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IGERTSummary
- 1. Real-time Formaldehyde Monitoring System IGERTSensor Science, Engineering and Informatics (SSEI) Clint Eaton J.C. Whittier Stacy Doore Delia Massey Paul Smitherman
- 2. • Introduction • ProjectGoal and Objectives • Phase I – Device Testing – Network Testing – System Informatics • Phase II – Site Testing • Commercialization Applications • Conclusions and Future Work Formaldehyde Monitoring in Built Environments O C H H
- 3. Formaldehyde Monitoring inBuilt Environments Problem: • Formaldehyde emissions and toxic exposure concerns, • Formaldehyde Standards Act (2010) • EPA regulations: all wood based building materials meet new, lower emissions standards by 2013, • Can we detect formaldehyde emission levels in real time and provide a web-based user interface for facility managers?
- 4. Agency/Industry Group ExposureLimit (TWA-8hr average) Short-term Exposure Limit (15 minute maximum) Threshold Limit Value (Ceiling) NIOSH/CDC (2009) 0.016 ppm (REL) 0.10 ppm 0.10 ppm ACGIH (2009) ----- ----- 0.30 ppm (TLV) OSHA (2008) 0.75 ppm (PEL) 2.0 ppm 5.0 ppm FEMA (2008) 0.016 ppm 0.10 ----- HUD (1985) 0.40 ppm ---- ----- Formaldehyde Monitoring in Built Environments Effective Date Hardwood Plywood Particleboard Medium density fiberboard By 1/1/2011 0.05ppm (veneer) 0.09ppm 0.11ppm (MDF) By 7/1/2012 0.08ppm (composite) ----- 0.13ppm (Thin MDF) U.S. Agency Formaldehyde Exposure Limits Formaldehyde Standards Act (2010) based on CARB standards
- 5. Objectives: 1. Compare theperformance of commercially available sensors and their ability to detect formaldehyde within a dynamic range of 0.01 and 0.80 ppm. 2. Design and build wireless network architecture to transmit real-time formaldehyde concentration data. 3. Identify formaldehyde emission patterns in a real world environment. 4. Develop prototype model for assessing low-level formaldehyde environmental exposures in real-time. 5. Explore possible changes in existing engineering controls and exposure assessment processes to improve worker safety. Goal: Design and build wireless sensor network to detect/monitor formaldehyde emissions in industry setting. Formaldehyde Monitoring in Built Environments
- 6. Formaldehyde Monitoring inBuilt Environments Issues raised during alpha test… • Office question: Were the high readings result of power flux, lighting, cleaning products, etc.? • Device Calibration: Could personal exposure badges help? • Previous EHS testing at AEWC? • Formaldehyde emissions from human breath? • Could we use RFID badges to track the location of people?
- 7. •Phase I – DeviceTesting – Network Design – System Informatics Formaldehyde Monitoring in Built Environments
- 8. • Sensor interface– RSR-232 serial with USB adapter • Sampling method – 10 mL snatch-sample of air taken by internal pump • Programmable data collection scheduler • Supplied calibration standard • Temperature and humidity corrected • Response time < 10 sec • Cost range: $1,100-$3,700 • High sensitivity • Linear response • Stable sensitivity Formaldehyde Monitoring in Built Environments Formaldemeter htV-M
- 9. • Anode: whereoxidation occurs, positive polarity contact • Cathode: where reduction occurs, negative polarity contact • First experiments by Sir William Grove in 1839 • Electrons generated flow from anode to cathode Formaldemeter Electrochemical Sensor Operation Formaldehyde Monitoring in Built Environments
