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Remedios An Overview

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An overview of the work carried out by Remedios

An overview of the work carried out by Remedios

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Remedios An Overview Remedios An Overview Presentation Transcript

  • Remedios Technology Solutions to Environmental Problems Graeme Paton, Technical Director
  • Remedios Background 4 E t bli h d i A Established in August 1999 t 4 Spin-out from University of Aberdeen 4 Exclusive access to all University IP relating to environmental biosensors 4 Multi-disciplinary team (7) with international reputations 4 Deploys biosensors to complement traditional approaches in the diagnosis, remediation and monitoring of pollution and contamination p 4 Access to high quality recruits through University connection 4 Unique access to laboratory facilities 4 Utilisation of multi-million environmental technology resource
  • Awards/Recognition/ Achievements 4 Sir Ian Wood Award for Innovation 1999 4 SMART Award for product development 1999 4 Millennium Product status- biosensors 4 Best new Biotech Company 2000 – industry peers 4 2004- selected by BP for exploring sustainable remediation of hydrocarbon wastes 4 2005- core founders of DTI, KTN-Net (FirstFaraday) 4 2007- DTI Promise Bioremediation Programme 2007 4 2007- Scottish Environmental Technology Network, Board
  • The Technology Platform Contaminant concentration Leading lights in environmental technology and solutions
  • Remedios and the Environmental Protection Act 4 Since 1999, new Policy and Guidelines have transformed environmental protection p 4 The biosensor is applied in parallel with chemical analysis and hazard/ risk assessment as required 4 Remedios perform complete contaminated land studies from desktop to intrusive investigation and remediation 4 Remedios retain a close and complementary relationship with regulators 4 Remedios have acted with regulators and as policy advisors
  • Remedios The Case Studies: from Risk Assessment to Remediation f i k di i
  • Risk management = risk assessment + risk reduction selection of actions
  • implementation Risk management = risk assessment + risk reduction of actions
  • Remediation Decision Support Tool 4 Developed support tool based on 3 tiers  designed to reduce uncertainty in  Developed support tool based on 3 tiers, designed to reduce uncertainty in  technology selection. 4 The tool assists in the decision making process of remediation  technologies:  • Enabling transparent justification of selection • Gives focussed and streamlined support for targeting best options. • Interfaces with web to enable continual updating as practices become  established and lessons are learned 
  • Predicting Hydrocarbon Remediation? • Empirical data from thirty sites have been generated & applied to appraise and validate. lid t BF = bioremediation function I = induction Resp [ [TPH] ] [TPH] =TPH concentration BF = x x Inhibition (I x [TPH] log (MPN) MPN = most probable number Resp = respiration
  • BF & Rate of Degradation 10000 1000 Rate (mg/kg 100 g/day) 10 1 1e+1 1e+2 1e+3 1e+4 1e+5 1e+6 1e+7 1e+8 BF
  • Decision Support Biosensing Diplock et al., 2009. Environmental Pollution Volume 157, Issue 6, 1831-1840
  • Cement Factory
  • Cement Factory 4 Long term spill 4 Generator oils 4 Extensive area 4 Local complaints 4 Remedios trained the environmental staff
  • Compressor Area, Novi Popovac Cement Factory Soil Hydrocarbon Contamination, 0-1m bgl PC1 mg/kg PC2 PC9 1400 1300 PC3SB1 1200 PC10 SB2 1100 PC4 PC5 1000 900 PC11 PC6 800 PC12 700 600 500 PC7 400 300 200 100 0 PC8 Not To Scale
  • Compressor Area, Novi Popovac Cement Factory Soil Hydrocarbon Contamination, 2-3m bgl PC1 mg/kg PC2 PC9 1400 1300 PC3SB1 1200 1100 PC10 SB2 PC4 1000 PC5 900 PC6 800 PC11 PC12 700 600 500 PC7 400 300 200 100 0 PC8 Not To Scale
