Estimation of Measurement Uncertainty for the Analysis of Arsenic in Water by Hydride Vapor
Generation–Atomic Absorption Spectrometry
Admer Rey C. Dablio1*, Noel Angelo P. Kalacas2, Vernalyn R. Abarintos2, Isaiah E. Ubando1, Ruth L. Damian1, and Johanna Andrea C. Valdueza1
1Standards and Testing Division, Industrial Technology Development Institute, Department of Science and Technology, Bicutan, Taguig City 1631 Philippines
2Science Education Institute, Department of Science and Technology, Bicutan, Taguig City 1631 Philippines
Every person should have the right to have access to an adequate supply of safe drinking water.
However, the presence of different chemical contaminants compromises its quality. Among these chemical contaminants is arsenic, particularly its inorganic species, which when consumed at certain levels in drinking water can cause cancer and heart, respiratory, and neurological problems. For this reason, the determination of arsenic in water requires the use of a validated test method. In this context, the estimation of measurement uncertainty (MU) is an important tool that identifies the impact of each step of the measurement protocol on the overall accuracy and reliability of measurement results. This study is important for drinking water consumers to have accurate test results and can be used by testing laboratories as a guide in their MU calculations for arsenic analysis. In this paper, MU estimation was presented as a validated test method for the analysis of arsenic in a water sample by hydride vapor generation–flame atomic absorption spectrophotometry (HVG-FAAS) using the bottom-up approach. The concentration of arsenic found in the water sample was 0.530 ± 0.07 μg L–1 (k = 2, norm.), which complies with the maximum allowable level (MAL) of arsenic at 10 μg L–1 set in Philippine National Standards for Drinking Water (PNSDW) of 2017. The concentration of arsenic in the sample solution is the major contributory component to the estimated uncertainty with 90.60%; 7.07% is due to overall bias and 1.87% to method precision. Based on this outcome, this study can provide a suitable procedure for estimating MU in HVG-FAAS analysis of arsenic in clean water. Further studies can be done for dialysis water, wastewater, and environmental water.
Keywords: arsenic, HVG-FAAS, measurement uncertainty, water
*Corresponding author: [email protected]
INTRODUCTION
Accuracy and reliability are the foundations of establishing the quality of a measurement result. Thus, standards are set that present quality assurance processes to guarantee that laboratories are capable of achieving and generating quality results (Ellison and Williams 2012).
One of the main standards that regulate requirements in testing and calibration laboratories is the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 17025 titled
“General requirements for the competence of testing and calibration laboratories”(ISO/IEC 2017), to which they must be accredited in order to be deemed technically competent. This standard demands a metrological ISSN 0031 - 7683
Date Received: 17 Dec 2022
approach to measurement processes that requires method validation, the establishment of metrological traceability, and estimation of measurement uncertainty (MU), among many others (Gasljevic 2015).
Prior to performing a new, unknown method, it should be validated to ascertain if it is fit for its intended purpose. This is normally a prerequisite of a laboratory’s accreditation to perform an analytical method. Method validation provides a good understanding of the extent of experiments conducted and their sources, and as such, can be used for the estimation of MU (Gasljevic 2015).
According to the International Vocabulary of Basic and General Terms in Metrology (JCGM 2012), MU is defined as a “non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information,” with measurand being the “quantity intended to be measured.” Basically, the estimation of MU determines the confidence level of a measurement result(Torres et al. 2015), thus, it plays a vital role in all analytical measurements. Every measurement result must be interpreted with associated uncertainty, which conforms to the requirements of ISO/IEC 17025.
