dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, a HBM study identifying an increasing trend toward ubiquitous exposures to DINCH (diisononylcyclohexane-1,2-dicarboxylate), a phthalate substitute, since it was introduced on the market  (Schütze et al. 2014). Longitudinal prospective HBM studies can be used in identifying subpopulations at risk of higher exposures, as well as in tracking exposures to new chemicals and new biomarkers  (Lermen et al. 2020). Thus, a consistent implementation of HBM programmes can identify timerelated and/or geographical trends. It is essential to understand regional differences or regional-specific exposures that might exist, as well as potential differences between age groups (i. e. especially vulnerable groups), as consumer and dietary habits differ across age groups. The data from population-representative HBM can also provide a basis for the identification of the exposure of vulnerable groups and to assess if a separate, ad hoc strategy is needed.
EU-OSHA is currently implementing a pilot study in six EU Member States collecting data on occupational exposure to cancer risk factors 2  (EU-OSHA 2018). The survey is aimed at identifying vulnerable groups, such as workers exposed to multiple cancer risk factors. For some substances, in particular those that can be absorbed through the skin or bioaccumulated, including HBM in health surveillance could provide additional insights into the internal exposure and for setting workplace prevention measures. 3 BM is also applied to study spatial distribution in the environment, e. g. to compare exposures by distance to an identified source and to determine to what extent such exposure estimates can be associated with observed health effects that may be clustered around a (suspected) hot spot of environmental pollution  (Abecasis et al.
2022; Odabasi et al. 2015; Wilhelm et al. 2005). Contaminated sites may be related to industrial activity, sanitary landfill or certain applications of high-risk chemicals, such as pesticides in agricultural settings  (Figueiredo et al. 2021; Silva et al. 2021; Trushna et al. 2022). Exposure studies may help to respond to questions and concerns within society about direct and indirect routes of exposures, particularly if they involve vulnerable groups living at a close distance to (suspected) sources of environmental pollution. In such settings, HBM can be used as an approach to complement other types of data collection and environmental monitoring. This type of use requires the application of contextual data and exposure modelling approaches to understand how environmental pollution may contribute to elevated levels of internal exposures and to identify groups with increased susceptibility  (Oerlemans et al.
Monitoring the effectiveness of risk management measures (RMMs) As illustrated in Fig. 1 , HBM can be used for post-marketing surveillance, monitoring compliance and measuring the effectiveness of RMMs for regulated chemical substances  (Kolossa-Gehring et al. 2012). In occupational settings, for example, HBM has been used for many decades and is still regularly used. This may be due to the employers' regulatory obligations, such as compliance with occupational exposure limits aimed at health promotion and disease prevention  (Galea et al. 2021; Louro et al. 2019; Viegas et al. , 2020). HBM implementation depends on the country's legislation. In some cases, HBM is used as a method for occupational hygiene assessments and in other cases for health surveillance  (Viegas et al. , 2020). In the latter application, the data are considered medical data, and therefore restrictions may apply to further/other uses, such as in the context of exposure and risk assessment.
, 2022). Concrete uses of HBM in regulatory risk assessment processes for REACH restriction and authorisation purposes are limited. They mainly relate to high production volume chemicals (REACH), some well-known environmental contaminants (European Food Safety Authority (EFSA))  (Verhagen et al. 2018) , and workers' exposures to pesticides  (Louro et al. 2019). The lead, mercury, phthalates and bisphenol A (BPA) restrictions and 2,2 ′ -dichloro-4,4 ′ -methylenedianiline (MOCA) authorisation process provide good examples on how HBM data can be used to strengthen exposure assessment in the regulatory risk assessment underlying and evaluating the effectiveness of RMM  (Louro et al. 2019). In particular, HBM can support model evaluation and/or validation where individual data are strongly preferred.
Priority setting for policy actions based on health endpoints Integrating HBM data into risk assessment for human health and chemicals regulations in general would further protect the human population  (Lange et al. 2021; Ougier et al. 2021; Wilhelm 2021). To this end, it is necessary to develop scientifically sound and up-to-date health-based HBM guidance values (HBM-GVs), which can be used for direct interpretation of HBM data and to protect the general population by integrate them into public health systems. HBM-GVs are derived from epidemiological and/or toxicological data. They indicate the concentration of a compound or its metabolite(s) in a biological matrix (e. g. blood, urine) at or under which a health risk is not anticipated, according to current knowledge  (Apel et al. 2020). In occupational health, these values are set by several expert bodies including the American Conference of Governmental Industrial Hygienists (ACGIH) in the US, the MAK Commission in Germany, the French Agency for Food, Environmental and Occupational Health & Safety (ANSES), and the former Scientific Committee on Occupational Exposure Limits [(SCOEL) now in RAC] (ACGIH 2021; ANSES 2020;  Forschungsgemeinschaft, 2017; SCOEL 2017).
