Update on the Global Monitoring Plan (GMP) in the UNEP...

Update on the Global Monitoring Plan (GMP) in the UNEP Stockholm Convention on Persistent Organic Pollutants

(POPs), technical guidance , data analysis, modeling, assessment and workplan.

Ramon Guardans1, 2 1Ministry of the Environment, Rural and Marine Affairs MARM, Madrid Spain 2 Stockholm Convention Regional Activity Center CPRAC, Barcelona, Spain.

E-mail contact: [email protected]

1. Introduction The Stockholm Convention on Persistent Organic Pollutants (POPs) [1] was adopted on 22 May 2001 and entered into force on 17 May 2004. As of November 2011 the Convention has 176 Parties.

The objective of the Stockholm Convention on POPs is to: Protect human health and the environment from persistent organic pollutants by reducing or eliminating releases to the environment.

Parties have agreed that they need a mechanism to measure whether this objective is reached. According to Article 16 of the Convention, its effectiveness shall be evaluated starting four years after the date of entry into force of the Convention and periodically thereafter at intervals to be decided by the Conference of the Parties (COP)

The objective of the GMP is to evaluate whether the levels of POPs in core media Air and Human tissue, (milk and serum) are actually reduced or eliminated as requested by Articles 3 and 5 of the Convention, information on environmental levels of the chemicals listed in the annexes should enable detection of trends over time. Therefore focus is upon monitoring of background levels of POPs in air at locations not influenced by local sources. The GMP has established a comprehensive regional inventory of capacities and a corresponding needs assessment conducted by the Secretariat with contributions from national Stockholm Convention focal points, in cooperation with UNEP Chemicals has implemented Capacity building programs in sevral world regions. A network of databases containing monitoring information has been developed.

The first monitoring reports from all five UN regions and the Global monitoring report were presented By the GMP at the fourth meeting of the Conference of the Parties in 2009

2. Technical guidance The technical guidelines,[2] are available in an extensive document describing recommended methods and procedures to monitor POPs in the GMP core media, air and human tissues (milk, serum) The first edition of the guidance for the Global Monitoring Plan was developed and published in 2004, by UNEP Chemicals. Further to the second meeting of the Conference of the Parties to the Stockholm Convention, a Technical Working Group (TWG) was mandated to revise the original guidance document. The Conference of the Parties (COP4 and COP5) decided to add ten new chemicals to Annexes A, B and C of the Stockholm Convention. The Conference also mandated the global coordination group and the Stockholm Convention Secretariat to update the guidance document for the global monitoring plan with additional chapters on long-range transport and specimen banking,

3. Data Analysis The first monitoring reports from all five UN regions [3] and the Global monitoring report [4] were presented by the GMP at the fourth meeting of the Conference of the Parties in 2009 providing the best available data to establish baselines for the preliminary work to asses the effectiveness of the measures undertaken to decrease the the concentrations of the initial16 listed POPs.

3.1. Air samples For interpreting changes in levels of persistent organic pollutants over time, data collected within a programme should be comparable. Ensuring data comparability between various air monitoring programmes is difficult, given the numerous sources of uncertainty. For modelling and evaluation of long-range transport or for semi-quantitative spatial comparisons of persistent organic pollutant levels, however, comparability of

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data between the programmes is desirable, and could be assessed and resolved through inter-comparison studies. Some of the physical and chemical properties of persistent organic pollutants are temperature-dependent and levels of persistent organic pollutants may be influenced by year-to-year climate variability. Climate variability also affects the meteorological patterns that deliver persistent organic pollutants to background sites. It is important to understand better those influences to ensure that data are interpreted correctly.

3.2. Human tissue For data from human milk or blood, comparability between programmes is hampered by the impact of various factors on the results: the location of the study; the age, sex and ethnicity of the subjects; social factors; and the laboratory conducting the analysis. The human milk survey coordinated by WHO, which uses a harmonized sampling protocol and a single laboratory, provides data sets that are comparable over time and between the regions.

4. Modeling and assessment 4.1 Air The work under the GMP has identified the relevance of modeling long range atmospheric transport including meteorological and climatic variability to understand better the effects of a changing climate on POPs . Important results have been published in UN-ECE/LRTAP [5] report on Hemispheric Transport of Air Pollution (HTAP) 2010 Part C and the United Nations Environment Programme (UNEP)/ Arctic Monitoring and Assessment Programme (AMAP) report [6].

4.2 Humans A central issue that has produced interesting and relevant work concerns modelling ecological and physiological pathways and time lags in exposure leading to health impacts [7]. Also the relevance of indoors exposure routes for critical groups (toddlers) for some substances (eg. BDEs, PCBs ) has been recently noted .

5. Workplan Challenges for future work include long term stability and coordination of the networks, the analytical and data quality assurance/quality control (QA/QC), data management and archiving, the development and use of models dealing with atmospheric and marine long range transport and climate change, as well as modeling ecological and physiological pathways and time lags in exposure leading to health impacts. The GMP is working towards second regional and global monitoring reports that will be presented to thee COP in 2015.

6. References [1] UNEP 2001 Stockholm Convention on Persistent Organic Pollutants (www.pops.int) [2] UNEP 2011 Draft Revised guidance on the Global Monitoring Plan for persistent organic pollutants

UNEP/POPS/COP.5/INF/27 http://chm.pops.int/Convention/COP/Meetings/COP5/COP5Documents/tabid/1268/Default.aspx

[3] UNEP 2009 Regional Monitoring Reports http://chm.pops.int/Programmes/GlobalMonitoringPlan/MonitoringReports/tabid/525/Default.aspx [4] UNEP 2009 Global Monitoring Report UNEP/POPS/COP.4/33 http://chm.pops.int/Portals/0/Repository/COP4/UNEP-POPS-COP.4-33.English.PDF [5] UN-ECE/LRTAP Task Force on Hemispheric Transport of Air Pollution (HTAP) 2010 report Part C http://www.htap.org/activities/2010_Final_Report.htm 6] UNEP/AMAP 2011 “Climate Change and POPs: Predicting the Impacts”

http://chm.pops.int/Programmes/GlobalMonitoringPlan/ClimateChangeandPOPsPredictingtheImpacts/tabid/1580/language/en-US/Default.aspx

[7] R.Ritter, M. Scheringer, M.MacLeod, U. Schenker and K. Hugerbüler 2009 A Multi-Individual Pharmacokinetic Model Framework for Interpreting Time Trends of Persistent Chemicals in Human Populations: Application to a Postban Situation. Environmental Health Prespectives 117, 1280-1286. Note: RG has served with Mr Vincent Maddadi , Kenya since 2008 as co chair of the Global Coordination Group of the GMP,. This document is based on GMP texts but repesents only the author.

http://chm.pops.int/Convention/COP/Meetings/COP5/COP5Documents/tabid/1268/Default.aspxhttp://chm.pops.int/Programmes/GlobalMonitoringPlan/MonitoringReports/tabid/525/Default.aspxhttp://chm.pops.int/Portals/0/Repository/COP4/UNEP-POPS-COP.4-33.English.PDFhttp://www.htap.org/activities/2010_Final_Report.htmhttp://chm.pops.int/Programmes/GlobalMonitoringPlan/ClimateChangeandPOPsPredictingtheImpacts/tabid/1580/language/en-US/Default.aspxhttp://chm.pops.int/Programmes/GlobalMonitoringPlan/ClimateChangeandPOPsPredictingtheImpacts/tabid/1580/language/en-US/Default.aspx

Adapting monitoring strategy to the contaminant source characteristic – chromium in the upper Dunajec River watershed

Ewa Szalinska1, and Janusz Dominik2, 3

1Cracow University of Technology, Institute of Water Supply and Environmental Protection, ul. Warszawska 24, 31-155 Cracow, Poland

2Istituto di Scienze Marine -- Consiglio Nazionale delle Ricerche, Arsenale - Tesa 104, Castello 2737/F 30122 Venezia, Italy

3 Institut F.-A. Forel, Université de Geneve, Switzerland E-mail contact: [email protected]

1. Introduction Application of the monitoring data instead of estimated or predicted values for contaminant emissions and concentrations is crucial for environmental risk assessment. Therefore, representativeness and reliability of these data should be assured. Enhanced knowledge about the temporal and spatial patterns of contaminant distribution improves effectiveness of monitoring data collection. Since these patterns are not uniform in aquatic environments carefully designed sampling regime should be considered for different types of aquatic systems, especially in the operational monitoring under WDF.

The goal of this study was to compare impact of sampling regime on the contaminant level assessments and load calculations and also suggest a sampling patterns which would produce reasonable load estimates.The upper Dunajec River watershed (Southern Poland) with the Czorsztyn Reservoir was selected for the study purpose as it constitutes a useful setup for single contaminant monitoring investigation. Chromium originating from local tanneries is the only metal contamination in this watershed. Tannery effluents contaminated with chromium, used as a tanning agent, are discharged into the Dunajec River and then transported to the reservoir [1, 2].

