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Resources","sub_category":"Air Quality","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# PM2.5-GNN\n\nPM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting\n\n## Dataset\n\n- Download dataset **KnowAir** from [Google Drive](https://drive.google.com/open?id=1R6hS5VAgjJQ_wu8i5qoLjIxY0BG7RD1L) or [Baiduyun](https://pan.baidu.com/s/18D6Etl5Lm1E4vOLVrX0ZAw) with code `t82d`.\n\n## KnowAir-V2\n\n🚀 Dataset Update: Announcing KnowAir-V2! 🚀\n\nWe are excited to announce a major upgrade to the original KnowAir (PM2.5-GNN) dataset with the official release of KnowAir-V2! This is a brand-new, higher-quality benchmark dataset for air quality forecasting.\n\nKey improvements in KnowAir-V2 include:\n- Longer Temporal Span: Data covers from 2016 to 2023.\n- Richer Variables: Includes not only PM2.5 but also O3 and more related meteorological variables.\n- Higher Data Quality: The data has undergone rigorous preprocessing and imputation, reaching an operational-level standard.\n\nFor all new research and projects, we strongly recommend using KnowAir-V2. This dataset is designed to provide a powerful benchmarking platform for more advanced spatio-temporal prediction models that integrate physical-chemical knowledge, such as PCDCNet.\n\nHow to Access and Cite\nDataset Download (KnowAir-V2):\n\n- Wang, S., Cheng, Y., Meng, Q., Saukh, O., Zhang, J., Fan, J., Zhang, Y., Yuan, X., \u0026 Thiele, L. (2025). KnowAir-V2: A Benchmark Dataset for Air Quality Forecasting with PCDCNet [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15614907\n\n- Related Paper (PCDCNet):\nPlease refer to the paper: \"PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraints\" (arXiv:2505.19842). https://www.arxiv.org/abs/2505.19842\n\n## Requirements\n\n```\nPython 3.7.3\nPyTorch 1.7.0\nPyG: https://github.com/rusty1s/pytorch_geometric#pytorch-170\n```\n\n```bash\npip install -r requirements.txt\n```\n\n## Experiment Setup\n\nopen `config.yaml`, do the following setups.\n\n- set data path after your server name. Like mine.\n\n![](https://tva1.sinaimg.cn/large/0081Kckwly1gjy8kojsfmj30i202g746.jpg)\n\n```python\nfilepath:\n  GPU-Server:\n    knowair_fp: /data/wangshuo/haze/pm25gnn/KnowAir.npy\n    results_dir: /data/wangshuo/haze/pm25gnn/results\n\n```\n\n- Uncomment the model you want to run.\n\n```python\n#  model: MLP\n#  model: LSTM\n#  model: GRU\n#  model: GC_LSTM\n#  model: nodesFC_GRU\n   model: PM25_GNN\n#  model: PM25_GNN_nosub\n```\n\n- Choose the sub-datast number in [1,2,3].\n\n```python\n dataset_num: 3\n```\n\n- Set weather variables you wish to use. Following is the default setting in the paper. You can uncomment specific variables. Variables in dataset **KnowAir** is defined in `metero_var`.\n\n```python\n  metero_use: ['2m_temperature',\n               'boundary_layer_height',\n               'k_index',\n               'relative_humidity+950',\n               'surface_pressure',\n               'total_precipitation',\n               'u_component_of_wind+950',\n               'v_component_of_wind+950',]\n\n```\n\n## Run\n\n```bash\npython train.py\n```\n\n## Reference\n\nPaper: https://dl.acm.org/doi/10.1145/3397536.3422208\n\n```\n@inproceedings{10.1145/3397536.3422208,\nauthor = {Wang, Shuo and Li, Yanran and Zhang, Jiang and Meng, Qingye and Meng, Lingwei and Gao, Fei},\ntitle = {PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting},\nyear = {2020},\nisbn = {9781450380195},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3397536.3422208},\ndoi = {10.1145/3397536.3422208},\nabstract = {When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.},\nbooktitle = {Proceedings of the 28th International Conference on Advances in Geographic Information Systems},\npages = {163–166},\nnumpages = {4},\nkeywords = {air quality prediction, graph neural network, spatio-temporal prediction},\nlocation = {Seattle, WA, USA},\nseries = {SIGSPATIAL '20}\n}\n```\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.15614907","https://doi.org/10.1145/3397536.3422208"],"works":{"https://doi.org/10.1145/3397536.3422208":{"id":"https://openalex.org/W3108376771","doi":"https://doi.org/10.1145/3397536.3422208","title":"PM2.5-GNN","display_name":"PM2.5-GNN","publication_year":2020,"publication_date":"2020-11-03","ids":{"openalex":"https://openalex.org/W3108376771","doi":"https://doi.org/10.1145/3397536.3422208","mag":"3108376771"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397536.3422208","pdf_url":null,"source":null,"license":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2002.12898","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036523453","display_name":"Shuo Wang","orcid":"https://orcid.org/0000-0003-1219-8678"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Wang","raw_affiliation_string":"School of Systems Science, Beijing Normal University Beijing China","raw_affiliation_strings":["School of Systems Science, Beijing Normal University Beijing China"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043892655","display_name":"Yanran Li","orcid":"https://orcid.org/0000-0001-8709-3497"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"CN","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanran Li","raw_affiliation_string":"Department of Computing The Hong Kong Polytechnic University Hong Kong, China#TAB#","raw_affiliation_strings":["Department of Computing The Hong Kong Polytechnic University Hong Kong, China#TAB#"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065286115","display_name":"Jiang Zhang","orcid":"https://orcid.org/0000-0003-0435-655X"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Zhang","raw_affiliation_string":"School of Systems Science, Beijing Normal University Beijing China","raw_affiliation_strings":["School of Systems Science, Beijing Normal University Beijing China"]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012400709","display_name":"Qingye Meng","orcid":"https://orcid.org/0000-0002-4980-4785"},"institutions":[],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingye Meng","raw_affiliation_string":"ColorfulClouds Pacific Technology Co., Ltd. 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