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This book introduces the companion modelling approach by presenting the stance that underpins it, the methods and tools used with stakeholders and the specific role of models during the process. It addresses the means to deal with the different levels of decision-making and to take into account the various power relationships. It proposes a methodology to assess the impact of the approach on the stakeholders involved in the process.The book includes 27 case studies and 7 teaching tools that describe the successful use of the approach in a variety of settings or teaching contexts. It is intended for researchers working on rural development or renewable resources management, as well as students and teachers.

Groundwater contamination is a global problem that has a significant impact on human health and ecological services. Studies reported in this special issue focus on contaminants in groundwater of geogenic and anthropogenic origin distributed over a wide geographic range, with contributions from researchers studying groundwater contamination in India, China, Pakistan, Turkey, Ethiopia, and Nigeria. Thus, this special issue reports on the latest research conducted in the eastern hemisphere on the sources and scale of groundwater contamination and the consequences for human health and the environment, as well as technologies for removing selected contaminants from groundwater. In this article, the state of the science on groundwater contamination is reviewed, and the papers published in this special issue are summarized in terms of their contributions to the literature. Finally, some key issues for advancing research on groundwater contamination are proposed.

Certain five heavy metals viz. arsenic (As), cadmium (Cd), chromium (Cr)(VI), mercury (Hg), and lead (Pb) are non-threshold toxins and can exert toxic effects at very low concentrations. These heavy metals are known as most problematic heavy metals and as toxic heavy metals (THMs). Several industrial activities and some natural processes are responsible for their high contamination in the environment. In recent years, high concentrations of heavy metals in different natural systems including atmosphere, pedosphere, hydrosphere, and biosphere have become a global issue. These THMs have severe deteriorating effects on various microorganisms, plants, and animals. Human exposure to the THMs may evoke serious health injuries and impairments in the body, and even certain extremities can cause death. In all these perspectives, this review provides a comprehensive account of the relative impact of the THMs As, Cd, Cr(VI), Hg, and Pb on our total environment.

The identification of microplastics becomes increasingly challenging with decreasing particle size and increasing sample heterogeneity. The analysis of microplastic samples by Fourier transform infrared (FTIR) spectroscopy is a versatile, bias-free tool to succeed at this task. In this study, we provide an adaptable reference database, which can be applied to single-particle identification as well as methods like chemical imaging based on FTIR microscopy. The large datasets generated by chemical imaging can be further investigated by automated analysis, which does, however, require a carefully designed database. The novel database design is based on the hierarchical cluster analysis of reference spectra in the spectral range from 3600 to 1250 cm−1. The hereby generated database entries were optimized for the automated analysis software with defined reference datasets. The design was further tested for its customizability with additional entries. The final reference database was extensively tested on reference datasets and environmental samples. Data quality by means of correct particle identification and depiction significantly increased compared to that of previous databases, proving the applicability of the concept and highlighting the importance of this work. Our novel database provides a reference point for data comparison with future and previous microplastic studies that are based on different databases.

Microplastics are a widespread contaminant found not only in various natural habitats but also in drinking waters. With spectroscopic methods, the polymer type, number, size, and size distribution as well as the shape of microplastic particles in waters can be determined, which is of great relevance to toxicological studies. Methods used in studies so far show a huge diversity regarding experimental setups and often a lack of certain quality assurance aspects. To overcome these problems, this critical review and consensus paper of 12 European analytical laboratories and institutions, dealing with microplastic particle identification and quantification with spectroscopic methods, gives guidance toward harmonized microplastic particle analysis in clean waters. The aims of this paper are to (i) improve the reliability of microplastic analysis, (ii) facilitate and improve the planning of sample preparation and microplastic detection, and (iii) provide a better understanding regarding the evaluation of already existing studies. With these aims, we hope to make an important step toward harmonization of microplastic particle analysis in clean water samples and, thus, allow the comparability of results obtained in different studies by using similar or harmonized methods. Clean water samples, for the purpose of this paper, are considered to comprise all water samples with low matrix content, in particular drinking, tap, and bottled water, but also other water types such as clean freshwater.

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

Groundwater is essential for the sustainable development of the Guanzhong Basin, China, and its quality is mainly controlled by hydrogeochemical processes and anthropogenic pollution. This study used statistical and multivariate statistical analysis approaches to recognize the hydrogeochemical processes and affecting factors of groundwater in the central part of the Guanzhong Basin. Correlations among 14 hydrochemical parameters were statistically examined. Principal component analysis (PCA), factor analysis (FA), and hierarchical cluster analysis (HCA) techniques were applied to analyze the physicochemical variables to understand the affecting factors of groundwater quality in the study area. The correlation analysis results indicate that cation exchange is the dominant process affecting the concentration of Na+ and Ca2+ in the groundwater. Both the PCA and FA indicate that minerals dissolution/precipitation and human activities are the key factors that affect groundwater quality. All parameters except CO32− and pH increase from C1 to C4 obtained through the Q mode HCA. C4 has a hydrochemical type of SO4–Na·K, indicating that the sample of this cluster is primarily influenced by anthropogenic processes.

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data” era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This study provides, first, an introductory overview of several methods for analyzing large spatial data. Second, this study describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Specifically, each research group was provided with two training datasets (one simulated and one observed) along with a set of prediction locations. Each group then wrote their own implementation of their method to produce predictions at the given location and each was subsequently run on a common computing environment. The methods were then compared in terms of various predictive diagnostics.