Based on OMI data product, the spatio-temporal variabilities characteristics of ozone column concentration and its correlation with influential factors in Fujian province are analysed using spatial information technology and Pearson correlational analysis method for the period of 2011 to 2020. The results show that the lowest monthly average value was 260.10 DU in April and the highest 283.97 DU in October. The monthly change was wavy, the order of seasonal mean values was autumn, summer, winter and spring, with autumn being obviously higher than the other three.. The variation range of annual average values was 265.18-275.09 DU, the lowest in 2013 and the highest in 2014, and the total mean value of ozone in recent 10 years was 270.19 DU. In terms of spatial distribution, monthly averages were generally distributed as a relatively high value area from July to December and as a relatively low value area from January to June. The spatial distribution of the mean values in autumn was in a relatively high area, and latitude changes in autumn and summer were more obvious than those in spring and winter. The spatial distributions of average annual values in 2013 and 2016 were in the relatively low value areas, while 2011, 2012, 2014 and 2018 in relatively high value areas. In the recent ten years, the distribution of the total mean values in the north was higher than that in the south, and the relatively high value areas were mainly in Ningde City, Nanping City and the north of Fuzhou City, while the relatively low value areas were mainly in Zhangzhou City, Xiamen City, and the south of Longyan City and Quanzhou City. The overall trend of distribution of ozone was that the ozone values decreased with the decrease of latitude in the north-south orientation, and it rose from inland areas to coastal areas in the east-west orientation. In terms of correlation of ozone with the influencing factors, temperature and precipitation presented both positive correlation and negative correlation, with positive correlation mainly distributed in coastal cities while negative correlation mainly in inland cities, presenting the characteristics of strong positive correlation gradually changed into strong negative correlation from coastal to inland. For precipitation and ozone, most regions showed negative correlation. The NDVI also showed the coexistence of positive and negative correlations but the area of negative correlation was significantly larger than that of positive correlation, showing a significant negative correlation on the whole.
SHI Yiqiang
,
ZHAO Dinglong
,
WANG Cuiping
,
XIAO Zhongyong
,
LIN Xiaofeng
,
LIU Shanhong
. Investigating spatial-temporal variabilities of ozone over Fujian province in recent decade using spatial information technology[J]. Science & Technology Review, 2023
, 41(11)
: 61
-69
.
DOI: 10.3981/j.issn.1000-7857.2023.11.006
[1] 陈雪萍. 基于卫星遥感的中国西部地区臭氧总量分布特征及其成因分析[D]. 兰州: 西北师范大学, 2019.
[2] 马明亮. 对流层臭氧时空分析影响因素研究及近地面臭氧估算[D]. 上海: 华东师范大学, 2019.
[3] 曹晓云, 祁栋林, 肖建设, 等. 基于卫星观测的青海高原对流层臭氧时空分布特征研究[J]. 环境科学学报, 2021, 41(5): 1640-1648.
[4] Ainsworth E A, Carmo-silva E. Editorial overview: Harnessing genetic variation in metabolic traits to understand trait evolution and improve the sustainability of crop production[J]. Current Opinion in Plant Biology, 2019, 49: 1-3.
[5] Nuvolone D, Petri D, Voller F. The effects of ozone on human health[J]. Environmental Science and Pollution Research, 2018, 25(9): 8074-8088.
[6] Sousa S I V, Martins F G, Pereira M C, et al. Influence of atmospheric ozone, PM10 and meteorological factors on the concentration of airborne pollen and fungal spores[J]. Atmospheric Environment, 2008, 42(32): 7452-7464.
[7] David L M, Nair P R. Tropospheric column O3 and NO2 over the Indian region observed by ozone monitoring Instrument (OMI): Seasonal changes and long-term trends[J]. Atmospheric Environment, 2013, 65: 25-39.
[8] Hong H, Lee H, Kim J, et al. First comparison of OMI-DOAS total ozone using ground-based observations at a megacity site in East Asia: Causes of discrepancy and improvement in OMI-DOAS total ozone during summer[J]. Journal of Geophysical Research-Atmospheres, 2014, 119(16): 10058-10067.
