Search results for: amazon forest

Authors: Henry Kuppen

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In the last decade, tree managers have faced many threats of pests and diseases and the effects of climate change. Managers will recognize that they have to put more energy and more money into tree management. By recognizing the cause behind this, the opportunity will arise to build sustainable tree populations for the future. More and more, unwanted larvae are sprayed, ash dieback infected trees are pruned or felled, and emerald ash borer is knocking at the door of West Europe. A lot of specific knowledge is needed to produce management plans and best practices. If pest and disease have a large impact, society loses complete tree species and need to start all over again building urban forest. But looking at the cause behind it, landscape design, and tree species selection, the sustainable solution does not present itself in managing these threats. Every pest or disease needs two important basic ingredients to be successful: climate and food. The changing climate is helping several invasive pathogens to survive. Food is often designed by the landscapers and managers of the urban forest. Monocultures promote the success of pathogens. By looking more closely at the basics, tree managers will realise very soon that the solution will not be the management of pathogens. The long-term solution for sustainable tree populations is a different design of our urban landscape. The use of tree species diversity can help to reduce the impact of climate change and pathogens. Therefore landscapers need to be supported. They are the specialists in designing the landscape using design values like canopy volume, ecosystem services, and seasonal experience. It’s up to the species specialist to show what the opportunities are for different species that meet the desired interpretation of the landscape. Based on landscapers' criteria, selections can be made, including tree species related requirements. Through this collaboration and formation of integral teams, sustainable plant design will be possible.

Keywords: climate change, landscape design, resilient landscape, tree species selection

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Authors: Armand Okende, Joëlle De Weerdt, Esther Fichtler, Maaike De Ridder, Hans Beeckman

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The rainforest plays a crucial role in regulating the climate balance. The biodiversity of tropical rainforests is undeniable, but many aspects remain poorly known, which directly influences its management. Despite the efforts of sustainable forest management, human pressure in terms of exploitation and smuggling of timber forms a problem compared to exploited species whose status is considered "vulnerable" on the IUCN red list compiled by. Commercial species in Class III of the Democratic Republic of Congo are the least known in the market operating, and their biology is unknown or non-existent. Identification of wood in terms of descriptions and anatomical measurements of the wood is in great demand for various stakeholders such as scientists, customs, IUCN, etc. The objective of this study is the qualitative and quantitative description of the anatomical characteristics of commercial species in Class III of DR Congo. The site of the Luki Biosphere Reserve was chosen because of its high tree species richness. This study focuses on the wood anatomy of 14 commercial species of Class III of DR Congo. Thirty-four wooden discs were collected for these species. The following parameters were measured in the field: Diameter at breast height (DBH), total height and geographic coordinates. Microtomy, identification of vessel parameters (diameter, density and grouping) and photograph of the microscopic sections and determining age were performed in this study. The results obtained are detailed anatomical descriptions of species in Class III of the Democratic Republic of Congo.

Keywords: sustainable management of forest, rainforest, commercial species of class iii, vessel diameter, vessel density, grouping vessel

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Authors: Tsedekech Gebremeskel Weldmichael, Tamas Szegi, Lubangakene Denish, Ravi Kumar Gangwar, Erika Micheli, Barbara Simon

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Following the 1992 Earth Summit in Rio de Janeiro, soil biodiversity has been recognized globally as a crucial player in guaranteeing the functioning of soil and a provider of several ecosystem services essential for human well-being. The microbial fraction of the soil is a vital component of soil fertility as soil microbes play key roles in soil aggregate formation, nutrient cycling, humification, and degradation of pollutants. Soil fauna, such as earthworms, have huge impacts on soil organic matter dynamics, nutrient cycling, and infiltration and distribution of water in the soil. Currently, land-use change has been a global concern as evidence accumulates that it adversely affects soil biodiversity and the associated ecosystem goods and services. In this study, we examined the patterns of soil microbial respiration (SMR) and earthworm (abundance, biomass, and species richness) across three land-use types (grassland, arable land, and forest) in Hungary. The objectives were i) to investigate whether there is a significant difference in SMR and earthworm (abundance, biomass, and species richness) among land-use types. ii) to determine the key soil properties that best predict the variation in SMR and earthworm communities. Soil samples, to a depth of 25 cm, were collected from the surrounding areas of seven soil profiles. For physicochemical parameters, soil organic matter (SOM), pH, CaCO₃, E₄/E₆, available nitrogen (NH₄⁺-N and NO₃⁻-N), potassium (K₂O), phosphorus (P₂O₅), exchangeable Ca²⁺, Mg²⁺, soil moisture content (MC) and bulk density were measured. The analysis of SMR was determined by basal respiration method, and the extraction of earthworms was carried out by hand sorting method as described by ISO guideline. The results showed that there was no statistically significant difference among land-use types in SMR (p > 0.05). However, the highest SMR was observed in grassland soils (11.77 mgCO₂ 50g⁻¹ soil 10 days⁻¹) and lowest in forest soils (8.61 mgCO₂ 50g⁻¹ soil 10 days⁻¹). SMR had strong positive correlations with exchangeable Ca²⁺ (r = 0.80), MC (r = 0.72), and exchangeable Mg²⁺(r = 0.69). We found a pronounced variation in SMR among soil texture classes (p < 0.001), where the highest value in silty clay loam soils and the lowest in sandy soils. This study provides evidence that agricultural activities can negatively influence earthworm communities, in which the arable land had significantly lower earthworm communities compared to forest and grassland respectively. Overall, in our study, land use type had minimal effects on SMR whereas, earthworm communities were profoundly influenced by land-use type particularly agricultural activities related to tillage. Exchangeable Ca²⁺, MC, and texture were found to be the key drivers of the variation in SMR.

