Search results for: noise pollution

Authors: Kaarthik K., Harshini S., Karthika M., Kripanandhini T.

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Water is an essential part of living organisms. Major water pollution is caused due to contamination of industrial wastewater in the river. The most important step in bringing wastewater contaminants down to levels that are safe for nature is wastewater treatment. The contamination of river water harms both humans who consume it and the aquatic life that lives there. We introduce a new cloud-based industrial IoT paradigm in this work for real-time control and monitoring of wastewater. The proposed system prevents prohibited entry of industrial wastewater into the plant by monitoring temperature, hydrogen power (pH), CO₂ and turbidity factors from the wastewater input that the wastewater treatment facility will process. Real-time sensor values are collected and uploaded to the cloud by the system using an IoT Wi-Fi Module. By doing so, we can prevent the contamination of industrial wastewater entering the river earlier, and the necessary actions will be taken by the users. The proposed system's results are 90% efficient, preventing water pollution due to industry and protecting human lives.

Keywords: sensors, pH, CO₂, temperature, turbidity

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Authors: Leili Esmaeilani, Jafar Ghaisari, Mohsen Ahmadian

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Hammerstein–Wiener model is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output and could be used to model various processes. This paper contains an optimization approach method for analysing the problem of Hammerstein–Wiener systems identification. The method relies on reformulate the identification problem; solve it as constraint quadratic problem and analysing its solutions. During the formulation of the problem, effects of adding noise to both input and output signals of nonlinear blocks and disturbance to linear block, in the emerged equations are discussed. Additionally, the possible parametric form of matrix operations to reduce the equation size is presented. To analyse the possible solutions to the mentioned system of equations, a method to reduce the difference between the number of equations and number of unknown variables by formulate and importing existing knowledge about nonlinear functions is presented. Obtained equations are applied to an instance H–W system to validate the results and illustrate the proposed method.

Keywords: identification, Hammerstein-Wiener, optimization, quantization

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Authors: Salim Ahmed

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In many countries, water pollution from industrial effluents is a real problem. It may have a negative impact on the environment. To minimize the adverse effects of these contaminants, various methods are used to improve effluent purification, including physico-chemical processes such as adsorption.The present study focuses on applying a naturally biodegradable adsorbent based on argan (southern Morocco) in a physico-chemical adsorption process to reduce the harmful effects of pollutants on the environment. Tests were carried out with the cationic dye methylene blue (MB) and revealed that removal is significantly higher within the first 15 minutes. The parameters studied in this study are adsorbent mass and concentration. The Freundlich model provides an excellent example of the adsorption phenomenon of BMs over argan powder. The results of this study show that argan kernels are a highly beneficial alternative for local communities, as they help to achieve a triple objective: pollution reduction, waste recovery and water recycling.

Keywords: environmental protection, activated carbon, water treatment, adsorption

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Authors: Christian Ndubisi Madu, Benjamin C. Ozumba, Ifeanyi E. Madu, Valentine E. Nnadi, Ikenna C. Ezeasor

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The ability to generate power and achieve energy security is one of the driving forces behind the emerging ‘microgrid’ concept. Traditional power supply often operates with centralized infrastructure for generating, transmitting and distributing electricity. The inefficiency and the incessant power outages associated with the centralized power supply system in Nigeria has alienated many users who frequently turn to electric power generator sets to power their homes and offices. Such acts are unsustainable and lead to increase in the use of fossil fuels, generation of carbon dioxide emissions and other gases, and noise pollution. They also pose significant risks as they entail random purchases and storage of gasolines which are fire hazards. It is therefore important that organizations rethink their relationships to centralized power suppliers in other to improve energy accessibility and security. This study explores the energy planning processes and learning taking place at the University of Nigeria Enugu Campus as the school lead microgrid feasibility studies in its community. There is need to develop community partners to deal with the issue of energy efficiency and also to create a strategic alliance to confront political, regulatory and economic barriers to locally-based energy planning. Community-based microgrid can help to reduce the cost of adoption and diversify risks. This study offers insights into the ways in which microgrids can further democratize energy planning, procurement, and access, while simultaneously promoting efficiency and sustainability.

