邯郸采暖期环境污染特征及空气质量预报方法研究

doi:10.19741/j.issn.1673-4831.2019.0569

摘要/Abstract

摘要： 利用河北邯郸气象和环境监测资料，分析了邯郸采暖期空气质量和环境气象条件特征；同时利用线性回归和BP神经网络统计方法对采暖期空气质量进行了预报研究。结果表明，PM₁₀、PM_2.5、SO₂、NO₂、CO（95）（CO日均值的第95百分位数）的空气质量指数（AQI）在冬季最高，夏季最低，O₃-8（90）（O₃日最大8 h值的第90百分位数）的AQI则相反。邯郸采暖期首要污染物以PM_2.5和PM₁₀为主，除O₃-8（90）外，其他5种污染物采暖期AQI均高于其年均值；同时采暖期降水少，温度低，小风出现频率明显高于非采暖期，而且局地逆温强，静稳天气指数高，是全年环境气象条件最差的时期。邯郸采暖期的环境气象条件1月最差，且夜晚差于白天，尤其是局地5-7时。邯郸采暖期首要污染物浓度与前一日污染物浓度、静稳指数、逆温、相对湿度和露点温度等呈正相关，与气温、风速、能见度和混合层高度等呈负相关。BP神经网络模型对污染物浓度的预报效果优于线性回归模型，可尝试应用于邯郸空气质量预报工作。

关键词: 邯郸, 采暖期, 空气质量, 环境气象条件, BP神经网络

Abstract: This study investigated characteristics of air quality and environmental meteorological conditions during heating period in Handan City of Hebei Province using local environmental and meteorological data, and statistically forecasted mass concentrations of six pollutants with regression and back propagation (BP) neural network methods. The results show that the air quality index (AQI) of PM_2.5, PM₁₀, SO₂, CO (95) and NO₂ are highest in winter and lowest in summer, as opposed to the O₃-8(90) AQI. During heating period, local primary pollutants are PM_2.5 and PM₁₀, and the AQI of abovementioned pollutants are higher than the annual average except for O₃-8(90). Compared with no-heating period, during heating period precipitation amount and surface air temperature are lower, weak wind occurs more frequently, and local inversion temperature and static stability index are obviously higher. The worst environmental conditions occur in January and the worse time is early morning, especially in 5-7 am. The mass concentrations of primary pollutants are positively correlated with their individual mass concentrations, inversion temperature, dew point temperature and mixing layer height in the previous day, but negatively correlated with air temperature, wind speed, visibility and mixed layer height. The results further show that the skill on forecast of pollutant mass concentration with the BP neural network method is better than those with the linear regression method, and the BP neural network method can be applied to the air quality prediction in Handan City.

Key words: Handan, heating period, air quality, environmental meteorological conditions, BP neural network

中图分类号:

X823

宋晓辉, 杜亮亮, 李剑东, 程晓丹, 张珺. 邯郸采暖期环境污染特征及空气质量预报方法研究[J]. 生态与农村环境学报, 2020, 36(8): 998-1005.

SONG Xiao-hui, DU Liang-liang, LI Jian-dong, CHENG Xiao-dan, ZHANG Jun. Study on Characteristics of Environmental Pollution and Methods of Air Quality Prediction During Heating Period in Handan City[J]. Journal of Ecology and Rural Environment, 2020, 36(8): 998-1005.

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