Environmetrics Special Issue on Exploring statistical strategies for addressing complex environmental challenges involving spatial-temporal data: Call for Papers

Call for Papers

Exploring statistical strategies for addressing complex environmental challenges involving spatial-temporal data

Submission start date: December 1, 2024 
Submission deadline: April 1, 2025

This special issue focuses on new statistical methodologies that can help overcoming some complex environmental problems. The need for more advanced statistical techniques for analysing and predicting these problems is of crucial importance in several key environmental issues, e.g. pollution, extreme weather, public health crises.

Contributions are invited on a wide range of statistical methods related to spatial and temporal modelling, point processes, machine learning, causal inference in space and time, data assimilation, uncertainty quantification, and risk assessment. This special issue will highlight applications that have direct relevance to modern environmental problems, particularly those that incorporate spatial or spatial-temporal data.

The goal of this issue is to highlight cutting-edge research to provide new insights into such complex issues surrounding the environmental challenges facing humans in today’s world.

 

Topics for this call for papers include but not restricted to:

  • Spatial-Temporal Modeling of Air Pollution and its Health Impacts
  • Machine Learning for Predicting Extreme Environmental Events
  • Causal Inference in Environmental Epidemiology
  • Data Assimilation Techniques for Climate Change Projections
  • Uncertainty Quantification in Ecosystem Risk Assessments
  • Bayesian Approaches to Climate Risk
  • Spatio-Temporal Analysis of Water Resources under Climate Stress
  • Impact of Environmental Policies: A Causal Analysis
  • Risk Modeling for Coastal Erosion and Sea-Level Rise
  • Estimation and assessment of spatial-temporal point process models for earthquake forecasting

 

Guest Editors:

Prof. Giada Adelfio,
Università di Palermo,
Italy

Prof. Frederic Schoenberg,
University of California,
Los Angeles, United States of America 

Keywords: Causal Inference in Environmental Science; Climate Change; Environmental Risk Assessment; Epidemiological Impacts of Pollution; Extreme Event Prediction; Machine Learning in Environmental Studies; Point Processes; Spatial-Temporal Modeling; Uncertainty Quantification

Submission Guidelines/Instructions

Please refer to the Author Guidelines to prepare your manuscript. When submitting your manuscript, please answer the question: “Is this submission for a special issue?” by selecting the special issue title from the drop-down list.