There exist two main approaches in estimating MU:
[1] the ISO Guide to the Expression of Uncertainty in Measurement (GUM) and [2] the Nordtest. The ISO/
GUM approach, which is universally accepted as the foundation of different uncertainty estimation approaches, is based on the harmonized publication of ISO issued in 1993 and corrected in 1995 as the “Guide to the Expression of Uncertainty in Measurement”(JCGM 2008) in collaboration with BIPM (Bureau International des Poids et Mesures), IEC, IFCC (International Federation of Clinical Chemistry), IUPAC (International Union of Pure and Applied Chemistry), IUPAP (International Union for Pure and Applied Physics), and OILM (International Organization of Legal Metrology). Based on this approach, which is often called the modeling or bottom- up approach, each uncertainty component at every step of the experiment is quantified by an estimated standard deviation, which is known as standard uncertainty, and then combined into combined standard uncertainty that takes into account the contributions from all major uncertainty components. ISO/GUM groups uncertainty components into two categories according to the method used to estimate the numerical values(Taylor and Kuyatt 1994):
[1] type A, which is estimated by statistical methods;
and [2] type B, which is estimated by other means aside from statistical methods, e.g. certificates of reference materials, manufacturer’s specifications or manuals of equipment, data from calibration report, etc. Although ISO/GUM is the standard document of MU estimation, the Nordtest approach(Magnusson et al. 2012) is also gaining popularity due to its simplicity and rapidness. This
approach, which is also called single-lab validation or top- down approach, contrary to ISO/GUM approach, does not go intensely into the measurement procedure and does not attempt to quantify each uncertainty component. In this approach, the uncertainty is based on two components: [1]
the within-lab reproducibility or intermediate precision, which accounts for uncertainty sources that are random in the long term; and [2] the bias component, which accounts for systematic effects that cause long-term bias.
Safe drinking water is one of the basic needs of every human being in order to sustain their life. However, the quality of drinking water is compromised due to the presence of various chemical contaminants, especially heavy metals, which can lead to acute or chronic diseases (EPA n/d).Thus, it is crucial to maintain and regulate the quality of drinking water. Based on the 2017 PNSDW (DOH 2017) embodied in the DOH Administrative Order No. 2017-0010, arsenic – a heavy metal – is among the mandatory parameters that all water service providers nationwide are required to test. According to this standard, the MAL of arsenic present in drinking water should be at 0.01 mg L–1. In order to satisfy their customers on the basis of providing safe and qualified drinking water, the analysts from the Standards and Testing Division (STD) of the Industrial Technology Development Institute (ITDI) under the Department of Science and Technology (DOST) have validated a test method for the analysis of total inorganic arsenic (As3+ and As5+)in water samples by hydride vapor generation–flame atomic absorption spectrophotometry (HVG-FAAS). The test method was validated in terms of linearity, detection limit, bias, and precision and is an accredited parameter of the STD based on the ISO/IEC 17025:2005 standard.
The objective of the study is to present the MU estimation of the validated test method for the analysis of arsenic in water sample by HVG-FAAS using the bottom-up approach. Uncertainty components of the measurement procedure are identified and quantified into standard, relative, and combined uncertainties. The final expanded uncertainty was computed and reported along with the test result. A study on the estimation of MU for arsenic was not yet done in the Philippines.
MATERIALS AND METHODS
Description of Sampling Sites
A water sample is collected from the tap water of the STD building at Saliksik St., DOST Compound, Bicutan, Taguig City, Philippines, and placed in sterilized wide- mouth bottles. The bottles were rinsed, capped three
times with the sample water, and filled to within one or two inches from the top. The water sample was filtered using a portable water treatment device that is under the intellectual property of a customer, which was being assessed for an alternative means of generating potable water from the surface, ground, and even wastewater.
Reagents
Concentrated nitric acid (65%) is used in the open digestion procedure. For the preparation of the pre- reducing agent, potassium iodide, ascorbic acid, and hydrochloric acid (35–37%) are mixed with reagent water. Sodium borohydride and sodium hydroxide are dissolved in a solution with reagent water to prepare the reducing agent.
