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Inter-laboratory study to improve the quality of the analysis of nutrients in rainwater chemistry
Atmospheric Environment 43 (2009) 3424–3430
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Inter-laboratory study to improve the quality of the analysis of nutrients in rainwater chemistry Sathrugnan Karthikeyan, Rajasekhar Balasubramanian*, Jun He Division of Environmental Science and Engineering, National University of Singapore Engineering Drive 1, Singapore 117576, Singapore
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 July 2008 Received in revised form 2 March 2009 Accepted 13 March 2009
This paper describes the results of an inter-laboratory study conducted for the analysis of nutrients (nitrate, ammonium, phosphate, total nitrogen (TN), and total phosphorus (TP)) in natural rainwater. For this purpose, rainwater samples were collected and aggregated in Singapore and homogenized. These samples were immediately filtered through 0.45 mm membrane filters and autoclaved for 15 min at 80 C in order to stabilize the nutrients. The homogeneity and the stability of nutrients were rigorously tested for a period of three months initially. Upon ensuring the homogeneity and stability, the samples were distributed to 15 different laboratories from various countries around the world (Australia, Brazil, India, Mauritius, Singapore, Slovenia, Spain, Taiwan, and USA). Almost all laboratories have reported the analytical results for nitrate whereas only 8 of the 15 laboratories reported results for other nutrients such as ammonium, phosphate, TN, and TP. The discrepancy was mainly due to the presence of these nutrients in low concentration levels (particularly ammonium ion and phosphate). Not all the laboratories were equipped with analytical capabilities to conduct the analysis of nutrients in low concentration levels. Further, the uncertainty associated with the analysis of TN and TP restricted the number of laboratories that could report their analytical data on nutrients. All 14 laboratories reported nitratenitrogen results which were in good agreement with each other (0.68 0.07 mg l1). Similarly, the results of TN and TP were also comparable among at least 8 laboratories. This inter-laboratory study on the analysis of nutrients in natural rainwater, conducted for the first time, provided an opportunity to the participating laboratories to assess and improve their laboratory performance, thereby, improving the quality of their analytical data. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Precipitation chemistry Nitrogen deposition Quality control Quality assurance Uncertainty analysis
1. Introduction The atmosphere has been recognized as an important pathway for the transfer of nutrients to surface waters through wet and dry deposition. The two main nutrients which may cause eutrophication, if in high concentrations, are nitrogen (N) and phosphorus (P). Both elements are not active as nutrients in their elemental form. While phosphorus is available to primary production in a dissolved inorganic phase as phosphate (PO4–P), nitrogen is supplied to aquatic plants mostly as ammonium (Ammonium-N: NH4–N; the total of ammonium plus ammonia), nitrate (Nitrate-N: NO3–N), and to a lesser degree nitrite (Nitrite-N: NO2–N). These forms of nitrogen are commonly referred to as Dissolved Inorganic Nitrogen (DIN) (Sharp, 1983; Janet, 1998; Guildford and Hecky, 2000; Erisman et al., 2001). Wet deposition of nutrients occurs when
* Corresponding author. Tel.: þ65 6516 5135; fax: þ65 6774 4202. E-mail address: [email protected] (R. Balasubramanian). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.03.025
gaseous, or particulate N/P is transferred from the air onto an underlying surface via precipitation. For example, atmosphericallyderived nitrogen contains a mixture of biologically-available dissolved inorganic compounds (NO3–N, NO2–N, NH4–N; DIN) in rainfall. In order to understand the influence of atmosphericallyderived nutrients on eutrophication, field studies on measurement of the levels of nutrients in hydrometeors, particularly in rainwater, have been actively pursued over the last two decades in urban, rural, and remote sites at different parts of the world using different analytical techniques (Nadim et al., 2001; Hu et al., 2003; Holland et al., 2005; Zhang et al., 2004, 2007). The acquisition of relevant and reliable analytical data is a primary component in all research and monitoring programs associated with the assessment of nutrients in natural waters and related environmental issues such as eutrophication. The analysis of nutrients in rainwater is relatively straightforward particularly in the case of dissolved inorganic nitrogen (NO3–N, NO2–N and NH4–N) since most laboratories employ ion chromatography for this purpose (Nadim et al., 2001; Hu et al., 2003; Zhang et al., 2007).
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However, phosphate analysis is sometimes carried out spectrophotometrically (by using longer path length cell) since it is present in very low concentration (2–30 mg l1). On the other hand, dissolved organic nitrogen and dissolved organic phosphorus are estimated only by the difference between the total and inorganic N and P. Measuring total nitrogen (and phosphorus) involves releasing the nitrogen (and phosphorus) from organic molecules by chemical oxidation to NO3–N (and to PO4–P). The uncertainties associated with estimation of DON (Dissolved Organic Nitrogen) or DOP (Dissolved Organic Phosphorous) are relatively large compared to that of DIN (Dissolved Inorganic Nitrogen) or DIP (Dissolved Inorganic Phosphorous). The major sources of error result from incomplete conversion of DON/DOP to DIN/DIP or losses during sampling and storage. Cornell et al. (2003) have critically reviewed the importance of DON and the challenges involved in its analysis. In the context of quality assurance, while a research laboratory can test the repeatability of its own measurements of nutrients, it cannot sufficiently assess their accuracy. The essential tools needed for this assessment are inter-laboratory comparison and certified reference materials (CRMs), those certified by the Standards, Measurement and Testing program of the commission of European community, or by the US National Institute of Standards and Technology (NIST). In the absence of suitable CRMs, inter-laboratory comparison provides laboratories with a valuable third party verification of data quality and acceptability. CRMs have been developed from inter-laboratory data for various natural water matrices (river water, rainwater and seawater) over the years (Alkema et al., 1998; Clancy and Willie, 2004; Karthikeyan and Balasubramanian, 2006a,b). Regular inter-laboratory study (ILS) generates a wealth of data for the many parameters important in environmental programs (Aminot and Kirkwood, 1994; Otoshi et al., 2001; Mosello et al., 2004). In recognition of the need to improve the quality of analytical data on nutrients in rainwater, use of CRMs and regular participation in inter-laboratory studies are critically needed. In response to this growing need, we have conducted an interlaboratory study for the analysis of nutrients in rainwater. Extensive literature search revealed that only limited reports (Alkema et al., 1997; Mosello et al., 2004) were available on interlaboratory data pertaining to the analysis of nutrients in rainwater. To the best of our knowledge, no ILS has been conducted previously using natural rainwater. Furthermore, neither ILS data nor CRMs are available in the literature for the analysis of total nitrogen and total phosphorus in rainwater. In this paper, we present the results obtained from an inter-laboratory exercise for the analysis of a wide range of nutrients in rainwater. This study has been conducted with two main objectives: (1) to verify the precision and accuracy of the methods commonly used (ion chromatography, spectrophotometry) for the analysis of various nutrients in rainwater (NO3–N, NH4–N, PO4–P TN, and TP); (2) to establish reference values for these nutrients in rainwater in order to develop a certified reference material for routine internal quality assurance/quality control (QA/QC) assessment. The details of the production, homogeneity, stability of the reference materials, and the inter-laboratory analytical data are presented and discussed. 2. Materials and methods 2.1. Sample bottles A 10 L capacity HDPE tank (NalgeneÒ) was used for the initial storage of rainwater. HPDE bottles (250 mL, NalgeneÒ) were used for storing the homogenized rainwater samples. These bottles were filled with 2 N HNO3 for 3 days and subsequently with ultrapure water for 3 days. Finally, they were rinsed with ultrapure water
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three times and kept safely in plastic bags until use. No residual nitrate was found in the rinse water after washing. 2.2. Preparation of rainwater samples for the intended ILS During peak monsoon periods, rainwater samples were collected in large quantities using an automated wet-only precipitation collector (Model 301, Aerochem Metric Inc.). This collector consists of a High Density Polyethylene bucket with a diameter of 28.6 cm, which is equipped with a sensor that detects the precipitation episodes and activates a mechanism by which the lid opens with the first drop of a shower and closes just after the rain event. The automated wet collector thus prevents the contamination of rainwater by dust fall, taking place before and after a rain event. Rainwater samples were collected and aggregated upto 10 L in Singapore. The rainwater was immediately filtered through a 0.45 mm membrane filter, and it was autoclaved for 15 min at 80 C. This sample was homogenized through circulation (bubbling with high purity N2), and stored in a 10 L HPDE container. Then six sub-samples (10 mL) were collected from this tank and analyzed for nitrate and phosphate to confirm the homogeneity within the container (10 L). Finally, these rainwater samples were transferred to 250 mL HDPE sample bottles to their capacity, preserved in polythene bags, and stored in a refrigerator @ 4 C for 3 months. These samples were kept safely in a clean place to avoid contamination. 2.3. Homogeneity and stability studies The homogeneity of the sample was investigated by analyzing 4 randomly selected bottles from the 40 bottles stored with rainwater, and analyzed for nutrients. Then, the stability of a few nutrients (DIN, TN, and TP) was investigated over a period of time (6 months). Ion chromatography (IC) (Hu et al., 2003; Karthikeyan and Balasubramanian, 2006a,b) was employed for the analysis of nitrate, ammonium ion, and total nitrogen. DIP and TP were analyzed by the molybdenum blue method (APHA, 1995). For internal quality assurance, a standard addition method was employed for all parameters. The recovery results were in the range of 80–105% at different concentration levels. 2.4. Inter-laboratory study After ensuring the homogeneity and stability of the rainwater samples, an inter-laboratory study was conducted. Fourteen laboratories from various countries (Australia, Brazil, India, Mauritius, Poland, Slovenia, Spain, Taiwan, and USA) were invited to participate in this inter-comparison exercise. Upon their agreement, one bottle (250 mL) was sent out to each participant safely. The participants were requested to analyze mainly nutrients (nitrate, ammonium ion, total nitrogen, dissolved inorganic phosphate, and total phosphorus) as per their existing analytical protocols (refer Table 2) in their respective laboratories. In view of the potential use of the rainwater sample as a reference material for atmospheric research, participants were also asked to analyze for other ions such as chloride, sulfate, and sodium, if possible. Each participant was requested to report the concentration values for six independent replicates. Only 13 laboratories reported the results in time and the remaining lab was unable to complete the analysis due to some technical problems. The organizers guaranteed the full respect of confidentiality as to the identity of the laboratories participating in this study. At the completion of the study, each laboratory received a report with comments about its performance and overall outcome of the exercise.
