EOE 2204 RESEARCH METHODS IN ENVIRONMENTAL SCIENCE

Prof Paul Worsfold Portland Square Development B520 Email [email protected]

(But not too often!) Contents stored on sharepoint EOE 2204 1

Introduction to Environmental Analytical Chemistry

This presentation is intended to provide support material for the Wembury field and lab work. Any of the material in steps 1-5 could be included in an exam question.

The six steps in the “Analytical Approach” to any problem are: 1.

Defining the problem 2.

Sampling (Waters and Sediments) 3.

Sample Treatment (Digestion of sediments, preservation of waters) 4.

Measurement 5.

Data Treatment (Precision, Accuracy, Calibration, Limit of Detection) 6.

Report Writing EOE 2204 2

Analytical Chemistry Definition

The science which deals identification and quantification with of the detection, chemical species in matrices of chemical, biological or environmental origin.

It is a multidisciplinary subject that has chemistry at its core but also requires a knowledge of other sciences, e.g. biology, biochemistry, physics, mathematics, statistics and computing.

It is concerned with the real world, with the primary aim of determining WHAT constituents ( qualitative analysis ) are present in a sample and HOW MUCH of each constituent ( quantitative analysis ) is present.

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Environmental Analytical Chemistry Common matrices/Application areas include;

• Hydrosphere •e.g. sea, river, estuary, lake, porewater • Atmosphere •e.g. outdoor air, workplace air, air-sea interface • Lithosphere •e.g. soil, sediment. rocks • Biosphere •e.g. plants, animals, humans EOE 2204 4

1. DEFINING THE PROBLEM

1. Why am I doing this analysis?

2. What sites am I interested in?

3. What analytes am I interested in?

4. What concentrations do I expect?

5. What techniques should I use for 6.

sampling and measurement?

What do my “customers” want from this study/report?

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2. SAMPLING

Outline of the strategies which need to be considered when designing and conducting a sampling programme for environmental analyses

1.

2.

3.

4.

5.

6.

Sampling design Economic and safety considerations Sampling locations Sampling time and frequency Methods of sample collection Sample storage and preservation EOE 2204 6

Design of sampling programme for environmental analytical chemistry

Apply this to e.g. Wembury field trip for stream water and sediment analysis Definition of objectives Select determinands and sampling positions Select number of samples and time/frequency of sampling Select appropriate analytical method Select methods for collecting samples Select methods for sample preservation Feedback from sampling, analysis and interpretation EOE 2204 7

Wembury Field and Laboratory Work

• • • • •

Field work at Wembury (collection of water and sediment samples and field measurements) at 3 locations (both morning and afternoon) Treatment of water and sediment samples Atomic absorption determination of Ca, Fe, Mg and Zn in water samples and sediment extracts Determination of nitrate and phosphate in water and sediment (P only) samples using a hand held spectrophotometer Environmental assessment report

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Sampling strategy Samples must be representative of;

•

the bulk

•

temporal material (body) being sampled variations

•

spatial variations Discrete (or Random/Grab)

environment or time – does not account for variation in spatial

Composite

– pooled number of discrete samples

Systematic (or Grid/Transect)

- a planned sampling strategy to accomplish a specific objective e.g. a transect of an estuary to account for salinity changes, identify different inputs, tidal cycle or time

(Pseudo)Continuous

– ongoing monitoring to e.g. establish baseline values for the environment and/or evaluate changes e.g. Atlantic Meridional Transect (AMT) programme.

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Safety Considerations

• Appropriate health and safety precautions followed.

• Risk Assessment.

• Awareness of hazards: sample toxicity, site access, site conditions, traffic, weather. • Accessibility of sampling locations.

• Transportation.

Economic considerations

• Main constraints are time and resources.

• Set realistic/affordable objectives.

• Cost effective sampling design.

• Consider automated sampling (and analysis)?

