Environmental Science Community Research Articles

Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management

Aims: This study is significant as it elucidates the relationship between floristic composition and soil nutrient availability of India's Saranda Sal (Shorea robusta Gaertn.) forest. Study Design: Vegetation sampling was done following a grid of 5 km x 5 km, and soil sampling was done in each sampling plot using a Z-pattern. Place and Duration of Study: The study was conducted in Saranda Forest of West Singhbhum district, Jharkhand, Eastern India, during 2021-2022 Methodology: Sampling has been designed as grid methods (5 km X 5 km) following a forest survey of India, and vegetation sampling was done by quadrat method. The soil samples were collected from three depths (i.e., surface: 0–30 cm, sub-surface: 30–60 cm and inner: 60-90 cm from each selected site. ANOVAs were used to compare the chemical properties of soil samples from various forests. Pearson correlation analyses to examine the effects of climatic variables on chemical properties of the soil of selected Sal forest as well as their relation with plant diversity. Results: A comprehensive analysis of 5432 vascular plants from 65 species and 34 families was conducted across the 17 sites. Fabaceae is the most dominant family with 07 species. The study also examined soil chemical parameters and micronutrients in different sites and established their relations with vegetation dynamics. Notably, tree density showed a significant positive correlation with soil pH (r=0.59, p<0.05), but a significant negative correlation with Shannon diversity (H') (r=-0.53, p<0.05). Similarly, there was a significant positive correlation of Organic carbon with copper (r=0.59, p<0.05) and iron (r=0.61, p<0.00); however, there was a significant negative correlation with Available Phosphorus (r=-0.52, p<0.05). Conclusion: Research findings underscore the importance of soil nutrients in promoting forest health and growth. Importantly, they can guide the formulation of practical and effective soil-forest management strategies for S. robusta and its associated forests, directly benefiting the forestry and environmental science community.

Indigenous community governance in marine conservation: A bibliometric analysis

Maintaining marine sustainability and biodiversity requires support and cooperation from the government, stakeholders, and community. Indigenous communities have the right to restoration and protection of the environment and productive capacity of land, territory, sea, and all their natural resources. Research on community governance has been carried out by many researchers with increasingly developing topics. Including topics about indigenous peoples and natural resource conservation. This research aims is to determine the development of research trends regarding community governance of indigenous communities in marine conservation. The data source is literature in the Scopus database and the data analysis uses bibliometric analysis of indigenous community governance in marine conservation publications. The results are the research of indigenous communities governance in marine conservation from the first publication in 2006 has tended to increase. Although in certain years it has decreased or there have been no publications at all. The research become an interesting topic because it related with environmental science and indigenous communities have a big role in implementing marine conservation. This conservation has been going for a long time and passed down from generation to generation. The recommendation of this research is legal recognition for indigenous peoples to protect their rights.

Opening a conversation on responsible environmental data science in the age of large language models

Abstract The general public and scientific community alike are abuzz over the release of ChatGPT and GPT-4. Among many concerns being raised about the emergence and widespread use of tools based on large language models (LLMs) is the potential for them to propagate biases and inequities. We hope to open a conversation within the environmental data science community to encourage the circumspect and responsible use of LLMs. Here, we pose a series of questions aimed at fostering discussion and initiating a larger dialogue. To improve literacy on these tools, we provide background information on the LLMs that underpin tools like ChatGPT. We identify key areas in research and teaching in environmental data science where these tools may be applied, and discuss limitations to their use and points of concern. We also discuss ethical considerations surrounding the use of LLMs to ensure that as environmental data scientists, researchers, and instructors, we can make well-considered and informed choices about engagement with these tools. Our goal is to spark forward-looking discussion and research on how as a community we can responsibly integrate generative AI technologies into our work.