Sample Preparation
The filtered sample was homogenized by mixing and shaking. Afterward, three independent 250-mL aliquots from the bulk sample were transferred into 400-mL beakers using 50- and 100-mL volumetric pipettes and were subjected to open digestion using concentrated HNO3 until near dryness in order to decompose any organic arsenic and other co-existing organic substances.
The digested samples were then filtered, collected, and diluted quantitatively into 25-mL volumetric flasks, which were then transferred into Nalgene plastic containers.
Afterward, 10-mL aliquots from the filtered samples were transferred into 25-mL volumetric flasks and were added with 2.5 mL of a pre-reductant mixture of KI and ascorbic acid solution and 2.5 mL of concentrated HCl.
The resulting solution was left to react for 45 min prior to dilution. The working standard solutions were prepared similarly starting with the addition of KI-ascorbic acid solution and concentrated HCl.
Instrumentation
The sample solutions were analyzed following the validated test method for the analysis of total inorganic arsenic using HVG-FAAS (Shimadzu AA6880) at 193.7 nm and carried out at the instrumentation room of the Inorganic Chemistry Section under the Chemistry Laboratory of STD of ITDI at the DOST Compound in Bicutan, Taguig City, Philippines.
Determination of Arsenic Concentration in Water Samples and Its Uncertainties
Sample preparation and analysis of arsenic in water by HVG-FAAS were adapted from Baird et al. (2017) with minor modifications.
Specification of Measurand
The measurand in this study is the concentration of arsenic expressed as μg L–1 in a water sample. The general mathematical equation for quantifying arsenic is:
𝐶𝐴𝑠 � 𝐴 − 𝐵₀ (1) 𝐵₁
where cAs is the concentration of arsenic in the sample solution, A is the absorbance of arsenic in the sample, B0 is the y-intercept and B1 is the slope. However, when calculating the actual concentration of arsenic in the bulk water sample, the following equation was employed:
(2) where cAs_sx is the actual concentration of arsenic in the bulk water sample, Vf is the final volume of the sample solution after acid digestion and filtration, DF is the dilution factor of the sample solution, and Vi is the initial volume taken from the bulk sample prior to acid digestion.
Identification of MU Components
The major uncertainty components of the method were identified, listed, and presented using a cause-and-effect diagram, also known as the fishbone structure or the Ishikawa diagram (Ishikawa 1976).
Figure 1. Ishikawa diagram for uncertainty components in the analysis of arsenic in water.
Ishikawa Diagram
The uncertainty components for the analysis of arsenic in water were identified and prepared using the Ishikawa diagram as shown in Figure 1.
The following uncertainty components were defined as Vsx1i for the volume of the aliquot from the bulk sample for acid digestion, Vsx1f for the final volume of the sample after acid digestion and filtration, Vsx2i for the volume of the aliquot from the acid-digested and filtered sample, Vsx2f for the final volume of diluted sample solution, cstd for preparation of 99.9 μg L–1 arsenic intermediate standard solution, P for purity of 999 mg L–1 arsenic stock solution, cAs for the concentration of arsenic in the
sample solution, method trueness for uncertainty arising from possible bias and method precision for uncertainty from dispersion of repeated measurements based on method validation.
Type A Evaluations
Uncertainty components from Type A evaluations were obtained from glassware repeatability/reproducibility, method trueness, method precision, and concentration of arsenic in the sample solution through glassware verification data, bias, the standard deviation of the mean, and residual sum of squares and residual standard deviation. Repeatability/reproducibility from glassware verification data was calculated using standard deviation s the and standard deviation of the mean sx̅:
(3)
(4)
where xi is the value of the ithmeasurement in a data set, x̅ is the mean value of the data set, and n is the number of measurements in a data set.
The uncertainty component of method trueness was calculated using standard deviation, s, for repeated measurements of a reference material and percent relative standard deviation, % RSD, standard uncertainty of the certified value of certified reference material (CRM) u(Cref), relative standard uncertainty of u(Cref), and bias through:
(6) (5)
where Clab is the mean of the repeated measurements of the reference material and Cref is the certified value of the reference material based on the certificate of analysis.