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Sampling location

Consider the following issues; • Programme objectives • Heterogeneity of determinand distribution • Accessibility (

e.g.

at bridge, tides) • Exact position (

e.g.

centre of river, mid-depth) • Point sources (e.g. STWs) • Stratified water bodies (lakes, estuaries, seas) • Mixing zones (merging streams, estuaries) EOE 2204 11

Sampling Time and Frequency

Representative of temporal variations: Random or cyclic (

e.g.

diurnal, seasonal, annual, decadal).

Chemical, biological and physical processes.

Sampling options: a)Occasional grab samples b)At fixed times c) At each part of a cycle d)Continuously Frequency of sampling constrained by economics EOE 2204 12

Methods of Sample Collection Sampling devices:

Water: Submersible pumps, Glass or plastic (polyethylene) bottles, Depth sampling bottles.

Sediment and soils:, Scoops and trowels, Corers and dredgers

Need to avoid Contamination

from sampling device and storage container.

Glass

– Use for organics. Ion exchange sites can remove metal ions from solution.

Plasticware

e.g. HDPE – Use for inorganics. Possible leaching of organics from container.

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Sample bottles

Glass – is used for organic analysis. Easily cleaned but cant be frozen.

Plasticware - e.g. high density polyethylene (HDPE) is used for nutrients and trace metals. Can be frozen.

Cleaning protocols – should be defined in the sampling strategy from the outset and can differ depending on the analyte under investigation and containers used.

Cleaning protocols - trace level analysis

Pre-washing by soaking in hot detergent for 24 h copious rinsing with pure water and then ultra high purity water (UHP) 5 days in an acid bath (HCl) copious (3x) rinsing with UHP 5 days in an acid bath (HNO 3 ) copious (3x) rinsing with UHP EOE 2204 14

3. SAMPLE TREATMENT (WATERS)

Sample Preservation and Storage

The aim is to minimise potential changes to the sample during storage. Processes that can affect sample integrity include: •

Biodegradation

•

Oxidation

(

e.g.

(

e.g.

N and P compounds) Fe(II), organic compounds) •

Absorption of CO2

(

e.g.

pH, alkalinity) •

Precipitation

•

Volatilisation

•

Adsorption

( ( (

e.g.

e.g.

e.g.

CaCO O 2 3 , Al(OH) , HCN) dissolved metals) 3 ) EOE 2204 15

Changes can be retarded (not prevented) by Refrigeration

(reduces biological activity)

Freezing

(plastic bottles only)

Filtration

(removes biotic and abiotic particulate matter but not all bacteria and viruses)

Preserving agents:

Concentrated; added during sampling, Acidification (

e.g.

HCl) for trace metals, Biocides (

e.g.

HgCl 2 ) for biodegradable determinands EOE 2204 16

Filtration

• Separation of dissolved and particulate determinands (0.45  m membrane filter) is operationally defined • Can use other sizes, e.g. 0.2  m • Perform immediately • Filters should be pre-washed • Removes most biotic and abiotic particles but not all bacteria, viruses and colloids EOE 2204 17

3. SAMPLE TREATMENT (SEDIMENTS)

A sample size of ~ 0.1

– 5 g is usually enough for analysis.

This is obtained by dividing up the originally collected sample.

The less than 180 um size fraction of inorganic material is often used and may require grinding.

This helps provide a more homogeneous sample and assists the dissolution (digestion) process.

“Coning and quartering” - the reduction of the bulk sample to a suitable sample size for analysis whereby the sample is dumped to form a cone, which is then flattened. The circular layer is divided into four equal segments and two opposite quarters are discarded. The operation is repeated with the remainder until the required amount is left.

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Methods of Sample Digestion Wet digestion

(ashing) in an open vessel (flask) is the most common approach for elemental analysis.

Various acids can be used, e.g. a mixture of nitric (oxidising) and hydrochloric (reducing) acids which is known as

aqua regia

or nitric acid alone. To speed up the process a closed pressurised vessel (e.g. PTFE bomb) and/or a microwave can be used.

Dry ashing

involves heating the sample in an open platinum or glazed porcelain crucible at 450 - 550 o C in a muffle furnace until the residue is white or nearly so. It is then extracted with hot hydrochloric acid.