Keywords: microgrid, energy efficiency, sustainability, energy security

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Authors: Y. Landkocz, C. Lepers, P.J. Martin, B. Fougère, F. Roy Saint-Georges. A. Verdin, F. Cazier, F. Ledoux, D. Courcot, F. Sichel, P. Gosset, P. Shirali, S. Billet

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In 2013, the International Agency for Research on Cancer (IARC) classified air pollution and fine particles as carcinogenic to humans. Causal relationships exist between elevated ambient levels of airborne particles and increase of mortality and morbidity including pulmonary diseases, like lung cancer. However, due to a double complexity of both physicochemical Particulate Matter (PM) properties and tumor mechanistic processes, mechanisms of action remain not fully elucidated. Furthermore, because of several common properties between air pollution PM and tobacco smoke, like the same route of exposure and chemical composition, potential mechanisms of synergy could exist. Therefore, smoking could be an aggravating factor of the particles toxicity. In order to identify some mechanisms of action of particles according to their size, two samples of PM were collected: PM0.03 2.5 and PM0.33 2.5 in the urban-industrial area of Dunkerque. The overall cytotoxicity of the fine particles was determined on human bronchial cells (BEAS-2B). Toxicological study focused then on the metabolic activation of the organic compounds coated onto PM and some genetic and epigenetic changes induced on a co-culture model of BEAS-2B and alveolar macrophages isolated from bronchoalveolar lavages performed in smokers and non-smokers. The results showed (i) the contribution of the ultrafine fraction of atmospheric particles to genotoxic (eg. DNA double-strand breaks) and epigenetic mechanisms (eg. promoter methylation) involved in tumor processes, and (ii) the influence of smoking on the cellular response. Three main conclusions can be discussed. First, our results showed the ability of the particles to induce deleterious effects potentially involved in the stages of initiation and promotion of carcinogenesis. The second conclusion is that smoking affects the nature of the induced genotoxic effects. Finally, the in vitro developed cell model, using bronchial epithelial cells and alveolar macrophages can take into account quite realistically, some of the existing cell interactions existing in the lung.

Keywords: air pollution, fine and ultrafine particles, genotoxic and epigenetic alterations, smoking

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Aparajita Mukherjee, D. P. Mukherjee

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This paper critically assessed pollution problems in small and medium enterprises with unique references to foundries and sponge iron industries to survey the adverse impact on human societies and the environment. The objective of this paper was to show how cleaner production concept was implemented in one foundry through improvisation of existing technology in India. Incremental advancement of existing technology minimized environmental issues and resource utilization. This study depicted that poor fiscal help, poor enforcement of government regulations, owners’ attitude and lacking specialized technical workers were the significant hindrances towards cleaner production. The paper explored the possible ways to overcome these hindrances for cleaner production. On a more general level, findings raise important questions regarding the need for a new paradigm for the implementation of cleaner production. Improvisation of existing technology in these enterprises would be cost effective towards sustainable development.

Keywords: SME pollution, ecological crisis, sustainable development, cleaner production, training

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Authors: Onur Yel

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This study represents the removal of harmful dye substances from wastewaters by using waste and cheap adsorbents. Rapid population growth and industrialization occasion anthropogenic pollution which gives irreversible damage to the environment. One of the ways in which water pollution occurs is caused by the release of the dyestuffs in the textile industry. The release of dyestuffs to the environment directly damages the living creatures that have acquired water habitat. Especially, wastewater cannot be used for nutritional purposes. In addition, some adsorbents have mutagenic and/or carcinogenic effects. By blocking photosynthesis, it hinders the inhibition of photosynthetic bacteria in the water, which damages the ecological balance and also causes the formation of malodorous compounds. Moreover, the lack of oxygen can pose a serious danger to the lives of other living organisms that need oxygen. In recent years, some physical and chemical methods are preferred for the removal of dyestuffs. However, the utilization of these methods is expensive. For this reason, the availability of new and cheap adsorbents becomes the more significant issue. In this study, an investigation of various variables on the removal of Methylene Blue and Acid Red-88 dyestuffs from wastewaters by the usage of pulverized cypress cones has been carried out. Thus, various masses of absorbent (0.1-0.25-0.5-1-2-4-5 grams) are used in 50, 100, 150, 200, 300 ppm concentrations of Methylene Blue and Acid Red-88 dyestuffs’ solutions, and with a variety of the interaction time (0.25-0.5-1-2-4-5 hours). The mixtures were centrifuged and the absorbance of the filtrates was measured on a UV spectrophotometer to determine their remaining concentrations. In the study, the highest removal ratio of Acid Red-88 dyestuff was found to be 81% at 200 ppm of dyestuff with 2 grams of adsorbent at 300 minutes. For Methylene Blue experiments, the removal percentage was found as 98% where 2 grams of adsorbent is used in 200 ppm dyestuff solution at 120 minutes of interaction.

