Hongyan Ren

Estimation of As and Cu Contamination in Agricultural Soils Around a Mining Area by Reflectance Spectroscopy:A Case Study

Concentrations of Iron (Fe), As, and Cu in soil samples from the fields near the Baoshan Mine in-Hunan Province, China, were analyzed and soil spectral reflectance was measured with an ASD FieldSpec FR spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory condition. Partial least square regression (PLSR) models were constructed for predicting soil metal concentrations. The data pre-processing methods, first and second derivatives (FD and SD), baseline correction (BC), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR), were used for the spectral reflectance data pretreatments. Then, the prediction results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R(2)). According to the criteria of minimal RRMSE and maximal R(2), the PLSR models with the FD pretreatment (RRMSE = 0.24, R(2) = 0.61), SNV pretreatment (RRMSE = 0.08, R(2) = 0.78), and BC-pretreatment (RRMSE = 0.20, R(2) = 0.41) were considered as the final models for predicting As, Fe, and Cu, respectively. Wavebands at around 460, 1400, 1900, and 2 200 nm were selected as important spectral variables to construct final models. In conclusion, concentrations of heavy metals in contaminated soils could be indirectly assessed by soil spectra according to the correlation between the spectrally featureless components and re; therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.

Scale Effect of Climate and Soil Texture on Soil Organic Carbon in the Uplands of Northeast China

Abstract Understanding how spatial scale influences commonly-observed effects of climate and soil texture on soil organic carbon (SOC) storage is important for accurately estimating the SOC pool at different scales. The relationships among climate factors, soil texture and SOC density at the regional, provincial, city, and county scales were evaluated at both the soil surface (0–20 cm) and throughout the soil profile (0–100 cm) in the Northeast China uplands. We examined 1 022 profiles obtained from the Second National Soil Survey of China. The results indicated that the relationships between climate factors and SOC density generally weakened with decreasing spatial scale. The provincial scale was optimal to assess the relationship between climate factors and SOC density because regional differences among provinces were covered up at the regional scale. However, the relationship between soil texture and SOC density had no obvious trend with increasing scale and changed with temperature. There were great differences in the impacts of climate factors and soil texture on SOC density at different scales. Climate factors had a larger effect on SOC density than soil texture at the regional scale. Similar trends were seen in Heilongjiang and eastern Inner Mongolia at the provincial scale. But, soil texture had a greater effect on SOC density compared with climate factors in Jilin and Liaoning. At the city and county scales, the influence of soil texture on SOC density was more important than climate factors.

Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China

Abstract Predicting soil organic carbon (SOC) content distribution accurately from limited soil samples has received a great deal of attention recently in an effort to support soil fertility mapping and to improve our understanding of carbon sequestration variability. Kriging methods combined with auxiliary variables are frequently used at present. However, studies using categorical information, such as soil type and land use, which are closely related to local trends in SOC spatial variation, as auxiliary variables are seldom conducted. In the present investigation, a total of 254 surficial soil samples were collected in the study area, Yujiang county in the hilly red soil region of South China, and a comparison of performance of four kriging approaches was conducted, ordinary kriging (OK), kriging combined with soil-type information (KST), land use (KLU) and combined land use–soil type information (KLUST). Results of the assessment were based on 85 validation samples. The results indicate that the best correlation between the measured and predicted values for validation location was obtained with KLUST (ru2009=u20090.854), whereas the lowest was obtained using OK (ru2009=u20090.383). Furthermore, the root mean square error (RMSE) from KLUST (3.47u2009gu2009kg−1) is the lowest, whereas the one obtained using OK (6.49u2009gu2009kg−1) is the highest. The correlation coefficient and RMSE from KST (ru2009=u20090.784, RMSEu2009=u20094.15u2009gu2009kg−1) and KLU (ru2009=u20090.795, RMSEu2009=u20093.95u2009gu2009kg−1) are the second and third most correlated, respectively. Comparing the SOC distribution maps generated by the four prediction approaches, the KLUST rendering best reflects the local change associated with soil types and land uses, whereas the map from the OK is the least representative. The results demonstrate that soil type and land use have an important impact on SOC spatial distribution, and KLUST, which reduces their influence as a local trend, is an efficient and practical prediction approach for the hilly red soil region of South China.

The influences of temperature on spatiotemporal trends of hand-foot-and-mouth disease in mainland China.

Understanding the influence of temperature on hand-foot-and-mouth disease (HFMD) is an important public health concern as well as being a major climate-infection issue in mainland China. City-scale data of incidence rates (IRs) of HFMD and temperature from 2008 to 2009 in mainland China has been analyzed. There were two peak periods for HFMD prevalence from April to July and August to November. Regions with higher monthly IR of HFMD periodically shifted following the pattern of south–north–south from March to December. Monthly IR of HFMD at city scale were closely associated with both average monthly temperature and monthly temperature range. Our study shows that spatiotemporal trends of HFMD infection were sensitive to temperature variation, and suggest that preventive measures should be considered for limiting the epidemic of HFMD in the cities with higher monthly IR during the peak periods.

Spatiotemporal variation of surface water quality for decades: a case study of Huai River System, China

Characterization of spatiotemporal variation of water quality is a basic environmental issue with implications for public health in China. Trends in the temporal and spatial distribution of water quality in the Huai River System (HRS) were analyzed using yearly surface water quality data collected from 1982 to 2009. Results showed that the water quality of the main stream deteriorated in the 1990s and early 2000s but has been ameliorated since 2005. The sections that were classified as severely polluted from the monitoring data were located largely in the middle reach. The water quality of HRS fluctuated during the period 1997-2009; it has improved and stabilized since 2005. In terms of spatialized frequency of serious pollution, heavily polluted regions were mostly concentrated in the area along several tributaries of the Ying, Guo and New Sui Rivers as well as the area north of Nansi Lake. These regions decreased from 1997 to 2009, especially after 2005. Our analysis indicated that water pollution in HRS had a close relation with population and primary industry during the period 1997-2009, and implied that spatiotemporal variation of surface water quality could provide a scientific foundation for human health risk assessment of the Huai River Basin.