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Table 1 Variance-component analysis data of the homogeneity test. Element
Sum of squares
NO3 TNN NH4 DIP SO4
Mean squares
Significance (a ¼ 0.05)
F
BB
WB
BB
WB
BB
WB
0.083 0.144 0.010 5.47 E005 0.160
0.017 0.051 0.000 8.17 E006 0.039
0.028 0.048 0.003 1.82, E005 0.053
0.009 0.025 0.000 4.08, E006 0.020
3.504 5.528 1.955 1.522 2.296
1.073 2.927 0.065 0.341 0.842
0.089 0.037 0.222 0.302 0.178
0.400 0.130 0.938 0.724 0.476
BB – between the bottles; WB – within the bottles.
2.5. Statistical analysis Homogeneity has been tested under univariate variancecomponent analysis at 5% significance level by using SPSS 13.0 for Windows (SPSS Inc., Chicago, IL, USA). A robust statistical method (Analytical Methods Committee, 1989) has been applied in the study which has gained wide acceptance. Robust statistics do not discard any observations, but down-weight extreme data. As a result, robust statistics are relatively insensitive to extreme values and to ‘tailing’ distributions. The median was calculated using all data points. Those values that deviated from the median by 1.5 times (5% tolerance) of Sb (standard deviation of participating laboratories) were identified and eliminated. The performance of individual laboratories was assessed based on the assigned values for each parameter and the actual values measured by the participants through the calculation of Z scores. This is a simple method of giving each participant a normalized performance score for bias; the method was adopted as a standard by ISO/IUPAC [ISO guide 431, 1997]. Z scores for each laboratory and each determinant were calculated using the following equation:
z ¼
jxi xj Sb
(1)
where, xi ¼ the reported value for the analyte concentration in the test sample (from a given laboratory), x ¼ the assigned value, and Sb ¼ Standard deviation of laboratory mean or the target value performance (12.5%). 3. Results and discussion 3.1. Homogeneity and stability The average concentrations of major ions in six sub-samples contained within the main storage bottle were 0.72 0.03 mg l1
(NO3–N), 0.19 0.05 mg l1 (NH4–N), 1.22 0.12 mg l1 (TN), 0.013 0.003 mg l1 (DIP–P), 3.45 0.31 mg l1 (SO2 4 ) and 0.75 0.15 mg l1 (Cl), respectively. The homogeneity of the rainwater sample, after transferring into small bottles, was tested by choosing 4 bottles randomly from the lot (40 bottles) and analyzing 2 them for NO 3 N, TNN, DIP, and SO4 in triplicate. The mean concentrations between the bottles were 0.67 0.03 mg l1(NO3–N), 0.94 0.15 mg l1(TNN), 0.19 0.03 mg l1(NH4–N), 0.010 0.003 mg l1(IP–P), and 3.29 0.18 mg l1(SO2 4). The coefficient of variance (% RSD) was calculated from mean values of four bottles and was less than 10% which indicates homogeneity of the sample between the bottles. Further, the homogeneity of the sample within the bottle and between the bottles was tested by variancecomponent analysis using SPSS software. The results are presented in Table 1. For NO3–N, NH4–N, DIPP and SO2 4, no statistically significant difference was observed between bottles and for the samples analyzed within bottles since all the p-values are greater than 0.05. For TN, no significant difference was found within the bottle, but some difference was observed with p-value of 0.037 (<0.05) for samples drawn from different bottles. However, the homogeneous subsets did not show any big difference between 2, 3 and 4 with p-value ¼ 0.551. Therefore, it can be said that the rainwater prepared for the inter-lab study was homogeneous within as well as between the bottles. The stability is another important criterion to be checked since biological activity can alter nutrient concentration rapidly if not preserved properly. Based on the previous reports, we have done a two-step pretreatment to stabilize the rainwater samples (Aminot and Kerouel, 1991, 1995; Cofino and Wells, 1994). Samples were initially filtered through 0.45 mm filters and autoclaved at 80 C for 15 min to ensure the complete removal of micro-organisms from these water samples. In order to check the stability, two of the samples were tested on a monthly basis over a period of six months.
Table 2 Summary of inter-laboratory results [concentrations in mg l1]. Lab
NO 3 –N (method)
NHþ 4 N (method)
TNN (method)
DIPP (method)
TPP (method)
SO2 4 (method)
Cl (method)
Lab1 Lab2 Lab3 Lab4 Lab5 Lab6 Lab7 Lab8 Lab9 Lab10 Lab12 Lab 13 Lab 14 Org.
0.75 0.06 (A) 0.59 0.05 (A) 0.84 0.07 (B) 0.73 0.02 (B) 0.63 0.09 (A) 0.78 0.06 (A) 0.63 0.01 (B) 0.34 0.23 (D) 0.67 0.07 (B) 0.67 0.02 (A) 0.61 0.03 (B) 0.71 0.02 (A) 0.71 0.02 (B) 0.72 0.12 (A)
0.29 0.014 (A) NR 0.09 0.01 (A) 0.16 0.003 (B) 0.02 0.01 (A) 0.28 0.01 (A) NR 0.16 0.01 (D) NR 0.09 0.05 (A) 0.147 0.03 (B) NR NR 0.24 0.04 (A)
1.0 NR 1.2 0.9 NR NR NR 2.84 NR 1.1 0.85 NR 1.07 0.9
0.001 0.0008 (B) NR ND (A) 0.007 0.0 (B) 0.430 0.08 (A) <0.05 0.109 0.007 (B) 0.004 0.004 (B) NR 0.005 0.002 (B) <0.005 (B) NR 0.001 0.0005 (B) 0.005 0.003 (B)
0.005 0.003 (B) NR 0.018 0.003 (C) 0.015 0.001 (C) NR 0.220 0.05 (C) 0.036 0.003 (C) 0.004 0.002 (C) NR 0.012 0.003 (C) 0.008 003 (C) NR 0.005 0.001 (C) 0.010 0.003 (B)
NR 2.40 4.30 2.70 NR 2.89 NR NR NR 2.95 NR NR NR 3.79
NR 0.90 0.1 (A) 0.80 0.27 (A) 0.98 0.03 (A) NR 1.03 0.04 (A) NR NR NR 0.67 0.20 (A) NR NR NR 0.74 0.20 (A)
0.01 (C) 0.39 (B) 0.01 (B)
0.18 (D) 0.30 (B) 0.05 (B) 0.184 (C) 0.14 (A)
A – Ion chromatography. B – Spectrophotometery (HACH or flow injection analysis following APHA). C – Persulfate oxidation followed by Spectrophotometery (HACH or flow injection analysis). D TKN steam distillation & Modified Berthelot method.
0.3 (A) 0.70 (A) 0.06 (A) 0.04 (A)
0.50 (A)
0.18 (A)
S. Karthikeyan et al. / Atmospheric Environment 43 (2009) 3424–3430
5
4
Nitrate
Total Nitrogen
Ammonia
Sulfate
mg/l
3
2
1
0 Apr
May
Jun
Jul
Aug
Sept
Fig. 1. Monthly variation of selected ions in rainwater.