[9] Ul-Haq Z, Tariq S, Rana A D, et al. Satellite remote sensing of total ozone column (TOC) over Pakistan and neighbouring regions[J]. International Journal of Remote Sensing, 2015, 36(4): 1038-1054.
[10] Jin X M, Holloway T. Spatial and temporal variability of ozone sensitivity over China observed from the ozone monitoring instrument[J]. Journal of Geophysical Research-Atmospheres, 2015, 120(14): 7229-7246.
[11] Chang C Y, Faust E, Hou X T, et al. Investigating ambient ozone formation regimes in neighboring cities of shale plays in the Northeast United States using photochemical modeling and satellite retrievals[J]. Atmospheric Environment, 2016, 142: 152-170.
[12] 李菁, 戴竹君, 李正金, 等 . 基于卫星观测的南京臭氧时空分布及变化特征[J]. 生态环境学报, 2019, 28(10): 2012-2019.
[13] 李紫微 . 京津唐地区近地面臭氧反演与时空分布特征分析[D]. 西安: 西安科技大学, 2021.
[14] 戚佩霓 . 多源卫星遥感分析中国区域污染气体特征[D]. 南京: 南京信息工程大学, 2021.
[15] 谢静晗, 李飒, 肖钟湧. 50年来中国臭氧总量的时空变化特征研究[J].中国环境科学, 2022, 42(7): 2977-2987.
[16] 徐芯蓓, 刘涛涛, 徐玲玲, 等 . 2017 年厦门金砖会晤期间人为减排和气象条件变化对臭氧污染特征的影响[J]. 环境科学学报, 2020, 40(12): 4380-4389.
[17] 王宏, 陈晓秋, 余永江, 等 . 福州近地层臭氧分布及其与气象要素的相关性[J]. 自然灾害学报, 2012, 21(4): 175-181.
[18] 王宏, 蒋东升, 谢祖欣, 等 . 福建省近地层臭氧时空分布与超标天气成因[J]. 中低纬山地气象, 2018, 42(1): 1-6.
[19] 王宏, 郑秋萍, 温珍治, 等. ENSO循环对福建省近地层臭氧浓度变化的影响[J]. 热带气象学报, 2021, 37(2): 145-153.
[20] 宋佳颖, 刘旻霞, 孙瑞弟, 等 . 基于 OMI 数据的东南沿海大气臭氧浓度时空分布特征研究[J]. 环境科学学报, 2020, 40(2): 438-449.
[21] 但扬彬, 于瑞莲, 卞雅慧, 等 . 基于 OMI 数据的新冠疫情影响下福建省臭氧敏感性变化[J]. 中国环境科学, 2021, 41(5): 2056-2063.
[22] 李阳 . 基于 OMI数据的中国臭氧时空分布及影响因素分析[D]. 兰州: 西北师范大学, 2020.
[23] 卢秀娟 . 基于 OMI卫星数据的甘肃省臭氧时空分布及影响因素分析[D]. 兰州: 西北师范大学, 2018.
[24] 陈雪萍, 咸龙, 巨天珍, 等 . 基于 OMI 的宁夏臭氧时空分布特征及影响因素研究[J]. 生态与农村环境学报, 2019, 35(2): 167-173.
[25] 孙雷 . 华北地区近地面臭氧长期变化特征及影响因素研究[D]. 济南: 山东大学, 2019.
[26] 杜君平, 朱玉霞, 刘锐, 等 . 基于 OMI 数据的中国臭氧总量时空分布特征[J]. 中国环境监测, 2014, 30(2): 191-196.
[27] 郭云飞, 包云轩, 刘端阳. 基于卫星和地面观测的中国典型城市群对流层内臭氧时空变化特征[J]. 环境科学研究, 2022, 35(3): 719-730.
[28] 苏静明, 洪炎, 唐超礼, 等. 基于OMI的中国区域2005-2020年间臭氧柱总量随经纬度变化特性研究[J]. 大气与环境光学学报, 2021, 16(6): 495-503.
[29] 庄马展. 厦门一次典型臭氧污染过程与成因分析[J]. 环境科学与技术, 2022, 45(9): 118-125.
