Search

Ozone is a very important trace gas in the atmosphere, it is like a "double-edged sword". Because the ozone in the stratosphere can effectively help the earth's organisms to avoid the sun's ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA's Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a "-" distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit. [ABSTRACT FROM AUTHOR]

To accurately evaluate the development level of ecological civilization in coastal cities, this paper proposes an evaluation algorithm of coastal city ecological civilization development based on Improved BP neural network, constructs the evaluation index system, and establishes the BP neural network-based evaluation model. By collecting the relevant data of 30 cities in China Line standardization, as the input learning matrix, entropy and coefficient of variation are used to evaluate the development level of ecological civilization. Through 5000 training times, the error accuracy is close to 10–7, and the cities with low ecological civilization level and less cultivated land resources per capita are found out. Based on the improved BP neural network method, according to the development level of coastal cities, the ecological culture of coastal cities is put forward as a whole. The countermeasures and suggestions of construction are given. • In this study, the ecological civilization development level of the coastal cities in Jiangsu was evaluated. • The evaluation results showed the overall development level of ecological civilization. • In the assessment, the comprehensive level of the secondary indexes is better than that of Donghai and Guanyun. • Jiangsu coastal cities have higher ecological civilization development levels, and Donghai and Guanyun is relatively low. [ABSTRACT FROM AUTHOR]