邢台市影响PM<sub>2.5</sub>和O<sub>3</sub>浓度多时间尺度演变的重要因素

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1540 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To reveal the evolution characteristics and influencing factors of atmospheric PM_2.5-O₃ complex pollution in Xingtai City, this study, based on the meteorological elements and pollutant concentration data provided by the online monitoring platform, deployed the KZ filtering method to decompose the original concentration sequences of PM_2.5 and O₃ in Xingtai City, and combined the multiple stepwise regression method to quantitatively identify the contribution of source emissions and meteorological factors to pollutant concentration in long-term component. Meanwhile, Random Forest method was used to qualitatively explore the effects of specific source emissions and meteorological factors on the original concentration series of PM_2.5 and O₃ in Xingtai City. The results showed that the long-term component of PM_2.5 concentration in Xingtai City showed a significant downward trend, however, short-term component was major contributor to PM_2.5 concentration. The long-term component of O₃ concentration showed an increasing trend, and the seasonal component was main contributor to O₃ concentration. The contribution ratio of source emissions to meteorological factors in the long-term component concentrations of PM_2.5 was about 5:1, and that for O₃ was close to 3.5:1. The source emission processes were the main cause of long-term atmospheric PM_2.5-O₃ complex pollution in Xingtai City. Relative humidity (RH) presented a significant influence on the original concentration series of PM_2.5 in Xingtai City, followed by industrial source emission and vehicle exhaust emission. The three factors effectively affecting the original O₃ concentration sequences were the short-wave radiation intensity (SR), temperature (T) and vehicle exhaust emission.

Key words： Xingtai City PM_2.5 O₃ KZ filtering random forest influencing factors

Received: 24 February 2024

PACS:

X513

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LAN Tong
	HAN Li-hui
	TIAN Jian
	QI Chao-nan
	XIAO Qian

Cite this article:

LAN Tong,HAN Li-hui,TIAN Jian等. Significant factors influencing the multi-temporal-scale evolutions of PM_2.5 and O₃ concentrations in Xingtai City[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(9): 4786-4795.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I9/4786