For the general population, different health-related assessment values are established such as HBM-I and HBM-II values from the German HBM Commission, biomonitoring equivalents (BE) by Summit Toxicology and Health Canada, health-related guidance values (HBM-GVs) referring to the internal body burden set by HBM4EU  (Lange et al. 2021). Health-based HBM-GVs can be used directly for the interpretation of HBM data as a solid scientific basis for chemicals policy and risk communication. These values can help to refine the public health risk assessment by identifying exposures of potential concern, but also indicate potential regulatory priorities and the need for (additional) measures to reduce exposures  (Apel et al. 2020). In addition, HBM data can support derivation of a possible mixture assessment factor (MAF), suggested for single substance risk assessments, to account for unknown co-exposures  (Socianu et al.
Establishing more national surveys in a range of countries Several countries (inter alia, the United States (US), Canada, Germany, France, Czech Republic, Belgium, South Korea, Japan) have ongoing nationwide HBM programmes to evaluate exposures to existing and emerging pollutants. HBM is a key component in the US National Health and Nutrition Examination Survey (NHANES), which also includes health parameters. NHANES data have provided exposure information to establish reference ranges as well as highlighting specific chemical exposures and inherent factors of variability (e. g. sex, age)  (Calafat 2012). The Canadian Health Measures Survey (CHMS) is a HBM survey of the general population. CHMS has established population baseline concentrations levels and informed regulatory risk management decisions for safeguarding public health  (Haines et al.
2017). In Germany, two major HBM programmes, the population representative German Environmental Survey and the German Environmental Specimen Bank, provide population HBM data used to identify exposures of concern, identify sources and higher exposed sub-population, control the effectiveness of measures and derive risk reduction measures  (Kolossa-Gehring et al. 2012). These national programmes have demonstrated that HBM can play an important role in evaluating environmental chemical exposures and, in general, health protection policies. In addition, HBM programmes provide population representative datasets, which may be used both to trigger and to provide benchmarks for the assessment of groups deserving special attention. Some examples are for children  (Hendryx and Luo 2018)  and occupationally exposed groups  (Varshavsky et al.
Further improving quality assurance (QA) / quality control (QC) for HBM methods It is not always clear whether HBM results generated by different laboratories are comparable or this comparability may be insufficient, even though quality assurance systems such as G-EQUAS (https://www. g-equas. de) and comparable systems in the US and worldwide offer internationally recognised quality assurance systems and regular round robins in which a large number of international HBM laboratories systematically participate. As demonstrated and promoted within the COPHES/DEMOCOPHES projects and more extensively in the European Joint Programme HBM4EU  (López et al. 2021 ), dedicated QA/QC programmes safeguard comparability of data for a number of biomarkers/biological matrices pairs, and result in a network of competent and highly qualified expert laboratories.
Better understanding of toxicokinetics Better knowledge of biomarker kinetics, including the identity of biotransformation products (e. g. primary and secondary metabolites) and biomarker concentrations in easily obtained biological fluids and/or matrices (e. g. quantification of urinary excretion rates of parent compounds to ensure extrapolation to internal concentrations), is a key factor for the identification of specific and sensitive markers which are a prerequisite for further improving the use of HBM. A more systematic generation and use of toxicokinetic knowledge in view of a better interpretation of HBM data  (Carusi et al. , 2022) , e. g. by enabling the calculation of uptake rates  (Koch et al. 2014; Kolossa-Gehring et al. 2017 ) is another building block of making better use of HBM.
Increasing findability of HBM data suitable for regulatory purpose The relevance of HBM in a worldwide context is often underestimated, in part due to the unharmonised (heterogenous) presentation of the HBM data (not well structured or aligned), unclear quality (e. g. lacking QA/QC for chemical analysis), and missing contextual information (metadata). In practice, finding data from studies that are suitable for a specific regulatory purpose is challenging. Some studies are fragmented and heterogeneous in terms of design, terminology, biomarker nomenclature, and data formats (Zare  Jeddi et al. , 2021b). In addition, policymakers are often required to produce quick responses when problems arise and therefore need timely access to reliable HBM data to come up with sound policy actions  (Joas et al.
2017 ). These shortcomings limit our capacity to compare and integrate data sets retrospectively (i. e. reuse). As an important step in this direction, HBM4EU has made a big contribution to improving the availability, accessibility and findability of European HBM data  (Gilles et al. 2021 ). More than 61 harmonised datasets from European studies and the HBM4EU Aligned Studies can be found through HBM4EU Dashboard (https://www. hbm4eu. eu/what-we-do/european-hbm-platform/eu-hbm-dashboard/). In order to feed HBM4EU data into the Information Platform for Chemical Monitoring (IPCHEM), HBM4EU has harmonised vocabularies and formats for reporting (meta)data  (Bopp et al. 2020 ). HBM4EU has managed to post-harmonise existing data from former studies and produced harmonised data in the HBM4EU Aligned Studies  (Eva et al.