Keywords: acid red-88, biosorption, methylene blue, cypress cones, water pollution

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Authors: Mohammad Mohammadzadeh, Alireza Ghasempour

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In this paper, a new design of monopole antenna has been proposed that increases the contrast of intravascular magnetic resonance images through increasing the homogeneity of the intrinsic signal-to-noise ratio (ISNR) distribution around the antenna. The antenna is made of a coaxial cable with three parasitic elements. Lengths and positions of the elements are optimized by the improved genetic algorithm (IGA) for 1.5, 3, 4.7, and 7Tesla MRI systems based on a defined cost function. Simulations were also conducted to verify the performance of the designed antenna. Our simulation results show that each time IGA is executed different values for the parasitic elements are obtained so that the cost functions of those antennas are high. According to the obtained results, IGA can also find the best values for the parasitic elements (regarding cost function) in the next executions. Additionally, two dimensional and one-dimensional maps of ISNR were drawn for the proposed antenna and compared to the previously published monopole antenna with one parasitic element at the frequency of 64MHz inside a saline phantom. Results verified that in spite of ISNR decreasing, there is a considerable improvement in the homogeneity of ISNR distribution of the proposed antenna so that their multiplication increases.

Keywords: intravascular MR antenna, monopole antenna, parasitic elements, signal-to-noise ratio (SNR), genetic algorithm

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Authors: Md. Moniruzzaman, Thirayoot Limanond

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Being the most visible indicator, the transport system of a city points out how developed the city is. Dhaka megacity holds a mixed composition of motorized and non-motorized modes of transport and the number of vehicle figure is escalating over times. And this obviously poses associated environmental costs like air pollution, noise etc. which is degrading the quality of life in the city. Eventually sustainable transport or more importantly green transport from environmental point of view has become a prime choice to the transport professionals in order to cope up the crisis. Currently the city authority is planning to execute such sustainable transport systems that could serve the pressing demand of the present and meet the future needs effectively. This study focuses on the selection and evaluation of green transportation systems among potential alternatives on a priority basis. In this paper, Fuzzy TOPSIS - a multi-criteria decision method is presented to find out the most prioritized alternative. In the first step, Twenty-one individual specific criteria for sustainability assessment are selected. In the following step, experts provide linguistic ratings to the potential alternatives with respect to the selected criteria. The approach is used to generate aggregate scores for sustainability assessment and selection of the best alternative. In the third step, a sensitivity analysis is performed to understand the influence of criteria weights on the decision making process. The key strength of fuzzy TOPSIS approach is its practical applicability having a generation of good quality solution even under uncertainty.

Keywords: green transport, multi-criteria decision approach, urban transportation system, sustainability assessment, fuzzy theory, uncertainty

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Authors: Jiawen Wang, Qijun Chen

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The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics.

Keywords: image inpainting, generative adversary nets, genetic algorithm, small-sample size

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Authors: Madikeri Rohit

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As energy efficiency is the key concern in many aircraft manufacturing companies regenerative braking is a technique using which energy lost due to friction while braking can be regained. In the operation of an aircraft, significant energy is lost during deceleration or braking which occurs during its landing phase. This problem can be overcome using Regenerative Breaking System (RBS) in landing gear. The major problem faced is regarding the batteries and the overall efficiency gained in competence with the added weight. As the amount of energy required to store is huge we need batteries with high capacity for storage. Another obstacle by using high capacity batteries is the added weight which undermines the efficiency obtained using RBS. An approach to this problem is to either use the obtained energy immediately without storage or to store in other forms such as mechanical, pneumatic and hydraulic. Problem faced with mechanical systems is the weight of the flywheel needed to obtain required efficiency. Pneumatic and hydraulic systems are a better option at present. Using hydraulic systems for storing energy is efficient as it integrates into the overall hydraulic system present in the aircraft. Another obstacle is faced with the redundancy of this system. Conventional braking must be used along with RBS in order to provide redundancy. Major benefits obtained using RBS is with the help of the energy obtained during landing which can be used of engine less taxing. This reduces fuel consumption as well as noise and air pollution. Another added benefit of using RBS is to provide electrical supply to lighting systems, cabin pressurization system and can be used for emergency power supply in case of electric failure. This paper discusses about using RBS in landing gear, problems, prospects and new techniques being pursued to improve RBS.

