湖北农业科学 ›› 2022, Vol. 61 ›› Issue (1): 74-79.doi: 10.14088/j.cnki.issn0439-8114.2022.01.013

• 资源·环境 • 上一篇    下一篇

天门市PM2.5和PM10颗粒污染物特征及其预测模型

鞠英芹1, 马德栗2,3, 杜良敏2, 黄忠3   

  1. 1.中国气象局气象干部培训学院湖北分院,武汉 430074;
    2.武汉区域气候中心,武汉 430074;
    3.天门市气象局,湖北 天门 431700
  • 收稿日期:2020-09-01 出版日期:2022-01-10 发布日期:2022-01-26
  • 通讯作者: 马德栗,男,河南南阳人,高级工程师,(电子信箱)506556793@qq.com。
  • 作者简介:鞠英芹(1983-),女,山东威海人,高级工程师,主要从事气象教育培训、环境气象等方面研究,(电话)15871438780(电子信箱)407065070@qq.com。
  • 基金资助:
    湖北省气象局科技发展基金青年专项(2019Q05); 天门市环境气象业务服务项目(2200506)

The characteristic of PM2.5 and PM10 atmospheric particulate matter pollution and its prediction model in Tianmen city

JU Ying-qin1, MA De-li2,3, DU Liang-min2, HUANG Zhong3   

  1. 1. Hubei Province Meteorological Training Center, Wuhan 430074,China;
    2. Wuhan Regional Climate Centre, Wuhan 430074,China;
    3. Tianmen Meteorological Bureau, Tianmen 431700,Hubei,China
  • Received:2020-09-01 Online:2022-01-10 Published:2022-01-26

摘要: 利用2017年1月1日至2020年5月30日PM2.5、PM10大气颗粒污染物质量浓度逐时监测数据,分析了大气颗粒污染物与气温、降水、相对湿度和风速风向等气象因子的关系。结果表明,PM2.5和PM10颗粒物质量浓度与日平均气温呈先上升后下降的关系,10 ℃以下颗粒物浓度随着气温的上升而升高,而10 ℃以上随着气温的逐渐升高,浓度则下降;降水对PM2.5、PM10污染物有明显的清除作用,降水每增加1 mm,浓度分别减少0.72 μg/m3和1.22 μg/m3;相对湿度在30%~70%, PM2.5和PM10质量浓度随相对湿度增大而增加;相对湿度在70%~100%,浓度则随相对湿度增大而减少;PM2.5和PM10质量浓度随着风速的增大而显著下降。比较分析多元非线性回归预测模型、多元线性回归模型和自适应线性神经网络模型的预测能力,PM2.5、PM10逐日质量浓度与当日气温、降水、风速所建立的多元非线性回归模型适合天门市颗粒污染物质量浓度的预测。

关键词: PM2.5, PM10, 气象条件, 多元非线性回归, 天门市

Abstract: The annual variation, diurnal variation characteristics of atmospheric pollutant and its relationship with meteorological factor were studied by using the monitoring data of two major air pollutants (PM2.5 and PM10) from environmental monitoring stations in Tianmen city from January 1, 2017 to May 30, 2020. Results showed that the relationship between PM2.5 and PM10 concentrations and daily average air temperature increased first and then decreased. However the concentration of particulate matter below 10 ℃ increased with the ascend of air temperature, decreased with the increased of air temperature; Precipitation had obvious removal effects on PM2.5 and PM10 pollutants, meanwhile the concentration of PM2.5 and PM10 decreased by 0.72 μg/m3 and 1.22 μg/m3 respectively with each increase of 1 mm. The relative humidity was 30%~70%, the concentrations of PM2.5 and PM10 increased with the increase of relative humidity; When the relative humidity was 70%~100%, the concentration decreases with the increase of relative humidity; The concentrations of PM2.5 and PM10 decreased significantly with the increase of wind speed. Compare and analyze the prediction ability of multiple nonlinear regression prediction model, multiple linear regression model and adaptive linear neural network model, the multiple nonlinear regression model established by the daily mass concentration of PM2.5, PM10 and the daily temperature, precipitation and wind speed was suitable for the prediction of the mass concentration of particulate pollutants in Tianmen city.

Key words: PM2.5, PM10, meteorological conditions, multiple nonlinear regressions, Tianmen city

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