Spatial Variability of Soil Carbon to Nitrogen Ratio and Its Driving Factors in Ili River Valley,Xinjiang,Northwest China

Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.

Progress on spatial prediction methods for soil particle-size fractions

Soil particle-size fractions(PSFs),including three components of sand,silt,and clay,are very improtant for the simulation of land-surface process and the evaluation of ecosystem services.Accurate spatial prediction of soil PSFs can help better understand the simulation processes of these models.Because soil PSFs are compositional data,there are some special demands such as the constant sum(1 or 100%) in the interpolation process.In addition,the performance of spatial prediction methods can mostly affect the accuracy of the spatial distributions.Here,we proposed a framework for the spatial prediction of soil PSFs.It included log-ratio transformation methods of soil PSFs(additive log-ratio,centered log-ratio,symmetry log-ratio,and isometric log-ratio methods),interpolation methods(geostatistical methods,regression models,and machine learning models),validation methods(probability sampling,data splitting,and cross-validation) and indices of accuracy assessments in soil PSF interpolation and soil texture classification(rank correlation coefficient,mean error,root mean square error,mean absolute error,coefficient of determination,Aitchison distance,standardized residual sum of squares,overall accuracy,Kappa coefficient,and Precision-Recall curve) and uncertainty analysis indices(prediction and confidence intervals,standard deviation,and confusion index).Moreover,we summarized several paths on improving the accuracy of soil PSF interpolation,such as improving data distribution through effective data transformation,choosing appropriate prediction methods according to the data distribution,combining auxiliary variables to improve mapping accuracy and distribution rationality,improving interpolation accuracy using hybrid models,and developing multi-component joint models.In the future,we should pay more attention to the principles and mechanisms of data transformation,joint simulation models and high accuracy surface modeling methods for multi-components,as well as the combination of soil particle size curves with stochastic simulations.We proposed a clear framework for improving the performance of the prediction methods for soil PSFs,which can be referenced by other researchers in digital soil sciences.

Assessment of pollution of potentially harmful elements in soils surrounding a municipal solid waste incinerator, China

We assessed the contamination levels of Mn, Zn, Cr, Cu, Ni, Pb, As and Hg and the risks posed by these potentially harmful elements in top-soils around a municipal solid waste incinerator （MSWI）. We collected 20 soil samples, with an average pH of 8.1, and another fly ash sample emitted from the MSWI to investigate the concentrations of these elements in soils. We determined the concentrations of these elements by inductively coupled plasma-optical emission spectrometer （ICP-OES）, except for Hg, which we measured by AF-610B atomic fluorescence spectrometer （AFS）. We assessed the risks of these elements through the use of geoaccumulation index （/geo）, potential ecological risk index （R/）, hazard quotient （HQi） and cancer risk （Riski）. The results showed that concentrations of potentially harmful elements in soil were influenced by the wind direction, and the concentrations of most elements were higher in the area northwest of the MSWI, compared with the area southeast of the incinerator, with the exception of As; these results were in accordance with those results acquired from our contour maps. According to the I^o values, some soil samples were clearly polluted by Hg emissions. However, the health risk assessment indicated that the concentrations of Hg and other elements in soil did not pose non-carcinogenic risks to the local populations. This was also the case for the carcinogenic risks posed by As Cr and Ni. The carcinogenic risk posed by As was higher in the range 6.49 × 10 -9.58 × 10 -6, but this was still considered to be an acceptable level of risk.