2. Materials and methods Instantaneous (discrete) and integrated (composed) water samples were collected in three sites (Figure 1) for 19 weeks. Sampling sites were localized in the vicinity of the local impoundment reservoir system. Discrete samples were collect weekly by hand, while integrated samples were collected using automatic water samplers (ISCO) in the time-integrated mode. Time integrating mode rather than discharge proportional mode was chosen for a better comparison with the discrete sampling.

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Figure 1: Localization of sampling sites [3].

Dissolved and particulate Cr loads in instantaneous and integrated samples were estimated using averaging calculation methods. Loads were computed as the product of the flow volume and Cr concentration measured during the corresponding time interval.


3. Results and discussion

3.1. Monitoring of chromium contamination level A 19-week continuous sampling in the upper Dunajec river watershed proved that chromium contamination in this river is variable and discontinuous. Temporal distribution of chromium in instantaneous samples at site 1 did showed high variability and no specific pattern. A more detailed picture of the chromium occurence was obtained through the results of integrated sampling. Dissolved Cr concentrations in the latter were usually higher than in instantaneous samples collected in the same days (Figure 2). These differences reaching 10−78% resulted from the diverse time and mode of sampling (integrated samples represent daily average Cr concentrations while instantaneous samples were collected in the morning of the sampling day). Taking into consideration that these concentrations were not related to the river flow we can assume high variability in tannery wastewater discharge released clandestinely. The means for each day of week showed that the illegal discharges occured mainly during the weekend.

Figure 2: Distribution of Cr concentrations in instantaneous and integrated samples at site 1 (Dunajec River) [3].

Such discrepancies in Cr concentrations raise the question of timing and frequency in water monitoring for this system. Since, there is no significant correlation between dissolved Cr concentrations in samples collected in instantaneous and integrated modes (r2=0.05 at p=0.39) we can suppose that not only the choice of the week day but also sampling hour matters for the reliability of the monitoring in this area.

3.2. Estimation of chromium loads The issue of sampling regime affected also estimation of the chromium load introduced into the Czorsztyn Reservoir. Dissolved Cr loads obtained for integrated samples were 3.6−5.4 times higher than for instantaneous ones. Taking these differences into consideration and also the uncertainty of errors estimated for the Cr loads for instantaneous samples (up to 75%), it becomes clear that time integrated sampling is the only reliable way of collecting data for load estimations of contaminants from fluctuating point sources. Instantaneous sampling, even if frequent, is not suitable for contaminant budget calculations for sites were the presence of highly variable sources of contamination is expected.

4. Conclusions Study results showed that discrete sampling can lead to an underestimation of chromium contamination level and load, especially when illegal/unexpected discharges occur in the watershed. From chromium load computations based on results for both types of samples we can conclud that integrated sampling produces more reliable data, with acceptable range of estimated errors. Instantaneous sampling, even if frequent, should not be used for contaminant budget calculations in the localities were strong variability of contamination is anticipated.

5. References [1] Szalinska E. 2002. Chromium transformations in the water environment contaminated with tannery waste

water. Monographs of Cracow University of Technology 283 (in Polish). [2] Szalinska E., Dominik J, Vignat DAL, Bobrowski A, Bas B. 2010. Seasonal transport pattern of chromium

(III and VI) in a stream receiving wastewater from tanneries. Applied Geochemistry, 25, 116−122. [3] Szalinska E., Smolicka A, Dominik J (submitted) Monitoring of chromium transport in a watershed with an

impoundment reservoir. Environmental Monitoring and Assessment, EMAS9470 Acknowledgement - The study was supported by the Swiss National Science Foundation (grant No : 200020-1179). The Authors thank Ms Agnieszka Smolicka for her help in this project.

Figure 1: Concentrations of Chromium associated with SPM from 2000 to 2009 at the station Isère at Grenoble campus.

Figure 2: Concentrations of PCB 153 associated with SPM from 2000 to 2009 at the station Rhône at Pougny. In 2008 LOQ decreased allowing PCB 153 to be quantified. When PCB 153 was not successfully quantified, LOQ is reported on the graph.

Factors influencing the quality of river monitoring data used for environmental risk assessment of particulate/hydrophobic

chemicals Hélène Angot1, Marina Launay1,2, Laetitia Roux2, Jérôme Le Coz2 and Marina Coquery1

1Irstea (ex-Cemagref) – UR MALY– Freshwater Systems Ecology and Pollution Research Unit, 3 bis quai Chauveau – CP 220, F-69336 Lyon, France

2Irstea (ex-Cemagref) – UR HHLY– Hydrology-Hydraulics Research Unit, 3 bis quai Chauveau – CP 220, F-69336 Lyon, France


1. Introduction An environmental risk assessment (ERA) of a chemical allows to predict whether a substance may adversely impact the environment. When chemical monitoring data are compared to no-effect concentrations, their quality, in terms of sampling and analytical uncertainties, can directly affect the outcome of the ERA. From 2000 to 2009, under the framework of the European Framework Directive (2000/EC/60), about 300 chemicals were analysed in water, bed sediment and suspended particulate matter (SPM) at 17 sites throughout the Rhône river network by the regional water authority. This study focuses on factors influencing the quality of this comprehensive database. Examples based on concentrations of hydrophobic organic contaminants and metals associated with SPM will be highlighted, given the scarcity of such data due to expensive and cumbersome sampling and analytical procedures [1].

2. Materials and methods Monitoring data were obtained as huge text files comprising up to 106 lines. Data of interest were selected using bash scripts and finally processed with software R (2.9.0).


3.1. Analytical methods Concentrations of chromium associated with suspended particulate matter (SPM) from 2000 to 2009 at the station Isère at Grenoble campus are shown in Figure 1. In 2003, the contracting analytical laboratory changed, together with the analytical method. The extraction step is suspected to have changed, leading to higher extraction yields and thus to higher concentrations. This trend was observed at all sampling sites. Besides, limits of quantification (LOQ) can be improved over time and previously unquantified contaminations can be brought to light (Fig. 2). On the other hand, information can be lost when an analytical method with a higher and inappropriate LOQ is used (Fig. 3).

Figure 3: Concentrations of anthracene associated with SPM from 2000 to 2009 at the station Isère at Grenoble campus. In 2003 the LOQ increased leading to an absence of quantification of the chemical. When anthracene was not successfully quantified, LOQ is reported on the graph.

Figure 5: Water discharge at the Grenoble sampling station from January to September 2004. The red crosses represent contaminant analyses.

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These results show that a critical review of quality of collected data is essential to avoid the calculation of inaccurate average concentrations. Moreover all analytical methods should match minimal performance standards [2], i.e., appropriate quantification limits to reach the operational objectives. When several laboratories are involved in the analytical process, analytical methods must be described beforehand, and interlaboratory comparison tests and quality controls are necessary.

3.2. SPM sampling methods In a recent study of the Rhône Sediment Observatory, the comparison of various SPM sampling techniques (a continuous-flow centrifuge, an integrative sediment trap and large volume water sampling) showed differences in the particle-size distributions of SPM sampled (Fig.4). The observed grain size biases could have an effect on the concentrations of contaminants associated with SPM due to the particle-size/contamination level correlation. One study indicated that finest particles are not as efficiently retained by an integrative sediment trap as slightly larger ones, which is partly confirmed by our results [3].

3.3. Sampling frequency Most of the concentrations reported in the dataset were sampled during baseflow periods, ignoring the periods with highest discharge and SPM concentration (Fig.5). Given the high temporal variability of the water discharge, 4 samplings and analyses randomly distributed over the year can not be representative of the river contamination variability, especially for contaminants associated with SPM. Higher sampling frequency during flood events is required to document the possible evolution of contamination rates with increasing discharge.

4. Conclusions Factors such as analytical methods, quantification limits, SPM sampling methods and sampling frequency may severely impact the measurement of spot concentrations or annual average concentrations of particulate/hydrophobic chemicals in rivers, and thus the quality of the dataset. Inaccurate or imprecise average contaminant concentrations can lead to inadequate environmental risk assessments. The dataset quality is thus essential and interlaboratory comparison tests, quality controls, beforehand description of appropriate analytical methods, quantification limits and sampling frequency, as well as a comparison of sampling techniques are among possible answers to improve it.

5. References [1] Mahler BJ, Van Metre PC. 2003. A simplified approach for monitoring hydrophobic organic contaminants

associated with suspended sediment: methodology and applications. Arch. Environ. Contam. Toxicol. 44, 288-297.

[2] European Commission. 2009. Commission Directive 2009/90/EC of 31 July 2009 laying down, pursuant to Directive 2000/60/EC of the European Parliament and of the Council, technical specifications for chemical analysis and monitoring of water status. Official Journal of the European Union.

[3] Allan IJ. 2009. Measuring concentrations of persistent organic pollutants and trace metals in Norwegian rivers. Riverpop (TA-2521/2009). 106p.

Figure 4: Particle-size distribution of SPM sampled with different sampling techniques (continuous-flow centrifuge, large volume water sampling and integrative sediment trap).

