沈阳城市热岛和污染岛特征及其与向下长波辐射的关系

doi:10.13287/j.1001-9332.202512.027

应用生态学报 ›› 2025, Vol. 36 ›› Issue (12): 3810-3818.doi: 10.13287/j.1001-9332.202512.027

沈阳城市热岛和污染岛特征及其与向下长波辐射的关系

李丽光^1,2,3*, 赵梓淇^1,2,3, 李晓岚^1,2,3, 刘宁微^1,2,3, 孟鑫⁴, 丁抗抗⁵, 温日红^1,2,3

¹中国气象局沈阳大气环境研究所辽宁省农业气象灾害重点实验室, 沈阳 110166;
²中国气象科学研究院沈阳农业与生态气象研究院, 沈阳 110166;
³盘锦国家气候观象台, 辽宁盘锦 124010;
⁴丹东市气象局, 辽宁丹东 118000;
⁵辽宁省气象信息中心, 沈阳 110166

收稿日期:2025-05-26 修回日期:2025-10-20 出版日期:2025-12-18 发布日期:2026-07-18
通讯作者: ^*E-mail: liliguang@iaesy.cn
作者简介:李丽光, 女, 1973年生, 研究员。主要从事城市热环境和全球气候变化研究。E-mail: liliguang@iaesy.cn
基金资助:
辽宁省气象局开放基金项目(202407、202406)、辽宁省自然科学基金面上项目(2024-MS-245)、辽宁省科技计划联合计划项目(2024-MSLH-504)和中国气象局沈阳大气环境研究所辽宁省农业气象灾害重点实验室开放基金项目(2025SYIAEKFZD07)

Characteristics of urban heat island and urban pollution island and their relationships with downward long-wave radiation in Shenyang, Northeast China

LI Liguang^1,2,3*, ZHAO Ziqi^1,2,3, LI Xiaolan^1,2,3, LIU Ningwei^1,2,3, MENG Xin⁴, DING Kangkang⁵, WEN Rihong^1,2,3

¹Key Laboratory of Agrometeorological Disasters in Liaoning Province, Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China;
²Shenyang Institute of Agricultural and Ecological Meteorology, Chinese Academy of Meteorological Science, Shenyang 110166, China;
³Panjin National Climate Observatory, Panjin 124010, Liaoning, China;
⁴Dandong Meteorological Service, Dandong 118000, Liaoning, China;
⁵Liaoning Meteorological Information Center, Shenyang 110166, China

Received:2025-05-26 Revised:2025-10-20 Online:2025-12-18 Published:2026-07-18

摘要/Abstract

摘要： 城市热岛效应和城市大气污染一直是政府和民众关注的热点问题。本研究利用2018年12月1日至2019年11月30日沈阳9个气象站、10个环境监测站、1个辐射监测站的温度、PM_2.5浓度和向下长波辐射资料,根据站点的位置和距离等,将气象站和环境监测站分为城市站和郊区站,采用城郊差值法计算相对于西南和东北郊区的沈阳城市热岛强度和污染岛强度的季、月、日变化,并探讨了城市热岛强度和污染岛强度与向下长波辐射之间的关系。结果表明: 研究期间,沈阳的季、月、日温度均表现为城区>西南郊区>东北郊区,相对于西南郊区,基于东北郊区的沈阳城市热岛强度更高,季、月、日热岛强度最大值分别出现在冬季(城区分别比东北和西南郊区高2.82和 1.75 ℃)、1月(城区分别比东北和西南郊区高3.04和 1.87 ℃)、00:00(城区比东北郊区高3.09 ℃)或01:00(城区比西南郊区高2.20 ℃)。沈阳季、月、日PM_2.5浓度均为东北郊区最低,夏季、6—8月、11:00—17:00城区PM_2.5浓度高于西南郊区,其他季节和时段均为西南郊区高于城区。城市热岛强度与污染岛强度呈显著负相关。向下长波辐射与城市污染岛强度在昼夜均呈显著相关,但与城市热岛强度仅在夜间呈显著相关。向下长波辐射通过温度和PM_2.5影响城市热岛强度和污染岛强度。

