短周期高强度旅游活动对城市空气质量的影响

大气与环境光学学报 ›› 2023, Vol. 18 ›› Issue (1): 36-46.

短周期高强度旅游活动对城市空气质量的影响

杜娟 1,2, 欧阳文言 1, 刘春琼 3*, 吴波 1, 张娇 1, 史凯 3

1 吉首大学数学与统计学院, 湖南吉首 416000; 2 洪江高新技术产业开发区 (洪江市) 管理委员会, 湖南怀化 418000; 3 吉首大学生物资源与环境科学学院, 湖南吉首 416000

收稿日期:2020-10-09 修回日期:2020-12-12 出版日期:2023-01-28 发布日期:2023-02-08
通讯作者: E-mail: liuchunqiong@163.com E-mail:liuchunqiong@163.com
作者简介:杜娟 (1995- ), 女, 贵州铜仁人, 硕士研究生, 主要从事应用统计方面的研究。 E-mail: dumin0014@163.com
基金资助:
国家自然科学基金项目 (52160024), 湖南省自然科学基金项目 (2022JJ30475)

Influence of short-duration high-strength tourism activities on urban air quality

DU Juan 1, 2, OUYANG Wenyan 1, LIU Chunqiong 3*, WU Bo 1, ZHANG Jiao 1, SHI Kai 3

1 College of Mathematic and Statistics, Jishou University, Jishou 416000, China; 2 Management Commission of High-Tech Industrial Development Zone in Hongjiang, Huaihua 418000, China; 3 College of Biology and Environmental Science, Jishou University, Jishou 416000, China

Received:2020-10-09 Revised:2020-12-12 Published:2023-01-28 Online:2023-02-08
Contact: Chunqiong Liu E-mail:liuchunqiong@163.com

摘要/Abstract

摘要： 为定量研究短周期高强度旅游活动对城市空气质量的影响，提出一种综合集合经验模态分解和去趋势互相关分析的模型 (EEMD-DCCA)，研究了2015 年张家界市武陵源景区旅游人数的变化对大气NO2浓度的影响。首先，通过EEMD方法，获得武陵源景区旅游人数与城市空气中NO2浓度的高频分量；进一步，通过DCCA方法研究旅游人数高频分量和NO2浓度之间的相关性。结果表明，两组旅游人数高频分量与NO2浓度之间均呈正相关性，且具有长期持续性特征，这意味着短周期节假日旅游人数的剧增会持续影响未来一定时间尺度内大气NO2浓度的变化趋势。在武陵源景区的研究表明所提的分析模型可以为定量评估旅游活动对自然景区空气质量的影响提供新的研究途径。

关键词: 旅游, NO2, 集合经验模态分解法, 去趋势互相关分析法, 长期持续性

Abstract: To quantitatively study the impact of short-duration high-strength human tourism activities on urban air quality, a novel model (EEMD-DCCA) integrating ensemble empirical mode decomposition (EEMD) and detrended cross-correlation analysis (DCCA) was proposed and applied to study the impact of the number of tourists in Wulingyuan scenic spot of Zhangjiajie on local atmospheric NO2 concentration in 2015. The high-frequency components of the number of tourists in Wulingyuan scenic spot and the concentration of NO2 in Zhangjiajie City were obtained firstly through EEMD. And then, DCCA method was used to analyze the correlation between high-frequency components of the number of tourists and NO2 concentration. The results show that there is a significant positive correlation between the two groups of the high-frequency components of the tourist number and NO2 concentration in Zhangjiajie, and the positive correlation has a long-term sustainability feature, which means that the rapid increase of the number of tourists in short-duration holidays will continue to affect the future change trend of NO2 concentration in a certain time scale. The research in Wulingyuan scenic area shows that the model proposed can provide a new research approach for quantitative assessment of the impact of tourism activities on the air quality of natural scenic spots.

Key words: tourism;NO2, ensemble empirical mode decomposition, detrended cross-correlation analysis; long term correlation

中图分类号:

杜娟, 欧阳文言, 刘春琼, 吴波, 张娇, 史凯 . 短周期高强度旅游活动对城市空气质量的影响[J]. 大气与环境光学学报, 2023, 18(1): 36-46.

