Uncertainty estimation of atmospheric ammonia ...

Uncertainty estimation of atmospheric ammonia concentration by passive samplers. M.A. Leiva • B. Gonzales • D. Vargas • R. Toro • R.G.E. Morales S. Microchemical Journal ISSN: 0026-265X Microchemical Journal (2013) 110, 340–349 DOI: 10.1016/j.microc.2013.05.004

Microchemical Journal 110 (2013) 340–349

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Estimating the uncertainty in the atmospheric ammonia concentration in an urban area by Ogawa passive samplers Manuel A. Leiva G. a,b,⁎, Benjamin Gonzales b, Daniela Vargas b, Richard Toro b, Raul G.E. Morales S. b a b

Department of Land, Air and Water Resources, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA Centro de Ciencias Ambientales and Departamento de Química, Facultad de Ciencias, Universidad de Chile, Casilla 653, Santiago, Chile

a r t i c l e

i n f o

Article history: Received 2 April 2013 Accepted 13 May 2013 Available online 21 May 2013 Keywords: Atmospheric ammonia concentration GUM Ogawa passive sampler Uncertainty estimation

a b s t r a c t Ammonia, one of the ambient gasses that require environmental monitoring, is typically measured using a passive sampling method. The present work presents an evaluation of the uncertainty according to the Guide to the Expression of Uncertainty in Measurement for the measurement of the atmospheric ammonia concentration as determined by an Ogawa passive sampler, using a colorimetric method. The analytical results report the uncertainty only as a standard deviation of repeated measurements, but not all sources of uncertainty are considered. In this work, the major sources of uncertainty in the measurements are identified as contributions to the linear least-square regression lines, repeatability and recovery. The result, including the expanded uncertainty (k = 2) at a level of confidence of 95%, is 39.2%. The aforementioned results indicate that the Ogawa sampler can be successfully deployed to estimate the atmospheric NH3 and could find wide application in environmental monitoring. However, to obtain correct conclusions, the uncertainty in the measurements must be considered. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Ammonia (NH3) is the primary alkaline gas in the atmosphere and thus plays a major role in the neutralization of acidic atmospheric gasses. Through reactions with acid pollutants to form ammonium nitrate, ammonium sulfates and ammonium chloride, NH3 is a main contributor to secondary particulate matter [1,2]. The need to elucidate the role of this important air pollutant has been underscored in recent years as NH3 emissions from sources [1,3–5] such as agricultural activities, animal feedlot operations, wetland, biomass burning and, to a lesser extent, fossil fuel combustion have increased. However, the development of cost-effective strategies for measuring the atmospheric NH3 concentration hinges on a thorough understanding of the sampling and analysis methodologies; in an effort to mitigate environmental concerns, the relative abundance and spatial distribution of the important precursors to secondary particulate matter must be estimated [6–8]. Passive sampling, which is currently a main area of analytical development particularly in the monitoring of environmental pollutants, is widely used to monitor NH3 [5,9,10]. Compared with other active and automatic sampler methodologies, passive sampling has

⁎ Corresponding author at: Centro de Ciencias Ambientales and Departamento de Química, Facultad de Ciencias, Universidad de Chile, Casilla 653, Santiago, Chile. Tel.: +56 2 2978 73 70; fax: +56 2 2978 72 52. E-mail addresses: [email protected], [email protected] (M.A. Leiva G.). 0026-265X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.microc.2013.05.004

many advantages, including a low cost with no required power supply. For example, the Teledyne Instruments model 201E chemiluminescent NH3 analyzer for ambient air-quality monitoring (Teledyne API, San Diego, California, USA) costs $18,000 USD. Passive diffusion tubes (Ogawa & Co. USA, Inc., Pompano Beach, Florida) cost $60 USD each and incur minimal analysis costs. In addition to simplicity and flexibility of deployment and use, the passive diffusion tubes can be used almost anywhere with large numbers deployed to provide detailed spatial and temporal surveys. In addition, no special training or maintenance is required to deploy the tubes in the field, further reducing the operational costs of these samplers [9,10]. All passive samplers operate on the principle of gasses diffusing from the atmosphere according to Fick's Law [11] through a sampler of defined dimensions onto an absorbing medium. The concentration of a particular gas i (Ci) in air can be estimated according to the following equation: Ci ¼

mi ri  t

ð1Þ

where mi is the mass of the gas i adsorbed by the collection pad filter, ri is a sampling flow rate and t is the duration of exposure. The sampling flow rate is a function of the diffusion path length (L) and the cross-sectional area (A), 2 characteristics of the geometry of the passive sampling device that are also functions of the diffusion coefficient of the gas i (Di). Numerous types of passive samplers have been developed for ambient air monitoring [12]. In each, the geometry (the diffusion path length