The lack of data for exposure and HBM approaches presents a challenge as the exposome assessment involves analysis of life course data. Additional emphasis should be placed on addressing the lack of data through multiple imputation and machine learning approaches. Therefore, exchange and complementarity of the European human biomonitoring projects, programmes and partnerships (ESBIO, COPHES, DEMOCOPHES, HBM4EU, PARC) and the European human exposome initiatives (HELIX, HEALS, EXPOsOMICS and European Human Exposome Network) will be critical to broaden our understanding of environmental exposures and health outcomes. Another approach for combined exposure is to map the spatial coincidence of multiple stressors by overlaying environmental monitoring data from different media, as well as social and demographic data.
Inclusion of HBM in chemical regulations against the background of legal requirements The potential of HBM has not been appropriately recognised in EU chemicals regulations to date and, except for the occupational setting, there is neither a requirement to perform HBM nor are there any specific legal requirements providing for this. One challenge in systematically recognising HBM in the regulatory context is the EU General Data Protection Regulation (GDPR) that came into force in the European Union in 2018. In HBM4EU, (which took place 2017-2022), for instance, it has become clear that compliance with the GDPR required additional efforts. This resulted, for example, in intense discussions about the strengths and limitations of anonymisation and pseudonymisation of individual data, which is particularly relevant when data are to be shared.
Strategic objective 3 -Further development of QA/QC throughout HBM research life-cycle Conducting the necessary QA/QC, method performance assessment, and harmonisation ensures a high level of data quality and comparability. It is essential to have insight into the intra-and inter-laboratory precision of HBM data generated by laboratories. We therefore encourage the adoption of common definitions and terminology by the whole community working in this field  (Heinemeyer et al. 2021). This would help in harmonising workflows used to treat complex chemical analytical datasets and improving reporting of results. The involvement of multiple laboratories in data generation over time and across different countries is inevitable. One way to gain insight into the comparability of data and inter-laboratory precision is through EQUAS, also known as proficiency tests (PTs).
This platform provides HBM specific metadata catalogues, harmonised terminologies, ontologies, and harmonised open-access protocol templates for better designing HBM studies,  (Zare Jeddi et al. , 2021b). Regulatory enforcement of HBM harmonisation is also conceivable. Along with the rapidly evolving HBM programmes and advanced omics technologies, large amounts of data are generated. Statistical (or other advanced) models help in establishing associations between exposure and health-related outcomes. Such relationships will only be elucidated when HBM data are (re)usable and comparable. Overall, for each step of conducting an HBM study, there is a need for developing relevant guidance. Further research and guidance are also needed on how to store HBM data in a database adhering to FAIR principles  (Zare Jeddi et al.
, 2021b). (Meta)data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. In all the activities of data sharing, data protection plays an important role. If HBM data are aggregated, they can be considered anonymous and are not considered personal data under the European GDPR  (EC, 2018; Eva et al. , 2022). Single measurement HBM data must be handled in a stringent way to prevent the risk of re-identification of data subjects. GDPR requirements need to be strictly followed when processing such pseudonymised data. One solution would be to establish federated FAIR database systems  (Collins et al. , 2018)  in which data contained in constituent databases can be used for further analyses by researchers or policy makers without holding or storing the data themselves on an in-house server.
Acknowledgment The authors are grateful to all experts from academia, institutes and industry that have participated in discussions that helped formulate the present strategy paper. They would also like to thank  ISES Europe  for initiating the discussions to prepare a European Exposure Science Strategy 2030. The  German Federal Institute for Occupational Safety and Health (BauA) ,  National Institute for Public Health and the Environment (RIVM)  and the  European Commission Joint Research Centre (JRC)  are also acknowledged for each hosting of a meeting in 2018, 2019 and 2020 respectively during which the foundations of the current strategy were built.
: Improve harmonisation throughout the HBM Research Life-cycle Ensuring/improvement of reliability, comparability and reusability of HBM data Web-based registry with mandatory fields based on web-based Academia and RI's Mid-term templates for the registration process to optimise HBM study designs National Authorities and standardisation of research protocols (under HBM Global Registry WHO Framework) Agencies Publishers Guidance how to implement FAIR (Findability, Accessibility, Academia and RI's Mid-term Interoperability, and Reusability) principles in HBM value chain (a European Open science FAIRification workflow) could Create a FAIR catalog specifically for HBM to promote 'FAIR by design' Academia and RI's Mid-term studies European Open science could Include research communities to encourage reporting in standard Academia and RI's Mid-term formats useful also for regulatory process European Commission Active communication between all stakeholders regarding databases Academia and RI's Long-term and data repositories European Commission Unique coding of analytes (substances and their metabolites) e.