As can be seen from Fig. 1, the concentrations of nitrate, total nitrogen, and sulfate were within 20% of variation compared to those observed in the first month after sample collection, which ensures the stability of these analytes. However, it was observed that the ammonium ion concentration was decreasing progressively. To minimize further reduction, samples were refrigerated at 4 C after 3 months, and it was then quite stable. To improve the stability of nutrients during long-term storage of rainwater at room temperature, a g-irradiation, or microwave irradiation of rain water sample should be tested in future studies (Clancy and Willie, 2004). In summary, the homogeneity and stability test results were satisfactory. Rainwater samples were then sent to the participants. 3.2. Comparison of inter-laboratory data Table 2 presents the summary of analytical results reported by various laboratories during this inter-laboratory study. All the participating laboratories have reported the results for NO 3 N. However, only 50–70% of them reported the results for other nutrients. Many participants did not analyze, or report NH4–N, DIPP, TN, and TP because of two different reasons. Firstly, ammonium and phosphorus were present in extremely low concentrations. Secondly, some participants attributed the lack of analytical data to inadequate sample size. Total phosphorus was the second most important nutrient reported by many participants even though the concentration was very low (w0.01 mg l1). Most of the participants have employed a spectrophotometer with longer path length cells (5 or 10 cm) to attain better sensitivity. Ammonium ion concentrations were also equally reported by as many as nine participants. However, the reported values showed large deviation (>50%). It could be presumably due to loss of volatile ammonia during storage. We have observed a reduction in the concentration of ammonium within 3 months when the rainwater samples were stored at room temperature. However, the concentration did not change significantly after storing the samples at 4 C. Merry (1995) reported a slight reduction in the concentration of ammonium ion during prolonged storage of seawater in autoclaving approach (Aminot and Kerouel, 1991, 1995). Since the ammonium was present in trace levels, even a small variation in the analytical measurements could affect the overall data quality. A more comprehensive study is required to stabilize the nutrients present in low levels at room temperature. As mentioned earlier, exposure of rainwater samples to g-irradiation, or microwave irradiation, should be tested and its merits evaluated. The DIP data were not considered for any statistical analysis since there were only few values (four) reported but with large deviation. The
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presence of other nutrients in low concentrations with the exception of DON was a major factor which contributed to the inconsistency in the analytical data reported by the participants. All the reported analytical data were treated with robust statistical methods (Analytical Methods Committee, 1989; Hund et al., 2000) to identify and eliminate the outliers. As described in Section 2, the median concentration was calculated without discarding any data points and subsequent outliers were eliminated by the cut off values. However, the rejected outliers were also considered in graphical presentation to justify their rejection. These laboratories with outliers could improve their performance if they carry out standard addition recovery tests. For example, lab 8 used different measurement methods (TKN steam distillation & Modified Berthelot method) compared to other standard methods (ion chromatography, spectrophotometry). The former methods need further validation in order to be consistent with standard methods. The laboratory consensus value (median) and standard deviation between laboratory values were calculated after eliminating the outliers and are presented in Table 3 along with other relevant information. The standard deviations between the laboratories were in the range of 10–15% of the target mean. This is in an acceptable range based on AOAC guidelines (2002) considering the low concentration levels. The inter-comparison of laboratory results against the grand mean is graphically shown in Fig. 2 (a–g). It could be seen that NO 3 results reported by all participants were within acceptable variation. One of the laboratories has reported the concentration with a negative bias. This particular laboratory was informed of the problem with their results for NO 3 . They have reanalyzed the sample, and the results were within the expected range. The discrepancy in the first set of measurements was due to a calibration error which they could identify only after our feedback. This example clearly shows that the third party verification sufficiently improves the quality of analytical data on nutrients in rainwater through the inter-comparison study. In the case of TN, TP, sulfate, and chloride, the reported results are in good agreement within the reported results. Spectrophotometry (APHA method) or ion chromatography was employed for nitrate analysis and almost all laboratory results were consistent. However, one of the laboratories reported the results with negative bias. The negative bias could be due to partial reduction of nitrate before measurement with the spectrophotometric technique. This problem could have been easily identified if standard addition experiment was carried out. The participating lab was informed about this negative bias with a suggestion to revalidate their method. For total nitrogen or total phosphorus, persulfate oxidation followed by spectrophotometric measurements was utilized by many participants. Only two of the TP results were not acceptable, presumably due to low concentration. Quantitative detection of TP can be improved by using longer path length cells during measurements. The IC results for other major anions (Cl & SO 4 ) were acceptable within the results. Although the IC or the
Table 3 Laboratory Grand Mean and SD of nutrients analysis in rainwater. Parameter
Median (mg l1)
S.D (mg l1)
No of acceptable data/total no of data
Nitrate (as NO 3) Ammonium (as NH4þ) DIP (as PO3 4 ) Total Nitrogen (N) Total P (as P) Sulfate (as SO2 4 ) Chloride (as Cl)
0.69 0.16 0.005 1.04 0.010 2.89 0.85
0.07 0.06 0.002 0.1 0.005 0.36 0.14
14/14 7/9 6/8 8/8 7/10 5/6 6/6
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1.50
b TN-N (mg/l)
0.90 0.60
0.90 0.60
b1 La b2 La b3 La b4 La b5 La b6 La b7 La b8 La b9 La b1 0 or g La b1 2 La b1 La 3 b1 4
La
b1 2 La b1 La 3 b1 4
La
b9 b1 0 or g La
b8
La
b7
La
b6
La
La
La
La
La
La
b5
0.00 b4
0.00 b3
0.30
b2
0.30
La
c
1.50 1.20
1.20
b1
NO3-N (mg/l)
a
0.40
d
0.220
2 b1
g
0
La
or
b1
b9
La
b8
La
La
b7 La
b6 La
b5 La
b4 La
b3
b2
La
La
b1
f
1.5
Cl (mg/l)
1.2 0.9 0.6 0.3
La 3 b1 4
2
b1
b1
La
La
0
g
or
b9
b1
La
b8
La
b7
La
La
b5
b6
La
La
b4
La
b3
La
b2
b1
La
La
b1 La 3 b1 4
2
La
b1
0
g
or
b9
b1
La
La
b8
b7
La
b6
La
b5
La
b4
La
La
b3
b2
La
La
b1
0.0 La
La
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 La
La b
1
La b
-0.100
0.00
SO4 (mg/l)
La b4 La b5 La b6 La b7 La b8 La b9 La b1 0 or g La b1 2 La b1 La 3 b1 4
-0.020
0.08
e
0.060
2
0.16
0.140
3
0.24
La b
TP (mg/l)
NH4+ (mg/l)
0.32
Fig. 2. Graphical representation of analytical data reported by different laboratories: (a) Nitrate; (b) total nitrogen; (c) ammonium; (d) total phosphorus; (e) sulfate; and (g) chloride. Solid line represents grand mean, calculated from individual laboratories data and dotted line is Uex as given in Table 5.
spectrophotometric method is suitable for these applications, an internal quality control protocol is an essential step to improve the laboratory performance. 3.3. Laboratory performance (Z score) Laboratory performances areis classified according to the following criteria: for an absolute value of Z 2, the performance of the laboratory is considered acceptable. When the absolute value of Z lies between 2 and 3, the result is of questionable quality, whereas for an absolute value of Z-score > 3, the result is regarded as unacceptable. The Z score values are shown in Table 4 in terms of the number of labs according to different categories. Z scores were not calculated for the ammonium ion and for DIP since their results were reported with larger deviation. In particular, the discrepancy of Table 4 Performance characteristics of the participating laboratories. Parameter
Total number Data
jZj < 2
2 < jZj <3
jZj > 3
NO 3 –N TNN TPP SO2 4 Cl DIPP
14 8 10 6 6 8
14 8 6 5 6 6
0 0 2 0 0 0
0 0 2 1 0 2
NHþ 4 N results was partially due to its instability during storage of rainwater at room temperature. As evident from Table 4, most of the laboratories produced satisfactory results (jZj < 2) for NO 3 N, TN_N, Cl, and SO2 4. The laboratories that scored between 2 & 3 could improve their performance by implementing appropriate internal quality control measures which include the use of QC standards (synthetic rainwater samples) as samples during the course of analysis of rainwater samples and recovery checks following standard addition of specific analytes to samples being analyzed. Regular participation in ILS of the type conducted in this work could help laboratories to identify sources of error in analytical data on nutrients and appropriate corrective measures that could be taken to rectify the problem. The laboratories that scored above 3 should investigate and revalidate their analytical method(s) to obtain satisfactory results. The Z scores of a few laboratories were higher than 3 for TP. This problem is thought to be due to detection and measurement of its very low concentration in rainwater; some of the laboratories were not able to achieve such low detection limits with the analytical methods used due to which they reported the results with positive bias.
