The Experts below are selected from a list of 67215 Experts worldwide ranked by ideXlab platform
Eddy Y Zeng - One of the best experts on this subject based on the ideXlab platform.
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impact of Anthropogenic Activities on urban stream water quality a case study in guangzhou china
Environmental Science and Pollution Research, 2014Co-Authors: Jinsong Liu, Lingchuan Guo, Xianlin Luo, Fanrong Chen, Eddy Y ZengAbstract:Anthropogenic Activities are increasingly impacting the quality of urban surface water, particularly in regions undergoing intensive urbanization, such as Guangzhou of South China with a large urban stream network. To examine such impacts, we conducted field sampling on December 24, 2010, May 24, 2011, and August 28, 2011, representative of the low-, normal-, and high-flow periods, respectively. The first sampling was timed immediately after the closing of the 16th Asian Games (November 12–27, 2010) and the 10th Asian Para Games (December 12–19, 2010) held in Guangzhou. Assessments based on a pollution index method showed that the urban streams under investigation were extremely polluted, with direct discharge of untreated domestic sewage identified as the main pollution contributor. In addition, stream water quality around urban villages with high population densities was worse than that within business districts away from the urban villages. Pollution control measures implemented in preparation for the Asian Games were effective for urban streams within the business districts, but less effective for those adjacent to the urban villages. However, short-term efforts may not be able to achieve sustainable urban water quality improvements. In the case of Guangzhou, minimizing or even eliminating direct point-source inputs to the urban streams is perhaps the best option.
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Anthropogenic Activities have contributed moderately to increased inputs of organic materials in marginal seas off china
Environmental Science & Technology, 2013Co-Authors: Liangying Liu, Gaoling Wei, Jizhong Wang, Yufeng Guan, Charles S Wong, Eddy Y ZengAbstract:Sediment has been recognized as a gigantic sink of organic materials and therefore can record temporal input trends. To examine the impact of Anthropogenic Activities on the marginal seas off China, sediment cores were collected from the Yellow Sea, the inner shelf of the East China Sea (ECS), and the South China Sea (SCS) to investigate the sources and spatial and temporal variations of organic materials, i.e., total organic carbon (TOC) and aliphatic hydrocarbons. The concentration ranges of TOC were 0.5–1.29, 0.63–0.83, and 0.33–0.85%, while those of Σn-C14–35 (sum of n-alkanes with carbon numbers of 14–35) were 0.08–1.5, 0.13–1.97, and 0.35–0.96 μg/g dry weight in sediment cores from the Yellow Sea, ECS inner shelf, and the SCS, respectively. Terrestrial higher plants were an important source of aliphatic hydrocarbons in marine sediments off China. The spatial distribution of Σn-C14–35 concentrations and source diagnostic ratios suggested a greater load of terrestrial organic materials in the Yellow S...
Kunwar P Singh - One of the best experts on this subject based on the ideXlab platform.
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evaluating influences of seasonal variations and Anthropogenic Activities on alluvial groundwater hydrochemistry using ensemble learning approaches
Journal of Hydrology, 2014Co-Authors: Kunwar P Singh, Shikha Gupta, Dinesh MohanAbstract:Chemical composition and hydrochemistry of groundwater is influenced by the seasonal variations and Anthropogenic Activities in a region. Understanding of such influences and responsible factors is vital for the effective management of groundwater. In this study, ensemble learning based classification and regression models are constructed and applied to the groundwater hydrochemistry data of Unnao and Ghaziabad regions of northern India. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) models were constructed. Predictive and generalization abilities of the proposed models were investigated using several statistical parameters and compared with the support vector machines (SVM) method. The DT and SVM models discriminated the groundwater in shallow and deep aquifers, industrial and non-industrial areas, and pre- and post-monsoon seasons rendering misclassification rate (MR) between 1.52–14.92% (SDT); 0.91–6.52% (DTF); 0.61–5.27% (DTB), and 1.52–11.69% (SVM), respectively. The respective regression models yielded a correlation between measured and predicted values of COD and root mean squared error of 0.874, 0.66 (SDT); 0.952, 0.48 (DTF); 0.943, 0.52 (DTB); and 0.785, 0.85 (SVR) in complete data array of Ghaziabad. The DTF and DTB models outperformed the SVM both in classification and regression. It may be noted that incorporation of the bagging and stochastic gradient boosting algorithms in DTF and DTB models, respectively resulted in their enhanced predictive ability. The proposed ensemble models successfully delineated the influences of seasonal variations and Anthropogenic Activities on groundwater hydrochemistry and can be used as effective tools for forecasting the chemical composition of groundwater for its management.
Yangjia Zhang - One of the best experts on this subject based on the ideXlab platform.
