Work

MERRA2_CNN_HAQAST_PM25: Hourly Bias‐Corrected PM2 .5 Datasets for Global Air Quality Assessment

Year: 2026

Type: article

Abstract: 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/m 3 . 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.

Source: Geoscience Data Journal

DOI: 10.1002/gdj3.70070

Authors: Pawan Gupta, Alqamah Sayeed

Institutions: Goddard Space Flight Center, Marshall Space Flight Center, University of Alabama in Huntsville

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,290