Horizontal Distribution and Annual Variations of Fecal Coliform Bacteria in Luoyuan Bay

Fecal Coliform Bacteria （FCB） of marine waters was monitored in Luoyuan Bay from January, 2003 to December, 2005. The results showed that number of FCB in marine water samples ranged from no detection （ 〈2 cfu/100 mL） to 540 cfu/100 mL in Luoyuan Bay. Values of FCB during August to October were significantly higher than those during January to April during three year period. Monthly changes of FCB values at each year period were mainly due to monthly precipitation. In addition, compared with FCB values in difference sample sites, values of FCB in Bay-heed were significantly higher than those in middle of bay and Bay-mouth, Luoyuan Bay. However, values of FCB in Bay-mouth were significantly less than those in middle of bay. The ratios to the par of FCB in 2003, 2004, and 2005 years were 100%, 98.0%, and 97.9%, respectively. Therefore, we considered that the pollution of FCB of surface marine water in Luoyuan Bay was not serious.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Estimating emissions and concentrations of road dust aerosol over China using the GEOS-Chem model

Paved road dust is one of the most important aerosols in China. The authors estimated road dust emissions using an empirical model （AP-42 model） developed by the U.S. Environmental Protection Agency, and simulated road dust concentrations over China for the years 2006-2011 using the GEOS-Chem model.The annual road dust emissions amount averaged over 2006-2011 is estimated to be 2331.4 kt, with much higher emissions in eastern China than in western China. Because of heavy traffic and a dense road network, emissions are high over Beijing-Tianjin-Tanggu （BTT）, Henan Province, and Shandong Province. Meanwhile, emissions are calculated to be 459.1, 112.0, and 102.7 kt, respectively, over BTT, the Pearl River Delta （PRD） region, and the Yangtze River Delta （YRD）. Due to the monthly variation of precipitation, road dust emissions over China are simulated to be highest in December and lowest in June. The highest annual mean road dust concentration is simulated to be 14.5 tJg m-3 in Beijing. Over 2006-2011, because of the increases in road length and number of vehicles, annual road dust emissions for China as a whole, Bl-r, the PRD, and the YRD, are simulated to increase by 260%, 239%, 266%, and 59%, respectively, leading to 233%, 243%, 273%, and 100% increases in road dust concentrations in these regions, respectively. Our results have important implications for air pollution control in China.