  • Compressor Area, Novi Popovac Cement Factory Soil Hydrocarbon Contamination, 4-6m bgl PC1 mg/kg PC2 PC9 1400 1300 PC3SB1 1200 PC10 SB2 1100 PC4 PC5 1000 900 PC11 PC6 800 PC12 700 600 500 PC7 400 300 200 100 0 PC8 Not To Scale
  • Compressor Area, Novi Popovac Cement Factory Groundwater Hydrocarbon Contamination PC1 mg/l PC2 PC9 0.14 PC3SB1 0.12 PC10 SB2 PC4 PC5 0.1 PC11 PC6 0.08 PC12 0.06 0.04 PC7 0.02 0.01 PC8 0 Not To S l N t T Scale
  • Cement Factory 4 Hydrocarbons are localised 4 Other measures reveal attenuation 4 Monitoring strategy to reflect this 4 SI led to cost effective sustainable remediation
  • Case Study 4 Hilden location
  • An overview of the Hilden site 4 A 8 ha working site with complex pollution problems, paints and problems coatings produced for over 100 years 4 Contamination of both surface soils and groundwater 4 O i i l estimate for remediation using standard clean-up Original ti t f di ti i t d d l technologies was £40 million in 1991
  • Aerial Photo 1997 TF5 TF1/C-B A/B TF2/F-B Project Rheingold j g KCL 18
  • Plan of Hilden site
  • Toxicity map of Hilden site today 90 85 High toxicity g y 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 Low toxicity y 5 0
  • 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 2004 15 10 5 0 100 1200 95 90 85 1000 80 75 70 65 800 60 55 50 1998 600 45 40 35 400 30 25 20 15 200 10 5 400 600 800 1000 1200 1400 1600 1800 2000 0
  • Benzene specific Biosensor result 80 75 70 High Response 65 60 55 50 45 40 35 30 25 2 20 15 10 5 0 Low Response p
  • BTEX Analysis 14 13 12 11 10 9 8 7 Total 6 BTEX (mg/l) 5 4 3 2 1 0
  • Comparison between maps 90 85 80 75 70 65 General toxicity 60 55 50 45 40 35 30 25 20 15 10 5 0 80 75 70 65 60 TVA8 Response 55 50 45 40 35 30 25 20 15 10 5 0 14 13 12 11 10 9 8 7 6 Total BTEX analysis 5 4 3 2 1 0
  • Assessing C A i Constraints t i t Charcoal pH adjustment Air sparging Removal of Removal volatile of non-volatile Removal of adverse pH organic (resuspension) organic ( p ) compounds compounds d high low high low high low Further sample manipulation Inorganic pH constraint Further sample p manipulation non-VOC’s constraint VOC’s constraint constraint
  • Bioremediation stage 1 Total toxicity 100 1200 95 Low toxicity 90 85 1000 80 75 70 65 800 60 55 50 600 45 40 35 400 30 25 20 200 15 High toxicity 10 5 400 600 800 1000 1200 1400 1600 1800 2000 0
  • Bioremediation stage 2 Toxicity after sparging 100 Low toxicity 1200 95 90 85 80 1000 75 70 65 800 60 55 50 600 45 40 35 400 30 25 20 15 200 10 5 High toxicity 400 600 800 1000 1200 1400 1600 1800 2000 0
  • Bioremediation stage 3 Toxicity after pH adjustment 100 1200 95 Low toxicity 90 85 1000 80 75 70 800 65 60 55 50 600 45 40 35 400 30 25 20 200 15 10 5 High toxicity 400 600 800 1000 1200 1400 1600 1800 2000 0
  • Case St d 4 C Study Railway Yard Development
  • Excavation area 1 exposure of underlying clay at 3.2mbgl
  • Excavation area 2 – exposure of hydrocarbon contaminated soils, confirmed by elevated FID readings
  • Conclusions 5
  • Conclusions 5
  • Data Collection Material Being Excavated Assess against Risk Nutrient level Pass- then stockpile Moisture Fail- then biopile pH TPH MPN TPH Characterisation Biosensor On site FID FID low levels CO2 Off site analysis Group material and send to AlControl O2 Phase material Group material; do not add to biopiles End-point Criteria TPH levels Olfactory If TPH between 0.05 and 10 g/kg Risk-based Consider for biopiling Non-TPH Physical/ engineering Group material and send t AlC t l G t i l d d to AlControl Site Status Excessive TPH Difficult Substances Base of biopile area Levels exceeding 10 g/kg Group and stockpile Made ready as Page 6 For decision later Material Management Data collection for characterisation Nutrient Amendment Moisture pH of soil TPH MPN Biosensor If trace levels are present, Determine the water Amend with lime or Calibrate