Uncertainty arising from the concentration of arsenic in the sample solution, u(cAs), was computed by the equation of the line, residual standard deviation S, and residual sum of squares Sxx:
(7) where Aj is the jth measurement of the absorbance of the ith calibration standard, ci is the concentration of the ith calibration standard, B1 is the slope, and B0 is the intercept;
(8)
(9)
(10) where S is the residual standard deviation, p is the number of measurements for determining cAs, n is the number of total measurements for all calibration standards, c̅ is the mean value of the different calibration standards (from n number of measurements), Sxx is the residual sum of squares, i is the index for the number of calibration standards, and j is the index for the number of measurements for obtaining the calibration curve.
Type B Evaluations
Uncertainty components from Type B evaluations were obtained from calibration certificates, certificates of reference materials, and manufacturer’s specifications of the equipment. The standard uncertainty from these data was calculated using the rectangular or triangular distribution. Rectangular distribution is assumed when a confidence interval specified by a certificate of analysis or manufacturer’s equipment specifications is likely to fall at extreme values. The standard uncertainty u(y) obtained from rectangular distribution was calculated by:
(11) where a is the confidence interval. On the other hand, triangular distribution is assumed when a given confidence interval is unlikely to fall at the extreme values. The standard uncertainty obtained from triangular distribution was calculated by:
(12) These assumptions are only applicable if the confidence interval is stated without the confidence level.
Uncertainty from Possible Bias
The uncertainty from possible bias, u(bias), was calculated using the following formula:
(13)
where RMSbias is the root mean square of bias estimates, sbias is the standard deviation of the bias estimates obtained using a single reference material and u(Cref) is the standard uncertainty of repeated measurements of the reference material. This equation only holds true if bias determinations were carried out using a single CRM.
Relative Standard Uncertainty
The relative standard uncertainty, ur(y), was calculated for each uncertainty component through the following:
(14) where u(y)is the standard uncertainty and y is the value of the uncertainty component.
Combined Standard Uncertainty
The combined standard uncertainty uc(y) was computed using the model equations below:
For models involving a sum or difference of quantities, the combined standard uncertainty is given by:
(15) where u(p) is the standard uncertainty of parameter p, u(q) is the standard uncertainty of parameter q, and u(r) is the standard uncertainty of parameter r.
For models involving a product or quotient, the com- bined standard uncertainty is given by:
(16)
where y is the output quantity, u(p)/p is the relative standard uncertainty of parameter p, u(q)/q is the relative standard uncertainty of parameter q, and u(r)/r is the relative standard uncertainty of parameter r.
Expanded Uncertainty
The expanded uncertainty was calculated using the formula:
𝑈� = 𝑢�₍�₎ × 𝑘 (17)
where Uc is the expanded uncertainty, uc(y) is the combined standard uncertainty of value y, and k is the coverage factor.
RESULTS AND DISCUSSION
Uncertainty from Measuring Apparatus
The digestion and dilution of the sample and preparation of the intermediate standard solution were performed using volumetric glassware and a micropipette. The sources of uncertainty related to this cover mainly the calibration of the volumetric pipettes, flasks, and micropipette, temperature variation effects, and repeatability.
According to the micropipette calibration certificate for the volume of 1000 μL, the expanded uncertainty is 2.3 μL (k = 2; 95% confidence level). Based on the calibration reports of the volumetric pipettes for volumes of 10 and 50 mL, the expanded uncertainties were both 0.01 mL (k = 2; 95% confidence level). Accordingly, the calibration reports of the volumetric flasks for the volumes of 25 and 100 mL specified expanded uncertainties (k = 2; 95% confidence level) at 0.02 and 0.05 mL, respectively. For the conversion of these values into standard uncertainty, the expanded uncertainty should be divided by k. Conversely, the 100-mL volumetric pipette was not subjected to calibration from a metrological laboratory; hence, the tolerance of this pipette was used as the confidence interval, assuming triangular distribution based on Equation 12.
