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DOI: 10.1039/C1EM10237K (Paper) J. Environ. Monit., 2012, 14, 79-84

Statistical evaluation of photon count rate data for nanoscale particle measurement in wastewaters†

Josh Smeraldi a, Rajagopalan Ganesh *b, Jana Safarik c and Diego Rosso a
aDept. of Civil and Environmental Engineering, University of California, Irvine, CA, USA 92697-2175
bKennedy/Jenks Consultants, 2355 Main St, Suite 140, Irvine, CA, USA 92614. E-mail: RGanesh@KennedyJenks.com; Fax: +1 (949) 261 2134; Tel: +1 (949) 261 1577
cOrange County Water Districts, 18700 Ward St, Fountain Valley, CA, USA 92708-6930

Received 15th March 2011 , Accepted 6th October 2011

First published on 3rd November 2011

Abstract

The dynamic light scattering (DLS) technique can detect the concentration and size distribution of nanoscale particles in aqueous solutions by analyzing photon interactions. This study evaluated the applicability of using photon count rate data from DLS analyses for measuring levels of biogenic and manufactured nanoscale particles in wastewater. Statistical evaluations were performed using secondary wastewater effluent and a Malvern Zetasizer. Dynamic light scattering analyses were performed equally by two analysts over a period of two days using five dilutions and twelve replicates for each dilution. Linearity evaluation using the sixty sample analysis yielded a regression coefficient R2 = 0.959. The accuracy analysis for various dilutions indicated a recovery of 100 ± 6%. Precision analyses indicated low variance coefficients for the impact of analysts, days, and within sample error. The variation by analysts was apparent only in the most diluted sample (intermediate precision ∼12%), where the photon count rate was close to the instrument detection limit. The variation for different days was apparent in the two most concentrated samples, which indicated that wastewater samples must be analyzed for nanoscale particle measurement within the same day of collection. Upon addition of 10 mg l−1 of nanosilica to wastewater effluent samples, the measured photon count rates were within 5% of the estimated values. The results indicated that photon count rate data can effectively complement various techniques currently available to detect nanoscale particles in wastewaters.


Environmental impact

Recent studies indicate that sub-micron and nanoscale particles in water and wastewater streams play a significant role in transport of contaminants as well as in the efficiency of treatment processes. For example, suspended particles of nanoscale size appear to contribute to the permanent fouling (pore plugging) of microfiltration membranes during water reclamation. The emerging use of manufactured nanomaterials in everyday products is likely to introduce new and more nanoscale particles into waters and wastewaters. Analytical methods to detect and measure nanoscale particles in wastewaters are still evolving and hence, still face limitations. This study, through statistical analyses, evaluates the use of photon count rates to monitor the levels of sub-micron particles in wastewater streams. This simple and fast measurement technique can help enhance our understanding of fate and transport of nanoscale particles in the environment, and possibly provide strategies to mitigate their environmental impacts.

1. Introduction

Particle size distribution has long been known to impact water and wastewater treatment processes. Most of the published studies on particle size effects have focused on the fraction at a micron or larger scale. These large size particles can impact filtration, coagulation, sedimentation, and solid dewatering during water and wastewater treatment.1–5 Recent studies indicate that sub-micron and nanoscale colloidal particles can affect membrane and other treatment processes. For example, 2 to 500 nm-size fractions of suspended particles appear to contribute to the permanent fouling of microfiltration membranes, resulting in increased energy footprint for water reclamation processes.6,7 In addition to biogenic nanoscale particles, the emerging use of manufactured nanomaterials in many everyday products is likely to introduce new and more nanoscale particles in wastewaters.8 Toxic effects of nanomaterials on human and other organisms have been reported in the literature.9,10 Furthermore, the presence of nanomaterials in biosolids and model wastewater effluents has already been reported.11–13 Qualitative and quantitative measurement of manufactured nanomaterials in water and wastewater samples are required to facilitate their fate and behavior modeling, and environmental risk estimation.