- 10. Formaldemeter System Blockdiagram PRECISION AMPLIFIER HIGH RESOLUTION A-D CONVERTER MICROPROCESSOR DISPLAY EXHAUST VANEPU MP FUEL CELL GAS INLET ELECTRICAL SIGNAL • Electrochemical technology • Snatch sampling via micro pump • Air drawn into sensor • Formaldehyde oxidizes to generate a small current • Three amplification stages • High resolution digital sampling system Formaldehyde Monitoring in Built Environments
- 11. Advantages •High sensitivity tolow vapor concentrations •Linear response over wide conc. range •Small, rugged and reliable •Stable sensitivity over long period of time •Long working life Disadvantages •High cost of production materials (e.g. precious metals) •Can be susceptible to interference effects from certain other compounds capable of oxidation •Different modes of calculating concentrations (peak, area, time) Formaldemeter Summary Formaldehyde Monitoring in Built Environments
- 12. Calibration Method The FormaldemeterhtV-M: • calibrated bi-weekly with calibration tool supplied by the manufacturer • exposed to formaldehyde using a permeation tube from Vici at 5 concentrations (.03, .05, .075, .10, .15 ppm) Formaldehyde Monitoring in Built Environments
- 13. Dart HCHO Sensor •11 mm two-electrode diffusion sensor • 250-300 nA/ppm output signal • Response time is 15 seconds at 20o C • Dynamic Range: 0-25 ppm • Cost: $30-$110 Commercialization Potential • Circuitry for current to voltage required • Combine with temperature/humidity sensors • Develop concentration curves that are temperature/humidity dependent • Algorithm development for marriage of output voltage to temperature and humidity data Formaldehyde Monitoring in Built Environments
- 14. ACS Badge: • Adsorptiontechnology consisting of 2,4 Dinitrophenylhydrazine • Lab analyzed with GC-MS • OSHA gold standard • provides 8 hour TWA, however... Scenario 1: .09, .09, .09, .09, .09, .09, .09, .09 = .09 ppm Scenario 2: .02, .02, .12, .2, .2, .13, .02, .02 = .09 ppm Mean ppm exposure statistics obscure the actual risk associated with shorter term exposure to higher level formaldehyde emissions. Formaldehyde Monitoring in Built Environments
- 15. Formaldehyde Monitoring inBuilt Environments Phase I: Device Calibration, Validation and Selection
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- 17. Network Design/Configuration • Thenetwork consists of 5 wireless sensor node network, a base station, a Sensor Observation Service (SOS), database system, and a web-based user interface. • Each Formaldemeter was connected to an XBee radio. Readings were output from the serial port to the XBee UART. • Base station (laptop) collected data from all nodes and pass it onto the SOS to be stored in a database. • Web-based user interface displayed latest readings and could be queried for historical analysis. Formaldehyde Monitoring in Built Environments
- 18. Node Radio Design PPMRS232 Voltage 11 V peak to peak Xbee Radio TTL Voltage 0-5 V To Base Station Voltage Conversion Max232a Voltage Conversion Max232a
- 19. Sensor nodes DetectReceive/TransmitProcess/AnalyzeKnowledge/Actio Base stationWeb-based User Interface ase I: Network Design and Testing Formaldehyde Monitoring in Built Environments SOS/SQL Server Database Raw Data = Package/StringProcessed Data = Dashboard
