HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method

TL;DR

HighAir is a hierarchical graph neural network approach that models complex interactions among pollution sources, weather, and land use to improve air quality forecasting accuracy across cities and stations.

Contribution

It introduces a hierarchical GNN architecture with inter- and intra-level interaction strategies and dynamic edge weights based on wind direction for enhanced air quality prediction.

Findings

HighAir outperforms state-of-the-art methods on Yangtze River Delta dataset.

The hierarchical approach effectively captures city and station-level patterns.

Dynamic edge weighting improves modeling of environmental influences.

Abstract

Accurately forecasting air quality is critical to protecting general public from lung and heart diseases. This is a challenging task due to the complicated interactions among distinct pollution sources and various other influencing factors. Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region. In this paper, we propose HighAir, i.e., a hierarchical graph neural network-based air quality forecasting method, which adopts an encoder-decoder architecture and considers complex air quality influencing factors, e.g., weather and land usage. Specifically, we construct a city-level graph and station-level graphs from a hierarchical perspective, which can consider city-level and station-level patterns, respectively. We design two strategies, i.e., upper delivery and lower updating, to implement the inter-level interactions, and introduce message passing mechanism to implement the intra-level interactions. We dynamically adjust edge weights based on wind direction to model the correlations between dynamic factors and air quality. We compare HighAir with the state-of-the-art air quality forecasting methods on the dataset of Yangtze River Delta city group, which covers 10 major cities within 61,500 km2. The experimental results show that HighAir significantly outperforms other methods.