FID with AlControl/ A count of less than Use MeOH and use 100:10:1 holding capacity of the soil sulphur to reach pH of 6- lab data and record for site 104 is too low and water to assess Select N source to suit pH and maintain at levels as 7.5. characterisation augmentation is bioavailability of co- (urea, ammonium nitrate) per manual. Use standard agricultural requirted pollutant and TPH Add before biopiling and mix calculation but remember well. CEC will be low Biopile Algorithm Amend and Optimise before biopiling Put derived data into equation (p29) and calculate decay Verify Algorithm Use microcosm to check algorithm prediction- max 2 weeks
  • Algorithm calculated Defined Targets to assess most Microcosm Olfactory suitable set-up Microcosm Risk-based experiment used to Physical/ engineering verify algorithm Constructed Biopile TPH Measurements Analysis carried out YES Continue monitoring with calibrated FID Levels change as expected b t d by CO2 Measurements algorithm Specific Analysis Routine Analysis Gas Analysis NO Plot data To assess trends Nutrient Amendment O2 Measurements Verify bioavailable status in KCl Gas Analysis WHC Confirm the values are Percent WHC Turning between 60 and 80% Biosensor Analysis Derived from If the system is air Use MeOH Verify presence of toxic moisture limited then turn and water to metabolites or co-pollutant pH assess Make sure 6-7.5 or bioavailability pH Moisture In water amend: check buffering Add/ remove as of co-pollutant Physical constraints and TPH required i d Trial pits to make sure the biopile is homogenised Temperature Temperature Using probe If the temp is too low MPN pH A count of Assessment of Bioaugmentation (<10) then add steam; too Trickle filter to adjust less than 104 If the biopile was amended or high (>40) remove is too low and not,there should be a minimum covers. None of the above re- number of degraders present The cause of Temperature T t augmentation reduced degradation Assess site weather is requirted rate cannot be and forecasts related to one of the above factors
  • Biopile 1 - view to west
  • Gantt Chart for Biopile Progress 18/07 25/07 01/08 08/08 15/08 22/08 29/08 05/09 12/09 19/09 26/09 03/10 10/10 17/10 24/10 31/10 07/11 14/11 21/11 28/11 Biopile 1 2 3 t t t t t t t t te te t t t t Biopile 1A pH, N A t t t t t t t t te te t t t t 2A pH, N A t t t t t t t t te te t t t t 3A pH, N A t t t t t t t t te te t t t t 4A pH, N A slippage pH adjust/ optimise pH impact of slippage N add nutrients t turning sampling (week start) e excavator for sampling sign off (week end) A Addition of inoculum back fill (week start)
  • Backfilling of Excavation (under buildings)
  • Compaction of Material in Backfill Area
  • Economics on-halogenated d sediments, peat sand, silt, clay, Co in £ cum eavy metals emi-Volatile alogenated xplosives orrosives No metal est/Herb sbestos yanides CDD ost OC AH CB on VO PA PC TC No Co Ha He As Cy Se Pe Ex Surface Amendments 20-35 all x x x x Biopile 40-70 s, s, -, s, - x x x x x Windrow turning 50 110 50-110 s, s, -, s, - , x x x x x x Bioventing and Air Sparging 80-85 s, s, -, s, - x x x x x Landfarming 80-180 s, s, -, s, - x x x x x x x Cement and Pozzolan-based 40-171 s, s, c, s, - x x x Pump and Treat 65-170 65 170 s, s, s, s s -, s - x x x x x x x x x Soil washing 80-370 s, s, -, s, p x x x x x x x Solvent Extraction 90-600 s, s, -, s, - x x x x x x x x Slurry Phase Bioreactor 110-140 all x x x x x x x x Thermal Desorption Th lD ti 115-400 115 400 all ll x x x x x Chemical Dehalogenation 150-370 s, s, c, s, - x x x x x x Incineration 140-400 all x x x x x x x x x x x x x x
  • General Conclusions 4 Bioremediation being more widely used Bioremediation- 4 End-point issues are important as is the re-use potential of p p p the material 4 Many techniques to tailor to sites 4 Regulator inclusion 4 Algorithm has great potential 4 Integrated mechanisms and approaches