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- 21. Use case 1: Data Discovery Unitof Measurement Unit of Measurement Feature & Properties Feature & Properties ObservationsObservations Use case 2: Sensor Selection & Discovery Sensor typesSensor types Sensor capabilities/ restrictions Sensor capabilities/ restrictions Deployment Systems Deployment Systems Use case 3: Provenance & Diagnostics Use case 4: Tasking & Programming Operating Restrictions Operating Restrictions Process model Process model ensor Network Uses Cases and Concepts
- 22. Why an Ontology? Ontology:Provides standardized vocabulary and specification of concepts and relationships within or across domains. Expressed in Web Ontology Language (OWL) which is grounded in Description Logics (DLs). Defines concepts, properties (relationships), and logical combinations of concepts to support inference. Advantages: • Can use existing ontologies, languages, querying, and reasoning tools • W3C standards and guidelines in process • Need system to be able to store and query very large dynamic datasets • Semantic modeling of multiple domains allows for creation of reusable data and interoperability across domains Formaldehyde Monitoring in Built Environments
- 23. Ontology based LinkedData Approach Domain ontology Domain ontology Observation ontology Observation ontology Sensor Network ontology Sensor Network ontology Spatio- temporal ontology Spatio- temporal ontology Exposure ontology Exposure ontology System & sensors node capabilities Measurement units Sensor/Person location Properties of Entity Exposure events Units ontology Units ontology hase I: System Informatics Formaldehyde Monitoring in Built Environments
- 24. Database System • Recordsobservations from sensors at nodes • Simplified schema based on OGC sensor ontologies • Can store data for PPM, DART, badge, and location • Supports changes to the sensors deployed at a node and location of the node Formaldehyde Monitoring in Built Environments
- 25. User Interface Formaldehyde Monitoringin Built Environments
- 26. Phase I Accomplishments •Built wireless sensor network for formaldemeters • Calibrated the formaldemeters to Vici permeation device • Investigation into Dart sensor properties • Conceptual framework guided database development Formaldehyde Monitoring in Built Environments
- 27. Formaldehyde Monitoring inBuilt Environments Resin Blender • Phase II – Site Testing Resin blender
- 28. Resin Blender Press Office lient Facility: AEWC FormaldehydeMonitoring in Built Environments
- 29. Exploratory Questions forSite Tests: 1. Under what conditions is it safe to open the blender door during the resin application process? 2. Where/when are the highest HCHO concentrations occurring? 3. How do existing HVAC controls impact the HCHO emissions during typical processes? 4. Are sensors/badges accurately reflecting concentration measurements? 5. Is there evidence that other work areas are being impacted by HCHO emissions during manufacturing processes? 6. Can the system help to identify environmental events Formaldehyde Monitoring in Built Environments
- 30. ~8’ ~8’ N2 Breathing Zone= ~1ft ofnose/mouth N3 ~6’ N4 Side views 1 and 2 N1 N2 N3 N4 N5 Top View N5N4 Formaldehyde Monitoring in Built Environments esting Variables: Sensor Placement short, long, plume, break) Resin Type and Volume Wood Total Engineering Controls N1 Test 1,4 and 5 Test 2 and 3
- 31. Alpha Test April20-21 Mean = 0.0244 Median = .0103 Mode = .007 SD = .022 SEM = .0004 Variance = .00 Skew = .89 Range .068 Min = .005 Max = .073 “Dry Run” Test conducted during AEWC client testing. Formaldehyde resin used with all normal engineering controls in place. OSHA exceed PEL events (≥ 0.75 ppm) NIOSH exceed REL events (≥ 0.016 ppm) OSHA exceed STEL events (≥ 2.0 ppm) NIOSH > STEL/ceiling events (≥ 0.1 ppm) Event 1 (4/21 12:32am-8:32am)