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Properties of commonly used acids for digestion of soils and sediments NITRIC

acid (70%) is acidic and oxidising and forms soluble salts (except oxides of Al, Nb, Ta, Ti, Sn, Sb, W). Used alone or with bromine or hydrochloric acid.

HYDROCHLORIC

acid (36%) is acidic and non-oxidising (reducing for some higher oxidation states) and chlorides are generally soluble (except Ag, Hg, Tl, Pb). Some chlorides volatile (Hg, Ge, As, Sb, Se).

Widely used for inorganic matrices but not for organic matrices. Used alone or plus either (1) a reducing agent such as tin(II) chloride or hyrazine hydrochloride, or (2) an oxidising agent such as bromine, nitric acid, perchloric acid.

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Sources of Contamination

•Reagents, e.g. acids, water •Containers, material, cleaning protocols •Environment, e.g. air quality, clean room, laminar flow hood. Specific contamination sources include;

Facilities.

Walls, floors and ceilings. Paint and coatings. Construction material (sheet rock, saw dust). Air conditioning debris. Room air and vapours. Spills and leaks.

People.

Skin flakes and oil, Cosmetics and perfume. Perspiration. Clothing debris (lint, fibres). Hair. A motionless person generates 100,000 particles of <0.3 um per minute!

Fluids.

Particulates floating in air (dust). Bacteria, organics and moisture. Floor finishes or coatings. Cleaning chemicals. Plasticizers (outgasses). Deionized water.

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Seastar (Canada) Baseline nitric acid specification EOE 2204 22

Milli-Q water system (from Millipore Corp.) •

Specifications

• Resistivity (MΩ·cm at 25 °C) 18.2 • TOC (ppb) 5–10 • Pyrogens (EU/mL) NA • Bacteria (cfu/mL) <1 • Flow Rate (L/min) 1.5

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4. MEASUREMENT

The major categories of techniques for the chemical analysis of environmental matrices Chemical Analysis “Wet” methods Classical gravimetric analysis Classical volumetric analysis (titrations) Optical methods Instrumental methods Separation methods Other methods uv/visible spectrometry fluorescence infrared atomic absorption (FAAS, ETAAS) atomic emission (ICP AES, ICP-MS) nmr spectroscopy

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thin layer chromatography gas chromatography liquid chromatography electrophoresis Electroanalytical methods voltammetry potentiometry amperometry

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5. DATA TREATMENT

Precision

– associated with random (indeterminate) error where replicate measurements give different results. Individual results distributed (Gaussian) around a mean value. Can be quantified by the standard deviation.

Standard deviation

(s.d.) - assessment of precision. Formula?

Relative s.d. (RSD) is s.d. expressed as a % RSD (%) = (s/x) x 100

Accuracy –

associated with systematic (determinate) error (bias). How close is mean experimental value to the ‘true’ value?

Can be evaluated by intercomparison (round robin) exercises and use of certified reference materials (CRMs).

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PRECISION AND ACCURACY

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Precision Standard deviation (measure of random error) Population standard deviation

. The population standard deviation (usually represented by the Greek letter sigma) measures the variability of data in a population.

Sample standard deviation

. The sample standard deviation (usually represented by S) measures the variability of data in a sample. It is easy to compute (compared to a population standard deviation) because it is based on a small and manageable number of measurements.

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The standard deviation assumes a Normal (Gaussian) distribution of data from replicate determinations (usually 4 or 5).

It is symmetrical about the mean and the greater the value of s the greater the spread of the ‘bell shape’.

+/- 1s - includes 68 % of sample population +/- 2s - includes 95 % of sample population +/- 3s - includes 99 % of sample population

Mean

(x) =

Arithmetic

estimate of true value ( μ)

Spread or range

measured value = difference between largest and smallest

Confidence intervals

errors can be used to test for systematic – measure CRM, derive confidence interval and if known value is not within confidence interval then systematic error is probably present.

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Accuracy Seawater Reference Material for Trace Metals NASS-5.

The following table shows the twelve metals for which certified values have been established. Certified values are based on the results of determinations by at least two independent methods of analysis. The uncertainties represent 95 percent confidence limits for an individual sub-sample. That is, 95 percent of samples from any bottle would be expected to have concentrations within the specified range 95 percent of the time.