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Making management decisions with imperfect data: assessing potential aquatic metal risks with biotic ligand models

Graham Merrington, Adam Peters and Peter Simpson

wca environment, Brunel House, Volunteer Way, Faringdon, Oxfordshire, SN7 7YR E-mail contact: [email protected]

1. Introduction Accounting for bioavailability, through the use of sophisticated Biotic Ligand Models (BLMs), represents the most technically robust method assessing potential metal risks in the freshwater aquatic environment. New user-friendly BLMs (ufBLM) are now available to facilitate their regulatory use. These new models are based on the outputs of the more sophisticated BLMs but require data on fewer water physicochemical parameters to run (i.e. limited to just pH, calcium and dissolved organic carbon). However, there remain obstacles to using the tools, specifically the lack of site-specific physicochemical input data. Absence of required input data means the models cannot be run. Yet, while not always starting with the perfect dataset for all sites of interest, it is rarely the situation that there is a complete absence of “fit of purpose” input data. There are several ways by which input data gaps can be filled in a robust, precautionary, manner to deliver a screening level assessment which can then be used to develop focussed monitoring programmes, identify sensitive sites and broadly characterise risks. This presentation will give an example of how, through the use of imperfect data, management decisions can be made in relation to the assessment of potential aquatic risks of metals.

2. Assessment aims and datasets Regulatory decisions, such as compliance assessment, waterbody classification under the Water Framework Directive (WFD) and discharge permitting, involving metals that have BLMs can only be made reliably when there is adequate supporting information available to paramatise BLM models. This means that, in addition to data on the concentrations of dissolved metals (at an appropriate limit of quantification), it is necessary for data on the supporting parameters to also be accessible. Many of the BLMs currently available utilise a relatively large number of input parameters, and undertaking monitoring for all of these at all monitoring sites would be costly. However, the number of sites for which this additional supporting data is required can be minimised by taking a tiered approach to assessment of potential risks. It is possible that early tiers in such a risk assessment framework can adopt less stringent criteria on what constitutes acceptable supporting data, as long as these are conservative and unlikely to introduce a type II error (false negative). The use of surrogate data, estimated from other parameters, or conservative default data can be used in some cases to minimise analytical requirements [1].

Information on DOC concentrations is currently the most significant hurdle to assessing metal bioavailability. However, in the absence of site-specific data, it may be possible to calculate an alternative value calculated from another surrogate variable, for which data is more readily available or to use a default input value. It has often been assumed that DOC concentrations cannot be predicted from other water quality properties (e.g. pH, Ca, etc.). However, co-variation between DOC concentrations and dissolved iron has been observed in the UK [2]. The most plausible explanation for this relationship seems to be that the dissolved iron concentrations observed are due to associations between iron and organic matter. Transport of organic matter from soil horizons to surface waters may result in the transport of iron which is already associated with the organic matter, although this is speculative. “Dissolved” iron in circumneutral waters is unlikely to be present as truly dissolved material, and is more likely to be present either as Fe ions which are bound to organic matter, or as fine colloids which also tend to be associated with organic matter in natural waters. UV analysis can also be used to provide simple and rapid determination of DOC concentrations [3]. A series of waterbody and hydrometric area default input values for DOC and Ca have also been developed for England and Wales [4] Defaults were derived as the 25th percentile of historic DOC monitoring data or the 50th percentile of available Ca monitoring data within a hydrometric area or waterbody. As such, they may not genuinely reflect average conditions on a local scale, but are likely to be conservative.


3. Interpretation of imperfect data in the assessment of potential aquatic risks of metals

wca environment recently undertook a study of waterbodies impacted by abandoned metalliferous mine sites for the Environment Agency of England and Wales [5]. One of the objectives of the study was to provide guidance on their appropriate classification under the WFD, particularly to provide guidance on the course of action when biological and chemical measurements of status differ i.e. EQS for metals are breached but the biological community is in a good condition. Application of user-friendly BLMs, within a tiered assessment approach, was investigated as a suitable methodology to rationalise biological and chemical measures under the WFD. The Environment Agency had previously identified 470 waterbodies that were potentially impacted by historic metalliferous mining. Of these 470 only 257 were suitable candidates for further risk assessment within the project because they either had no surface water classification data, they were failing chemical status because of pesticide, nutrient and industrial chemical quality elements, or they had no metal monitoring data. All of the remaining 257 sites had site-specific pH monitoring data, but only 48 (19%) had site-specific monitoring data for pH, DOC and Ca. In the absence of site-specific data for DOC and Ca hydrometric area or waterbody default values were used in user-friendly BLM calculations. Of the 209 waterbodies that lacked site-specific data 1 did not have a suitable BLM (only Fe data monitored), 128 (50%) had hydrometric area defaults, 63 (25%) had waterbody defaults, whilst only 17 (7% of waterbodies) lacked any suitable input data for the user-friendly BLMs. Despite the availability of default values the majority of waterbodies only had monitoring data for two metals (copper and zinc).

Use of default values allowed the potential benefit of accounting for bioavailability in WFD surface water classification to be undertaken across a large number of waterbodies and clearly identified where existing, non-bioavailability based, metals EQS were resulting in failure of chemical status that could be considered as Type I errors (false positives), particularly for copper. A follow-up targeted field programme, informed by these screening results was undertaken in a limited number of waterbodies to refine the initial risk assessment.

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Figure 1: Compliance of individual metals across the screening assessment.

4. Conclusions The use of default or derived input data for the user-friendly BLMs in the way suggested is not the end of the process of assessing metal risk, but marks the beginning. The use of pragmatic, scientifically credible risk-based methods to utilise existing, imperfect monitoring data can be used to inform the next step in monitoring strategy and allow accountable methods for site prioritisation and hazard assessment.

5. References [1] Peters A, Merrington G, de Schamphelaere K, Delbeke K. 2011a. Regulatory consideration of bioavailability for metals:

Simplification of input parameters for the chronic copper biotic ligand model. Integr Environ Assess Manag. 7:437–444. [2] Peters A, Crane M, Adam W. 2011b. Effects of iron on benthic macroinvertebrate communities in the field. Bull Environ Contam

Toxicol. 86:591-5. [3] Tipping E, Corbishley H, Kiprovnjak J, Lapworth D, Miller M, Vincent C, Hamilton-Taylor J. 2009. Environ Chem. 6:472-6. [4] Environment Agency. 2010. The importance of dissolved organic carbon in the assessment of environmental quality standard

compliance for copper and zinc. Environment Agency, Bristol, UK (in press). [5] Environment Agency. 2011. Ecological Indicators for abandoned mines. Science Report SC090024/2, Environment Agency, Bristol,

UK.

Mapping the chemical environment of London; the London Earth Project

Christopher Johnson, Catherine Scheib, Andreas Scheib, Deirdre Flight, Mark Cave, Louise Ander, Bob Lister, Neil Breward and Don Appleton

British Geological Survey, Keyworth, Nottingham, NG12 5GG, United Kingdom E-mail contact: [email protected]

1. Introduction The British Geological Survey (BGS) has had a programme of geochemical mapping since the 1960s currently referred to as the Geochemical Baseline Survey of the Environment (G-BASE) project [1]. Since the 1990s the systematic sampling of soils at a high density (1 site per 2 km squares in rural areas and 4 sites per 1 km square in built areas) has provided a geochemical baseline giving valuable information with which to address environment and health issues. During 2005 – 2009 the Greater London Authority (GLA) area was systematically sampled (Figure 1). This represents one of the world’s largest systematic geochemical mapping exercises of an urban area. Over 6,400 topsoil samples were analysed for more than 50 elements by X-ray fluorescence spectrometry (XRFS) with pH and loss on ignition also determined. Phase I of the work was completed in May 2011 with the release of the data and web publication of some example maps accompanied by guidance notes and summary element interpretations [2]. Phase II of the work for the period 2011 – 2012 has involved more detailed interpretation of the results and further analyses is ongoing on subsets of the samples including: Hg and organic analyses; profile lines mapping the occurrence of Au and the platinum group elements (PGEs); and bioaccessibility studies. Applications of the London Earth data to environment and health issues will be presented.

Figure 1: Soil sampling sites within the Greater London Authority area.

2. Sampling and analytical methods Soils were sampled using a 1 m auger and are a composite of 5 sub-samples collected at the corners and centre of a 20 m square. At each site three soil sample types were collected – surface (0 – 2 cm); topsoil (5 – 20 cm); and deep soil (35 – 50 cm). Only the topsoils have been systematically analysed; other samples are archived and are available for further study. After oven drying (


3.1. Presentation of results The topsoil results are available as a stand-alone digital product [3]. Initially, data have been presented as interpolated geochemical images ([2] and Figure 2) and these are accompanied by a discussion and interpretation of each element’s distribution. An electronic geochemical atlas is being prepared which will consider the results in the context of a larger regional area.

Figure 2: Pb in London topsoils. Interpolated image ( ArcGIS v9.3) using IDW, search radius 750 m and cell size 80 m.