Analytical methods to detect and measure nanoscale particles in wastewaters are still evolving and hence, still face limitations. For example, dialysis followed by chromatographic analysis can be used to measure concentrations of nanoscale particles in wastewater samples.14,15 However, this technique is very labor-intensive and may not be viable for routine monitoring in treatment plants. Similarly, single particle analyses such as scanning electron microscopy (SEM) are also labor-intensive, and do not yield quantitative information. Chemical composition analyses such as inductively coupled plasma (ICP) analyses do not distinguish between nanoscale particles and dissolved constituents. Recently, a “nanoparticle tracking analysis (NTA)” technique that employs laser-induced optical microscopy for direct particle count and concentration estimate for nanoscale particles in liquid matrices has been developed.16 This technique is currently limited to relatively concentrated samples (106 particles per ml) and typically, for measurement of particles larger than 30 to 50 nm in environmental samples. In general, a combination of technologies is currently required for monitoring nanoscale particles in water and wastewater samples.

Dynamic Light Scattering (DLS) (also referred to as Photon Correlation Spectroscopy (PCS)) techniques have recently been used for measuring nanoscale particles in industrial and municipal wastewaters.17,18 Briefly, nanoscale/sub-micron particles in suspension undergo Brownian motion. If the particles or molecules are illuminated with a laser (photons), the intensity of the scattered light fluctuates at a rate that is dependent upon the size of the particles. Analysis of these intensity fluctuations yields the velocity of the Brownian motion and hence, the particle size.19 These data are then used to estimate the size (hydrodynamic diameter) distribution of the particles, assuming that the particles are spherical. Currently, the primary use of DLS techniques has been limited to determination of nanoscale particle size distribution, with the additional measurements in some cases of zeta potential and molecular weight.20

During DLS analyses, the fluctuation in light intensity (photons) measured by the instrument per unit time is reported as photon count rate (kilo counts per second, kCPS). In most cases, the photon count rate required for analysis is a function of size and concentration of scattering particles. The interpretation of photon count rate data during nanoscale particle analyses varies with the application or sample type. For example, a steadily increasing or decreasing count rate over the duration of a sample's analysis would indicate aggregation or sedimentation of particles, respectively.20 In some cases where nanoscale particles are not expected to undergo significant transformation, the photon count rate can be used as a surrogate measurement for concentration of nanoscale particles.21 In samples where the particles tend to aggregate, the photons and hence, the photon count rate required to detect these particles, increases. In these cases, the photon count rates, together with particle size distribution data, provide meaningful information about the transformation of the particles.22 To date, the photon count rate data have not been used widely to understand the fate of biogenic nanoscale particles in wastewater samples. Understanding the photon count rate output in wastewater samples can complement the suite of techniques currently used to detect and monitor biogenic and manufactured nanoscale particles in wastewaters.

This study was performed to evaluate: (i) the applicability of photon count rate data, using statistical methods, for measuring biogenic nanoscale particles in wastewater; (ii) the change in count rates when a manufactured nanomaterial (nanosilica) is spiked in wastewater samples; and (iii) how the photon count rate data complement the data from chemical composition (ICP) analyses for measurement of a manufactured nanomaterial (nanocopper, CuNP).

2. Materials and methods

2.1 Wastewater samples

Secondary effluent samples were collected from Plant 1 of Orange County Sanitation District (OCSD), Fountain Valley, CA. At the time of sampling the activated sludge process was operating in ammonia bypass mode (i.e., carbon oxidation only) with 1.2 day sludge retention time. All samples were pre-filtered with 0.20 μm filters. Samples were then diluted with Milli-Q 18 MΩ water.

