Open AccessInferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument
Author(s) -
M. J. Cooper,
Randall V. Martin,
Chris A. McLinden,
Jeffrey R. Brook
Publication year - 2020
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/aba3a5
Subject(s) - satellite , environmental science , nitrogen dioxide , troposphere , air quality index , remote sensing , population , atmospheric sciences , meteorology , physics , geology , demography , astronomy , sociology
Satellite-based estimates of ground-level nitrogen dioxide (NO 2 ) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO 2 concentrations are biased low with respect to ground-based measurements. In this work we demonstrate that these biases are due to both the coarse resolution of previous satellite NO 2 products and inaccuracies in vertical mixing assumptions used to convert satellite-observed tropospheric columns to surface concentrations. We develop an algorithm that now allows for different mixing assumptions to be used based on observed NO 2 conditions. We then apply this algorithm to observations from the TROPOMI satellite instrument, which has been providing NO 2 column observations at an unprecedented spatial resolution for over a year. This new product achieves estimates of ground-level NO 2 with greater accuracy and higher resolution compared to previous satellite-based estimates from OMI. These comparisons also show that TROPOMI-inferred surface NO 2 concentrations from our updated algorithm have higher correlation and lower bias than those found using TROPOMI and the prior algorithm. TROPOMI-inferred estimates of the population exposed to NO 2 conditions exceeding health standards are at least three times higher than for OMI-inferred estimates. These developments provide an exciting opportunity for air quality monitoring.