干旱区研究

Arid Zone Research >

2022 , Vol. 39 >Issue 4: 1056 - 1065

DOI: https://doi.org/10.13866/j.azr.2022.04.07

Weather and Applied Climate

Optimal time period for PM2.5 control based on decision tree model: A case study of Guanzhong, China

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  • 1. School of Environment & Nature Resources, Renmin University of China, Beijing 100872, China
    2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
    3. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China
    4. Shaanxi Provincial Investigation and Ecological Assessment Center, Xi’an 710054, Shaanxi, China

Received date: 2022-01-18

  Revised date: 2022-04-23

  Online published: 2022-09-26

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Abstract

A simple spatiotemporal identification method for PM2.5 pollution control based on a machine learning model is proposed. A spatiotemporal clustering algorithm was used to cluster PM2.5 in the winter prevention period, and different PM2.5 polluted areas were identified. Furthermore, a decision tree model was constructed using the meteorological data of different areas to identify the most unfavorable influences on PM2.5 concentration in different areas. The changes in PM2.5 under the most unfavorable meteorological conditions were analyzed, and the optimal time period for PM2.5 pollution control in the different study areas was determined. Using the Guanzhong area as an example, the correlation analysis of the spatial clustering of mean daily PM2.5 in winter showed that the Guanzhong area is mainly divided into a low-altitude plain area (the Guanzhong Plain) and a relatively high mountain range. The classification tree model was then constructed, using the meteorological data of the plain and mountain areas. The analysis showed that the mountain elevations have a higher PM2.5 concentrations under Ⅰ-10 (1.57 h ≤ sunshine hours < 7.88 h; maximum wind speed < 3.72 m·s-1) and Ⅰ-11 (sunshine hours < 1.57 h; maximum wind speed < 3.72 m·s-1) meteorological conditions; low-altitude areas have a higher PM2.5 concentrations under Ⅱ-10 (small-scale evaporation ≥ 0.96 mm; average relative humidity ≥ 45.38%; sunshine hours < 8.55 h; average wind speed ≥ 2.43 m·s-1) and Ⅱ-11 (small evaporation < 0.96 mm) meteorological conditions. Lastly, the regression analysis results showed that in the mountain elevations area in the Ⅰ-10, Ⅰ-11 category and the plain altitude area under the most unfavorable meteorological conditions (Ⅱ-10 and Ⅱ-11 category), the PM2.5 concentration will continue to increase for 4.76 days on average, before reaching a maximum concentration.

Key words： decision tree model; PM2.5; meteorological classification; heavy pollution

Cite this article

JIA Ce,CHEN Zhen,HAN Mei . Optimal time period for PM2.5 control based on decision tree model: A case study of Guanzhong, China[J]. Arid Zone Research, 2022 , 39(4) : 1056 -1065 . DOI: 10.13866/j.azr.2022.04.07