2.2 Nanoscale particle analyses

In this study, a Zetasizer Nano (Malvern Instruments, Westborough, MA) was used to analyze particle size distribution and photon count rates of nanoscale/sub-micron particles. These include particles already present in the wastewater (biogenic particles) as well as manufactured nanomaterials spiked to the wastewater during this study. The Zetasizer uses dynamic light scattering (DLS) technology to detect nanoscale/sub-micron suspended particles in aqueous samples. A schematic of the Zetasizer setup is shown in the ESI†. Similar to other DLS-based instruments, the Zetasizer measures the fluctuation in light intensity caused by the Brownian motion to detect the particles in suspension. Furthermore, the Zetasizer assumes that the detected particles are spherical in shape while transforming the light intensity data to determine the hydrodynamic diameter and size distribution. The instrument uses a 4.0 mW He–Ne laser with a wavelength of 633 nm (red). Depending on the characteristics of the samples this instrument uses 11 different laser intensities (attenuator index) and also, alters the measurement position within the sample cell. For example, for dilute samples DLS measurement occurs near the center of the sample cell, and for concentrated samples measurement occurs near the wall of the sample cell (to minimize multiple scattering of light by the large number of particles). Fig. 1 shows the schematic indicating the measurement position of the sample cell. Additional details regarding laser intensity are provided in the ESI†. For this study we used the highest laser intensity (no. 11, i.e. 100% of the laser power), and set the measurement position to 4.65 mm, which corresponds to near the center of the sample cell for analyses of all the samples. These settings are often used for analyses of dilute samples. Conversely, for a different study using a sample with a large number of suspended particles, a lower attenuation index (e.g. # 9 corresponding to 10% of the instrument laser power) and measurement position (e.g. # 1 corresponding to a position near the cell wall) may be required. The samples were sonicated (VWR 75T Aquasonic sonicator) for 5 minutes to minimize particle aggregation prior to Zetasizer analyses. Glass sample cells containing approximately 2 ml of sample were analyzed by the Zetasizer. An instrument equilibration time of 2.5 minutes was used during sample analysis.
Schematic indicating the measurement position in the sample cell. A lower number indicates a measurement position closer to the cell wall and a higher number indicates a position closer to the center of the sample cell.20
Fig. 1 Schematic indicating the measurement position in the sample cell. A lower number indicates a measurement position closer to the cell wall and a higher number indicates a position closer to the center of the sample cell.20

2.3 Statistical evaluation of photon count rate data

A modified procedure based on International Conference of Harmonization (ICH) guidelines for effective protocol design and data validation was used for statistical evaluation of the photon count rate during the analyses of OCSD wastewater filtrate.23 This procedure involved determination of detection and quantification limits, linearity, accuracy, precision, impact of analyst and impact of time.

Accordingly, the detection limit for analysis was determined by analyzing six replicates of a Milli-Q water sample. The mean of the six replicates, plus three times the standard deviation, is considered the detection limit, while 10 times the standard deviation is considered the quantification limit.

The OCSD secondary effluent filtrate was diluted with Milli-Q water for evaluating linearity, accuracy and precision. Five dilutions (wastewater[thin space (1/6-em)]:[thin space (1/6-em)]Milli-Q water ratios of 100[thin space (1/6-em)]:[thin space (1/6-em)]0, 75[thin space (1/6-em)]:[thin space (1/6-em)]25, 50[thin space (1/6-em)]:[thin space (1/6-em)]50, 25[thin space (1/6-em)]:[thin space (1/6-em)]75, and 10[thin space (1/6-em)]:[thin space (1/6-em)]90) spanning the expected range of the photon count rates were each tested using 12 replicates. Two analysts were employed to analyze equal number of samples from each dilution over two days.

Linearity during DLS analyses was measured by least squares regression analysis by combining the data for analysts and days for each dilution. Accuracy is the difference between the measured value and the true value. For this study, the photon count rate of the undiluted wastewater sample was considered the “true value”. Accuracy was measured by using all data for analyst and day for each dilution, and then comparing the mean photon count rate of each dilution with the mean value of the undiluted sample.

