The probabilistic tsunami hazard assessment along Karnataka Coast from Makran Subduction Zone,west coast of India

Coastal tsunami amplitudes were calculated to identify areas susceptible to tsunami hazard at selected locations of the coast of Karnataka, west of India, due to earthquakes in the Makran Subduction Zone. This is the first time that the probabilistic tsunami hazard assessment along this study area has been attempted. A series of earthquake source scenarios with magnitudes of Mw 8.0, Mw 8.5, and a mega thrust of Mw 9.1 were modeled by using the Community Model Interface for Tsunami interface with the MOST model of Titov and Synolakis (J. Waterway, Port, Coastal and Ocean Engineering, 121(6), 308–316, 1995). As per the previous occurrences, the two least magnitudes are probable, while Mw 9.1 is a worst-case scenario as described by Heidarzadeh et al. (Ocean Engineering, 36(5), 368–376, 2009) and the same is reported by Burbidge et al. (Geoscience Australia Professional Opinion No. 2009/11) as high magnitude. These are not at all historical earthquakes or specifically from historical catalogues. The results of modeling show that all the seven coastal locations are inundated barely in our worst-case scenario with maximum water levels in the range of 100–200 cm. The first tsunami wave strikes the coast within 4–5 h of earthquake occurrence.     

Monitoring of meteorological and hydrological droughts in the Vistula basin (Poland)

The article presents the course of meteorological droughts in Vistula subcatchments in years 1981–2010 and their influence on the occurrence of hydrological droughts. Using the Standardized Precipitation Index (SPI) as an indicator of meteorological drought on the one hand and the Standardized Water-level Index (SWI) and Standardized Runoff Index (SRI) as indicators of hydrological drought on the other, the mutual relationships between precipitation conditions and hydrological conditions were evaluated, as well as the relationships between the two drought types. Studies were conducted for three cumulative periods of these indices, of 12, 24, and 48 months. It was determined that meteorological droughts occurred earliest in the north-western and central part of the basin, and latest in areas lying above 300 m a.s.l. and in the south of Poland. Total duration, depending on the cumulative period, for SPI comprised from 38 to 41% of the analyzed period and for SWI (35–47%) and SRI (24–51%). The strongest relationships were identified in the central part of the Vistula (0.8?<?r?<?0.85), while the weakest relationships were recorded in the foothill region (r?<?0.5). There were also indicated non-climate-related factors influencing those relationships (underground reservoirs, diverse Vistula water resource usage for municipal and industrial intake).     

Spectral trend of vegetation with rainfall in events of El Niño-Southern Oscillation for Atlantic Forest biome,Brazil

This study aimed to analyze the spectral trend of vegetation with rainfall in El Niño-Southern Oscillation events (ENSO) in the Atlantic Forest, Brazil. Monthly rainfall data were collected from 85 conventional meteorological stations (EMC), data from the Enhanced Vegetation Index 2 (EVI2) and ENSO events (El Niño, La Niña, and Neutral) in the period from 2001 to 2013. Afterwards, state cluster analysis was performed using the results of non-parametric tests. The Mann-Kendall (MK) non-parametric test did not identify a trend pattern in rainfall distribution in the Atlantic Forest. The results for EVI2 by state and region showed that the trend is decreasing in the Northeast Region, except for the states of Alagoas and Pernambuco. Southeast region showed an increasing trend of EVI2 (except for Rio de Janeiro and São Paulo), while the South region showed a decreasing trend. In the Midwest, the trend was significantly decreasing. In the prognosis elaborated for the future, the regions with significant declines of the vegetation were the Northeast and Midwest. This study shows that the Atlantic Forest in some regions of Brazil has been suffering from the growing urbanization process and there is a trend of soil degradation.     

