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生态学杂志 ›› 2022, Vol. 41 ›› Issue (10): 2035-2042.doi: 10.13292/j.1000-4890.202209.006

• 研究报告 • 上一篇    下一篇

基于移动观测的城市街道峡谷大气污染物时空分布特征

崔爱伟1,2,3,苗纯萍1,2,何欢4,熊在平1,胡远满1,2,3,陈玮1,2,3*   

  1. 1中国科学院沈阳应用生态研究所, 中国科学院森林生态与管理重点实验室, 沈阳 110016; 2中国科学院沈阳树木园, 沈阳 110016; 3中国科学院大学, 北京 100049; 4沈阳建筑大学国际学院, 沈阳 110168)

  • 出版日期:2022-10-10 发布日期:2022-10-13

Spatiotemporal distribution characteristics of air pollutants in urban street canyons as observed by mobile monitoring.

CUI Ai-wei1,2,3, MIAO Chun-ping1,2, HE Huan4, XIONG Zai-ping1, HU Yuan-man1,2,3, CHEN Wei1,2,3*#br#

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  1. (1Chinese Academy of Sciences Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; 2Shenyang Arboretum, Chinese Academy of Sciences, Shenyang 110016, China; 3University of Chinese Academy of Sciences, Beijing 100049, China; 4International School, Shenyang Jianzhu University, Shenyang 110168, China).

  • Online:2022-10-10 Published:2022-10-13

摘要: 为了解城市街道峡谷内大气污染物浓度的分布特征及其与气象因子的相互关系,本研究在良好及污染两种天气状况下的通勤高峰时段和通勤平峰时段,采用便携式空气污染物监测设备对沈阳市青年大街内的5种大气污染物(CO、SO2、PM1、PM2.5和PM10)进行移动观测,比较不同天气状况、不同通勤时段大气污染物的分布差异,利用Pearson相关性分析方法研究了气象因子对5种大气污染物质量浓度的影响。结果表明:沈阳市青年大街内的空气质量指数(AQI)分布具有显著的时空分异特征。在清洁天气下AQI空间分布表现为“北高南低”,而在污染天气下表现为“南高北低”,且在道路交叉口处的污染物浓度较高。5种污染物在时间分布上均显示出污染天气>清洁天气,通勤高峰>整体时段>通勤平峰。Pearson相关性分析表明,清洁天气下5种大气污染物与温度和湿度呈显著正相关,与大气压呈显著负相关;污染天气下,温度与大气污染物的相关性减弱,湿度与SO2和CO浓度的相关性变为负相关,大气压与CO、SO2、PM1和PM2.5呈显著负相关。本研究为日后在街道峡谷开展大气污染治理和区域大气污染物传输等研究提供了参考依据。

关键词: 大气污染物, 街道峡谷, 移动观测, 时空分布, Pearson相关性分析, 沈阳市

Abstract: To explore the variation of air pollutant concentration and the influence of meteorological factors on air pollutant concentration in urban street canyon, we used portable air pollution monitoring equipment to detect five kinds of air pollutants (CO, SO2, PM1, PM2.5, and PM10) in the Qingnian Street of Shenyang during the peak and off peak periods under clean weather and polluted weather. Pearson correlation analysis was used to assess the influence of meteorological factors on the mass concentrations of five air pollutants. The results showed that the distribution of air quality index (AQI) in the Qingnian Street had significant spatial and temporal variations over short-term. The spatial distribution of AQI showed a pattern of “high in the north and low in the south” under clean weather, and a pattern of “high in the south and low in the north” under polluted weather, with high pollutant concentration at road intersections. Temporally, the concentrations of the five pollutants were higher under polluted weather than that under clean weather. They were the highest in peak commuting time and the lowest in off peak commuting time. The concentrations of air pollutants were significantly positively correlated with air temperature and relative humidity, and negatively correlated with air pressure in clean weather. In polluted weather, air temperature was negatively correlated with PM1; relative humidity was negatively correlated with SO2 and CO concentrations, positively correlated with PM2.5 and PM10 concentrations; and pressure was negatively correlated with CO, SO2, PM1 and PM2.5 concentrations. This study provided a reference for future studies on air pollution control and regional air pollutant transport in street canyons.


Key words: air pollutant, urban street canyon, mobile monitoring, spatial and temporal distribution, Pearson correlation analysis, Shenyang.