Keywords: regenerative braking, types of energy conversion, landing gear, energy storage

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Authors: Maydariana Ayuningtyas, Bambang Riyanto, Akhiruddin Maddu

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Industrial waste of crustacean shells, such as shrimp and crab, has been considered as one of the major issues contributing to environmental pollution. The waste processing mechanisms to form new, practical substances with added value have been developed. Chitosan, a derived matter from chitin, which is obtained from crab and shrimp shells, performs prodigiously in broad range applications. A chitosan composite-based diaphragm is a new inspiration in fiber optic acoustic sensor advancement. Elastic modulus, dynamic response, and sensitivity to acoustic wave of chitosan-based composite film contribute great potentials of organic-based sound-detecting material. The objective of this research was to develop chitosan diaphragm application in fiber optic microphone system. The formulation was conducted by blending 5% polyvinyl alcohol (PVA) solution with dissolved chitosan at 0%, 1% and 2% in 1:1 ratio, respectively. Composite diaphragms were characterized for the morphological and mechanical properties to predict the desired acoustic sensor sensitivity. The composite with 2% chitosan indicated optimum performance with 242.55 µm thickness, 67.9% relative humidity, and 29-76% light transmittance. The Young’s modulus of 2%-chitosan composite material was 4.89×104 N/m2, which generated the voltage amplitude of 0.013V and performed sensitivity of 3.28 mV/Pa at 1 kHz. Based on the results above, chitosan from crustacean shell waste can be considered as a viable alternative material for fiber optic acoustic sensor sensing pad development. Further, the research in chitosan utilisation is proposed as novel optical microphone development in anthropogenic noise controlling effort for environmental and biodiversity conservation.

Keywords: acoustic sensor, chitosan, composite, crab shell, diaphragm, waste utilisation

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Authors: Nkwachukwu Chukwuchekwa, Joy Ulumma Chukwuchekwa

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Magnetic characteristics of grain-oriented electrical steel (GOES) are usually measured at high flux densities suitable for its typical applications in power transformers. There are limited magnetic data at low flux densities which are relevant for the characterization of GOES for applications in metering instrument transformers and low frequency magnetic shielding in magnetic resonance imaging medical scanners. Magnetic properties such as coercivity, B-H loop, AC relative permeability and specific power loss of conventional grain oriented (CGO) and high permeability grain oriented (HGO) electrical steels were measured and compared at high and low flux densities at power magnetising frequency. 40 strips comprising 20 CGO and 20 HGO, 305 mm x 30 mm x 0.27 mm from a supplier were tested. The HGO and CGO strips had average grain sizes of 9 mm and 4 mm respectively. Each strip was singly magnetised under sinusoidal peak flux density from 8.0 mT to 1.5 T at a magnetising frequency of 50 Hz. The novel single sheet tester comprises a personal computer in which LabVIEW version 8.5 from National Instruments (NI) was installed, a NI 4461 data acquisition (DAQ) card, an impedance matching transformer, to match the 600  minimum load impedance of the DAQ card with the 5 to 20  low impedance of the magnetising circuit, and a 4.7 Ω shunt resistor. A double vertical yoke made of GOES which is 290 mm long and 32 mm wide is used. A 500-turn secondary winding, about 80 mm in length, was wound around a plastic former, 270 mm x 40 mm, housing the sample, while a 100-turn primary winding, covering the entire length of the plastic former was wound over the secondary winding. A standard Epstein strip to be tested is placed between the yokes. The magnetising voltage was generated by the LabVIEW program through a voltage output from the DAQ card. The voltage drop across the shunt resistor and the secondary voltage were acquired by the card for calculation of magnetic field strength and flux density respectively. A feedback control system implemented in LabVIEW was used to control the flux density and to make the induced secondary voltage waveforms sinusoidal to have repeatable and comparable measurements. The low noise NI4461 card with 24 bit resolution and a sampling rate of 204.8 KHz and 92 KHz bandwidth were chosen to take the measurements to minimize the influence of thermal noise. In order to reduce environmental noise, the yokes, sample and search coil carrier were placed in a noise shielding chamber. HGO was found to have better magnetic properties at both high and low magnetisation regimes. This is because of the higher grain size of HGO and higher grain-grain misorientation of CGO. HGO is better CGO in both low and high magnetic field applications.

