Study of the Climate Change Impacts on Water Quality in the Upstream Portion of the Cau River Basin,Vietnam

This paper presents simulations of climate change impacts on water quality in the upstream portion of the Cau River Basin in the North of Vietnam. The integrated modeling system GIBSI was used to simulate hydrological processes, pollutant and sediment wash-off in the river basin, and pollutant transport and transformation in the river network. Three projections for climate change based on emission scenarios B1, B2, and A2 of IPCC Special Report on Emission Scenarios (SRES) were considered. By assuming that the input pollution sources and watershed configuration were constant, based on 2008 data, water quality in the river network was simulated up to the terminal year 2050. For each climate change scenario, patterns of precipitation in wet and dry year were considered. The change in annual and monthly trends for dissolved oxygen (DO), biochemical oxygen demand (BOD), and ammonium ions (NH4+) load and concentration for different portions of the watershed have been analyzed. The results of these simulations show that climate change has more impact on changing the seasonal water quality parameters than on altering the average annual load of the pollutants. The percent change and change pattern in water quality parameters are different for wet and dry year, and the changes in wet year are smaller than those in dry year.     

Effect of HPLC binary mobile phase composition on the analysis of carbonyls

The relative performance of the binary mobile phase in the high-performance liquid chromatography analysis of carbonyl compounds (CCs) was tested using the liquid-phase standards containing 15 aldehyde/ketone-DNPH mixture. The Hichrome column was employed for the analysis of CCs at a flow rate of 1.5 mL min(?-?1). The binary mobile phases prepared using both acetonitrile/water (AW) and a possible alternative of methanol:water (MW) mixture were examined by their calibration results. The data derived from these two binary phases were then evaluated in terms of three key variables (i.e., resolution, relative sensitivity, and retention time). The relative water content (or the water to organic solvent ratio (W/A) or (W/M)) of the binary phase was found as the key variable for the performance. The results indicate that the optimal resolution of AW combination was attained consistently for most composition, while MW generally suffered from overpressure problem. The changes of water content in the AW mixture led to the changes of all three variables in the quantitative analysis of CCs. The obtained results confirm that the AW mixture should be the optimal elutant for the CC analysis, as other simple binary compositions like MW are limited in many respects.     

Application of WRF-Chem to simulate air quality over Northern Vietnam

Environmental Science and Pollution Research - The WRF-Chem (Weather Research and Forecasting with Chemistry) model is implemented and validated against ground-based observations for meteorological...     

Mission assurance policy and risk management in cybersecurity

Mission assurance policy and risk management are essential in enabling decision makers to ensure successful completion of missions by addressing the security status of cyber assets. This paper presents a novel mission assurance policy that adapts to the dynamic security status of all mission assets to quickly and automatically determine mission assurance level and to decide what changes are needed accordingly. The novelty of this mission assurance policy stems from using a time Petri net model for determining the security status of cyber assets, and then employing binary or multi-valued logic decision diagrams to assess the mission assurance level. The ability of a mission assurance policy to successfully complete its objectives depends mainly on whether a risk management scheme is provided to reduce risk to an acceptable level. To that end, this paper also describes a risk management scheme to systematically deal with the main factors of risk management such as the temporal interdependencies of cyber assets, impact of attacks, and risk mitigation. Given that the status of cyber assets changes due to the dynamic cybersecurity environment of asset vulnerabilities, threats, and recovery, the proposed mission assurance policy and risk management scheme enable decision makers to cope with the real-time assessment of mission assurance level.     

Characterization of metal aerosols in PM10 from urban, industrial, and Asian Dust sources

Metallic elements (As, Be, Ca, Cd, Co, Cr, Fe, K, Mn, Ni, Pb, Sb, Se, and Zn) in PM10 aerosols were determined at urban and industrial sites, which are affected by traffic and residential sources, metallurgical activity, and petrochemical and steel works. The effect of the long-range transported Asian Dust on the metal content of aerosols was also examined. At the urban sampling site, concentrations of As, Cd, Pb, Se, and Zn were assigned to road traffic and combustion sources, Ca and Fe to soil dust sources from long-range transported Asian Dusts, and Cr and Ni to metallurgical sources transported from the nearby industrial complex, based on Principal Component Analysis (PCA). Enhanced Cr and Ni concentrations at the metallurgical industrial site suggest that local emissions from metal-assembly facilities and manufacture of alloys contributed to elevated levels of those metals. We also observed that petrochemical activities contributed to increased levels of Sb and Zn. When Asian Dust events occurred, Ca, Fe, K, and Zn concentrations dramatically increased compared to values without the Asian Dust. Two different types of Asian Dust events were observed. For the Asian Dust event 1 (4/1/2007), the Fe and K concentrations were much higher by a factor of 2–3 than those for the Asian Dust event 2 (3/2/2008), while As, Mn, and Zn concentrations were significantly higher on the Asian Dust event 2. Backward trajectory analysis showed that for the Asian Dust event 2, the air mass had passed over the heavily industrialized zones in China during long-range transport to the current sampling site, suggesting that the As, Mn, and Zn may have originated from industrial sources.     