- 32. Blender Tests April26 Tests used 5 node wireless system deployed inside/outside blender. Formaldehyde resin used both with/without normal engineering controls. Potential Event Thresholds OSHA exceed PEL events (≥ 0.75 ppm) NIOSH exceed REL events (≥ 0.016 ppm) OSHA exceed STEL events (≥ 2.0 ppm) NIOSH exceed STEL/ceiling events (≥ 0.1 ppm) Test Hood Fan Blender Fan Start Time Stop Time Sensor Height (ft) Fan on Hood (s) Time Door Open Resin Type 1 ON ON 903 907 5 15 910 PUF 2 ON ON 924 927 5 15 930 PUF 3 OFF OFF 951 954 5 0 956 PUF 4 OFF OFF 1044 1047 5 0 1050 PUF 5 OFF OFF 1144 (1147)* 1149 5 0 1151 PUF
- 33. Blender Tests (1-5) InsideBlender
- 34. Test 1 Door open 9:10 Test2 Door open 9:30 Test 3 Door open 9:56 Test 4 Door open 10:50 Test 5 Door open 11:51 Outside Blender
- 36. Blender Test 1 Event (4/26 9:02am-9:17am) Test1 Door open 9:10 Outside Blender (Nodes 2-5) Inside Blender (Node 1) Mean = .077 Median = .0688 Mode = .068 SD = .017 SEM = .0027 N = 40 Variance = .000 Skew = .863 Range .049 Min = .058 Max = .107 Descriptives Peak PPM (Nodes 2-5)
- 37. Event (4/26 9:20am-9:35am) Test 2 Dooropen 9:30 Blender Test 2 Outside Blender (Nodes 2-5) Inside Blender (Node 1) Mean = .071 Median = .065 Mode = .066 SD = .016 SEM = .002 N = 56 Variance = .000 Skew = 1.00 Range .048 Min = .055 Max = .103 Descriptives Peak PPM (Nodes 2-5)
- 38. Event (4/26 9:56am-10:18am) Test 3 Dooropen 9:56 Blender Test 3 Outside Blender (Nodes 2-5) Inside Blender (Node 1) Mean = .088 Median = .084 Mode = .085 SD = .026 SEM = .022 N = 116 Variance = .001 Skew = 1.486 Range .149 Min = .056 Max = .205 Descriptives Peak PPM (Nodes 2-5)
- 39. Event (4/26 10:50am-11:10am) Test 4 Dooropen 10:50 Blender Test 4 Outside Blender (Nodes 2-5)Inside Blender (Node 1) Mean = .087 Median = .0733 Mode = .073 SD = .038 SEM = .004 N = 72 Variance = .001 Skew = 2.228. Range .179 Min = .056 Max = .235 Descriptives Peak PPM (Nodes 2-5)
- 40. Event (4/26 11:51am-12:10am) Test 5 Dooropen 11:51 Blender Test 5 Outside Blender (Nodes 2-5)Inside Blender (Node 1) Mean = .086 Median = .079 Mode = .060 SD = .0311 SEM = .003 N = 88 Variance = .001 Skew = 1.708 Range .130 Min = .060 Max = .190 Descriptives Peak PPM (Nodes 2-5)
- 41. Outside Press/Inside Curtain(Nodes2-InsidePress (Node 1) Board Press Test 6 Mean = .182 Median = .094 Mode = .060 SD = .350 SEM = .035 N = 96 Variance = .123 Skew = 4.959 Range 2.00 Min = .057 Max = 2.057 Descriptives Peak PPM (Nodes 2-5)
- 42. Tues. April 26Wed. April 27 Board Emission Test Mean = .190 Median = .190 Mode = .15 SD = .036 SEM = .0006 N = 2960 Variance = .001 Skew = -0.388 Range .26 Min = .01 Max = .27 Descriptives Peak PPM (Nodes 2-5)
- 43. Office Test Conducted over8 days to assess ambient air quality in non-lab areas using only 1 node in data logging mode. No formaldehyde resin was directly introduced into the environment at any time. Event (≥ 2.0 ppm) on 4/26 Events (≥ 0.016 ppm) on 4/21, 4/23, 4/26, 4/27 Events (≥ 0.1 ppm) on 4/23, 4/24, 4/26, 4/27
- 44. Mean = 0.018Mean = 0.018 Mean = 0.016 Mean = 0.03 Mean = 0.094 Mean = 0.013 Mean = 0.188 Mean = 0.163
- 45. Office Test (April20-27)
- 46. Office Test MEANPPM by Day * sig. at .01 level * * * * One way ANOVA Post Hoc: Tukey HSD Bonferroni
- 47. Office Test HCHO,Temp, Humidity (Zscores)
- 48. Office Test HCHO,Temp, Humidity (Zscores) Pre-Weekend
- 49. No Maps? Insufficient Numberof Data Points!
