Stable isotope-guided analysis of biomagnification profiles of arsenic species in a tropical mangrove ecosystem

We performed stable carbon and nitrogen-guided analyses of biomagnification profiles of arsenic (As) species, including total As, lipid-soluble As, eight water-soluble As compounds (arsenobetaine (AB), arsenocholine (AC), tetramethylarsonium ion (TETRA), trimethylarsine oxide (TMAO), dimethylarsinic acid (DMA), monomethylarsonic acid (MMA), arsenate (As[V]), and arsenite (As[III])), and non-extracted As in a tropical mangrove ecosystem in the Ba Ria Vung Tau, South Vietnam. Arsenobetaine was the predominant As species (65-96% of water-soluble As). Simple linear regression slopes of log-transformed concentrations of total As, As fractions or individual As compounds on stable nitrogen isotopic ratio (δ15N) values are regarded as indices of biomagnification. In this ecosystem, lipid-soluble As (slope, 0.130) and AB (slope, 0.108) were significantly biomagnified through the food web; total As and other water-soluble As compounds were not. To our knowledge, this is one of the first reports on biomagnification profiles of As compounds from a tropical mangrove ecosystem.     

Freezing as a method of sample preservation for the analysis of dissolved inorganic nutrients in seawater

It is often desirable or necessary to store collected seawater samples prior to analysis for dissolved inorganic nutrients. It is therefore important to establish preservation and storage techniques that will ensure sample integrity and will not alter the precision or accuracy of analysis. We have performed a series of experiments on the storage of nutrient samples collected at the oligotrophic North Pacific benchmark Station ALOHA, using both standard autoanalyses and low-level techniques. Our results reveal that for oligotrophic oceanic waters, the immediate freezing of an unfiltered water sample in a clean polyethylene bottle is a suitable preservation method. This procedure is simple, it avoids potentially contaminating sample manipulations and chemical additions, and it adequately preserves the concentrations of nitrate + nitrite, soluble reactive phosphate, and soluble reactive silicate within a single water sample.     

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA) based on Deep Belief Network(DBN) with Back Propagation(BP) algorithm optimized by the Genetic Algorithm(GA).For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE) technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy, root mean square error(RMSE), and area under the receiver operatic characteristic curve(AUC) were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC = 0.989) and prediction accuracy(AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR(0.885), LMT(0.934), BLR(0.936), ADT(0.976), NBT(0.974), REPTree(0.811), ANFIS-BAT(0.944), ANFIS-CA(0.921), ANFIS-IWO(0.939), ANFIS-ICA(0.947), and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province,Vietnam

Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In...     

Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique

Natural Resources Research - In this paper, we used artificial intelligence (AI) techniques to investigate the relation between the rock size distribution (RSD) and blasting parameters for rock...     

An Application of HFE-D for Evaluating Sea Water Intrusion in Coastal Aquifers of Southern Vietnam

The coastal aquifers and inland waters of the Long Xuyen Quadrangle and Ca Mau Peninsula of southern Vietnam have been significantly impacted by sea water intrusion (SI) as a result of recent anthropogenic activities. This study identified the evolution and spatial distribution of hydrochemical conditions in coastal aquifers at this region using Hydrochemical Facies Evolution Diagram (HFE-D) and Geographical Information System mapping. Hydraulic heads and water chemistry were measured at 31 observation wells in four layered aquifers during dry and rainy seasons in early (2005), and more recent (2016), stages of agricultural development. Hydrochemical facies associated with intrusion or freshening stages were mapped in each aquifer after assigning mixing index values to each facies. The position of groundwater freshening and SI phases differed in Holocene, Upper Pleistocene, Middle Pleistocene, and Lower Pleistocene aquifers. The geographic position of freshening and intrusion fronts differ in dry and rainy seasons, and shifted after 11 years of groundwater abstraction in all four aquifers. The spatial and temporal differences in hydrochemical facies distributions according to HFE-D reflect the relative impact of SI in the four aquifers. The study results provide a better understanding of the evolution of groundwater quality associated with SI in a peninsular coastal aquifer system, and highlight the need for improving groundwater quality and management in similar coastal regions.     