Precision tests involved determination of repeatability and intermediate precision. Repeatability, also referred to as intra-assay precision, indicates precision under the same operating conditions over a short interval of time. Intermediate precision indicates the within-laboratory variations including different analysts and different days. Initially, variance components for analysts, days and error were calculated using the restricted maximum likelihood estimation (RMLE) method.24 The variance component is a measure of variation from measurement to measurement of different factors (e.g. analyst, days). Then, analyst and day variability were combined to estimate intermediate precision, and the variation after accounting for the analyst and day (error variance) was used to determine repeatability. Accordingly,

 
ugraphic, filename = c1em10237k-t1.gif(1)
where σ2 is the variance component of error and μt is the mean value. Also,
 
ugraphic, filename = c1em10237k-t2.gif(2)
where σa2 is the variance component for analysts and σd2 is the variance component for days.

2.4 Photon count rate for analyses of manufactured nanomaterials in wastewaters

Silica nanoparticle stock (5%) was obtained from NEI Corporation (Somerset, NJ). The silica particles had a primary size of 50 nm and aggregated size of 200 nm. The photon count rate response in wastewater samples in the presence of manufactured nanomaterials was evaluated by diluting the nanosilica stock in Milli-Q water to 10 and 20 mg l−1, and mixing them in filtered wastewater effluent to yield a silica concentration of 5 and 10 mg l−1, respectively. Initially, photon count rates of the filtered effluent and the nanosilica suspensions were independently measured. Subsequently, the nanosilica samples were mixed with the wastewater (1[thin space (1/6-em)]:[thin space (1/6-em)]1 volume ratio), analyzed by the DLS technique, and the measured photon count rates were compared with the estimated values.

Copper nanoparticles were obtained from QSI Company, Santa Ana, CA. The CuNP obtained from the vendor is a mixture of copper and copper oxide particles with a copper content of approximately 51% by mass. The primary and aggregated sizes of these particles are approximately 50 and 125 nm, respectively. More information about copper nanoparticles and stock suspension preparation are published elsewhere.25 Undigested and digested samples of 10 mg l−1copper nanoparticle suspensions were analyzed by Hach Bicinchoninate Method 8506 for concentration,26 and by Malvern Zetasizer for nanoscale particle size and photon count rates. Samples were digested using nitric acid and the pH adjusted to 3.5 prior to analyses.

3. Results and discussion

Table 1 shows the photon count rate data during replicate analyses of Milli-Q water, diluted, and undiluted wastewater samples.
Table 1 Photon count rate (kCPS) data for various dilutions of the OCSD filtrate (0.2 μm). Milli-Q water was used to dilute the OCSD filtrate. Photon count rates of Milli-Q water are also shown
Sample Photon count rate/kCPS
Analyst 1 Analyst 2
WW[thin space (1/6-em)]:[thin space (1/6-em)]Milli-Q water Day 1 Day 2 Day 1 Day 2
10[thin space (1/6-em)]:[thin space (1/6-em)]90 16.4, 20.7, 17.7 20.7, 18.5, 18.6 20.5, 21.1, 24.4 20.7, 27.9, 20.1
25[thin space (1/6-em)]:[thin space (1/6-em)]75 27.8, 29.1, 26 28.6, 24.1, 26.1 27.5, 25.7, 26.6 29.9, 25.1, 24.3
50[thin space (1/6-em)]:[thin space (1/6-em)]50 43.8, 36.4, 44.9 39.6, 38.7, 39.9 37.1, 33.6, 40.5 40.4, 44.5, 41.5
75[thin space (1/6-em)]:[thin space (1/6-em)]25 49.6, 50.9, 44.2 50.8, 53.8, 53.8 53.7, 45.6, 47.9 48.8, 53.4, 53
100[thin space (1/6-em)]:[thin space (1/6-em)]0 56.6, 57.6, 57.4 65.4, 64, 64.3 62.3, 57.8, 59.8 63, 61.6, 68.5
Milli-Q water 13.4, 13.8, 13.2, 12.9, 12.4, 12.4

3.1 Estimation of detection and quantification limits

The photon count rate response for the Milli-Q water ranged from 12.4 to 13.8 kCPS with a mean value of 13.2 and a standard deviation of 0.56. The detection limit (mean + 3 standard deviations) was 14.7 kCPS and the quantification limit (10 standard deviations) was 5.6 kCPS.

