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Ma K, Lin Y, Fang F, Tan H, Li J, Ge L, Wang F, Yao Y. Spatiotemporal dynamics of near-surface ozone concentration and potential source areas in northern China during 2015-2020. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:89123-89139. [PMID: 37452250 DOI: 10.1007/s11356-023-28713-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 07/05/2023] [Indexed: 07/18/2023]
Abstract
Near-surface ozone (O3) pollution has become one of the main factors hampering urban air quality in northern China. However, on a spatiotemporal scale, dynamic transport paths and potential source areas of O3 in northern China are ambiguous. In addition, we suspect that the contribution of transportation activities to urban O3 concentrations developed in northern China may be underestimated. In this study, the HYSPLIT, PSCF, CWT and GTWR model were used to study the transmission paths, potential source areas and driving factors of urban O3 concentration on a spatiotemporal scale. The average annual concentration of surface O3 (the 90th percentile of MDA8) was 172 ± 29 μg/m3 in northern China from 2015 to 2020. In terms of inter-annual variation, the urban O3 concentration increased from 2015 to 2018, and decreased after 2018. On the spatial scale, the areas with high O3 concentration were mainly clustered in industrial cities (Tangshan, Baoding, Shijiazhuang, Xingtai and Handan). During the study period, the area with high O3 concentration in northern China shifted from northwest to southeast. From 2015 to 2020, the influence of long-distance air mass trajectories from Xinjiang and Siberi on airflow transport in Beijing city dominates (78.60%) The average percentage of short-distance transport trajectories from Shandong Peninsula region is about 21.40%. The core potential source areas of O3 pollution shifted from northwest to southeast, but the contribution to O3 pollution in Beijing gradually weakened during the same period. Temperature and relative humidity were the main meteorological driving factors affecting O3 concentration in the study area, while population density, the proportion of secondary industry in GDP, industrial smoke (dust) emissions, and passenger traffic were the main non-meteorological factors. During the period study, the influence of industrial and traffic emissions had a more significant impact on O3 concentration in northern China, which will require that more attention be paid to emission mitigation in the regional industrial and passenger transportation sector, as well as the joint prevention and control of O3 pollution in northern China in the future.
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Affiliation(s)
- Kang Ma
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, Wuhu, 241002, China
| | - Yuesheng Lin
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, Wuhu, 241002, China
| | - Fengman Fang
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, Wuhu, 241002, China
| | - Huarong Tan
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
| | - Jingwen Li
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
| | - Lei Ge
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
| | - Fei Wang
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
| | - Youru Yao
- School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China.
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, Wuhu, 241002, China.
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Wu S, Zhang Y, Hao G, Chen X, Wu X, Ren H, Zhang Y, Fan Y, Du C, Bi X, Bai L, Tan J. Interaction of air pollution and meteorological factors on IVF outcomes: A multicenter study in China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 259:115015. [PMID: 37201423 DOI: 10.1016/j.ecoenv.2023.115015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Previous studies revealed associations between air-pollutant exposure and in vitro fertilization (IVF) outcomes. However, modification effects of air pollution on IVF outcomes by meteorological conditions remain elusive. METHODS This multicenter retrospective cohort study included 15,217 women from five northern Chinese cities during 2015-2020. Daily average concentrations of air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) and meteorological factors (temperature, relative humidity, wind speed, and sunshine duration) during different exposure windows were calculated as individual approximate exposure. Generalized estimating equations models and stratified analyses were conducted to assess the associations of air pollution and meteorological conditions with IVF outcomes and estimate potential interactions. RESULTS Positive associations of wind speed and sunshine duration with pregnancy outcomes were detected. In addition, we observed that embryo transfer in spring and summer had a higher likelihood to achieve a live birth compared with winter. Exposure to PM2.5, SO2, and O3 was adversely correlated with pregnancy outcomes in fresh IVF cycles, and the associations were modified by air temperature, relative humidity, and wind speed. The inverse associations of PM2.5 and SO2 exposure with biochemical pregnancy were stronger at lower temperatures and humidity. Negative associations of PM2.5 with clinical pregnancy were only significant at lower temperatures and wind speeds. Moreover, the effects of O3 on live birth were enhanced by higher wind speed. CONCLUSIONS Our results suggested that the associations between air-pollutant exposure and IVF outcomes were modified by meteorological conditions, especially temperature and wind speed. Women undergoing IVF treatment should be advised to reduce outdoor time when the air quality was poor, particularly at lower temperatures.
