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 consists of ambient air quality data and ERA5 atmospheric reanalysis data. Having undergone rigorous data 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 evolution characteristics of pollutant concentrations and capture the driving effects of macro-scale meteorological conditions on the migration and transformation of pollutants. By providing high-quality, multi-dimensional training samples for the models, it significantly improves the models' prediction accuracy for heavy pollution episodes and cross-regional transport incidents, 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

该数据集是环境空气质量预测模型训练数据，由环境空气质量数据和ERA5大气再分析数据构成，经过清洗治理后适配多时长预测需求。它通过结合本地污染物演变特征和宏观气象驱动作用，为模型提供高质量训练样本，旨在提升重污染过程和跨区域传输事件的预测准确性，支撑精准空气质量预报。