We use the aerosol optical depth (AOD) measured by the moderate resolution imaging spectrometer (MODIS) onboard the Terra satellite, air pollution index (API) daily data measured by the Shanghai Environmental Monitoring Center (SEMC), and the ensemble empirical mode decomposition (EEMD) method to analyze the air quality variability in Shanghai in the recent decade. The results indicate that a trend with amplitude of 1.0 is a dominant component for the AOD variability in the recent decade. During the World Expo 2010, the average AOD level reduced 30% in comparison to the long-term trend. Two dominant annual components decreased 80% and 100%. This implies that the air quality in Shanghai was remarkably improved, and environmental initiatives and comprehensive actions for reducing air pollution are effective. AOD and API variability analysis results indicate that semi-annual and annual signals are dominant components implying that the monsoon weather is a dominant factor in modulating the AOD and API variability. The variability of AOD and API in selected districts located in both downtown and suburban areas shows similar trends; i.e., in 2000 the AOD began a monotonic increase, reached the maxima around 2006, then monotonically decreased to 2011 and from around 2006 the API started to decrease till 2011. This indicates that the air quality in the entire Shanghai area, whether urban or suburban areas, has remarkably been improved. The AOD improved degrees (IDS) in all the selected districts are (8.6±1.9)%, and API IDS are (9.2±7.1)%, ranging from a minimum value of 1.5% for Putuo District to a maximum value of 22% for Xuhui District.
The middle Yangtze Reach is one of the most developed regions of China. As a result, most lakes in this area have suffered from eutrophication and serious environmental pollution during recent decades. The aquatic biodiversity in the lakes of the area is thus currently under significant threat from continuous human activities. Testate amoebae (TA) are benthic (rarely planktonic) microorganisms characterized by an agglutinated or autogenous shell. Owing to their high abundance, preservation potential in lacustrine sediments, and distinct response to environmental stress, they are increasingly used as indicators for monitoring water quality and reconstructing palaeoenvironmental changes. However this approach has not yet been developed in China. This study presents an initial assessment of benthic TA assemblages in eight lakes of Lake Donghu in the region of Wuhan, China. Testate amoeba community structure was most strongly correlated to water pH. In more alkaline conditions, communities were dominated by
Assessing the extent to which all bio-fuels that are claimed to be renewable are in fact renewable is essential because producing such renewable fuels itself requires some amount of non-renewable energy (NE) and materials. Using hybrid life cycle analysis (LCA)—from raw material collection to delivery of pellets to end users—the energy cost of wood pellet production in China was estimated at 1.35 J/J, of which only 0.09 J was derived from NE, indicating that only 0.09 J of NE is required to deliver 1 J of renewable energy into society and showing that the process is truly renewable. Most of the NE was consumed during the conversion process (46.21%) and delivery of pellets to end users (40.69%), during which electricity and diesel are the two major forms of NE used, respectively. Sensitivity analysis showed that the distance over which the pellets are transported affects the cost of NE significantly. Therefore the location of the terminal market and the site where wood resources are available are crucial to saving diesel.
It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are mainly used to estimate the real-time traffic conditions on road segments, and has done little for turn delay estimation. However, turn delays on road intersections contribute significantly to the overall travel time on road networks in modern cities. In this paper, we present a technical framework to calculate the turn delays on road networks with float car data. First, the original floating car data collected with GPS equipped taxies was cleaned and matched to a street map with a distributed system based on Hadoop and MongoDB. Secondly, the refined trajectory data set was distributed among 96 time intervals (from 0: 00 to 23: 59). All of the intersections where the trajectories passed were connected with the trajectory segments, and constituted an experiment sample, while the intersections on arterial streets were specially selected to form another experiment sample. Thirdly, a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm argued is not only statistically fitted the real traffic conditions, but also is insensitive to data sparseness and missing data problems, which currently are almost inevitable with the widely used floating car data collecting technology. We adopted the floating car data collected from March to June in Beijing city in 2011, which contains more than 2.6 million trajectories generated from about 20000 GPS-equipped taxicabs and accounts for about 600?GB in data volume. The result shows the principal curve based algorithm we presented takes precedence over traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy (about 10%–15% higher in RMSE), as well as reflecting the changing trend of traffic congestion. With the estimation result for the travel delay at intersections, we analyzed the spatio-temporal distribution of turn delays in three time scenarios (0: 00–0: 15, 8: 15–8: 30 and 12: 00–12: 15). It indicates that during one’s single trip in Beijing, average 60% of the travel time on the road networks is wasted on the intersections, and this situation is even worse in daytime. Although the 400?main intersections take only 2.7% of all the intersections, they occupy about 18% travel time.
Using the hydrological and meteorological data in the Kaidu River Basin during 1957–2008, we simulated the hydro-climatic process by back-propagation artificial neural network (BPANN) based on wavelet analysis (WA), and then compared the simulated results with those from a multiple linear regression (MLR). The results show that the variation of runoff responded to regional climate change. The annual runoff (AR) was mainly affected by annual average temperature (AAT) and annual precipitation (AP), which revealed different variation patterns at five time scales. At the time scale of 32-years, AR presented a monotonically increasing trend with the similar trend of AAT and AP. But at the 2-year, 4-year, 8-year, and 16-year time-scale, AR presented nonlinear variation with fluctuations of AAT and AP. Both MLR and BPANN successfully simulated the hydro-climatic process based on WA at each time scale, but the simulated effect from BPANN is better than that from MLR.