Spatial–Temporal Variation and the Influencing Factors of NO2 Column Concentration in the Plateau Mountains of Southwest China

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO2 column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO2 column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10−5 mol/m2 and lowest in 2022 at 2.85 × 10−5 mol/m2. Spatially, NO2 column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO2 column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO2 column concentration.

Authors

Institutions

Publication Details

Journal
Atmosphere
Published
2024-10-22
DOI
https://doi.org/10.3390/atmos15111263
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatial–Temporal Variation and the Influencing Factors of NO2 Column Concentration in the Plateau Mountains of Southwest China

Denghong Huang, Zhongfa Zhou, Shuanglong Du, Xiandan Du et al.
Atmosphere
Atmospheric chemistry and aerosols
article

Spatial–Temporal Variation and the Influencing Factors of NO2 Column Concentration in the Plateau Mountains of Southwest China

Denghong Huang, Zhongfa Zhou, Shuanglong Du, Xiandan Du, Fei Dong
article en

Abstract

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO2 column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO2 column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10−5 mol/m2 and lowest in 2022 at 2.85 × 10−5 mol/m2. Spatially, NO2 column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO2 column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO2 column concentration.

AtmosphereVol. 15(11)
Guizhou Normal University (CN), Gansu Desert Control Research Institute (CN)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 78%
Atmospheric chemistry and aerosols
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.