Subsurface nutrient modelling using finite element model under Boro rice cropping system

Boro rice, an emerging low-risk crop variety of rice, cultivated using residual or stored water after Kharif season. To enhance the quality and production of rice, potassium (K) and phosphorus (P) are the common constituents of agricultural fertilizers. However, excess application of fertilizers causes leaching of nutrients and contaminates the groundwater system. Therefore, assessment and optimization of fertilizer dose are needed for better management of fertilizers. Towards this, the present study determines the path, persistence, and mobility of K and P under the Boro rice cropping system. The experimental site consisted of four plots having Boro rice with four different fertilizer doses of nitrogen (N), P, K viz. 100%, 75%, 50%, and 25% of the recommended dose. Disturbed soil samples were analysed for K and P from pre-sown land to tillering stage at 0–5, 5–10, 10–15, 15–30, 30–45, and 45–60 cm depths. Simultaneously, K and available P were also simulated in the subsurface soil layers through the HYDRUS-1D model. The statistical comparisons were made with RMSER, E, and PBIAS between the modelled values and laboratory-measured values. Although, the results showed that all the treatments considered had agreeable simulations for both K and P, the K simulations were found to be better as compared to P simulations except for 25% where P simulations outperformed K. The simulated concentration at all doses was found most appropriate when measured for the subsurface layers (up to 45 cm), while showed an underestimation in the bottom layers (45–60 cm) of soil.

     

Determinants of CO2 emissions in upper middle-income group countries: an empirical investigation

Environmental Science and Pollution Research - This study investigates the kinked exponential growth, degree of association, and causation between economic growth, energy consumption, population,...     

An Experimental Methodology for Monitoring Contaminant Transport Through Geotechnical Centrifuge Models

In this paper, an attempt has been made to highlight an experimental methodology for monitoring contaminant transport through locally available silty soil and commercially available clay in geotechnical centrifuge models, for different compaction states. Use of multiple depth sensors to determine depth distribution of sodium chloride in the soil column has been detailed. The obtained results have been compared with argentometric method. To validate the centrifuge modelling, modelling of models has been used. The test setup developed can simulate contaminant transport mechanisms through the soil mass, which is approximately 10 m deep, over a period of 600 days. R _e and P _e are found to be N times higher in the centrifuge models. These numbers are found to be several orders less than unity. This indicates that laminar flow prevails and the dominating Cl⁻ transport mechanism in centrifuge is diffusion. The study also highlights the fact that the geotechnical centrifuge modelling can be used as a viable alternative to field scale experimentation.     

Organochlorine Pesticide Residues in Milk and Blood of Women from Anupgarh, Rajasthan, India

Anupgarh is the most fertile area of Rajasthan state where a variety of seasonal crops are grown. The availability of three manmade canals has enhanced the agricultural activities in this area. The farmers use huge amounts of pesticides to increase the crop productivity. Exposure of humans to these hazardous chemicals occurs directly in the fields and indirectly due to consumption of contaminated diet, or by inhalation or by dermal contact. The organochlorine pesticides are reported to be lipophilic and their presence in human milk and blood has been documented in different parts of the world. Blood and milk samples were collected from lactating women who were divided into four groups on the basis of different living standards viz residence area, dietary habits, working conditions and addiction to tobacco. The level of total organochlorine pesticides in blood ranged from 3.319mg/L—6.253mg/L while in milk samples it ranged from 3.209Mdash;4.608 mg/L. The results are in concurrence with the reports from other countries.     