DU Juan , , OUYANG Wenyan , LIU Chunqiong , WU Bo , ZHANG Jiao , SHI Kai . Influence of short-duration high-strength tourism activities on urban air quality[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(1): 36-46.

参考文献 J

[1]	Qiao X, Xiao W Y, Tang Y, et al. Contributions of local tourism and regional air pollution to atmospheric aerosols in
	Jiuzhaigou, Sichuan, China [J]. China Environmental Science, 2014, 34(1): 14-21.
	乔雪, 肖维阳, 唐亚, 等. 旅游和区域大气污染对四川九寨沟气溶胶的贡献 [J]. 中国环境科学, 2014, 34(1): 14-21.
[2]	Zhou C C, Wang X Q, Sun X Y, et al. A test analysis of environmental changes of tourism Karst caves and study on
	influencing factors — A case of Jiutian cave in Yiyuan County, Shandong Province [J]. Tourism Tribune, 2009, 24(2): 81-86.
	周长春, 王晓青, 孙小银, 等. 旅游洞穴环境变化监测分析及其影响因素研究: 以山东沂源九天洞为例 [J]. 旅游学刊,
20	09, 24(2): 81-86.
[3]	Zheng K L, Huang Y, Yao X Y, et al. Correlation between PM2.5, NO2 and tourism activities, weather factors in Zhangjiajie
	City [J]. Journal of Atmospheric and Environmental Optics, 2020, 15(5): 347-356.
	郑凯莉, 黄毅, 姚小云, 等. 张家界市PM2.5、NO2与旅游活动及天气因素的相关性分析 [J]. 大气与环境光学学报, 2020,
15	(5): 347-356.
[4]	Shi P H. Constructing the national tourism innovation system and accelerating the strategy of China tourism 4.0 [J]. Tourism
	Tribune, 2015, 30(11): 13-14.
	石培华. 构建国家旅游创新体系, 加快推进中国旅游4.0 战略 [J]. 旅游学刊, 2015, 30(11): 13-14.
[5]	Shi K, Liu C Q, Wu S H. Long range correlation of air pollution between Chengdu City and its surrounding towns [J].
	Resources and Environment in the Yangtze Basin, 2014, 23(11): 1633-1640.
	史凯, 刘春琼, 吴生虎. 基于DCCA方法的成都市市区与周边城镇大气污染长程相关性分析 [J]. 长江流域资源与环境,
20	14, 23(11): 1633-1640.
[6]	Shi K, Liu C Q, Ai N S, et al. Using three methods to investigate time-scaling properties in air pollution indexes time series
[J]	Nonlinear Analysis: Real World Applications, 2008, 9(2): 693-707.
[7]	Lee C K, Juang L C, Wang C C, et al. Scaling characteristics in ozone concentration time series (OCTS) [J]. Chemosphere,
20	06, 62(6): 934-946.
[8]	Shi K, Liu C Q, Huang Y. Multifractal processes and self-organized criticality of PM2.5 during a typical haze period in
	Chengdu, China [J]. Aerosol and Air Quality Research, 2015, 2015(3): 926-934.
[9]	Shi K. Detrended cross-correlation analysis of temperature, rainfall, PM10 and ambient dioxins in Hong Kong [J]. Atmospheric
	Environment, 2014, 97: 130-135.
[10]	Wu S H, Shi K, Liu C Q, et al. Fractal feature and DFA analysis of PM10 evolution in a typical fog-haze episode in Chengdu
[J]	Journal of Safety and Environment, 2014, 14(5): 285-291.
	吴生虎, 史凯, 刘春琼, 等. 成都市一次重雾霾期间PM10自组织演化的分形特征及DFA 分析 [J]. 安全与环境学报,
20	14, 14(5): 285-291.
[11]	Wu Z H, Huang N. Ensemble empirical mode decomposition: A noise-assisted data analysis method [J]. Advances in Adaptive
	Data Analysis, 2009, 1(01): 1-41.
[12]	Tang J, Liu X Q, Fu M X. Multiscale analysis of precipitation in Shaanxi Province based on ensemble empirical mode
	decomposition [J]. Journal of Shaanxi University of Technology (Natural Science Edition), 2018, 34(1): 87-92.
	唐洁, 刘晓琴, 傅明星. 集合经验模态分解在陕西降水多尺度分析中的应用 [J]. 陕西理工大学学报(自然科学版), 2018,
34	(1): 87-92.
[13]	Kim T, Shin J Y, Kim S, et al. Identification of relationships between climate indices and long-term precipitation in South