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- 51. • Engineering controls(hood, fan, door) seem to be working well to protect lab area during blender process • Possible emission issues may arise once boards are pressed and left in lab to dry • Highest HCHO concentrations occurred during press processes • Unexplained HCHO events in office • System functioned with few node failures Formaldehyde Monitoring in Built Environments hase II Exploratory Findings
- 52. Next Steps • Workon new circuit board for DART to design implement, and test second sensor network • Concentration verification • Continue data analysis to identify events • Collect more data from AEWC offices • Submit proposals to NSF and EPA for additional R & D funding Formaldehyde Monitoring in Built Environments
- 53. Unique product tomeet identified industry need for indoor air quality/exposure assessment tools/methods: • wood product manufacturing • manufactured homes and RVs • furniture, carpet, wallboard, paint manufacturing • mortuary supplies • hair care products ommercialization Applications Formaldehyde Monitoring in Built Environments
- 54. So why isthis important? International Center for Toxicology and Medicine http://www.ictm.com/Investigation/Root-Cause.aspx Formaldehyde Monitoring in Built Environments
- 55. Conclusions • Identified immediateneed for wireless formaldehyde monitoring application, • Tested several commercially available sensors to deploy in network, • Designed and implemented wireless network to collect formaldehyde data in real world setting, and • Exploratory analysis and representation of formaldehyde emissions and exposure processes. Formaldehyde Monitoring in Built Environments
- 56. Acknowledgements • NSF IGERTSensor Science, Engineering and Informatics DGE Award number 0504494 • Dr. Kate Beard, SSEI Director • Dr. Brian Frederick, Project Advisor • Dr. Steven Shaler, Dr. Doug Gardner, Russell Edgar, and UMaine’s Advanced Structure & Composite Center Formaldehyde Monitoring in Built Environments
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- 59. Calibration Calculations C(ppm) =(P * (24.46/MW))/Fc) C = the concentration in PPM by volume P = the permeation rate in ng/min MW = the molecular weight of the pollutant gas Fc = the total flow of the calibration mixture in cc/min The constant 24.46 is the molar volume at the reference conditions. P (ng/min) 29 MW (g/mol) 30 24.46 Fc (ppm) Fc (ppm) Fc (ppm) Fc (ppm) Fc (ppm) 0.03 0.05 0.075 0.1 0.15 23.6 23.6 23.6 23.6 23.6 788.2 472.9 315.3 236.4 157.6 mL/min mL/min mL/min mL/min mL/min
- 60. Calibration Calculations Log P1= log P0 + a (T1 - T0) P0 = Rate at temp T0 (°C) P1 = New rate at temp T1 (°C) a = 0.030 for high emission tubes estimate 1°C decrease in temperature decreases the rate by 10%. rate in ng/min P1 29 1.46 P0 60 1.46 T1 19.5 T0 30
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- 62. Sensitivity and Selectivityof Formaldemeter Formaldehyde Sensor Type Electrochemical, two noble metal electrodes and electrolyte (proprietary) Formaldehyde Sensor Dynamic Range 0.05 -10 ppm Formaldehyde Sensor Resolution 0.001 ppm Formaldehyde Sensor Precision 10% at 2ppm level Temperature/Humidity Interchangeable digital CMOSens® Temperature/Humidity Range -40 to 128 °C, 0-100% Temperature/Humidity Accuracy ±0.4°C, ±3% RH Temperature/Humidity Calibration Calibrated to ISO/IEC17025 by manufacturer
- 63. Interference Compound 1 ppminterference concentration Comments Acetone, Acetic Acid, Butanol -- -- Acetaldehyde 8-12 Linear Ammonia 71000 High Concentration Carbon Monoxide 100 Linear Ethylene 160 -- Ethanol, Methanol 12-20, 50 Linear Phenol, Resorcinol 5,5 Filterable
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- 65. Performance Characteristics ofSensors Technology Specification Chemisresistive Sensor Optical Sensor Biological Sensor Sensing Mechanism Metal Oxide Semiconductor Difference Frequency Generator Biological enzymatic reaction Transduction Process Resistivity change results in electrical output MCT Detector Photomultiplier Tube (PMT) Signal Processing Principal Component Analysis Spectral analysis Algorithms for photon counts Sensitivity 10’s of ppb (part per billion) 10’s of ppt (parts per trillion) 1’s of ppb Response/analysis time < 1 minute (ppm) >1 minute (ppb) 1 minute 2 minutes Sampling system Micro pump and tubing Multipass absorption cell fed with ambient air by vacuum pump Flow cell comprised of silicone tube and PMMA cell Selectivity constraints Poor selectivity Unaffected by humidity Highly selective with minimal interference