3.2 Linearity evaluation

Results of the linearity evaluation (Fig. 2) showed a good relationship between the amount of wastewater in each dilution and the photon count rate measurements. The R2 value for this sixty sample analyses was greater than 0.95. This suggested that the biogenic nanoscale particles in the OCSD effluent did not undergo substantial transformation in any of the dilutions, and that the other constituents in the effluent did not significantly affect nanoscale particle measurement by photon count rate.
Photon count rate for OCSD effluent filtered with 0.2 μm filter and diluted by Milli-Q water. Twelve replicate samples were analyzed for each dilution by two analysts equally over two days.
Fig. 2 Photon count rate for OCSD effluent filtered with 0.2 μm filter and diluted by Milli-Q water. Twelve replicate samples were analyzed for each dilution by two analysts equally over two days.

3.3 Accuracy analysis

For accuracy analysis, the particle count rate in the undiluted sample was considered the “true” value. The accuracy of the photon count rate data was then evaluated by comparing the estimated value (i.e. “actual value” adjusted for dilution) and the measured value for various dilutions. Table 2 shows the data from this analysis. The analyses yielded a recovery of ±6% with confidence intervals that are relatively narrow. While a recovery of ±5% is desired for many analyses, the recoveries obtained were reasonably acceptable. These results reinforce the linearity between kCPS and the concentration of nanoscale particles in wastewater.
Table 2 Accuracy evaluation of photon count rate data. The mean of the measured value of the undiluted sample (100% wastewater) was used as the “true value” to estimate the percent recovery
Wastewater concentration (%) Estimated meana/kCPS Measured mean/kCPS Percent recovered Upper 95% confidence interval Lower 95% confidence interval
a Estimated values indicate the mean measured particle count rate in the undiluted sample (61.51 kCPS) adjusted for dilution using Milli-Q water (17.4 kCPS).
10 19.38 20.61 106.34 18.88 22.34
25 26.41 26.73 101.23 25.68 27.79
50 38.11 40.08 105.17 38.16 41.99
75 49.82 50.46 101.29 48.58 52.34
100 61.52 61.52 100 59.39 63.64

3.4 Precision analyses

Precision analyses were performed by calculating variance components using RMLE between analysts, days, and within each sample type (Table 3, and Fig. 3 and 4). Overall, the data showed relatively good precision among the samples evaluated. Variance components for the days and analysts were lower than the variance within analyses in all but one case. The intermediate and repeatability estimates were less than 10% for all but the most diluted sample (Fig. 3 and Table 3). However, there are some areas in the data where slightly higher variations were observed. First, variation between analysts was apparent with the most diluted (10% wastewater) sample, as indicated by the high variance component (48% of the total variance). Furthermore, the intermediate precision for this sample was about 11.7%. The higher variability in the most diluted samples may have occurred since the photon count rates were close to the detection limit for this analysis (approximately 15 kCPS). Second, variation between days became apparent with the sample containing 75% of wastewater effluent, but more pronounced with the undiluted (100%) effluent sample as indicated by the high variance components (39 and 77% of the total variance, respectively, Fig. 4 and Table 3). This suggested that the wastewater samples may have to be analyzed within the same day for photon count rate determination. Note that the ionic strengths of the 75% and 100% wastewater samples were higher than that of the remaining samples. Dissolved salts that contribute to higher ionic strength are known to aggregate and settle some nanoscale particles in aqueous samples. The relatively higher variability in these two samples between the two days may be due to higher dissolved salt effects or other transformation that may have happened to the samples during the two days. Data from two one-way ANOVA tests performed with analysts and days as factors also confirmed these trends (see ESI†).
Table 3 Variance component estimations between days and analysts. The analyst and day variability combine to give the intermediate precision, whereas the variation after accounting for the analyst and day is the repeatability
Wastewater concentration (%) Variance components (% of total variance) Intermediate precision (%) Repeatability (%)
Day Analyst Within
10% 0 48.1 51.9 11.69 12.1
25% 0 0 100 0.00 7
50% 0 0 100 0.00 8.4
75% 38.8 0 61.2 4.52 5.7
100% 76.9 0 23.1 6.62 3.6
Overall 20.2 0 79.8 3.72 7.4


