About the Journal

Aims & Scope

Environmental Analytics (EA) publishes transformative research at the intersection of advanced analytical methodologies and environmental applications. The journal emphasizes methodological innovation—advancing data science, sensing technologies, and computational tools to solve complex environmental problems, rather than documenting phenomena alone.
 
We welcome rigorous contributions across the following areas:
 
Environmental Data Science & Informatics
  • Environmental big data: acquisition, management, fusion, and visualization
  • Machine learning and AI for environmental modeling and prediction
  • Statistical and spatiotemporal data analysis
  • Informatics platforms and open-source tool development
Advanced Sensing & Observation Technologies
  • Remote sensing (satellite, UAV, LiDAR) for environmental monitoring
  • IoT, wireless sensor networks, and in-situ sensing
  • Novel sensor development and calibration (e.g., nanosensors, biosensors)
  • Data quality frameworks for citizen science and low-cost sensor networks
Environmental Modeling & Simulation
  • Process-based model improvement and data assimilation (hydrological, atmospheric, ecological)
  • High-performance computing and digital twins for environmental systems
  • Uncertainty quantification and model validation
Applications
  • Climate change: carbon monitoring, source attribution, and impact assessment
  • Pollution control: real-time source tracking, dispersion modeling, and risk assessment
  • Water resources: quality forecasting, flood/drought early warning, and optimal allocation
  • Ecosystem health: biodiversity monitoring, services assessment, and habitat change detection
  • Environment and health: exposure science and risk analysis
  • Sustainable cities: smart urban environmental management, urban metabolism, and circular economy
Outside Scope: Purely descriptive environmental surveys without quantitative analysis; conventional chemical/biological studies lacking deep data-analytic or modeling integration; and policy or economics-focused papers where methodological advancement is not the central contribution.