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Affiliation(s)
- Shanshan Wu
- Centre of Reproductive Medicine, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning 110022, PR China; Key Laboratory of Reproductive Dysfunction Disease and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning 110022, PR China
| | - Yunshan Zhang
- Tianjin Central Hospital of Obstetrics and Gynecology, Tianjin 300100, PR China
| | - Guimin Hao
- Department of Reproductive Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, PR China
| | - Xiujuan Chen
- Reproductive Medicine Centre, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010050, PR China
| | - Xueqing Wu
- Reproductive Medicine Center, Children's Hospital of Shanxi and Women Health Center of Shanxi, Taiyuan, Shanxi 030013, PR China
| | - Haiqin Ren
- Jinghua Hospital, Shenyang, Liaoning 110022, PR China
| | - Yinfeng Zhang
- Tianjin Central Hospital of Obstetrics and Gynecology, Tianjin 300100, PR China
| | - Yanli Fan
- Department of Reproductive Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, PR China
| | - Chen Du
- Reproductive Medicine Centre, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia 010050, PR China
| | - Xingyu Bi
- Reproductive Medicine Center, Children's Hospital of Shanxi and Women Health Center of Shanxi, Taiyuan, Shanxi 030013, PR China
| | - Lina Bai
- Jinghua Hospital, Shenyang, Liaoning 110022, PR China
| | - Jichun Tan
- Centre of Reproductive Medicine, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning 110022, PR China; Key Laboratory of Reproductive Dysfunction Disease and Fertility Remodeling of Liaoning Province, Shenyang, Liaoning 110022, PR China.
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Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China. SUSTAINABILITY 2022. [DOI: 10.3390/su14159041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM2.5, PM10, and O3 and meteorological conditions were conducted based on observations from radar wind profiler, microwave radiometer, automatic weather station, and air quality monitoring sites in Beijing during the spring of 2019. The results showed that the boundary layer height and temperature inversion were negatively (positively) correlated with PM (O3) concentrations, modulating the degree of air pollution. Five identified synoptic patterns were derived using geopotential height data of the ERA5 reanalysis, among which Type 1, characterised by south-westerly prevailing winds with high pressure to the south, was considered to be associated with severe PM and O3 contamination. This indicates that air pollutants originating from southern regions exert a major influence on Beijing through the transportation effect. In addition, high temperature, relative humidity, and low wind velocity exacerbate pollution. Overall, this study provides significant information for understanding the vital roles played by meteorological elements at both the regional and local scales in regulating air contamination during spring in Beijing.
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Characteristics and Weekend Effect of Air Pollution in Eastern Jilin Province. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050681] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Using the hourly monitoring data of pollutants from 16 automatic atmospheric monitoring stations in eastern Jilin Province from 2015 to 2020, this paper analyzed the temporal and spatial distribution laws of CO, SO2, NO2, PM10, PM2.5, and O3 in eastern Jilin Province. At the same time, the regional transport pathways of pollutants were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model; the potential source contribution function (PSCF) analyzed the potential source area of PM2.5. Finally, the “weekend effect” of CO, NO2, PM2.5, and O3 was analyzed. The results showed that the six pollutants showed a downward trend year by year. The concentrations of O3, PM10, and PM2.5 were higher in northwest Jilin, and the concentrations of SO2 and CO were higher in southwest Jilin. Except for CO, the seasonal variation of pollutants was pronounced. Except for O3, most pollutants had the highest concentration in winter. Hourly variation analysis described that SO2 and O3 had only one peak in a day, and the other four pollutants showed “double peak” hourly variation characteristics. The study area was mainly affected by the airflow pathway from northwest and southwest. The weight potential source contribution function (WPSCF) high-value area of PM2.5 was northwest and southwest. O3 showed a “negative weekend effect”, and NO2 and CO showed a “positive weekend effect”.
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Atmospheric NO2 Distribution Characteristics and Influencing Factors in Yangtze River Economic Belt: Analysis of the NO2 Product of TROPOMI/Sentinel-5P. ATMOSPHERE 2021. [DOI: 10.3390/atmos12091142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nitrogen dioxide (NO2) has a great influence on atmospheric chemistry. Scientifically identifying the temporal-spatial characteristics of NO2 distribution and their driving factors will be of realistic significance to atmospheric governance in the Yangtze River Economic Belt (YREB). Based on the NO2 data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO2 vertical column density (NO2 VCD) in the YREB from 2019 to 2020. The results showed that the NO2 VCD in the YREB was high in winter and autumn and low in spring and summer (temporal distribution), and high in the northeast and low in the southwest (spatial distribution), with significant spatial agglomeration. High-value agglomeration zones were collectively and stably distributed in the east region, while low-value zones were relatively dispersed. The explanatory power of each potential factor for the NO2 VCD showed regional and seasonal variations. Surface pressure was found to be a core influencing factor. Synergistic effects of factors presented bivariate enhancement or nonlinear enhancement, and interaction between any two factors strengthened the explanatory power of a single factor for the NO2 VCD.
