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  • Modeling and Statistical Approaches for Air Pollution Analysis
    The goal of the research is to investigate several methods of assessing air pollution, such as creating models to explain the phenomenon from a historical standpoint, comprehending its dynamics using statistical and modeling tools, challenges and limitations of air pollution modeling, and projecting future paths for pollution analysis
  • Mathematical Modeling of the Effect of Emission Sources on . . .
    First, the pollutants and the time and spatial scales of interest are defined, and then an appropriate model(s) is chosen Models have been formulated in a number of ways Each formulation involves certain approximations and has certain strengths and limitations This chapter shows how models can be used for relating health effects to sources
  • Artificial intelligence in environmental monitoring . . .
    Similarly, in energy management, interpretable AI models can help utilities understand energy consumption patterns and optimize grid operations Shortcomings of AI models In the realm of AI for environmental monitoring, alongside its numerous advantages, there exist several noteworthy shortcomings and challenges that must be recognized ( Shalu
  • Models, Tools and Databases for Air Research - US EPA
    There are many sources of indoor air pollution, such as building materials, furnishings, and chemical cleaners Since most people spend a large portion of their time indoors, it is important to be able to estimate exposure to these pollutants IAQX helps users analyze the impact of pollutant sources and sinks, ventilation, and air cleaners
  • Modeling for Environmental Pollution and Change - Frontiers
    Several research topics related to environmental pollution and change are being rapidly advanced by modeling technologies including machine learning (ML), artificial intelligence (AI), molecular simulation, computational fluid dynamics (CFD) and other types of computational techniques On the one hand, this promotion is driven by the flourish of high-performance computing in the past few years
  • Pollution Prevention Strategies for Environmental Planners
    AI tools are increasingly used to predict pollutant behavior and recommend timely intervention measures With continuous learning algorithms, these tools can assimilate vast amounts of environmental data and generate insights that were previously unattainable using conventional methods
  • Air pollution modelling: what is it and what can it tell us?
    Modelling can be used to run scenarios, to test theories and understand environmental impact under different emission rates, weather and development scenarios There are lots of different methods and techniques, but the goal is always the same – make an assessment of pollutant impact over a given area using an existing set of data


















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