[1] Zhao C, Wang Q, Ban J, et al. Estimating the daily PM_2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01°×0.01°spatial resolution [J]. Environment International, 2020, 134:105297.
[2] 郝静,孙成,郭兴宇,等.京津冀内陆平原区PM_2.5浓度时空变化定量模拟[J]. 环境科学, 2018,39(4):1455-1465. Hao J, Sun C, Guo X Y, et al. Simulation of the spatio-temporally resolved PM_2.5 aerosol mass concentration over the inland plain of the Beijing-Tianjin-Hebei region [J]. Environmental Science, 2018,39(4): 1455-1465.
[3] 贾飞虎,贾艳乐,李飞,等.邢台市秋冬季大气污染综合治理对策分析及建议[J]. 环境保护与循环经济, 2019,39(2):53-57. Jia F H, Jia Y L, Li F, et al. Analysis and suggestions on comprehensive control of air pollution in autumn and winter of Xingtai City [J]. Environmental Protection and Circular Economy, 2019,39(2):53-57.
[4] 侯刚波.从邢台PM_2.5“退后十”探究大气污染问题[J]. 邢台学院学报, 2023,38(1):21-26. Hou G B. Exploring the problem of air pollution from the perspective of Xingtai PM_2.5"Jumping out of the last Ten [J]. Journal of Xingtai University, 2023,38(1):21-26.
[5] 姚青,唐颖潇,蔡子颖,等.京津冀区域典型城市PM_2.5污染特征及其成因研究[J]. 环境科学学报, 2022,42(7):43-52. Yao Q, Tang Y X, Cai Z Y, et al. Evaluation of PM_2.5 pollution characteristics and formation mechanisms of typical cities in Beijing-Tianjin-Hebei region [J]. Acta Scientiae Circumstantiae, 2022,42(7):43-52.
[6] 邢台市生态环境局.2022年邢台市生态环境状况公报[R]. 邢台:邢台市生态环境局, 2023 Xingtai Ecological Environment Bureau. 2022 Xingtai ecological environment status bulletin [R]. Xingtai: Xingtai Environment Bureau, 2023.
[7] 马东玲,任永强,陈星彤,等.2019~2021年疫情前后唐山市大气污染物变化特征及成因分析[J]. 自然资源遥感, 2024,36(1):275-280. Ma D L, Ren Y Q, Chen X T, et al. Analysis of the variations and causes of air pollutants in Tangshan City before and after the COVlD-19pandemic from 2019 to 2021[J]. Remote Sensing for Natural Resources, 2024,36(1):275-280.
[8] 代武俊,周颖,王晓琦,等.典型输送通道城市冬季PM_2.5污染与传输变化特征[J]. 环境科学, 2024,45(1):23-35. Dai W J, Zhou Y, Wang X Q, et al. Variation characteristics of PM_2.5 pollution and transport in typical transport channel cities in winter [J]. Environmental Science, 2024,45(1):23-35.
[9] Yang J, Chen X, Li M C, et al. The division of PM_2.5-O₃ composite airborne pollution across China based on spatiotemporal clustering. [J]. Journal of Cleaner Production, 2023,401,136706.
[10] 梁晓宇,单春艳,孟瑶,等.唐山一次冬季重污染过程污染特征及成因分析[J]. 中国环境科学, 2019,39(5):1804-1812. Liang X Y, Shan C Y, Meng Y, et al. Characteristics and formation mechanism of a heavy winter air pollution [J]. China Environmental Science, 2019,39(5):1804-1812.
[11] Liu X, Yi G H, Zhou X B, et al. Spatio-temporal variations of PM_2.5 and O₃ in China during 2013~2021: Impact factor analysis. [J]. Environmental Pollution, 2023,334,122189.
[12] Duan W J, Wang, X Q, Cheng S Y, et al. Regional collaboration to simultaneously mitigate PM_2.5 and O₃ pollution in Beijing-Tianjin-Hebei and the surrounding area: Multi-model synthesis from multiple data sources [J]. Science of the Total Environment, 2022,820,153309.
[13] 王少博,王涵,张敬巧,等.邢台市秋季PM_2.5及水溶性离子污染特征[J]. 中国环境科学, 2020,40(5):1877-1884. Wang S B, Wang H, Zhang J Q, et al. Characterization analysis of PM_2.5 and water-soluble ions during autumn in Xingtai City [J]. China Environmental Science, 2020,40(5):1877-1884.
[14] 王丛梅,杨永胜,李永占,等.2013年1月河北省中南部严重污染的气象条件及成因分析[J]. 环境科学研究, 2013,26(7):695-702. Wang C M, Yang Y S, Li Y Z, et al. Analysis on the meteorological condition and formation mechanism of serious pollution in south Hebei Province in January 2013[J]. Research of Environmental Sciences, 2013,26(7):695-702.
[15] 姬梦怡.大气污染时空特征及传输规律模拟研究——以京津冀城市群为例[D]. 天津:天津大学, 2020. Ji M Y. Simulation study on spatial and temporal characteristics and transport of air pollution: Take the Beijing-Tianjin-Hebei city cluster as an example [D]. Tianjin: Tianjin University, 2020.
[16] 郝巨飞,齐佳慧,李永占,等.邢台市颗粒污染物变化特征和后向轨迹分析[J]. 中国环境监测, 2018,34(5):46-53. Hao J F, Qi J H, Li Y Z, et al. Analysis of variation characteristics of atmospheric particulate matter pollution and the influence of Backward Trajectory in Xingtai [J]. Environmental Monitoring in China, 2018,34(5):46-53.
