Prediction of air quality indicators for the Beijing-Tianjin-Hebei region

Wu, Lifeng; Li, Nu; Yang, Yingjie

doi:10.1016/j.jclepro.2018.06.068

Cited by 93 publications

(26 citation statements)

References 19 publications

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“…To understand the trend of haze in Beijing in the future, the linear time-varying GM(1,3) model proposed in this paper is used to forecast PM 10 concentration in Beijing for 2019 to 2021, the forecast results are shown in Table 5. According to Table 5, PM 10 concentration in Beijing will decrease slowly from 2019 to 2021, but still exceed the China's environmental air quality standard [13]. According to all the results considered, we will perform a discussion as follows: the average predicted relative errors of the linear time-varying GM(1,3) model are less than those of the original GM(1,3) model, which is attributed to the improved adaptability of the model to the dynamic change characteristics data.…”

Section: Forecast Results and Discussionmentioning

confidence: 99%

“…In other words, due to the constraints of data acquisition tools, acquisition conditions and errors by the acquisition personnel, the value of haze may contain inevitable measurement errors in a certain range, thus, haze has obvious grey number features. Therefore, Wu et al established a grey prediction model to forecast annual pollutant concentrations effecting haze by limited data for the first time [13]. Grey prediction model is an important part of grey system.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

et al. 2019

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Haze is the greatest challenge facing China's sustainable development, and it seriously affects China's economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM 10 , SO 2 and NO 2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM 10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

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Section: Forecast Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

et al. 2019

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“…Since the grey prediction model was proposed, it has been widely concerned by scholars. The existing studies mainly focus on the theoretical development from the aspects of background value optimization [3,4], parameter optimization [5][6][7], model expansion [8][9][10], and the application of natural gas [11,12], electric power [13][14][15][16], environment [17][18][19][20], economy [21,22], and transportation [23,24].…”

Section: Introductionmentioning

confidence: 99%

Fractional‐Order Accumulative Linear Time‐Varying Parameters Discrete Grey Forecasting Model

Gao

Zhan

Liu

2019

Mathematical Problems in Engineering

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Traditional discrete grey forecasting model can effectively predict the development trend of the stabilizing system. However, when the system has disturbance information, the prediction result will have larger error, and there will appear significant downward trend in the stability of the model. In the presence of disturbance information, this paper presents a fractional-order linear time-varying parameters discrete grey forecasting model to deal with the system that contains both linear trend and nonlinear trend. The modeling process of the model and calculation method are given. The perturbation bounds of the new model are analyzed by using the least-squares method of perturbation theory. And it is compared with that of the first-order linear time-varying parameters discrete grey forecasting model. Finally, two real cases are given to verify the effectiveness and practicality of the proposed method.

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“…e traditional grey model has been used to provide particulate matter information for the roadside inhabitants [13]. e grey model with fractional order accumulation has been used to predict air quality [14], and grey relational analysis has been used to determine whether carbon price has multiple timescales [15]. e purpose of this study is to assess the relationship between AQI and housing price.…”

Section: Introductionmentioning

confidence: 99%

Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

Zhang

Chen

2019

Complexity

Self Cite

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To analyze the relationship between air quality index (AQI) and housing price, six relationship indexes between air quality index and housing price were calculated using grey spectrum theory, specifically grey association spectrum, grey cospectrum, grey amplitude spectrum, grey phase spectrum, grey lag time length, and grey condense spectrum. Three main change periods were extracted. There was a negative correction between the air quality and the housing price in Handan. The results provide a basis for the government’s measures to prevent haze.

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Prediction of air quality indicators for the Beijing-Tianjin-Hebei region

Cited by 93 publications

References 19 publications

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Fractional‐Order Accumulative Linear Time‐Varying Parameters Discrete Grey Forecasting Model

Grey Spectrum Analysis of Air Quality Index and Housing Price in Handan

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