2022
DOI: 10.3390/su14127289
|View full text |Cite
|
Sign up to set email alerts
|

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

Abstract: Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a hig… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 50 publications
(82 reference statements)
0
2
0
Order By: Relevance
“…To accurately describe the spatiotemporal distribution characteristics and variation rules of GOCs, we collected multiple geospatial datasets (Table 3) according to predictive abilities and data accessibility for the establishment of a high spatial resolution concentration retrieval model [32]. As described in Table 3, the variables numbered 1-10 were obtained from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).…”
Section: Ground-level Ozone Datasetsmentioning
confidence: 99%
See 1 more Smart Citation
“…To accurately describe the spatiotemporal distribution characteristics and variation rules of GOCs, we collected multiple geospatial datasets (Table 3) according to predictive abilities and data accessibility for the establishment of a high spatial resolution concentration retrieval model [32]. As described in Table 3, the variables numbered 1-10 were obtained from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).…”
Section: Ground-level Ozone Datasetsmentioning
confidence: 99%
“…The best-fit GLM is the one that explains the greatest amount of information using the fewest possible variables. A stepwise regression method based on the Akaike Information Criterion (AIC) was utilized to identify the variables included in the final GLM [32]. The smaller the AIC value is, the better the model fit.…”
Section: Glm Analysis Modulementioning
confidence: 99%