Box plot showing the photon count rate data by the two analysts. The data include analyses over the two days. The variations between the analysts were high in the most diluted (10% wastewater) sample. The count rates were closer to the detection limit for these samples. The photon count rate for sample labeled ‘o’ is outside of the inner fence of the box plot.
Fig. 3 Box plot showing the photon count rate data by the two analysts. The data include analyses over the two days. The variations between the analysts were high in the most diluted (10% wastewater) sample. The count rates were closer to the detection limit for these samples. The photon count rate for sample labeled ‘o’ is outside of the inner fence of the box plot.

Box plot showing the photon count rate data over the two days of analyses. The data include analyses by the two analysts. The variations were high in the most concentrated (75 and 100% wastewater) samples. The data suggested that the samples have to be analyzed on the same day of collection. Photon count rates for samples labeled * are outside of the outer fence of the box plot. Photon count rates for samples labeled ‘o’ are outside of the inner fence of the box plot.
Fig. 4 Box plot showing the photon count rate data over the two days of analyses. The data include analyses by the two analysts. The variations were high in the most concentrated (75 and 100% wastewater) samples. The data suggested that the samples have to be analyzed on the same day of collection. Photon count rates for samples labeled * are outside of the outer fence of the box plot. Photon count rates for samples labeled ‘o’ are outside of the inner fence of the box plot.

3.5 Particle size distribution and polydispersity analyses

Preliminary DLS analyses performed using OCSD effluent indicated that an equilibration time of about 5 minutes is required to clear the internal QA requirements for this instrument. However, the photon count rate data between the samples that were equilibrated for 2.5 minutes, which did not pass the internal QA, and those equilibrated for 5 minutes, which cleared the internal QA, were not substantially different (see ESI†). The samples with 2.5 minute equilibration had poor multimodal and “cumulant fit” criteria. Due to the large number of samples (>30) that had to be analyzed each day, the samples were analyzed using an equilibration time of 2.5 minutes for this study. The median polydispersity index of 0.28 (against the QA requirement of <1) and the correlation intercept i of 0.68 (against the QA requirement of 0.1 < i < 0.9) were in compliance with the instrument QA criterion (see ESI†). However, the median in-range value for the analyses was 88.3%, while the instrument QA criterion required this value to be >90%. The mean particle diameter estimates based on intensity, volume, and number counts were 163, 88, and 42 nm, respectively. Although, analyses of many samples did not pass the internal QA due to the aforementioned reasons, which indicated sedimentation of some particles, the number of particles settling in these samples was not large enough to affect the photon count rate measurements in effluent filtrate samples from OCSD.