Keywords: flux density, electrical steel, LabVIEW, magnetization

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Authors: Yassets Egaña, Patricio Núñez, Juan C. Rios, Ivan Balic, Alex Manquez, Yarela Flores, Maria C. Gatica, Sergio Diez De Medina, Rocio Tijaro-Rojas

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Globally, humans produce millions of tons of waste per year. An important percentage of this waste is plastic, which frequently ends up in landfills and oceans. During the last decades, the greatest plastics production in history have been made, a few amount of this plastic is recycled, the rest ending up as plastic pollution in soils and seas. Plastic pollution is disastrous for the environment, affecting essential species, quality of consumption water, and some economic activities such as tourism, in different parts of the world. Due to its durability and decomposition on micro-plastics, animals and humans are accumulating a variety of plastic components without having clear their effects on human health, economy, and wildlife. In dry regions as the Atacama Desert, up to 95% of the water consumption comes from underground reservoirs, therefore preventing the soil pollution is an urgent need. This contribution focused on isolating, genotyping and optimizing microorganisms that use plastic waste as the only source of food to construct a batch-type bioreactor able to degrade in a faster way the plastic waste before it gets the desert soils and groundwater consumed by people living in this areas. Preliminary results, under laboratory conditions, has shown an improved degradation of polyethylene when three species of bacteria and three of fungi act on a selected plastic material. These microorganisms have been inoculated in dry soils, initially lacking organic matter, under environmental conditions in the laboratory. Our team designed and constructed a prototype using the natural conditions of the region and the best experimental results.

Keywords: biological breakdown, plastic bags, prototype, desert regions

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Authors: Victor Vasques Ribeiro

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The Santa Cruz dos Navegantes beach is located in the city of Guarujá, in the central portion of the coast of the state of São Paulo. Next to this beach is located the Channel of the Port of Santos, configured as a source of plastic pellets for marine environments. On sandy beaches near the sources, especially during the winter and after cold front entrance events, the amounts of pellets can be very high. This study aimed to determine the influence of a cold front entry event of the winter of 2019 on the amount of pellets found on Santa Cruz dos Navegantes beach, besides assuming the proximity of the sources. During six consecutive collection campaigns, three of which were previous and three after the cold front entry peak, 30.0 square meters of surface sediments were sampled in each campaign. The color and shape of the pellets were determined to assume the length of the permanence of these granules in the marine environment and, consequently, the proximity of the sources. This beach was considered ideal for this type of research. The pellet pollution index (PPI) was from moderate to very high right after the peak of the cold front entry. The cold front peak event significantly influenced the amount of pellets found on the beach of Santa Cruz dos Navegantes. The factors that can bury the pellets in the sediments were classified as low when compared to other beaches in the region. Most of the pellets found were recently produced and lost to aquatic environments. Like the other beaches near Santos Bay, Santa Cruz dos Navegantes beach receives significant amounts of pellets that have nearby origins. Therefore, it was supposed that the activities of the Santos port complex are sources of pellets for the marine environment. This pollution can be further worsened in certain meteoceanographic events. The beaches of this region need to be constantly monitored and evaluated for pollution by pellets.

Keywords: beach, cold front, pellets, sources

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Authors: L. Alejandro Cárdenas, Fernando Herrera, David Nova, Juan Ballesteros