CHH: Health community accessing environmental information from the Copernicus Programme

Abstract Copernicus is the Earth observation component of the European Union's Space programme, looking at our planet and its environment to benefit all European citizens. The Copernicus services transform wealth of satellite and ground-based measurements into value-added information by processing and analysing the products. All the information is provided with an open and free data policy to help public national and European authorities, policy makers, international organisations and service providers to improve European citizens’ quality of life. There are six operational Copernicus Services covering the whole Earth System including ocean, land, atmosphere and more horizontal domains such as climate change, emergency and security. To facilitate the use of these information by the different user communities, some Thematic Hubs have been created and are under development. One of the first hubs is the Copernicus Health Hub (CHH) and it is focusing on the health community. The CHH collects and provides all the Copernicus environmental information that are pertinent to Health, following WHO definition: Physical, Mental and Well-being. The health Hub is also supporting the users in better exploiting and uptake Copernicus data and products (via documentation, access to catalogues, inspirational use case stories, ...). In addition, the CHH should collect new requirements for the evolution of the Copernicus programme. In this presentation, the CHH will be introduced in more details, the different types of environmental information will be presented accompanied by some use cases to inspire further developments and new applications for the health community. Key messages • The Copernicus Health Hub is a facilitator between the Health community and the Earth Observation and environmental science community. • The Copernicus Health Hub provide all environmental data at Globale and European scale.

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

SummaryCloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.

A Case for Accelerating Standards to Achieve the FAIR Principles of Environmental Health Research Experimental Data.

Funding agencies, publishers, and other stakeholders are pushing environmental health science investigators to improve data sharing; to promote the findable, accessible, interoperable, and reusable (FAIR) principles; and to increase the rigor and reproducibility of the data collected. Accomplishing these goals will require significant cultural shifts surrounding data management and strategies to develop robust and reliable resources that bridge the technical challenges and gaps in expertise. In this commentary, we examine the current state of managing data and metadata-referred to collectively as (meta)data-in the experimental environmental health sciences. We introduce new tools and resources based on invivo experiments to serve as examples for the broader field. We discuss previous and ongoing efforts to improve (meta)data collection and curation. These include global efforts by the Functional Genomics Data Society to develop metadata collection tools such as the Investigation, Study, Assay (ISA) framework, and the Center for Expanded Data Annotation and Retrieval. We also conduct a case study of invivo data deposited in the Gene Expression Omnibus that demonstrates the current state of invivo environmental health data and highlights the value of using the tools we propose to support data deposition. The environmental health science community has played a key role in efforts to achieve the goals of the FAIR guiding principles and is well positioned to advance them further. We present a proposed framework to further promote these objectives and minimize the obstacles between data producers and data scientists to maximize the return on research investments. https://doi.org/10.1289/EHP11484.

Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications

Abstract Neural networks (NN) have become an important tool for prediction tasks—both regression and classification—in environmental science. Since many environmental-science problems involve life-or-death decisions and policy making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely, to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: 1) estimating vertical profiles of atmospheric dewpoint (a regression task) and 2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research. Significance Statement Neural networks are used for many environmental-science applications, some involving life-or-death decision-making. In recent years new methods have been developed to provide much-needed uncertainty estimates for NN predictions. We seek to accelerate the adoption of these methods in the environmental-science community with an accessible introduction to 1) methods for computing uncertainty estimates in NN predictions and 2) methods for evaluating such estimates.

People, infrastructure, and data: A pathway to an inclusive and diverse ecological network of networks

AbstractMacrosystem‐scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade‐offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner.

Study becomes insight: Ecological learning from machine learning.

