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11 articles for “Air pollution forecast”
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Prediction of Air Pollution LSTM Model Use in Machine Learning
Abstract: A major environmental problem that has an impact on the health and happiness of millions of people is air pollution. Accurate air quality forecasting is crucial for maintaining human health, the environment, and urban planning. In recent years, machine learning algorithms, such as Long Short-Term Memory (LSTM) models, have been increasingly used to predict air pollution levels. This study presents an abstract for a system that uses an LSTM model …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 1, 2023 · pp. 90–98 Read article
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Prediction of Air Quality Index (AQI)
Abstract: AbstractGrowing economy of a country has its pros and cons. Swift development of industries on an extensive scale has led to immense growth in air pollution. It happens to be elementary root of maladies and early deaths around the world. The UN responsible agency for international public health has estimated that outside atmosphere consisting of air pollution takes about three million people’s lives every year. Therefore, contamination amount in air …
Published in Journal of Electronic Design Technology · Vol. 11, Issue 2, 2020 · pp. 28–34 Read article
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Remote Sensing in Atmospheric Studies: Enhancing Understanding of Climate Dynamics and Air Quality, Atmospheric Monitoring and Analysis; Emerging Technologies and Future Directions
Abstract: Remote sensing has emerged as a transformative tool in atmospheric sciences, providing detailed and comprehensive insights into various atmospheric phenomena. Its advanced capabilities have revolutionized our understanding of climate dynamics, air quality, and atmospheric composition, enabling more accurate monitoring and analysis. This paper reviews the critical role of remote sensing technologies in enhancing knowledge of atmospheric processes and their practical applications in areas such as climate change monitoring, air pollution …
Published in International Journal of Atmosphere · Vol. 2, Issue 1, 2025 · pp. 1–5 Read article
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An Application of Artificial Neural Networks for Air Quality Prediction of SO2 Concentrations in Calabar Metropolis
Abstract: The past few years have seen intense enhancements in our aptitude to simulate complicated physical systems using computers, which has led to the use of artificial neural networks (ANNs) in various capacities of scientific research. The artificial neural network has been applied to various ecological pollution complications and which has revealed some degree of precision. The study aims to advance a neural network air quality forecast model for the complex …
Published in Research & Reviews : Journal of Physics · Vol. 9, Issue 3, 2020 · pp. 8–11 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article
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Integrated, Geospatial Risk Assessment of Air, Water, and Soil Pollution Impacts on Agricultural Sustainability using Advanced Digital Technologies
Abstract: The systemic threat posed by the convergence of air, water, and soil contaminants represents a critical challenge to global agricultural resilience and food security. Traditional, site-specific pollutant monitoring methods are insufficient for capturing the dynamic, diffuse, and often nonlinear nature of environmental risk pathways that permeate agrarian landscapes. This study presents a robust framework for comprehensive risk assessment utilizing a synergistic suite of modern tools designed for spatial, temporal, and …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 2, 2025 · pp. 28–37 Read article
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Assessing Air Quality, Climate Change, and Migration Dynamics in Delhi NCR: A System Dynamics Approach
Abstract: As climate change accelerates and environmental degradation worsens, urban centers like Delhi NCR are under increasing pressure from internal migration. Poor air quality—especially in rural and peri-urban regions—emerges both as a driver of out-migration and a deterrent for in-migration to already burdened cities. This study develops a system dynamics (SD) model that integrates climate variables, air pollution metrics, economic indicators, governance quality, and migration behavior to simulate population flows into …
Published in Recent Trends in Mathematics · Vol. 2, Issue 1, 2025 · pp. 7–11 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
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Atmospheric Boundary Layer Processes: Impacts on Weather Patterns and Climate Change
Abstract: The atmospheric boundary layer (ABL) is a vital component of the Earth's climate system, acting as the interface between the terrestrial surface and the overlying atmosphere. This layer, typically extending from the surface to a height of a few hundred meters, is characterized by strong gradients in meteorological variables such as temperature, humidity, wind speed, and atmospheric pressure. Understanding the dynamics of the ABL is essential for comprehending weather phenomena, …
Published in International Journal of Atmosphere · Vol. 1, Issue 2, 2024 · pp. 22–25 Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 Read article
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Integrating Atmospheric Science: Understanding Greenhouse Gases, Aerosols, and Air Quality Dynamics
Abstract: Atmospheric science investigates the Earth’s atmospheric systems to understand their composition, dynamics, and the implications for climate, weather, and air quality. This review explores five primary areas within the field: atmospheric composition, atmospheric modeling, remote sensing, air pollution, and boundary layer dynamics, highlighting critical challenges and advancements. Rising levels of greenhouse gases (GHGs), including carbon dioxide and methane, continue to drive global warming, while feedback mechanisms—like cloud interactions and surface …
Published in International Journal of Atmosphere · Vol. 1, Issue 1, 2024 · pp. 32–35 Read article