Assessing potential reduction in greenhouse gas : an integrated approach

Abstract: Greenhouse gases remain as threat to the environment. Various models employed in greenhouse gases are either to determine the causative factors responsible for emission, forecast emission or to optimize. Integrating these models would reduce the limitations of individual models to better assess possible greenhouse mitigation. This paper addresses the management technique for analyzing, assessing and mitigating industry’s carbon dioxide (CO2) emission. The current work offers a different technique based on an integrated model utilizing the functions of Index Decomposition Analysis (IDA), Artificial Neural Network (ANN) and Data Envelopment Analysis (DEA) composed of activity, structure, intensity and energy-mix as inputs responsible for CO2 emission. By considering how the three different models are integrated into one system, it will be demonstrated how much percentage of an industry’s CO2 can be reduced. The Canadian industrial sector was analyzed using the integrated model and it was discovered that 3.13% of emitted CO2 from year 1991 to year 2035 could be mitigated

Assessing the Possible Potential in Energy Consumption and Greenhouse Gas Emission: Application of a Proven Hybrid Method

Energy evaluation together with greenhouse gas mitigation goes a long way in sustaining the growth and economy of a nation. Various evaluation methods have been adopted by researchers, academia and various country wise energy department ministries to achieve this aim. The most effective method is the hybrid evaluation method. This takes into consideration strength of a particular method to overcome the weakness of another method. This chapter focuses on a recently proven integrated method on energy and greenhouse gas studies—integrated IDA‐ANN‐DEA (index decomposition analysis—artificial neural network—data envelopment analysis). Case studies were exemplified using this approach in evaluating possible energy potential that could be saved in the manufacturing industries in Canada and South Africa as well as a particular food and beverage industry. Another case study focused on the amount of possible greenhouse gas that could be mitigated in the Canadian industry. The hybrid model proved very useful in its analysis

Life Cycle Assessment of Ordinary Portland Cement (OPC) Using both Problem Oriented (Midpoint) Approach and Damage Oriented Approach (Endpoint)

The concern for environmental related impacts of the cement industry is fast growing in recent times. The industry is challenged with high environmental impact which spans through the entire production process. Life cycle assessment (LCA) evaluates the environmental impact of product or process throughout the cycle of production. This can be done using either or both midpoint (process-oriented) and endpoint (damage-oriented) approaches of life cycle impact assessment (LCIA). This study assessed the environmental impact of 1 kg Ordinary Portland Cement (OPC) using both approaches of LCIA. This analysis was carried out using a data modeled after the rest of the world other than China, India, Europe, US and Switzerland. The dataset was taken from Ecoinvent database incorporated in the SimaPro 9.0.49 software. The result of the analysis showed that clinker production phase produced the highest impact and CO2 is the highest pollutant emitter at both endpoint and midpoint approaches. This is responsible for global warming known to affect both human health and the ecosystem. Also, toxicity in form of emission of high copper affects the ecosystem as well as humans. In addition, high fossil resources (crude oil) are consumed and pose the possibility for scarcity

Impacts of flood disasters in Nigeria: A critical evaluation of health implications and management

Floods lead to tremendous losses of property, infrastructure, business and increased risk of diseases. Floods are also the most frequent natural disasters, affecting over 2.8 billion people in the world and causing over 200 000 deaths over the past three decades. The World Health Organization categorised the 2012 flood disaster in Nigeria as the worst flood to have hit the country in the past 50 years. This study reviews flood disasters in Nigeria and how they have been managed over the past two decades. The extensive review of the literature is complemented by data obtained from Ajegunle, a community in Ajeromi–Ifelodun Local Government Area. Because of its proximity to water bodies, its large population and its small land mass, the Ajegunle community is highly susceptible to floods and outbreaks of waterborne diseases. The study also discusses the institutionalisation and development of disaster management in Nigeria. Further, it critically evaluates the disaster management framework and other current disaster management policies as well as the effectiveness and functions of the disaster management focus areas and government response. The study takes a historic approach to flood disasters, linking disaster management to human health with a special focus on flood-related infectious diseases, isolating waterborne diseases as being predominant. Quantitative and qualitative data were collected to develop an understanding of how the people of Ajegunle are affected by flood disasters. This study reveals poorly managed health reforms and argues that in spite of government’s disaster management policies, there is an absence of organised and coordinated institutional structures to plan and respond to flood emergencies. It also revealed that diarrhoea outbreak was the predominant waterborne disease associated with flood disasters. Although Lagos State has been said to have the best flood preparedness plan in Nigeria, it has failed to reduce the yearly flood disasters and their impact on the health of the people. The article suggests a holistic approach by the government to get stakeholders, especially the health sector, more actively involved in disaster management planning