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and/or cross-sectional area) of the sampler is modified to achieve the desired sampling flow rate. In addition, the absorbent material in the coated pad can be modified: for NH3, a number of acids have been used, including citric, phosphoric, sulfuric and tartaric acids [13]. To obtain accurate and reliable data from passive diffusion samplers, one must understand the operating principles of diffusion sampling, have knowledge of the factors that may affect the sampler performance and estimate the uncertainty in the measurements. The uncertainty can be defined as “a parameter associated with the result of a measurement that characterizes the dispersion of values reasonably attributed to the measurement” [14]. Uncertainty is useful for establishing the quality of a measurement and determining whether the results are sufficient for the purposes of the study [15]. In principle, when estimating the uncertainty in an analytical measurement, all significant components of the uncertainty must be identified and quantified [16]. Components that affect the uncertainty in the analytical measurement include sampling, handling, transport, storage, preparation, testing, and so forth. The measurement of uncer-

341

tainty appears simple, but many steps can be difficult to identify or quantify, and the process can be time consuming [17]. No information was found in the literature regarding the assessment of measurement uncertainties associated with the use of an Ogawa sampler, i.e., assessments performed according to the Guide to the Expression of Uncertainty in Measurement (GUM) [18]. The GUM ISO is the most detailed, popular and key document used by the National Measurement Institutes and industrial calibration laboratories to evaluate the uncertainty in the output of a measurement system. This study aimed to develop a methodology for estimating the uncertainty in the determination of the atmospheric concentration of NH3 using Ogawa passive samplers (OPSs) with an ultraviolet–visible (UV–vis) spectrophotometric method and the GUM approach for calculating the uncertainty. Thus, we demonstrated that the calculated uncertainties were higher than the values typically obtained via repeated measurements, which are commonly considered synonymous with the uncertainty. In addition, we focused on minimizing the effect of the principal sources of uncertainty.

2. Materials and methods 2.1. Materials, reagents and standards All reagents were analytical grade commercial products (Merck or Aldrich). Solutions were prepared with ultra-pure Milli-Q water (18.2 MΩ cm, Milli-Q System, Millipore). The calibration standard was prepared using appropriate dilutions of a concentrated stock solution (1000 ± 2) mg L−1 (CertiPur, Merck). A blank solution was used to measure the baseline. All glass and volumetric materials were decontaminated by washing them with a common detergent and rinsing them 3 times with Milli-Q water. The materials were then soaked for 24 h in a 20% (v/v) HNO3 solution, washed with Milli-Q water and finally dried in a clean environment. 2.2. NH3 passive sampling NH3 samples were collected using OPSs as shown in Fig. 1 [19]. An OPS is a small solid Teflon cylinder with 2 open but unconnected ends. Each side contains an impregnated cellulose filter mounted between 2 stainless steel screens (open area, Asc = 0.152 cm2; thickness, lsc = 0.02 cm) and situated behind a diffusion-barrier end cap containing 25 holes (open area, Aend = 0.785 cm2; thickness, lend = 0.6 cm). The OPSs were mounted on shielding plates with their open sides oriented downward for protection from direct exposure to sunlight, wind and dust. The assembly was hung approximately 2 m above the ground. An exposure time of 14 days was used for all samplers, after which the samplers were transferred to the laboratory for analysis. Prior to sampling, the OPS assemblies were thoroughly cleaned with a citric acid solution (5% w/v) for each use (to avoid contamination and carryover), rinsed thoroughly with deionized water (ASTM Type I) and dried. The components were assembled into the sampler using clean forceps. Clean latex gloves were used when handling the samplers. Exposure of the materials, samplers and reagents to the ambient air was

Fig. 1. Schematic view of the OPSs from 2 perspectives.