Keywords: earthworm community, land use, soil biodiversity, soil microbial respiration, soil property

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Authors: Y. Cochet, A. Achim, Tom Flatman, J-C. Domec, J. Ogée, L. Wingate, Ram Oren

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It is accepted that atmospheric CO2 concentration will increase in the future. For the past 30 years, researchers have used FACE (Free-Air Carbon Dioxide Enrichment) facilities to study the development of terrestrial ecosystems under elevated CO2 (eCO2). Forest responses to eCO2 are likely to impact timber industries with potential feedbacks towards the atmosphere. The main objectives of this study were to examine whether eCO2 alone or in combination with N-fertilization alter wood properties and to identify changes in wood anatomy related to water transport. Wood disks were sampled at breast height from mature loblolly pine trees (Pinus taeda L.) harvested at the Duke FACE site (NC, USA). By measuring ring width and intra-ring changes in density (X-ray densitometry) and tracheid size (lumen and cell wall thickness) from pith to bark, the following hypotheses were tested: 1) eCO2 and N-fertilization interact positively to increase significantly above-ground primary productivity; 2) eCO2 and N-fertilization lead to a decrease in density; 3) eCO2 and N-fertilization increase lumen diameter and decrease cell wall thickness, thus affecting water transport capacity. Our results revealed a boost in earlywood tracheid production induced by eCO2 lasting a few years. The following decrease seemed to be buffered by N-fertilization. X-ray profiles did not show a marked decrease in wood density under eCO2 or N-fertilization, although there were changes in cell anatomical properties such as a reduction in cell-wall thickness and an increase in lumen diameter. If such effects of eCO2 are confirmed, forest management strategies for example N-fertilization should be redesigned.

Keywords: wood density, Duke FACE (free-air carbon dioxide enrichment), N fertilization, tree ring

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Authors: Miseri Roeslan Afany, Nur Ainun Pulungan

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The unique characteristics of Andisol differentiate them from other soils. These characteristics become a guideline in determining management and usage with regards to agriculture. Especially in the tropical area, Andisols may have fast mineral alteration due to intensive water movement in the soils. Four soil chemical tests were conducted for evaluating soils in the study area. Al/Fe humus, allophane, pH, and P retention were used to differentiate Andisols under different practices. Non-cultivation practice (e.g. natural forest) and cultivation practices (e.g. horticulture systems and intensive farming systems) are compared in this study. We applied Blackmore method for P retention analysis. The aims of this study are: (i) to analyze the specific behavior of Al/Fe humus, pH, and allophane towards P retention in order (ii) to evaluate the effect of cultivation practices on their behavior changes among Andisols, and (iii) to gain the sustainable agriculture through proposing an appropriate soil managements in the study area. 5 observation sites were selected, and 75 soil sampling were analyzed in this study. The results show that the cultivation decreases P retention in all sampling sites. There is a declining from ±90% to ±50% of P retention in the natural forest where shifts into cultivated land. The average of P retention under 15 years of cultivation down into 63%, whereas, the average of P retention more than 15 years of cultivation down into 54%. Many factors affect the retention of P in the soil such as: (1) type and amount of clay, (2) allophone and/or imogolit, (3) Al/Fe humus, (4) soil pH, (5) type and amount of organic material, (6) Exchangeable bases (Ca, Mg, Na, K), (7) forms and solubility of Al/Fe. To achieve the sustainable agriculture in the study area, conventional agriculture practices should be preserved and intensive fertilizing practices should be applied in order to increase the soil pH, to maintain the organic matter of andisols, to maintain microba activities, and to release Al/Fe humus complex, and thus increase available P in the soils.

Keywords: Andisols, cultivation, P retention, sustainable agriculture

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: M. Mohan, J. P. Roise, G. P. Catts

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Despite living in an era where conservation zones are de-facto the central element in any sustainable wildlife management strategy, we still find ourselves grappling with several pareto-optimal situations regarding resource allocation and area distribution for the same. In this paper, a multi-objective decision making (MODM) model is presented to answer the question of whether or not we can establish mutual relationships between these contradicting objectives. For our study, we considered a Red-cockaded woodpecker (Picoides borealis) habitat conservation scenario in the coastal plain of North Carolina, USA. Red-cockaded woodpecker (RCW) is a non-migratory territorial bird that excavates cavities in living pine trees for roosting and nesting. The RCW groups nest in an aggregation of cavity trees called ‘cluster’ and for our model we use the number of clusters to be established as a measure of evaluating the size of conservation zone required. The case study is formulated as a linear programming problem and the objective function optimises the Red-cockaded woodpecker clusters, carbon retention rate, biofuel, public safety and Net Present Value (NPV) of the forest. We studied the variation of individual objectives with respect to the amount of area available and plotted a two dimensional dynamic graph after establishing interrelations between the objectives. We further explore the concept of interdependency by integrating the MODM model with GIS, and derive a raster file representing carbon distribution from the existing forest dataset. Model results demonstrate the applicability of interdependency from both linear and spatial perspectives, and suggest that this approach holds immense potential for enhancing environmental investment decision making in future.

