MVAR: MultiVariate AutoRegressive Air Pollutants Forecasting Model

• URL: http://arxiv.org/abs/2507.12023v1
• Date: Wed, 16 Jul 2025 08:30:41 GMT
• Title: MVAR: MultiVariate AutoRegressive Air Pollutants Forecasting Model
• Authors: Xu Fan, Zhihao Wang, Yuetan Lin, Yan Zhang, Yang Xiang, Hao Li,
• Abstract summary: Existing studies predominantly focus on single-pollutant forecasting, neglecting the interactions among different pollutants and their diverse spatial responses.<n>We propose MultiVariate AutoRegressive air pollutants forecasting model, which reduces the dependency on long-time-window inputs.<n>We construct a comprehensive dataset covering 6 major pollutants across 75 cities in North China from 2018 to 2023, including ERA5 reanalysis data and FuXi-2.0 forecast data.
• Score: 18.785110680719235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Air pollutants pose a significant threat to the environment and human health, thus forecasting accurate pollutant concentrations is essential for pollution warnings and policy-making. Existing studies predominantly focus on single-pollutant forecasting, neglecting the interactions among different pollutants and their diverse spatial responses. To address the practical needs of forecasting multivariate air pollutants, we propose MultiVariate AutoRegressive air pollutants forecasting model (MVAR), which reduces the dependency on long-time-window inputs and boosts the data utilization efficiency. We also design the Multivariate Autoregressive Training Paradigm, enabling MVAR to achieve 120-hour long-term sequential forecasting. Additionally, MVAR develops Meteorological Coupled Spatial Transformer block, enabling the flexible coupling of AI-based meteorological forecasts while learning the interactions among pollutants and their diverse spatial responses. As for the lack of standardized datasets in air pollutants forecasting, we construct a comprehensive dataset covering 6 major pollutants across 75 cities in North China from 2018 to 2023, including ERA5 reanalysis data and FuXi-2.0 forecast data. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods and validate the effectiveness of the proposed architecture.

Related papers

• FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning [22.270124698874934]
  An air quality forecasting model, named FuXi-Air, has been constructed based on multimodal data fusion to support high-precision air quality forecasting.<n>The model successfully completes 72-hour forecasts for six major air pollutants at an hourly resolution across multiple monitoring sites within 25-30 seconds.
  arXiv  Detail & Related papers  (2025-06-09T10:27:50Z)
• Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network [47.699409089023696]
  Air quality prediction plays a crucial role in public health and environmental protection.<n>Existing works underestimate the critical role atmospheric conditions in air quality prediction.<n> MDSTNet is an encoder framework explicitly that captures atmosphere-pollution dependencies for prediction.<n>ChinaAirNet is the first dataset combining air quality records with multi-pressure-level meteorological observations.
  arXiv  Detail & Related papers  (2025-04-14T09:18:11Z)
• Offline Meteorology-Pollution Coupling Global Air Pollution Forecasting Model with Bilinear Pooling [5.236306661644172]
  Traditional physics-based models forecast global air pollution by coupling meteorology and pollution processes.<n>Existing deep learning (DL) solutions employ online coupling strategies for global air pollution forecasting.<n>This study pioneers a DL-based offline coupling framework that utilizes bilinear pooling to achieve offline coupling between meteorological fields and pollutants.
  arXiv  Detail & Related papers  (2025-03-24T07:24:31Z)
• A HEART for the environment: Transformer-Based Spatiotemporal Modeling for Air Quality Prediction [0.0]
  llull-environment is a sophisticated and scalable forecasting system for air pollution.<n>It contains an encoder-decoder convolutional neural network to forecast mean pollution levels for four key pollutants.<n>This paper investigates the augmentation of this neural network with an attention mechanism to improve predictive accuracy.
  arXiv  Detail & Related papers  (2025-02-26T10:54:27Z)
• AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment [46.56288727659417]
  Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization.<n>We introduce AirCast, a novel multi-variable air pollution forecasting model.<n>AirCast employs a multi-task head architecture that simultaneously forecasts atmospheric conditions and pollutant concentrations.
  arXiv  Detail & Related papers  (2025-02-25T07:34:18Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Variable importance measure for spatial machine learning models with application to air pollution exposure prediction [2.633085745593072]
  The objective is to predict air pollution exposures for study subjects at locations without data in order to optimize our ability to learn about health effects of air pollution. We tackle these challenges in two datasets: sulfur (S) from regulatory United States national PM2.5 sub-species data and ultrafine particles (UFP) from a new Seattle-area traffic-related air pollution dataset. Our key contribution is a leave-one-out approach for variable importance that leads to interpretable and comparable measures for a broad class of models.
  arXiv  Detail & Related papers  (2024-06-04T05:51:36Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.