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minimized to avoid potential contamination. All collection filters were sealed and stored in the refrigerator prior to being loaded into the samplers. 2.3. Chemical analysis NH3 gas that diffuses through a sampler filter impregnated with citric acid is converted into ammonium ions according to Eq. (2). þ

−

NH3 þ C6 H8 O7 →NH4 þ C6 H7 O7

ð2Þ

The ammonium concentration in the filter extracts was determined colorimetrically using the phenol–hypochlorite method [19]. The filters were impregnated with 100 μL 2% (w/v) citric acid and 2% (v/v) glycerin in methanol. After sampling, the filters were transferred (using forceps) to acid-washed glass vials containing 10 mL of deionized water to extract the ammonium citrate that formed on the cellulose pads. The glass vials were then shaken for 15 min and sonicated for 5 min. The extract was filtered through a 13-mm-diameter filter to remove any filter particles, which could cause a positive absorbance artifact during the analysis. A 10-mL aliquot of the filtered extract was added to a 400-μL phenol solution (11% w/v, Fisher Scientific, Ultrapure MP Biomedicals), 400 μL of sodium nitroprusside solution (0.5% w/v, Merck Millipore) and 1 mL of oxidizing solution in a 1:4 ratio of the sodium hypochlorite (5%, Merck Millipore) and alkaline citrate solutions (0.2% w/v, Merck Millipore). NH3 reacts to form indophenol, which has an intense blue color that absorbs light at 640 nm. The color intensity was stable for 24 h. The blank and calibration standards were prepared by diluting the stock NH3 solution. The main stock solution was prepared from certified commercial solutions of 1000 ± 2 mg L−1 nominal ion concentration (CertiPur, Merck). All prepared solutions were stored in a refrigerator at 4 ± 2 °C. The standards were treated in the same manner as the samples. The absorption spectra were measured on a Perkin Elmer Lambda 11 UV–vis spectrophotometer at room temperature using a quartz cell. The ammonium ions were identified and quantified by interpolation on a proper calibration curve. The calibration standard solutions were prepared by successive additions of deionized water to the principal standard solution. The calibration curve ranged from 0 to 2.0 mg L−1. The calibration curves were constructed by plotting the absorbance against the concentration. The quality requirement for the acceptance of a calibration function was established as a correlation coefficient of R2 ≥ 0.995 [20]. The limit of detection (LOD) for the passive sampler corresponded to 3 times the standard deviation (σ) of the blank values, and the limit of quantification (LOQ) was defined as 10 times the standard deviation (σ) of the blank values [21]. All calculations were performed using MS Excel© Version 2010 (Microsoft Corporation, Redmond, California, USA). 2.4. Experimental design and uncertainty estimation procedure The procedure used to evaluate the uncertainty associated with determining the concentration of atmospheric NH3 via UV photometry and in accordance with GUM [18] can be divided into the following steps: Step 1. Description of the measurement procedures. Step 2. Specification of the measurand and the relationship between the measurand and the variables. Step 3. Identification of the sources of uncertainty. Step 4. Creation of the cause-and-effect diagrams and quantification of the individual uncertainties. Step 5. Calculation of the combined uncertainty. Step 6. Calculation of the expanded uncertainty. Step 7. Expression of the results. A brief description of each step is provided as follows: Step 1. Description of the measurement procedures. The atmospheric NH3 was measured using OPS [19]. Fig. 2 provides a flowchart for the measurement procedures. Each box represents the analytical process used to obtain the results. Step 2. Specification of the measurand and the relationship between the measurand and the variables. The ambient concentration of NH3 as determined by the passive sampler depends on the mass of NH3 adsorbed by the collection filter, the exposure duration and the diffusion sampling flow rate. The NH3 concentration in air is calculated as follows: C NH3 ¼


 cNHþ Ex−bl  vEx 4

r NH3  t

6

 10  f MW  f rep  f rec

ð3Þ

where C NH3 is the ambient NH3 gas concentration (μg m−3), cNHþ Ex−bl is the filter extract ammonium ion concentration less the 4 extract concentration of an unexposed travel blank (mg L−1), vEx is extract solution volume (mL), r NH3 is the sampling flow −1 6 rate (mL min ), t is a sampler exposure time (min), 10 is a unit conversion (μg L mg−1 m−3), fMW is the molecular weight ratio for converting the measured ammonium ion to NH3 gas (unitless), frep is the repeatability factor (unitless) and frec is the instrumental recovery factor (unitless). Steps 3 and 4. Identification of uncertainty sources, creation of the cause-and-effect diagrams and quantification of the individual uncertainties. An Ishikawa diagram, or cause-and-effect diagram, is a useful tool for identifying the influential parameters (i.e., the sources of uncertainty) in the entire measurement procedure. From this diagram, the identified sources of uncertainty were as follows: (a) the calibration curve and blank, (b) the extraction volume, (c) the sampling flow rate, (d) the exposure time, (e) the repeatability factor, (f) the instrumental recovery factor and (g) the molecular weight factor. All influential parameters are shown in the cause-and-effect diagram in
Fig. 3. We provide a brief description of the sources of uncertainty in these influential parameters. Calibration curve and blank cNHþ Ex−Bl ; cNHþ Ex ; cNHþ Bl ; uc þ ; uc þ uc þ Þ. The linear regression model provided in Eq. (4) was ap4

4

4

NH Ex−Bl 4

NH Ex 4

NH Bl 4

plied for calibration: A ¼ b  cNHþ þ a 4

ð4Þ

M.A. Leiva G. et al. / Microchemical Journal 110 (2013) 340–349

Fig. 2. Flowchart for the analytical process used to measure the ambient NH3 gas with an OPS.