3.2. Discussion Geological and anthropogenic controls on element distributions are observed to varying degrees. A notable feature is a central zone of elevated concentrations for elements such as Pb, Sb, Ca, Zn, Cu, Sn and As in the oldest, most intensely urbanised parts of the city where traffic density is highest. Localised areas of higher concentrations, related to particular anthropogenic activities, can also be identified. For example, Se, Cd, Ni, Cu and Zn show particularly elevated concentrations close to an industrial area on the banks of the River Lee in N London. Despite these anthropogenic controls in a predominantly built environment, strong geological influence on soil chemistry is observed for many elements. This is particularly evident in south London where high baseline concentrations of Ca, Ce, I, La, Mn, Nd, P, Sr, Y and Zr, relate to the influence of the Cretaceous chalk bedrock. Elevated levels of Hf and Zr correspond to areas of Eocene marine and Quaternary wind-blown deposits. An interesting feature shown is the consistently low concentrations of metals associated with the Royal Parks (Bushy and Richmond), Hampton Court and nearby Wimbledon Common in southwest London, which contrast with surrounding areas. These parks have avoided significant residential or industrial activity and remain free of imported soil, wastes or ‘made ground’. Consequently, comparison of geochemical baselines within and outside the parks, where underlying geology is consistent, can help to provide an estimation of anthropogenic chemical modification to London’s environment.

4. Conclusions Systematic baseline geochemical mapping provides valuable evidence and information that can be used to address some of the major environment and health issues in urban areas. On such example is the use of the London Earth topsoil data in a project in support of the revision of statutory guidance for the contaminated land regime to determine what are normal soil contaminant concentrations.

5. References [1] BGS 2011. G-BASE Project. http://www.bgs.ac.uk/gbase/ Accessed 30 November 2011. [2] BGS 2011. London Earth. http://www.bgs.ac.uk/gbase/londonearth.html Accessed 30 November 2011 [3] Johnson CC, Scheib A, Lister TR. 2011. London Earth topsoil chemical results: user guide. British Geological Survey,12pp (OR/11/035). http://nora.nerc.ac.uk/14402/ Accessed 30 November 2011. Acknowledgement - We acknowledge the efforts of the many undergraduate students involved in the London Earth sample collection. This extended abstract is published with permission of the BGS Director.

http://www.bgs.ac.uk/gbase/http://www.bgs.ac.uk/gbase/londonearth.htmlhttp://nora.nerc.ac.uk/14402/http://nora.nerc.ac.uk/14402/

Active local sources of PCBs in the Arctic Ola A. Eggen1, Rolf Tore Ottesen1 and Morten Jartun2

1Geological Survey of Norway, TRONDHEIM, Norway 2current employer: OSL, GARDERMOEN, Norway


1. Introduction Ecotoxicological surveys have previously demonstrated high concentrations of polychlorinated biphenyls (PCBs) in biological material on and around Svalbard, Norway. Predators in particular, such as polar bears which occupy a top in the food chain, have accumulated high concentrations of PCBs [1,2]. 30 years after their banning, PCBs are still showing up in our environment.

Studies of PCBs in local sources, were initiated on Svalbard in 2007. These sources include: including building materials, such as paint and concrete; small electrical capacitors; and local surface soil, Previous to these studies, Akvaplan-niva found increasing levels of various pollutants in marine sediments outside specific settlements, suggesting active, local sources of PCBs [3,4].

In this presentation the work done at Svalbard will be emphasized, but other similar work will be shortly reviewed.

2. Materials and methods

2.1. Sampling Samples were collected of several types of materials, such as surface soil, paint, concrete and electrical capacitors. The paint was scraped from building facades, ideally from flaking paint. When possible, an effort was made to collect paint samples from each location which reflected the varying paint types. Generally, samples of concrete were collected from buildings by breaking off pieces with a small hammer, e.g. from foundation wall corners or steps. The surface soil (0–2 cm) in settlements was collected from evenly dispersed points. Background soil samples were collected from sites around the archiopelago, far from settlements. Sampling took place in the period 2007-2010.

2.2. Chemical analysis Samples of soil from the settlements were analysed for PCBs by GC-ECD (gas chromatography with electron capture detector). Paint were anaysed by either GC-ECD or GC-MS (gas chromatography with mass spectrometry) to determine concentrations of PCBs. Background soil samples were analysed using HRGC-HRMS (high resolution GC-MS). The analytical methods were based on Nordtest Technical Report 329 which is a regularly used method in Norway for analysing PCBs, as well as the Swedish standard SS-EN 11465.


3.1. Svalbard, Norway During the sampling period more than 1100 single samples of paint, concrete, soil and small capacitors from 12 different active and abandoned settlements were collected. 78 soil samples from 24 background sites were also collected. Figure 1 shows that the exterior paint in the settlements can be regarded as a primary source of PCBs. In a dry and extreme climate such as exists on Svalbard, paint will eventually flake off and fall to the ground. The settlement soil can then be regarded as a secondary source of PCBs, which can spread the pollution to terrestial or marine ecosystems by wind and/or water erosion. The background sites will be affected by a mixture of long-range transported PCBs and PCBs from local sources.


Figure 1: PCBs on Svalbard in exterior paint, surface soil

from settlements, and surface soil from background sites.

3.2. Other surveys

3.2.1. Nuuk, Greenland

Five samples of exterior paint and concrete from the Nuuk settlement, Greenland were collected in 2008. The PCB concentrasions range from

GEMAS: Geochemical mapping of agricultural and grazing land soils at the European and national scales

Manfred Birke1, Clemens Reimann2, Uwe Rauch1, Enrico Dinelli3, Alecos Demetriades4, Volodymyr Klos5, GEMAS Project Team2

1Federal Institute for Geosciences and Natural Resources, Berlin Office, Wilhelmstrasse 25 – 30, 13593 Berlin, Germany

2Geological Survey of Norway, PO Box 6315 Sluppen, 7491 Trondheim, Norway 3 Earth Science Department, University of Bologna, Piazza di Porta San Donato 1 40126 Bologna Italy 4Institute of Geology and Mineral Exploration (IGME), Spirou Louis Street 1, 13677 Acharnae, Greece

5 PivnichGeologia, Northern State Regional Geological Enterprise, 10 GeophysicistsStreet, Kyiv 02088, Ukraine


1. Introduction The Geological Surveys of Europe have a long tradition of geochemical mapping at a variety of scales – from the local (mineral exploration or urban geochemistry, [1]) to regional (covering a single country, [2]) and finally the continental [3, 4] scale.

In Europe the new REACH (Registration, Evaluation and Authorisation of Chemicals) regulation [5] requires that industry must prove that it can safely manufacture and use its products at the local, regional and European scale. Consequently, there is a need for a harmonised dataset for metal exposure across Europe. EuroGeoSurvey and Eurometaux decided to join forces to provide such a database at the European scale. The data will be released to the general public in 2013. The GEMAS project started in 2008 with a joint field campaign of almost all Geological Surveys in Europe in cooperation with some external organisations.

2. Materials and methods Sampling at a density of 1 site per 2500 km2 (grid 50 x 50 km) was completed at the beginning of 2009 by collecting 2211 samples of arable soil (Ap samples, 0-20 cm), and 2118 samples from land under permanent grass cover (Gr samples, 0-10 cm), according to an agreed field protocol, [6]). All the samples were shipped to the central laboratory of the Geological Survey of Slovakia for sample preparation (air-drying and sieving

Figure 1: spatial distribution of Cd concentrations and pH values in European agricultural soils

The distribution of pH values is clearly influenced by a combination of geology and climate (e.g. acidic soils on crystalline bedrocks in the cold and wet Scandinavia), and land use. Maps for many other elements and several parameters determining the availability of metals show also a strong influence of climate. On all maps the effect of diffuse contamination remains invisible at the European scale. Element concentrations decrease rapidly towards background with distance from source (geological or anthropogenic).

4. Conclusions The GEMAS dataset provides high quality and completely harmonised data for both metal concentrations and corresponding soil properties that determine the availability of metals in arable and grazing land soils for all Europe (except Malta, Albania and Romania). The natural variation is large for most elements, usually serveral orders of meagnitude (between one and four orders of magnitude for total concentrations and up to five orders of magnitude in AR extraction). For the most elements the median values in arable and grazing land soils are very similar. The median element concentrations of total Y, Na, Sr, Ba and Ca are higher in Ap soils than in Gr soils. The Gr soils are characterised by higher median values of LOI, Th, Pb and La.

The results show that low density geochemical mapping delivers very informative maps displaying robust element distribution patterns. The data allow the study of the spatial distribution of chemical elements and soil properties at the European scale, and clarification of the processes driving the observed patterns. Metal concentrations in northern and southern Europe are different. The break in concentration occurs along the maximum extent of the last ice age. The continental scale geochemical maps show no evidence of the importance of diffuse contamination on the observed element distribution patterns. Finally the maps demonstrate that the selected sample density of one site per 2500 km2 is ideal for working at the European scale. The GEMAS data allow a directly comparable country-specific regional risk characterisation based on PEC and PNEC data for EU 27 countries.