Assessment and monitoring of nutrient loading in the sediments of tidal creeks receiving shrimp farm effluent in Quang Ninh, Vietnam

Coastal shrimp farming may lead to the contamination of sediments of surrounding estuarine and marine ecosystems as shrimp farm effluent often contains high levels of pollutants including a range of organic compounds (from uneaten feed, shrimp feces, and living and dead organisms) which can accumulate in the sediments of receiving waterways. The assessment and monitoring of sediment quality in tidal creeks receiving shrimp farm effluent can support environmental protection and decision making for sustainable development in coastal areas since sediment quality often shows essential information on long-term aquatic ecosystem health. Within this context, this paper investigates nutrient loadings in the sediments of tidal creeks receiving shrimp farm effluent in Quang Ninh, Vietnam, which now have a high concentration of intensive and semi-intensive shrimp farms. Sediment samples taken from inside creek sections directly receiving effluent from concentrated shrimp farms (IEC), from main creeks adjacent to points of effluent discharge outside concentrated shrimp farms (OEC), and few kilometers away from shrimp farms (ASF) as reference sites were collected and analyzed before and after shrimp crops to investigate spatial and temporal variation. The results showed that there were statistically significant differences in the concentrations of total nitrogen, total phosphorus, and total organic carbon among IEC, OEC, and ASF sites while the seasonal variation being limited over study times. A sediment nutrient index (SNI) computed from coefficient scores of the factor analysis efficiently summarizes sediment nutrient loads, which are high, albeit quite variable, in canals directly receiving effluents from farms but then decline sharply with distance from shrimp farms. The visualization and monitoring of sediment quality data including SNI on maps can strongly support managers to manage eutrophication at concentrated shrimp farming areas, contributing to sustainable development and management at coastal zones.     

Land use Systems and Erosion in the Uplands of the Central Coast, Vietnam

Soil erosion is a significant problem in the uplands of the Central Coast, Vietnam. It affects the livelihood of farmers and could hinder the long-term economic development efforts in the uplands. Yet, trapped in poverty, upland farmers, especially the ethnic minority, are still mining the soil using erosive land use systems to meet their urgent needs. This study demonstrates that the level of soil erosion varies across the typical land use systems. The fruit tree-based agroforestry (AF) system is least erosive and most financially profitable. The choice of land use system is influenced by farmers' attributes, land plot characteristics, and policy-related variables. The set of policy variables is important in explaining land use choice by upland farmers. Promoting the switch to AF system and the adoption of soil conservation is of great importance to reducing soil erosion and sustaining development in the uplands. However, it is a very challenging task.     

Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

ABSTRACT

Time-series and machine-learning methods are being strongly exploited to improve the accuracy of short-term load forecasting (STLF) results. In developing countries, power consumption behaviors could be suddenly changed by different customers, e.g. industrial customers, residential customers, so the load-demand dataset is often unstable. Therefore, reliability assessment of the load-demand dataset is obviously necessary for STLF models. Hence, this paper proposes a novel and unified statistical data-filtering method with the best confidence interval to eliminate unexpected noises/outliers of the input dataset before performing various short-term load forecasting models. This proposed novel data-filtering method, so-called the data pre-processing method, is also compared to other existing data-filtering methods (e.g. Kalman filter, Density-Based Spatial Clustering of Applications with Noise, Wavelet transform, and Singular Spectrum Analysis). By using an SCADA system?-based database of a typical 22kV distribution network in Vietnam, NYISO database, and PJM-RTO database, case studies of short-term load forecasting have been conducted with a conventional ARIMA model, an ANN forecasting model, an LSTM-RNN model, an LSTM-CNN combined model, a deep auto-encoder (DAE) network, a Wavenet-based model, a Wavenet and LSTM hybrid model, and a Wavelet Neural Network (WNN) model, which are to validate the novel and unified statistical data-filtering method proposed. The achieved numerical results demonstrate which the accuracy of the aforementioned STLF models can be significantly improved due to the proposed statistical data-filtering method with the best confidence interval of the input load dataset. The proposed statistical data-filtering method can considerably outperform the existing data-filtering methods.     

Can non-breeding be a cost of breeding dispersal?

Breeding habitat selection and dispersal are crucial processes that affect many components of fitness. Breeding dispersal entails costs, one of which has been neglected: dispersing animals may miss breeding opportunities because breeding dispersal requires finding a new nesting site and mate, two time- and energy-consuming activities. Dispersers are expected to be prone to non-breeding. We used the kittiwake (Rissa tridactyla) to test whether breeding dispersal influences breeding probability. Breeding probability was associated with dispersal, in that both were negatively influenced by private information (previous individual reproductive success) and public information (average reproductive success of conspecifics) about patch quality. Furthermore, the probability of skipping breeding was 1.7 times higher in birds that settled in a new patch relative to those that remained on the same patch. Finally, non-breeders that resumed breeding were 4.4 times more likely to disperse than birds that bred in successive years. Although private information may influence breeding probability directly, the link between breeding probability and public information may be indirect, through the influence of public information on breeding dispersal, non-breeding thus being a cost of dispersal. These results support the hypothesis that dispersal may result in not being able to breed. More generally, non-breeding (which can be interpreted as an extreme form of breeding failure) may reveal costs of various previous activities. Because monitoring the non-breeding portion of a population is difficult, non-breeders have been neglected in many studies of reproduction trade-offs.