Fig. 3. Ishikawa diagram, or cause-and-effect diagram, for the sources of uncertainty.

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where the predicted content, cNHþ , is calculated from the absorbance (A) at 640 nm. The regression coefficients, b (slope) and a (in3 tercept) were estimated using the calibration data (cj,Aj) and the least-squares method. The uncertainty of the term cNHþ Ex was obtained from the calibration curve [22]. The following equation is used to calculate the stan3 dard measurement uncertainty in the content of a sample:

uc

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2 u u cNHþ Ex −c s u 1 1 4 u ¼ t þ þ
 b m p ∑n c −c 2

NH þ Ex 4

j¼1

ð5Þ

j

where s is a residual standard deviation calculated according to Eq. (5), b is the slope of the calibration curve, m is the total number of data points used for the calculation, p is the number of measurements made to determine a particular value, c is the mean concentration value of the various stock standard solutions and cj is the concentration of each calibration standard observed at each calibration point. A rigorous explanation of the uncertainty calculation for the calibration curve is outside the scope of this paper. More details can be found in the literature [20,22]. vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2 u u cNHþ Ex −c u1 1 4 s¼u 2 tm þ p þ n
∑ c −c j¼1

ð6Þ

j

The field blank (c(NH3 + Bl)) consisted of a loaded sampler taken to and from the field with other samplers but never removed from its airtight vial. These blanks were prepared and processed simultaneously and in the same manner as the deployed samplers to determine whether contamination occurred during the sampler loading, transport or analysis. Ten replicates (nBl) were measured to obtain a standard deviation that could be used directly as the standard uncertainty (sBl). A normal distribution was assumed. The uncertainty of the field blank can be estimated according to Eq. (7). uc

NH þ Bl 4

sBl ¼ pffiffiffiffiffiffi nBl

ð7Þ

The uncertainty in cNHþ Ex−Bl (i.e., cNHþ Ex−Bl −cNHþ Bl ) can be estimated according to Eq. (8). 3

uc

NH þ Ex−Bl 4

3

3

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ c2NHþ Ex þ u2NHþ Bl 4

ð8Þ

4


 Extraction volume vEx ; uvEx . The uncertainty in the volumetric operations is associated with the following sources: (i) the uncertainty in the certified internal volume of the flask [23], (ii) the variation in filling the flask to the mark [24] and (iii) differences between the flask or solution temperatures and the temperature at which the volume of the flask was calibrated [25]. These 3 sources of uncertainty provide a standard uncertainty for a 10-mL volumetric flask (Hirschmann Laborgeräte GmbH & Co, Germany) of 0.01 mL.
 Sampling flow rate rNH3 ; urNH . To reach the reactive filter, atmospheric NH3 diffuses through the diffusive barrier end cap 3 and outer screen. Using an analogy to laminated solids [26], the
 bulk rNH3 for each
side  of the sampler can be related to the ec sc component mass transport coefficients for the end cap r NH3 and the screen r NH3 as follows: r NH3 ¼

sc r ec NH 3  r NH 3

ð9Þ

sc r ec NH 3 þ r NH 3

where r iNH3 can be calculated from the effective cross-sectional area (Ai) and the diffusion distance (li) of the end cap (i = end) or screen (i = sc) using Eq. (10). i

r NH3 ¼

DNH3 Ai

ð10Þ

li

The uncertainty associated with the recovery r NH3 is then estimated in Eq. (11) as follows:

uri

NH 3

i

¼ r NH3

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 0 u
u  12 !2 !2 u ec sc ursc u urecNH r NH þr NH NH 3 3 3 B 3 C u þ þ @
A t ec ec r NH3 r sc NH 3 r þ r sc NH3

ð11Þ

NH 3

sc
 is the where urec and ursc are the combined uncertainties of the sources of the r ec NH3 and r NH3 , respectively, and u ec NH 3 NH 3 r NH þr sc sc NH 3 3 þ r . The uncertainty sources can be estimated as follows: combined uncertainty associated with r ec NH 3 NH3

uri

NH 3

i

¼ r NH3

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u u uDNH 2 uA 2 ul 2 t 3 i i  þ þ DNH3 Ai li