Regarding the temperature variation during the usage of volumetric glassware, the standard uncertainty was set to 0.03 mL within 3 °C of the stated operating temperature of the glassware.
Considering the uncertainty arising from the repeatability of volumetric glassware, the standard uncertainties were obtained from the conduct of glassware verification within the laboratory using the standard deviation of triplicate measurements, then calculating the standard deviation of the mean by Equation 4.
The standard uncertainties of the volumetric glassware calibration, temperature effect, and repeatability were combined by Equation 15.
Uncertainty from Intermediate Standards
Prior to generating a calibration curve, an intermediate standard solution of arsenic with a concentration of 99.9 μg L–1 was prepared by diluting 9.99 mg L–1 or 9,990 μg L–1 intermediate standard solution, which was prepared beforehand by diluting 999 mg L–1 or 999,000 μg L–1 arsenic stock solution. The uncertainty components for the preparation of 99.9 μg L–1 intermediate standard solution involve the calibration of the micropipette and volumetric flask, temperature variation effects, and repeatability. Accordingly, the uncertainty components arising from the preparation of 9.99 mg L–1 intermediate standard solution, which entailed the purity of the stock solution and calibration of the micropipette and volumetric flask, temperature variation effects, and repeatability,
contributed further to the uncertainty components in the preparation of 99.9 μg L–1 intermediate standard solution.
According to the certificate of analysis for 999 mg L–1 arsenic stock solution, the expanded uncertainty is 4 mg L–1 (k = 2; 95% confidence level). Based on the previous section, the expanded uncertainty of the micropipette at 1000 μL is 2.3 μL and the combined standard uncertainty of 100-mL volumetric flask is 0.0393 mL. Both preparations of 9.99 mg L–1 and 99.9 μg L–1 intermediate standard solutions utilized 1000-µL micropipette and 100- mL volumetric flasks. The combined relative standard uncertainties for the preparation of 9.99 mg L–1 and 99.9 μg L–1 intermediate standard solutions were calculated using Equation 16. The combined standard uncertainty for the preparation of the intermediate standard solution used to subsequently prepare the working standard solutions is 0.2636 μg L–1.
Uncertainty from Calibration Curve
The method adopted in the present study utilizes a calibration curve that is based on the linear least squares regression used to generate the analytical signal obtained from measurements. It was emphasized in the EURACHEM/CITAC guide(Ellison and Williams 2012) that the most significant source for calibration comes from the random variations in the measurement signal, which affects both the experimental and calculated y-values.
Figure 2. Calibration curve for the linear range of arsenic in water.
Thus, the estimated MU for the calibration curve was calculated using Equations 8, 9, and 10.
The calibration curve that was generated from the calibration standards (prepared from 99.9 µg L–1 As intermediate standard solution) by the instrument is shown in Figure 2. Based on the data above, the estimated value for the uncertainty related to the calibration curve was 0.1343 μg L–1 of arsenic.
Uncertainty from Method Precision
The uncertainty for method precision was evaluated through the standard deviation of the mean calculated using Equation 4 from the repeatability data of spiked ultrapure water samples from the method validation. Three concentration levels at 10 trials each were used to assess the repeatability of the method: low (2 μg L–1), medium (6 μg L–1), and high (12 μg L–1). The low level was chosen since it has the concentration closest to the mean solution concentration of the sample at 2.121 μg L–1. The standard deviation of the mean was directly used to obtain the standard uncertainty related to method precision.
The actual mean value was way beyond the expected value of 2 μg L–1 with a recovery of 161.35%. This can be attributed to systematic error, in particular, instrument drift. Despite the existence of a variation in the analyte recovery at a low level, the precision of the method was still approved given the low standard deviation among the concentration values, as well as the inclusion of QC samples (e.g. CRM, internal calibration check standards) during the assessment of precision.