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Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China. SUSTAINABILITY 2021. [DOI: 10.3390/su13147724] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The intensification of global urbanization has exacerbated the negative impact of atmospheric environmental factors in urban areas, thus threatening the sustainability of future urban development. In order to ensure the sustainability of urban atmospheric environments, exploring the changing laws of urban air quality, identifying highly polluted areas in cities, and studying the relationship between air quality and land use have become issues of great concern. Based on AQI data from 340 air quality monitoring stations and urban land use data, this paper uses inverse distance weight (IDW), Getis-Ord Gi*, and a negative binomial regression model to discuss the spatiotemporal variation of air quality in the main urban area of Lanzhou and its relationship with urban land use. The results show that urban air quality has characteristics of temporal and spatial differentiation and spatially has characteristics of agglomeration of cold and hot spots. There is a close relationship between urban land use and air quality. Industrial activities, traffic pollution, and urban construction activities are the most important factors affecting urban air quality. Green spaces can reduce urban pollution. The impact of land use on air quality has a seasonal effect.
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Yang G, Liu Y, Li X. Spatiotemporal distribution of ground-level ozone in China at a city level. Sci Rep 2020; 10:7229. [PMID: 32350319 PMCID: PMC7190652 DOI: 10.1038/s41598-020-64111-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 03/20/2020] [Indexed: 01/24/2023] Open
Abstract
In recent years, ozone (O3) pollution in China has shown a worsening trend. Due to the vast territory of China, O3 pollution is a widespread and complex problem. It is vital to understand the current spatiotemporal distribution of O3 pollution in China. In this study, we collected hourly data on O3 concentrations in 338 cities from January 1, 2016, to February 28, 2019, to analyze O3 pollution in China from a spatiotemporal perspective. The spatial analysis showed that the O3 concentrations exceeded the limit in seven geographical regions of China to some extent, with more serious pollution in North, East, and Central China. The O3 concentrations in the eastern areas were usually higher than those in the western areas. The temporal analysis showed seasonal variations in O3 concentration, with the highest O3 concentration in the summer and the lowest in the winter. The weekend effect, which occurs in other countries (such as the USA), was found only in some cities in China. We also found that the highest O3 concentration usually occurred in the afternoon and the lowest was in the early morning. The comprehensive analysis in this paper could improve our understanding of the severity of O3 pollution in China.
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Affiliation(s)
- Guangfei Yang
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China.
| | - Yuhong Liu
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China
| | - Xianneng Li
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China
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Yao Y, He C, Li S, Ma W, Li S, Yu Q, Mi N, Yu J, Wang W, Yin L, Zhang Y. Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 660:384-394. [PMID: 30640107 DOI: 10.1016/j.scitotenv.2019.01.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 01/02/2019] [Accepted: 01/04/2019] [Indexed: 06/09/2023]
Abstract
Characteristics of the spatial and temporal distribution of air pollutants may reveal the cause of air pollution, especially for large regions where the anthropogenic pollutant emission is concentrated. This study addresses this issue by focusing on Shandong province, which has the highest air pollutant emissions in China. First, the spatial and temporal variation characteristics of the observed concentrations of conventional pollutants are analyzed in detail. The most prominent indicator of the problem (PM2.5), was selected as the key analytical object. On the spatial scale, the Multivariate Moran model was used to identify factors affecting the spatial distribution of PM2.5. On the time scale, wavelet analysis was used to explore the fluctuation characteristics of PM2.5 at different time periods. Results show that there are significant regional differences in pollutant concentration within Shandong province. The concentration of particulate matter and gaseous pollutants in western and northern Shandong is significantly higher than eastern Shandong. The average concentrations of PM2.5, PM10, SO2 and NO2 were highest in winter and lowest in summer, whereas concentration of O3 peaked in summer. For PM2.5, the annual mean concentration has a significant spatial correlation with SO2 emission, GDP per capita, population density and energy consumption per unit of GDP; in addition, the correlation between different regions and various indices is different. On the time scale, the fluctuation energy of PM2.5 concentrated in Dezhou and Liaocheng is the strongest on December 18 and 19, 2015. The inversion temperature has a strong influence on the daily variation of PM2.5 concentration. The formation and evolution of atmospheric pollution, therefore, can be explored by combining the temporal and spatial distribution of pollutants, providing a comprehensive analytical method for atmospheric pollution in different regions.