The ecological and environmental science communities have embraced machine learning (ML) for empirical modelling and prediction. However, going beyond prediction to draw insights into underlying functional relationships between response variables and environmental ‘drivers’ is less straightforward. Deriving ecological insights from fitted ML models requires techniques to extract the ‘learning’ hidden in the ML models.We revisit the theoretical background and effectiveness of four approaches for deriving insights from ML: ranking independent variable importance (Gini importance, GI; permutation importance, PI; split importance, SI; and conditional permutation importance, CPI), and two approaches for inference of bivariate functional relationships (partial dependence plots, PDP; and accumulated local effect plots, ALE). We also explore the use of a surrogate model for visualization and interpretation of complex multi‐variate relationships between response variables and environmental drivers. We examine the challenges and opportunities for extracting ecological insights with these interpretation approaches. Specifically, we aim to improve interpretation of ML models by investigating how effectiveness relates to (a) interpretation algorithm, (b) sample size and (c) the presence of spurious explanatory variables.We base the analysis on simulations with known underlying functional relationships between response and predictor variables, with added white noise and the presence of correlated but non‐influential variables. The results indicate that deriving ecological insight is strongly affected by interpretation algorithm and spurious variables, and moderately impacted by sample size. Removing spurious variables improves interpretation of ML models. Meanwhile, increasing sample size has limited value in the presence of spurious variables, but increasing sample size does improves performance once spurious variables are omitted. Among the four ranking methods, SI is slightly more effective than the other methods in the presence of spurious variables, while GI and SI yield higher accuracy when spurious variables are removed. PDP is more effective in retrieving underlying functional relationships than ALE, but its reliability declines sharply in the presence of spurious variables. Visualization and interpretation of the interactive effects of predictors and the response variable can be enhanced using surrogate models, including three‐dimensional visualizations and use of loess planes to represent independent variable effects and interactions.Machine learning analysts should be aware that including correlated independent variables in ML models with no clear causal relationship to response variables can interfere with ecological inference. When ecological inference is important, ML models should be constructed with independent variables that have clear causal effects on response variables. While interpreting ML models for ecological inference remains challenging, we show that careful choice of interpretation methods, exclusion of spurious variables and adequate sample size can provide more and better opportunities to ‘learn from machine learning’.

Image of the environmental scientist.

The environmental scientist has been playing an important role in preventing and mitigating environmental pollution. Individuals' images of the environmental scientist will likely impact and meditate their interest and attitudes toward environmental science as well as their willingness to take up an environmental science profession. However, prior studies focusing on the image of a professional are primarily of the general scientist. The environmental scientist and the image thereof remains out of the limelight of environmental science community and has been under-researched. In this article, on the basis of the retrospect on the stereotypical images of the general and environmental scientist and the gaps in previous studies, we attempted to report young individuals' images of the environmental scientist in an alternative context. Drawing technique was employed and administered to 127 junior students aged from 13 to 15 year-old from a junior high school. The individuals' image of environmental scientists can be generally abstracted as a male with environmental protection knowledge and commitment to environmental protection who does work more like a green chemist, ecology restorer, or plant protector and observes or cleans outdoors, primarily in forest, jungle, and/or by riverside.

Towards ‘Creative Participatory Science’: Exploring Future Scenarios Through Specialist Drought Science and Community Storytelling