3.4. Establishing reference values Reference and indicative were values established using ILS data. These will be useful to the participating laboratories to assess the
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analytical quality of data obtained on nutrients and to improve the existing QA/QC protocols, if necessary. New samples with reference values are also available for other researchers upon request. Two main criteria were used for assigning a reference value for each ion as suggested in the ISO Guidelines of reference material preparation (ISO guide, 2000): (1). the overall mean obtained from the analytical data from more than 10 laboratories (mean values) and (2) nutrient concentration obtained by at least two different analytical techniques. Only the nitrate data met these two criteria, and therefore a reference value is proposed for nitrate in rainwater. However, indicative values were established for all other species analyzed in rainwater. It is important to calculate measurement uncertainty when the sample is used for QA/QC purposes. Therefore, an uncertainty budget was calculated by considering all relevant sources of error that could affect the reference values. Combined and expanded uncertainties were calculated following the procedure recommended in the ISO guide (ISO guide, 1993; Thomson and Wood, 1993; Ellison et al., 2001). The value of ‘‘Uc’’ was determined from the combined uncertainties of the various methods used to generate the characterization data (Uchar) as well as uncertainties related to possible between-sample bottle variation (Uhom) and instability derived from the effects relating to long-term storage and short-term transport (Ustab). The combined uncertainty was calculated using following equation:
Uc ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 Uchar þ Uhom þ Ustab
(2)
where Uchar is characterization uncertainty which was calculated from the mean of laboratory standard deviation divided by the number of laboratories [XSD/n]; Uhom is the uncertainty component for homogeneity which was set equal to between the unit standard deviation; Ustab is the uncertainty component due to stability which was set equal to the standard deviation of stability data (6 months). The expanded uncertainty was calculated using a k factor of 2 (95% confidence level), and the calculated values are given in Table 5. As can be seen from the table that expanded uncertainty values were within 20% of target value and considered to be satisfactory as per AOAC guidelines (2002) based on their concentration levels.
4. Conclusion The report presents a summary of the inter-laboratory study on the analysis of nutrients and few selected anions (SO2 4 and Cl ) in rainwater samples. The details of sample preparation, homogeneity and stability tests, and the inter-laboratory study were presented and discussed. Using the inter-laboratory results, a reference value for nitrate and indicative values for other nutrients in rainwater
Table 5 Reference and Indicative values for rain water based on ILS data. Parameter Reference values 1 Nitrate (NO 3 mg l ) Indicative Value Total Nitrogen (N mg l1) Total Phosphorus (P mg l1) 1 Sulfate (SO2 4 mg l ) Chloride (Cl mg l1) 1 Ammonium (NHþ 4 mg l ) 1 Dissolved Inorganic Phosphate (PO3 4 mg l )
Mean *Uex
Analytical techniques
0.68 0.07
IC & SP
1.00 0.28 0.011 0.006 2.87 0.49 0.85 0.20 0.16 0.08 0.004
IC & SP SP IC IC IC SP
Uex represents expanded uncertainty with a coverage factor of 2 (95% confidence level). SP – spectrophotometry.
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were established. However, a more comprehensive study is required to find an optimal storage condition under which the concentration of ammonium ion can remain stable for longer duration even at room temperature. Such a study is currently underway in our laboratory with investigation of different protocols for a long-term storage stability of rainwater for a complete analysis of nutrients. The overall results indicated that the performance of individual laboratories can be improved if internal quality control protocols are used for routine analysis. Acknowledgements The authors wish to thank all the participants for their significant contributions in making this inter-laboratory comparison project successful: Maher Bill (University of Canberra, Australia), R.E. Santelli (Universidade Federal Fluminense, Brazil), Kishore Shenoy (Sriram Institute for Industrail Research, India), Dinesh Goel (Delhi Test House, India), P. Mani (Central Pollution Control Board, India), M.A. Bholah (Mauritius Sugar Industry Research Institute, Mauritius), Rajmund Michalski (Institute of Environmental Engineering of Polish Academy of Science, Poland), Y.J. Jie (Singapore Test Service Singapore), Andreaja Drolac (National Institute of Chemistry, Slovenia), Isabel Rache (Dept of Ecology, University of Granada, Spain), Hung Yu Chen (National Taiwan Ocean University, Taiwan) and J. A. Downing (Iowa state University, USA). References Alkema, H., Simser, J., Hjelm, L., 1997. Interlaboratory quality assurance studies: their use in certifying natural waters for major constituents and trace elements. Fresenius Journal of Analytical Chemistry 360, 339–343. AOAC guidelines for Single laboratory validation, 2002. APHA, AWWA, WPCF, 1995. Standard Methods for the Examination of Water and Wastewater, 19th ed. American Public Health Association, American Water Works Association, and Water Pollution Control Federation, Washington, DC. Alkema, H., Simser, J., Hjelm, L., 1998. Inter-laboratory quality assurance studies: their use in certifying natural waters for major constituents and trace elements. Fresenius Journal of Analytical Chemistry 360, 339–343. Aminot, A., Kerouel, R., 1991. Autoclaved seawater as reference material for the determination of nitrate and phosphate in seawater. Analytica Chimica Acta 248, 277–283. Aminot, A., Kerouel, R., 1995. Reference material for nutrients in seawater – stability of nitrate, nitrite, ammonia and phosphate in autoclaved sample. Marine Chemistry 49, 221–232. Aminot, A., Kirkwood, D.S., 1994. The 1993 QUASIMEME laboratory performance study: nutrients in seawater and standard solutions. Marine Pollution Bulletin 29, 159–165. Analytical Methods Committee, 1989. Robust statistics, parts 1 and 2. Analyst 114, 1693–1702. Clancy, L., Willie, S., 2004. Preparation and certification of a reference material for the determination of nutrients in seawater. Analytical Bioanalytical Chemistry 378, 1239–1242. Cofino, W.P., Wells, D.E., 1994. Design and evaluation of the QUASIMEME interlaboratory performance studies: a test case for robust statistics. Marine Pollution Bulletin 29, 149–158. Cornell, S.E., Jickells, T.D., Capeb, J.N., Rowlandc, A.P., Duced, R.A., 2003. Organic nitrogen deposition on land and coastal environments: a review of methods and data. Atmospheric Environment 37, 2173–2191. Ellison, S.L.R., Burke, S., Walker, R.F., Heydorn, K., Mansson, M., Pauwels, J., Wegscheider, W., Nijenhuis, B., 2001. Uncertainty for reference materials certified by interlaboratory study: recommendations of an international study group. Accreditation and Quality Assurance 6, 274–277. Erisman, J.W., de Vries, W., Kros, J., O van der, L., van Zeijts, E.H., 2001. An outlook for integrated nitrogen. Environmental Science and Policy 4, 87–95. Guildford, S.J., Hecky, R.E., 2000. Total nitrogen, total phosphorus, and nutrient limitation in lakes and oceans: is there a common relationship? Limnology and Oceanography 45, 1213–1223. Holland, E.A., Braswell, B.H., Sulzman, J., Lamarque, J.F., 2005. Nitrogen deposition onto the United States and Western Europe: synthesis of observations and models. Ecological Applications 15, 38–57. Hu, G.P., Balasubramanian, R., Wu, C.D., 2003. Chemical characterization of rain water in Singapore. Chemosphere 51, 747–755. Hund, E., Massart, D.L., Smeyers-Verbeke, J., 2000. Inter-laboratory studies in analytical chemistry. Analytica Chimica Acta 423, 145–165. Guide to the expression of uncertainty in measurement, 1993. ISBN: 92-67-10188-9, first ed. ISO, Geneva, Switzerland.
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ISO Guide 43-1, 1997. Proficiency testing by inter-laboratory comparisons part 1: development and operation of proficiency testing schemes. ISO Guide 34, 2000. General requirement for the competence of reference material producers. Janet, P., 1998. Is coastal eutrophication out of control? Environmental Science and Technology 1, 462A–466A. Karthikeyan, S., Balasubramanian, R., 2006a. Interlaboratory study to improve the quality of trace element determinations in rainwater. Analytica Chimica Acta 576, 9–16. Karthikeyan, S., Balasubramanian, R., 2006b. Determination of water soluble inorganic and organic species. Michrochemical Journal 82, 49–55. Merry, J., 1995. Reference material for monitoring nutrients in seawater environment: approach, preparation, certification and their use in environmental laboratories. Fresenius Journal of Analytical Chemistry 352, 148–151. Mosello, R., Tartart, G.A., Marchetto, A., Polesello, S., Blanchi, M., Muntau, H., 2004. Ion chromatography performance evaluated from the third AQUACON freshwater analysis interlaboratory exercise. Accreditation and Quality Assurance 9, 242–246. Nadim, F., Trahiotis, M.M., Stapcinskaite, S., Perkins, C., Carley, R.J., Hoaga, G.E., Yang, X., 2001. Estimation of wet, dry and bulk deposition of
atmospheric nitrogen in Connecticut. Journal of Environmental Monitoring 3, 671–680. Otoshi, T., Fukuzaki, N., Li, H., Hoshino, H., Sase, H., Saito, M., Suzuki, K., 2001. Quality control and its constraints during the preparatory phase activities of the acid deposition monitoring network in East Asia [EANET]. Water, Air and Soil Pollution 130, 1613–1618. Sharp, J.H., 1983. The distribution of inorganic nitrogen and dissolved and particulate organic nitrogen in the sea. In: Carpenter, E.J., Capone, D.G. (Eds.), Nitrogen in the Marine Environment. Plenum, pp. 124–138. Thomson, M., Wood, R., 1993. ISO/IUPAC/AOAC International harmonized protocol for proficiency testing of analytical laboratories. Journal of AOAC International 76, 926–940. Zhang, J., Zou, L., Wu, Y., Lin, Y.A., 2004. Atmospheric wet deposition and changes in phytoplankton biomass in the surface ocean. Geophysical Research Letters 31 Art. No. L11310. Zhang, G., Zhang, J., Liu, S., 2007. Characterization of nutrients in the atmospheric wet and dry deposition observed at the two monitoring sites over Yellow Sea and East China Sea. Journal of Atmospheric Chemistry 57, 41–57.
