Spatiotemporal Variation Trend Decision Support Other % All Sources PM % (6) 63% (12) 5% (1) 26% (5) 5% (1) 40% (19) PM 10 23% (3) 46% (6) 15% (2) 38% (5) 0% (0) 27% (13) Carbon Monoxide (CO) Nitrogen Dioxide (NO 2 ) Sulfur Dioxide (SO 2 ) 35% (6) 65% (11) 18% (3) 24% (4) 0% (0) 35% (17) 32% (7) 68% (15) 18% (4) 27% (6) 0% (0) 46% (22) 20% (1) 40% (2) 20% (1) 60% (3) 0% (0) 10% (5) Ozone (O 3 ) 20% (5) 72% (18) 20% (5) 20% (5) 0% (0) 52% (25) ( ) represents the number of references used in the statistic

27 Frequency of Monitoring Applications Application PM 2.5 PM 10 Monoxide Carbon (CO) Nitrogen Dioxide (NO 2 ) Sulfur Dioxide (SO 2 ) Ozone (O 3 ) Air Quality Forecasting 16% (3) 23% (3) 12% (2) 14% (3) 40% (2) 8% (2) Air Quality Index Reporting 26% (5) 31% (4) 24% (4) 23% (5) 40% (2) 16% (4) Community Near-Source Monitoring 42% (8) 38% (5) 35% (6) 36% (8) 60% (3) 48% (12) Control Strategy 32% (6) 46% (6) 18% (3) 18% (4) 40% (2) 24% (6) Data Fusion 16% (3) 23% (3) 12% (2) 18% (4) 40% (2) 8% (2) Emergency Response 21% (4) 31% (4) 18% (3) 14% (3) 40% (2) 8% (2) Epidemiological Studies 42% (8) 46% (6) 24% (4) 27% (6) 40% (2) 28% (7) Exposure Reduction 16% (3) 15% (2) 35% (6) 23% (5) 40% (2) 20% (5) Hot Spot Detection 42% (8) 38% (5) 18% (3) 23% (5) 60% (3) 20% (5) Model Input 16% (3) 23% (3) 12% (2) 18% (4) 40% (2) 8% (2) Model Verification 21% (4) 31% (4) 18% (3) 18% (4) 40% (2) 16% (4) Process Study Research 16% (3) 23% (3) 12% (2) 14% (3) 40% (2) 8% (2) Public Education 37% (7) 38% (5) 29% (5) 32% (7) 60% (3) 16% (4) Source Identification 16% (3) 23% (3) 35% (6) 32% (7) 40% (2) 20% (5) Supplemental Monitoring 68% (13) 62% (8) 47% (8) 50% (11) 80% (4) 56% (14) Other 11% (2) 8% (1) 12% (2) 23% (5) 20% (1) 12% (3) % All Information Sources 40% (19) 27% (13) 35% (17) 46% (22) 10% (5) 52% (25) ( ) represents the number of references used in the statistic

28 Frequency of DQOs/DQIs Reported Performance Characteristic/DQI PM 2.5 PM 10 Monoxide Carbon (CO) 84% (16) 77% (10) Nitrogen Dioxide (NO 2 ) Sulfur Dioxide (SO 2 ) Ozone (O 3 ) 65% (11) 68% (15) 80% (4) 76% (19) Accuracy/Uncertainty Bias 5% (1) 8% (1) 18% (3) 9% (2) 40% (2) 16% (4) Completeness 26% (5) 31% (4) 12% (2) 14% (3) 40% (2) 16% (4) Detection Limit 26% (5) 8% (1) 47% (8) 32% (7) 80% (4) 24% (6) Measurement Duration 26% (5) 8% (1) 18% (3) 14% (3) 0% (0) 20% (5) Measurement Frequency 26% (5) 15% (2) 35% (6) 23% (5) 0% (0) 32% (8) Measurement Range 47% (9) 46% (6) 35% (6) 32% (7) 80% (4) 40% (10) Precision 42% (8) 31% (4) 29% (5) 36% (8) 80% (4) 32% (8) Response Time 0% (0) 0% (0) 29% (5) 32% (7) 80% (4) 20% (5) Selectivity 11% (2) 8% (1) 24% (4) 23% (5) 80% (4) 16% (4) Other 5% (1) 8% (1) 0% (0) 0% (0) 0% (0) 8% (2) % All Information 40% 27% 35% (17) 46% (22) 10% (5) 52% (25) Sources (19) (13) ( ) represents the number of references used in the statistic

29 r 2 of Sensor Comparison with Reference Monitors PM 2.5 PM 10 Monoxide Carbon (CO) Nitrogen Dioxide (NO 2 ) Sulfur Dioxide (SO 2 ) Ozone (O 3 ) (0.78) (0.36) (0.89) (0.9) ( ) represents median values where 3 or more references existed

30 What s Next? Currently in the midst of a 4-year strategic research planning process the ORD Air and Energy Strategic Plan to be published in Fall 2018 Anticipating -Continued evaluation of performance characteristics for low cost sensors -Application of EPA developed sensor pods at select locations to meet State and Regional EPA air quality research needs -Publication of findings and sensor application tools on the Air Sensor Toolbox FEM/FRM Air Toxics Sensors Special purpose monitoring What is on the horizon for EPA/ORD 33

31 Resources and Contact Information Ron Williams U.S. EPA Disclaimer: Name or inclusion of any sensor here is not endorsement or recommendation for use