Regional partitioning of agricultural non-point source pollution in China using a projection pursuit cluster model

A projection pursuit cluster（PPC） model was used to analyze the regional partitioning of agricultural non-point source pollution in China.The environmental factors impacting the agricultural non-point source pollution were compiled into a projection index to set up the projection index function.A novel optimization algorithm called Free search（FS） was introduced to optimize the projection direction of the PPC model.By making the appropriate improvements as we explored the use of the algorithm,it became simpler,and developed better exploration abilities.Thus,the multi-factor problem was converted into a single-factor cluster,according to the projection,which successfully avoided subjective disturbance and produced objective results.The cluster results of the PPC model mirror the actual regional partitioning of the agricultural non-point source pollution in China,indicating that the PPC model is a powerful tool in multi-factor cluster analysis,and could be a new method for the regional partitioning of agricultural non-point source pollution.

Two Ultraviolet Radiation Datasets that Cover China

Ultraviolet（UV） radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization＇s measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes.

Pedotransfer functions for predicting bulk density of coastal soils in East China

Soil bulk density(BD) is an important physical property and an essential factor for weight-to-volume conversion. However, BD is often missing from soil databases because its direct measurement is labor-intensive, time-consuming, and sometimes impractical, particularly on a large scale. Therefore, pedotransfer functions(PTFs) have been developed over several decades to predict BD. Here, six previously revised PTFs(including five basic functions and stepwise multiple linear regression(SMLR)) and two new PTFs, partial least squares regression(PLSR) and support vector machine regression(SVMR), were used to develop BD-predicting PTFs for coastal soils in East China. Predictor variables included soil organic carbon(SOC) and particle size distribution(PSD). To compare the robustness and reliability of the PTFs used, the calibration and prediction processes were performed 1 000 times using the calibration and validation sets divided by a random sampling algorithm. The results showed that SOC was the most important predictor, and the revised PTFs performed reasonably although only SOC was included. The PSD data were useful for a better prediction of BD, and sand and clay fractions were the second and third most important properties for predicting BD. Compared to the other PTFs, the PLSR was shown to be slightly better for the study area(the average adjusted coefficient of determination for prediction was 0.581). These results suggest that PLSR with SOC and PSD data can be used to fill in the missing BD data in coastal soil databases and provide important information to estimate coastal carbon storage, which will further improve our understanding of sea-land interactions under the conditions of ongoing global warming.

The compositions,sources,and size distribution of the dust storm from China in spring of 2000 and its impact on the global environment

The average mass concentration of the aerosols in Beijing during the dust storm in the spring of 2000 was -6000 μg · m-3, -30 times as high as that in the non-dust storm days. The enrichment factors of the pollution elements As, Sb and Se were higher than those in the non-dust storm days. This indicated that As, Sb and Se resulted from the pollution sources of those areas, through which the dust storm passed during their long-range transport, in addition to the local pollution sources in Beijing. The enrichment factors of the Pb, Zn, Cd and Cu were much less than those in the non-dust storm days, suggesting that the local pollution sources in Beijing area contributed to them mostly. The enrichment factors of elements Al, Fe, Sc, Mn, Na, Ni, Cr, V and Co were close to 1, showing that these elements originated from crust. The concentration of S in the dust storm was -10 μg · m-3,4 times as high as that in non-dust storm. S in the aerosols resulted from the adsorption of gaseous SO2 and the consequent

Effect of groundwater depth on riparian plant diversity along riverside-desert gradients in the Tarim River

Aims Riparian plant diversity is sensitive to changes in groundwater in arid regions.However,little is known about how plant diversity responds to changes in environment along riverside-desert gradi-ents in riparian ecosystem.Our objectives were to(i)identify ri-parian plant diversity along riverside-desert gradients in Tarim desert riparian forests,(ii)analyze the impact of environment variables on plant diversity,(iii)determine the optimum groundwater depth for different plant life-forms.Methods Six transects 90 quadrats(with each size 100 m×100 m)distributed vertically to river bed along riverside-desert gradients~30 km in length were surveyed.At each quadrat,the morphological features of riparian plant communities were measured,and the groundwater depth,soil water,soil salinity,soil nutrient were also monitored at same sites.Important Finding Three distinct vegetation communities were identified based on cover and richness in the tree,shrub and herb layers:the riparian zone,the transitional zone and the desert margin zone.Twelve spe-cies were indicators of the three vegetation communities.Riparian plant diversity was influenced by groundwater depth,distance from river,soil moisture content,soil salinity and soil nutrient by redundancy analysis.In response to groundwater depth,the op-timal groundwater depths for species diversity,evenness and shrub cover were 2.8,2.7 and 3.7 m,respectively.Therefore,maintaining high plant diversity requires managers to ensure stable groundwater depth for different plant life-forms rather than for some of them.