5. References [1] Johnson CC, Demetriades A, Locutura J, Ottesen RT (Eds), 2011. Mapping the chemical environment of

urban areas. Wiley-Blackwell, Chichester, 616p. [2] Koljonen T (Ed.), 1992. The geochemical atlas of Finland, part 2: till. Geological Survey of Finland,

Espoo, Finland, 218p. [3] Reimann C, Siewers U, Tarvinen T et al., 2003. Agricultural Soils in Northern Europe: A Geochemical

Atlas. Geologisches Jahrbuch, Heft SD 5, Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, 279p. [4] Salminen R (Ed.), 2005. Geochemical Atlas of Europe. Part 1, Geological Survey of Finland, Espoo, 525p [5] Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006, 849 [6] EuroGeoSurveys Geochemistry Working Group, 2008. EuroGeoSurveys Geochemical mapping of

agricultural and grazing land soil of Europe (GEMAS) - Field manual. NGU Report 2008:038, 46 pp. [7] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurvey Geochemistry expert group, 2009.

The EuroGeoSurveys geochemical mapping of agricultural and grazing land soils project (GEMAS) - Evaluation of quality control results of aqua regia extraction analysis. NGU Report 2009.049, 94p.

[8] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurvey Geochemistry Expert Group, 2011. The EGS Geochemical Mapping of Agricultural and Grazing Land Soils (GEMAS). NGU Report 2011.043

.

Metal bioaccessibility in Canadian soils: Using the North American Geochemical Landscape Project:

Matt Dodd1, Mark Richardson2, Andy Rencz3

1Royal Roads University, 2005 Sooke Road, Victoria, British Columbia, Canada, V9B 5Y2; e-mail: 2SNC-Lavalin Environment, 20 Colonnade Road, Suite 110, Ottawa, ON K2E 7M6

3Andy Rencz – Geological Survey Canada, 601 Booth St, Ottawa, ON, Canada K1A 0E8 E-mail contact: [email protected]

1. Introduction The North American Soils Geochemical Landscapes Project (NASGLP), a collaborative effort among the US Geological Survey (USGS), the Geological Survey of Canada (GSC) and the Mexican Geological Survey was initiated to provide a soil geochemical database for a broad-based group of users in the field of environmental and human health. The survey is based on low-density sampling (within a 40 km by 40 km grid) yielding 6,018 sites in Canada and a total of 13,487 sites across North America. This Tri-national Survey will ultimately produce a database of the regional natural-occurring differences in concentrations and physicochemical characteristics which can be used to assess background conditions and identify anthropogenic impacts. Soil sampling and analysis in Canada was initiated by the GSC in partnership with other provincial and federal agencies including Health Canada in 2004. A sub-set of the samples collected from the surface (0-5 cm) and C horizon were analyzed for bioaccessibility using a simplified physiologically based extraction test as a surrogate for bioavailability. Bioavailability data can be used to provide realistic information on potential health effects of contamination, establish site-specific soil clean-up levels and help prioritize sites for subsequent evaluation. The main objectives of the investigations presented in this paper include an assessment of the differences in bioaccessibility between the public health layer (0-5 cm) and the C horizon (parent material) among the Canadian provinces and how these data might influence derivation of generic soil quality guidelines.

2. Materials and methods Site selection and soil sampling were based on the NASGLP protocols [1]. Briefly, a 40 km x 40 km grid was set up over the entire continent. A 1-km2 target area was chosen at random within each grid and a sample site was selected from the most representative landscape within the most common soil type. A soil pit was dug to bedrock, water table or C Horizon depending on which was encountered first. Samples were then collected from the public health layer (0-5, cm), A, B (mineral enriched,), and C (parent material, typically till) horizons.

The soil samples were analyzed for total metals, pH, TOC, IC and LOI using standard methods. In vitro bioaccessibility assay (IVBA) was conducted on a sub-set of the samples collected from the PH layer and C-horizon. The IVBA methodology was based on USEPA standard operating protocols [2]. Briefly, the soil sample was air dried and sieved to Pb> Cu> Zn> Ni>Co> As>Ni. This trend was comparable to that noted for metal bioaccessibility in two transects across the United States and Canada [3]. The bioaccessible fraction for each element in the soil was variable. As an example arsenic bioaccessibility ranged from 0.3 to 94%. The 95th percentile for arsenic (42%) and lead (75%) were sligthly lower than that obtained for a data set compiled from individual studies of oral bioaccessibility on contaminated sites for arsenic (59%) and lead (80%) [4].

The variability in bioaccessibility between the public health layer and the C horizon among the provinces is illustrated in Figure 1. Apart from arsenic, bioaccessibility was generally higher in the C horizon soils compared to the PH layer. There were also some statistically significant relationships between metals bioaccessibility and soil pH, total organic carbon and loss on ignition for a sub-set of the samples. However the data suggested that these physicochemical properties could not be used to predict metal bioaccessibility.


Elemental Bioaccessibility (%) Element As Cd Cr Cu Co Pb Ni Zn Count 292 217 209 264 234 237 291 295 Mean 16 75 6 39 20 41 20 24 Median 11 80 4 36 18 42 13 14 Std Dev 14 24 6 22 14 22 21 25 Minimum 0.3 7.4 0.6 0.2 1.8 0.2 0.2 0.7 Maximum 94 116 40 112 79 93 111 115 95 Percentile 42 107 18 81 44 75 64 80

Table 1: Statistical summary of elemental bioaccessibility in Canadian NASGLP soil samples

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Public Heath layer (0-5 cm) C Horizon

Figure 1: Comparison of mean provincial elemental bioaccessibility for the Public Health layer and C horizon

4. Conclusions Differences in metal bioaccessibility between the surface layer and the C horizon and among the provinces may reflect geological processes.

5. Ongoing research - Complete analysis of additional soil samples to expand the data base. - Complete assessment of relationship between metal bioaccessibility and geological conditions as well as

soil physicochemical properties. - Implications for human health risk assessment and Canadian generic environmental quality guidelines.

6. References [1] Friske PWB, Ford KL, Kettles IM, McCurdy, MW, McNeil RJ, Harvey, BA. 2010. North American soil geochemical landscapes project: Canadian field protocols for collecting mineral soils and measuring soil gas radon and natural radioactivity.Geological Survey of Canada, Open File 6282; 177 p. [2] United States Environmental Protection Agency (USEPA). 2007. Estimation of Relative Bioavailability of Lead in Soil and Soil-Like Materials Using In Vivo and In Vitro Methods. [3] Jacques Whitford. 2006. Ingestion bioavailability of arsenic, lead and cadmium in human health risk assessments: Critical review, and recommendations. Submitted to Health Canada, Ottawa, ON http://www.bioavailabilityresearch.ca/Health%20Canada%20Bioavailability.final.pdf. Accessed 29 Nov, 2011. [4] Morman SA, Plumlee GS, Smith DB. 2009. Application of in vitro extraction studies to evaluate element bioaccessibility in soils from a transect across the United States and Canada. App Geochem 24: 1454-1463. Acknowledgement - The authors thank Health Canada for funding.

http://www.bioavailabilityresearch.ca/Health%20Canada%20Bioavailability.final.pdf

Quality Assurance in the GEMAS Project and results of the connected proficiency test

Cornelia Kriete1, Clemens Reimann2, Manfred Birke1, Alecos Demetriades3, GEMAS Project Team2

1Federal Institute for Geosciences and Natural Resources, Stilleweg 2, D-30655 Hannover, Germany 2Geological Survey of Norway (NGU), PO Box 6315 Sluppen, N-7491 Trondheim, Norway

3Institute of Geology and Mineral Exploration (IGME), Spirou Louis Street 1, 13677 Acharnae, Greece E-mail contact: [email protected]

1. Introduction The main aim of the GEMAS project (GEochemical Mapping of Agricultural and grazing land Soils) was to produce harmonized data of agricultural and grazing land soils at the European scale and to present them in geochemical maps. Quality assurance is since long recognised as one of the keystones to the success of any regional geochemical mapping project. Here the quality assurance measures taken for the GEMAS project are described and selected results are presented.

2. Materials and methods A set of stringent quality control (QC) measures were introduced into the GEMAS analytical program to detect quality problems in time and to evaluate precision of the data (1) randomization of all samples prior to analysis, (2) collection of a field duplicate at every 20th sample site, (3) production of an analytical replicate of all duplicate samples for blind insertion near the original sample, and (4) the preparation and insertion of two project standards (Ap and Gr) as internal reference samples for regular blind insertion at a rate of 1 in 20 between all samples

Although above procedures are sufficient for providing fit for purpose data for geochemical mapping, the insertion of two project standards will not provide any idea of how close the results are to the “true values”. Thus, based on the two project standards, a proficiency test (PT) was carried out in 2011 to check for the trueness of the analytical results used for the geochemical maps. This interlaboratory comparison also serves for confirming the comparability of individual national laboratory data with the data used for mapping.

In total 21 institutions from 16 countries submitted their analytical data for a variety of analytical procedures and elements/parameters, mainly for total and AR element concentrations. Since different analytical methods were treated as different laboratories, 36 data sets were available for the PT, including the mean values from QC data [1, 2] as “normal” participants.

Assigned values were estimated as consensus values from all participants using robust statistics (Q-method and Hampel estimator) according to ISO 17043 [3]. For assessment of individual laboratory results modified zu scores were used. As assigned standard deviations the calculated standard deviations were limited to achieve reasonable tolerance limits. Analytical results with a |zu| score less than 2 were assessed as satisfying.


