The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

Song, Yiliao; Qin, Shanshan; Qu, Jianhua; Liu, Feng

doi:10.1016/j.atmosenv.2015.06.032

Cited by 76 publications

(24 citation statements)

References 22 publications

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“…Therefore, knowing the 24-h rainfall can help to understand the category and sustainability of dust-haze, i.e., large amounts of rainfall may help to reduce the occurrence of dust-haze, while no rain occurring in the following days may increase the probability of sustained dust-haze. Such rainfall information may also help to correct air quality forecast models which are mainly based on the dynamics and chemistry of aerosols (Otte et al, 2005;Saide et al, 2011;Song et al, 2015). Different lag period lengths were found following the same rain between the two cities, e.g., lag periods for PM 2.5 following 20 mm rainfall were 3 days in Guangzhou and 6 days in Xi'an, indicating the different durations in air quality improvement.…”

Section: Discussionmentioning

confidence: 96%

The washout effects of rainfall on atmospheric particulate pollution in two Chinese cities

Guo

Zhang

Lin

et al. 2016

Environmental Pollution

134

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Section: Discussionmentioning

confidence: 96%

The washout effects of rainfall on atmospheric particulate pollution in two Chinese cities

Guo

Zhang

Lin

et al. 2016

Environmental Pollution

134

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“…In [9], the experimental results show that artificial intelligent optimization is superior to MLS or MLE in the process of searching for optimal distribution parameters. Accordingly, in this paper, we utilized artificial intelligence optimization to search for the optimal distribution function parameters.…”

Section: Simulation Modeling and Analysismentioning

confidence: 99%

“…Accordingly, compared to empirical models, CTMs is less accurate. Empirical models mainly involve multiple linear regression (MLR), autoregressive integrated moving average model (ARIMA), hidden Markov model and artificial intelligence models, which are generally applied in air pollutant forecasting [5,7,8,9]. However, the most prevalent model for air pollutant forecasting is based on the theory of artificial intelligence, which is efficient and accurate in practical application.…”

Section: Introductionmentioning

confidence: 99%

“…Song et al applied a cuckoo search optimization algorithm to optimize the parameters of Weibull, Rayleigh, Lognormal and Gamma distribution functions, which can be conducive to implement interval forecasting further. Besides, the paper applied an adaptive neuro-fuzzy model to perform deterministic forecasting of PM 2.5 and PM 10 from three cities in China [9]. Kanchan Prasad et al developed an adaptive neuro-fuzzy inference system to comprehensively forecast daily air pollution concentrations of five air pollutants, namely SO 2 , NO 2 , CO, O 3 and PM 10 .…”

Section: Introductionmentioning

confidence: 99%

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Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Wang

Niu

Wang

2017

IJERPH

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The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

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“…At present, energy consumption carbon emissions (ECCE) are a practical issue which runs through the political, economy, social and other fields more than a scientific problem. In this context, carrying out systematic studies on ECCE has become a priority in ecological environment, natural disaster and so on, such as river drought and pollution [14,15], air quality [16], earthquake [17] and floods [18], mainly include the following methods: Markov model [19], Gray model (GM) [20], support vector machine [21], neural networks [22] and so forth. Among these methods, the back propagation neural network (BPNN) which was proposed based on a neural network put forward by Rumelhart and McClelland [22], has better performance in forecasting with its strong non-linear mapping ability, high self-learning and self-adaptability [23,24].…”

Section: Introductionmentioning

confidence: 99%

Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

et al. 2017

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Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is "Severe warning"-"Moderate warning"-"Slight warning"; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of "Semi-secure" for a long time and the corresponding alarm is still in the state of "Slight warning", reflecting that the situation is still not optimistic. Keywords:energy consumption carbon emissions (ECCE); security early warning; pressure-state-response (P-S-R) model; time and space analysis; back propagation neural network based on kidney-inspired algorithm (KA-BPNN)

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The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

Cited by 76 publications

References 22 publications

The washout effects of rainfall on atmospheric particulate pollution in two Chinese cities

The washout effects of rainfall on atmospheric particulate pollution in two Chinese cities

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

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