Keywords: conservation, interdependency, multi-objective decision making, red-cockaded woodpecker

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Authors: Richard G. Boakye, Dara A Stanley, Mathavan Vickneswaran, Blanaid White

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The cultivation of cocoa (Theobroma cocoa), an important cash crop that contributes immensely towards the economic growth of several Western African countries, depends almost entirely on pesticide application owing to the plant’s vulnerability to pest and disease attacks. However, the extent to which pesticides inputted for cocoa cultivation impact bees and bee products has rarely received attention in research. Through this study, the effects of pesticides applied for cocoa cultivation on honey in Ghana were examined by evaluating honey samples from cocoa and forest ecosystems in Ghana. An analysis of five honey samples from each land use type confirmed pesticide contaminants from these land use types at measured concentrations for acetamiprid (0.051mg/kg); imidacloprid (0.004-0.02 mg/kg), thiamethoxam (0.013-0.017 mg/kg); indoxacarb (0.004-0.045 mg/kg) and sulfoxaflor (0.004-0.026 mg/kg). None of the observed pesticide concentrations exceeded EU maximum residue levels, indicating no compromise of the honey quality for human consumption. However, from the results, it could be inferred that toxic effects on bees may not be ruled out because observed concentrations largely exceeded the threshold of 0.001 mg/kg at which sublethal effects on bees have previously been reported. One of the most remarkable results to emerge from this study is the detection of imidacloprid in all honey samples analyzed, with sulfoxaflor and thiamethoxam also being detected in 93% and 73% of the honey samples, respectively. This suggests the probable prevalence of pesticide use in the landscape. However, the conclusions reached in this study should be interpreted within the scope of pesticide applications within Bia West District and not necessarily extended to other cocoa-producing districts in Ghana. Future studies should therefore include multiple cocoa-growing districts and other non-cocoa farming landscapes. Such an approach can give a broader outlook on pesticide residues in honey produced in Ghana.

Keywords: honey, cocoa, pesticides, bees, land use, landscape, residues, Ghana

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Authors: P. J. Kagosi, M. Mndolwa, E. Japhate

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The study was conducted to communities adjacent MNR, Lushoto district, Tanzania. The MNR is one of the nine nature reserves in the Eastern Arc Mountains of Tanzania with an area of 8,700ha with high biological diversity. However, biodiversity in MNR have been threatened by increasing human activities for livelihood in 1970s. The AF systems in the study area was practised since 1980s however, no study was conducted on AF impacts. This paper presents the influence of AF on livelihood of communities adjacent MNR and biodiversity conservation. Qualitative and quantitative data were collected using socio-economic survey and botanical surveys. Data were analysed using Statistical Packages for Social Sciences and content analysis. The study found that in 1970s free livestock grazing caused considerable surface runoff, soil erosion and reduction of crop production. Since 1980s, the study area received various interventions based on the land conservations and improved livelihood through practising AF systems. It was further found that the AF farming improved crop productivity, reduced soil erosion, increased firewood (80.2%) and other forest products availability and AF encouraged community members practicing indoor livestock keeping.The dominant agroforestry tree found in the study area is grevillea reported by 74.1% of respondents planting an average of 40 trees. The study found that the AF reduced pressure to MNR as forest products and fodders were obtained from community's farms in turn, currently water flow from MNR has been increased. Thus AF products support livelihood needs and conserve biodiversity. The study recommends continuity education on new AF technology packages.

Keywords: impact of agroforestry, biodiversity management, communities’ livelihoods, Magamba nature reserve

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Authors: Maffo Maffo Nicole Liliane, Mounmeni Kpoumie Hubert, Mbaire Matindje Karl Marx, Zapfack Louis