References

[1] Guo Y M, Jia Y P, P X C, et al. The association between fine particulate air pollution and hospital emergency room visits for cardiovascular diseases in Beijing, China[J]. Science of the Total Environment, 2009, 407(17): 4826-4830.
[2] Guo P, Feng W R, Zheng M R, et al. Short-term associations of ambient air pollution and cause-specific emergency department visits in Guangzhou, China[J]. Science of the Total Environment, 2018, 613-614: 306-313.
[3] 张增凯, 路雨婷, 赵鹏宇, 等. 2019年京津冀重污染天气应急响应经济损失评估[J]. 中国环境科学, 2021, 41(7): 3399-3408.
[3] [Zhang Zengkai, Lu Yuting, Zhao Pengyu, et al. The assessment of economic cost induced by emergency responses for heavy pollution weather within Jing-Jin-Ji area in 2019[J]. China Environmental Science, 2021, 41(7): 3399-3408.]
[4] Nie T, Nie L, Zhou Z, et al. Exploring the heavy air pollution in Beijing in the fourth quarter of 2015: Assessment of environmental benefits for red alerts[J]. Frontiers of Earth Science, 2017, 12(2):361-372.
[5] Zhao H P, Wang F P, Niu C C, et al. Red warning for air pollution in China: Exploring residents’ perceptions of the first two red warnings in Beijing[J]. Environmental Research, 2018, 161: 540-545.
[6] Tribby C P, Miller H J, Song Y, et al. Do air quality alerts reduce traffic? An analysis of traffic data from the Salt Lake City metropolitan area, Utah, USA[J]. Transport Policy, 2013, 30: 173-185.
[7] Saberian S, Heyes A, Rivers N. Alerts work! Air quality warnings and cycling[J]. Resource and Energy Economics, 2017, 49: 165-185.
[8] Zivin J G, Neidell M. Days of haze: Environmental information disclosure and intertemporal avoidance behavior[J]. Journal of Environmental Economics and Management, 2009, 58(2): 119-128.
[9] Neidell M J. Air pollution, health, and socio-economic status: the effect of outdoor air quality on childhood asthma[J]. Journal of Health Economics, 2004, 23(6): 1209-1236.
[10] Troncoso R, Grange L D, Cifuentes L A. Effects of environmental alerts and pre-emergencies on pollutant concentrations in Santiago, Chile[J]. Atmospheric Environment, 2012, 61: 550-557.
[11] 王力, 姜晓群. 重污染天气应急预警下的固定源“一厂一策”实施机制探讨[J]. 环境保护, 2019, 47(7): 53-56.
[11] [Wang Li, Jiang Xiaoqun. Research on the implementation mechanism of One Factory & One Policy at stationary source in heavy air pollution days[J]. Environmental Protection, 2019, 47(7): 53-56.]
[12] 贺璇. 技术赋能与空气重污染应急管理智慧化演进路径[J]. 华中科技大学学报(社会科学版), 2020, 34(3): 131-140.
[12] [He Xuan. Starting Characteristics of air pollution emergency management and intelligent evolution by technology empowerment[J]. Journal of Huazhong University of Science and Technology (Social Science Edition), 2020, 34(3): 131-140.]
[13] 李名升, 任晓霞, 于洋, 等. 中国大陆城市PM2.5污染时空分布规律[J]. 中国环境科学, 2016, 36(3): 641-650.
[13] [Li Mingsheng, Ren Xiaoxia, Yu Yang, et al. Spatiotemporal pattern of ground-level fine particulate matter (PM2.5) pollution in mainland China[J]. China Environmental Science, 2016, 36(3): 641-650.]
[14] 徐大海, 王郁, 朱蓉. 大气环境容量系数A值频率曲线拟合及其应用[J]. 中国环境科学, 2016, 36(10): 2913-2922.
[14] [Xu Dahai, Wang Yu, Zhu Rong. The atmospheric environmental capacity coefficient cumulative frequency curve fitting and its application[J]. China Environmental Science, 2016, 36(10): 2913-2922.]
[15] 朱蓉, 张存杰, 梅梅. 大气自净能力指数的气候特征与应用研究[J]. 中国环境科学, 2018, 38(10): 3601-3610.
[15] [Zhu Rong, Zhang Cunjie, Mei Mei. The climate characteristics of atmospheric self-cleaning ability index and its application in China[J]. China Environmental Science, 2018, 38(10): 3601-3610.]
[16] Wang J Z, Wang Y Q, Liu H, et al. Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing[J]. Atmospheric Environment, 2013, 81:158-165.
[17] Yang J H, Ji Z M, Kang S C, et al. Spatiotemporal variations of air pollutants in western China and their relationship to meteorological factors and emission sources[J]. Environmental Pollution, 2019, 254(Pt A): 112952.
[18] 高爽, 李时蓓, 马岩, 等. 基于气象分型的大气环境允许排放量测算方法[J]. 中国环境科学, 2021, 41(2): 588-595.