Trace Metal Concentrations (micrograms/litre).

Arsenic 1.27 ± 0.12, Cadmium 0.023 ± 0.003, Chromium 0.110 ± 0.015, Cobalt 0.011 ± 0.003, Copper 0.297 ± 0.046, Iron 0.207 ± 0.035, Lead 0.008 ± 0.005, Manganese 0.919 ± 0.057, Molybdenum 9.6 ± 1.0, Nickel 0.253 ± 0.028, Selenium(IV) (0.018)*, Uranium (2.6)*, Vanadium (1.2)*, Zinc 0.102 ± 0.039.

*information value only EOE 2204 29

CRM data sheets produced at Institute for National Measurement Standards, NRCC CASS -4 NASS -5 SLEW -3 SLRS-4 HISS -1 MESS -3 PACS -2 CARP -2 DOLT -3 DORM -2 LUTS-1 TORT -2 ORMS -2 MOOS -1 Nearshore Seawater Reference Material for Trace Metals Open Ocean Seawater Reference Material for Trace Metals Estuarine Water Reference Material for Trace Metals Riverine water Reference Material for Trace Metals Marine Sediment Reference Material for Trace Elements and Other Constituents Marine Sediment Reference Material for Trace Elements and Other Constituents Marine Sediment Reference Material for Trace Elements and Other Constituents Fish Reference Material for Dioxins, Furans and PCBs Dogfish Liver Reference Materials for Trace Metals Dogfish Muscle Reference Materials for Trace Metals Non Defatted Lobster Hepatopancreas Reference Material for Trace Metals Lobster Hepatopancreas Marine Reference Material for Trace Metals Elevated Mercury in River Water Reference Material Seawater Certified Reference Material for Nutrients

http://inms-ienm.nrc-cnrc.gc.ca/en/calserv/crm_e.php

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Calibration

Prepare series (at least 4) of standard solutions of known concentration, analyse exactly as for sample.

Produce calibration graph – if linear, then derive the concentration of the analyte by interpolation or use of equation of straight line (y = mx + c).

y

r 4 .

.

e.g. y = 1.17x + 5.0833

r 2 = 0.9988

Response signal r 3 r 2 r 1 .

More sensitive .

.

.

.

Less sensitive . Unknown analyte . standards c 1 c 2

x

Concentration EOE 2204 31

Equation of a straight line y = mx + c (obtained from e.g. Excel) e.g. y = 1.17x + 5.0833, r 2 = 0.9988

y – signal, m – gradient, x - analyte conc, c – intercept of line r 2 value, known as ‘correlation coefficient’, (actually product-moment correlation coefficient) represents line of best fit through the data points. Indicates ‘quality’ of the calibration graph. Equation used to interpolate unknowns, i.e. samples. The gradient of the line defines the SENSITIVITY of the method.

Important considerations •Always include blank value in calculation (never subtract blank from other points).

•Always make rough manual plot of data in lab to check for errors.

•Is response linear? For analytical data need at least 0.99.

•What is the best straight line (slope and intercept) through the data points?

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Limit of Detection (LOD)

This is a very important term that is used to determine the lowest analyte concentration that can be confidently detected. It is defined as the concentration giving a signal (e.g. absorbance units) equal to the blank signal, Y B , plus three standard deviations of the blank, S B .

It is calculated by analysing the blank solution several (4 or 5) times and calculating both the mean and standard deviation of the blank data.

The value Y B + (3 x S B ) is then substituted into the equation of the line (y = mx + c) and the equivalent x value (concentration) determined. Assuming that the line goes through Y that

x (LOD) = (3 x S B )/m .

