Recent Developments in Quantitative Passive Soil Gas Sampling for VOCs

Transcription

1 Recent Developments in Quantitative Passive Soil Gas Sampling for VOCs Hester Groenevelt, Todd McAlary, Robert Ettinger Geosyntec Consultants, Inc. Tadeusz Gorecki, Faten Salim, Marios Ionnadis University of Waterloo 28 th Annual AEHS International Conference On Soil, Water, Energy and Air San Diego, CA 21 March,

2 Outline Introduction to passive sampling Four keys to quality retention, recovery, starvation, calibration Starvation modeling vapor diffusion into a borehole field results Calibration modeling uptake rate over time lab results Future Applications Summary 2 There are four keys to accurate and precise concentration data when using passive sorbent-based sampling. Two of them retention and recovery have been evaluated extensively for active sorbent-based sampling methods such as EPA Method TO-17. This presentation will focus on the other two starvation and calibration which are important for quantitative passive sampling. 2

3 Basic Principle of Passive Sampling CTWA = M (UR)(t) C TWA M UR t = time-weighted average concentration = mass adsorbed = uptake rate = sampling duration M and t are measured (very accurately) UR is calibrated experimentally 3 M is measured in the laboratory under controlled condition and is usually accurate and precise within very fine tolerances. t is measured in the field at the time of sampler deployment and retrieval. If the sample duration is long (days to months) and the uncertainty in the start and stop times is one minute or so, the uncertainty in t is negligible. Therefore, the uptake rate is the critical parameter for accurate and precise concentration measurements. This is usually calibrated using controlled laboratory experiments. It can also be field-calibrated using side-by-side samples of a different method, for example, one Summa canister sample for analysis by EPA Method TO-15 beside one passive sampler (both with the same sample duration), which is referred to as an inter-method duplicate and can be implemented at a frequency of one in every ten or 20 investigative samples. 3

4 Benefits of Passive Sampling Time-integrated sampling minimizes temporal variability No electricity, mechanical parts, connections minimizes sampling errors Cost effective vs. active sampling quick and simple protocols shipping Less obtrusive 4 Some of the benefits of passive sampling are shown in this slide. Overall, the quality of data is comparable to other methods such as whole air sampling with Summa canisters, but the sampling can be performed more easily and cheaply with passive samplers. For more information on the demonstration and validation of passive samplers, see ESTCP ER final report (click link to final report on the right side of this page: Restoration/Contaminated-Groundwater/Emerging-Issues/ER /ER ) 4

5 Shipping and Handling 72 Summa canisters 72 passive samplers Key Point: passive samplers are much easier to work with 5 The expense and labor associated with shipping and handling a large number of Summa canisters can be alleviated using passive samplers. The sampling protocols are also easier and there are no risks of leaks in the sample train, so leak-checks are not required. 5

6 Passive Samplers Quantitative Radiello Semi-Quantitative Gore Module ATD Tubes SKC Ultra III 3M OVM 3500 Waterloo Membrane Sampler Beacon Key Points: different samplers have different sizes, uptake rates, sorbents, wind screen, and methods of analysis not all samplers have calibrated uptake rates 6 Passive samplers come in two categories: 1) Quantitative passive samplers which have calibrated uptake rates and are of fixed geometry 2) Semi-quantitative passive samplers which have uptake rates that vary due to their unfixed geometry 6

7 Four Keys to Quality 1) Retention sorbent must retain the VOCs of interest during sampling 2) Recovery sorbent must release the VOCs of interest during analysis 3) Starvation if the uptake rate of the sampler is higher than the delivery rate of analytes to the sampler, the sampler will scrub its environment, causing a negative bias in the measured concentration 4) Calibration factors that can affect the uptake rate must be understood (Retention and recovery have been extensively studied for active sorptive sampling like Method TO-17) 7 As mentioned earlier, this presentation will focus on starvation and calibration. Starvation is a phenomenon that is well-known in passive soil gas sampling, and calibration must be well-understood in order to get quantitative passive sampling results. 7

