摘要
基于深圳市环境监测站的PM_(2.5)浓度数据以及深圳市国家气候观象台发布的月度气象监测公报,研究了2012—2019年深圳市PM_(2.5)浓度的变化规律,分析了PM_(2.5)浓度与月尺度气候要素的关系,并利用多元线性回归分析法建立了PM_(2.5)月均浓度的预测模型。结果表明:2012—2019年深圳PM_(2.5)浓度呈明显下降趋势,PM_(2.5)浓度有季节性特征,干季(1~3月及10~12月)PM_(2.5)浓度比较高,也是PM_(2.5)污染防控的重要时段。月降水日数、月降水量以及月平均温度与PM_(2.5)浓度的负相关较明显,偏北风频率与PM_(2.5)浓度呈显著正相关,可一定程度上帮助预判月均PM_(2.5)浓度。与前人研究结果相反,月平均相对湿度与PM_(2.5)浓度呈显著负相关。包含气象因素项以及PM_(2.5)浓度项的月平均PM_(2.5)浓度预报模型拟合度较高,偏北风频率、月平均相对湿度是对月平均PM_(2.5)浓度影响最大的气象因素。利用深圳市2020年数据对模型进行检验,结果证明方程对于月平均PM_(2.5)浓度的预报有一定适用性,可较好预报PM_(2.5)浓度月增量。
Based on the PM_(2.5) concentration data from Shenzhen environmental monitoring station and the monthly meteorological monitoring bulletin issued by Shenzhen National Climate Observatory,the variation tendencies of PM_(2.5) concentration in Shenzhen during the period of 2012 to 2019 were studied.The relationship between PM_(2.5) concentration and monthly climate elements was also analyzed,and the prediction model of PM_(2.5) monthly average concentration was established by using multivariate linear regression analysis.The results show that the concentration of PM_(2.5) in Shenzhen showed an obvious downward trend from 2012 to 2019.The concentration of PM_(2.5) has seasonal characteristics.The concentration of PM_(2.5) in dry season(January to March and October to December)is relatively high,indicating that dry seasons are important periods for PM_(2.5) pollution prevention and control.The negative correlation between monthly precipitation days,monthly precipitation and monthly average temperature and PM_(2.5) concentration is obvious,and the northerly wind frequency is significantly positively correlated with PM_(2.5) concentration,which can help predict the monthly average PM_(2.5) concentration.Contrary to the previous research results,the monthly average relative humidity was significantly negatively correlated with PM_(2.5) concentration.The monthly average PM_(2.5) concentration prediction model including meteorological factor terms and PM_(2.5) concentration terms performs well.Northerly wind frequency and monthly average relative humidity are the meteorological factors that have the greatest impact on the monthly average PM_(2.5) concentration.The model was tested by using the data of Shenzhen in 2020.The results show that the equation is applicable to the prediction of monthly average PM_(2.5) concentration,especially it can be used to predict the monthly increment of PM_(2.5) concentration.
作者
何钰清
李磊
杨红龙
兰紫娟
邵应泉
张文海
HE Yu-qing;LI Lei;YANG Hong-long;LAN Zi-juan;SHAO Ying-quan;ZHANG Wen-hai(School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China;Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai 519082, China;Atmosphere-Ocean System (Sun Yat-Sen University), Ministry of Education Key Laboratory, Zhuhai 519082, China;Shenzhen National Climate Observatory, Shenzhen 518040, China;Shenzhen Research Academy of Environmental Sciences, Shenzhen 518001, China;Zhuhai National Climate Observatory, Zhuhai 519000, China;Shenzhen Academy of Severe Storms Science, Shenzhen 518057, China)
出处
《科学技术与工程》
北大核心
2022年第1期400-408,共9页
Science Technology and Engineering
基金
广东省科技计划项目(科技创新平台类)2019B121201002
国家自然科学基金(42075059)
深圳市环境保护科技专项。
作者简介
第一作者:何钰清(1998—),女,汉族,江西赣州人,硕士研究生,研究方向:大气污染、城市气象,E-mail:heyq73@mail2.sysu.edu.cn;通信作者:李磊(1977—),男,土家族,重庆人,博士,教授,研究方向:大气边界层物理、城市气候、应用气象、气象大数据分析,E-mail:lilei68@mail.sysu.edu.cn。