ð12Þ

M.A. Leiva G. et al. / Microchemical Journal 110 (2013) 340–349

u


r ec þr sc NH NH 3

¼

345

r
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 
 urec

NH 3

2

þ ursc

NH 3

2

:

ð13Þ

3

We provide a brief description of the source of uncertainty in the influential parameters for the sampling flow rate—the gas diffusion
 DNH3 and the geometry of the passive sampler, i.e., the effective cross-sectional area (Ai) and diffusion distance (li).
 Gas diffusion coefficient DNH3 ; uiDNH . The uncertainty of the diffusion coefficientDNH3 is associated with the following sources: (i) the 3

 uncertainty associated with the variability in the reported conventional values ucon and (ii) the temperature variability uTDNH . DNH coefficient

3

3

The variability associated with reported results can be estimated in a manner similar to that in Eq. (5). The reported values of the DNH3 at 25 °C were 13.8 [27], 14.16 [28], 16.8 [29] and 13.92 [30] cm2 min−1. At 25 °C, the value of DNH3 ; considering the average of the reported values, was 14.7 cm2 min−1, and the estimated standard uncertainty was 0.4 cm2 min−1, a difference of 2.7%. The DNH3 is a function of temperature (K) at 1 atm of pressure [31]. The estimated variability of DNH3 was approximately 9%, within the range of normal atmospheric temperature [30]. The standard uncertainty, assuming a rectangular distribution, for DNH3 associated with the temperature variability was 5.2%. Finally, the combined standard uncertainty of DNH3 at 25 °C, considering the variability of the conventional values and temperature,
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi was 5.9% 2:7% 2 þ 5:2% 2 . Geometry of the passive sampler ðlend ; lsc ; Aend ; Asc ; ulend ; ulsc ; uAend ; uAsc Þ. For the geometry of the passive sampler, there were 2 sources of uncertainty: the repeatability, estimated from 10 independent OPS measurements, and the accuracy of the measuring instrument. Assuming a normal distribution and measurement scale (0.0005 cm), a standard uncertainty was associated with the accuracy of the measuring instrument, assuming a triangular distribution. The measurement was performed using a digital slide caliper (L.S. Starrett Company, Athol, MA, model 798B-6/150 W/SLC with NIST Traceable Calibration). The uncertainty source provided a

 standard uncertainty for the diffusion distance at the end cap ulend and the screen ulsc of 0.001 cm. The standard uncertainty for

 the effective cross-sectional area at the end cap uAend and screen uAsc was 0.004 cm2. Exposure time. The exposure time was determined from the official time of continental Chile provided by the government agency Servicio Hidrografico y Oceanografico (Hydrographic and Oceanographic Service of Chile, SHOA, Spanish acronym). This agency provides a computerized clock to sync to a timeserver, which we referenced to a portable digital clock with a precision of 0.1%. The standard uncertainty of 0.7% was estimated from 10 successive time measurements taking into consideration the uncertainty of the time deployments of the passive samplers. Repeatability factor. The repeatability factor can be estimated using a repeatability study [32]. A series of 10 replicate samples were collected to obtain a standard deviation for direct use as the standard uncertainty (a normal distribution is assumed). The repeatability can be estimated in a manner similar to Eq. (7) as follows: sf ffiffiffi : uf rep ¼ prep n

ð14Þ

Instrumental recovery factor. The samples from the recovery method with the ammonium standard spiked onto the pad filters were processed in the same manner as the deployed samplers, and the quantities observed in the recovery method samples were compared with the spiked concentrations. The recovery factor is the only input quantity that takes into account the sample preparation. The recovery is calculated according to Eq. (15) [20,33]: f rec ¼

cobs : CM

ð15Þ

The uncertainty associated with the recovery, uf rec is evaluated as follows: uf rec

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s   ffi uC M 2 uobs 2 ¼ f rec  þ cobs CM

ð16Þ

where sobs is the standard deviation of the n measurements of the reference material, cobs is the average of the measurements, CM is the reference concentration and uC M is the uncertainty of the reference value. The expected concentration and the standard uncertainty in each extract was 2 mg L−1 with 0.04 mg L−1 standard uncertainty. The main stock solution was prepared from certified commercial solutions of 1000 mg L−1 nominal ion concentration with a standard uncertainty of 1 mg L−1 (CertiPur, Merck). A significance test was conducted to determine whether the measured recovery deviated significantly from 1.0. A statistical t-test was calculated using the following equation:   1−f   rec  tc ¼  :  uf rec 

ð17Þ

The t-test value was compared with the 2-tailed critical t-test value for n − 1 degrees of freedom with 95% confidence. If the t-test value was equal to or exceeded the critical t-test value, the recovery deviated significantly from 1.0, and the concentration values were then corrected according to the recovery factor; otherwise, frec was equal to 1.0.