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Inter-laboratory study to improve the quality of the analysis of nutrients in rainwater chemistry Sathrugnan Karthikeyan, Rajasekhar Balasubramanian*, Jun He Division of Environmental Science and Engineering, National University of Singapore Engineering Drive 1, Singapore 117576, Singapore
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 July 2008 Received in revised form 2 March 2009 Accepted 13 March 2009
This paper describes the results of an inter-laboratory study conducted for the analysis of nutrients (nitrate, ammonium, phosphate, total nitrogen (TN), and total phosphorus (TP)) in natural rainwater. For this purpose, rainwater samples were collected and aggregated in Singapore and homogenized. These samples were immediately filtered through 0.45 mm membrane filters and autoclaved for 15 min at 80 C in order to stabilize the nutrients. The homogeneity and the stability of nutrients were rigorously tested for a period of three months initially. Upon ensuring the homogeneity and stability, the samples were distributed to 15 different laboratories from various countries around the world (Australia, Brazil, India, Mauritius, Singapore, Slovenia, Spain, Taiwan, and USA). Almost all laboratories have reported the analytical results for nitrate whereas only 8 of the 15 laboratories reported results for other nutrients such as ammonium, phosphate, TN, and TP. The discrepancy was mainly due to the presence of these nutrients in low concentration levels (particularly ammonium ion and phosphate). Not all the laboratories were equipped with analytical capabilities to conduct the analysis of nutrients in low concentration levels. Further, the uncertainty associated with the analysis of TN and TP restricted the number of laboratories that could report their analytical data on nutrients. All 14 laboratories reported nitratenitrogen results which were in good agreement with each other (0.68 0.07 mg l1). Similarly, the results of TN and TP were also comparable among at least 8 laboratories. This inter-laboratory study on the analysis of nutrients in natural rainwater, conducted for the first time, provided an opportunity to the participating laboratories to assess and improve their laboratory performance, thereby, improving the quality of their analytical data. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Precipitation chemistry Nitrogen deposition Quality control Quality assurance Uncertainty analysis
1. Introduction The atmosphere has been recognized as an important pathway for the transfer of nutrients to surface waters through wet and dry deposition. The two main nutrients which may cause eutrophication, if in high concentrations, are nitrogen (N) and phosphorus (P). Both elements are not active as nutrients in their elemental form. While phosphorus is available to primary production in a dissolved inorganic phase as phosphate (PO4–P), nitrogen is supplied to aquatic plants mostly as ammonium (Ammonium-N: NH4–N; the total of ammonium plus ammonia), nitrate (Nitrate-N: NO3–N), and to a lesser degree nitrite (Nitrite-N: NO2–N). These forms of nitrogen are commonly referred to as Dissolved Inorganic Nitrogen (DIN) (Sharp, 1983; Janet, 1998; Guildford and Hecky, 2000; Erisman et al., 2001). Wet deposition of nutrients occurs when
* Corresponding author. Tel.: þ65 6516 5135; fax: þ65 6774 4202. E-mail address: [email protected] (R. Balasubramanian). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.03.025
gaseous, or particulate N/P is transferred from the air onto an underlying surface via precipitation. For example, atmosphericallyderived nitrogen contains a mixture of biologically-available dissolved inorganic compounds (NO3–N, NO2–N, NH4–N; DIN) in rainfall. In order to understand the influence of atmosphericallyderived nutrients on eutrophication, field studies on measurement of the levels of nutrients in hydrometeors, particularly in rainwater, have been actively pursued over the last two decades in urban, rural, and remote sites at different parts of the world using different analytical techniques (Nadim et al., 2001; Hu et al., 2003; Holland et al., 2005; Zhang et al., 2004, 2007). The acquisition of relevant and reliable analytical data is a primary component in all research and monitoring programs associated with the assessment of nutrients in natural waters and related environmental issues such as eutrophication. The analysis of nutrients in rainwater is relatively straightforward particularly in the case of dissolved inorganic nitrogen (NO3–N, NO2–N and NH4–N) since most laboratories employ ion chromatography for this purpose (Nadim et al., 2001; Hu et al., 2003; Zhang et al., 2007).
S. Karthikeyan et al. / Atmospheric Environment 43 (2009) 3424–3430
However, phosphate analysis is sometimes carried out spectrophotometrically (by using longer path length cell) since it is present in very low concentration (2–30 mg l1). On the other hand, dissolved organic nitrogen and dissolved organic phosphorus are estimated only by the difference between the total and inorganic N and P. Measuring total nitrogen (and phosphorus) involves releasing the nitrogen (and phosphorus) from organic molecules by chemical oxidation to NO3–N (and to PO4–P). The uncertainties associated with estimation of DON (Dissolved Organic Nitrogen) or DOP (Dissolved Organic Phosphorous) are relatively large compared to that of DIN (Dissolved Inorganic Nitrogen) or DIP (Dissolved Inorganic Phosphorous). The major sources of error result from incomplete conversion of DON/DOP to DIN/DIP or losses during sampling and storage. Cornell et al. (2003) have critically reviewed the importance of DON and the challenges involved in its analysis. In the context of quality assurance, while a research laboratory can test the repeatability of its own measurements of nutrients, it cannot sufficiently assess their accuracy. The essential tools needed for this assessment are inter-laboratory comparison and certified reference materials (CRMs), those certified by the Standards, Measurement and Testing program of the commission of European community, or by the US National Institute of Standards and Technology (NIST). In the absence of suitable CRMs, inter-laboratory comparison provides laboratories with a valuable third party verification of data quality and acceptability. CRMs have been developed from inter-laboratory data for various natural water matrices (river water, rainwater and seawater) over the years (Alkema et al., 1998; Clancy and Willie, 2004; Karthikeyan and Balasubramanian, 2006a,b). Regular inter-laboratory study (ILS) generates a wealth of data for the many parameters important in environmental programs (Aminot and Kirkwood, 1994; Otoshi et al., 2001; Mosello et al., 2004). In recognition of the need to improve the quality of analytical data on nutrients in rainwater, use of CRMs and regular participation in inter-laboratory studies are critically needed. In response to this growing need, we have conducted an interlaboratory study for the analysis of nutrients in rainwater. Extensive literature search revealed that only limited reports (Alkema et al., 1997; Mosello et al., 2004) were available on interlaboratory data pertaining to the analysis of nutrients in rainwater. To the best of our knowledge, no ILS has been conducted previously using natural rainwater. Furthermore, neither ILS data nor CRMs are available in the literature for the analysis of total nitrogen and total phosphorus in rainwater. In this paper, we present the results obtained from an inter-laboratory exercise for the analysis of a wide range of nutrients in rainwater. This study has been conducted with two main objectives: (1) to verify the precision and accuracy of the methods commonly used (ion chromatography, spectrophotometry) for the analysis of various nutrients in rainwater (NO3–N, NH4–N, PO4–P TN, and TP); (2) to establish reference values for these nutrients in rainwater in order to develop a certified reference material for routine internal quality assurance/quality control (QA/QC) assessment. The details of the production, homogeneity, stability of the reference materials, and the inter-laboratory analytical data are presented and discussed. 2. Materials and methods 2.1. Sample bottles A 10 L capacity HDPE tank (NalgeneÒ) was used for the initial storage of rainwater. HPDE bottles (250 mL, NalgeneÒ) were used for storing the homogenized rainwater samples. These bottles were filled with 2 N HNO3 for 3 days and subsequently with ultrapure water for 3 days. Finally, they were rinsed with ultrapure water