Uncertainty from Overall Bias
According to the EURACHEM/CITAC guide(Ellison and Williams 2012), the uncertainty associated with the determination of bias remains an essential component of overall uncertainty. Hence, the bias arising from laboratory performance and the method itself was evaluated using repeated CRM measurements. Thirteen (13) independent trials were measured on the CRM, which underwent the same measurement procedure as the sample.
The CRM used in the measurement procedure, which also served as the quality control sample, was ERA WasteWatR™ Trace Metals (Cat. #500).
The overall bias was calculated by Equation 6 and its corresponding uncertainty was obtained using Equation 13.a
As seen in Figure 3, the overall bias component is considered significant compared to the combined uncertainty, hence, it cannot be reasonably neglected.
Uncertainty Budget
After quantifying the uncertainty components, the standard uncertainties (or combined standard uncertainties for components with corresponding subparts) were transformed into relative standard uncertainties by Equation 14 and further combined using Equation 15.
Table 1 shows the overall uncertainty budget of all the quantified uncertainty components.
Based on the obtained relative standard uncertainties, a graph was plotted to compare the contribution of each
Table 1. Summary of results for the estimation of standard and relative standard uncertainties based on the validated test method for the analysis of arsenic in water.
Uncertainty component Value
Unit Standard uncer- tainty
Relative standard
uncertainty Type of evalu-
ation Source of data
Y u(y) u(y)/y
Volume of aliquot from bulk sample for acid digestion (Vsx1i)
250 mL 0.2247855 0.0008991 Type B 2 x Pyrex No. 7100 Vol Pipette Class A Tolerance, ± 0.080 mL and LMS Germany 50 mL Vol Pipette Class A Calibration Re- port No. 02-97-2013: U = ±0.01 mL; k = 2
Final volume of sample after acid digestion and filtration (Vsx1f)
25.00 mL 0.0316525 0.0012661 Type B Pyrex USA 25 mL Vol Flask
Class A Calibration Report No.
02-97-2014: U = ± 0.02 mL; k
= 2 Volume of aliquot from ac-
id-digested and filtered sam- ple (Vsx2i)
10.00 mL 0.0306026 0.0030602 Type B IVA 10 mL Volumetric Pipette Class A Calibration Report No.
02-97-2015: U = ± 0.01 mL; k
= 2 Final volume of diluted sam-
ple (Vsx2f) 25.00 mL 0.031652521 0.0012661 Type B Pyrex 25 mL Vol Flask Class A
Calibration Report No. 02-97- 2014: U = ± 0.02 mL; k = 2 99.9 µg L–1 As intermediate
standard solution (cstd) 99.90 µg L–1 0.2635856 0.0026384 Type B Refer to Supplemental Tables S2 and S3
Concentration of arsenic in
the sample solution (cAs) 2.121 µg L–1 0.134335685 0.0633390 Type A Refer to Supplemental Table S5 Overall bias 833.91 µg L–1 14.75989434 0.0176995 Type A Certificate of Analysis US ERA WasteWatR™ Trace Metals Cat.
No. 500, Lot No. P243-500: U = 5.49764 μg L–1; k = 2
Method precision 3.23 µg L–1 0.02933300 0.0090901 Type A Repeatability from Method Validation Data of Arsenic in Water at Low Level (2 ppb): s = 0.0927591; n = 10
component, as shown in Figure 3.
According to this diagram, the component with the highest contribution is the concentration of arsenic in the sample solution (cAs) due to the residual standard deviation and residual sum of squares, followed by overall bias and method precision. With this in mind, the other components – Vsx1i, Vsx1f, Vsx2i, Vsx2f, and cstd – can
Figure 3. Comparison of the percent contributions of each uncertainty component.
be considered negligible to the estimation of the overall combined uncertainty.
