季节指数改进的PM<sub>2.5</sub>质量浓度组合预测模型研究

曾江毅; 李志生; 欧耀春; 金宇凯

doi:10.12052/gdutxb.210055

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季节指数改进的PM_2.5质量浓度组合预测模型研究

更新时间：2026-05-27

- 季节指数改进的PM_2.5质量浓度组合预测模型研究
- PM_2.5 Concentration Improving Prediction Modeling of Seasonal Index
- 广东工业大学学报 2022年39卷第3期页码：89-94
- 作者机构：
  
  广东工业大学土木与交通工程学院,广东,广州,510006
- 作者简介：
  
  曾江毅(1998–)，男，硕士研究生，主要研究方向为室内空气品质
  李志生(1972–)，男，副教授，主要研究方向为建筑节能、室内环境控制及污染物去除，E-mail：chinaheat@163.com
- 基金信息：
  
  广东省自然科学基金资助项目(2016A030313711，S2011040003755)
- DOI：10.12052/gdutxb.210055
  中图分类号： X831;X513
- 网络首发：2022-05-10，
  
  纸质出版：2022
- 稿件说明：
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曾江毅, 李志生, 欧耀春, 金宇凯. 季节指数改进的PM_2.5质量浓度组合预测模型研究[J]. 广东工业大学学报, 2022, 39(3): 89-94. DOI: 10.12052/gdutxb.210055.

Zeng Jiang-yi, Li Zhi-sheng, Ou Yao-chun, et al. PM_2.5 Concentration Improving Prediction Modeling of Seasonal Index[J]. 2022, 39(3): 89-94. DOI: 10.12052/gdutxb.210055.
曾江毅, 李志生, 欧耀春, 金宇凯. 季节指数改进的PM_2.5质量浓度组合预测模型研究[J]. 广东工业大学学报, 2022, 39(3): 89-94. DOI: 10.12052/gdutxb.210055. DOI：

Zeng Jiang-yi, Li Zhi-sheng, Ou Yao-chun, et al. PM_2.5 Concentration Improving Prediction Modeling of Seasonal Index[J]. 2022, 39(3): 89-94. DOI: 10.12052/gdutxb.210055. DOI：

摘要

随着我国的经济和城市化迅速发展，PM

2.5

主导的区域空气污染已成为紧迫、突出的环境问题。据相关研究表明，PM

2.5

在不同季节质量浓度差异较大。根据广州市2015~2019年的PM

2.5

月均质量浓度数据，结合大气污染物及气象因素，引入季节指数，建立预测PM

2.5

质量浓度的改进多元线性回归和多层感知器组合预测模型，探析广州市大气污染物中PM

2.5

质量浓度的变化规律。结果表明，用季节指数改进的组合预测模型对PM

2.5

质量浓度进行预测分析，拟合结果良好。使用不同评价指标将组合模型与传统的多层感知器预测模型和多元线性回归模型进行对比，该组合模型的均方根误差(Root Mean Square Error，RMSE)、平均绝对百分比误差(Mean Absolute Percentage Error，MAPE)、平均绝对误差(Mean Absolute Error，MAE)分别比多层感知器模型减少了23.1%、31%、24.2%；比多元线性回归模型减少了35.3%、41.3%、41%。该模型精度均优于传统的多元线性回归模型和多层感知器模型，能更好地预测环境PM

2.5

质量浓度，为优化环境提供参考。

Abstract

In recent years

China's economy and urbanization have developed rapidly

and the development of cities often comes at the expense of the environment. Regional air pollution dominated by PM

2.5

has become the most pressing and prominent environmental problem in China. According to relevant studies

2.5

concentrations vary greatly in different seasons. Based on the PM

2.5

monthly mean concentration data of Guangzhou from 2015 to 2019

combined with atmospheric pollutants and meteorological factors

with the seasonal index introduced

an improved multiple linear regression and multi-layer perceptual combination prediction model for PM

2.5

concentration is established

to analyze the variation law and future development trend of PM

2.5

concentration in Guangzhou. The results show that the combined prediction model with the improved seasonal index is used to predict and analyze PM

2.5

and the fitting results are good. Compared with the multi-layer perceptron prediction model by using different evaluation indexes

the RMSE(Root Mean Square Error)

MAPE(Mean Absolute Percentage Error) and MAE(Mean Absolute Error) of the combined model are reduced by 23.1%

31% and 24.2% . Compared with the multiple linear regression model

the reduction is 35.3%

41.3% and 41% . The accuracy of the model is better than the traditional multiple linear regression model and multi-layer perceptron model

which can better predict environmental PM

2.5

concentration and provide reference for optimizing the environment.

关键词

Keywords

references

FAN H, ZHAO C F, YANG Y K. A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018 [J]. Atmospheric Environment, 2020, 220(117066): 1-12.

WANG W, TANG D G, LIU H J, et al . Research on corrent pollution sataus and pollution characteristics of PM 2.5 in China [J ] . Research of Environmental Sciences, 2000(1): 1-5.