Biomethane Production and Applications

Biomethane production generally involves the cleaning to remove minor unwanted components of biogases such as hydrogen sulfide (H2S) and moisture (H2O) and upgrading in a process that involves the removal of carbon dioxide (CO2) to increase the concentration of CH4 to 95–99% and reduce CO2 concentration to 1–5%, with little or no hydrogen sulfide (H2S). Biomethane gas is a flexible and easy to store fuel having similar properties and applications as natural gas with no need to modify the settings for natural gas devices and equipment. Biomethane can be used for industrial and domestic applications ranging from thermal and power generation and feedstock for processes like the Fischer-Tropsch (FT) for fuel manufacturer and direct power generation in hydrogen or biogas fuel cells like production of green hydrogen. Therefore, biomethane promises to play a leading role in the energy transition through hydrogen, electricity, and other renewable fuels production. Biomethane production by biogas upgrading methods include the pressure swing adsorption, which has an option of temperature swing adsorption, absorption technics based on amine, membrane separation, cryogenic separation, and biological separation. The technology adopted may depend on factors such as costs, quality of products, location, and technology maturity and requirements

Voice Assisted Key-In Building Quantities Estimation System

Voice recognition technology has been in existence over several decades but its application in the construction industry has been minimal. Despite the several advantages it offers, its application has been limited to smart building integration only. This study has made a significant contribution by integrating voice recognition technology into key-in building quantities estimation software. The Visual Basic programming language was used to design and code the interface of the voice recognition system and key-in estimating software model. The prototype model continues to have some challenges because it cannot work efficiently in a noisy work environment and there is limited range of vocabulary it can recognize. This paper seeks to challenge the stakeholders of the construction industry to maximize the benefits of voice recognition technology and integrate it into other construction tasks. In addition, future research can consider integrating building information modeling and voice recognition technology

Assessing possible energy potential in a food and beverage industry: Application of IDA-ANN-DEA approach

In the food and beverage industry, where growing, processing, packaging, distribution, storage, preparation, serving and disposing of food is the order of the day; energy consumption becomes an important input. Various energy models have been developed since the early 1970s, the period when energy caught the attention of policymakers due to the sudden price increase. Among the models are the index decomposition analysis (IDA), artificial neural network (ANN) and data envelopment analysis (DEA). The purpose of this study is to combine the strengths of these models, i.e., IDA, ANN and DEA, to allow biases in one model to offset biases in the other, so as to examine the effectiveness of energy management policies in a particular food and beverage industry. The integrated model applied to the food and beverage revealed that approximately 11% of energy consumed could be saved

Comparing performance of MLP and RBF neural network models for predicting South Africa’s energy consumption

In view of the close association between energy and economic growth, South Africa’s aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa’s industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model

Assessing possible energy potential in a food and beverage industry: Application of IDA-ANN-DEA approach

In the food and beverage industry, where growing, processing, packaging, distribution, storage, preparation, serving and disposing of food is the order of the day; energy consumption becomes an important input. Various energy models have been developed since the early 1970s, the period when energy caught the attention of policymakers due to the sudden price increase. Among the models are the index decomposition analysis (IDA), artificial neural network (ANN) and data envelopment analysis (DEA). The purpose of this study is to combine the strengths of these models, i.e., IDA, ANN and DEA, to allow biases in one model to offset biases in the other, so as to examine the effectiveness of energy management policies in a particular food and beverage industry. The integrated model applied to the food and beverage revealed that approximately 11% of energy consumed could be saved

Evaluation of the awareness and experiences of the primary and secondary school teachers on asthma: A cross-sectional study in Ilorin, Nigeria

Objectives: Children spend the daytime participating in classes and sports. Hence, as the general caregiver of children during school hours, the teacher has a role in managing those with asthma. The present study aims to identify teachers’ knowledge, attitude, and experiences about childhood asthma in primary and secondary schools in Ilorin, Nigeria. Materials and Methods: A cross-sectional study was conducted among 1532 teachers from 64 schools (24 primary and 40 secondaries) selected through multistage sampling. A 40-item Asthma Knowledge Questionnaire was used to assess teachers’ knowledge about childhood asthma. A score of <22 out of 40 was considered suboptimal knowledge of childhood asthma. Results: The response rate was 92.8%. Two-thirds of respondents were female. The overall mean age was 33.8 ± 8.1 years. The median (interquartile range [IQR]) teaching duration was 6 (3–11) years. The teachers had the highest median score (60.0%) in the triggers domain and the lowest median score (33.3%) in the symptoms domain. The overall median (IQR) knowledge score of the primary school teachers was 50.0 (30.0–65.0%), and for the secondary school teachers was 52.5 (30.0–65.0%), P = 0.689. Two hundred and ninety-one (55.1%) primary teachers and 479 (53.6%) secondary teachers had suboptimal knowledge. Teachers with an asthmatic relative, an asthmatic child in the class, or who previously witnessed a child with an asthmatic attack had – significantly better asthma knowledge, with each P < 0.05. Conclusion: About half of the teachers surveyed had suboptimal knowledge of asthma. Strategies to improve teachers’ knowledge are crucial for improved childhood asthma management in Ilorin schools