3.6 Photon count rates in silica nanoparticle-spiked samples

This test was performed to evaluate how the photon count rate response would differ when manufactured nanomaterials were released to wastewaters that contain biogenic nanoscale particles. Two silica nanoparticle (SiNP) suspensions (∼10 and 20 mg l−1) were mixed at 1[thin space (1/6-em)]:[thin space (1/6-em)]1 volume ratio with a filtered OCSD effluent for this evaluation. The initial photon count rates for the two silica suspensions and wastewater filtrates were 275, 564, and 40 kCPS, respectively. The estimated photon count rates (i.e. weighted average), based on these initial measurements of the two 1[thin space (1/6-em)]:[thin space (1/6-em)]1 mixed samples are 157.5 and 302 kCPS, respectively, and the measured photon count rates of the two mixed samples were 164.4 and 313.7 kCPS, respectively. The measured photon count rates were within 5% of the estimated values which indicated that, at the concentrations of nanosilica added, potential differences in physical, chemical, and optical characteristics between the biogenic nanoscale particles in the wastewater and nanosilica did not substantially alter the photon count rate measurements. In addition to particle count data, the particle size distribution data for the 1[thin space (1/6-em)]:[thin space (1/6-em)]1 mixed sample were consistent with the expectations (ESI†).

3.7 Photon count rates during chemical composition analyses

Stock suspensions of copper nanoparticles (CuNP, 10 mg l−1) were diluted to 2 mg l−1 and analyzed using Hach Method 8506. A portion of the sample was digested to pH 3.5 prior to copper analysis. Control tests were performed using an ionic copper (copper chloride) sample. Table 4 shows the measured copper concentrations and the count rates for the Milli-Q water used for dilution, digested, and the undigested CuNP suspensions. The percent copper recovery in the undigested CuNP suspension was approximately 16%. The DLS analyses indicated that this sample contained a significantly higher photon count rate than that for the ionic copper solution. For the digested CuNP sample, the copper recovery was more than 90%. The photon count rate for this sample was similar to that of the ionic copper sample. These data suggested that the digestion process facilitated dissolution of CuNP and its subsequent analyses. Photon count rate measurements helped verify the dissolution of CuNP.
Table 4 Concentration and photon count rate for ionic copper and nanocopper samples. Concentrations were measured by Hach Bicinchoninate Method (# 8506)
Sample Pretreatment Measured concentrationa/mg l−1 Photon count rateb/kCPS
a Samples were diluted 5 fold with Milli-Q water prior to analyses by Hach Method 8506. b Samples were diluted 10 fold prior to DLS analyses. The kCPS values shown are for diluted samples. c Copper chloride salt was used as control.
Copper salt c None 9.55 23.9
Copper nanoparticles None 1.6 320
Copper nanoparticles Acidified to pH 3.5 9.5 27

4. Conclusions

Statistical evaluation photon count rate data yielded very good linearity (R2 > 0.95), accuracy (% recovery within ±6%), and precision (variability <10% in most samples) values. Biogenic nanoscale particles in secondary effluent tested appeared to be mostly stable and have a lower tendency to sediment or aggregate. Hence, photon count rate data could be useful for evaluating membrane fouling potential or other effects that are influenced by biogenic nanoscale particles in wastewater.

Introduction of a stable manufactured nanomaterial (silica NP) into the wastewater containing biogenic nanoscale particles did not significantly alter the photon count rate profile. The measured values were within 5% of the estimated values in Milli-Q water suspensions. However, if a manufactured nanomaterial that is less stable and has a tendency to undergo transformation is released into the wastewater stream, the photon count rate must be used in conjunction with other analytical measurements (e.g. particle size distribution, chemical quantification) to understand its fate. Overall, the data from this study indicate that photon count rate analyses can be useful and complementary analyses to various techniques currently used to measure nanoscale particles in wastewaters.

Acknowledgements

We thank Dr Harold Dyck, UC Irvine Center for Statistical Consulting, Department of Statistics for assistance with statistical analyses. We thank Orange County Sanitation District and Orange County Water District, Fountain Valley, CA for their help in providing wastewater samples.

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Footnote

† Electronic supplementary information (ESI) available. See: DOI: 10.1039/c1em10237k
This journal is © The Royal Society of Chemistry 2012
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