[17] Ma Y J, Huang Y H, Wu J H, et al. A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing. [J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022,44(2):4783-4807.
[18] Tian Y F, Li J Y, Yang C L, et al. Effects of the annular eclipse on the surface O₃ in Yunnan province, China [J]. Frontiers in Environmental Science, 2022,10:968507.
[19] Zhang X, Ma Q, Chu W H, et al. Identify the key emission sources for mitigating ozone pollution: A case study of urban area in the Yangtze River Delta region, China [J]. Science of the Total Environment, 2023,892,164703.
[20] 卢鋆镆,曾穗平,曾坚,等.基于随机森林的高分辨率PM_2.5浓度时空变化模拟——以中原城市群核心区为例[J]. 中国环境科学, 2023,43(7):3299-3311. Lu J M, Zeng S P, Zeng J, et al. High resolution simulation of temporal and spatial variation of PM_2.5 concentration based on random forest——A case study of Central Plains Urban Agglomeration Core Area [J]. China Environmental Science, 2023,43(7):3299-3311.
[21] Do, Woo-gon, Jung, Woo-Sik. Analysis of the long-term trend of PM₁₀ Using KZ Filter in Busan, Korea [J]. Journal of Environmental Science International, 2017,26(2):221-230.
[22] Sa Elisa, Tchepel O, Carvalho A, et al. Meteorological driven changes on air quality over Portugal: a KZ filter application [J]. Atmospheric Pollution Research, 2015,6(6):979-989.
[23] Botlaguduru V S V, Kommalapati R R, Huque Z. Long-term meteorologically independent trend analysis of ozone air quality at an urban site in the greater Houston area [J]. Journal of the Air & Waste Management Association, 2018,68(10):1051-1064.
[24] 余益军,孟晓艳,王振,等.京津冀地区城市臭氧污染趋势及原因探讨[J]. 环境科学, 2020,41(1):106-114. Yu Y J, Meng X Y, Wang Z, et al. Driving factors of the significant increase in surface Ozone in the BeijingTianjin-Hebei Region, China, during 2013~2018[J]. Environmental Science, 2020,41(1):106-114.
[25] 陈红,张运江,占青,等.基于KZ滤波法分析2015~2021年长三角大气颗粒物趋势变化特征[J]. 环境科学学报, 2023,43(4):102-110.Chen H, Zhang Y J, Zhan Q, et al. Characterization of atmospheric particulate matter trends in the Yangtze River Delta from 2015 to 2021 based on KZ filtering approach analysis [J]. Acta Scientiae Circumstantiae, 2023,43(4):102-110.
[26] Yin C Q, Deng X J, Zou Y, et al. Trend analysis of surface ozone at suburban Guangzhou, China. [J]. Science of the Total Environment, 2019,695:133880.
[27] 李灿然,布多,谢民生,等.青藏高原城市PM_2.5和O₃污染特征及影响因素[J]. 中国环境科学, 2024,44(6):3060-3069. Li C R, Bu D, Xie M S, et al. Pollution characteristics and influencing factors of PM_2.5 and O₃ pollution in the cities of Qinghai-Tibet Plateau [J]. China Environmental Science, 2024,44(6):3060-3069.
[28] Vaishali, Gaurav Verma, Rupesh M. Das. Influence of Temperature and Relative Humidity on PM_2.5 Concentration over Delhi. [J]. MAPAN, 2023,38(3):759-769.
[29] 夏晓圣,陈菁菁,王佳佳,等.基于随机森林模型的中国PM_2.5浓度影响因素分析[J]. 环境科学, 2020,41(5):2057-2065. Xia X S, Chen J J, Wang J J, et al. PM_2.5 concentration influencing factors in China based on the Random Forest Model [J]. Environmental Science, 2020,41(5):2057-2065.
[30] Guo S S, Tao, X L, Liang L W, et al. Exploring natural and anthropogenic drivers of PM_2.5 concentrations based on random forest model: Beijing-Tianjin-Hebei Urban Agglomeration, China [J]. Atmosphere, 2023,14(2),381.
[31] Tian Y, DeSouza P, Mora S, et al. Evaluating the meteorological effects on the urban form-air quality relationship using mobile monitoring [J]. Environmental Science and Technology, 2022,56(11): 7328-7336.
[32] McDonald J D, Chow J C, Peccia J, et al. Influence of collection region and site type on the composition of paved road dust [J]. Air Quality, Atmosphere & Health, 2013,6(3):615-628.
