环境空气质量预测模型训练数据

该数据集由环境空气质量数据和ERA5大气再分析数据组成。经过严格的清洗和治理，能够适配不同预测时长的模型训练需求。将其直接应用到空气质量预测模型的训练中，既能让模型学习污染物浓度的本地演变特征，又能捕捉宏观气象条件对污染物迁移转化的驱动作用，为模型提供高质量、多维度的训练样本，显著提升模型对重污染过程、跨区域传输事件的预测准确性，最终支撑短期、中期乃至长期的环境空气质量精准预报。

This dataset comprises ambient air quality data and ERA5 atmospheric reanalysis data. Having undergone rigorous cleaning and curation, it meets the training requirements of models with varying prediction horizons. When directly applied to the training of air quality prediction models, it enables the models to learn the local evolutionary characteristics of pollutant concentrations while capturing the driving effects of macroscale meteorological conditions on the transport and transformation of pollutants. By providing high-quality, multi-dimensional training samples, it significantly improves the models' prediction accuracy for heavy pollution episodes and cross-regional transport events, ultimately supporting accurate short-term, medium-term and even long-term ambient air quality forecasting.

搜集汇总

背景与挑战

背景概述

该数据集整合了环境空气质量数据和ERA5大气再分析数据，经过严格清洗和治理，适用于不同预测时长的模型训练。它能帮助模型学习污染物浓度的本地演变特征和宏观气象条件对污染物迁移转化的影响，从而提升对重污染过程和跨区域传输事件的预测准确性，支持短期、中期和长期的环境空气质量精准预报。