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Sacred forests play a valuable role in conserving local biodiversity and provide numerous ecosystem services in Cameroon. The study was carried out in the sacred forests of Bandrefam and Batoufam (western Cameroon). The aim was to estimate the diversity of woody species, carbon stocks and degradation factors in these sacred forests. The floristic inventory was carried out in plots measuring 25m × 25m for trees with diameters greater than 10 cm and 5m × 5m for trees with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and the allometric equations. Data on degradation factors were collected using semi-structured surveys in the Bandrefam and Batoufam neighborhoods. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam Sacred Forest and 45 species divided into 42 genera and 27 families in the Batoufam Sacres Forest. The families common to both sacred forests are as follows: Phyllanthaceae, Fabaceae, Moraceae, Lamiaceae, Malvaceae, Rubiaceae, Meliaceae, Anacardiaceae, and Sapindaceae. Three genera are present in both sites. These are: Albizia, Macaranga, Trichillia. In addition, there are 27 species in common between the two sites. The total carbon stock is 469.26 tC/ha at Batoufam and 291.41 tC/ha at Bandrefam. The economic value varies between 15 823 877.05 fcfa at Batoufam and 9 825 530.528 fcfa at Bandrefam. The study shows that despite the sacred nature of these forests, they are subject to degradation factors such as bushfires (35.42 %), the creation of plantations (23.96 %), illegal timber exploitation (21.88 %), young people's lack of interest in the notion of conservation (9.38 %), climate change (7.29 %) and growing urbanization (2.08 %). These factors threaten biodiversity and reduce carbon storage in these forests.

Keywords: sacred forests, degradation factors, carbon stocks, semi-structured surveys

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Authors: Younmin Shin, Jin Kyu Kim, Mirim Jin, Jeong June Choi

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Demilitarized Zone (DMZ) is a peace region located between South and North Korea border to avoid accidental armed conflict. Because human accessing to the area was forced to be prohibited for more than 60 years, DMZ is one of the cleanest land keeping wild lives as nature itself in South Korea. In this study, we evaluated the biological efficacies of plants (SS, PC, and AR) inhabiting in DMZ for the development of functional cosmetics. First, we tested the cytotoxicity of plant extracts in keratinocyte and melanocyte, which are the major cell components of skin. By 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay with the cell lines, we determined the safety concentrations of the extracts for the efficacy tests. Next, we assessed the anti-wrinkle cosmetic function of SS by demonstrating that SS treatment decreased the expression of Matrix metalloproteinase-1 (MMP-1) in UV-irradiated keratinocytes via real-time PCR. The suppressive effect of SS was greatly potentiated by combination with other DMZ-inhabiting plants, PC and AR. The expression of tyrosinase, which is one the main enzyme that producing melanin in melanocyte, was also down-regulated by the DMZ-inhabiting SS extract. Wound healing activity was also investigated by in vitro test with HaCat cell line, a human fibroblast cell line. All the natural materials extracted form DMZ habiting plants accelerated the recovery of the cells. These results suggested that DMZ is a treasure island of functional plants and DMZ-inhabiting natural products are warranted to develop functional cosmetic materials. This study was carried out with the support of R&D Program for Forest Science Technology (Project No. 2017027A00-1819-BA01) provided by Korea Forest Service (Korea Forestry Promotion Institute).

Keywords: anti-wrinkle, Demilitarized Zone, functional cosmetics, whitening

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Dessie Tegegne Tibebu, Kirsi Mononen, Ari Pappinen

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In areas with forest management, agricultural, and industrial activity, sediments and biomass are accumulated in lakes through drainage system, which might be a cause for biodiversity loss and health problems. One possible solution can be utilization of lake bottom biomass and sediments for biogas production. The main objective of this study was to investigate the potentials of lake bottom materials for production of biogas by anaerobic digestion and to study the effect of pretreatment methods for feed materials on biogas yield. In order to study the potentials of biogas production lake bottom materials were collected from two sites, Likokanta and Kutunjärvi lake. Lake bottom materials were mixed with straw-horse manure to produce biogas in a laboratory scale reactor. The results indicated that highest yields of biogas values were observed when feeds were composed of 50% lake bottom materials with 50% straw horse manure mixture-while with above 50% lake bottom materials in the feed biogas production decreased. CH4 content from Likokanta lake materials with straw-horse manure and Kutunjärvi lake materials with straw-horse manure were similar values when feed consisted of 50% lake bottom materials with 50% straw horse manure mixtures. However, feeds with lake bottom materials above 50%, the CH4 concentration started to decrease, impairing gas process. Pretreatment applied on Kutunjärvi lake materials showed a slight negative effect on the biogas production and lowest CH4 concentration throughout the experiment. The average CH4 production (ml g-1 VS) from pretreated Kutunjärvi lake materials with straw horse manure (208.9 ml g-1 VS) and untreated Kutunjärvi lake materials with straw horse manure (182.2 ml g-1 VS) were markedly higher than from Likokanta lake materials with straw horse manure (157.8 ml g-1 VS). According to the experimental results, utilization of 100% lake bottom materials for biogas production is likely to be impaired negatively. In the future, further analyses to improve the biogas yields, assessment of costs and benefits is needed before utilizing lake bottom materials for the production of biogas.