There is a growing interest in different forms of participatory modeling that bring science and lay knowledge into the same space. This recognizes that, traditionally, the environmental science community has mostly seen stakeholder engagement as a ‘follow on’ activity to be undertaken once the key scientific research has been completed. By excluding communities from the scientific process, or at best approaching communities in one-way communication, scientists are missing out on the wealth of local community knowledge about the very facets of the environment which they seek to understand. The challenge, however, is in identifying, developing and adopting appropriate platforms for communication and co-creation to allow scientists and local communities to have effective dialogue, efficiently gather, interpret and evaluate lay knowledge, and develop relevant, scientifically robust, but widely comprehensible, results. DRY (Drought Risk and You) was a 4-year project, funded under the RCUK Drought and Water Scarcity Program, with the aim of developing an evidence-based resource to support better decision-making in United Kingdom drought risk management. In DRY, scientific data and multiple narrative approaches have been brought together to facilitate decision-making processes and improve community resilience. Creative experiments were designed by the DRY interdisciplinary team to engage local communities in using specialist science as a stimulus for storytelling at catchment level, but also to give scientists the insight required to develop meaningful scenarios of local change to explore potential drought impacts in a particular river catchment. One challenge of working with storytelling is that it is very often retrospective and linked to past experiences and memories. It can be seen as a backward-looking activity, learning principally from what has happened before. The participatory approaches applied in DRY demonstrated that storytelling can be also used to imagine, interrogate and plan for a future that communities might collectively wish to subscribe or adapt to. In particular, by co-designing and facilitating storyboarding workshops, the DRY team, together with local stakeholders, have been exploring the ‘scenario-ing’ of possible futures as a way of creating a story and visualizing a picture for the future of the community. By allowing the scientists, community and local stakeholders to develop model drought scenarios iteratively together using storytelling, these scenarios should not only be scientifically accurate, but should also reflect local interests and aspirations, as well as local drought mitigation practices. This process integrates valuable knowledge exchange and the building of mutual capital to support local risk decision-making - scaling up from the level of the individual to the collective.

Gathering evidence of impact from research support services: Examining impact in the context of the Centre for Environmental Data Analysis

AbstractThe Centre for Environmental Data Analysis (CEDA) is a provider of two major services to the environmental science community; JASMIN and the CEDA Archive. CEDA is frequently required to evidence the impact it has on researchers and wider society. However, this is challenging as there are currently no formal or standard processes for collecting impact information. To understand how CEDA could collect impact information, and to allow its users to shape this monitoring, over 500 users provided their opinions, preferences and suggestions as to how to share impact, via six focus groups and an online survey. The results suggest that whilst there was a high degree of willingness to provide impact information to CEDA there remains confusion around what ‘impact’ is. Users are keen to share impact in ways which utilize existing processes, and at times which make sense to both the research and the impact, whilst also understanding the need and purpose for sharing that information.

Low-cost electronic sensors for environmental research: Pitfalls and opportunities

Repeat observations underpin our understanding of environmental processes, but financial constraints often limit scientists’ ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IoT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on 6 years of developmental work with Arduino-based open-source hardware and software, extensive laboratory and field testing, and incorporation of such technology into active research programmes, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap, and a sonic anemometer for monitoring sand transport. Schematics, code and purchasing guidance to reproduce our sensors are described in the paper, with detailed build instructions hosted on our King’s College London Geography Environmental Sensors Github repository and the FreeStation project website. We show in each case study that manual design and construction can produce research-grade scientific instrumentation (mean bias error for calibrated sensors –0.04 to 23%) for a fraction of the conventional cost, provided rigorous, sensor-specific calibration and field testing is conducted. In sharing our collective experiences with build-it-yourself environmental monitoring, we intend for this paper to act as a catalyst for physical geographers and the wider environmental science community to begin incorporating low-cost sensor development into their research activities. The capacity to deploy denser sensor networks should ultimately lead to superior environmental monitoring at the local to global scales.

The impact of below detection limit samples in residual risk assessments for decommissioning nuclear power plant sites

As the number of nuclear power plants (NPPs) being decommissioned increases internationally, many issues are being raised. One such issue is related to site soil analyses for the determination of residual risk for license termination. In a typical site-cleanup analysis, the majority of soil samples at the site are at or below the detection limit (BDL). Conventional approaches to managing BDL data are to simply ignore or substitute the data with a value of zero or the detection limit itself. However, these approaches are statistically biased, limiting their usefulness. Within the environmental science community, the issue of how to treat BDL data has been examined by a number of investigators. This study reviewed the issue of BDL data in nuclear decommissioning using the analytical methods suggested by studies in the environmental science, including the Kaplan-Meier method, robust regression on order statistics, and maximum likelihood estimation. The use of these methods to handle BDL data was examined using a case study with respect to its potential impact on dose/risk assessment, the soil volume removal estimate, and the associated costs. The case study was based on the Colorado School of Mines Research Institute's site soil data. Our analysis included the consideration of the uncertainties associated with residual dose/risk, waste soil volume estimation, and costs. An uncertainty analysis was conducted using a Latin hypercube sampling approach. Results showed that using BDL data can have a large impact on the estimation of dose/risk, waste volume, and waste disposal cost of a NPP decommissioning project.

