Tag Archives: E1AF

The rapid industrial development has led to the intermittent outbreak of

The rapid industrial development has led to the intermittent outbreak of pm2. cities, accompanied by a large population, consumption, and pollution. Together with Tianjin city and Hebei province, North China is becoming probably one of the most polluted and productive areas on the planet. By 2013, the transient human population of Beijing was 37.5 million, as well as the intermittent outbreak of 226256-56-0 manufacture polluting of the environment has greatly impacted every citizen’s life: physiological diseases [1, 2], depression, and poor visibility in traffic [3, 4]. The primary element 226256-56-0 manufacture of haze can be pm2.5 (particulate matters significantly less than 2.5?is correlated with normal severely polluted times negatively. The paper [12] founded a cubic exponential smoothing model by presenting dirt emission into haze prediction. Liang 226256-56-0 manufacture et al. remarked that there are many transmission and distribution patterns of pm2.5 [20]. Actually, Wang et al. described how the national government control policy is highly recommended in model simulations [9]. Many researches make use of backpropagation neural network as the simulation model [19, 21]. Statistical period series evaluation can be used in haze prediction, therefore long-term haze prediction can be problematic for current solutions to accomplish [22]. Multiple linear regression versions also perform well on daily scale prediction [23, 24]. However, the test data of existing researches is not ample; for example, [21] tested the prediction accuracy on only 3 days. Besides, Zhang et al. pointed out that pm2.5 accumulation in previous days significantly affects the present daily pm2.5 concentration, which should also be a concern in the modeling process [22]. Considering the above points, this paper presents a new AQI prediction model integrated with natural factor, humanity factor, and self-evolution factor. 3. The Prediction Model of Beijing’s Daily AQI 3.1. The Parameters and Architecture of the Prediction Model The change of daily pm2.5 concentration depends on two factors: daily overall production of pm2.5 by human activities and daily overall natural diffusion or overall natural accumulation of pm2.5 ? could be directly observed. is generated by a semimanual method. is mainly related to daily human activities, and we calculate from AQI sequences of no less than five consecutive sunny and windless days. Special circumstances are also considered. In winter, will be larger because the heating system is on. The car usage restrictions and temporary stoppage of factories during Beijing APEC 2014 are also taken into consideration. is then calculated as ? (? is greater than zero, which means pm2.5 accumulates because of nonhuman factors. Thus, the daily net growth of pm2.5 (? represents other factors which affect present day’s pm2.5. 3.2. Complexity Reduction of the Prediction Model In order to facilitate the research and modeling process, we 226256-56-0 manufacture have proved that this model could be reduced to a vector autoregressive model. Proposition 1 . Formula (1) is a vector autoregressive model. Proof Assume that there exists sequence autocorrelation in formula (1). The autocorrelation is is white noise. Here, we apply the Cochrane-Orcutt iteration to rewrite formula (2): is the lag operator E1AF ( through successive iteration method. Specifically, this method uses residual error to estimate the unknown days’ AQI to predict present day’s AQI. Multiply (1 ? is an endogenous variable. And the policy control index depends on present day’s and previous days’ accumulation of history pm2.5, the wind power, the daily production of pm2.5, and daily diffusion of pm2.5: represents the influence brought about by other policies. The net growths of previous days’ pm2.5 and policy control index also have.