Keywords: anaerobic digestion, biogas, lake bottom materials, sediments, pretreatment

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Authors: Yogita Tulsian, A. Yadav

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Background: India reports 3,600 cholera cases annually. In August 2013, a cholera outbreak was reported in Jalgaon district, Maharashtra state. We sought to describe the epidemiological characteristics,identify risk factors, and recommend control measures. Methods: We collected existing stool and water testing laboratory results, and conducted a1: 1 matched case-control study. A cholera case was defined as a resident of Vishnapur or Malapur villagewith onset of acute watery diarrhea on/ after 1-July-2013. Controls were matched by age, gender and village and had not experienced any diarrhea for 3 months. We collected socio-demographic characteristics, clinical presentation, and food/travel/water exposure history and conducted conditional logistic regression. Results: Of 50 people who met the cholera case definition, 40 (80%) were from Vishnapur village and 30 (60%) were female. The median age was 8.5 years (range; 0.3-75). Twenty (45%) cases were hospitalized, twelve (60%) with severe dehydration. Three of five stool samples revealed Vibrio cholerae 01 El Tor, Ogawa and samples from 7 of 14 Vishnapur water sources contained fecal coliforms. Cases from Vishnapur were significantly more likely to drink from identified contaminated water sources (matched odds ratio (MOR) 3.5; 95% confidence interval (CI): 1-13), or from a river/canal (MOR=18.4;95%CI: 2-504). Cases from Malapur were more likely to drink from a river/canal (MOR=6.2; 95%CI: 0.6-196). Cases from both villages were significantly more likely to visit the forest (MOR 6.3; 95%CI: 2-30) or another village (MOR 3.5; 95%CI; 0.9-17). Conclusions: This outbreak was caused by Vibrio cholerae, likely through contamination of water in Vishnapur village and/or through drinking river/canal water. We recommended safe drinking water for forest visitors and all residents of these villages and use of regular water testing.

Keywords: cholera, case control study, contaminated water, river

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Katie Strong, Brian P. O'Connor, Jacqueline M. Kanippayoor

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To date, self-report measures of psychopathy have largely been conceptualized with a male-focused understanding of the disorder, with the presumption that psychopathy expression is uniform across genders. However, generalizing this understanding to the female population may be misleading. The objective of this research was to explore gender differences in the expression of psychopathy and to assess current self-report psychopathy measures for gender bias. It was hypothesized that some items in commonly used measures of psychopathy may show gender bias and that existing measures may not contain enough items that are relevant to the manifestation of psychopathy in women. An exploratory investigation was conducted on statistical bias in common measures of psychopathy, and novel, relevant, but previously neglected items and measures were included in a new data collection. The participant pool included a sample of 403 university students and 354 participants recruited using Amazon Mechanical Turk. Item Response Theory methods - including Differential Item Functioning - were used to assess for the item- and test- level bias across several common self-report measures of psychopathy. Analyses indicated occasional and modest levels of item-level bias, and that some additional female-relevant items merit consideration for inclusion in measures of psychopathy. These findings suggest that current self-report measures of psychopathy may be demonstrating gender-bias and warrant further examination.

Keywords: gender, measurement bias, personality, psychopathy

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Authors: Ombir Singh

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In spite of the religious and cultural pre-dominance of the river Ganga in the Indian ethos, fragmentation and degradation of the river continued down the ages. Recognizing the national concern on environmental degradation of the river and its basin, Ministry of Water Resources, River Development & Ganga Rejuvenation (MoWR,RD&GR), Government of India has initiated a number of pilot schemes for the rejuvenation of river Ganga under the ‘Namami Gange’ Programme. Considering the diversity, complexity, and intricacies of forest ecosystems and pivotal multiple functions performed by them and their inter-connectedness with highly dynamic river ecosystems, forestry interventions all along the river Ganga from its origin at Gaumukh, Uttarakhand to its mouth at Ganga Sagar, West Bengal has been planned by the ministry. For that Forest Research Institute (FRI) in collaboration with National Mission for Clean Ganga (NMCG) has prepared a Detailed Project Report (DPR) on Forestry Interventions for Ganga. The Institute has adopted an extensive consultative process at the national and state levels involving various stakeholders relevant in the context of river Ganga and employed a science-based methodology including use of remote sensing and GIS technologies for geo-spatial analysis, modeling and prioritization of sites for proposed forestation and conservation interventions. Four sets of field data formats were designed to obtain the field based information for forestry interventions, mainly plantations and conservation measures along the river course. In response, five stakeholder State Forest Departments had submitted more than 8,000 data sheets to the Institute. In order to analyze a voluminous field data received from five participating states, the Institute also developed a software to collate, analyze and generation of reports on proposed sites in Ganga basin. FRI has developed potential plantation and treatment models for the proposed forestry and other conservation measures in major three types of landscape components visualized in the Ganga riverscape. These are: (i) Natural, (ii) Agriculture, and (iii) Urban Landscapes. Suggested plantation models broadly varied for the Uttarakhand Himalayas and the Ganga Plains in five participating states. Besides extensive plantations in three type of landscapes within the riverscape, various conservation measures such as soil and water conservation, riparian wildlife management, wetland management, bioremediation and bio-filtration and supporting activities such as policy and law intervention, concurrent research, monitoring and evaluation, and mass awareness campaigns have been envisioned in the DPR. The DPR also incorporates the details of the implementation mechanism, budget provisioned for different components of the project besides allocation of budget state-wise to five implementing agencies, national partner organizations and the Nodal Ministry.