[33] 付文星,黄琳,丁嘉豪,等.基于机器学习方法研究气象及排放变化对长三角地区主要城市大气污染物的影响[J]. 环境科学, 2023, 44(11):5879-5888. Fu W X, Huang L, Ding J H, et al. Elucidating the impacts of meteorology and emission changes on concentrations of major air pollutants in major cities in the Yangtze River Delta Region using a machine learning De-weather method [J]. Environmental Science, 2023,44(11):5879-5888.
[34] 熊江荷,孔少飞,郑煌,等.排放和气象对疫情前后武汉不同类型点位大气污染物的影响[J]. 环境科学, 2023,44(2):670-679. Xiong J H, Kong S F, Zheng H, et al. Impacts of emission and meteorological conditions on air pollutants at various sites around the COVlD-19 Lockdown in Wuhan [J]. Environmental Science, 2023, 44(2):670-679.
[35] HJ 818-2018环境空气气态污染物(SO₂、NO₂、O₃、CO)连续自动监测系统运行和质控技术规范[S]. HJ 818-2018 Technical specifications for operation and quality control of ambient air quality continuous automated monitoring system for SO₂, NO₂, O₃ and CO [S].
[36] HJ 817-2018环境空气颗粒物(PM₁₀和PM_2.5)连续自动监测系统运行和质控技术规范[S]. HJ 817-2018 Specifications and test procedures for ambient air quality continuous automated monitoring system for PM₁₀ and PM_2.5 [S].
[37] Wise E K, Comrie A C. Extending the Kolmogorov-Zurbenko filter: Application to ozone, particulate matter, and meteorological trend [J]. Journal of the Air & Waste Management Association, 2005,55:1208-1216.
[38] Kumari S, Jayaraman G, Ghosh C. Analysis of long-term ozone trend over Delhi and its meteorological adjustment [J] International Journal of Environmental Science and Technology, 2013,10(6):1325-1336.
[39] Rao S T, Zurbenko I G. Detecting and tracking changes in ozone air quality [J]. Air Waste, 1994,44(9):1089-1092.
[40] Russell A R, Valin L C, Bucsela E J, et al. Space-based constraints on spatial and temporal patterns of NO_x emissions in California, 2005~ 2008. Environmental Science & Technology [J]. 2010,44(9):3608-3615.
[41] Chen Z Y, Chen D L, Kwan M P, et al. The control of anthropogenic emissions contributed to 80% of the decrease in PM_2.5 concentrations in Beijing from 2013 to 2017. [J]. Atmospheric Chemistry and Physics, 2019,19(21):13519-13533.
[42] 秦人洁,张洁琼,王雅倩,等.基于KZ滤波法的河北省PM_2.5和O₃浓度不同时间尺度分析研究[J]. 环境科学学报, 2019,39(3):821-831. Qin R J, Zhang J Q, Wang Y Q, et al. Study on different time scales of PM_2.5 and O₃ concentrations in Hebei province based on KZ filter [J]. Journal of Environmental Science, 2019,39(3):821-831.
[43] 邬双双,陈梓超,薛鑫,等.基于KZ滤波分析气象对珠三角臭氧趋势的影响[J]. 中国环境科学, 2024,44(4):1905-1911. Wu S S, Chen Z C, Xue X, et al. Analysis of meteorological effects on the long-term trend of Ozone in the Pearl River Delta using the KZ Filtering method. [J/OL]. China Environmental Science, 2024,44(4): 1905-1911.
[44] Breiman Leo. Random Forests. [J]. Machine Learning, 2001,45(1): 5-32.
[45] 张志豪,陈楠,祝波,等.基于随机森林模型的武汉市城区大气PM_2.5来源解析[J]. 环境科学, 2022,43(3):1151-1158. Zhang Z H, Chen N, Zhu B, et al. Source analysis of ambient PM_2.5 in Wuhan City based on Random Forest Model [J]. Environmental Science, 2022,43(3):1151-1158.
[46] Vu T V, Shi Z B, Cheng J, et al. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique. [J]. Atmospheric Chemistry and Physics, 2019,19:11303-11314.
[47] 辛泊达,吕连宏,王培,等.基于随机森林模型的臭氧浓度时空变化特征及关键影响因子识别:以滁州市为例[J/OL]. 环境科学:1-16(网络首发) https://doi.org/10.13227/j.hjkx.202310113. Xin B D, Lv L H, Wang P, et al. Spatio-temporal variation characteristics of Ozone and identification of key influencing factors based on Random Forest Model: A case study of Chuzhou City [J/OL]. Environmental Science:1-16.
[48] Tang Y M, Deng R R, Liang Y H, et al. Estimating high-spatial-resolution daily PM_2.5 mass concentration from satellite top-of-atmosphere reflectance based on an improved random forest model. [J]. Atmospheric Environment, 2023,302,119724.
[49] 国家统计局.《中国城市统计年鉴—2015》[M]. 北京:中国统计出版社, 2016. National Bureau of Statistics. China city statistical yearbook -2015[M]. Beijing: China Statistics Press, 2016.
[50] 国家统计局.《中国城市统计年鉴—2019》[M]. 北京:中国统计出版社, 2020. National Bureau of Statistics. China city statistical yearbook – 2019[M]. Beijing: China Statistics Press, 2020.