3.1. Quality control data X-Charts for the two standards indicated a number of problems (sample mix-ups, time trends) that needed to be solved before the analytical results could be accepted. The within-lab-reproducibility for the standards Ap and Gr based on the QC data is generally good, but strongly dependent on the analyte. It is generally higher for aqua regia extractable (AR) contents than for the total XRF analyses.

The nested design of duplicate field and analytical replicate samples allowed an unbalanced statistical ANOVA to estimate relative contributions of regional, sampling and analytical variances. The natural variance is well over 80% and often up to 99% for the vast majority of parameters. Thus reliable geochemical maps can be plotted.

3.2. Proficiency test A report containing the statistical results and a comprehensive overview on the acquired data was distributed to the participating laboratories, including the individual laboratory assessment [4]. Seven laboratories scored 100 % acceptable results, while only three participants achieved a rate of 60% satisfying results. In total 2012 of 2295 analytical results were assessed as satisfying, that is a rate of 87.7 %.

The between-lab-reproducibility is usually below 10% for total contents of the main components but partially rather high for low level traces. As expected, the between-lab-reproducibilities exceed the within-lab-reproducibilities due to variations in the analytical techniques and conditions.

For evaluation of the PT performance the Horwitz Ratio (HorRat, quotient of robust sd and empirically predicted Horwitz sd, [5]) can be used. The usual range for HORRAT is 0.5 – 2, and in the GEMAS proficiency test most Horwitz ratios (with the exceptions of low level traces and AR extractable main components) lie within this range.

The assessment of the QC data for the standards Ap and Gr in this PT confirms the trueness of total element contents and most AR extractable contents (with a few exceptions due to the rather “soft” nature of the aqua regia extraction chosen for the GEMAS project).

The comprehensive data set of this PT also allows the estimation of the AR extractability of elements: it varies between 1 % (Na2O) and 98 % (Cu). Though the ratios are similar in both sample materials (Ap and Gr) it is questionable, whether they are transferable to other soils. The AR extractable content therefore allows no direct conclusions about the true total element concentration. Furthermore the data set allows statistical evaluation of method equivalence, i.e. whether systematic effects due to the analytical method or the sample preparation occur. Some significant method bias’s for certain preparation – analytical method combinations could be detected.

4. Conclusions GEMAS quality control measures were successful in eliminating several analytical problems prior to using the data. Sampling and within-lab precision is sufficient to detect regional differences and allow geochemical mapping for the vast majority of analytical parameters.

The large attendance in the GEMAS proficiency test enables a broad statistical evaluation in addition to the laboratory assessment. The between-laboratory-reproducibilities meet the precision predicted by empirical Horwitz function with the exception of aqua regia extractable main components. Some method-specific systematic errors could be detected. The assessment of the quality control data in the PT confirms the trueness of the geochemical mapping data for most parameters.

5. References [1] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurveys Geochemistry Expert Group. 2011. The EuroGeoSurveys Geochemical Mapping of Agricultural and grazing land Soils project (GEMAS) - Evaluation of quality control results of aqua regia extraction analysis. Trondheim, Norway. Geological Survey of Norway. 94p. [2] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurveys Geochemistry Expert Group. 2011. The EuroGeoSurveys Geochemical Mapping of Agricultural and grazing land Soils project (GEMAS) - Evaluation of quality control results of total C and S, total organic carbon (TOC), cation exchange capacity (CEC), XRF, pH, and particle size distribution (PSD) analysis. Trondheim, Norway. Geological Survey of Norway. 90p. [3] International Standardization Organisation. ISO/IEC17043 Conformity Assessment – General Requirements for Proficiency Testing, 2010, 40 p. [4] Kriete C 2011 Report on the GEMAS project proficiency test - Laboratory Assessment. Report. Hannover, Germany. Federal Institute for Geosciences and Natural Resources. 57 p. [5] Horwitz W Albert R. 2006. The Horwitz Ratio (HorRat): A useful index of method performance with respect to precision. Journal of AOAC International 89: 1095-1109.

Acknowledgement - The authors thank all the participating laboratories for their contribution.

Comparison of XRF and Aqua Regia data from agricultural soil in Europe: results from the GEMAS project

Enrico Dinelli1, Manfred Birke2, Clemens Reimann3, Alecos Demetriades4, Benedetto De Vivo5, , GEMAS Project Team3

1 Earth Science Department, University of Bologna, Piazza di Porta San Donato 1 40126 Bologna Italy 2Federal Institute for Geosciences and Natural Resources, Stilleweg 2, 30655 Hannover, Germany

3Geological Survey of Norway, PO Box 6315 Sluppen, 7491 Trondheim, Norway 4Institute of Geology and Mineral Exploration (IGME), Spirou Louis Street 1, 13677 Acharnae, Greece

5Earth Science Department, University of Naples Federico II, Via Mezzocannone 8, 80138 Napoli, Italy E-mail contact: [email protected]

1. Introduction One key factor when dealing with soil quality and soil critical values is the choice of analytical methods to be used in a project. Aqua regia (AR) extraction is one of the most common methods applied in the analyses of soil samples, because it combines practical advantages in laboratory routine with the environmental relevance of acid extraction results. Metals adsorbed on mineral surfaces, associated with carbonates, absorbed by iron and manganese oxides and organic matter and partially by silicate minerals, essentially clay minerals, are extracted by AR. Total concentrations obtained by X-ray Fluorescence (XRF) spectrometry provide additional information on elements bound in the lattices of silicates and other minerals that will not dissolve in AR. XRF results do, thus, better reflect bedrock geochemistry.

Results from both AR extraction and total XRF analyses from the GEMAS (GEochemical Mapping of Agricultural and grazing land Soils) project are compared. One of the aims of the GEMAS project is to produce soil geochemical data at the continental scale consistent with REACH (Registration, Evaluation and Authorisation of Chemicals) requirements (e.g., sampling depth, land use). The project involved the collection of samples from agricultural soil (Ap, Ap-horizon, 0-20 cm) and from land under permanent grass cover ("grazing land" - Gr, topsoil 0-10 cm) at the continental scale, with a sample density of 1 site/2500 km2. The strength of the project is that critical issues, such as the standardisation of sampling procedures [1], sample preparation and analysis for a large suite of parameters were carried out in a single laboratory and under strict quality control [2, 3]. Differences between total and acid-soluble element concentrations are presented for the Ap-samples, and the factors that control the changes in the ratio between AR and XRF analytical results are evalutated.

2. Materials and methods Sampling was carried out in 2008 with some last samples arriving at the beginning of 2009. In total 2211 Ap samples (0-20 cm, composite from five pits within an area of ~100 m2) were collected from regularly ploughed agricultural fields, and 2118 samples from land under permanent grass cover (Gr samples, 0-10 cm), both evenly spread over 33 European countries. Sample preparation was conducted at the central laboratory of the Geological Survey of the Slovak Republic (air dried, sieved through a 2 mm nylon screen, homogenised and splitted). Aqua regia analyses were carried out at ACME Labs, and XRF analyses at the Federal Institute for Geosciences and Natural Resources, Germany.

3. Results and discussion Element recovery by AR, compared to total XRF concentration (extractability) is very different ranging from

Even elements with very low extractability show wide variation and spatial patterns that are affected by soil parent material (see the Ti distribution in Figure 1). Titanium exhibits relatively high extractability in soil developed on granitic rocks (e.g., Portugal/Spain, Massif Central, Bohemian massif, Hercynian granites in Sardinia and southern Italy) or volcanic rocks in central Italy. Scandinavia, with its predominace of crystalline bedrocks takes an outstanding position.

Total element concentrations are not generally related to element recovery by AR. In contrast soil pH, reflecting weathering intensity on soil minerals, appears to influence extractability. Under acidic conditions most of the easily available elements were already leached from minerals resulting in a low extractability, that increases with increasing pH and is highest in the 7-8 pH range. Critical also is the clay content which controls the extractability of many elements, providing higher extractability with increasing clay content. The example presented (Ti), shows an element with low average extractability, but with a peculiar spatial distribution. The processes governing its extractability are complicated, and contrary to what is observed for most other elements, extractability is highest in the 4-5 pH range and in samples with a low clay content (Figure 1). This behaviour is shared with Nb and V suggesting that there could be an important mineralogic effect (biotite content).

Figure 1: Spatial distribution of Ti extractability in Ap GEMAS samples. Classes are identified according to the boxplot

distribution. The effect of pH and clay content on Ti extractability is also shown.

4. Conclusions The results illustrate that there exist significant differences in the aqua regia extractability of elements from soils. They further show that the extractability of an element is not constant, but is influenced by the bedrock material and weathering intensity of soil, soil pH, soil organic matter and clay content. Consequently, soil risk assessments for elements or metals should be made using effects and exposure values, based on the same extraction method.

5. References [1] EuroGeoSurveys Geochemistry Working Group, 2008. EuroGeoSurveys Geochemical mapping of

agricultural and grazing land soil of Europe (GEMAS) - Field manual. NGU Report 2008:038, 46p. [2] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurvey Geochemistry expert group, 2009.