Keywords: conservation, Ganga, river, water, forestry interventions

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Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

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Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

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Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: Amir Rashid

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Cloud Computing frameworks empower organizations to cut expenses by outsourcing computation resources on-request. As of now, customers of Cloud service providers have no methods for confirming the privacy and ownership of their information and data. To address this issue we propose the platform of a trusted cloud computing program (TCCP). TCCP empowers Infrastructure as a Service (IaaS) suppliers, for example, Amazon EC2 to give a shout box execution condition that ensures secret execution of visitor virtual machines. Also, it permits clients to bear witness to the IaaS supplier and decide if the administration is secure before they dispatch their virtual machines. This paper proposes a Trusted Cloud Computing Platform (TCCP) for guaranteeing the privacy and trustworthiness of computed data that are outsourced to IaaS service providers. The TCCP gives the deliberation of a shut box execution condition for a client's VM, ensuring that no cloud supplier's authorized manager can examine or mess up with its data. Furthermore, before launching the VM, the TCCP permits a client to dependably and remotely acknowledge that the provider at backend is running a confided in TCCP. This capacity extends the verification of whole administration, and hence permits a client to confirm the data operation in secure mode.

Keywords: cloud security, IaaS, cloud data privacy and integrity, hybrid cloud

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Authors: Luis M. Ribeiro, Jorge Raposo, Ricardo Oliveira, David Caballero, Domingos X. Viegas

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Under the scope of European project WUIWATCH a set of experimental tests on house vulnerability was performed in order to assess the resistance of selected house components during the passage of a forest fire. Among the individual elements most affected by the passage of a wildfire the windows are the ones with greater exposure. In this sense, a set of exploratory experimental tests was designed to assess some particular aspects related to the vulnerability of windows and blinds. At the same time, the importance of leaving them closed (as well as the doors inside a house) during a wild fire was explored in order to give some scientific background to guidelines for homeowners. Three sets of tests were performed: 1. Windows and blinds resistance to heat. Three types of protective blinds were tested (aluminium, PVC and wood) on 2 types of windows (single and double pane). The objective was to assess the structures resistance. 2. The influence of air flow on the transport of burning embers inside a house. A room was built to scale, and placed inside a wind tunnel, with one window and one door on opposite sides. The objective was to assess the importance of leaving an inside door opened on the probability of burning embers entering the room. 3. The influence of the dimension of openings on a window or door related to the probability of ignition inside a house. The objective was to assess the influence of different window openings in relation to the amount of burning particles that can enter a house. The main results were: 1. The purely radiative heat source provides 1.5 KW/m2 of heat impact in the structure, while the real fire generates 10 Kw/m2. When protected by the blind, the single pane window reaches 30ºC on both sides, and the double pane window has a differential of 10º from the side facing the heat (30ºC) and the opposite side (40ºC). Unprotected window constantly increases temperature until the end of the test. Window blinds reach considerably higher temperatures. PVC loses its consistency above 150ºC and melts. 2. Leaving the inside door closed results in a positive pressure differential of +1Pa from the outside to the inside, inhibiting the air flow. Opening the door in half or full reverts the pressure differential to -6 and -8 times respectively, favouring the air flow from the outside to the inside. The number of particles entering the house follows the same tendency. 3. As the bottom opening in a window increases from 0,5 cm to 4 cm the number of particles that enter the house per second also increases greatly. From 5 cm until 80cm there is no substantial increase in the number of entering particles. This set of exploratory tests proved to be an added value in supporting guidelines for home owners, regarding self-protection in WUI areas.

Keywords: forest fire, wildland urban interface, house vulnerability, house protective elements

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Authors: Tamirat Solomon, Aberash Yohannis, Efrem Gulfo

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The plantation of trees, which harmonizes the agroecology of the environment, has been implemented in Ethiopia with great concern for a noticeably degraded environment. This study was designed to evaluate the effectiveness of green legacy, species selection and, the rate of survival, and the management status in the study areas. A systematic sampling method was employed to collect the required data from 144 quadrants measuring a 15m radius with an interval of 40m apart. Additionally, 244 sample households were selected for the socioeconomic study in addition to secondary data collected from office recordings. The data collected was analyzed using multivariate analysis, considering exposure and outcome variables. The findings of this study indicated that four exotic tree species, namely; A. salgina, C. fistula, A. indica, and G. robusta, were commonly selected tree species for degraded land restoration in the study areas. Among the seedlings planted at the four study sites, a total of 79.9% survived, and A. salgina was the dominant and best performed species, A. indica was the least survived species in the entire study area. The age of the seedling before planting significantly (p = 0.05) affected the survival potential of most seedlings of species, and the majority (82%) of local communities expressed their positive attitudes and willingness to manage the restoration works in the study areas. It was recommended to consider the inclusion of native species in the restoration effort and evaluate the co-existence of native flora with exotic and its competition for nutrients, water, and light in addition to the invading potentials in the ecosystem. In general, before embarking on degraded land restoration, species selection, adequate preparation of seedlings, and species diversity composition that exactly fit the socioeconomic and ecological demands of the areas must get the attention for the success of the restoration.