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Monitoring of ambient PM₁₀ (particulate matter which passes through a size selective impactor inlet with a 50% efficiency cut-off at 10 μm aerodynamic diameter) has been done at residential (Kasba) and industrial (Cossipore) sites of an urban region of Kolkata during November 2003 to November 2004. These sites were selected depending on the dominant anthropogenic activities. Metal constituents of atmospheric PM₁₀ deposited on glass fibre filter paper were estimated using Inductively Coupled Plasma Atomic Emission Spectrometer (ICP-AES). Chromium (Cr), zinc (Zn), lead (Pb), cadmium (Cd), nickel (Ni), manganese (Mn) and iron (Fe) are the seven toxic trace metals quantified from the measured PM₁₀ concentrations. The 24 h average concentrations of Cr, Zn, Pb, Cd, Ni, Mn and Fe from ninety PM₁₀ particulate samples of Kolkata were found to be 6.9, 506.1, 79.1, 3.3, 7.4, 2.4 and 103.6 ng/m³, respectively. The 24 h average PM₁₀ concentration exceeded national ambient air quality standard (NAAQS) as specified by central pollution control board, India at both residential (Kasba) and industrial (Cossipore) areas with mean concentration of 140.1 and 196.6 μg/m³, respectively. A simultaneous meteorology study was performed to assess the influence of air masses by wind speed, wind direction, rainfall, relative humidity and temperature. The measured toxic trace metals generally showed inverse relationship with wind speed, relative humidity and temperature. Factor analysis, a receptor modeling technique has been used for identification of the possible sources contributing to the PM₁₀. Varimax rotated factor analysis identified four possible sources of measured trace metals comprising solid waste dumping, vehicular traffic with the influence of road dust, road dust and soil dust at residential site (Kasba), while vehicular traffic with the influence of soil dust, road dust, galvanizing and electroplating industry, and tanning industry at industrial site (Cossipore).     

Arsenic exposure in Hungary, Romania and Slovakia

Inorganic arsenic is a potent human carcinogen and toxicant which people are exposed to mainly via drinking water and food. The objective of the present study was to assess current exposure to arsenic via drinking water in three European countries. For this purpose, 520 individuals from four Hungarian, two Slovakian and two Romanian countries were investigated by measuring inorganic arsenic and methylated arsenic metabolites in urine by high performance liquid chromatography with hydride generation and inductively coupled plasma mass spectrometry. Arsenic in drinking water was determined by atomic absorption spectrometry. Significantly higher concentrations of arsenic were found in both the water and the urine samples from the Hungarian counties (median: 11 and 15 microg dm(-3), respectively; p < 0.001) than from the Slovakian (median: 0.94 and 4.5 microg dm(-3), respectively) and Romanian (median: 0.70 and 2.1 microg dm(-3), respectively) counties. A significant correlation was seen between arsenic in water and arsenic in urine (R(2)= 0.46). At low water arsenic concentrations, the relative amount of dimethylarsinic acid (DMA) in urine was increased, indicating exposure via food. Also, high body mass index was associated with higher concentrations of arsenic in urine (p= 0.03), mostly in the form of DMA. Smokers had significantly higher urinary arsenic concentrations than non-smokers (p= 0.03). In conclusion, elevated arsenic exposure via drinking water was prevalent in some of the counties. Exposure to arsenic from food, mainly as DMA, and cigarette smoke, mainly as inorganic arsenic, are major determinants of arsenic exposure at very low concentrations of arsenic in drinking water.     

Model-based analysis of urban settlement process in eco-sensitive area of developing country: a study with special reference to hills of an Indian city

Rapid and unplanned expansion of a city into its eco-sensitive areas like hills, wetlands, and forests is becoming a major concern, particularly in developing countries. Understanding the process and causes of such unplanned urban expansion is of paramount importance for framing sustainable urban development policies. This paper presents a modelling concept that relates urban settlement in such eco-sensitive areas with potential socio-economic, demographic and geographical factors. The model is applied to an Indian city Guwahati, which is experiencing serious environmental degradation due to unplanned urban settlement in its eco-sensitive hilly areas. While topographical and settlement data were derived using satellite data in GIS platform, all other necessary data were collected from relevant government organizations. On validation of the multi-linear regression model, the coefficient of determination and the root mean square error are obtained as 0.938 and 1.7, respectively. Model results show that geographical parameters are less influencing as compared to the other socio-economic and demographic factors. Sensitivity analysis of urban settlements in hills of Guwahati city carried out with respect to the considered factors reveals that land value and free space availability in the surrounding area of a hill are the most sensitive parameters. This indicates that city development plans should give more importance to outward spatial expansion in plain areas with regulated land value and zoning scheme to minimize unauthorized settlement in eco-sensitive hilly areas of Guwahati city.     

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.     

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.     

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.