Development of Life Cycle Assessment Based Air Quality Modeling and Decision Support System for the Mining Industry

Air quality in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. Mining operations emit various types of pollutants which could violate the environmental guidelines. The development of an integrated approach is conceptualized in this thesis as life cycle assessment based air quality modeling system (LCAQMS) for the mining industry. LCAQMS consists of four primary models which are: (1) life cycle inventory model, (2) artificial neural network model, (3) mining-zone air dispersion model, and (4) decision analysis model. A graphical user interface (GUI) is built to integrate primary models to understand the pollutant’s fate from its generation (emission inventory) to its management (control decisions). The life cycle inventory (LCI) model is developed to determine emission inventory using inverse matrix method, and defined characterization methods are investigated to assess the environmental impact. Artificial neural network model is developed to analyze carbon footprints (CO2 equivalent) using backpropagation method. Mining-zone air dispersion model (MADM) is developed to generate the predicted concentration of air pollutants at various receptor levels by considering the deposition effect. The meteorological factors based on atmospheric stability conditions are determined by employing the Pasquill-Turner method (PTM). The decision analysis model comprises multi-criteria decision analysis (MCDA) method and air pollution control model (APCM) to provide air pollution control alternatives and optimize the cost-effective solutions, respectively. Monte Carlo simulation accomplishes the uncertainties in the system. Moreover, an environmental risk assessment (ERA) method is extended by integrating the APCM with a fuzzy set. The applicability of LCAQMS is explored through three different case studies of open-pit metal mining in North America. Inventory results first show the air emission load for each mining activity and allow to evaluate the emission impact by linking the inventory to each impact category. Then this study helps to quantify the carbon footprints for the gold and copper mines. Also, prediction of significant pollutants such as PM10, PM2.5, SO2, and NOx at ground level has been calculated. The results depict that dry deposition is a dominate physical removal mechanism in the mining area. The LCAQMS results are evaluated with the monitoring field values, particularly MADM results are statistically tested against California puff (CALPUFF) model. Additionally, atmospheric stability is examined by analyzing the relationship between modeled PM2.5 concentrations and mixing height based on seasonal variation and the diurnal cycle. In conclusion, LCAQMS can serve as a useful tool for the stakeholders to assess the impact, predict the air quality, and aid planners to minimize the pollutants at a marginal cost by suggesting control pollution techniques

Long-Term Variability of Aerosol Concentrations and Optical Properties over the Indo-Gangetic Plain in South Asia

Emissions of atmospheric pollutants are rapidly increasing over South Asia. A greater understanding of seasonal variability in aerosol concentrations over South Asia is a scientific challenge and has consequences due to a lack of monitoring and modelling of air pollutants. Therefore, this study investigates aerosol patterns and trends over some major cities in the Indo-Gangetic Plain of the South Asia, i.e., Islamabad, Lahore, Delhi, and Dhaka, by using simulations from the Modern -Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) model and satellite measurements (Moderate Resolution Imaging Spectroradiometer, (MODIS)) from 2000 to 2020. The results show that seasonal MODIS–aerosol optical depth (AOD) during 2000−2020 in Lahore is 0.5, 0.52, 0.92, and 0.71, while in Islamabad 0.25, 0.32, 0.45, and 0.38, in Delhi 0.68, 0.6, 1.0, and 0.77, and in Dhaka 0.79, 0.75, 0.78 and 0.55 values are observed during different seasons, i.e., winter, spring, summer, and autumn, respectively. The analysis reveals a significant increase in aerosol concentrations by 25%, 24%, 19%, and 14%, and maximum AOD increased by 15%, 14%, 19%, and 22% during the winter of the last decade (2011–2020) over Islamabad, Lahore, Delhi, and Dhaka, respectively. In contrast, AOD values decreased during spring by −5%, −12%, and −5 over Islamabad, Lahore, and Delhi, respectively. In Dhaka, AOD shows an increasing trend for all seasons. Thus, this study provides the aerosol spatial and temporal variations over the South Asian region and would help policymakers to strategize suitable mitigation measurements.This research was funded by the Higher Education Commission (HEC) Pakistan PBAIRP grant # 22-17 for funding to complete this project

Comparison of Evaporation in Conventional Diesel and Bio-Fuel Droplets in Engine Cylinder

Renewable energy resources are need of the hour at the current energy scarcity scenario in the world. Scientist and researchers are finding the ways to replace the conventional energy resources with the renewable ones. It is fact that fossils are going to be obsolete in future. One third of global energy is being consumed by the transportation sector. All the amount of this energy comes from the fossils that contain the hydrocarbons in their composition. Efforts are being made to replace the fossils with the renewable energy resources. In this regard, biofuels are emerged as a replacement of the diesel fuels. There are several processes in the engine cylinder from atomization of fuel until the exhaust of gases. One of them is the evaporation of fuel droplets. In the present work, evaporation characteristics of conventional diesel fuel and biofuels is described by comparing them in different working conditions. Modeling of evaporation phenomenon using computational fluid dynamics (CFD) techniques and the effects of in cylinder conditions is also explained. Results show that biofuel droplets show a better evaporation rate at the high operating conditions in the engine cylinder