关键词: 城市热岛, 城市污染岛, 向下长波辐射, PM_2.5, 关联度

Abstract: The urban heat island (UHI) effect and urban air pollution (UPI) have received concerns from the government and the public. Based on the data of temperature, PM_2.5 concentration and downward long-wave radiation (DLR) from 9 meteorological stations, 10 environmental monitoring stations and 1 radiation monitoring station in Shenyang for one whole year from December 1, 2018 to November 30, 2019, we divided the meteorological stations and environmental monitoring stations into urban and suburb stations according to their locations and the distance between stations. We used an urban-suburb temperature difference method calculated the seasonal, monthly, diurnal intensities of the UHI and UPI in Shenyang urban compared with the southwest and northeast suburbs, and analyzed the relationships among UHI, UPI and DLR. Results showed that the seasonal, monthly, and hourly temperature in Shenyang showed an order of urban>southwest suburb>northeast suburb. Compared to basing on the southwest suburb, the urban heat island intensity in Shenyang was much higher when based on the northeast suburb. The maximum seasonal, monthly, and daily heat island intensities occurred in winter (urban were 2.82 ℃ and 1.75 ℃ higher than the northeast and southwest suburbs, respectively), January (urban were 3.04 ℃ and 1.87 ℃ higher than the northeast and southwest suburbs, respectively), and at 00:00 (urban were 3.09 ℃ higher than the northeast suburb) or 01:00 (urban were 2.20 ℃ higher than the southwest suburb). The PM_2.5 concentrations in Shenyang were lowest in the northeast suburb across seasons, months, and days. During summer, from June to August and between 11:00 and 17:00, urban PM_2.5 concentrations were higher than those in the southwest suburb. In other seasons and time periods, the southwest suburb exceeded urban area. Urban heat island intensity showed a significant negative correlation with pollution island intensity. Downward long-wave radiation was significantly correlated with urban pollution island intensity both day and night, but only with urban heat island intensity during nighttime. Downward long-wave radiation affected urban heat island intensity and pollution island intensity through temperature and PM_2.5.

Key words: urban heat island, urban pollution island, downward long-wave radiation, PM_2.5, correlation degree

李丽光, 赵梓淇, 李晓岚, 刘宁微, 孟鑫, 丁抗抗, 温日红. 沈阳城市热岛和污染岛特征及其与向下长波辐射的关系[J]. 应用生态学报, 2025, 36(12): 3810-3818.

LI Liguang, ZHAO Ziqi, LI Xiaolan, LIU Ningwei, MENG Xin, DING Kangkang, WEN Rihong. Characteristics of urban heat island and urban pollution island and their relationships with downward long-wave radiation in Shenyang, Northeast China[J]. Chinese Journal of Applied Ecology, 2025, 36(12): 3810-3818.