[3] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurvey Geochemistry expert group, 2011. The EuroGeoSurveys Geochemical Mapping of Agricultural and grazing land Soils (GEMAS). NGU Report 2011.043, 90p.

Use of monitoring data for environmental risk assessment of metals in soil

Koen Oorts1, Clemens Reimann 2, Ilse Schoeters3 and GEMAS Project Team2 1 ARCHE (Assessing Risks of CHEmicals), Stapelplein 70 box 104, 9000 Gent, Belgium

2Geological Survey of Norway (NGU), PO Box 6315 Sluppen, N-7491 Trondheim, Norway 3Rio Tinto, 2 Eastbourne Terrace, London, W2 6LG, United Kingdom


1. Introduction The European REACH Regulation specifies that industry must prove that it can produce and use its substances safely. Risks due to the exposure to a substance during production and use at the local, regional and European scale, all need to be reliably assessed. Both the natural background concentration and bioavailability of trace elements vary largely across soils and consequently, a sound risk assessment for trace elements in soil must preferentially take into account the spatial variation for both exposure and effects concentrations. Data availability for both aspects differs largely across different countries or regions in Europe, resulting in varying degrees of (worst-case) assumptions on exposure and effects of metals at the regional scale. The use of different sampling protocols and analytical methods further complicate comparison of results. Therefore, there is a need for a harmonised European monitoring dataset for metal exposure and soil properties known to affect metal bioavailability. A cooperative project on geochemical mapping of agricultural and grazing land soil (GEMAS) was carried out by the EuroGeoSurveys (EGS) Geochemistry Expert Group and Eurometaux. The aim of this project was to produce at the European scale harmonised data with respect to sampling density, analytical methodology and land-use (comparable level of diffuse emissions). These GEMAS monitoring data form a strong basis for the environmental risk assessment of metals in soil.

2. Materials and methods Soil sampling took place during the summer and autumn of 2008, with some very last samples arriving in early 2009. In total 2211 samples of agricultural soil (0-20 cm) and 2118 samples of grazing land soil (0-10 cm) were collected at an average sampling density of 1 site per 2500 km2 (grid of 50 x 50 km) across Europe.

All samples were shipped to a central laboratory for preparation (air-drying and sieving 100-fold and 10-fold, respectively, across soils in Europe (Table 1). The risk characterisation ratio (RCR) for all individual sites were calculated from site-specific data for both exposure and effects concentrations. This avoids interpolations or assumptions on the spatial distribution of either exposure or effects data. Predicted risk ratios for metals typically vary over 2 orders of magnitude across soils in Europe (Table 1).


Table 1: Distribution of aqua regia concentrations, PNEC values and risk characterisation ratio (RCR) for Cu in agricultural land across Europe.

Parameter minimum 10th percentile Median 90th percentile Maximum

Cu (mg/kg) 0.3 4.6 14.5 37.7 395.1

PNEC (mg Cu/kg) 13.4 52.3 89.9 131.8 201.4

RCR (-) 0.01 0.07 0.16 0.41 3.91

All sampled sites are geo-referenced and European maps for exposure, effects or risk characterisation ratios could be prepared (Figure 1). Variation in RCR values can be generally explained by natural patterns in metal background concentrations and soil properties. The example of the distribution of risk characterisation ratio for Cu in agricultural soils does not show a regional risk in agricultural soils across Europe (Figure 1). The largest RCR values were observed for countries in Southern Europe, which could be explained by higher Cu concentrations due to a difference in the weathering history of these soils. Only few, isolated sites were predicted to be at risk (i.e. with a risk characterisation ratio above 1).

Figure 1: Spatial distribution of risk characterisation of Cu in European agricultural soils.

The GEMAS dataset allows discussions on regional risks based on the distribution of site-specific RCR values, instead of on assumptions on exposure and effects separately. This is especially important because metal bioavailability (effects) and background concentrations (exposure) are often significantly correlated (e.g. higher metal background concentrations and lower bioavailability in clay soils compared to sand soils). Consequently, exposure and effects data should not be treated independently to derive generic reasonable worst-case scenarios for risk assessment, as is done by default through comparison of reasonable worst-case PNEC with reasonable worst-case PEC values. Whenever possible, regional and local environmental risk assessments for metals must be based on comparison of site-specific exposure and effects data.

4. Conclusions Harmonised monitoring data for both soil metal concentrations and general soil properties across Europe allow a consistent risk characterisation across Europe and increase the transparency and credibility of a regional risk characterisation at the European scale. Such data provide a strong basis for taking into account the spatial variability of both exposure (metal concentrations) and effect concentrations (considering bioavailability through variation in soil properties) in a risk assessment for metals in soils and avoid the need for (worst-case) assumptions on both aspects.

5. References [1] Smolders E, Oorts K, Van Sprang P, Schoeters I, Janssen CR, McGrath SP, McLaughlin MJ. 2009.

Toxicity of trace metals in soils as affected by soil type and aging after contamination: using calibrated bioavailability models to set ecological soil standards. Environ Toxicol Chem 28:1633-1642.

Lead and lead isotopes in agricultural soils of Europe: natural distribution or contamination?

Clemens Reimann1, Belinda Flem1, Karl Fabian1, Manfred Birke2, Anna Ladenberger3, Philippe Negrel4, Alecos Demetriades5, Jurian Hoogewerff6, GEMAS Project Team1

1Geological Survey of Norway (NGU), PO Box 6315 Sluppen, N-7491 Trondheim, Norway 2Federal Institute for Geosciences and Natural Resources, Stilleweg 2, D-30655 Hannover, Germany

3Geological Survey of Sweden (SGU), Box 670, S-751 28 Uppsala, Sweden 4BRGM, Service Métrologie Monitoring, 3 Avenue Claude Guillemin, BP 6009, 45060 Orléans Cedex 2,

France 5Institute of Geology and Mineral Exploration (IGME), Spirou Louis Street 1, 13677 Acharnae, Greece

6Oritain Global Ltd., Invermay Agricultural Centre, Mosgiel 9053, New Zealand


1. Introduction In environmental sciences the 206Pb/207Pb isotope ratio is used to “prove” Pb contamination of different compartments of the environment. However, the lead-isotope variation at the continental scale has never been established and, thus, the natural background is unknown. Geochemical Mapping of Agricultural Soils (GEMAS) is a cooperative project between the Geochemistry Expert Group of EuroGeoSurveys (EGS) and Eurometaux. The main aim was to produce harmonised data, at the European scale, for metals and soil properties that will influence the availability of metals in soils in the context of agricultural and grazing land soils. For this purpose 2108 samples were collected from regularly ploughed agricultural fields from 33 European countries, at an average sample density of 1 site/2500 km2, and are used to map the Pb concentration as well as the 206Pb/207Pb and 207Pb/208Pb isotope ratios at the European scale. Agricultural soils are most exposed to wind-blown erosion and will thus substantially influence atmospheric chemistry. What is the regional variation of Pb isotopes in these soils? What are the predominant processes influencing the distribution of Pb isotopes in soil at the European scale? Do the soils primarily reflect differences in natural conditions or is the Pb-isotopic landscape of Europe dominated by anthropogenic inputs?

2. Materials and methods Sampling took place during the summer and autumn of 2008, with the very last samples arriving in early 2009. In total, 2108 samples of agricultural soil (Ap, Ap-horizon, 0-20 cm) and 2024 samples of grazing land soil (Gr, 0-10 cm) were collected at an average sampling density of 1 site per 2500 km2 (grid of 50 x 50 km) across Europe. All samples were shipped to a central laboratory for preparation (air-drying, sieving to

The 206Pb/207Pb isotope ratios in agricultural soils of Europe show a large variation (1.12 – 1.71), with a median at 1.20 (Fig. 1, right). The patterns displayed on the map are again governed by geological processes, most dominant is the extent of the last ice age: a lower 206Pb/207Pb isotope ratio is observed in older, SW European soils, while soils in NE Europe are dominated by high ratios. The occurrence of granitic bedrock is often marked by elevated 206Pb/207Pb isotope ratios. Climatic effects on Pb-isotope ratios can be detected in coastal zones. None of the large-scale patterns can be linked to documented contamination sources or long-range atmospheric transport (LRT). Isotope ratios in the soils commonly reflect the isotope ratios found in the local ore bodies of Europe. Traffic, or traffic density have clearly no influence on the pattern displayed on the map (Fig. 1).

Figure 1: Spatial distribution of Pb concentration and the 206Pb/207Pb isotope ratio in the GEMAS Ap samples. Note that additional results from eastern Ukraine are included in the Pb concentration map (left).

4. Conclusions The pattern dominating the Pb maps is the southern limit of the last glaciation, almost coincident with the tectonic border between the old Precambrian craton of NE Europe and the younger Palaeozoic platform of SW Europe. Evidently, a close relation of Pb concentrations and isotopic ratios with geology is preserved in agricultural soils at the European scale. This proves that the majority of Pb in European agricultural soils is at present still of natural origin. Many studies carried out on a local scale have used too few samples, and neglected the existence of a natural background variation of element concentrations and isotope ratios. Such studies can provide erroneous perceptions with regard to the anthropogenic impact on the regional background, because the large-scale regional variations and the processes governing them were not well understood.