	Korea using ensemble empirical mode decomposition [J]. Journal of Hydrology, 2018, 557: 726-739.
[14]	Wang J, Wang X, Lei X, et al. Teleconnection analysis of monthly streamflow using ensemble empirical mode
	decomposition [J]. Journal of Hydrology, 2020, 582: 124411.
[15]	Xu M, Shang P, Lin A. Cross-correlation analysis of stock markets using EMD and EEMD [J]. Physica A: Statistical
	Mechanics and Its Applications, 2016, 442: 82-90.
[16]	Zhang N N, Lin A J, Shang P J. Multidimensional k-nearest neighbor model based on EEMD for financial time series
	forecasting [J]. Physica A: Statistical Mechanics and Its Applications, 2017, 477: 161-173.
[17]	Gong X, Lin B. Modeling stock market volatility using new HAR-type models [J]. Physica A: Statistical Mechanics and Its
	Applications, 2019, 516: 194-211.
[18]	Podobnik B, Stanley H E. Detrended cross-correlation analysis: A new method for analyzing two nonstationary time series [J].
	Physical Review Letters, 2008, 100(8): 084102.
[19]	Vassoler R T, Zebende G F. DCCA cross-correlation coefficient apply in time series of air temperature and air relative
	humidity [J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(7): 2438-2443.
[20]	Yuan N M, Fu Z T. Different spatial cross-correlation patterns of temperature records over China: A DCCA study on different
	time scales [J]. Physica A: Statistical Mechanics and Its Applications, 2014, 400: 71-79.
[21]	Shi K, Di B F, Zhang K S, et al. Detrended cross-correlation analysis of urban traffic congestion and NO2 concentrations in
	Chengdu [J]. Transportation Research Part D: Transport and Environment, 2018, 61: 165-173.
[22]	Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary
	time series analysis [J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and
	Engineering Sciences, 1998, 454(1971): 903-995.
[23]	Ouyang Q, Lu W X, Xin X, et al. Monthly rainfall forecasting using EEMD-SVR based on phase-space reconstruction [J].
	Water Resources Management, 2016, 30(7): 2311-2325.
[24]	Ferreira P, Loures L, Nunes J, et al. Are renewable energy stocks a possibility to diversify portfolios considering an
	environmentally friendly approach: The view of DCCA correlation coefficient [J]. Physica A: Statistical Mechanics and Its
	Applications, 2018, 512: 675-681.
[25]	Xue L, Chen F, Guo S, et al. Time varying correlation structure of Chinese stock market of crude oil related companies
	greatly influenced by external factors [J]. Physica A: Statistical Mechanics and Its Applications, 2019, 530: 121086.
[26]	Zhang C, Ni Z W, Ni L P. Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors [J].
	Physica A: Statistical Mechanics and Its Applications, 2015, 438: 114-123.
[27]	Zebende G F, Brito A A, Silva Filho A M, et al. ρDCCA applied between air temperature and relative humidity: An hour/hour
	view [J]. Physica A: Statistical Mechanics and Its Applications, 2018, 494: 17-26.
[28]	Zhuang D C, Deng X Z, Zhan J Y. Assessment of the environmental quality for Wulingyuan Scenic Spot in Hunan Province
[J]	Geographical Research, 2004, 23(2): 192-200.
	庄大春, 邓祥征, 战金艳. 武陵源风景区环境质量评估 [J]. 地理研究, 2004, 23(2): 192-200.

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