Transformation potential of cannabinoids during their passage through engineered water treatment systems: A perspective

Cannabinoids are incipient contaminants with limited literature in the context of water treatment. With increasing positive public opinion toward legalization and their increasing use as a pharmaceutical, cannabinoids are expected to become a critical class of pollutant that requires attention in the water treatment industry. The destructive removal of cannabinoids via chlorination and other oxidation processes used in drinking water and wastewater treatment requires careful investigation, because the oxidation and disinfection byproducts (DBPs) may pose significant risks for public health and the environment. Understanding transformation of cannabinoids is the first step toward the development of management strategies for this emerging class of contaminant in natural and engineered aquatic systems. This perspective reviews the current understanding of cannabinoid occurrence in water and its potential transformation pathways during the passage through drinking water and wastewater treatment systems with chlorination process. The article also aims to identify research gaps on this topic, which demand attention from the environmental science and engineering community.

EcoEd: Cohesive training and skill development for digital ecoscience tools

Digital research infrastructures such as data portals and virtual laboratories enable easier access to data and analytical tools. Such infrastructures are essential to deliver research excellence that drives innovation, but we also need to ensure that we have a skilled workforce that can use these infrastructures. Therefore, training and skill development of students, researchers, government practitioners and industry professionals is key to the long-term success of this investment. In Australia, a suite of digital infrastructures has been developed for environmental sciences to enhance our understanding of the natural world and making forward projections into novel conditions (e.g. Atlas of Living Australia, Biodiversity and Climate Change Virtual Laboratory, ecocloud, Terrestrial Ecosystem Research Network). To provide users with a holistic approach to environmental spatial data discovery and analysis, these infrastructures have joined forces to deliver an exciting and innovative new training program. This program, called ecoEd, provides cohesive training and skill development to university lecturers, researchers and industry professionals enabling them to combine theoretical concepts with real-world applications. In this presentation, I will present how ecoEd was developed and the outcomes of the training sessions in which a group of ecoEd Champions absorbed ready-to-use lecture and workshop modules along with tools and knowledge on how to use the platforms. These resources can immediately be used in undergraduate courses that focus on topics such as ecology, biogeography, conservation biology, environmental management and spatial analysis as well as in stand alone workshops for researchers and practitioners. The training program aims to provide the Champions with the resources and knowledge required so that they can confidently re-deliver the lectures and workshops in their own institutions. As such, ecoEd is increasing the capacity of Australia’s environmental science community to advance science and deliver outcomes that underpin the sustainable use of our ecosystems using the latest advances in digital technologies. Moreover, it is enabling first-rate science education in Australia by supporting and nurturing our future scientists.

From a Guided Teacher into Leader: A Three-Stage Professional Development (TSPD) Model for Empowering Teachers

A three-stage professional development (TSPD) model for training experienced teachers to become teachers’ leaders is presented here, along with a study assessing its value. The three stages of the model are:  a “basic training” stage, a “master-teacher” stage, and an “independent implementation” stage. This qualitative study included open questionnaires and interviews of participants and course leader after the various stages. Statements were classified accordingly to three main themes: ‘teachers as pedagogues’, ‘teachers’ involvement in environmental science (ES) community’, and ‘teachers’ as leaders’. Results show that participating in the TSPD course enabled teachers to unify into one coherent community with similar goals, increased their self-confidence, empowered them as teachers by improving classroom function, and intensified their abilities to act as teachers’ leaders. This model, although tested on environmental science teachers, is applicable to any teacher community.