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three times and kept safely in plastic bags until use. No residual nitrate was found in the rinse water after washing. 2.2. Preparation of rainwater samples for the intended ILS During peak monsoon periods, rainwater samples were collected in large quantities using an automated wet-only precipitation collector (Model 301, Aerochem Metric Inc.). This collector consists of a High Density Polyethylene bucket with a diameter of 28.6 cm, which is equipped with a sensor that detects the precipitation episodes and activates a mechanism by which the lid opens with the first drop of a shower and closes just after the rain event. The automated wet collector thus prevents the contamination of rainwater by dust fall, taking place before and after a rain event. Rainwater samples were collected and aggregated upto 10 L in Singapore. The rainwater was immediately filtered through a 0.45 mm membrane filter, and it was autoclaved for 15 min at 80 C. This sample was homogenized through circulation (bubbling with high purity N2), and stored in a 10 L HPDE container. Then six sub-samples (10 mL) were collected from this tank and analyzed for nitrate and phosphate to confirm the homogeneity within the container (10 L). Finally, these rainwater samples were transferred to 250 mL HDPE sample bottles to their capacity, preserved in polythene bags, and stored in a refrigerator @ 4 C for 3 months. These samples were kept safely in a clean place to avoid contamination. 2.3. Homogeneity and stability studies The homogeneity of the sample was investigated by analyzing 4 randomly selected bottles from the 40 bottles stored with rainwater, and analyzed for nutrients. Then, the stability of a few nutrients (DIN, TN, and TP) was investigated over a period of time (6 months). Ion chromatography (IC) (Hu et al., 2003; Karthikeyan and Balasubramanian, 2006a,b) was employed for the analysis of nitrate, ammonium ion, and total nitrogen. DIP and TP were analyzed by the molybdenum blue method (APHA, 1995). For internal quality assurance, a standard addition method was employed for all parameters. The recovery results were in the range of 80–105% at different concentration levels. 2.4. Inter-laboratory study After ensuring the homogeneity and stability of the rainwater samples, an inter-laboratory study was conducted. Fourteen laboratories from various countries (Australia, Brazil, India, Mauritius, Poland, Slovenia, Spain, Taiwan, and USA) were invited to participate in this inter-comparison exercise. Upon their agreement, one bottle (250 mL) was sent out to each participant safely. The participants were requested to analyze mainly nutrients (nitrate, ammonium ion, total nitrogen, dissolved inorganic phosphate, and total phosphorus) as per their existing analytical protocols (refer Table 2) in their respective laboratories. In view of the potential use of the rainwater sample as a reference material for atmospheric research, participants were also asked to analyze for other ions such as chloride, sulfate, and sodium, if possible. Each participant was requested to report the concentration values for six independent replicates. Only 13 laboratories reported the results in time and the remaining lab was unable to complete the analysis due to some technical problems. The organizers guaranteed the full respect of confidentiality as to the identity of the laboratories participating in this study. At the completion of the study, each laboratory received a report with comments about its performance and overall outcome of the exercise.
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Table 1 Variance-component analysis data of the homogeneity test. Element
Sum of squares
NO3 TNN NH4 DIP SO4
Mean squares
Significance (a ¼ 0.05)
F
BB
WB
BB
WB
BB
WB
0.083 0.144 0.010 5.47 E005 0.160
0.017 0.051 0.000 8.17 E006 0.039
0.028 0.048 0.003 1.82, E005 0.053
0.009 0.025 0.000 4.08, E006 0.020
3.504 5.528 1.955 1.522 2.296
1.073 2.927 0.065 0.341 0.842
0.089 0.037 0.222 0.302 0.178
0.400 0.130 0.938 0.724 0.476
BB – between the bottles; WB – within the bottles.
2.5. Statistical analysis Homogeneity has been tested under univariate variancecomponent analysis at 5% significance level by using SPSS 13.0 for Windows (SPSS Inc., Chicago, IL, USA). A robust statistical method (Analytical Methods Committee, 1989) has been applied in the study which has gained wide acceptance. Robust statistics do not discard any observations, but down-weight extreme data. As a result, robust statistics are relatively insensitive to extreme values and to ‘tailing’ distributions. The median was calculated using all data points. Those values that deviated from the median by 1.5 times (5% tolerance) of Sb (standard deviation of participating laboratories) were identified and eliminated. The performance of individual laboratories was assessed based on the assigned values for each parameter and the actual values measured by the participants through the calculation of Z scores. This is a simple method of giving each participant a normalized performance score for bias; the method was adopted as a standard by ISO/IUPAC [ISO guide 431, 1997]. Z scores for each laboratory and each determinant were calculated using the following equation:
z ¼
jxi xj Sb
(1)
where, xi ¼ the reported value for the analyte concentration in the test sample (from a given laboratory), x ¼ the assigned value, and Sb ¼ Standard deviation of laboratory mean or the target value performance (12.5%). 3. Results and discussion 3.1. Homogeneity and stability The average concentrations of major ions in six sub-samples contained within the main storage bottle were 0.72 0.03 mg l1
(NO3–N), 0.19 0.05 mg l1 (NH4–N), 1.22 0.12 mg l1 (TN), 0.013 0.003 mg l1 (DIP–P), 3.45 0.31 mg l1 (SO2 4 ) and 0.75 0.15 mg l1 (Cl), respectively. The homogeneity of the rainwater sample, after transferring into small bottles, was tested by choosing 4 bottles randomly from the lot (40 bottles) and analyzing 2 them for NO 3 N, TNN, DIP, and SO4 in triplicate. The mean concentrations between the bottles were 0.67 0.03 mg l1(NO3–N), 0.94 0.15 mg l1(TNN), 0.19 0.03 mg l1(NH4–N), 0.010 0.003 mg l1(IP–P), and 3.29 0.18 mg l1(SO2 4). The coefficient of variance (% RSD) was calculated from mean values of four bottles and was less than 10% which indicates homogeneity of the sample between the bottles. Further, the homogeneity of the sample within the bottle and between the bottles was tested by variancecomponent analysis using SPSS software. The results are presented in Table 1. For NO3–N, NH4–N, DIPP and SO2 4, no statistically significant difference was observed between bottles and for the samples analyzed within bottles since all the p-values are greater than 0.05. For TN, no significant difference was found within the bottle, but some difference was observed with p-value of 0.037 (<0.05) for samples drawn from different bottles. However, the homogeneous subsets did not show any big difference between 2, 3 and 4 with p-value ¼ 0.551. Therefore, it can be said that the rainwater prepared for the inter-lab study was homogeneous within as well as between the bottles. The stability is another important criterion to be checked since biological activity can alter nutrient concentration rapidly if not preserved properly. Based on the previous reports, we have done a two-step pretreatment to stabilize the rainwater samples (Aminot and Kerouel, 1991, 1995; Cofino and Wells, 1994). Samples were initially filtered through 0.45 mm filters and autoclaved at 80 C for 15 min to ensure the complete removal of micro-organisms from these water samples. In order to check the stability, two of the samples were tested on a monthly basis over a period of six months.
Table 2 Summary of inter-laboratory results [concentrations in mg l1]. Lab
NO 3 –N (method)
NHþ 4 N (method)
TNN (method)
DIPP (method)
TPP (method)
SO2 4 (method)
Cl (method)
Lab1 Lab2 Lab3 Lab4 Lab5 Lab6 Lab7 Lab8 Lab9 Lab10 Lab12 Lab 13 Lab 14 Org.
0.75 0.06 (A) 0.59 0.05 (A) 0.84 0.07 (B) 0.73 0.02 (B) 0.63 0.09 (A) 0.78 0.06 (A) 0.63 0.01 (B) 0.34 0.23 (D) 0.67 0.07 (B) 0.67 0.02 (A) 0.61 0.03 (B) 0.71 0.02 (A) 0.71 0.02 (B) 0.72 0.12 (A)
0.29 0.014 (A) NR 0.09 0.01 (A) 0.16 0.003 (B) 0.02 0.01 (A) 0.28 0.01 (A) NR 0.16 0.01 (D) NR 0.09 0.05 (A) 0.147 0.03 (B) NR NR 0.24 0.04 (A)
1.0 NR 1.2 0.9 NR NR NR 2.84 NR 1.1 0.85 NR 1.07 0.9
0.001 0.0008 (B) NR ND (A) 0.007 0.0 (B) 0.430 0.08 (A) <0.05 0.109 0.007 (B) 0.004 0.004 (B) NR 0.005 0.002 (B) <0.005 (B) NR 0.001 0.0005 (B) 0.005 0.003 (B)
0.005 0.003 (B) NR 0.018 0.003 (C) 0.015 0.001 (C) NR 0.220 0.05 (C) 0.036 0.003 (C) 0.004 0.002 (C) NR 0.012 0.003 (C) 0.008 003 (C) NR 0.005 0.001 (C) 0.010 0.003 (B)
NR 2.40 4.30 2.70 NR 2.89 NR NR NR 2.95 NR NR NR 3.79
NR 0.90 0.1 (A) 0.80 0.27 (A) 0.98 0.03 (A) NR 1.03 0.04 (A) NR NR NR 0.67 0.20 (A) NR NR NR 0.74 0.20 (A)
0.01 (C) 0.39 (B) 0.01 (B)
0.18 (D) 0.30 (B) 0.05 (B) 0.184 (C) 0.14 (A)
A – Ion chromatography. B – Spectrophotometery (HACH or flow injection analysis following APHA). C – Persulfate oxidation followed by Spectrophotometery (HACH or flow injection analysis). D TKN steam distillation & Modified Berthelot method.
