Environmental Evidence最新文献

Background: Automobiles are ubiquitous in the modern world, and chemicals leaching from car tires and from the tire wear particles produced during driving can be toxic to the environment, particularly in aquatic ecosystems. 6PPD-Quinone (6PPD-Q), a recently identified tire and tire wear particle leachate, has been identified as highly toxic to coho salmon and other aquatic species. Research on the distribution and impacts of 6PPD-Q in aquatic ecosystems is rapidly developing, while research on 6PPD-Q in other environmental media is just beginning. With research efforts developing on many fronts, there is a need to better map emerging knowledge about this toxin. To do that, we ask the question: "What research exists on the presence of the 6PPD-Q in different environmental media (water (freshwater), soil, sediment, and air, including dust)?" The ultimate purpose of this systematic map is to generate a literature catalog that serves as a searchable database about 6PPD-Q in different environmental media.

Methods: The systematic map will follow the Collaboration for Environmental Evidence guidelines and conform to the Reporting Standards for Systematic Evidence Syntheses (ROSES). Relevant English language only literature searches will use a search string using the specified Boolean description of our PECO elements (Population: Environmental media water, soil, sediment, air: including dust; Exposure: N/A; Comparator: N/A; Outcome: The presence of 6PPD-Q/ The concentration of 6PPD-Q). Two bibliographic databases, Web of Science (WOS) Core Collection and ScienceDirect, will be searched. Additional literature will be located through searches of targeted search engines and specialist websites. Screening of titles, abstracts, and full texts will be completed in series using established eligibility criteria. The results of the systematic map will contain a searchable open-access database formatted in Microsoft Excel. Furthermore, the outcome will be presented in a global map of the geographical distribution of included studies and their PICO/PECO elements, including a narrative synthesis, descriptive statistics, tables, and figures.

Background: Coastal ecosystems, including seagrass meadows, saltmarshes, and macroalgae, are crucial in the sequestration and storage of organic carbon. These ecosystems provide essential ecosystem services, such as supporting biodiversity, coastal protection, and water quality enhancement. Despite their significance, they face substantial threats from human activities, including pollution, habitat degradation, and overexploitation, further exacerbated by climate change phenomena like heatwaves and ocean acidification. Efforts to protect, restore, or alleviate pressures on blue carbon ecosystems can yield multifaceted benefits beyond climate mitigation, including preserving biodiversity, enhancing climate resilience, and safeguarding vital services for human well-being. Understanding the factors affecting the biodiversity and carbon capacity i.e. the capacity for carbon uptake, storage and sequestration, of these ecosystems is crucial for effective conservation efforts. The goal of the present study is to assess the available quantitative and qualitative evidence on the impacts of human activities on the biodiversity and carbon storage capacity of blue carbon ecosystems in the North-East Atlantic. Developing a systematic map of the available evidence could significantly enhance our understanding of the pressures faced by blue carbon ecosystems in the North-East Atlantic and facilitate the identification of knowledge clusters and gaps thereby determining the scope and depth of the current knowledge base.

Methods: A systematic map on existing evidence of human impacts on the biodiversity and carbon capacity of blue carbon ecosystems in the North-East Atlantic will be conducted using relevant bibliographic databases and a web-based search engine. All searches will be conducted in English and will gather peer reviewed publications from 1980 to 2024. The resulting literature will be screened by two independent screeners at the level of title and abstract followed by full text against a set of eligibility criteria (i.e. population, intervention, outcome, study type). Metadata will be extracted from studies that meet the eligibility criteria and summarize with heatmaps, bar plots, geographic distribution maps, and tabular summaries.

Background: As people work towards environmental sustainability for urban environments and everyday lives, tensions have been seen in different efforts on food, housing, environmental management, urban planning, and many cross-cutting issues touching on multiple aspects of social-ecological systems. Urban agriculture (UA) as one multifaceted, cross-cutting arena, has had one particular tension regarding relationships with housing and the built environment: its gentrification potential. However, different accounts have provided evidence and theorization of gentrification as a possible outcome of UA activities, as a risk for UA initiatives, and showing still other relationships between UA and gentrification. These different accounts may be partially explained by different theoretical engagements with gentrification, as well as multiple activities constituting a broad notion of urban agriculture. An overview of the scholarly work regarding these two topics can provide a starting point for understanding how they have been approached and theoretically engaged together, and demonstrate gaps in dominant academic discourses.

Methods: This research for a systematic mapping of literature seeks to assess the academic work around relationships between urban agriculture and gentrification. The protocol outlines a comprehensive and reliable search and review strategy based on the core components of urban, agriculture, and gentrification in search strings and inclusion criteria. Texts in English, French, and German will be scanned as historically and currently dominant academic languages, while searching nine bibliographic databases or platforms. The protocol details a data coding strategy for metadata, empirical content, and analytic content. The results are expected to uncover sources of evidence for links between urban agriculture and gentrification, producing interoperable datasets of the evidence base, insights of the overall research landscape, and possibilities to find research gaps.

Artificial intelligence (AI) is increasingly being explored as a tool to optimize and accelerate various stages of evidence synthesis. A persistent challenge in environmental evidence syntheses is that these remain predominantly monolingual (English), leading to biased results and misinforming cross-scale policy decisions. AI offers a promising opportunity to incorporate non-English language evidence in evidence syntheses screening process and help to move beyond the current monolingual focus of evidence syntheses. Using a corpus of Spanish-language peer-reviewed papers on biodiversity conservation interventions, we developed and evaluated text classifiers using supervised machine learning models. Our best-performing model achieved 100% recall meaning no relevant papers (n = 9) were missed and filtered out over 70% (n = 867) of negative documents based only on the title and abstract of each paper. The text was encoded using a pre-trained multilingual model and class-weights were used to deal with a highly imbalanced dataset (0.79%). This research therefore offers an approach to reducing the manual, time-intensive effort required for document screening in evidence syntheses-with minimal risk of missing relevant studies. It highlights the potential of multilingual large language models and class-weights to train a light-weight non-English language classifier that can effectively filter irrelevant texts, using only a small non-English language labelled corpus. Future work could build on our approach to develop a multilingual classifier that enables the inclusion of any non-English scientific literature in evidence syntheses.

The global demand for high-quality, robust and up-to-date evidence to guide decision-making has never been higher. The vast quantity of scientific literature being produced and made accessible presents an unparalleled opportunity for evidence-based decision-making to become a widespread reality. In addition, the world has at its fingertips cutting-edge technologies, such as AI, to make sense of this extensive knowledge base and deliver insights more quickly to decision-makers most in need. AI-powered evidence syntheses promises to be transformative, saving many lives and enhancing livelihoods globally. However, achieving this requires substantial cultural shifts in the evidence community, including amongst both AI developers and users to shape both trustworthy AI and trust in AI. Current efforts to establish best practices are emerging, but progress is hindered by the lack of clear consensus on what constitutes trustworthy AI for evidence synthesis. Philanthropic investments in trustworthy AI systems, alongside robust evaluations of trust in AI for evidence synthesis, must be prioritised to determine the conditions required for an enabling environment. Mainstreaming AI for reliable, faster and cheaper evidence synthesis demands a better understanding of trustworthy AI and trust in these systems. Funders should prioritise aspects of trustworthiness and trust whilst balancing the drive towards ongoing innovation.