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This paper presents a methodology to optimize the maintenance of grid-connected photovoltaic systems, considering the cleaning and module replacement periods based on an asset management strategy. The methodology is based on the analysis of the energy production of the PV plant, the energy feed-in tariff, and the cost of cleaning and replacement of the PV modules, with the overall revenue received being the optimization variable. The methodology is evaluated as a case study of a 5.6 kWp solar PV plant located on the Bogotá campus of the Universidad Nacional de Colombia. The asset management strategy implemented consists of assessing the PV modules through visual inspection, energy performance analysis, pollution, and degradation. Within the visual inspection of the plant, the general condition of the modules and the structure is assessed, identifying dust deposition, visible fractures, and water accumulation on the bottom. The energy performance analysis is performed with the energy production reported by the monitoring systems and compared with the values estimated in the simulation. The pollution analysis is performed using the soiling rate due to dust accumulation, which can be modelled by a black box with an exponential function dependent on historical pollution values. The pollution rate is calculated with data collected from the energy generated during two years in a photovoltaic plant on the campus of the National University of Colombia. Additionally, the alternative of assessing the temperature degradation of the PV modules is evaluated by estimating the cell temperature with parameters such as ambient temperature and wind speed. The medium-term energy decrease of the PV modules is assessed with the asset management strategy by calculating the health index to determine the replacement period of the modules due to degradation. This study proposes a tool for decision making related to the maintenance of photovoltaic systems. The above, projecting the increase in the installation of solar photovoltaic systems in power systems associated with the commitments made in the Paris Agreement for the reduction of CO2 emissions. In the Colombian context, it is estimated that by 2030, 12% of the installed power capacity will be solar PV.

Keywords: asset management, PV module, optimization, maintenance

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Authors: Zaccheaus Olasupo Apotiola

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This study evaluates the level of pollution in underground water from Ogijo and Odo-nla areas in lkorodu, Lagos State. Water sample were collected around various industries and transported in ice packs to the laboratory. Temperature and pH was determined on site, physicochemical parameters and total plate were determined using standard methods, while heavy metal concentration was determined using Atomic Absorption spectrophotometry method. The temperature was observed at a range of 20-28 oC, the pH was observed at a range of 5.64 to 6.91 mol/l and were significantly different (P < 0.05) from one another. The chloride content was observed at a range 70.92 to 163.10 mg/l there was no significant difference (P > 0.05) between sample 40 GAJ and ISUP, but there was significant difference (P < 0.05) between other samples. The acidity value varied from 11.0 – 34.5 (mg/l), the samples had no alkalinity. The Total plate count was found at 20-125 cfu/ml. Asernic, Lead, Cadmium, and Mercury concentration ranged between 0.03 - 0.09, 0.04 - 0.11, 0.00 -0.00, and 0.00 – 0.00(mg/l) respectively. However there was significant difference (p < 0.05) between all samples except for sample 4OGA, 5OGAJ, and 3SUTN that were not significantly different (P > 0.05). The results revealed all samples are not safe for human consumption as the levels of Asernic and Lead are above the maximum value of (0.01 mg/l) recommended by NIS 554 and WHO.

Keywords: arsenic, cadmium, lead mercury, WHO

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: Y. M. Al-Wahaibi, S. N. Al-Bahry, A. E. Elshafie, A. S. Al-Bemani, S. J. Joshi, A. K. Al-Bahri

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Crude oil is a major source of global energy. The major problem is its widespread use and demand resulted is in increasing environmental pollution. One associated pollution problem is ‘oil spills’. Oil spills can be remediated with the use of chemical dispersants, microbial biodegradation and microbial metabolites such as biosurfactants. Four different minimal salt media for biosurfactant production by Bacillus isolated from oil contaminated sites from Oman were screened. These minimal salt media were supplemented with either glucose or sucrose as a carbon source. Among the isolates, W16 and B30 produced the most active biosurfactants. Isolate W16 produced better biosurfactant than the rest, and reduced surface tension (ST) and interfacial tension (IFT) to 25.26mN/m and 2.29mN/m respectively within 48h which are characteristics for removal of oil in contaminated sites. Biosurfactant was produced in bulk and extracted using acid precipitation method. Thin Layer Chromatography (TLC) of acid precipitate biosurfactant revealed two concentrated bands. Further studies of W16 biosurfactant in bioremediation of oil spills are recommended.