Keywords: plantation forest, degraded land, forest restoration, plantation survival, species selection

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Authors: Debit Datta, Jeffrey J. Coleman, Scott A. Enebak, Lori G. Eckhardt

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Loblolly pine (Pinus taeda) is a leading productive timber species in the southeastern USA. Over the past three years, an emerging threat is expressed by successive needle defoliation followed by stunted growth and tree mortality in loblolly pine plantations. Considering economic significance, it has now become a rising concern among landowners, forest managers, and forest health state cooperators. However, the symptoms of the disease were perplexed somewhat with root disease(s) and recurrently attributed to invasive Phytophthora species due to the similarity of disease nature and devastation. Therefore, the study investigated the potential causal agent of this disease and characterized the fungi associated with loblolly pine needle defoliation in the southeastern USA. Besides, 70 trees were selected at seven long-term monitoring plots at Chatom, Alabama, to monitor and record the annual disease incidence and severity. Based on colony morphology and ITS-rDNA sequence data, a total of 28 species of fungi representing 17 families have been recovered from diseased loblolly pine needles. The native brown-spot pathogen, Lecanosticta acicola, was the species most frequently recovered from unhealthy loblolly pine needles in combination with some other common needle cast and rust pathogen(s). Identification was confirmed using morphological similarity and amplification of translation elongation factor 1-alpha gene region of interest. Tagged trees were consistently found chlorotic and defoliated from 2019 to 2020. The current emergence of the brown-spot pathogen causing loblolly pine mortality necessitates the investigation of the role of changing climatic conditions, which might be associated with increased pathogen pressure to loblolly pines in the southeastern USA.

Keywords: brown-spot needle blight, loblolly pine, needle defoliation, plantation forestry

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Authors: Lydia Murugi Mugendi

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The promotion of tree growing as a lucrative enterprise is the focus of this paper as management practices have shifted focus from protection of natural forest resources to community/government partnerships with the aim of resource conservation, management and increase of on-farm tree growing. Using KEFRI as (the source) of information pertaining Melia volkensii (the medium or message) being transferred, this paper investigates the current perception towards forestry and the behavioural attitudes of recipients of forest intervention activities. The two objectives explored in this paper are to find out the level of adoption of Mukau in Kitui, Kibwezi and Samburu/Taru and secondly, to find out the characteristics of the adoption process between Kitui, Kibwezi and Samburu/Taru. The methodologies used during data collection were participatory rural appraisal tools in conjunction with the social survey questionnaires. Simple random sampling and snowball sampling were used to identify respondents within the three target sites and analysis was done using SPSS. Results of the study of indicating that adoption rates of the Mukau in Samburu/Taru, where forestry-related activities were introduced within the past one decade had significantly increase despite initial resistance. The other areas, which had benefited from numerous decades of intense forestry extension projects and activities, indicated a decline in re-adoption rates of Mukau as an enterprise. This study has brought out the reality of adoption trends and state of Mukau population within the three counties while providing a glimpse towards the communities’ perception in regards to adoption of forestry and other environmental innovations. The outcome of the study is to provide a guideline for extension/ dissemination officers in KEFRI and related stakeholders to promote seamless cohesive interaction between the recipient communities of the proposed interventions.

Keywords: adoption, innovation, enterprise, extension, DOI Theory

Procedia PDF Downloads 86

Authors: Stephen Dankwa, Zheng Wenfeng, Xiaolu Li

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Climate change is recently gaining the attention of many countries across the world. Climate change, which is also known as global warming, referring to the increasing in average surface temperature has been a concern to the Environmental Protection Agency of Ghana. Recently, Ghana has become vulnerable to the effect of the climate change as a result of the dependence of the majority of the population on agriculture. The clearing down of trees to grow crops and burning of charcoal in the country has been a contributing factor to the rise in temperature nowadays in the country as a result of releasing of carbon dioxide and greenhouse gases into the air. Recently, petroleum stations across the cities have been on fire due to this climate changes and which have position Ghana in a way not able to withstand this climate event. As a result, the significant of this research paper is to project how the rise in the average surface temperature will be like at the end of the mid-21st century when agriculture and deforestation are allowed to continue for some time in the country. This study uses the Coupled Modeled Intercomparison Project phase 5 (CMIP5) experiment RCP 8.5 model output data to monitor the future climate changes from 2041-2050, at the end of the mid-21st century over the ten (10) major cities (Accra, Bolgatanga, Cape Coast, Koforidua, Kumasi, Sekondi-Takoradi, Sunyani, Ho, Tamale, Wa) in Ghana. In the models, Support Vector Machine and Random forest, where the cities as a function of heat wave metrics (minimum temperature, maximum temperature, mean temperature, heat wave duration and number of heat waves) assisted to provide more than 50% accuracy to predict and monitor the pattern of the surface air temperature. The findings identified were that the near-surface air temperature will rise between 1°C-2°C (degrees Celsius) over the coastal cities (Accra, Cape Coast, Sekondi-Takoradi). The temperature over Kumasi, Ho and Sunyani by the end of 2050 will rise by 1°C. In Koforidua, it will rise between 1°C-2°C. The temperature will rise in Bolgatanga, Tamale and Wa by 0.5°C by 2050. This indicates how the coastal and the southern part of the country are becoming hotter compared with the north, even though the northern part is the hottest. During heat waves from 2041-2050, Bolgatanga, Tamale, and Wa will experience the highest mean daily air temperature between 34°C-36°C. Kumasi, Koforidua, and Sunyani will experience about 34°C. The coastal cities (Accra, Cape Coast, Sekondi-Takoradi) will experience below 32°C. Even though, the coastal cities will experience the lowest mean temperature, they will have the highest number of heat waves about 62. Majority of the heat waves will last between 2 to 10 days with the maximum 30 days. The surface temperature will continue to rise by the end of the mid-21st century (2041-2050) over the major cities in Ghana and so needs to be addressed to the Environmental Protection Agency in Ghana in order to mitigate this problem.