[51] 国家统计局.《中国城市统计年鉴—2017》[M]. 北京:中国统计出版社, 2018. National Bureau of Statistics. China city statistical yearbook -2017[M]. Beijing: China Statistics Press, 2018.
[52] 彭猛,崔璐璐,王磊,等.唐山市大气PM_2.5季节污染特征及来源分析[J]. 生态环境学报, 2020,29(9):1855-1861. Peng M, Cui L L, Wang L, et al. Seasonal characteristics and sources of PM_2.5 in Tangshan [J]. Ecology and Environmental Sciences, 2020,29(9):1855-1861.
[53] Deng C X, Qin C Y, Li Z W, et al. Spatiotemporal variations of PM_2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region [J]. Chemosphere, 2022,301,134640.
[54] 柯碧钦,何超,杨璐,等.华北地区地表臭氧时空分布特征及驱动因子[J]. 中国环境科学, 2022,42(4):1562-1574. Ke B Q, He C, Yang L, et al. The spatiotemporal variation of surface ozone and the main driving factors in North China [J]. China Environmental Science, 2022,42(4):1562-1574.
[55] Xiao Q Y, Geng G N, Xue T, et al. Tracking PM_2.5 and O₃ pollution and the related health burden in China 2013~2020[J]. Environmental Science & Technology, 2022,56(11):6922-6932.
[56] 张南,熊黑钢,葛秀秀,等.北京市冬季雾霾天人体呼吸高度PM_2.5变化特征对气象因素的响应[J]. 环境科学, 2016,37(7):2419-2427. Zhang N, Xiong H G, Ge X X, et al. Response of human respiratory height PM_2.5 variation Characteristics to meteorological factors during winter haze Days in Beijing [J]. Environmental Science, 2016, 37(7):2419-2427.
[57] Li C L, Li F, Cheng Q, et al. Divergent summertime surface O₃ pollution formation mechanisms in two typical Chinese cities in the Beijing-Tianjin-Hebei region and Fenwei Plain [J]. Science of the Total Environment, 2023,870,161868.
[58] 国家统计局.《中国城市统计年鉴—2021》. [M]. 北京:中国统计出版社, 2022. National Bureau of Statistics. China city statistical yearbook -2021[M]. Beijing: China Statistics Press, 2022.
[59] 李会杰,闫帅磊,胡少倩.邢台市工业燃烧源大气污染物排放清单及特征[J]. 绿色科技, 2023,25(12):99-104. Li H J, Yan H L, Hu S Q, et al. Atmospheric pollutant emission inventory and its characteristics from industrial stationary combustion source in Xingtai [J]. Journal of Green Science and Technology, 2023,25(12):99-104.
[60] 王琰玮.京津冀及周边地区PM_2.5与O₃污染潜在源区和传输特征研究[D]. 天津:天津大学, 2021. Wang Y W. The Study on the potential source area and transmission characteristics of PM_2.5 and O₃ pollution in the Beijing-Tianjin-Hebei and surrounding areas [D]. Tianjin: Tianjin University, 2021.
[61] 荆琦,盛立芳,张玮航,等.2018~2021年京津冀及周边地区“2+26”城市PM_2.5与O₃污染特征及气象影响[J]. 环境科学研究, 2023, 36(5):875-886. Jing Q, Sheng L F, Zhang W H, et al. Characteristics of PM_2.5 and O₃ Pollution and related meteorological impacts in ‘2+26’ cities of Beijing-Tianjin-Hebei and its surrounding areas from 2018 to 2021[J]. Research of Environmental Sciences, 2023,36(5):875-886.
[62] Jacob D J, Winner D A. Effect of climate change on air quality [J]. Atmospheric Environment, 2009,43(1):51-63.
[63] 李军,王京丽,屈坤.相对湿度和PM_2.5浓度对乌鲁木齐市冬季能见度的影响[J]. 中国环境科学, 2020,40(8):3322-3331. Li J, Wang j l, Qu K. Impacts of relative humidity and PM_2.5 concentration on atmospheric visibility during winter in Urumqi Urban Area [J]. China Environmental Science, 2020,40(8):3322-3331.
[64] 汪兵,唐钰寒,刘玉芝,等.长三角地区O₃与PM_2.5复合污染机制研究[J]. 环境保护科学, 2021,47(4):38-46. Wang B, Tang Y H, Liu L Z, et al. Mechanism of complex pollution of O₃ and PM_2.5 in Yangtze River Delta region [J]. Environmental Protection Science, 2021,47(4):38-46.
[65] Narumi D, Kondo A, Shimoda Y. The effect of the increase in urban temperature on the concentration of photochemical oxidants [J]. Atmospheric Environment, 2009,43(14):2348-2359.
[66] Kong L W, Song M D, Li X, et al. Analysis of China’s PM_2.5 and ozone coordinated control strategy based on the observation data from 2015 to 2020[J]. Journal of Environmental Sciences, 2024,138(4): 385-394.
[67] 陈多宏,沈劲,陈瑶瑶,等.2020年珠三角O₃污染特征及主要成因[J]. 中国环境科学, 2022,42(11):5000-5007. Chen D H, Shen J, Chen Y Y, et al. Characteristics and main causes of ozone pollution in the Pearl River Delta in 2020[J]. China Environmental Science, 2022,42(11):5000-5007.