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Molecular weight factor (fMW). These conversion factors permit a change in the measured concentration of the NH+ 4 ion to the NH3 gas and correspond to the following: f MW ¼

MW NH3 MW NHþ

:

ð18Þ

4



 The uncertainty in the molecular weights of the ammonium ion MW NHþ and NH3 MW NH3 is determined from the latest atom4

ic weight (AWi) for the ith element [34]. The standard uncertainty is determined by assuming a rectangular distribution. The uncertainty of fMW is estimated according to the following equation: ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi v0 u
2
2 12 0
2
2 12 u uB uAW N þ 3  uAW H C B uAW N þ 4  uAW H C u A þ@ A : t@ MW NH3 MW NHþ

uf MW ¼ f MW

ð19Þ

4

The value of fMW was therefore 0.94412, and the standard uncertainty was 0.00005. Steps 5, 6 and 7. Calculation of the combined uncertainty, expanded uncertainty and the expression of the results. For each main source of uncertainty (uqi) (i.e., the calibration curve, extraction volume, gas diffusion coefficient, geometric factors, exposure time, repeatability factor, recovery factor and molecular weight factor), the combined standard uncertainty can be calculated as a function of the identified i
 sources uqi according to Eq. (20) [16,18]: 2 uC NH

3

¼ C NH3

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 n uqi  ∑ qi i¼1

ð20Þ


 where qi represents the i-source of uncertainty cNHþ Ex ; cNHþ Bl ; vEx ; r NH3 ; t; f MW ; f rep ; f rec . The final results should be stated togeth3 3
 er with the expanded uncertainty U C NH , which is calculated using a coverage factor (k) [14] and provides a level of confidence 3

according to the following expression: U C NH ¼ k  uC NH : 3

ð21Þ

3

The following form is therefore recommended to express the result: C NH3  U C NH :

ð22Þ

3

3. Results and discussion 3.1. Calibration curve, blank, LOD and LOQ A calibration curve was constructed to determine the concentration of NH3 in ambient air using a colorimetric method. The concentrations of the working calibration solutions for curves (cj) in mg L−1, the average of 3 absorbance measurements at 640 nm (Aj) in absorbance units (a.u.) and the relative standard deviation (RSDj) in absorbance units are provided in Table 1. The results are provided in Fig. 4, and the calibration concentration range, number of calibration points, replicates per calibration point, slope, intercept, coefficient of correlation and residual standard deviation are provided in Table 2. A high correlation coefficient was obtained for the calibration curves (r2 > 0.995).

Table 1 Data for the calibration curve concentration (cj) in mg L−1, absorbance (Aj) in a.u. and standard deviation (SD) in a.u. cj (mg L−1)

Aj (a.u.)

RSDj (a.u.)

0 0.2 0.4 0.8 1.2 1.5 1.7 2.0

0.019 0.135 0.334 0.521 0.768 0.994 1.148 1.328

0.04 0.03 0.02 0.03 0.05 0.06 0.09 0.10

The 10 unexposed field blank tubes were found to have a concentration of 0.03 mg L−1 with a standard deviation of 0.01 mg L−1. The standard deviation of blank values was used to calculate the detection limit for the passive sampling method. The LOD and LOQ for the NH3 measurement were 0.03 and 0.10 mg L−1, respectively. Those limits correspond to 0.9 μg m−3 (LOD) and 2.9 μg m−3 (LOQ) for an ambient concentration. 3.2. Repeatability study The descriptive statistics and repeatability data for NH3 are provided in Table 3. The average value and the standard deviation were calculated from independent measured replicates. Eq. (14) was used to calculate the standard uncertainty. The results of this study indicate that the relative standard uncertainty for the repeatability was 3.8%, and the repeatability factor was 1.000 ± 0.013. 3.3. Recovery study The recovery studies were performed by spiking the filter pad with ammonium ion and extracting samples (Table 4). The row labeled “Means” in Table 4 is the average of 8 injections, and the theoretical row corresponds to the quantity of certified reference solution added, which was maintained within the range of the calibration limits. From Table 4, we can conclude that the quantitative recovery, calculated according to Eq. (15), was 100.7%. The statistical significance (Eq. (17)) was calculated, and the results of the calculated t-statistic were lower than those of the 2-tailed critical