B it follows EOE 2204 33

6. REPORT WRITING

See practical handbook for details of structure, length, presentation and deadline Presentation of data

The number of

Significant figures

precision of the experiment given indicates the – in practice quote as sig. figs. the digits which are certain e.g. 10.09, 10.10, 10.09, 10.11 - mean is 10.102, s = 0.01304

– clearly uncertainty in 2nd decimal place. Therefore best quoted as x +/- s = 10.10 =/- 0.01

Rounding up or down of figures – if consistent in one direction e.g. up, introduces bias - therefore round to the nearest even no e.g. 9.65 to 9.6, 4.75 to 4.8

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7. Supplementary information and exercises/answers t-test used to c

ompare experimental mean with a known value (thus checking for systematic errors) by 1. establishing a null hypothesis (H 0 ) (e.g. there is no difference between observed and known values other than that due to random errors), 2. then testing whether it is true, i.e. the statistical probability that difference between x and m arises from random chance.

The lower the probability that difference occurs by chance the less likely the null hypothesis is true.

Usually null hypothesis rejected if probability is < 1 in 20 (i.e. p = 0.05 or 5 %) i.e. the difference is significant at the 0.05 or 5 % level. Increased certainty by using p = 0.01 or 0.001 (1 % or 0.1

%) 3. The value of the

t

is calculated, if this is less than a critical value (taken from tables) then the null hypothesis is accepted.

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t-tests (cont’d) - also used to Compare 2 experimental means

e.g. those from a new method with a reference method (Ho is that the 2 means are equal and then test whether difference between the means differs significantly from zero). Calculate

t

(nb diff calc than for previous

t

) and use tables as before (n.b. assumes samples are drawn from populations with equal s.d.). Can also use this test to decide whether a change in experimental. conditions effects the results. (n.b. if s.d. of the populations is different, different equations are used).

Paired t test

– comparison of differences in results obtained by 2 analytical methods, when there are also differences in the analyte concentrations in the samples analysed. Achieved at p = 0.05 by testing the difference (

d

) between each pair of results - Ho =

d

does not differ significantly from 0 EOE 2204 37

•

Outliers

– when one or more results differs substantially from the others – test data using

Dixons Q test

- H o (null hypothesis) is that all measurements come from the same population.

Usually use p = 0.05 (95 % confidence).

Q = Suspect value largest value – nearest value/ – smallest value If calc Q value exceeds critical Q value (from tables) then suspect value rejected.

•

Grubbs test

– compares the deviation of the suspect value from the sample mean with the standard deviation of the sample. It is now the preferred outlier test.

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Other useful statistical tests F-test

- Significance test comparing standard deviations and thus used to compare random errors between two data sets. 1 sided

F

test - tests whether method A is more precise than method B e.g. whether new analytical method is more precise than old method.

2 sided

F

test – tests whether methods A and B differ in their precision.

ANOVA

– Analysis of variance. Comparison of more than 2 means e.g. several different analytical methods or different analysts using the same equipment. 2 different sources of variation – that always present due to random errors and the variation due to controlled or fixed-effect factors e.g. the solution storage conditions, 39

UNITS ppm

= 1 part per million (10 6 ) = 1 mg L -1 (for solutions) = 1 mg kg -1 = 1 ug mL = 1 ug g -1 (for -1 solids)

ppb

= 1 part per billion (10 9 ) = 1 ug L-1 = 1 ng mL -1 (for solutions) = 1 ug kg -1 = 1 ng g-1 (for solids)

ppt

= 1 part per trillion (10 12 ) = 1 ng L -1 etc.

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MORE UNITS % ‰

= 1 part per hundred (percent) = 1 part per thousand (often used for seawater)

Mass (weight) percent

(or m/m %) = (mass of substance / mass of total solution) x 100

Volume percent

(or v/v %) = (volume of substance / volume of total solution) x 100

Mass/volume percent

(or m/v %) = (mass of substance in grams / volume of total solution in mL) x 100 EOE 2204 41

Prefix Symbol Factor (10) Prefix Symbol Factor (10)

tera giga mega kilo hecto deca deci T G M k h da d 12 9 6 centi milli micro 3 2 nano pico 1 femto -1 EOE 2204 atto c m  f a n p -2 -3 -6 -9 -12 -15 -18 42

Molarity

The most common unit of concentration is molarity (moles per litre), abbreviated as M (or mol L -1 ). A mole is defined as the number of atoms of 12 C in exactly 12 g of 12 C. This number of atoms is called

Avogadros number

and its value is 6.022045

x 10 23 . A mole is simply 6.022045 x 10 23 of anything.