Keywords: climate changes, CMIP5, Ghana, heat waves, random forest, SVM

Procedia PDF Downloads 177

Authors: Saloni Khandelwal

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The Aravalli hills are one of the oldest and most distinctive mountain chains of peninsular India spanning in around 692 Km. More than 60% of it falls in the state of Rajasthan and influences ecological equilibrium in about 30% of the state. Because of natural and human-induced activities, physical gaps in the Aravallis are increasing, new gaps are coming up, and its physical structure is changing. There are no strict regulations to protect and monitor the Aravallis and no comprehensive research and study has been done for the enhancement of ecosystem functions of these ranges. Through this study, various factors leading to Aravalli’s degradation are identified and its impacts on selected areas are analyzed. A literature study is done to identify factors responsible for the degradation. To understand the severity of the problem at the lowest level, two tehsils from different districts in Rajasthan, which are the most affected due to illegal mining and increasing physical gaps are selected for the study. Case-1 of three-gram panchayats in Kishangarh Tehsil of Ajmer district focuses on the expanding physical gaps in the Aravalli range, and case-2 of three-gram panchayats in Tijara Tehsil of Alwar district focuses on increasing illegal mining in the Aravalli range. For measuring the degradation, physical, biological and social indicators are identified through literature review and for both the cases analysis is done on the basis of these indicators. Primary survey and focus group discussions are done with villagers, mining owners, illegal miners, and various government officials to understand dependency of people on the Aravalli and its importance to them along with the impact of degradation on their livelihood and environment. From the analysis, it has been found that green cover is continuously decreasing in both cases, dense forest areas do not exist now, the groundwater table is depleting at a very fast rate, soil is losing its moisture resulting in low yield and shift in agriculture. Wild animals which were easily seen earlier are now extinct. Cattles of villagers are dependent on the forest area in the Aravalli range for food, but with a decrease in fodder, their cattle numbers are decreasing. There is a decrease in agricultural land and an increase in scrub and salt-affected land. Analysis of various national and state programmes, acts which were passed to conserve biodiversity has been done showing that none of them is helping much to protect the Aravalli. For conserving the Aravalli and its forest areas, regional level and local level initiatives are required and are proposed in this study. This study is an attempt to formulate conservation and management strategies for the Aravalli range. These strategies will help in improving biodiversity which can lead to the revival of its ecosystem functions. It will also help in curbing the pollution at the regional and local level. All this will lead to the sustainable development of the region.

Keywords: Aravalli, ecosystem, LULC, Rajasthan

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Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

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Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

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Authors: Stellina Jolly

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July 18th to August 19th 2013 has gone down in the history of India for undertaking the country’s first environment referendum. The Supreme Court had ruled that the Vedanta Group's bauxite mining project in the Niyamgiri Hills of Orissa will have to get clearance from the gram sabha, which will consider the cultural and religious rights of the tribals and forest dwellers living in Rayagada and Kalahandi districts. In the Niyamgiri hills, people of small tribal hamlets were asked to voice their opinion on bauxite mining in their habitat. The ministry has reiterated its stand that mining cannot be allowed on the Niyamgiri hills because it will affect the rights of the Dongria Kondhs. The tribal person who occupies the Niyamgiri Hills in Eastern India accomplished their first success in 2010 in their struggle to protect and preserve their existence, culture and land against Vedanta a London-based mining giant. In August, 2010 Government of India revoked permission for Vedanta Resources to mine bauxite from hills in Orissa State where the Dongria Kondh live as forest dwellers. This came after various protests and reports including amnesty report wherein it highlighted that an alumina refinery in eastern India operated by a subsidiary of mining company. Vedanta was accused of causing air and water pollution that threatens the health of local people and their access to water. The abuse of human rights by corporate is not a new issue it has occurred in Africa, Asia and other parts of the world. Paper focuses on the instances and extent of human right especially in terms of environment violations by corporations. Further Paper details on corporations and sustainable development. Paper finally comes up with certain recommendation including call for a declaration by United Nations on Corporate environment Human Rights Liability.

Keywords: environment, corporate, human rights, sustainable development

Procedia PDF Downloads 450