Expanded Uncertainty and Presentation of Analytical Result
The relative standard uncertainties listed in Table 1 can now be used to determine the overall combined standard uncertainty of the actual concentration of arsenic in the water sample. Using Equation 2, the actual concentration was found to be 0.530 μg L–1. The overall combined standard uncertainty was calculated using this concentration by Equation 16 and was found to be ±0.035 μg L–1. At a 95% confidence level (k = 2), this overall combined standard uncertainty is expressed as expanded uncertainty using Equation 17, with a value of ± 0.07 μg L–1 that amounts to 13.31% of uncertainty to the test result. Thus, the presentation of the analytical result for arsenic in the water sample is expressed as 0.530 ± 0.07 μg L–1 (k = 2, norm.), which complies with the MAL of arsenic at 10 μg L–1 set in the PNSDW of 2017.
In comparison to related literature, not many articles exist that focused on the estimation of MU water by HVG-AAS.
A study by Faustino et al.(2018) on top-down uncertainty measurement of total As content in water samples from Brazil using graphite furnace–AAS produced a 16%
expanded uncertainty (k = 2, norm.) for a concentration range of 20–80 μg L–1. Based on their Water Framework Directive, the maximum standard expanded uncertainty with 10% precision and 10% trueness (which are the required conditions for As measurement at 10 μg L–1 concentration range) should be at 15%, to which the study does not conform to by a slight amount.
Another study by Komorowicz and Baralkiewicz(2016) involved the determination of total arsenic and arsenic species concentration by inductively coupled plasma mass spectrometry (ICP-MS) and high-performance liquid chromatography/ inductively coupled plasma mass spectrometry (HPLC/ICP-MS), respectively, in different types of water: drinking water, wastewater, surface water, and snow collected in Poland. The lowest concentration of total arsenic among the examined drinking water samples was 0.141 ± 0.0010 μg L–1 (0.71% uncertainty), whereas the highest concentration determined was 1.010 ± 0.072 μg L–1 (7.13% uncertainty), which was significantly lower than the 10 μg L–1 maximum permissible concentration of arsenic in drinking water. For surface water samples, they found a high concentration of arsenic up to 3778 ± 268 μg L–1 (7.09% uncertainty) from a stream, whereas the total arsenic concentration within the range of 0.928 ± 0.066 μg L–1 (7.11% uncertainty) to 2.840 ± 0.202 μg L–1 (7.11%
uncertainty) from a river situated in the same province as the stream was determined. Lastly, the concentration of total arsenic in wastewater samples ranging from 0.1020
± 0.0072 μg L–1 (7.06% uncertainty) to 1.82 ± 0.13 μg L–1 (7.14% uncertainty) was also determined.
CONCLUSION
This study presented a MU estimation to a validated test method for the analysis of arsenic limited in a clean water sample. The identified uncertainty components were the volume of the sample aliquot transferred for acid digestion, the final volume of the sample after acid digestion and filtration, preparation of 99.9 µg L–1 arsenic intermediate standard solution, the volume of the aliquot from the acid-digested and filtered sample, the final volume of the diluted sample, the concentration of arsenic in the sample solution, overall bias, and method precision. However, the components with the most major contributions to the uncertainty budget were the concentration of arsenic in the sample solution at 90.60%, followed by overall bias at 7.07%, and finally, method precision at 1.87%. The
overall combined standard uncertainty was found to be
± 0.035 μg L–1, whereas the expanded uncertainty is ± 0.07 μg L–1, accounting for 13.31% uncertainty to the test result. The final result of the concentration of arsenic in the water sample is reported as 0.530 ± 0.07 μg L–1 (k = 2, norm.). The obtained result complies with the PNSDW of 2017 and based on this outcome, this study can provide a suitable procedure for estimating MU in the HVG-FAAS analysis of arsenic.
