There is an increasing body of evidence showing the impact of air pollutants on human health such as on the respiratory, and cardio- and cerebrovascular systems. In China, as people begin to pay more attention to air quality, recent research focused on the quantitative assessment of the effects of air pollutants on human health. To assess the health effects of air pollutants and to construct an indicator placing emphasis on health impact, a generalized additive model was selected to assess the health burden caused by air pollution. We obtained Baidu indices (an evaluation indicator launched by Baidu Corporation to reflect the search popularity of keywords from its search engine) to assess daily query frequencies of 25 keywords considered associated with air pollution-related diseases. Moreover, we also calculated the daily concentrations of major air pollutants (including PM₁₀, PM_2.5, SO₂, O₃, NO₂, and CO) and the daily air quality index (AQI) values, and three meteorological factors: daily mean wind level, daily mean air temperature, and daily mean relative humidity. These data cover the area of Beijing from 1 March 2015 to 30 April 2017. Through the analysis, we produced the relative risks (RRs) of the six main air pollutants for respiratory, and cardio- and cerebrovascular diseases. The results showed that O₃ and NO₂ have the highest health impact, followed by PM₁₀ and PM_2.5. The effects of any pollutant on cardiovascular diseases was consistently higher than on respiratory diseases. Furthermore, we evaluated the currently used AQI in China and proposed an RR-based index (health AQI, HAQI) that is intended for better indicating the effects of air pollutants on respiratory, and cardio- and cerebrovascular diseases than AQI. A higher Pearson correlation coefficient between HAQI and RR_Total than that between AQI and RR_Total endorsed our efforts.

The rapid development of urban rail transit brings high efficiency and convenience. At the same time, the increasing passenger flow also remarkably increases the risk of emergencies such as passenger stampedes. The accurate and real-time prediction of dynamic passenger flow is of great significance to the daily operation safety management, emergency prevention, and dispatch of urban rail transit systems. Two deep learning neural networks, a long short-term memory neural network (LSTM NN) and a convolutional neural network (CNN), were used to predict an urban rail transit passenger flow time series and spatiotemporal series, respectively. The experiments were carried out through the passenger flow of Beijing metro stations and lines, and the prediction results of the deep learning methods were compared with several traditional linear models including autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and space–time autoregressive integrated moving average (STARIMA). It was shown that the LSTM NN and CNN could better capture the time or spatiotemporal features of the urban rail transit passenger flow and obtain accurate results for the long-term and short-term prediction of passenger flow. The deep learning methods also have strong data adaptability and robustness, and they are more ideal for predicting the passenger flow of stations during peaks and the passenger flow of lines during holidays.

Multipronged statistical approaches were applied to give spatiotemporal insight into the long-term patterns of vegetative drought over the contiguous United States (CONUS) from a weekly NOAA/AVHRR VHI product of 1981-2017. Based on the analysis results, the following characteristics have been found: First, the category of the most frequently occurring drought is the moderate drought (D1), and the drought frequency successively decreases from abnormally dry conditions (D0) to exceptional drought (D4). The frequencies of moderate drought (D1), severe drought (D2), and extreme drought (D3) in the West Mountains region are consistently higher than the frequency of the corresponding severity levels in other regions, while the frequency of exceptional drought (D4) in the Great Plains is the highest. The frequencies of three worst drought levels (D2, D3, and D4) in the East region are consistently the lowest in the CONUS. Second, there are gradual changes in the trends of vegetative drought from March to October and the change patterns are significantly different for newly identified four vegetation classes (evergreen forest, deciduous forest, grass, and crop). There is more increase in VHI values in March-May and less in June-October for deciduous forest and grass. Evergreen forest presents similar results except more in October. There are obviously more pixels with significant increase in VHI values in March, April and July, August for crop. Third, the trajectories of the centroids of vegetative drought have significantly moved southwestward in the past 37 years. With drought severity levels increasing, the centroid movement got larger and there were regular translations toward southwest.