Keywords: oil contamination, remediation, Bacillus spp, biosurfactant, surface tension, interfacial tension

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Authors: A. Miškinytė, A. Dėdelė

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Road traffic is one of the main sources of air pollution in urban areas associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered as traffic-related air pollutant, which concentrations tend to be higher near highways, along busy roads and in city centres and exceedances are mainly observed in air quality monitoring stations located close to traffic. Atmospheric dispersion models can be used to examine emissions from many various sources and to predict the concentration of pollutants emitted from these sources into the atmosphere. The study aim was to compare modelled concentrations of nitrogen dioxide using ADMS-Urban dispersion model with air quality monitoring network in cold and warm seasons in Kaunas city. Modelled average seasonal concentrations of nitrogen dioxide for 2011 year have been verified with automatic air quality monitoring data from two stations in the city. Traffic station is located near high traffic street in industrial district and background station far away from the main sources of nitrogen dioxide pollution. The modelling results showed that the highest nitrogen dioxide concentration was modelled and measured in station located near intensive traffic street, both in cold and warm seasons. Modelled and measured nitrogen dioxide concentration was respectively 25.7 and 25.2 µg/m3 in cold season and 15.5 and 17.7 µg/m3 in warm season. While the lowest modelled and measured NO2 concentration was determined in background monitoring station, respectively 12.2 and 13.3 µg/m3 in cold season and 6.1 and 7.6 µg/m3 in warm season. The difference between monitoring station located near high traffic street and background monitoring station showed that better agreement between modelled and measured NO2 concentration was observed at traffic monitoring station.

Keywords: air pollution, nitrogen dioxide, modelling, ADMS-Urban model

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: cross-validation, decision tree, lagged variables, short-term forecasting

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Authors: Hajar Hajmohammadi, Paul Pfeffer, Anna De Simoni, Jim Cole, Chris Griffiths, Sally Hull, Benjamin Heydecker

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Background: There is strong interest in the relationship between short-term air pollution exposure and human health. Most studies in this field focus on serious health effects such as death or hospital admission, but air pollution exposure affects many people with less severe impacts, such as exacerbations of respiratory conditions. A lack of quantitative analysis and inconsistent findings suggest improved methodology is needed to understand these effectsmore fully. Method: We developed a time series regression model to quantify the relationship between daily NOₓ concentration and Asthma exacerbations requiring oral steroids from primary care settings. Explanatory variables include daily NOₓ concentration measurements extracted from 8 available background and roadside monitoring stations in east London and daily ambient temperature extracted for London City Airport, located in east London. Lags of NOx concentrations up to 21 days (3 weeks) were used in the model. The dependent variable was the daily number of oral steroid courses prescribed for GP registered patients with asthma in east London. A mixed distribution model was then fitted to the significant lags of the regression model. Result: Results of the time series modelling showed a significant relationship between NOₓconcentrations on each day and the number of oral steroid courses prescribed in the following three weeks. In addition, the model using only roadside stations performs better than the model with a mixture of roadside and background stations.

Keywords: air pollution, time series modeling, public health, road transport

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Authors: Dhananjoy Dutta

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Field studies in lower Gangetic plains of India reveal that over-abstraction of groundwater for irrigation to paddy leads to a substantial depletion of groundwater over the decades, resulting in negative effects on lowering of the water table, drying up of surface water sources and aquifer pollution with leached-out arsenic. The aggravating arsenic toxicity in drinking water is manifested in health problems and ‘arsenicosis’ of people. A social conflict arises between farmers, who intend to grow paddy for livelihoods, and the groundwater authority, which enacts the ‘Regulation Laws’ by putting a check on the excessive installation of private tube-wells for irrigation. Hence, considering the challenges of resource sustainability, health issues, and food security, the study calls for a paradigm shift in policy from further groundwater development to sustainable water resources management and evaluates some strategies integrating supply and demand side management for mitigating the problems.

Keywords: groundwater, irrigation, paddy farming, water table depletion, arsenic pollution, gangetic plains

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Authors: V. Radulescu, S. Dumitru

