DOI: 10.1016/j.atmosenv.2014.12.011
Scopus记录号: 2-s2.0-84918500080
论文题名: Real time air quality forecasting using integrated parametric and non-parametric regression techniques
作者: Donnelly A ; , Misstear B ; , Broderick B
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 103 起始页码: 53
结束页码: 65
语种: 英语
英文关键词: Air quality forecasting
; Nitrogen dioxide
; Nonparametric kernel regression
; Statistical modelling
Scopus关键词: Air quality
; Computational efficiency
; Forecasting
; Linear regression
; Nitrogen
; Nitrogen oxides
; Wind
; Air quality forecasting
; Meteorological parameters
; Multiple linear regressions
; Nitrogen dioxides
; Non-parametric regression
; Nonparametric kernel regressions
; Statistical modelling
; Wind speed and directions
; Urban growth
; factor A
; nitrogen dioxide
; air quality
; forecasting method
; model validation
; nitrous oxide
; numerical model
; parameterization
; real time
; regression analysis
; temporal variation
; wind velocity
; accuracy
; air quality
; Article
; circadian rhythm
; environmental parameters
; explanatory variable
; forecasting
; kernel method
; meteorological phenomena
; meteorology
; multiple linear regression analysis
; nonparametric test
; parametric test
; predictor variable
; real time air quality forecasting
; seasonal variation
; time series analysis
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: This paper presents a model for producing real time air quality forecasts with both high accuracy and high computational efficiency. Temporal variations in nitrogen dioxide (NO2) levels and historical correlations between meteorology and NO2 levels are used to estimate air quality 48h in advance. Non-parametric kernel regression is used to produce linearized factors describing variations in concentrations with wind speed and direction and, furthermore, to produce seasonal and diurnal factors. The basis for the model is a multiple linear regression which uses these factors together with meteorological parameters and persistence as predictors. The model was calibrated at three urban sites and one rural site and the final fitted model achieved R values of between 0.62 and 0.79 for hourly forecasts and between 0.67 and 0.84 for daily maximum forecasts. Model validation using four model evaluation parameters, an index of agreement (IA), the correlation coefficient (R), the fraction of values within a factor of 2 (FAC2) and the fractional bias (FB), yielded good results. The IA for 24hr forecasts of hourly NO2 was between 0.77 and 0.90 at urban sites and 0.74 at the rural site, while for daily maximum forecasts it was between 0.89 and 0.94 for urban sites and 0.78 for the rural site. R values of up to 0.79 and 0.81 and FAC2 values of 0.84 and 0.96 were observed for hourly and daily maximum predictions, respectively. The model requires only simple input data and very low computational resources. It found to be an accurate and efficient means of producing real time air quality forecasts. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82043
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Museum Building, College Green, Dublin 2, Ireland
Recommended Citation:
Donnelly A,, Misstear B,, Broderick B. Real time air quality forecasting using integrated parametric and non-parametric regression techniques[J]. Atmospheric Environment,2015-01-01,103