GAN Y Y, LI Z S. A numerical effect on PM 2.5 in the kitchen [J ] . Journal of Guangdong University of Technology, 2020, 37(3): 82-87.

张洪. 京津冀地区空气质量时空分布特征及PM 2.5 浓度预测[D ] . 哈尔滨: 哈尔滨师范大学, 2020.

XU X D, TONG T, ZHANG W, et al . Fine-grained prediction of PM 2.5 concentration based on multisource data and deep learning [J ] . Atmospheric Pollution Research, 2020, 11(10): 1728-1737.

MASOOD A, AHMAD K. A model for particulate matter PM 2.5 prediction for Delhi based on machine learning approaches [J ] . Procedia Computer Science, 2020, 167(1): 2101-2110.

ZHANG Y W, GUO A D, WU H L, et al . Seasonal prediction of PM 2.5 based on the PCA-BP neural network [J ] . Journal of Nanjing Forestry University (Natural Sciences Edition), 2020, 44(5): 231-238.

HE H D, LI M, WANG W L, et al . Prediction of PM 2.5 concentration based on the similarity in air quality monitoring network [J ] . Building and Environment, 2018, 137(1): 11-17.

ASHA B C. Estimating PM 2.5 concentration from satellite derived aerosol optical depth and meteorological variables using a combination model [J ] . Atmospheric Pollution Research, 2019, 10(3): 847-857.

LIU Z R, HU B, WANG L L, et al . Seasonal and diurnal variation in particulate matter (PM 10 and PM 2.5 ) at an urban site of Beijing: analyses from a 9-year study [J ] . Environmental Science and Pollution Research, 2015, 22(1): 627-642.

LI Z M, SUN Z B, SHAO X, et al . Using Morlet wavelet analysis to analyze multiple time scale periodically in PM 2.5 in Beijing [J ] . China Environmental Science, 2017, 37(2): 407-415.

HE D, XU B P, WU L F, et al . Analysis of the impact of meteorological factors on the pollution characteristics of PM 2.5 and PM 10 in the typical cities of Jiangxi Province—taking Pingxiang city as an example [J ] . Energy Research and Mangement, 2020(4): 42-46.

JIAO C, HUANG Y R, ZHAO Q F, et al . Application of time series decomposition model to the predictionof the number of diabetics in Shandong Province [J ] . Chinese Rural Health Service Administration, 2021, 41(2): 93-97.

ZHENG W, WANG C Q, LI W D. Forecasting model of agricultural products prices based on seasonal index adjustment and HGWO-SVR algorithm[J]. Statistics Decision, 2018, 34(19): 33-37.

GUO X Y, YIN X G, ZHANG Y Y. The analysis method of seasonal index number of time sequence of seasonal fluctuation [J]. Journal of Applied Statistics and Management, 2000(6): 60-64.

XU W, REN G, LIU Y, et al . Forecasting energy consumption using a new GM–ARMA model based on HP filter: the case of Guangdong Province of China [J ] . Economic Modelling, 2015, 45(1): 127-135.

CHEN T J. Seasonal traffic flow prediction model of expressway based on improved wavelet neural network[J]. Intelligent City, 2020, 6(3): 22-24.

SUN S, RAPPAPORT T S, RANGAN S, et al. Propagation path loss models for 5G urban micro-and macro-cellular scenarios[C]// 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). Nanjing: IEEE, 2016: 1-6.

XIE S F, ZHAO Y, LI G H, et al . Prediction of PM 2.5 concentration based on WPA-WOA-BP neural network [J ] . Journal of Geodesy and Geodynamics, 2021, 41(1): 12-16.

WANG X K, YU H Y, YANG G Y, et al . Prediction of nonthly average PM 2.5 concentration based on multilayer combination model: a case study of Wuhan [J ] . Journal of Mudanjiang Normal University(Natural Sciences Edition), 2021(1): 7-11.

GUO Q J, YAO Y B, ZHOU Y J. PM 2.5 random forest prediction model incorporating GNSS meteorological parameters[J ] . Science of Surveying and Mapping, 2021, 46(4): 37-42.

王赛兰. 基于多元方法和时间序列的PM 2.5 浓度分析与预测[D ] . 长沙: 湖南师范大学, 2019.

CHEN B H, JIN Q F, CHAI H L, et al. Spatiotemporal distribution and correlation factors of PM 2. 5 concentrations in Zhejiang Province[J ] . Acta Scientiae Circumstantiae, 2021: 41(3): 817-829.

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季节指数改进的PM2.5质量浓度组合预测模型研究

PM2.5 Concentration Improving Prediction Modeling of Seasonal Index

DOI：10.12052/gdutxb.210055

摘要

Abstract

关键词

Keywords

references

季节指数改进的PM_2.5质量浓度组合预测模型研究

PM_2.5 Concentration Improving Prediction Modeling of Seasonal Index