The maps presented here establish the natural geochemical background for both, Pb concentration and Pb isotope ratios in European agricultural soils in aqua regia/HNO3-extraction, and demonstrate the existence of two major continental-scale natural background regimes for NE and SW Europe, each in turn substantially modulated by local-scale internal variability. An important conclusion for any interpretation of local Pb isotope studies is that they have to demonstrate that a clearly defined and well known source of Pb contamination contrasts to the variations in the regional geochemical background. Using global average values, as representative for a certain source (e.g., the average upper crust for “geogenic Pb”), is simply not sufficient.

5. References [1] Reimann C, Demetriades A, Eggen OA, Filzmoser P, EuroGeoSurvey Geochemistry expert group, 2009.


Estimating and evaluating cumulative human exposures to ubiquitous pollutants: Integration of outdoor, food web, and

indoor fate models with exposure biomarkers Thomas E. McKone1,2, Srinandini Parthasarathy1,2

1University of California, 50 University Hall #7360, Berkeley CA 94720-7360 USA 2Lawrence Berkeley National Laboratory, Berkeley CA 94720 USA


1. Introduction Widespread observations of environmental contaminants in house dust, food, vegetation, soil, animals, and human tissue have motivated research on better understanding of exposure pathways for a broad range of contaminants over indoor, urban, regional, continental, and global scales. This paper addresses how fate models at different levels of geographic scale combined with environmental and biomarker measurements can be used to interpret and predict cumulative human exposure from multiple pollutants and emissions sources. We consider three case studies to address insight gained by simple but informative integration strategies. First we look at regional scale pesticide exposures in a farming community and compare local contributions to exposure from pesticide use on fields with contributions from food produced within and outside the region. We next look at cumulative exposures to combustion-produced polycyclic aromatic hydrocarbons (PAHs) across 3000 counties in the United States for which emissions data are available. Finally we look at commercial buildings with filtered and re-circulated air to assess how indoor exposures to particle-bound semi-volatile organic contaminants are impacted by the level of air recirculation.

2. Materials and methods Exposure assessment is the process of measuring and/or modeling the magnitude, frequency, and duration of contact between a potentially harmful substance and a receptor population—an activity that requires emissions data, fate models, and human activity data. An exposure pathway is the course that a pollutant takes from an environmental medium (outdoor air, indoor air, soil, water, biota, etc.) to an exposure medium (personal air, food, tap water, etc.) and then to an exposed individual. Exposure pathways are multiple and complex, but the magnitude and variation of exposures to environmental contaminants depend largely on the concentrations of contaminants in exposure media and the exposure factors of the target population. Organizations such as the US Centers for Disease Control and Prevention [1] are collecting and analysing samples of blood and urine to produce national reports on human exposure to hundreds of environmental chemicals, These reports capture both the magnitude and variation of exposures.

Figure 1: An illustration of the pathways from pollutant emissions to biomarker levels


We use the CalTOX multi-media fate and multi-pathway exposure model [2] to model the intake fraction for outdoor pollutant exposures and for food-pathway exposures. For indoor exposure, we use a modified version of the Bennett and Furtaw model [3] that can be coupled to the CalTOX ambient environmental system. CalTOX uses a fugacity-based mass balance approach to link chemical emissions to concentrations in outdoor air, indoor air, soil, water, etc. Fugacity is used to quantify chemical potential at low concentrations and expresses the tendency of a chemical to move from one environmental compartment to another.

3. Results and discussion In the sections below, we describe results from each of our three case studies—pesticide exposures in an agricultural region, the national variation of PAH exposures in the US, and the transport of outdoor pollutant exposures to the occupants of buildings with filtration and re-circulation.

3.1. Case Study 1: Cumulative exposure to a pesticide in an agricultural region We characterize cumulative intakes of organophosphorous (OP) pesticides in an agricultural region of California by drawing on human biomonitoring data, California pesticide use reporting (PUR) data, and limited environmental samples together with outputs from the CalTOX multimedia, multipathway source-to-dose model. Both model results and biomarker comparisons support the observation that, relative to national exposure variations, the agricultural-region population has a statistically significant added intake of OP pesticides with low inter-individual variability. We attribute the magnitude and small variance of this intake to residential non-dietary exposures from local agricultural OP uses. These results show that mass-balance models can estimate exposures for OP pesticides within the range measured by biological monitoring.

3.2. Case Study 2: Geographical variations of PAH emissions and exposure biomarkers For each of the PAHs considered, we use CalTOX applied to the county-scale National Air Toxics Assessment (NAATA) emissions data to plot the cumulative probability distribution of intake from mobile and stationary outdoor sources. On the same plot we determined the intake that could be inferred from biomarkers. We find that for both distributions, there is significant variation in the per-capita intake among the 3108 counties considered but that the standard deviation of both curves appears to be the same--they both vary over 4 orders of magnitude between the 1st and 99th percentile.

3.3. Case Study 3: Geographical variations of PAH emissions and exposure biomarkers With a modified version of the Bennett and Furtaw [3] fugacity based indoor-mass-balance model, we simulate and evaluate the impact of ventilation on semi-volatile organic pollutant concentrations indoors. We find that mechanisms such as filtration can be effective at removing pollutants such as particulate matter of certain size ranges, and contaminants with high octanol-air partitioning coefficients, and predominantly outdoor sources. Ventilation can significantly reduce exposure to outdoor contaminants with low octanol-air partitioning coefficients. Filtration competes with ventilation at certain air change rates to limit exposure to contaminants with high octanol-air partitioning coefficients.

4. Conclusions Research focused on the use of models in exposure assessment reveals that the strategic selection and integration of different model scales along with integration of models and exposure factor/exposure concentration measurements are more reliable for capturing source-to-receptor relationships than any single model or measurements alone. These three cases reveal that the relative contributions to cumulative pollutant intake via different exposure pathways depend on (a) persistence of chemicals at different levels of integration (regional, urban-scale, food-web, indoors), (b) basic chemical properties, (c) the retention of chemicals in food webs, and (d) the retention of chemicals by indoor surfaces.

5. References [1] Center for Disease Control and Prevention CDC. 2003-2005. National reports on human exposure to environmental chemicals. CDC: National Center Environmental Health, 2003 [2] McKone TE, Maddalena RL, Bennett DH. 2003. CalTOX 4.0, a multimedia total exposure model

Lawrence Berkeley National Laboratory, http://eetd.lbl.gov/ied/era [3] Bennett DH, Furtaw EJ. 2004. Fugacity-based indoor residential pesticide fate model. Environ. Sci. Technol. 2004, 38(7), 2142-2152. Acknowledgement - The authors thank the California Energy Commission for funding on Case Study 3.

http://eetd.lbl.gov/ied/era

Reducing empirical data need in fate and exposure models A. Jan Hendriks, Isabel A. O`Connor, Alessandra Pirovano, Aafke M. Schipper, Ad M. J.

Ragas and Mark A. J. Huijbregts

Radboud University Nijmegen, Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010, NL-6500 GL Nijmegen, The Netherlands.


1. Introduction Environmental chemistry and toxicology face the immense challenge of protecting thousands of species from thousands of substances released into thousands of different landscapes. Empirical studies are limited because of financial, practical, ethical and time-space restrictions. To cover all relevant cases, fate and exposure models have been developed. Yet, such models too, tend to become data hungry because of parameterization. Properties of chemicals have extensively been used to extrapolate knowledge, allowing one to estimate default values for parameters in the absence of experimental data. For instance, partitioning between and binding to water, soil and biota has been related to the octanol-water partition ratio Kow or the covalent index χ2r of organic chemicals and metals, respectively (Mackay 1979, Zhou et al. 2011).

By contrast, landscape characteristics and species traits have hardly been used to facilitate modelling. Risk assessment is often restricted to a “example” foodchain consisting of a few species thought to inhabit a “typical” landscape unit (Mackay 1979). Yet, much of the physical, chemical and biological variability noted can be covered by a few overarching principles, in particular size scaling. For instance, lake volume explained up to 85% of incoming run-off, while up to 95% of energy and water turnover in organism can be attributed to mass (Hendriks 1999, Hendriks 2007, Hendriks et al. 2012). As a result, flow rate of chemicals carried by these cycles are also strongly related to size (Hendriks et al. 2001).

Hence, overarching principles, in particular in size are remarkably similar among different systems. The aim of the present paper is to identify similarities and differences in scaling in various disciplines involved in fate and exposure modelling. We will demonstrate how scaling in hydrology, chemistry, biology and technology may help use to overcome data gaps and provide insight in overall principles driving systems.

2. Methods Parameters y of fate and exposure models were theoretically and empirically related to mass m (or volume, area) according equations and regressions of the type y = a·mb (Hendriks 1999, 2007). The coefficient “a” depends on the process or pattern studied and can be recognized as the intercept in log-log plots. The exponent “b” is a multiple of ⅓ and ¼, reflecting geometric and allometric scaling in 3-dimensional systems, respectively. In addition, 2-dimensional systems can be lin

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