摘要：
Soil is both an important sink and a source for contaminants in the agricultural ecosystem. To research the sources and ecological risk of potentially toxic elements in Xiangzhou, China, 326 soil samples from arable land were collected and analyzed for five potentially toxic elements: cadmium (Cd), mercury (Hg), arsenic (As), lead (Pb), and chromium (Cr). In this research, ecological risk assessment was used to determine the degree of contamination in the research area, the outcome of the Geographic Information System was as used to study the spatial distribution characteristics of potentially toxic elements, and random forest was used to evaluate the natural and artificial influencing factors. We surveyed the sources of potentially toxic elements through quantifying the indicators, which gave further opinions. The results were as follows: (1) The average contents of potentially toxic elements were 0.14 mg/kg (Cd), 0.05 mg/kg (Hg), 12.33 mg/kg (As), 28.39 mg/kg (Pb), and 75.21 mg/kg (Cr), respectively. The results compared with the background value of Hubei, neighboring regions, and countries for Cd, As, Pb, and Cr showed mild pollution. (2) The total evaluation of soil pollution via the comprehensive pollution index indicated slight contamination by Cd. Assessment by the potential ecological risk index indicated low ecological risk due to Cd and moderate contamination by Hg. Evaluation through the geo-accumulation index evinced the low ecological risk for Cd, As, and Pb and moderate contamination by Hg. (3) We found that in addition to natural factors (such as soil parent material, soil pH, etc.), long-term industrial pollution, mineral mining and processing, exhaust emissions from transportation, the application of manure from farms as farmyard manure, and sewage irrigation were the primary anthropogenic sources of potentially toxic element contamination in the soil.
摘要：
Understanding the spatial pattern of soil chemical properties (SCPs) together with topological factors and soil management practices is essential for land management. This study examines the spatial changes in soil chemical properties and their impact on China’s subtropical mountainous areas. In 2007 and 2017, 290 and 200 soil samples, respectively, were collected in Hefeng County, a mountainous county in central China. We used descriptive statistics and geostatistical methods, including ANOVA, semivariance, Moran’s I, and fractal dimensions, to analyze the characteristics and spatial autocorrelation changes in soil organic matter (OM), available phosphorus (AP), available potassium (AK), and pH value from 2007 to 2017. We explored the relationship between each SCP and the relationship between SCPs with topographic parameters, soil texture, and cropping systems. The results show that the mean value of soil OM, AP, AK, and pH in Hefeng increased from 2007 to 2017. The spatial variation and spatial dependency of each SCP in 2007, excluding AP and AK in 2007, were higher than in 2017. The soil in areas with high topographic relief, profile curvature, and planform curvature had less AP, AK, and pH. Soil at higher elevation had lower OM (r = −0.197, p < 0.01; r = −0.334, p < 0.01) and AP (r = −0.043, p < 0.05; r = −0.121, p < 0.05) and higher AK (r = −0.305, p < 0.01; r = 0.408, p < 0.01) in 2007 and 2017. Soil OM and AK in 2007 were significantly (p < 0.05) correlated with soil texture (p < 0.05). In contrast, oil AP and soil pH in 2007 and all SCPs in 2017 were poorly correlated with soil texture. The cropping systems played an important role in affecting all SCPs in 2007 (p < 0.01), while they only significantly affected AK in 2017 (p < 0.05). Our findings demonstrate that both topological factors, that is, the changes in cropping management and the changes in acid rain, impact soil chemical properties. The local government should place more focus on reducing soil acid amounts, soil AP content, and soil erosion by improving water conservancy facilities.
作者机构：
[Wang, Li; Li, Qing; Xu, Tao; Wu, Zhengxiang; Zhou, Yong; Liu, Jingyi] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Zhou, Yong] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
farmland quality;machine-learning algorithms;comprehensive assessment index system;random forest;county scale;Xiangzhou
摘要：
Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.
摘要：
In order to study the spatial distribution and anthropogenic sources of potentially toxic elements in Xiangzhou, soil samples were collected from arable land and were analyzed for five different potentially toxic elements: Cd, Hg, As, Pb, and Cr. Inverse distance weighting (IDW) was used to study the spatial distribution of potentially toxic elements in the soil, while principal component analysis (PCA) and random forest analysis (RFA) were applied to examine the anthropogenic sources. It was shown that the combination of multiple analysis tools provides an effective way of delineating multiple potentially toxic elements from anthropogenic sources. The results showed that the average contents of Cd, Hg, and Cr in soils were lower than the background values of Hubei, whereas the average concentrations of As and Pb in soils were higher than the background values of Hubei. Through PCA, it was concluded that human activities contributed more than 60% of the As, Pb, and Cr concentrations in Xiangzhou soils, which was verified by a random forest simulation methodology. Through random forest analysis, Pb, As, and Cr in the soil were found to originate from factories and enterprises, livestock farms, mining areas, and traffic; Cd in the soil was found to originate from mining and the processing of minerals, human production and construction activities, and agricultural irrigation; and Hg in the soil was found to originate from livestock manure, mining and processing of minerals, and human industrial production. The results of this study could provide support for better management of soil pollution through prevention practices such as specific industrial governance and layout optimization.