Testing and monitoring laboratories can employ this MU estimation in their routine analytical analyses for quality control measures. This will also help regulatory agencies, i.e. the DOH and local government units, in monitoring arsenic in drinking water. Competent quantification of arsenic in water can enhance the country’s trade, as well as promote consumer protection against these harmful effects of arsenic. Further studies can be done for dialysis water, wastewater, and environmental water
ACKNOWLEDGMENTS
The authors would like to acknowledge and thank the PCIEERD (Philippine Council for Industry, Energy, and Emerging Technology Research and Development) under the DOST for the financial support of this research project through their GIA (Grants-in-Aid) program.
REFERENCES
BAIRD RB, EATON AD, RICE EW eds. 2017.
Standard Methods for the Examination of Water and Wastewater, 23rd ed. American Water Works Association, Denver, CO.
[DOH] Department of Health. 2017. Philippine National Standards for Drinking Water of 2017 [AO No. 2017- 0010]. Manila, Philippines.
ELLISON SLR, WILLIAMS A eds. 2012. EURACHEM/
CITAC Guide: Quantifying Uncertainty in Analytical Measurement, 3rd ed. [QUAM:2012.P1].
EURACHEM, London, UK.
[USEPA] United States Environmental Protection Agency.
n/d. Potential Well Water Contaminants and their Impacts. Retrieved on 03 May 2020 from https://
www.epa.gov/privatewells/potential-well-water- contaminants-and-their-impacts
FAUSTINO MG, LANGE CN, MONTEIRO LR, FURUSAWA HA, MARQUES JR, STELLATO TB, SOARES SMV, DA SILVA TBSC, DA SILVA DB, COTRIM MEB, PIRES MAF. 2018. Top Down Arsenic
Uncertainty Measurement in Water and Sediments from Guarapiranga Dam (Brazil). J Phys Conf Ser 975 (1): 1–6. DOI: 10.1088/17426596/975/1/01202 4 GASLJEVIC V. 2015. Method Validation and
Measurement Uncertainty. Biochem Med 20(1): 57–63.
DOI: 10.11613/BM.2010.007
ISHIKAWA, K. 1976. Guide to Quality Control. Asian Productivity Organization.
[ISO/IEC] International Organization for Standardization/
International Electrotechnical Commission. 2017.
General Requirements for the Competence of Testing and Calibration Laboratories, 3rd ed. [ISO/IEC 17025:2017(E)]. Geneva, Switzerland.
[JCGM] Joint Committee for Guides in Metrology.
2008. Evaluation of Measurement Data—Guide to Expression of Measurement Uncertainty; JCGM 100:2008. Paris, France.
[JCGM] Joint Committee for Guides in Metrology.
2012. International Vocabulary of Metrology – Basic and General Concepts and Associated Terms, 3rd ed.
[JCGM 200:2012(E/F)]. Paris, France.
KOMOROWICZ I, BARALKIEWICZ D. 2016.
Determination of Total Arsenic and Arsenic Species in Drinking Water, Surface Water, Wastewater, and Snow from Wielkopolska, Kujawy-Pomerania, and Lower Silesia Provinces, Poland. Environ Monit Assess 188(504): 1–22. DOI: 10.1007/s10661-016-5477-y MAGNUSSON B, NAYKKI T, HOVIND H, KRYSELL
M. 2012. Handbook for Calculation of Measurement Uncertainty in Environmental Laboratories; NT TR 537 [Project 1589-02]. Nordic Innovation, Oslo, Norway.
TAYLOR BN, KUYATT CE. 1994. Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results [NIST Technical Note 1297].
NIST, Gaithersburg, MD.
TORRES DP, OLIVARES IR, QUEIROZ HM. 2015.
Estimate of the Uncertainty in Measurement for the Determination of Mercury in Seafood by TDA AAS. J Environ Sci Health B 50(8): 622–631. DOI:
10.1080/03601234. 2015.1028861