0.3 (A) 0.70 (A) 0.06 (A) 0.04 (A)
0.50 (A)
0.18 (A)
S. Karthikeyan et al. / Atmospheric Environment 43 (2009) 3424–3430
5
4
Nitrate
Total Nitrogen
Ammonia
Sulfate
mg/l
3
2
1
0 Apr
May
Jun
Jul
Aug
Sept
Fig. 1. Monthly variation of selected ions in rainwater.
As can be seen from Fig. 1, the concentrations of nitrate, total nitrogen, and sulfate were within 20% of variation compared to those observed in the first month after sample collection, which ensures the stability of these analytes. However, it was observed that the ammonium ion concentration was decreasing progressively. To minimize further reduction, samples were refrigerated at 4 C after 3 months, and it was then quite stable. To improve the stability of nutrients during long-term storage of rainwater at room temperature, a g-irradiation, or microwave irradiation of rain water sample should be tested in future studies (Clancy and Willie, 2004). In summary, the homogeneity and stability test results were satisfactory. Rainwater samples were then sent to the participants. 3.2. Comparison of inter-laboratory data Table 2 presents the summary of analytical results reported by various laboratories during this inter-laboratory study. All the participating laboratories have reported the results for NO 3 N. However, only 50–70% of them reported the results for other nutrients. Many participants did not analyze, or report NH4–N, DIPP, TN, and TP because of two different reasons. Firstly, ammonium and phosphorus were present in extremely low concentrations. Secondly, some participants attributed the lack of analytical data to inadequate sample size. Total phosphorus was the second most important nutrient reported by many participants even though the concentration was very low (w0.01 mg l1). Most of the participants have employed a spectrophotometer with longer path length cells (5 or 10 cm) to attain better sensitivity. Ammonium ion concentrations were also equally reported by as many as nine participants. However, the reported values showed large deviation (>50%). It could be presumably due to loss of volatile ammonia during storage. We have observed a reduction in the concentration of ammonium within 3 months when the rainwater samples were stored at room temperature. However, the concentration did not change significantly after storing the samples at 4 C. Merry (1995) reported a slight reduction in the concentration of ammonium ion during prolonged storage of seawater in autoclaving approach (Aminot and Kerouel, 1991, 1995). Since the ammonium was present in trace levels, even a small variation in the analytical measurements could affect the overall data quality. A more comprehensive study is required to stabilize the nutrients present in low levels at room temperature. As mentioned earlier, exposure of rainwater samples to g-irradiation, or microwave irradiation, should be tested and its merits evaluated. The DIP data were not considered for any statistical analysis since there were only few values (four) reported but with large deviation. The
3427
presence of other nutrients in low concentrations with the exception of DON was a major factor which contributed to the inconsistency in the analytical data reported by the participants. All the reported analytical data were treated with robust statistical methods (Analytical Methods Committee, 1989; Hund et al., 2000) to identify and eliminate the outliers. As described in Section 2, the median concentration was calculated without discarding any data points and subsequent outliers were eliminated by the cut off values. However, the rejected outliers were also considered in graphical presentation to justify their rejection. These laboratories with outliers could improve their performance if they carry out standard addition recovery tests. For example, lab 8 used different measurement methods (TKN steam distillation & Modified Berthelot method) compared to other standard methods (ion chromatography, spectrophotometry). The former methods need further validation in order to be consistent with standard methods. The laboratory consensus value (median) and standard deviation between laboratory values were calculated after eliminating the outliers and are presented in Table 3 along with other relevant information. The standard deviations between the laboratories were in the range of 10–15% of the target mean. This is in an acceptable range based on AOAC guidelines (2002) considering the low concentration levels. The inter-comparison of laboratory results against the grand mean is graphically shown in Fig. 2 (a–g). It could be seen that NO 3 results reported by all participants were within acceptable variation. One of the laboratories has reported the concentration with a negative bias. This particular laboratory was informed of the problem with their results for NO 3 . They have reanalyzed the sample, and the results were within the expected range. The discrepancy in the first set of measurements was due to a calibration error which they could identify only after our feedback. This example clearly shows that the third party verification sufficiently improves the quality of analytical data on nutrients in rainwater through the inter-comparison study. In the case of TN, TP, sulfate, and chloride, the reported results are in good agreement within the reported results. Spectrophotometry (APHA method) or ion chromatography was employed for nitrate analysis and almost all laboratory results were consistent. However, one of the laboratories reported the results with negative bias. The negative bias could be due to partial reduction of nitrate before measurement with the spectrophotometric technique. This problem could have been easily identified if standard addition experiment was carried out. The participating lab was informed about this negative bias with a suggestion to revalidate their method. For total nitrogen or total phosphorus, persulfate oxidation followed by spectrophotometric measurements was utilized by many participants. Only two of the TP results were not acceptable, presumably due to low concentration. Quantitative detection of TP can be improved by using longer path length cells during measurements. The IC results for other major anions (Cl & SO 4 ) were acceptable within the results. Although the IC or the
Table 3 Laboratory Grand Mean and SD of nutrients analysis in rainwater. Parameter
Median (mg l1)
S.D (mg l1)
No of acceptable data/total no of data
Nitrate (as NO 3) Ammonium (as NH4þ) DIP (as PO3 4 ) Total Nitrogen (N) Total P (as P) Sulfate (as SO2 4 ) Chloride (as Cl)
0.69 0.16 0.005 1.04 0.010 2.89 0.85
0.07 0.06 0.002 0.1 0.005 0.36 0.14
14/14 7/9 6/8 8/8 7/10 5/6 6/6
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1.50
b TN-N (mg/l)
0.90 0.60
0.90 0.60
b1 La b2 La b3 La b4 La b5 La b6 La b7 La b8 La b9 La b1 0 or g La b1 2 La b1 La 3 b1 4
La
b1 2 La b1 La 3 b1 4
La
b9 b1 0 or g La
b8
La
b7
La
b6
La
La
La
La
La
La
b5
0.00 b4
0.00 b3
0.30
b2
0.30
La
c
1.50 1.20
1.20
b1
NO3-N (mg/l)
a
0.40
d
0.220
2 b1
g
0
La
or
b1
b9
La
b8
La
La
b7 La
b6 La
b5 La
b4 La
b3
b2
La
La
b1
f
1.5
Cl (mg/l)
1.2 0.9 0.6 0.3
La 3 b1 4
2
b1
b1
La
La
0
g
or
b9
b1
La
b8
La
b7
La
La
b5
b6
La
La
b4
La
b3
La
b2
b1
La
La
b1 La 3 b1 4
2
La
b1
0
g
or
b9
b1
La
La
b8
b7
La
b6
La
b5
La
b4
La
La
b3
b2
La
La
b1
0.0 La
La
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 La
La b
1
La b
-0.100
0.00
SO4 (mg/l)
La b4 La b5 La b6 La b7 La b8 La b9 La b1 0 or g La b1 2 La b1 La 3 b1 4
-0.020
0.08
e
0.060
2
0.16
0.140
3
0.24
La b
TP (mg/l)
NH4+ (mg/l)
0.32
Fig. 2. Graphical representation of analytical data reported by different laboratories: (a) Nitrate; (b) total nitrogen; (c) ammonium; (d) total phosphorus; (e) sulfate; and (g) chloride. Solid line represents grand mean, calculated from individual laboratories data and dotted line is Uex as given in Table 5.
spectrophotometric method is suitable for these applications, an internal quality control protocol is an essential step to improve the laboratory performance. 3.3. Laboratory performance (Z score) Laboratory performances areis classified according to the following criteria: for an absolute value of Z 2, the performance of the laboratory is considered acceptable. When the absolute value of Z lies between 2 and 3, the result is of questionable quality, whereas for an absolute value of Z-score > 3, the result is regarded as unacceptable. The Z score values are shown in Table 4 in terms of the number of labs according to different categories. Z scores were not calculated for the ammonium ion and for DIP since their results were reported with larger deviation. In particular, the discrepancy of Table 4 Performance characteristics of the participating laboratories. Parameter
Total number Data
jZj < 2
2 < jZj <3
jZj > 3
NO 3 –N TNN TPP SO2 4 Cl DIPP
14 8 10 6 6 8
14 8 6 5 6 6
0 0 2 0 0 0
0 0 2 1 0 2
NHþ 4 N results was partially due to its instability during storage of rainwater at room temperature. As evident from Table 4, most of the laboratories produced satisfactory results (jZj < 2) for NO 3 N, TN_N, Cl, and SO2 4. The laboratories that scored between 2 & 3 could improve their performance by implementing appropriate internal quality control measures which include the use of QC standards (synthetic rainwater samples) as samples during the course of analysis of rainwater samples and recovery checks following standard addition of specific analytes to samples being analyzed. Regular participation in ILS of the type conducted in this work could help laboratories to identify sources of error in analytical data on nutrients and appropriate corrective measures that could be taken to rectify the problem. The laboratories that scored above 3 should investigate and revalidate their analytical method(s) to obtain satisfactory results. The Z scores of a few laboratories were higher than 3 for TP. This problem is thought to be due to detection and measurement of its very low concentration in rainwater; some of the laboratories were not able to achieve such low detection limits with the analytical methods used due to which they reported the results with positive bias.