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347

Fig. 4. UV–vis spectra and resulting calibration curve.

t-statistic, indicating that the recovery factor correction need not be applied because the concentration of CM did not differ significantly from the concentration in the spiked sample. The correction factor was therefore equal to 1.0 (100%). 3.4. Uncertainty budgets, combined and expanded uncertainty The uncertainty in the results calculated using the proposed methodology is provided in Table 5. The combined and expanded uncertainties (Eqs. (20) and (21)) were calculated for the uncertainty sources. The result was 18.6 ± 7.3 μg m−3 expressed with the expanded uncertainty calculated using a coverage factor (k) equal to 2, at a 95% level of confidence. The uncertainty expressed as a relative coefficient of variation was 39%. The results of the relative uncertainty are tabulated by source in Fig. 5. The largest contributions to the combined uncertainty were derived from the uncertainty flow rate, a function of the
of the sampling  gas diffusion coefficient DNH3 ; uDNH3 and the geometry of the pas sive sampler lend ; lsc ; Aend ; Asc ; ulend ; ulsc ; uAend ; uAsc . The other large sources of uncertainty were the repeatability and the calibration curve, clearly demonstrating the importance of colorimetric analysis and the blank contribution to the uncertainty estimation. 3.5. Comparison with a reference method The results of the simultaneous deployments of passive samplers and scrubbers have been reported in the literature [30,35] and show Table 2 Calibration range and parameters for the calibration curves: range of concentration, slope, correlation coefficient and residual standard deviation. Description

Value

Calibration range (mg L−1) No of calibration points No of replicates per calibration point Slope (cm−1 L mg−1) Intercept (cm−1) Correlation coefficient, r2 Residual standard deviation

0–2.0 8 3 0.65 ± 0.01 0.02 ± 0.01 0.996 0.0051

a close agreement between the 2 methods with a relative uncertainty of 5%. This comparison demonstrates that the OPSs are a viable alternative to other NH3(g) sampling methods. If the uncertainty source is included in the budget, the results expressed with the expanded uncertainty, calculated using a coverage factor (k) equal to 2, at a 95% level of confidence, expressed as a relative coefficient of variation are 40%.

3.6. Standard deviation versus uncertainty When conclusions are drawn from measurements, the uncertainty of the measurements must not be neglected. This uncertainty is particularly important when comparing different measurements [36]. As an

Table 3 Descriptive statistics for the repeatability study. Description

Value

Replicate number, ns Means (mg L−1) Standard deviation, srep (mg L−1) Standard uncertainty, urep (mg L−1) Repeatability factor Relative standard uncertainty, ur-rep

8 0.53 0.02 0.01 1.000 0.013

Table 4 Descriptive statistics for the recovery study. Description

Value

Spiked value, CM (mg L−1) Standard uncertainty, uM (mg L−1) Number of replicates, nobs Means, cobs (mg L−1) Standard deviation, sobs (mg L−1) Standard uncertainty, uobs (mg L−1) Relative standard uncertainty, ur-obs Recovery factor, frec Relative standard uncertainty, uf rec Critical value, t 2-Tailed critical value, t

1.00 0.02 8 1.01 0.09 0.03 0.02 1.01 0.04 0.1483 1.8595

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Table 5 Uncertainty budget for the estimation of ambient NH3 concentration by the OPS. Component
 Calibration curve andblank cNHþ Ex−Bl ; mg L−1
4 −1 þ Blank cNH Bl ; mg L
 4 Calibration curve cNHþ Ex ; mg L−1 4 Extract solution volume (vEx,mL) Certified internal volume Repeatability Variation in the temperature
 Sampling flow rate r NH3 ; mL min−1 2 Aend (cm ) lend (cm) Asc (cm2) lsc (cm) Gas diffusion coefficient DNH3 (cm2 min−1) Time (t,min) Repeatability (min) Precision (min) Molecular weight factor (fMW,unitless) Molecular weight NH3 Molecular weight NH+ 4 Repeatability factor (frep,unitless) Recovery factor (frec,unitless) Reference material (CM,mg L−1) Repeatability (cobs,mg L−1) Conversion factor (constant, μg L mg−1 m−3)

Source qi

Standard uncertainty uqi

Relative uncertainty

0.65 0.03 0.68 10.0 10.00 10.01 10.00 16.4 0.785 0.600 0.152 0.020 14.7 20.07 × 103 20.07 × 103 20.07 × 103 0.94412 17.0309 18.0390 1.000 1.00 1.00 1.01 106

