Abdugheni Abliz

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

Pollution characteristics and health risk assessment of heavy metals in the vegetable bases of northwest China

The objective of this study was to investigate heavy metal contamination in four major vegetable bases and determine the health risks of residents in the vicinity of the highly urbanized city Urumqi in Xinjiang, China. In this paper, we determined the contents of six heavy metals (i.e., As, Zn, Cd, Cr, Hg, and Pb) in surface soil and groundwater to evaluate the levels of heavy metal pollution and human health risks using the pollution index (PI), the Nemerow integrated pollution index (NIPI), the ecological risk factor (Eir), risk index (RI) and the health risk assessment model. The results showed that (1) The PI, NIPI, the ecological risk factor and risk index indicated that Cd and Hg were the primary pollutants in Sishihu village. These indices suggested moderate to slightly heavy potential ecological risks. In Anningqu town, Hg and Cd led to high levels of pollution and posed slightly heavy potential ecological risks. In Qinggedahu village, it was concluded that the metals Zn, Cr, Cd, Hg, and Pb caused moderate to heavy pollution. In Liushihu village, the pollution trends in the area were low. The results of the pollution level of the irrigation well water (i.e., groundwater) indicated that the well water was considerably safer than the soil, but Cr posed a slight pollution risk. (2) The non-carcinogenic risks for adults based on the HI values of these four vegetable bases were <1. However, when considering the non-carcinogenic risks for children, the HI values were larger than 1 in all areas, indicating the local children have a higher potential non-carcinogenic risk. In addition, CR (Carcinogenic risk) from dermal contact with the vegetables bases did not pose a high risk for residents. However, for adults, the carcinogenic risk posed by Arsenic (As) through trough inhalation was the primary pathway of exposure in three of the vegetable bases, generally in the order of Qinggedahu village > Sishihu village > Anningqu town. For children, the carcinogenic risks posed by As through trough inhalation and ingestion were the main exposure pathways. From the TCR results, it can be seen that in Sishihu village, Anningqu town, and Qinggedahu village, the TCR values for adults and children were >1 × 10-4 (unitless), and this degree of carcinogenic risk is unacceptable. (3) The identification of risk sources determined the main pollution sources affecting the vegetable bases were human activities and natural sources. Anthropogenic activities were most often related to traffic pollution sources and agricultural pollution sources, such as the irrational use of pesticides and fertilizers and stock farming. The results are important for designing remediation scenarios to control the spread of contamination as well as for serving as a reference point for soil environmental protection efforts in this region.

Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area

Abstract Spectroscopy is regarded as a quick and nondestructive method to classify and quantitatively analyze many elements of the soil. Visible and Near-infrared reflectance spectroscopy offers a conductive tool for investigating soil heavy metal pollution. The main goal of this work is to obtain spectral optimized indices (RSI, NPDI and NDSI) related to soil heavy metal Arsenic (As), to estimate the As contents in soil based on geographically weighted regression model (GWR), and to investigate the plausibility of using these spectral optimized indices to map the distribution of heavy metal Arsenic in the soil of coal mining areas. The spectral optimized indices (RSI, NPDI and NDSI) derived from the original and transformed reflectance (the reciprocal (1/R), logarithm (lg R ), logarithm-reciprocal (1/lg R ) and root mean square method ( R ) were used to construct the GWR models. Then, the variables (RSIs, NPDIs and NDIs) were applied in estimating the Arsenic (As) concentration and in the mapping of the As distribution in this study region. The NPDIs calculated by the original and transformed reflectance ( R , 1/ R , lg R , 1/lg R , and R ) indicated higher correlation coefficient values than NDSI and RSI. The highest correlation coefficient and lowest p -values ( r ≥0.73 and p =0.001) were found in thenear-infrared (NIR, 780–1100 nm) and shortwave infrared (SWIR, 1100–1935 nm). From the 4 prediction models (GWR) performances, it can be seen that Model-a ( R ) showed superior performance to the other three models (Model-b (1/ R ), Model-c ( R ) and Model-d (lg R )), and it has the highest validation coefficients ( R 2 = 0.831, RMSE =4.912 μg/g, RPD=2.321) and lowest AIC (Akaike Information Criterion) value (AIC=179.96). NPDI 1417 nm, 1246 nm is more sensitive and potential hyperspectral index for As in the study area. Thus, the two band optimized index (NPDI 1417 nm, 1246 nm ) might be recommended as an indicator for estimating soil As content. The hyperspectral optimized indices may help to quickly and accurately evaluate Arsenic contents in soil, and furthermore, the results provide theoretical and data support to access the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.