8 Starvation mass flux into passive sampler mass flux through void space mass flux through soil to void space Diffusion is usually the main transport mechanism for vapors in soil. Diffusion coefficients depend on soil moisture content. 8 This slide show a graphical representation of a cylindrical borehole in the vadose zone containing a passive sampler. The mass flux through the soil to the void space (the red arrows) occurs at a rate that depends on the soil texture and moisture. The mass flux through the void space will never be slower, because the tortuosity of the soil restricts the diffusion rate. If the uptake rate of the sampler is the rate-limiting step, the concentration in the void space will equilibrate with the surrounding soil. However, if the uptake rate of the sampler is high, it will become a scrubber that will cause the concentration in the void space to be lower than the surrounding soil, and cause a negative bias in the reported concentration (this is referred to as the starvation effect). 8

9 Modeling Diffusion to a Borehole 9 We developed two mathematical models (transient and steady-state) to simulate the rate of vapor diffusion from the soil to the void space as a function of the soil moisture content. 9

10 Model Results Transient Steady-State Steady-state is reached fairly quickly Uptake rates less than about 1 ml/min are preferred to minimize starvation (Lower is better in wet soils) 10 Results from transient modelling (left chart) show that steady-state is reached within minutes or hours for all but very wet soils, which may require up to a day to equilibrate. (Theta sub w is the water-filled porosity. The total porosity was fixed at 35% for these simulations). Results from steady state modeling (right chart) show the diffusive delivery rate (the rate at which analytes are transported from the soil to the cylindrical borehole, expressed in the same units as the uptake rate, ml/min) as a function of the waterfilled porosity of the soil. Delta is the concentration in the void space divided by the concentration in the surrounding soil (i.e., a delta of 0.95 corresponds to a negative bias of 5%, a delta of 0.75 corresponds to a negative bias of 25%, etc.). These simulations indicate that a passive sampler uptake rate less than about 10 ml/min is required for well-drained soils and a lower uptake rate (less than 1 ml/min) would be preferred in wetter soils. 10

11 Low-Uptake Rate Samplers Decrease sampling surface area Increase membrane thickness 11 Most commercially available passive samplers can be adapted to lower the uptake rate, either by decreasing the area of the sampling surface, or by increasing the membrane thickness of a permeation sampler. Lower uptake rates are advantageous in three scenarios: 1) To minimize or eliminate negative bias attributable to starvation, 2) To avoid overloading the sorbent in setting where there are very high vapor concentrations, and 3) To avoid overloading the sorbent when sampling over very long durations. 11

12 Field Methods 2 options: open holes for single use or sealed probes for periodic monitoring 12 This slide shows some photos of installations of passive samplers for soil gas and subslab sampling: 1) Use of a hammer drill and a 5-foot long, 1-inch diameter drill bit to install a temporary soil gas probes 2) Use of a hammer drill to install a sub-slab probe 3) A depiction of a open borehole (represented by the glass cylinder) with a passive sampler and a foam plug in a plastic sleeve to collect a depth-discrete soil gas sample 4) A depiction of a completed soil gas probe, fitted with a piece of steel strapping which creates a gap in the vadose zone in which to deploy the passive sampler (length to be adjusted as needed for sampling at any desired depth). 12

13 Results in Dry Soil Passive Sampler (µg/m³) Sub-Slab Soil Gas Uptake rates in the range of 3 to 5 ml/min provide good correlation to active sampling in dry soil (sub-slab is usually dry because the building is an umbrella) Active Sampler (µg/m³) 13 In dry soils, there is a very good correlation between concentrations measured in a passive sampler (the y-axis) and measured by an active method (the x-axis) over a span of several orders of magnitude, as long as uptake rates are fairly low (less than 5 ml/min) so the passive samplers do not experience significant starvation. 13

14 Results for Wetter Soils Passive Sampler (µg/m 3 ) 100,000,000 10,000,000 1,000, ,000 10,000 Uptake rate about 1 ml/min Uptake rate about 0.1 ml/min 1,000 1,000 10, ,000 1,000,000 10,000, ,000,000 Active Sampler (µg/m 3 ) Key point: reducing the uptake rate from 1 to 0.1 ml/min reduced starvation Simple modification of 10X thicker membrane 14 In wetter soils, we see that the passive samplers begin to demonstrate a negative bias due to starvation. The blue data points show results for a passive sampler with an uptake rate of about 1 ml/min and all fall below the diagonal 1-to-1 line, indicating a negative bias. The orange diamond symbol shows results for a passive sampler with a 10-times thicker membrane and the correlation to active sample results (Summa canister/to-15) improves significantly. 14