- 66. Formaldehyde detection competitors Sensor Company Real Time Data LCD Display Wireless Network Data storage/ analysis Compute r Interface PortableThreshol d Alarm OSHA certified List Price ($) Enmet PPM X X X X X X X 3800 CEA Instruments X X X X 2000 Advance Chemical Sensors X X 40 Interscan X X 2000 Environmental Sensor Company X X 1200
- 67. Dart Performance Characteristics DynamicRange 0.01-25 ppm Expected Life 5 years in non-corrosive environment Output signal 250-300 nA/ppm Temperature Range -10 to 40°C Pressure Range Up to 10 atmospheres pressure T90 response time 15 seconds at 20°C Relative humidity range 15%-90% non-condensing Typical baseline offset (20°C) 0.02 ppm formaldehyde equivalent Typical baseline offset (20°C-40°C) 0 to -0.30 ppm (Economy) Premium TBA Repeatability <+/-2% Output Linearity Linear Position Sensitivity None Storage Life 2 years at 20°C
- 68. Need Context for... •Entity of Interest: Object (Sensor) • Observation and Measurement • Entity of Interest: Object (Person) • Location Context • Intersection of Person-Location Event and Noteworthy O&M-Location Event = Exposure Event
- 70. o CHEMICAL ENERGY-> ELECTRICAL ENERGY o Typically consist of two electrodes in contact with an electrolyte o ANODE: where oxidation occurs o CATHODE: where reduction occurs o Electrons generated flow from anode to cathode o Similar to galvanic cell (battery): reactions occur spontaneously o Reactants are supplied from outside rather than forming an integral part of its construction Electrochemical Introduction Formaldehyde Monitoring in Built Environments
Editor's Notes
- #2 Hello, My name is Stacy Doore and I am an IGERT Trainee representing the University of Maine’s Sensor Science, Engineering and Informatics Program. Today I would like to very briefly tell you about the work my cohort and I have been doing as a part of our Spring Testbed project involving the development of a wireless formaldehyde monitoring network for industrial built environments.
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- #4 Stacy First, a little background into why we chose this topic for our project. Formaldehyde emissions in indoor air have long been a concern of public health researchers and in some cases has come to the national attention such as the health risks identified by the CDC study on the FEMA trailers deployed after Hurricane Katrina. Based on this and a number of other studies of formaldehyde emissions in indoor environments the Formaldehyde Standards Act was unanimously passed by congress in 2010. Under these new regulations, the EPA will be responsible for ensuring by July 2013 that all wood based building materials such as softwood veneer, plywood, and reconstituted wood products are in compliance with new, lower emissions standards first established by the California Air Resources Board or the CARB standards. These new standards impact many manufacturing businesses in Maine. UMaine has a wood composite research and testing facility on campus, the Advanced Structure & Composite Center that has been listening to industry concerns about regulatory changes Faculty working with these industries brought these concerns to our attention as a potential IGERT testbed project.
- #5 Stacy After spending a semester researching the problem from several different domain perspectives and running some preliminary tests, Nasal and eye irritation, neurological effects, and increased risk of asthma and/or allergy at 0.01 to 0.5 ppm. Eczema and changes in lung function at 0.6 to 1.9 ppm. Cannot be reliably measured in blood, urine, or body tissues following exposure.
- #6 Stacy we defined the testbed project goal and objectives. GOAL The goal of this research is to design and test a real-time formaldehyde monitoring system using wireless sensor network technology to provide an immediate decision support system for users. Objectives: Compare the performance of commercially available sensors and their ability to detect formaldehyde within a dynamic range of 0.01 and 0.80 ppm. Design and build wireless network architecture to transmit real-time formaldehyde emission data. Identify formaldehyde emission patterns in real world environment. Develop prototype model for assessing low-level formaldehyde environmental exposures in real-time. Explore possible changes in existing engineering controls and exposure assessment processes to improve worker safety. * detection range meets CDC NIOSH, FEMA, OSHA, USGBA and ACGIH standards
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- #8 Clint Phase I consisted of Prototype Development which involved Device selection, calibration and validation testing, Network development and testing, as well as the creation of data structures, analysis and visualization tools for the system based on identified user needs.