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Introduction: Determination of the temperature field inside a fluid in motion has many practical issues, especially in the case of turbulent flow. The phenomenon is greater when the solid walls have a different temperature than the fluid. The turbulent heat and mass transfer have an essential role in case of the thermal pollution, as it was the recorded during the damage of the Thermoelectric Power-plant Oradea (closed even today). Basic Methods: Solving the theoretical turbulent thermal pollution represents a particularly difficult problem. By using the semi-empirical theories or by simplifying the made assumptions, based on the experimental measurements may be assured the elaboration of the mathematical model for further numerical simulations. The three zones of flow are analyzed separately: the vicinity of the solid wall, the turbulent transition zone, and the turbulent core. For each area are determined the distribution law of temperature. It is determined the dependence of between the Stanton and Prandtl numbers with correction factors, based on measurements experimental. Major Findings/Results: The limitation of the laminar thermal substrate was determined based on the theory of Landau and Levice, using the assumption that the longitudinal component of the velocity pulsation and the pulsation’s frequency varies proportionally with the distance to the wall. For the calculation of the average temperature, the formula is used a similar solution as for the velocity, by an analogous mediation. On these assumptions, the numerical modeling was performed with a gradient of temperature for the turbulent flow in pipes (intact or damaged, with cracks) having 4 different diameters, between 200-500 mm, as there were in the Thermoelectric Power-plant Oradea. Conclusions: It was made a superposition between the molecular viscosity and the turbulent one, followed by addition between the molecular and the turbulent transfer coefficients, necessary to elaborate the theoretical and the numerical modeling. The concept of laminar boundary layer has a different thickness when it is compared the flow with heat transfer and that one without a temperature gradient. The obtained results are within the margin of error of 5%, between the semi-empirical classical theories and the developed model, based on the experimental data. Finally, it is obtained a general correlation between the Stanton number and the Prandtl number, for a specific flow (with associated Reynolds number).

Keywords: experimental measurements, numerical correlations, thermal pollution through pipelines, turbulent thermal flow

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Tao Feng, Xiaomei Ma, Xian Guo, Jing Wang

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Named Data Network (NDN) is one of the future Internet architecture, all nodes (i.e., hosts, routers) are allowed to have a local cache, used to satisfy incoming requests for content. However, depending on caching allows an adversary to perform attacks that are very effective and relatively easy to implement, such as content pollution attack. In this paper, we use a method of secure network coding based on homomorphic signature system to solve this problem. Firstly ,we use a dynamic public key technique, our scheme for each generation authentication without updating the initial secret key used. Secondly, employing the homomorphism of hash function, intermediate node and destination node verify the signature of the received message. In addition, when the network topology of NDN is simple and fixed, the code coefficients in our scheme are generated in a pseudorandom number generator in each node, so the distribution of the coefficients is also avoided. In short, our scheme not only can efficiently prevent against Intra/Inter-GPAs, but also can against the content poisoning attack in NDN.

Keywords: named data networking, content polloution attack, network coding signature, internet architecture

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Authors: Nida Rizvi, Deeksha Katyal, Varun Joshi

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River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

Keywords: cluster analysis, multivariate statistical techniques, river Hindon, water quality

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Authors: Alaa M. Younis, Ismail S. Ismail, Lamiaa I. Mohamedein, Shimaa F. Ahmed

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Sandy surface sediments collected from fourteen sites along the gulfs of Suez and Aqaba coasts, Egypt were analyzed for heavy metals including Iron, Manganese, Zinc, Chromium, Nickel, Lead, Copper and Cadmium in order to evaluate the pollution status and environmental risk assessment of the study area. The obtained results showed that the concentrations of investigated metals are represented in the following sequence; For Gulf of Aqaba sediments Fe > Mn > Zn > Pb > Cr > Ni > Cu > Cd. While for Gulf of Suez Sediments Fe > Mn > Pb > Zn > Cu > Cr > Ni > Cd. The degree of surface sediment contamination using Geo-accumulation index (I geo) and Metal Pollution Index (MPI) was computed. Higher MPI values were observed at the sites III (Nama Bay) and VIII (Rex Beach). According to Sediment quality guidelines (SQGs) approach, Pb and Cu in the gulf of Suez at station IX (Kabanon Beach) had probably adverse ecological effects to marine organisms.

Keywords: heavy metal, environmental risk, Suez gulf, Aqaba gulf

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Authors: Jyotismita Talukdar

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In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free environment and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient’s data has been collected and studied. The data is analysed and the results have been compared with the Doctor’s diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). It has been found that highest accuracy(93%), has been achieved in case of PPI( Post-Permanent-Pacemaker Implementation ), around 79% in case of CAD(Coronary Artery disease), 87% in DCM (Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75 % in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP (Ischemic Cardiomyopathy), about 38% and AF( Atrial Fibrillation), about 60 to 62%.

Keywords: coronary heart disease, chronic stable angina, sick sinus syndrome, cardiovascular disease, cholesterol, Thalach

Procedia PDF Downloads 159