15 Implications of Lower Uptake Rates Sample duration is related to target concentration Select the sample duration to provide a concentrations reporting limit equal to or less than the sub-slab or soil gas screening level (SL). But how might a long sample duration affect the uptake rate? 15 One consequence of reducing the uptake rate is that the sample duration must be extended proportionately in order achieve the same reporting limit. The reporting limit is the lowest concentration that can be quantified reliably and must be lower than the screening level that the sample is intended to satisfy. This raises the question of consistent the uptake rate is during a long sampling period. As an example: Consider a compound that has a subslab screening level (SSSL) of 100 µg/m3. If the laboratory can reliably detect as little as 0.05 µg of mass (M) for that compound, and the sampler uptake rate is 1 ml/min, the sample duration must be at least 12 hours. If the uptake rate was reduced to 0.1 ml/min, the sample duration would have to be at least 5 days. 15

16 Transient Modeling of Uptake Rate Environmental Science: Processes & Impacts, 2017, 19, Membrane 0 x L Sorbent bed L m m x L b Air Gap Sorbent Membrane VOC uptake L b L m x x= 0 Model considers: permeation of analytes though the membrane mass transfer between gas and sorbed phase in sorbent bed migration of analytes within the sorbent bed 16 A mathematical model was developed to characterize the uptake of analytes by a permeation passive sampler (the Waterloo Membrane Sampler, or WMS). 16

17 Model Results Uptake Rate vs Time Uptake rate (ml/min) Toluene/Carbopack B Co = 2.3 µg/m Time (days) Key Point: reducing the uptake rate by increasing the membrane thickness minimizes the change in uptake rate over time. 17 The scenario that was modelled was that of a worst case where the uptake rate is expected to vary over time: a weak sorbent (Carbopack B) and an analyte with low affinity to it (toluene). The reason for this is that we wanted the model to show a change in the uptake rate so that the results could be dissected. If a stronger sorbent, such as Anasorb 747, is used, the change in uptake rate over time is expected to be much less. The model results show that for a 100 µm thick membrane (the orange dashed line), the uptake rate declines over time. By reducing the uptake rate 2-fold by increasing the membrane thickness to 200 µm (the blue dashed line), the change of the uptake rate over time is greatly reduced. 17

18 Model vs Experimental Results Carbopack B (Non-porous particles) 2.5 Anasorb 747 (Porous particles) 100 µm membrane / toluene 100 µm membrane / toluene 2.0 Uptake 1.5 Rate (ml/min) Time (day) Time (days) Key Point: model matches experimental data which provides confidence in ability to predict the uptake rate for any sample duration 18 Experimental results show good agreement with the model results. And, as described on the previous slide, the stronger sorbent, Anasorb 747, shows a much smaller change in uptake rate over time, even with the 100 µm membrane. 18

19 Model Results - Toluene Distribution Moles of Toluene 1.E-03 1.E-04 1.E-05 1.E-06 1.E-07 1.E-08 1.E-09 1.E-10 1.E-11 1.E-12 Sorbed on the sorbent In the air in the packaging In the membrane In the air spaces between the sorbent particles >99% 1.E Storage time (day) <1% <0.1% <0.001% packaging sorbent bed with air spaces membrane Key Point: the vast majority of the analyte is trapped by the sorbent, as intended The model results also show the distribution of analytes in the different compartments of a permeation passive sampler. This allows us to see that the great majority of the analyte, >99%, is trapped by the sorbent, as intended. Only a very small amount (<0.1% for toluene) remains in the membrane. 19

20 3-Month Sample Durations 20 Experimental validation of the uptake rate vs time model was performed using this experimental set-up, in which samplers were exposed to a known concentration of >50 different VOCs over a 3-month period. 20

21 Measured versus Modeled Uptake Rates For most VOCs in the 8260 analyte list, the measured uptake rate was within 2x of the expected uptake rate 21 Results show that the experimental uptake rate over 3 months is mostly within a factor of 2 of the uptake rate measured for a 7-day sample duration, with only a few outliers. 21