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the impact of climate change and Anthropogenic Activities on alpine grassland over the qinghai tibet plateau
Agricultural and Forest Meteorology, 2014Co-Authors: Aoxiong Che, Xianzhou Zhang, Jia Tao, Jingsheng Wang, Peili Shi, Yangjia ZhangAbstract:Climate change and Anthropogenic Activities are two factors that have important effects on the carbon cycle of terrestrial ecosystems, but it is almost impossible to fully separate them at present. This study used process-based terrestrial ecosystem model to stimulate the potential climate-driven alpine grassland net primary production (NPP), and Carnegie-Ames-Stanford Approach based on remote sensing to stimulate actual alpine grassland NPP influenced by both of climate change and Anthropogenic Activities over the Qinghai-Tibet plateau (QTP) from 1982 to 2011. After the models were systematically calibrated, the simulations were validated with continuous 3-year paired field sample data, which were separately collected in fenced and open grasslands. We then simulated the human-induced NPP, calculated as the difference between potential and actual NPP, to determine the effect of Anthropogenic Activities on the alpine grassland ecosystem. The simulation results showed that the climate change and Anthropogenic Activities mainly drove the actual grassland NPP increasing in the first 20-year and the last 10-year respectively, the area percentage of actual grassland NPP change caused by climate change declined from 79.62% in the period of 1982-2001 to 56.59% over the last 10 years; but the percentage change resulting from human Activities doubled from 20.16% to 42.98% in the same periods over the QTP. The effect of human Activities on the alpine grassland ecosystem obviously intensified in the latter period compared with the former 20 years, so the negative effect caused by climate change to ecosystem could have been relatively mitigated or offset over the QTP in the last ten years. (C) 2014 Elsevier B.V. All rights reserved.
J M Forja - One of the best experts on this subject based on the ideXlab platform.
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seasonal variation of early diagenesis and greenhouse gas production in coastal sediments of cadiz bay influence of Anthropogenic Activities
Estuarine Coastal and Shelf Science, 2018Co-Authors: Macarena Burgos, T Ortega, Julio Bohorquez, Alfonso Corzo, Christophe Rabouille, J M ForjaAbstract:Abstract Greenhouse gas production in coastal sediments is closely associated with the early diagenesis processes of organic matter and nutrients. Discharges from Anthropogenic Activities, particularly agriculture, fish farming and waste-water treatment plants supply large amounts of organic matter and inorganic nutrients that affect mineralization processes. Three coastal systems of Cadiz Bay (SW Spain) (Guadalete River, Rio San Pedro Creek and Sancti Petri Channel) were chosen to determine the seasonal variation of organic matter mineralization. Two sampling stations were selected in each system; one in the outer part, close to the bay, and another more inland, close to a discharge point of effluent related to Anthropogenic Activities. Seasonal variation revealed that metabolic reactions were driven by the annual change of temperature in the outer station of the systems. In contrast, these reactions depended on the amount of organic matter reaching the sediments in the outermost part of the systems, which was higher during winter. Oxygen is consumed in the first 0.5 cm indicating that suboxic and anoxic processes, such as denitrification, sulfate reduction and methanogenesis are important in these sediments. Sulfate reduction seems to account for most of the mineralization of organic matter at the marine stations, while methanogenesis is the main pathway at the sole freshwater station of this study, located inside the estuary of the Guadalete River, because of the lack of sulfate as electron acceptor. Results point to denitrification being the principal process of N2O formation. Diffusive fluxes varied between 2.6 and 160 mmol m−2 d−1 for dissolved inorganic carbon (DIC); 0.9 and 164.3 mmol m−2 d−1 for TA; 0.8 and 17.4 μmol m−2 d−1 for N2O; and 0.1 μmol and 13.1 mmol m−2 d−1 for CH4, indicating that these sediments act as a source of greenhouse gases to the water column.
Dinesh Mohan - One of the best experts on this subject based on the ideXlab platform.
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evaluating influences of seasonal variations and Anthropogenic Activities on alluvial groundwater hydrochemistry using ensemble learning approaches
Journal of Hydrology, 2014Co-Authors: Kunwar P Singh, Shikha Gupta, Dinesh MohanAbstract:Chemical composition and hydrochemistry of groundwater is influenced by the seasonal variations and Anthropogenic Activities in a region. Understanding of such influences and responsible factors is vital for the effective management of groundwater. In this study, ensemble learning based classification and regression models are constructed and applied to the groundwater hydrochemistry data of Unnao and Ghaziabad regions of northern India. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) models were constructed. Predictive and generalization abilities of the proposed models were investigated using several statistical parameters and compared with the support vector machines (SVM) method. The DT and SVM models discriminated the groundwater in shallow and deep aquifers, industrial and non-industrial areas, and pre- and post-monsoon seasons rendering misclassification rate (MR) between 1.52–14.92% (SDT); 0.91–6.52% (DTF); 0.61–5.27% (DTB), and 1.52–11.69% (SVM), respectively. The respective regression models yielded a correlation between measured and predicted values of COD and root mean squared error of 0.874, 0.66 (SDT); 0.952, 0.48 (DTF); 0.943, 0.52 (DTB); and 0.785, 0.85 (SVR) in complete data array of Ghaziabad. The DTF and DTB models outperformed the SVM both in classification and regression. It may be noted that incorporation of the bagging and stochastic gradient boosting algorithms in DTF and DTB models, respectively resulted in their enhanced predictive ability. The proposed ensemble models successfully delineated the influences of seasonal variations and Anthropogenic Activities on groundwater hydrochemistry and can be used as effective tools for forecasting the chemical composition of groundwater for its management.