[18] [Gao Shuang, Li Shibei, Ma Yan, et al. Research on atmospheric environmental capacity accounting method based on classification of meteorological conditions[J]. China Environmental Science, 2021, 41(2): 588-595.]
[19] 许艳玲, 薛文博, 雷宇. 气象和排放变化对PM2.5污染的定量影响[J]. 中国环境科学, 2019, 39(11): 4546-4551.
[19] [Xu Yanlin, Xue Wenbo, Lei Yu. Impact of meteorological conditions and emission change on PM2.5 pollution in China[J]. China Environmental Science, 2019, 39(11): 4546-4551.]
[20] 兰紫娟, 卢超, 李磊, 等. 基于污染气象条件分级的深圳PM2.5污染回顾评价[J]. 环境科学学报, 2020, 40(12): 4410-4418.
[20] [Lan Zijuan, Lu Chao, Li Lei, et al. Review of PM2.5pollution in Shenzhen based on classification of pollution meteorological condition[J]. Acta Scientiae Circumstantiae, 2020, 40(12): 4410-4418.]
[21] Xue Y L, Xue W B, Lei Y, et al. Impact of meteorological conditions on PM2.5 pollution in China during winter[J]. Atmosphere, 2018, 9(11): 429.
[22] 郝志新, 耿秀, 刘可邦, 等. 关中平原过去1000年干湿变化特征[J]. 科学通报, 2017, 62(21): 2399-2406.
[22] [Hao Zhixin, Geng Xiu, Liu Kebang, et al. Dryness and wetness variations for the past 1000 years in Guanzhong Plain[J]. Chinese Science Bulletin, 2017, 62(21): 2399-2406.]
[23] 陈建文, 胡琳, 王娟敏, 等. 陕西省大气稳定度分布特征研究[J]. 水土保持研究, 2013, 20(3): 299-304.
[23] [Chen Jianwen, Hu Lin, Wang Juanmin, et al. Distribution characteristics of atmospheric stability in Shaanxi Province[J]. Research of Soil and Water Conservation, 2013, 20(3): 299-304.]
[24] Brownlee J. Machine learning mastery[J]. URL: http://machinelearningmastery.com/discover-feature-engineering-howtoengineer-features-and-how-to-getgood-at-it, 2014.
[25] 贾册, 王亚琼, 杨啸. 关中平原PM2.5浓度周期性变化规律及成因分析[J]. 干旱区研究, 2020, 37(6): 1528-1536.
[25] [Jia Ce, Wang Yaqiong, Yang Xiao. Analysis on the periodic variation of PM2.5 concentration and its causes in Guanzhong Plain[J]. Arid Zone Research, 2020, 37(6): 1528-1536.]
[26] 张波, 宋国君, 周芳. 基于PM2.5监测点空间聚类的关中五市空气污染区域识别[J]. 环境科学学报, 2021, 41(3): 1-9.
[26] [Zhang Bo, Song Guojun, Zhou Fang. Regional delimitation of PM2.5 pollution: A case study of five cities in Guanzhong Plain[J]. Acta Scientiae Circumstantiae, 2021, 41(3): 1-9.]
[27] 宋国君, 国潇丹, 杨啸, 等. 沈阳市PM2.5浓度ARIMA-SVM组合预测研究[J]. 中国环境科学, 2018, 38(11): 4031-4039.
[27] [Song Guojun, Guo Xiaodan, Yang Xiao, et al. ARIMA-SVM combination prediction of PM2.5 concentration in Shenyang[J]. China Environmental Science, 2018, 38(11): 4031-4039.]
[28] 胡琳, 程路, 林扬, 等. 关中区域大气重污染年份气象条件分析[J]. 干旱区研究, 2020, 37(6): 1496-1503.
[28] [Hu Lin, Cheng Lu, Lin Yang, et al. A conceptual model of regional pollution and meteorological conditions in Guanzhong region[J]. Arid Zone Research, 2020, 37(6): 1496-1503.]
[29] Zhao B, Wang S X, Ding D, et al. Nonlinear relationships between air pollutant emissions and PM2.5-related health impacts in the Beijing-Tianjin-Hebei region[J]. Science of The Total Environment, 2019, 661: 375-385.
[30] Zhao B, Wang S X, Xing J, et al. Assessing the nonlinear response of fine particles to precursor emissions: Development and application of an extended response surface modeling technique v1.0[J]. Geoscientific Model Development, 2015, 8(1): 115-128.
[31] 王静晞, 曹国良, 韩蕾, 等. 关中地区人为源大气污染物排放清单研究[J]. 安全与环境学报, 2015, 15(5): 282-287.
[31] [Wang Jingxi, Cao Guoliang, Han Lei, et al. Probe to the anthropogenic air pollutant emission inventory in Guanzhong Region, Shaanxi[J]. Journal of Safety and Environment, 2015, 15(5): 282-287.]
[32] 郭庆元, 杨晓春, 吴其重, 等. 基于数值模拟对西安地区冬季一次重污染过程PM2.5区域来源解析[J]. 环境科学学报, 2020, 40(9): 3103-3111.
[32] [Guo Qingyuan, Yang Xiaochun, Wu Qizhong, et al. An analysis of PM2.5 regional sources from heavy pollution in the winter of Xi’an based on numerical simulation[J]. Acta Scientiae Circumstantiae, 2020, 40(9): 3103-3111.]
[33] 李霞, 吴佳睿, 刘浪, 等. 模拟研究咸阳市2016年秋冬季雾霾期间PM2.5来源[J]. 地球环境学报, 2019, 10(4): 347-363.
[33] [Li Xia, Wu Jiarui, Liu Lang, et al. Simulating the sources of PM2.5 during heavy haze pollution episodes in the autumn and winter of 2016 in Xianyang City, China[J]. Journal of Earth Environment, 2019, 10(4): 347-363.]
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