新型冠状病毒肺炎疫情防控时期上海市空气质量特征及成因分析

孔琳; 胡婷莛; 牛植; 周梦鸽; 饶品华; 葛大逢; 彭王敏子

doi:10.13205/j.hjgc.202109016

新型冠状病毒肺炎疫情防控时期上海市空气质量特征及成因分析

doi: 10.13205/j.hjgc.202109016

1. 上海工程技术大学化学化工学院, 上海 201620;
2. 南京大学大气科学学院, 南京 210023;
3. 江西省气象科学研究所, 南昌 330046

基金项目:

国家自然科学基金青年科学基金项目（51508395）；中国博士后科学基金第57批面上基金项目（2015M571599）。

详细信息

作者简介:
孔琳(1999-),女,主要研究方向为大气环境监测。konglinflora@163.com

通讯作者:
胡婷莛(1984-),女,博士,讲师,主要从事城区大气污染监测与模拟、城区通风规划与设计方面研究。tingtinghu@sues.edu.cn

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出版历程
- 收稿日期: 2020-09-17
- 网络出版日期: 2022-01-21

CHARACTERISTICS AND CAUSES OF AIR CONDITION IN SHANGHAI DURING COVID-19 PREVENTION PERIODS

1. School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;
2. School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China;
3. Jiangxi Meteorological Science Institute, Nanchang 330046, China

摘要

摘要: 为研究新型冠状病毒肺炎（COVID-19）疫情防控政策实施对上海市大气污染物质量浓度的影响，利用上海市内环某高层顶楼微环境平台观测了政策实施前10 d（2020-01-14—23）和实施后20 d（2020-01-24—02-12）的PM_2.5和PM₁₀质量浓度及气象要素（温度、相对湿度、风向、风速、大气压及降雨），结合2019年同期观测数据和杨浦四漂空气质量监测点的气态污染物逐时数据，采用描述性统计、合成分析、拉格朗日粒子扩散模式和Spearman相关系数方法，分析了政策实施前、后大气污染物特征及其影响因素。结果表明：1）污染物浓度变化方面。政策实施后，ρ（PM_2.5）和ρ（PM₁₀）和ρ（NO₂）均明显降低，ρ（PM_2.5）和ρ（PM₁₀）分别由61.4，102.4 μg/m³降至38.1，63.5 μg/m³，降幅均为38.0%，ρ（NO₂）由57.3 μg/m³降至27.0 μg/m³，降幅达到52.9%，而ρ（O₃）由47.6 μg/m³增至69.5 μg/m³。ρ（PM_2.5）和ρ（PM₁₀）日变化特征由实施前的双峰双谷型变为单谷型。2）气象因素影响方面。上海地区南风异常减弱了冬季风强度，对流层中层正距平异常抑制了对流活动的发展，易导致大气污染物在近地面的汇聚。ρ（PM_2.5）和ρ（PM₁₀）与相对湿度呈负相关，风速对ρ（PM_2.5）和ρ（PM₁₀）的影响与风向有关。3）外源输入影响方面。长三角城市群及山东省、河南省等周边区域对上海市ρ（PM_2.5）和ρ（PM₁₀）贡献显著。
- 新型冠状病毒肺炎疫情 /
- 大气污染物 /
- 合成分析 /
- 拉格朗日粒子扩散模型 /
- 上海
Abstract: In order to explore the impact of the COVID-19 epidemic implementation policy on air pollution variation, ρ(PM_2.5), ρ(PM₁₀) and meteorological elements (temperature, relative humidity, wind direction, wind speed, atmospheric pressure and rainfall) were observed by the microenvironment platform on the roof of a high-rise dormitory building in the inner ring of Shanghai from January 14^th, 2020 to February 18^th, 2020. The characteristics and influencing factors of air pollutants before and after the implementation of the policy were analyzed through statistical analysis, synthetic analysis, Lagrangian particle dispersion model and Spearman correlation method, based on the above observation data in 2020 and the corresponding observation data in 2019, as well as the hourly monitoring data of gaseous pollutants (O₃, NO₂, CO, SO₂) at Yangpu Sipiao air quality monitoring station. The results showed that:1) Changes in pollutant concentrations:the implementation of the policy reduced the magnitude of ρ(PM_2.5), ρ(PM₁₀) and ρ(NO₂) significantly:ρ(PM_2.5) and ρ(PM₁₀) decreased from 61.4 μg/m³ to 38.1 μg/m³ and from 102.4 μg/m³ to 63.5 μg/m³, respectively, with a decrease rate of 38.0%, ρ(NO₂) decreased from 57.3 μg/m³ to 27.0 μg/m³, with a decrease rate of 52.9%. ρ(O₃) increased from 47.6 μg/m³ and 69.5 μg/m³. The diurnal bimodal and double-valley variation pattern of ρ(PM_2.5) and ρ(PM₁₀) became a single-valley pattern after the implementation of the policy; 2) The influence of meteorological factors:the anomaly of the southerly wind weakened the intensity of the winter monsoon, and the anomaly of the positive anomaly in the middle troposphere inhibited the development of convective activities, which easily led to the accumulation of atmospheric pollutants near the ground. ρ(PM_2.5) and ρ(PM₁₀) were negatively correlated with relative humidity. The influence of wind speed on ρ(PM_2.5) and ρ(PM₁₀) depended on the wind direction; 3) The impact of external sources:the urban agglomeration of the Yangtze River Delta and the surrounding provinces such as Shandong and Henan contributed significantly to ρ(PM_2.5) and ρ(PM₁₀) in Shanghai.
- COVID-19 /
- air pollutants /
- synthetic analysis /
- Lagrangian particle dispersion model /
- Shanghai

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