4.7 Article

Spatiotemporal Variation, Driving Mechanism and Predictive Study of Total Column Ozone: A Case Study in the Yangtze River Delta Urban Agglomerations

Journal

REMOTE SENSING
Volume 14, Issue 18, Pages -

Publisher

MDPI
DOI: 10.3390/rs14184576

Keywords

total column ozone; spatiotemporal characteristics; driving forces; forecast

Funding

  1. National Natural Science Foundation of China [41975036]
  2. Fundamental Research Funds for the Non-Key Project of South China Institute of Environmental Science, MEE [PM-zx703-202104-069]

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This paper analyzed the spatial and temporal variation characteristics of total column ozone (TCO) over the Yangtze River Delta Urban Agglomeration, exploring its relationship with meteorological and socio-economic factors. The results showed that TCO exhibited a quasi-latitudinal distribution and was influenced by precipitation and the absorbed aerosol index. The study also constructed mathematical models to fit and predict the future trend of TCO.
Total column ozone (TCO) describes the amount of ozone in the entire atmosphere. Many scholars have used the lower resolution data to study TCO in different regions, but new phenomena can be discovered using high-precision and high-resolution TCO data. This paper used the long time, high accuracy, and high-resolution MSR2 dataset (2000-2019) to analyze the spatial and temporal variation characteristics of TCO over the Yangtze River Delta Urban Agglomeration to explore the relationship between the TCO and meteorological and socio-economic factors. The correlations between the TCO and climatic factors and the driving forces of meteorological and socio-economic factors on the spatial and temporal variability of TCO were also analyzed, and different mathematical models were constructed to fit the TCO for the past 20 years and predict the future trend of the TCO. The results show the following. (1) The TCO over the study area exhibited a quasi-latitudinal distribution, following a slight downtrend during 2000-2019 (0.01 +/- 0.18 DU per year) and achieved its maximum in 2010 and minimum in 2019; throughout the year, an inverted V-shaped cycle characterizes the monthly variability of TCO; TCO was significantly higher in spring than in summer and autumn than winter. (2) Precipitation and the absorbed aerosol index (AAI) had critical effects on the spatial distribution of TCO, but meteorological factors were weakly correlated with the annual variation of TCO subject to the game interactions between different external driving factors. The monthly changes in the TCO were not in synergy with that of other meteorological factors, but with a significant hysteresis effect by 3-5 months. Socio-economic factors had a significant influence on TCO over the study area. (3) The Fourier function model can well describe the history and future trend of the annual TCO over the study area. The TCO over the study area shows a fluctuating upward trend (0.27 +/- 1.35 DU per year) over the next 11 years. This study enriches the theoretical and technical system of ozone research, and its results can provide the necessary theoretical basis for ozone simulation and forecasting.

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