参考文献

[1] Manley G. On the frequency of snowfall in metropolitan England. Quarterly Journal of Royal Meteorological Society, 1958, 84: 70-72
[2] Crutzen PJ. New directions: The growing urban heat and pollution “island” effect-impact on chemistry and climate. Atmospheric Environment, 2004, 38: 3539-3540
[3] Li HD, Meier F, Lee XH, et al. Interaction between urban heat island and urban pollution island during summer in Berlin. Science of the Total Environment, 2018, 636: 818-828
[4] Wang YQ, Zhang XY, Sun JY, et al. Spatial and temporal variations of the concentrations of PM₁₀, PM_2.5 and PM₁ in China. Atmospheric Chemistry and Physics, 2015, 15: 13585-13598
[5] Li XL, Tang GQ, Li LG, et al. Multilevel air quality evolution in Shenyang: Impact of elevated point emission reduction. Journal of Environmental Sciences, 2022, 113: 300-310
[6] Zhao ZQ, He BJ, Li LG, et al. Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China. Energy and Buildings, 2017, 155: 282-295
[7] 陈晨, 阿木拉堵, 李翠琳, 等. 基于多源数据的大气颗粒物对北京市城市热岛的影响研究. 地球环境学报,2020, 11(2): 146-150
[8] 郑宇. 基于地基观测的华北地区大气气溶胶微物理和光学特性及其辐射效应研究. 博士论文. 南京: 南京信息工程大学, 2020
[9] 贺婧. 不同排放情景对北京及周边地区空气质量影响的模式模拟研究. 硕士论文. 北京: 中国气象科学研究院, 2018
[10] 程先富, 周志凌, 蔡菁菁. 基于模式模拟的苏皖鲁豫交界区典型月份PM_2.5来源解析. 环境科学学报, 2023, 43(2): 365-375
[11] Liu Y, Miao C, Cui A, et al. Spatial distribution of air pollutants in different urban functional zones based on mobile monitoring and CFD simulation. International Journal of Environmental Science and Technology, 2025, 22: 11045-11058
[12] Arunab KS, Mathew A. Quantifying urban heat island and pollutant nexus: A novel geospatial approach. Sustainable Cities and Society, 2024, 101: 105117
[13] Chang YH, Guo XM. Disparities in the impact of urban heat island effect on particulate pollutants at different pollution stages: A case study of the “2 +36” cities. Urban Climate, 2025, 59: 102273
[14] 赵铖钰, 张淑怡, 朱泓恺, 等. 上海地表热环境演变趋势城乡分异及其对城市更新的响应. 应用生态学报, 2023, 34(7): 1923-1931
[15] Liu ZY, Zhang SS. Exploring the relationship between urban green development and heat island effect within the Yangtze River Delta Urban Agglomeration. Sustainable Cities and Society, 2025, 121: 106204
[16] Chen YH, Wu JT, Yu K, et al. Evaluating the impact of the building density and height on the block surface temperature. Building and Environment, 2020, 168: 10493
[17] Han L, Zhang RJ, Wang JQ, et al. Spatial synergistic effect of urban green space ecosystem on air pollution and heat island effect. Urban Climate, 2024, 55:101940
[18] 吴舒祺, 金囝囡, 郑冬阳, 等. “2+26”城市PM_2.5与气象因子的尺度依存关系及影响因素分析. 环境科学, 2023, 44(12): 6441-6451
[19] 谭洁, 危千骏, 廖朝阳, 等. 基于XGBoost-SHAP可解释机器学习模型的城市形态与地表温度的关系. 应用生态学报, 2025, 36(3): 659-670
[20] 陈岑, 梁德庄, 杨俊, 等. 基于CNKI文献计量的地表城市热岛效应研究方法综述. 应用生态学报, 2025, 36(3): 647-658
[21] Guan SY, Chen YD, Wang TW, et al. Mitigating urban heat island through urban-rural transition zone landscape configuration: Evaluation based on an interpretable ensemble machine learning framework. Sustainable Cities and Society, 2025, 123: 106272
[22] Kasimov N, Chalov S, Chubarova N, et al. Urban heat and pollution island in the Moscow megacity: Urban environmental compartments and their interactions. Urban Climate, 2024, 55: 101972
[23] Feng RD, Wang FY, Liu SH, et al. Synergistic effects of urban forest on urban heat island-air pollution-carbon stock in mega-urban agglomeration. Urban Forestry & Urban Greening, 2025, 103: 128590
[24] 徐祥德. 城市大气环境污染模型动力学问题. 应用气象学报, 2002, 13(特刊1) : 1-12
[25] Suthar G, Kaul N, Khandelwal S, et al. Predicting land surface temperature and examining its relationship with air pollution and urban parameters in Bengaluru: A machine learning approach. Urban Climate, 2024, 53: 101830
[26] 朱炎, 刘红年, 沈建, 等. 苏州城市热岛对污染扩散的影响. 高原气象, 2016, 35(6): 1584-1594
[27] 吴昊, 王体健, 方欢, 等. 南京细颗粒物对城市热岛强度的影响. 大气科学学报, 2014, 37(4): 425-431
[28] Cao C, Lee XH, Liu SD, et al. Urban heat islands in China enhanced by haze pollution. Nature Communications, 2016, 7: 12509
[29] Li J, Zhou M, Lenschow DH, et al. Observed relationships between the urban heat island, urban pollution island, and downward longwave radiation in the Beijing area. Earth and Space Science, 2020, 7: e2020EA001100
[30] Li LG, Zhao ZQ, Wang HB, et al. Variabilities of land surface temperature and frontal area index based on local climate zone. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 2166-2174
[31] 李丽光, 许申来, 王宏博, 等. 基于源汇指数的沈阳热岛效应. 应用生态学报, 2013, 24(12): 3446-3452
[32] 辽宁省技术监督局. 气象预报预测术语(DB21/1453—2006). 沈阳: 辽宁省技术监督局, 2006
[33] 李丽光, 许申来, 王宏博, 等. 基于气象资料的城市热环境研究. 北京: 化学工业出版社, 2014: 109-112
[34] 唐启义. DPS数据处理系统. 北京: 科学出版社, 2020: 1641-1650
[35] Jonsson P, Bennet C, Eliasson I, et al. Suspended particulate matter and its relations to the urban climate in Dares Salaam Tanzania. Atmospheric Environment, 2004, 38: 4175-4181
[36] Zheng ZF, Ren GY, Wang H, et al. Relationship between fine-particle pollution and the urban heat island in Beijing, China: Observational evidence. Boundary-Layer Meteorology, 2018, 169: 93-113
[37] 中国环境科学研究院, 中国环境监测总站. 环境空气质量标准 GB 3095—2012 [EB/OL]. (2016-01-01) [2025-01-03]. https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/201203/t20120302_224165.shtml
[38] Wang LM, Lee XH, Schultz N, et al. Response of surface temperature to afforestation in the Kubuqi Desert, Inner Mongolia. Journal of Geophysical Research: Atmospheres, 2018, 123: 948-964
[39] 李丽光, 王宏博, 赵梓淇, 等. 站点选择对城市热岛效应的影响分析. 气象与环境学报, 2013, 29(2): 54-60
[40] Li XL, Ma YJ, Wang YF, et al. Temporal and spatial analyses of particulate matter (PM₁₀ and PM_2.5) and its relationship with meteorological parameters over an urban city in northeast China. Atmospheric Research, 2017, 198: 185-193

沈阳城市热岛和污染岛特征及其与向下长波辐射的关系

Characteristics of urban heat island and urban pollution island and their relationships with downward long-wave radiation in Shenyang, Northeast China

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