Note that

M

is the symbol for moles per litre whereas the symbol for moles.

mol

is The molecular weight (MW), now called relative molecular mass (RMM), of a substance is the number of grams that contain Avogadros number of molecules. To convert from M to g L -1 convert from g L -1 to mg L -1 multiply by the RMM. Then to multiply by 1,000.

To convert from g L -1 to M divide by the RMM.

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Exercise 1

Data for nitrate in water (mg L -1 ). The true value, as determined by a CRM is 42.0 mg L -1 .

A 42.5

41.6

42.1

41.9 41.1

42.2

B C D E 39.8

43.5

35.0

42.2

43.6

42.8

43.0

41.6

42.1

43.8

37.1

42.0

40.1

43.1

40.5

41.8

43.9

42.7

36.8

42.6

41.9

43.3

42.2

39.0

Comment on the bias, precision and accuracy of each of these sets of data.

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Exercise 1 Answers

1. Mean results for labs A-E are 41.9, 41.9, 43.2, 39.1, 41.5. The true answer was 42.0.

A – precise, little bias, accurate.

B – poor precision, little bias, mean accurate but not very reliable.

C – precise but biased to high values, not very accurate.

D – poor precision, biased to low values.

E – similar to A but the last result might be an outlier.

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Exercise 2

The following data sets were obtained for the determination of copper, calcium and magnesium in river water (units mg L -1 ). Calculate the mean, standard deviation and relative standard deviation of each data set.

Cu 0.07

0.08

0.07

0.08

0.07

0.07

0.08

0.08

0.09

Ca 23.3

22.6

22.5

24.7

21.9

21.5

19.9

21.3

21.7

23.8

Mg 13.8

14.0

13.2

11.9

12.0

EOE 2204 12.1

46

Exercise 2 Answers

Means, standard deviations and relative standard deviations as follows:

Cu

Mean = 0.077 mg L -1 s.d. = 0.007 mg L -1 RSD = 9 %

Ca

Mean = 22.3 mg L -1

Mg

Mean = 12.83 mg L -1 s.d. = 1.4 mg L EOE 2204 -1 s.d. = 0.95 mg L -1 RSD = 6.2 % RSD = 7.4 % 47

Exercise 3

1. What dilutions would you use to prepare standards of 1, 2, 3, 4, 5 mg L -1 Ca from a stock solution of 1,000 mg L -1 Ca?

2. What dilutions would you use to prepare standards of 0.1, 0.5, 1.0 mg L -1 Mg from a stock solution of 1.000 mg L -1 Mg?

Exercise 3 Answers

1. Dilute stock 10X with water to give 100 mg L -1 Ca then pipette 1, 2, 3, 4 and 5 mL directly into 100 mL volumetric flasks and make each up to 100 mL with water.

2. Dilute stock 10X with water to give 100 mg L -1 a further 10X with water to give 10 mg L -1 Mg then dilute Mg then pipette 1, 5 and 10 mL directly into 100 mL volumetric flasks and make each up to 100 mL with water.

Note: Always use ultrapure water e.g. from Milli-Q unit.

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Guided Reading

1. 1. ACOL Environmental Analysis by R.N. Reeve, 543.REE.

2. 2. Statistics for Analytical Chemistry, 4th Ed, by J.C. Miller and J.N. Miller, 519.5024541.MIL.

3. 3. Any general Instrumental Analysis textbook, e.g. Principles of Instrumental Analysis, 4th Ed, by D.A. Skoog and J.L. Leary, 543.08.SKO

4. 4. An Introduction to Atomic Absorption Spectroscopy by L. Ebdon, 543.0888.EBD.

5. 5. Encyclopedia of Analytical Science, Various articles, 543.003.ENC.

6. The Chemical Analysis of Water, 2nd Ed, by D.T.E. Hunt

7.

and A.L. Wilson, 546.22.HUN.

http://ec.europa.eu/environment/guide/part2d.htm

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