Influence of different textile fibers on characterization of dyeing wastewater and final effluent

Textile industry needs to recover and reuse its wastewater as to fulfil the demand of increasingly strict regulations. The characterization of dyeing wastewater samples according to textile fiber and final textile effluent enables the application of different treatment methods. This study aims to characterize dyeing wastewater in black color of polyamide, polyester, and viscose fibers and final textile effluent. Samples were collected and characterized completely for major pollution indicator parameters. Dyeing wastewater of polyester showed higher values for some parameters, e.g., 4994.44% (49,944,400 mg L^?1 and 917 NTU) of turbidity and 4100.00% of phenol when compared to dyeing wastewater of other fibers. Other parameters such as pH, alkalinity, color, phosphorus, nitrogen, sulfides, chlorides, oil and grease, dissolved solids, and chemical and biochemical oxygen demand were also assessed. In addition to individual characterization, this study also presents a correlation of the contribution of each parameter to the final textile effluent. Although dyeing wastewater of polyamide contributes the most in terms of quantity for the final effluent, this study revealed that dyeing wastewater of polyester influenced the most on the final composition of the textile wastewater when evaluating color, turbidity, total iron, biochemical oxygen demand, chemical oxygen demand, phenol, mercury, oil and grease, and total phosphorus. The present study is focused on bringing new insights to provide future research with other strategies to improve the treatment of dyeing wastewater. In addition, some suggestions are also given for wastewater treatments according to type of textile fiber.     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Spatial distribution characteristics of soil organic carbon in subtropical forests of mountain Lushan,China

The study on the spatial distribution of forest soil organic carbon (SOC) is of great significance for accurate assessment of carbon storage in forest ecosystems. In the present study, by taking eight kinds of forest soils of Mountain Lushan in the subtropical area as the research object, we studied the spatial distribution characteristics of SOC in this mountainous area. The results showed that the SOC content and SOC density (SOCD) of main forest types in the Mountain Lushan were lower than the national and the world average. The soil layer of Lushan forest was thinner, and the SOC and active SOC (ASOC) contents of different forest types and SOCDs are the highest in the surface soil. SOCD of the topsoil accounts for 32.64–54.03% of the total SOCD in the whole soil profile. Surface litter is an important source of SOC, and the different vegetation types are the important reason for the different spatial distribution of SOC in this area. Soil SOC contents in the high-altitude forest (bamboo forest, deciduous broadleaf forest, Pinus taiwanensis forest, evergreen-deciduous forest, and coniferous-broadleaved mixed forest) were higher than those in the low-altitude forest (evergreen broadleaf forest, shrub, and Pinus massoniana forest). However, the difference in SOC content exhibited at the altitude gradient is significantly lower than that in SOC in the soil profile. This indicates that both soil depth and elevation are the important factors that affected SOC distribution. However, the influence of soil depth on spatial distribution of SOC may be more complex than that of altitude. Vegetation types and soil properties are the main reasons for the large differences of reduction rate in the contents of SOC and ASOC.     

Real<Emphasis Type="Bold">-</Emphasis>time water quality monitoring using Internet of Things in SCADA

Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.     

Silver near municipal wastewater discharges into western Lake Ontario,Canada

Because of the widespread use of silver nanoparticles in commercial products, discharges of municipal wastewater may be a point source of silver in the aquatic environment. We monitored two sites in western Lake Ontario impacted by discharges from wastewater treatment plants serving the City of Toronto. Concentrations of silver were elevated in bottom sediments and suspended sediments collected at the two sites. We also deployed two types of passive samplers in the water column at the two sites, the newly developed Carbon Nanotube Integrative Samplers for monitoring “CNIS-labile” silver and Diffusive Gradient in Thin Film samplers for monitoring “DGT-labile” silver. Results from these passive samplers indicated that the concentrations of silver at the two sites were either below detection limits or were in the ng/L range. In laboratory experiments where the sediments were re-suspended in Milli-Q water, a small proportion of the silver (i.e., <?25%) was labile and partitioned as colloidal or dissolved silver into the liquid phase after agitation. Nanoparticles tentatively identified as silver nanoparticles were detected by single-particle ICP-MS in suspension after agitation of both suspended and bottom sediments. Therefore, there is a need to assess whether silver species, including silver nanoparticles are transported from wastewater treatment plants into sediments in the aquatic environment. This study is unique in focusing on the in situ distribution of silver in natural waters and in sediments that are potentially impacted by urban sources of nanoparticles.