[Meta-analysis of the Effects of Metal Mining on Soil Heavy Metal Concentrations in Southwest China].

Metal mining is one of the main contributors of soil heavy metals. Previous studies examining the impact of metal mining on surrounding soil have mainly focused on one or a few metal mining areas. However, such studies cannot effectively inform the management of heavy metal pollution in soil at an inter-provincial scale. As part of this study, literature was collected on soil heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) affected by metal mining in regions of Southwest China (i.e., Yunnan Province, Sichuan Province, Guizhou Province, Chongqing Municipality, and Tibet Autonomous Region); Next, the impact of metal mining on the soil concentrations of these metals was quantified through meta-analysis, and the relationships between the selected factors (i.e., different sub-regions, metal minerals, and land-use types) and soil heavy metal concentrations were explored. Finally, the literature data was tested for publication bias. The results showed that metal mining in Southwest China has significantly increased the concentrations of heavy metals in topsoil. The different metals were ranked according to their weight effect sizes (ES+) in the following order Cd > Pb > Hg > Zn > As > Cu > Ni > Cr. Metal mining in both Sichuan and Yunnan led to higher effect sizes of soil Cd (ES+Sichuan=4.16, ES+Yunnan=3.20) and Pb (ES+Sichuan=3.47, ES+Yunnan=2.54) than those of the other heavy metals, while metal mining in Guizhou led to a higher effect size of soil Hg (ES+=2.80). The effect size of metal mining on soil heavy metals was higher in cultivated soil (ES+=1.42) than in forested soil (ES+=0.50). The mining of lead-zinc and tin significantly increased the concentrations of soil Cd, Pb, and Zn, and the mining of copper significantly increased the concentrations of soil Cu, Cd, and Pb. Of the investigated soil heavy metals in Southwest China, Pb and Zn showed slight potential publication biases (P<0.05). The above results can provide more effective information for the environmental protection of soil in metal mining areas of Southwest China.

[Assessment of the Availability of Soil Copper and Related Influencing Factors at a County Scale].

Understanding the spatial distribution of total copper, available copper, and the spatial non-stationary relationships between available copper and relevant environmental factors is important for the delineation of soil risk areas and the development of related control measures. This study was conducted in Zhangjiagang County of Jiangsu Province, China. The risk status for soil copper was assessed based on 357 soil samples in the study area. The effects of soil type and land-use type on the concentration of available soil copper were discussed first. Then, ordinary kriging was adopted to map the spatial distribution patterns of the total soil copper and available soil copper, and the spatial distribution map of the copper availability ratio (i.e., available copper/total copper) was also developed for the study area. The risk areas for soil copper were delineated based on the spatial distribution patterns of available soil copper and the copper availability ratio. Finally, a new spatial local regression technique, geographic weighted regression (GWR), was used to explore the local spatial regression relationships between available copper and its three main impact factors (i.e., total soil copper, soil pH, and SOM). Results showed that both soil type and land-use type had some effect on the concentration of available soil copper. The copper availability ratio had a strong spatial heterogeneity, with the higher values mainly in the northeast, southeast, and northwest of the study area and the lower values mainly in the middle and southwest of the study area. The range of the copper availability ratio is 13.56% to 29.15%. The results of the comparison of the traditional ordinary least squares regression (OLSR) and GWR showed that the GWR model had higher fitting accuracy than the OLSR model[i.e., a larger decision coefficient R2, and smaller corrected Akaike information criteria (AICc) and the sum of squares of residuals] in modeling the relationships between available copper and its three main impact factors. The GWR analysis showed that the effect of soil factors on the concentration of soil available copper was non-stationary. The GWR could effectively reveal the spatial non-stationary influence of the related soil factors on the concentration of available soil copper, and the results could explain the reasons for the accumulation of available soil copper in local areas. Potential risk areas for available soil copper were delineated based on the copper availability ratio and the concentration of available soil copper in the study area. The results should be crucial data for developing specific control measures for soil copper at a regional scale.

[Interpolation of daily mean temperature by using geographically weighted regression-Kriging].

Air temperature is the input variable of numerous models in agriculture, hydrology, climate, and ecology. Currently, in study areas where the terrain is complex, methods taking into account correlation between temperature and environment variables and autocorrelation of regression residual (e.g., regression Kriging, RK) are mainly adopted to interpolate the temperature. However, such methods are based on the global ordinary least squares (OLS) regression technique, without taking into account the spatial nonstationary relationship of environment variables. Geographically weighted regression-Kriging (GWRK) is a kind of method that takes into account spatial nonstationarity relationship of environment variables and spatial autocorrelation of regression residuals of environment variables. In this study, according to the results of correlation and stepwise regression analysis, RK1 (covariates only included altitude), GWRK1 (covariates only included altitude), RK2 (covariates included latitude, altitude and closest distance to the seaside) and GWRK2 (co-variates included altitude and closest distance to the seaside) were compared to predict the spatial distribution of mean daily air temperature on Hainan Island on December 18, 2013. The prediction accuracy was assessed using the maximum positive error, maximum negative error, mean absolute error and root mean squared error based on the 80 validation sites. The results showed that GWRK1's four assessment indices were all closest to 0. The fact that RK2 and GWRK2 were worse than RK1 and GWRK1 implied that correlation among covariates reduced model performance.

[Assessment for spatial uncertainty of daily minimum temperature by using sequential Gaussian simulation].

Understanding daily minimum temperature is of great importance for assessing low temperature damages to crops and guiding people to take timely remedial measures to ensure food security. Kriging is a widely used technology for mapping the spatial distribution of the near-surface temperature. However, the smoothing effect, commonly found in the Kriging maps, leads to low values to be overestimated and high values to he underestimated. For daily minimum temperature on Hainan Island which was affected by cold air on December 12, 2011, cross-validation was adopted to evaluate the prediction accuracy of ordinary Kriging (OK) and Kriging with external drift (KED). The spatial distribution maps of daily minimum temperature on Hainan Island on December 12, 2011 produced by OK and sequential Gaussian simulation (SGS) were compared. Results showed that the prediction accuracy of KED (r = 0.86) was not superior to OK (r = 0.86) significantly. SGS could generate multiple equiprobable simulation realizations, and the distribution and variance function of the original data could be reproduced in the realizations. The simulation realizations generated by SGS overcame the smoothing effect of Kriging and could more truly reflect the spatial distribution of minimum temperature on the day on Hainan Island. In the region where daily minimum temperature was low, and the temperature change was small, the conditional variance of the SGS results was less than the ordinary Kriging variance. Spatial uncertainty of a potential chilling damage area could be quantified by multiple simulation realizations generated by SGS. SGS was a valuable tool for assessing agro-meteorological disasters caused by low temperature.