The role of Earth and environmental science in addressing sustainable development priorities in Eastern Africa

Here we synthesise the results of three participatory workshops to explore sustainable development priorities in eastern Africa, and discuss these in the context of Earth and environmental science. The planet is a core pillar of sustainable development, and the engagement of Earth and environmental scientists is vital to achieving the UN Sustainable Development Goals (SDGs). In 2017, the British Geological Survey gathered 76 delegates from 48 organisations at three workshops in Nairobi (Kenya), Lusaka (Zambia), and Dar es Salaam (Tanzania). Using the SDGs as a reference tool, participants (i) identified development priorities at regional (eastern Africa) and national scales, (ii) explored the Earth and environmental science research and data needs to help address these, and (iii) co-designed relevant science-for-development projects. Participants identified sustainable development priorities to be basic (or immediate) needs, including zero hunger (SDG 2), education (SDG 4), ending poverty (SDG 1), and water and sanitation (SDG 6). Participants also described examples of Earth and environmental science research, training, technologies, monitoring and management to support sustainable development. Emerging themes included environmental data (collection, management, integration, access), policy and regulations (integrating environmental science, and policy coherence), resource management (degradation, pollution and environmental protection), and scientific education and understanding (training, knowledge exchange, public understanding of science). A comparative synthesis of existing regional and national development strategies indicates that current narratives of development interventions do not fully capture the opportunities from environmental data integration and policy coherence. Greater engagement with and by the Earth and environmental science community could help to advance these themes to support sustainable development in eastern Africa. This would support efforts to reduce environmental degradation, improve natural resource management, and inform the utilisation of natural resources to improve economic growth and social wellbeing.

Efficient Heterogeneous Activation of Persulfate by Iron-Modified Biochar for Removal of Antibiotic from Aqueous Solution: A Case Study of Tetracycline Removal

Waste reutilization is always highly desired in the environmental engineering and science community. In this study, Fe-SCG biochar was functionalized by modifying spent coffee grounds (SCG) with magnetite (Fe3+) at 700 °C and applied for the oxidative removal of tetracycline (TC) with the presence of persulfate (PS). The effects of pH, dosage of biochar and sodium persulfate and initial TC concentration on TC degradation were investigated in a batch system. Our results show that higher TC degradation efficiency was obtained at low pH, low initial TC concentration, and at high dosages of PS and biochar. The highest removal efficiency (96%) was achieved by Fe-SCG/PS under the conditions of pH = 2.0, [Fe-SCG] = 2.5 g/L, [PS] = 60 mM and [TC] = 1 mM. The proposed Fe-SCG catalyst could be a promising effective biochar for the remediation of other emerging organic contaminants.

Using a linked table-based structure to encode self-describing multiparameter spatiotemporal data

Multiparameter data with both spatial and temporal components are critical to advancing the state of environmental science. These data and data collected in the future are most useful when compared with each other and analyzed together, which is often inhibited by inconsistent data formats and a lack of structured documentation provided by researchers and (or) data repositories. In this paper we describe a linked table-based structure that encodes multiparameter spatiotemporal data and their documentation that is both flexible (able to store a wide variety of data sets) and usable (can easily be viewed, edited, and converted to plottable formats). The format is a collection of five tables (Data, Locations, Params, Data Sets, and Columns), on which restrictions are placed to ensure data are represented consistently from multiple sources. These tables can be stored in a variety of ways including spreadsheet files, comma-separated value (CSV) files, JavaScript object notation (JSON) files, databases, or objects in a software environment such as R or Python. A toolkit for users of R statistical software was also developed to facilitate converting data to and from the data format. We have used this format to combine data from multiple sources with minimal metadata loss and to effectively archive and communicate the results of spatiotemporal studies. We believe that this format and associated discussion of data and data storage will facilitate increased synergies between past, present, and future data sets in the environmental science community.