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

Bibliographic Data

ID15468321
AuthorsJin Chen (0000-0002-9490-9981, Harbin Normal University), Li Sun (0000-0002-9084-1318, Harbin Normal University, corresponding author), Hongjie Jia (0000-0003-2907-2895, Harbin Normal University), Chunlei Li (0000-0003-4017-6820, Harbin Normal University), Xin Ai (0000-0001-6079-5237, Harbin Normal University), Shuying Zang (0000-0001-5275-1760, Harbin Normal University)
Year2022
Volume19
Issue23
Pages15862-15862
Publication date2022-11-29
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph192315862
PMID36497936
OpenAlexW4310182848
LanguageEN
References cited28

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

Air pollution · Atmospheric sciences · China · Climatology · Common spatial pattern · Geography · Meteorology · Ozone · Physical geography · Spatial analysis · Spatial distribution · Spatial ecology · Spatial variability · Urban agglomeration · Air Quality and Health Impacts · Atmospheric chemistry and aerosols · Climate Change and Health Impacts · Environmental Science · Ecology · Geology · Pollution

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