Comparison of FNR and GNR Based on TROPOMI Satellite Data for Ozone Sensitivity Analysis in Chinese Urban Agglomerations

Currently, ozone (O3) has become one of the primary air pollutants in China, underscoring the importance of analyzing ozone formation sensitivity (OFS) for effective pollution control. Ozone sensitivity indices serve as effective tools for OFS identification. Among them, the ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx)—such as the formaldehyde-to-nitrogen dioxide ratio (FNR, defined as HCHO/NO2, where HCHO represents VOCs and NO2 represents NOx)—is one of the most widely used satellite-based indicators. Recent studies have highlighted glyoxal (CHOCHO) as another critical ozone precursor, prompting the proposal of the glyoxal-to-nitrogen dioxide ratio (GNR, CHOCHO/NO2) as an alternative metric. This study systematically compares the performance of FNR and GNR across four major urban agglomerations in China: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Chengdu–Chongqing (CY) region, by integrating satellite remote sensing with ground-based observations. Results reveal that both indices exhibit consistent spatial trends in OFS distribution, transitioning from VOC-limited regimes in urban centers to NOx-limited regimes in surrounding suburban areas. However, differences emerge in threshold values and classification outcomes. During summer, FNR identifies urban areas as transitional regimes (or VOC-limited in regions such as YRD and PRD), while suburban areas are classified as NOx-limited. In contrast, GNR, which shows heightened sensitive to anthropogenic VOCs (AVOCs), exhibits a more restricted spatial extent in the transition regimes. By autumn, most urban areas shift toward VOC-limited regimes, while suburban regions remain NOx-limited. Thresholds for both VOCs and NOx increase during this period, with GNR demonstrating stronger sensitivity to NOx. These findings underscore that the choice between FNR and GNR directly influences OFS determination, as their differing responses to biogenic and anthropogenic emissions lead to different conclusions. Future research should focus on integrating the complementary strengths of both indices to develop a more robust OFS identification method, thereby providing a theoretical basis for formulating effective ozone control strategies.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2025-09-27
DOI
https://doi.org/10.3390/rs17193321
Citations
1
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
1.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparison of FNR and GNR Based on TROPOMI Satellite Data for Ozone Sensitivity Analysis in Chinese Urban Agglomerations

Yichen Li, Ying Zhang, Jing Fan, Chao Yu et al.
1 citations
Remote Sensing
Atmospheric chemistry and aerosols
1.30
article

Comparison of FNR and GNR Based on TROPOMI Satellite Data for Ozone Sensitivity Analysis in Chinese Urban Agglomerations

Yichen Li, Ying Zhang, Jing Fan, Chao Yu, Meng Fan, Jinhua Tao, Liangfu Chen
article en
1 citations

Abstract

Currently, ozone (O3) has become one of the primary air pollutants in China, underscoring the importance of analyzing ozone formation sensitivity (OFS) for effective pollution control. Ozone sensitivity indices serve as effective tools for OFS identification. Among them, the ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx)—such as the formaldehyde-to-nitrogen dioxide ratio (FNR, defined as HCHO/NO2, where HCHO represents VOCs and NO2 represents NOx)—is one of the most widely used satellite-based indicators. Recent studies have highlighted glyoxal (CHOCHO) as another critical ozone precursor, prompting the proposal of the glyoxal-to-nitrogen dioxide ratio (GNR, CHOCHO/NO2) as an alternative metric. This study systematically compares the performance of FNR and GNR across four major urban agglomerations in China: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Chengdu–Chongqing (CY) region, by integrating satellite remote sensing with ground-based observations. Results reveal that both indices exhibit consistent spatial trends in OFS distribution, transitioning from VOC-limited regimes in urban centers to NOx-limited regimes in surrounding suburban areas. However, differences emerge in threshold values and classification outcomes. During summer, FNR identifies urban areas as transitional regimes (or VOC-limited in regions such as YRD and PRD), while suburban areas are classified as NOx-limited. In contrast, GNR, which shows heightened sensitive to anthropogenic VOCs (AVOCs), exhibits a more restricted spatial extent in the transition regimes. By autumn, most urban areas shift toward VOC-limited regimes, while suburban regions remain NOx-limited. Thresholds for both VOCs and NOx increase during this period, with GNR demonstrating stronger sensitivity to NOx. These findings underscore that the choice between FNR and GNR directly influences OFS determination, as their differing responses to biogenic and anthropogenic emissions lead to different conclusions. Future research should focus on integrating the complementary strengths of both indices to develop a more robust OFS identification method, thereby providing a theoretical basis for formulating effective ozone control strategies.

Remote SensingVol. 17(19)
Chinese Academy of Sciences (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 18%
Atmospheric chemistry and aerosols
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.