{"title":"MERRA2_CNN_HAQAST_PM25: Hourly Bias-Corrected PM2.5 Datasets for Global Air Quality Assessment","authors":"Pawan Gupta, Alqamah Sayeed","doi":"10.1002/gdj3.70070","DOIUrl":null,"url":null,"abstract":"<p>This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the exact horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method. It is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST). The dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3-dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in μg/m<sup>3</sup>. QFLAG denotes the quality of data at each grid point, where four indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 2","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70070","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://rmets.onlinelibrary.wiley.com/doi/10.1002/gdj3.70070","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the exact horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method. It is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST). The dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3-dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in μg/m3. QFLAG denotes the quality of data at each grid point, where four indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis.
Geoscience Data JournalGEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍：
Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered.
An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices.
Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