22 Potential Applications Round-the-calendar monitoring 4 successive 3-month samples never miss a day What about short-term exposures? if a 10-day sample concentration is <0.1 µg/m 3, no single 24- hour period could have been above 1 µg/m 3 same for a 100-day sample concentration of <0.01 µg/m 3 Key Point: where there really is no vapor intrusion, this is a cost-effective way to document it 22 There are several potential applications for long-term indoor air monitoring. 22

23 Take-Home Messages Quantitative Passive Sampling for Soil Gas has arrived key is to have an uptake rate lower than the diffusive delivery rate this innovation was awarded a US patent (# ) in 2016 WMS sampler uptake rates are easily adjusted via membrane thickness Lower uptake rates require longer sample durations e.g., 1 week with UR = 0.1 ml/min for SGSL of 60 µg/m 3 Longer sampling durations can be a good thing talk to anyone in the radon field (90-days is a short-term sample) uptake rates for various sample durations can now be calculated field calibration may be challenging for durations >14 days creates new options for cost-effective monitoring 23 Quantitative passive soil vapor sampling is accurate and precise when the uptake rate of the sampler is lower than the diffusive delivery rate of VOC vapors through soil to the void space in which the passive sampler is deployed, as supported by mathematical modeling and a comprehensive demonstration/validation (ESTCP ER200830). A lower uptake rate requires a proportionally longer sample duration to achieve the same reporting limit. Another mathematical model validated with experimental data shows that uptake rates can decrease over long times, particularly for weak sorbents with weakly sorbed analytes; however, experimental results show that for strong sorbents, the uptake rate does not decrease significantly over a 3-month period. Longer sampling times have certain advantages, such as the ability to collect long-term time-weighted average samples easily and cheaply. Field calibration with inter-method duplicates is valuable when the highest level of accuracy is desired. This has been difficult when collecting samples over 14 days or longer, but Alan Rossner of Clarkson University is evaluating 14 day flow controllers for Summa canisters as part of an ESTCP project, and this will make it a lot easier to fieldcalibrate long-term passive samplers. 23

24 Peer-Reviewed Articles McAlary, T., X. Wang, A. Unger, H. Groenevelt and T. Górecki, Quantitative passive soil vapor sampling for VOCs- part 1: theory. Environ. Sci.: Processes Impacts, 2014, DOI: /C3EM00652B. McAlary, T., H. Groenevelt, S. Seethapathy, P. Sacco, D. Crump, M. Tuday, B. Schumacher, H. Hayes, P. Johnson and T. Górecki, Soil vapor sampling for VOCs using passive diffusive samplers laboratory experiments. Environ. Sci.: Processes Impacts, /c3em00128h. McAlary, T., H. Groenevelt, P. Nicholson, S. Seethapathy, P. Sacco, D. Crump, M. Tuday, H. Hayes, B. Schumacher, P. Johnson, T. Górecki and I. Rivers-Duarte, Quantitative passive soil vapor sampling for VOCs: field experiments. Environ. Sci.: Processes Impacts, /c3em00653k. McAlary, T., H. Groenevelt, S. Seethapathy, P. Sacco, D. Crump, M. Tuday, B. Schumacher, H. Hayes, P. Johnson, L. Parker and T. Górecki. Quantitative passive soil vapor sampling for VOCs: flow-through cells. Environ. Sci.: Processes Impacts, /c4em00098f. McAlary, T., H. Groenevelt, S. Disher, J. Arnold, S. Seethapathy, P. Sacco, D. Crump, B. Schumacher, H. Hayes, P. Johnson, T. Górecki. Passive Sampling for Volatile Organic Compounds in indoor Air Controlled Laboratory Comparison of Four Sampler Types. Environ. Sci.: Processes Impacts, /c4em00560k. Salim, F., M. Ioannidis, T. Górecki. Experimentally validated mathematical model of analyte uptake by permeation passive samplers. Environ. Sci.: Processes Impacts, /c7em00315c. Seethapathy, S. and T. Górecki, 2011a. Polydimethylsiloxane-based permeation passive air sampler. Part I: Calibration constants and their relation to retention indices of the analytes. J Chromatogr A Jan 7; 1218(1): Seethapathy, S. and T. Górecki, 2011b. Polydimethylsiloxane-based permeation passive air sampler. Part II: Effect of temperature and humidity on the calibration constants. J Chromatogr A Dec 10; 1217(50):

25 Questions? 25