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With the increase in inclement weather conditions, many countries would experience more and more landslide hazards in the process of planning, designing and construction for engineering projects, especially in the mountainous regions. How to quickly and accurately assess potential landslide risk in a large region (> 10,000 km2) is facing challenge due to its complex geological conditions and large amount of landslides in the region. To optimize the accuracy of the existing models for a large region, in this study, the genetic algorithm (GA) and particle swarm optimization (PSO) are, respectively, coupled with the backpropagation (BP) neural network to determine the initial weights and thresholds in the BP neural network, which can be called GA-BP model and PSO-BP model. To show the reliability and accuracy of the new models in large region, the BP, GA-BP and PSO-BP models are evaluated based on root mean square error (RMSE), coefficient of determination (R2), Kappa coefficient (k), receiver operating characteristic (ROC), training time and condition factor weights by using 100 landslide samples from Sichuan Province, China. Results show that the RMSE values of the GA-BP model and the PSO model are, respectively, 22.6% and 5.1% lower than those of the BP model; the R2 values of the GA-BP model and the PSO model are, respectively, 24.9% and 6.2% higher than those of the BP model; the k values of the GA-BP model and the PSO model are, respectively, 44.3% and 15.4% higher than those of the BP model, and the areas under ROC of the GA-BP model and the PSO model are, respectively, 32.4% and 9.6% larger than those of the BP model. The GA-BP model and the PSO-BP model have better accuracy in the assessment of the overall risk value and the risk-level classification. The difference of the training time is small, and the sequences of condition factor weights given by the three models are consistent. In general, the GA-BP model is more effective for landslide risk assessment in large region. At last, this study gives proposed models under different engineering conditions, which can increase efficiency of the risk assessment for landslides. [ABSTRACT FROM AUTHOR]

Although there is no direct major riverine input, a large quantity of waste produced in mainland China and Korea is transported continuously to the Yellow Sea (YS) through atmospheric deposition, currents and tides; therefore, the environment is distinctly influenced by man-made pollution. This study focuses on the associated pollutant transport mechanisms and fluxes by sampling polychlorinated biphenyls (PCBs). PCBs (Σ24PCB) in YS sediments ranged between 99 pg/g and 3.13 ng/g of dry sediment (with a mean value of 715 pg/g). PCBs produced unintentionally by industrial and other processes appeared to be the major source of PCBs in the sediments, accounting for 60.5%. Industrially synthesized PCBs with 3Cl and 5Cl accounted for 15.5% and 24.0%, respectively. PCBs were mainly from atmospheric deposition (84.5%), followed by continental runoff (15.5%). The average atmospheric deposition flux of technical PCBs was 789 ng/(m(2)a) and flux of surface runoff was 2.27 ng/L., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The Yangtze River estuary (YRE) and the adjacent East China Sea (ECS) inner shelf is an important sink of pollutants originated from mainland China. In this work, we studied the spatial and temporal variations and sources of polychlorinated biphenyls (PCBs) in sediments from the YRE and the ECS inner shelf. Total concentration of 23 PCBs (Σ23PCB) ranged between 24.3 and 343.3 pg/g (with a mean value of 126.7 pg/g), and higher values occurred in the estuarine region. The homolog profile was dominated by 3-5Cl CBs, accounting for 76.1% of Σ23PCB. The Cl proportion ranged from 45.1% to 58.8% (with a mean value of 49.9%); the lowest level appeared in the estuarine region, and it increased with distance from the YRE. Lower chlorinated congeners were carried mainly by the YR into the ocean, to be finally buried in sediments of the estuary region and vicinity. However, higher chlorinated ones were imported mainly via direct emission from local sources, to be finally buried in sediments farther away from the estuary. Of the PCBs in surface sediments, 40.1% came from the YR, 37.9% from the local sources and the rest 22.0% from atmospheric deposition. In a sediment core, we found that 81% of PCBs were technically produced and 19% unintentionally produced in the last century. Before the 1970s, PCB levels were influenced predominantly by the production and consumption of technical PCBs. While in recent decades, the effect of human activities on PCB levels has been increasing continuously., (© 2013.)