Sources of sedimentary PAHs in tropical Asian waters: Differentiation between pyrogenic and petrogenic sources by alkyl homolog abundance

We collected surface sediment samples from 174 locations in India, Indonesia, Malaysia, Thailand, Vietnam, Cambodia, Laos, and the Philippines and analyzed them for polycyclic aromatic hydrocarbons (PAHs) and hopanes. PAHs were widely distributed in the sediments, with comparatively higher concentrations in urban areas (∑PAHs: ∼1000 to ∼100 000 ng/g-dry) than in rural areas (∼10 to ∼100 g-dry), indicating large sources of PAHs in urban areas. To distinguish petrogenic and pyrogenic sources of PAHs, we calculated the ratios of alkyl PAHs to parent PAHs: methylphenanthrenes to phenanthrene (MP/P), methylpyrenes + methylfluoranthenes to pyrene + fluoranthene (MPy/Py), and methylchrysenes + methylbenz[a]anthracenes to chrysene + benz[a]anthracene (MC/C). Analysis of source materials (crude oil, automobile exhaust, and coal and wood combustion products) gave thresholds of MP/P = 0.4, MPy/Py = 0.5, and MC/C = 1.0 for exclusive combustion origin. All the combustion product samples had the ratios of alkyl PAHs to parent PAHs below these threshold values. Contributions of petrogenic and pyrogenic sources to the sedimentary PAHs were uneven among the homologs: the phenanthrene series had a greater petrogenic contribution, whereas the chrysene series had a greater pyrogenic contribution. All the Indian sediments showed a strong pyrogenic signature with MP/P ≈ 0.5, MPy/Py ≈ 0.1, and MC/C ≈ 0.2, together with depletion of hopanes indicating intensive inputs of combustion products of coal and/or wood, probably due to the heavy dependence on these fuels as sources of energy. In contrast, sedimentary PAHs from all other tropical Asian cities were abundant in alkylated PAHs with MP/P ≈ 1-4, MPy/Py ≈ 0.3-1, and MC/C ≈ 0.2-1.0, suggesting a ubiquitous input of petrogenic PAHs. Petrogenic contributions to PAH homologs varied among the countries: largest in Malaysia whereas inferior in Laos. The higher abundance of alkylated PAHs together with constant hopane profiles suggests widespread inputs of automobile-derived petrogenic PAHs to Asian waters.     

Spatio-temporal analysis of recent groundwater-level trends in the Red River Delta, Vietnam

A groundwater-monitoring network has been in operation in the Red River Delta, Vietnam, since 1995. Trends in groundwater level (1995?C2009) in 57 wells in the Holocene unconfined aquifer and 63 wells in the Pleistocene confined aquifer were determined by applying the non-parametric Mann-Kendall trend test and Sen??s slope estimator. At each well, 17 time series (e.g. annual, seasonal, monthly), computed from the original data, were analyzed. Analysis of the annual groundwater-level means revealed that 35?% of the wells in the unconfined aquifer showed downward trends, while about 21?% showed upward trends. On the other hand, confined-aquifer groundwater levels experienced downward trends in almost all locations. Spatial distributions of trends indicated that the strongly declining trends (>0.3?m/year) were mainly found in urban areas around Hanoi where there is intensive abstraction of groundwater. Although the trend results for most of the 17 time series at a given well were quite similar, different trend patterns were detected in several. The findings reflect unsustainable groundwater development and the importance of maintaining groundwater monitoring and a database in the Delta, particularly in urban areas.     

Cenozoic basement structure of the South China Sea and adjacent areas by modeling and interpreting gravity data

The South China Sea and adjacent areas is a large region with diversely complicated geological conditions. In spite of investigations carried out over the past many years, the marine geological structure in many places has remained poorly understood because of the deficient data, a thick seawater layer as well as of the sensitive areas among the countries in the region. In this paper, the authors study and apply a model-based methodology of the modeling and interpretation of the newest gravity data and others, which are 3D modeling, frequency filtering, horizontal gravity gradient and maximum horizontal gravity gradient, in order to determine clearly the basement structure. The basement features such as the main faults systems, uplift-depression zones and seafloor spreading axis, oceanic boundary in the region have been defined. The achieved results are checked by the seismic data available for the region. From the studied results, the authors have brought out some initial remarks on the structure and form of the basement in the South China Sea and adjacent areas.