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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
A Land Use Regression Application into Simulating
Spatial Distribution Characteristics of Particulate
Matter (PM2.5) Concentration in City
of Xi’an, China
1
College of Geomatics, Xi’an University of Science and Technology, Xi’an, China
2
School of Public Health, Xi’an JiaoTong University, Xi’an, China
3
Shaanxi Key Laboratory of Land Consolidation, Xi’an, China
4
School of Earth Science and Resources, Chang’an University, Xi’an, China
Submission date: 2019-12-25
Final revision date: 2020-02-08
Acceptance date: 2020-02-24
Online publication date: 2020-05-22
Publication date: 2020-08-05
Corresponding author
Pol. J. Environ. Stud. 2020;29(6):4065-4076
KEYWORDS
TOPICS
ABSTRACT
Decreasing of PM2.5 concentration in the heating season was not significant in Xi’an. This article
determined a land use regression (LUR) model and researched the distribution characteristics of PM2.5
in heating and non-heating seasons in Xi'an. The results showed that: (1) The R2 of LUR was larger
than 0.9, and the simulation results were better than previous studies. (2) The PM2.5 concentration
in the heating season was larger than in the non-heating season. In Xi’an, the distribution of PM2.5
concentration was low in the southeast and high in the northwest in the non-heating season and was low
in the southeast and high in the main urban region in the heating season. (3) The PM2.5 concentration
was affected by temperature, average air pressure, altitude, humidity and precipitation in non-heating
season and was influenced by precipitation, altitude, average air pressure, vegetation, and density of
roads in heating season. (4) This paper showed some improvements in selection of potential variables for
LUR model, and the conclusion can provide a scientific basis for PM2.5 pollution control and a reliable
method for simulating PM2.5 concentration in other cities.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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