Effects of Seasonal Variation on Spatial and Temporal Distributions of Ozone in Northeast China

Jin Chen, Li Sun (), Hongjie Jia, Chunlei Li, Xin Ai and Shuying Zang
Additional contact information
Jin Chen: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Li Sun: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Hongjie Jia: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Chunlei Li: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Xin Ai: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Shuying Zang: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China

IJERPH, 2022, vol. 19, issue 23, 1-18

Abstract: The levels of tropospheric ozone (O 3 ) are closely related to regional meteorological conditions, precursor emissions, and geographical environments, which have a significant negative impact on human health. The concentrations of O 3 were relatively low, while the spatial distribution was strongly heterogeneous in Northeast China; however, little is known about how the influencing factors affect the distribution of O 3 in Northeast China. Here, the O 3 concentration, meteorological observation data, precursors (NO 2 ), and vegetation coverage data from 41 monitoring cities in Northeast China from 2017 to 2020 were collected and analyzed. The spatial–temporal distributions and evolution characteristics of O 3 concentrations were investigated using statistical analysis, kriging interpolation, spatial autocorrelation analysis, cold–hot spot analysis, and geographic detectors, and the effects of meteorological factors, NO 2 , and green land area on O 3 concentrations were evaluated seasonally and spatially. The results showed that O 3 pollution in Northeast China was generally at a relatively low level and showed a decreasing trend during 2017–2020, with the highest concentrations in the spring and the lowest concentrations in the autumn and winter. May–July had relatively high O 3 concentrations, and the over-standard rates were also the highest (>10%). The spatial distribution showed that the O 3 concentration was relatively high in the south and low in the northeast across the study area. A globally significant positive correlation was derived from the spatial autocorrelation analysis. The cold–hot spot analysis showed that O 3 concentrations exhibited spatial agglomerations of hot spots in the south and cold spots in the north. In Northeast China, the south had hot spots with high O 3 pollution, the north had cold spots with excellent O 3 levels, and the central region did not exhibit strong spatial agglomerations. A weak significant negative correlation between O 3 and NO 2 indicated that the emissions of NOx derived from human activities have weak effects on the O 3 concentrations, and wind speed and sunshine duration had little effect on spatial differentiation of the O 3 concentrations. Spatial variability in O 3 concentrations in the spring and autumn was mainly driven by temperature, but in the summer, the influence of temperature was weakened by the relative humidity and precipitation; no factor had strong explanatory power in the winter. The temperature was the only controlling factor in hot spots with high O 3 concentrations. In cold spots with low O 3 concentrations, the relative humidity and green land area jointly affected the spatial distributions of O 3 .

Keywords: O 3 concentration; influencing factors; aggregation characteristics; geographical detector; Northeast China (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/23/15862/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/23/15862/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:23:p:15862-:d:987155

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().