Raging fires, wild storms: Is the rate of global climate change outpacing our progress in the assessment and management of chemical stressors to effectively protect humans and the environment for extreme climate conditions?
Bibliographic record
Abstract
Extremes of climate are occurring with ever greater frequency. Wildfires, floods, droughts, and cyclones are having devastating impacts on humans and ecosystems around the world. As this editorial was developed, at least 115 people are known to have died in the recent Maui wildfires, thousands have been evacuated as wildfires ravage parts of Canada and the Mediterranean, and thousands are homeless as flooding inundates areas of Chile. The 2022 floods in eastern Australia were some of the largest and costliest in Australia's recorded history (Callaghan, 2023). The 2015–2019 droughts in Europe and Australia were the worst experienced in over 2000 years (Büntgen et al., 2021). Unprecedented high temperatures, inundations of water and sediment, and record low and high rainfalls are predicted to continue with global climate change (GCC) (Commonwealth Scientific and Industrial Research Organization & Bureau of Meteorology, 2022). Thus, a new paradigm for methods and predictive tools is necessary in the fate and assessment of chemical stressors for effective response and resilience in this GCC-impacted world. Towards this intent, within the Society of Environmental Toxicology and Chemistry (SETAC), Wenning et al., in 2010, called for environmental toxicologists and chemists to have greater involvement in GCC debates (Wenning et al., 2010). This was followed by a SETAC Pellston Workshop, held in Wisconsin 2011, that brought together 36 scientists from 11 countries to address the question “How will GCC influence the environmental impacts of chemicals and stressors and the way we assess and manage them in the environment?” The resulting series of seven papers (https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.2037) provided an excellent overview of the key findings and important issues as summarized by Stahl et al. (2013). The consensus issues in environmental toxicology and chemistry of chemical stressors influenced by GCC were identified as occurrence, fate and bioavailability of chemicals, mechanisms of toxicity, populations and communities, human health risk assessment, ecological risk assessment, and assessment of injury to natural resources and restoration ecology. The next SETAC Pellston Workshop on the topic was held in Oslo last year, over 10 years later. This aimed to develop a framework for integrating climate information into risk assessment, and identify the still remaining knowledge gaps (Moe et al., 2022). A special series reporting the workshop outcomes will be published in IEAM. Other relevant collaborative initiatives have emerged in SETAC conferences and meetings of other learned societies and professional groups (e.g., workshops on Natural Hazards). Significant challenges exist to adapt and develop current knowledge in environmental toxicology and chemistry in the context of GCC, including the comprehensive integration of data from climate scientists (IPCC, 2023). A wide range of factors must be considered, and complex multidisciplinary, multirisk approaches are required (Landis et al., 2013; Terzi et al., 2019). Despite progress since the call to action by Wenning et al. (2010), large uncertainty still remains in understanding the variability in climate conditions, the spatial and temporal complexities across different world environments, and calibration of existing models (Moe et al., 2022; Schewe et al., 2019). Many collaborative opportunities have been disrupted by the COVID pandemic. Monitoring data are dispersed, often concentrated in developed nations, and even then, are frequently siloed with restricted sharing between stakeholders. This has undermined the development of comprehensive surveillance programs to detect shifts in baseline conditions and provide early warning of impacts. Funding flows are subject to changing budgets and economies, and often fall short of levels needed for effective action, despite government reviews prioritizing the research gaps after catastrophic events (Commonwealth of Australia, 2020). Necessary change has been hindered by slow policy implementation and lack of resources. We must adapt and transform our practice and policy to truly capture the extremes of global climate change and effectively support prevention, preparation, and responsive restoration and rehabilitation of potential impact scenarios into the future. Susan C. Wilson: Writing—original draft; writing—review and editing. The author acknowledges the input of S. Jannicke Moe and Wayne Landis in the review of this editorial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".