This chapter offers an introduction to the book and is specifically recommended for all readers intending to do the practical exercises it contains. It also provides readers with all the information they require to make the most of the book's contents. In this chapter, we explain the aim, structure and intended audience for this book. We also give the readers a few tips and guidelines about how to make best use of it. This is followed by a description of the software and the data used to do the practical exercises. In the last section of this chapter, we offer a detailed explanation about how we conducted the review of the LUC datasets carried out for Chap.
How to Use This Book? The book can be used in different ways, depending on the type of user and their particular background and interests. With this in mind, it has been conceived as a flexible tool that can be used for a wide variety of purposes. Beginners in this field are referred to Chap. "Land Use Cover Mapping, Modelling and Validation. A Background", as are other users interested in gaining an overall picture of LUC mapping, LUCC modelling and the essential concepts required for uncertainty and validation analyses. This short, yet comprehensive chapter sets out the basic theoretical principles on which the rest of the book is based and is therefore recommended reading for all users. For LUC data visualization and creation, readers are referred to Part II of this book. It provides an overview of the different options available for symbolizing LUC data and LUC change in GIS.
It also addresses some of the problems associated with the spatial visualization of LUC information. This part of the book also includes a tutorial on the creation of a set of sample points for LUC data validation with QGIS. Users interested in the validation of LUC datasets and Land Use Cover Change (LUCC) modelling exercises should refer to Chap. "Validation of Land Use Cover Maps: A Guideline". This provides guidelines for validating different LUC products: single LUC maps, LUC map series, and outputs from LUCC modelling exercises. The different tools and methods referred to in these guidelines are then described in detail and applied in practice in the example exercises in Part III of the book. Users interested in doing the example QGIS exercises appearing in this book should refer to Sect.
5 of this chapter, which presents all the data and the cases studied in this book. It also offers essential information about the particular version of QGIS that we use and about how to integrate R software into QGIS, a necessary step when carrying out some of the exercises set out in the book. Those interested in LUC data sources should refer to Chap. "Land Use Cover Datasets: A Review", which offers an introduction to LUC mapping at global and supra-national scales, including a review of the different datasets available. Part IV of the book offers in-depth descriptions of most of the datasets that are available for download, detailing their specific characteristics and how they can be accessed. The methodology followed in the review of the datasets is described in Sect. 6 of this chapter.
GIS Software Of all the Geographical Information Systems (GIS) currently available, in this book we use QGIS, a well-known, open-source GIS software that is widely used and recognized. It provides a unified interface to many other relevant open-source GIS software programmes, such as SAGA, GDAL, GRASS or LasTools  (Menke et al. 2016). It also allows integration with R, a powerful open-source software for statistical analysis. We opted for the QGIS 3. 10. 13 "A Coruña" version of QGIS for the practical exercises included in this book. This is because it was the newest long-term release version of QGIS available when we began writing the book. Users could try other versions of QGIS when doing the exercises included in this book. However, they should bear in mind that the exercises have been created and tested using the version indicated above and that certain issues and errors may arise when using any other version of QGIS.
Earlier versions of QGIS prior to QGIS 3 are strongly discouraged, as important changes were made in the software between versions 2 and 3 and many features of QGIS 3 do not work in earlier versions of the software. The latest version of QGIS is available at the QGIS website (www. qgis. org). Users who require a specific version of this software should visit: https://qgis. org/ downloads/. Full documentation relating to the software can also be found at the official website: https://www. qgis. org/en/docs/index. html, where inexperienced QGIS users will find a brief introduction to the software interface and the main tools. Several user manuals are also available to help beginners make the most of the software. These include the books published by Packt  (Graser et al. 2017; Cutts and Graser 2018)  and the series of manuals coordinated by  Baghdadi et al.
QGIS Plugins QGIS works with plugins written in the C++ and Python programming languages. These plugins are an easy way to expand the capabilities of the software, which is why many of the features of the software are currently implemented through these plugins. There are two types of plugins: core and external plugins (QGIS Project 2020). The core plugins are maintained by the QGIS Development Team and automatically form part of the distributed software. The external plugins are developed by a community of users and are available at the QGIS Python Plugins Repository (https://plugins. qgis. org/plugins/). The external plugins may be up-to-date or outdated and are usually available for specific QGIS versions. The official plugin repository includes information about all these questions. External plugins that are still in the early stages of development and have not been widely used are marked by QGIS as experimental plugins and are not directly available through the software.