0.03 0.01 0.03 0.04 0.02 0.03 0.01 3.0 0.004 0.001 0.004 0.001 0.9 0.14 × 103 0.144 × 103 0.002 × 103 0.0005 0.0006 0.0006 0.013 0.04 0.03 0.02 0

0.05 0.20 0.04 0.004 0.002 0.003 0.001 0.18 0.005 0.002 0.026 0.050 0.061 0.007 0.007 0.0001 0.0005 0.0004 0.0003 0.013 0.04 0.03 0.02 0

uqi qi

The result expressed with the expanded uncertainty (k = 2) at a 95% level of confidence is 18.6 ± 7.3 μg m−3. The expanded uncertainty is 39.2%.

illustration, we measured the atmospheric NH3 concentrations during 2 different sampling periods using the OPS at the same sampling site located in the Facultad de Ciencias at the University of Chile. The periods, designated as SP1 and SP2, were in autumn (11 January to 02 February, 2012) and winter (22 June to 06 July, 2012). Fig. 6 illustrates the comparison of the ambient NH3 concentration during the 2 sampling periods, considering the standard deviation, i.e., CNH3  1s (Fig. 6a) and the expanded uncertainty, i.e., CNH3  U CNH3 (Fig. 6b). When comparing the results and considering only the standard deviation, one can conclude that the results differ significantly. However, if the uncertainty in the results is considered, the conclusion changes, and results become comparable. From these results, the need to consider

the uncertainty in the analysis of environmental measurements to obtain appropriate conclusions becomes obvious.

4. Conclusions and summary This paper presents detailed measurement calculations and develops a full uncertainty budget for the analysis of ambient NH3 concentrations using passive sampling. The results of the estimated overall expanded uncertainty in the measurements are useful for analyzing the ambient NH3 concentration as determined by an OPS. The result expressed as a relative coefficient of variation was 39%, calculated using a coverage

Fig. 5. Uncertainty contribution by source for the measurement of the ambient NH3 concentration using an OPS.

M.A. Leiva G. et al. / Microchemical Journal 110 (2013) 340–349

Fig. 6. Comparison of the ambient NH3 concentration considering the standard deviation (a) and expanded uncertainty (b).

factor (k) equal to 2 at a 95% level of confidence during 2 weeks of Ogawa exposure. The aforementioned results indicate that the Ogawa sampler can be successfully deployed to estimate the atmospheric NH3 and could find wide application in environmental monitoring. However, to obtain correct conclusions, the uncertainty in the measurements must be considered. Examination of the uncertainty budget revealed the following: • The largest contributions to the combined uncertainty derive from the uncertainty associated with the sampling flow rate, which underlines the importance of considering the geometry of the passive sampler as an uncertainty source, and the gas diffusion coefficient. We highly recommend that the supplier of passive samplers provides information regarding the uncertainty in the geometry of the passive OPS. • The other source of uncertainty is that associated with the blank sample, often due to contamination artifacts. Field and laboratory protocols should allow for standardized field deployment, handling, storage and analysis to reduce this uncertainty. Acknowledgments The financial support of the Centro de Ciencias Ambientales of the Facultad de Ciencias of the Universidad de Chile is gratefully acknowledged. MALG acknowledges CONICYT — BECAS CHILE for partial financial support of the Postdoctoral stay. References [1] B.H. Baek, V.P. Aneja, Q.S. Tong, Chemical coupling between ammonia, acid gases, and fine particles, Environ. Pollut. 129 (2004) 89–98. [2] R.G.E. Morales, Atmospheric Urban Pollution. Critical Episodes of the Environmental Pollution in the City of Santiago of Chile (in Spanish), Editorial Universitaria SA, Santiago of Chile, 2006. [3] S.N. Behera, M. Sharma, Investigating the potential role of ammonia in ion chemistry of fine particulate matter formation for an urban environment, Sci. Total Environ. 408 (2010) 3569–3575. [4] Koutrakis, Monitoreo pasivo de NH3 en la RM, Informe Final, CONAMA, 1998. [5] A.L. Zbieranowski, J. Aherne, Spatial and temporal concentration of ambient atmospheric ammonia in southern Ontario, Canada, Atmos. Environ. 62 (2012) 441–450. [6] M.A. Puchalski, M.E. Sather, J.T. Walker, C.M.B. Lelunann, D.A. Gay, J. Mathew, W.P. Robargef, Passive ammonia monitoring in the United States: comparing three different sampling devices, J. Environ. Monit. 13 (2011) 3156–3167.

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