关键词：
deep learning;hyperparameter optimization;proximal soil sensing;Qinghai-Tibet Plateau;soil monitoring
摘要：
Soil quality in alpine ecosystems requires regular monitoring to assess its dynamics under changes in climate and land use. Visible near‐infrared (vis‐NIR) spectroscopy could offer an option, as sampling and transporting large numbers of soil samples in the Qinghai‐Tibet Plateau is extremely difficult. However, the potential for in situ vis‐NIR spectra and the optimal algorithms need to be defined in this region. We have therefore evaluated the performance of a deep learning method, multilayer perceptron (MLP), for in situ spectral measurement of soil organic carbon (SOC) with in situ vis‐NIR spectroscopy in southeastern Tibet, China. A total of 39 soil cores (maximum depth 1 m), including 547 soil samples taken from each 5‐cm depth interval, were collected. The spectra were also measured at each 5‐cm depth interval accordingly. After spectral preprocessing, 4,096 MLP models were generated by taking all the combinations from six parameters defined in the MLP. The 10‐fold‐core cross‐validation showed that MLP had a good performance for in situ SOC prediction, and the best MLP model had an R 2 of .92, which were much better than those of the partial least squares regression model (R 2 = .80). The results also suggested that the number of epochs, number of neurons, and dropout rate were the most important parameters in the MLP model. We concluded that in situ vis‐NIR spectroscopy coupled with an MLP model has high potential for large‐scale SOC monitoring in the Qinghai‐Tibet Plateau. Our results also provide a reference for rapid hyperparameter optimization using MLP for future soil spectroscopic modeling. We evaluated the in situ measurement of SOC using vis‐NIR spectra. A multilayer perceptron was used to predict SOC in alpine soils. Hyperparameter optimization was conducted by grid searching. A multilayer perceptron had good performance for in situ SOC prediction. The most vital parameters for a multilayer perceptron model were identified.
摘要：
Global climate change has led to significant changes in seasonal rhythm events of vegetation growth, such as spring onset and autumn senescence. Spatiotemporal shifts in these vegetation phenological metrics have been widely reported over the globe. Vegetation growth peak represents plant photosynthesis capacity and responds to climate change. At present, spatiotemporal changes in vegetation growth peak characteristics (timing and maximum growth magnitude) and their underlying governing mechanisms remain unclear at regional scales. In this study, the spatiotemporal dynamics of vegetation growth peak in northeast China (NEC) was investigated using long-term NDVI time series. Then, the effects of climatic factors and spring phenology on vegetation growth peak were examined. Finally, the contribution of growth peak to vegetation production variability was estimated. The results of the phenological analysis indicate that the date of vegetation green up in spring and growth peak in summer generally present a delayed trend, while the amplitude of growth peak shows an increasing trend. There is an underlying cycle of 11 years in the vegetation growth peak of the entire study area. Air temperature and precipitation before the growing season have a small impact on vegetation growth peak amplitude both in its spatial extent and magnitude (mainly over grasslands) but have a significant influence on the date of the growth peak in the forests of the northern area. Spring green-up onset has a more significant impact on growth peak than air temperature and precipitation. Although green-up date plays a more pronounced role in controlling the amplitude of the growth peak in forests and grasslands, it also affects the date of growth peak in croplands. The amplitude of the growth peak has a significant effect on the inter-annual variability of vegetation production. The discrepant patterns of growth peak response to climate and phenology reflect the distinct adaptability of the vegetation growth peak to climate change, and result in different carbon sink patterns over the study area. The study of growth peak could improve our understanding of vegetation photosynthesis activity over various land covers and its contribution to carbon uptake.
期刊：
Journal of Coastal Research,2020年104(sp1):593-600 ISSN：0749-0208
通讯作者：
Wang, Hongzhi
作者机构：
[Zhong, Hongping; Li, Shuo; Zhou, Yong; Wang, Hongzhi; Yan, Qingqing] Cent China Normal Univ, Fac Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zhong, Hongping] Dongguan Expt High Sch, Off Geog Teaching Grp, Guangzhou 523007, Peoples R China.;[Lu, Jianzhong] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
通讯机构：
[Wang, Hongzhi] C;Cent China Normal Univ, Fac Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
China;Poyang Lake;river-lake relationship;SWAT;TGR;Yangtze River
摘要：
Wang, H.Z., Zhong, H.P., Lu, J.Z., Yan, Q.Q., Li, S., and Zhou, Y., 2020. Understanding the river-lake relationship after the operation of TGR based on SWAT model. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 593–600. Coconut Creek (Florida), ISSN 0749-0208.It is an important issue how the operation of Three Gorges Reservoir (TGR) has impacted upon the hydrological relationship between the Yangtze River and the Poyang Lake. A SWAT model was built to simulate the runoff of the River and the Lake, and the energy difference (Fe) index was selected to characterize the river-lake relationship. The results show: (1) The SWAT model was verified suitable to simulate the runoff of the study area. (2) The monthly trend of River-lake relationship after the operation of the TGR from 2010 to 2018 was studied. The successive monthly strong effect of the River (Fe<-0.1) appeared in August and September of 2010, which corresponded to the period of severe flood of the Lake basin. The successive monthly strong effect of the Lake (Fe>0.1) appeared in November and December of 2013, which corresponded to the period of severe draught of the Lake basin. (3) The yearly trend showed the constant increase of the role of the Lake and the constant decrease of effect of the Yangtze River main stream. (4) Under the operation of the TGR, the Yangtze River plays a stronger role during the drainage and flooding periods, leading to the increase of the flood control pressure in the Poyang Lake basin; the effect of the Poyang Lake is enhanced during the water storage and dry period, leading to the increase of the risk of drought disasters in the Poyang Lake basin. The study systematically reveals the change pattern of the river-lake relationship after the operation of the TGR at different time scales.
作者机构：
[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Hubei Prov Key Lab Anal & Simulat Geog Proc, Wuhan 430079, Hubei, Peoples R China.;[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;[Nie Yan; Yu Lei; Yi Jun; Zhang Tao; Zhou Yong] Cent China Normal Univ, Res Inst Sustainable Dev, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Yu Lei] C;Cent China Normal Univ, Hubei Prov Key Lab Anal & Simulat Geog Proc, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Res Inst Sustainable Dev, Wuhan 430079, Hubei, Peoples R China.