- #9 Clint Describe the device down selected from technologies including chemiresistive, optical and biological. Choose this based on commercial availability.
- #10 Clint Reduction Reactant + e- = Products Oxidation Reactant = Product + e- electolyte contains free ions that make material electrically conductive
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- #13 Clint Correlation co efficient ranged from 0.9461 to 0.9923
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- #15 Clint Scenario 1 is below NIOSH REL throughout entire 8 hour minute sampling Scenario 2 is mixed levels with hazardous levels through portions of sampling
- #16 Clint Phase One Device testing involved calibrating and validating three commercially available sensors each with a different structure, capabilities and constraints. Sensor 1: was a prepackaged unit capable of collecting formaldehyde concentrations, temperature and humidity. It performed well in the calibration and validation tests but was very expensive, had a slow sampling rate of about a minute, was difficult to convert into a wireless device and used a “black box” approach to its sensor algorithms so it was difficult to see what was going on “under the hood” in terms of signal and response. Sensor 2 was a low cost raw sensor that had potentially a much larger dynamic range. We had to make custom circuit board to be able to convert its signal to voltage and ran into a number of problems during its calibration testing such as an unstable signal and a quick saturation response. Sensor 3 was a an OSHA compliant badge type formaldehyde diffusion sensor used to measure exposures over an 8 hour shift. The badges are then sent into a lab which certifies that the TWA or Time Weighted Average over that 8 hours was at a certain concentration level. The problem with these sensors is the lack of immediate feedback it took over a week to get back the results and the fact that acute exposures could have happened but might have been hidden within the mean.
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- #20 Paul So based on our device evaluation, we selected Sensor 1 to develop into the first wireless sensor network. After some trial and error and more circuit board development, the network consisted of six sensors which needed to be modified to be able to pull/send packages of data to an Xbee wireless radio. The radio sent the package to a base station which displayed the raw package data. Informatics of the system concentrated on making raw data useful for creating basic knowledge and decision support tools. The sensor nodes’ packages were sent from the base station to the Sensor Observation Service, an ontology created by 52degreesNorth based on O&M and Sensor ML ontologies designed to handle sensor network data streams. MS SQL server parsed and processed the string into sensor nodes readings in parts per million, temperature, and humidity units for the user. In addition, several user interfaces were created such as the dashboard you see here.
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- #28 Delia Phase 2 moved the prototype system into the AEWC to the main testing floor and the large resin blender you see in the picture, as well as an adjacent office. We are actually in the middle of all of this testing as we speak so we don’t have full results to report as of yet but should by the end of this semester.
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- #31 Delia We have been trying a number of different formaldehyde based resins, concentrations and engineering control scenarios to measure differences in emissions in each scenario.
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- #38 Show Map Series of Blender Test 2 Stacy
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- #54 delia We see commercialization potential with the development and refinement of this system that could be broadly applied to not only the wood products manufacturing industry, but also the manufactured and mobile home industry as well as other service based industries using formaldehyde based products such as mortuary services and hair care services.
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- #56 delia So in conclusion, we have been able to show some promising preliminary results for our prototype wireless formaldehyde monitoring system during Phase I and should be able to report results for the Phase II portion of our site testing. This project has Identified immediate need for wireless formaldehyde monitoring application, Tested several commercially available sensors to deploy in a wireless network, Designed and implemented wireless network to collect formaldehyde data in real world setting, Created prototype of conceptual model and informatics tools to represent formaldehyde emission and exposure data collection, organization and integration, Our Next Steps will be to continue working to amplify signal of low cost raw sensor for next phase of network development and submit proposals for additional funding.
- #57 delia I would like to thank NSF for the support of our IGERT program at the University of Maine, Dr. Kate Beard, our project director and Dr. Brian Frederick, Dr. Steven Shaler our project advisors, and, the SSEI IGERT Cohort 5 who have mentored us through this last year
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