3.4. Establishing reference values Reference and indicative were values established using ILS data. These will be useful to the participating laboratories to assess the
S. Karthikeyan et al. / Atmospheric Environment 43 (2009) 3424–3430
analytical quality of data obtained on nutrients and to improve the existing QA/QC protocols, if necessary. New samples with reference values are also available for other researchers upon request. Two main criteria were used for assigning a reference value for each ion as suggested in the ISO Guidelines of reference material preparation (ISO guide, 2000): (1). the overall mean obtained from the analytical data from more than 10 laboratories (mean values) and (2) nutrient concentration obtained by at least two different analytical techniques. Only the nitrate data met these two criteria, and therefore a reference value is proposed for nitrate in rainwater. However, indicative values were established for all other species analyzed in rainwater. It is important to calculate measurement uncertainty when the sample is used for QA/QC purposes. Therefore, an uncertainty budget was calculated by considering all relevant sources of error that could affect the reference values. Combined and expanded uncertainties were calculated following the procedure recommended in the ISO guide (ISO guide, 1993; Thomson and Wood, 1993; Ellison et al., 2001). The value of ‘‘Uc’’ was determined from the combined uncertainties of the various methods used to generate the characterization data (Uchar) as well as uncertainties related to possible between-sample bottle variation (Uhom) and instability derived from the effects relating to long-term storage and short-term transport (Ustab). The combined uncertainty was calculated using following equation:
Uc ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 Uchar þ Uhom þ Ustab
(2)
where Uchar is characterization uncertainty which was calculated from the mean of laboratory standard deviation divided by the number of laboratories [XSD/n]; Uhom is the uncertainty component for homogeneity which was set equal to between the unit standard deviation; Ustab is the uncertainty component due to stability which was set equal to the standard deviation of stability data (6 months). The expanded uncertainty was calculated using a k factor of 2 (95% confidence level), and the calculated values are given in Table 5. As can be seen from the table that expanded uncertainty values were within 20% of target value and considered to be satisfactory as per AOAC guidelines (2002) based on their concentration levels.
4. Conclusion The report presents a summary of the inter-laboratory study on the analysis of nutrients and few selected anions (SO2 4 and Cl ) in rainwater samples. The details of sample preparation, homogeneity and stability tests, and the inter-laboratory study were presented and discussed. Using the inter-laboratory results, a reference value for nitrate and indicative values for other nutrients in rainwater
Table 5 Reference and Indicative values for rain water based on ILS data. Parameter Reference values 1 Nitrate (NO 3 mg l ) Indicative Value Total Nitrogen (N mg l1) Total Phosphorus (P mg l1) 1 Sulfate (SO2 4 mg l ) Chloride (Cl mg l1) 1 Ammonium (NHþ 4 mg l ) 1 Dissolved Inorganic Phosphate (PO3 4 mg l )
Mean *Uex
Analytical techniques
0.68 0.07
IC & SP
1.00 0.28 0.011 0.006 2.87 0.49 0.85 0.20 0.16 0.08 0.004
IC & SP SP IC IC IC SP
Uex represents expanded uncertainty with a coverage factor of 2 (95% confidence level). SP – spectrophotometry.
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were established. However, a more comprehensive study is required to find an optimal storage condition under which the concentration of ammonium ion can remain stable for longer duration even at room temperature. Such a study is currently underway in our laboratory with investigation of different protocols for a long-term storage stability of rainwater for a complete analysis of nutrients. The overall results indicated that the performance of individual laboratories can be improved if internal quality control protocols are used for routine analysis. Acknowledgements The authors wish to thank all the participants for their significant contributions in making this inter-laboratory comparison project successful: Maher Bill (University of Canberra, Australia), R.E. Santelli (Universidade Federal Fluminense, Brazil), Kishore Shenoy (Sriram Institute for Industrail Research, India), Dinesh Goel (Delhi Test House, India), P. Mani (Central Pollution Control Board, India), M.A. Bholah (Mauritius Sugar Industry Research Institute, Mauritius), Rajmund Michalski (Institute of Environmental Engineering of Polish Academy of Science, Poland), Y.J. Jie (Singapore Test Service Singapore), Andreaja Drolac (National Institute of Chemistry, Slovenia), Isabel Rache (Dept of Ecology, University of Granada, Spain), Hung Yu Chen (National Taiwan Ocean University, Taiwan) and J. A. Downing (Iowa state University, USA). References Alkema, H., Simser, J., Hjelm, L., 1997. Interlaboratory quality assurance studies: their use in certifying natural waters for major constituents and trace elements. Fresenius Journal of Analytical Chemistry 360, 339–343. AOAC guidelines for Single laboratory validation, 2002. APHA, AWWA, WPCF, 1995. Standard Methods for the Examination of Water and Wastewater, 19th ed. American Public Health Association, American Water Works Association, and Water Pollution Control Federation, Washington, DC. Alkema, H., Simser, J., Hjelm, L., 1998. Inter-laboratory quality assurance studies: their use in certifying natural waters for major constituents and trace elements. Fresenius Journal of Analytical Chemistry 360, 339–343. Aminot, A., Kerouel, R., 1991. Autoclaved seawater as reference material for the determination of nitrate and phosphate in seawater. Analytica Chimica Acta 248, 277–283. Aminot, A., Kerouel, R., 1995. Reference material for nutrients in seawater – stability of nitrate, nitrite, ammonia and phosphate in autoclaved sample. Marine Chemistry 49, 221–232. Aminot, A., Kirkwood, D.S., 1994. The 1993 QUASIMEME laboratory performance study: nutrients in seawater and standard solutions. Marine Pollution Bulletin 29, 159–165. Analytical Methods Committee, 1989. Robust statistics, parts 1 and 2. Analyst 114, 1693–1702. Clancy, L., Willie, S., 2004. Preparation and certification of a reference material for the determination of nutrients in seawater. Analytical Bioanalytical Chemistry 378, 1239–1242. Cofino, W.P., Wells, D.E., 1994. Design and evaluation of the QUASIMEME interlaboratory performance studies: a test case for robust statistics. Marine Pollution Bulletin 29, 149–158. Cornell, S.E., Jickells, T.D., Capeb, J.N., Rowlandc, A.P., Duced, R.A., 2003. Organic nitrogen deposition on land and coastal environments: a review of methods and data. Atmospheric Environment 37, 2173–2191. Ellison, S.L.R., Burke, S., Walker, R.F., Heydorn, K., Mansson, M., Pauwels, J., Wegscheider, W., Nijenhuis, B., 2001. Uncertainty for reference materials certified by interlaboratory study: recommendations of an international study group. Accreditation and Quality Assurance 6, 274–277. Erisman, J.W., de Vries, W., Kros, J., O van der, L., van Zeijts, E.H., 2001. An outlook for integrated nitrogen. Environmental Science and Policy 4, 87–95. Guildford, S.J., Hecky, R.E., 2000. Total nitrogen, total phosphorus, and nutrient limitation in lakes and oceans: is there a common relationship? Limnology and Oceanography 45, 1213–1223. Holland, E.A., Braswell, B.H., Sulzman, J., Lamarque, J.F., 2005. Nitrogen deposition onto the United States and Western Europe: synthesis of observations and models. Ecological Applications 15, 38–57. Hu, G.P., Balasubramanian, R., Wu, C.D., 2003. Chemical characterization of rain water in Singapore. Chemosphere 51, 747–755. Hund, E., Massart, D.L., Smeyers-Verbeke, J., 2000. Inter-laboratory studies in analytical chemistry. Analytica Chimica Acta 423, 145–165. Guide to the expression of uncertainty in measurement, 1993. ISBN: 92-67-10188-9, first ed. ISO, Geneva, Switzerland.
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