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Affiliation(s)
- Youru Yao
- School of Environment, Nanjing Normal University, Nanjing 210023, China; School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China
| | - Cheng He
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200082, China.
| | - Shiyin Li
- School of Environment, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Weichun Ma
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200082, China
| | - Shu Li
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200082, China
| | - Qi Yu
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200082, China
| | - Na Mi
- School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Jia Yu
- School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Wei Wang
- School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Li Yin
- School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Yong Zhang
- Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA
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Risk Reduction Behaviors Regarding PM 2.5 Exposure among Outdoor Exercisers in the Nanjing Metropolitan Area, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15081728. [PMID: 30103552 PMCID: PMC6121644 DOI: 10.3390/ijerph15081728] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 07/20/2018] [Accepted: 08/07/2018] [Indexed: 01/12/2023]
Abstract
Aims: This study aimed to describe risk reduction behaviors regarding ambient particulate matter with a diameter of 2.5 μm or less (PM2.5) among outdoor exercisers and to explore potential factors influencing those behaviors in the urban area of Nanjing, China. Method: A cross-sectional convenience sample survey was conducted among 302 outdoor exercisers in May 2015. Descriptive analysis was used to describe demographics, outdoor physical activity patterns, knowledge of PM2.5 and risk reduction behaviors. Multivariate logistic regression analysis was then used to explore factors that influence the adoption of risk reduction behaviors. Results: The most common behavior to reduce PM2.5 exposure was minimizing the times for opening windows on hazy days (75.5%), and the least common one was using air purifiers (19.3%). Two thirds of respondents indicated that they wore face masks when going outside in the haze (59.5%), but only 13.6% of them would wear professional antismog face masks. Participants adopting risk reduction behaviors regarding PM2.5 exposure tended to be females, 50–60 year-olds, those with higher levels of knowledge about PM2.5 and those who had children. Conclusions: These findings indicate the importance of improving knowledge about PM2.5 among outdoor exercisers. Educational interventions should also be necessary to guide the public to take appropriate precautionary measures when undertaking outdoor exercise in high PM2.5 pollution areas.
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Wang Y, Du H, Xu Y, Lu D, Wang X, Guo Z. Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 631-632:921-933. [PMID: 29728003 DOI: 10.1016/j.scitotenv.2018.03.050] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 02/20/2018] [Accepted: 03/05/2018] [Indexed: 05/23/2023]
Abstract
Urbanization has led to an obvious urban heat island (UHI) effect in the Yangtze River Delta (YRD), China. The ozone (O3) pollution in the YRD is getting worse. The UHI effect is a key factor that affects the O3 level. Understanding the influences of the UHI effect on O3 concentrations is necessary for improving air quality. In this study, the temporal and spatial relationship between UHI and O3 in the YRD during 2015 was investigated. The influence factors of UHI effect and O3 are both natural and artificial. Multi-source remote sensing data, which include land cover, land surface temperature (LST), Normalization Difference Vegetation Index (NDVI), and digital elevation model (DEM) data, were used to extract surface landscape elements. The results showed that: (1) the average hourly O3 concentration was 61.83 μg/m3 (30.92 ppb), the highest value was 105.32 μg/m3 (52.66 ppb) at 15:00 and the O3 peak was 82.50 μg/m3 (41.25 ppb) in September. The O3 concentrations and temperature have a similar variation trend both in diurnal and monthly. The O3 concentrations in coastal stations are higher than those inland. (2) The average daytime UHI intensity was 1.24 °C, and the daytime O3 concentration was 80.66 μg/m3 (40.33 ppb). There is a positive relationship between UHI and O3 in the YRD. The relationship in the central developed cities is higher than that in the northern and southern cities. (3) The related factors influencing UHI and O3 include surface landscape, topography and population. The LST and NDVI are most important among these factors. (4) Due to various geographical backgrounds, the UHI intensities and O3 concentrations show obvious spatial differences. This study provides a reference with which to better understand the relationship among UHI, O3 and related factors. Furthermore, the issues of atmospheric and energy transmission in this region deserve further study.
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Affiliation(s)
- Yuanyuan Wang
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Hongyu Du
- Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China; Department of Environmental Science, East China Normal University, No. 500, Dongchuan Road, Minhang District, Shanghai, China
| | - Yanqing Xu
- Department of Geography and Planning, The University of Toledo, Toledo 43606, United States
| | - Debin Lu
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Xiyuan Wang
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Zhongyang Guo
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; School of Geographic Sciences, East China Normal University, Shanghai 200241, China.
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