Environmental Impacts and Challenges Associated with Oil Spills on Shorelines

Oil spills are of great concern because they impose a threat to the marine ecosystem, including shorelines. As oil spilled at sea is transported to the shoreline, and after its arrival, its behavior and physicochemical characteristics change because of natural weathering phenomena. Additionally, the fate of the oil depends on shoreline type, tidal energy, and environmental conditions. This paper critically overviews the vulnerability of shorelines to oil spill impact and the implication of seasonal variations with the natural attenuation of oil. A comprehensive review of various monitoring techniques, including GIS tools and remote sensing, is discussed for tracking, and mapping oil spills. A comparison of various remote sensors shows that laser fluorosensors can detect oil on various types of substrates, including snow and ice. Moreover, current methods to prevent oil from reaching the shoreline, including physical booms, sorbents, and dispersants, are examined. The advantages and limitations of various physical, chemical, and biological treatment methods and their application suitability for different shore types are discussed. The paper highlights some of the challenges faced while managing oil spills, including viewpoints on the lack of monitoring data, the need for integrated decision-making systems, and the development of rapid response strategies to optimize the protection of shorelines from oil spills

Environmental Impacts and Challenges Associated with Oil Spills on Shorelines

Oil spills are of great concern because they impose a threat to the marine ecosystem, including shorelines. As oil spilled at sea is transported to the shoreline, and after its arrival, its behavior and physicochemical characteristics change because of natural weathering phenomena. Additionally, the fate of the oil depends on shoreline type, tidal energy, and environmental conditions. This paper critically overviews the vulnerability of shorelines to oil spill impact and the implication of seasonal variations with the natural attenuation of oil. A comprehensive review of various monitoring techniques, including GIS tools and remote sensing, is discussed for tracking, and mapping oil spills. A comparison of various remote sensors shows that laser fluorosensors can detect oil on various types of substrates, including snow and ice. Moreover, current methods to prevent oil from reaching the shoreline, including physical booms, sorbents, and dispersants, are examined. The advantages and limitations of various physical, chemical, and biological treatment methods and their application suitability for different shore types are discussed. The paper highlights some of the challenges faced while managing oil spills, including viewpoints on the lack of monitoring data, the need for integrated decision-making systems, and the development of rapid response strategies to optimize the protection of shorelines from oil spills

Air quality modeling for effective environmental management in the mining region

<p>Air quality in the mining sector is a serious environmental concern and associated with many health issues. Air quality management in mining regions has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanisms. A modeling approach called the mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model takes into account the planet’s boundary conditions and assumes that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values for the predicted concentrations of PM₁₀, PM_2.5, TSP, NO₂, and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening in summer, and the minimum mixing height (380 m) is attained during the evening in winter. The dust fall (PM coarse) deposition flux is maximum during February and March with a deposition velocity of 4.67 cm/sec. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with <i>R</i>-squared ranging from 0.72 to 0.96 for PM_2.5, from 0.71 to 0.82 for PM₁₀, and from 0.71 to 0.89 for NO₂. The analyses illustrate that the presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. Comparisons of MADM and CALPUFF modeling values are made for four different pollutants (PM_2.5, PM₁₀, TSP, and NO₂) under three different atmospheric stability classes (stable, neutral, and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.</p> <p><i>Implications</i>: The mathematical model (MADM) is developed by extending the Gaussian equation particularly when examining the settling process of important pollutants for the industrial region. Physical removal effects of air pollutants with field data have been considerred for the MADM development and for an extensive field case study. The model is well validated in the field of an open pit mine to assess the regional air quality. The MADA model helps to facilitate the management of the mining industry in doing estimation of emission rate around mining activities and predicting the resulted concentration of air pollutants together in one integrated approach.</p

A survey of the Vision Transformers and its CNN-Transformer based Variants

Vision transformers have recently become popular as a possible alternative to convolutional neural networks (CNNs) for a variety of computer vision applications. These vision transformers due to their ability to focus on global relationships in images have large capacity, but may result in poor generalization as compared to CNNs. Very recently, the hybridization of convolution and self-attention mechanisms in vision transformers is gaining popularity due to their ability of exploiting both local and global image representations. These CNN-Transformer architectures also known as hybrid vision transformers have shown remarkable results for vision applications. Recently, due to the rapidly growing number of these hybrid vision transformers, there is a need for a taxonomy and explanation of these architectures. This survey presents a taxonomy of the recent vision transformer architectures, and more specifically that of the hybrid vision transformers. Additionally, the key features of each architecture such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. This survey highlights the potential of hybrid vision transformers to achieve outstanding performance on a variety of computer vision tasks. Moreover, it also points towards the future directions of this rapidly evolving field.Comment: Pages: 58, Figures: 1

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Climate change has been observed worldwide in recent decades, posing challenges to the coastal and offshore oil and gas infrastructure. It is crucial to identify how climate change affects these infrastructures and the associated oil spill risk. This paper provides an analysis of the vulnerability of coastal and offshore oil and gas infrastructure in response to climate change. The paper examines oil spill incidents worldwide and addresses climate change’s possible influences on oil spill risk. Moreover, available oil spill modeling and decision support tools for oil spill response are reviewed considering climate change. The paper signals the need for emerging decision and modeling tools considering climate change effects, which can help decision-makers to evaluate the risk on time and provide early warnings to adapt or prevent the unforeseen impacts on the oil industry partially resulting from global warming, including oil spill accidents

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Climate change has been observed worldwide in recent decades, posing challenges to the coastal and offshore oil and gas infrastructure. It is crucial to identify how climate change affects these infrastructures and the associated oil spill risk. This paper provides an analysis of the vulnerability of coastal and offshore oil and gas infrastructure in response to climate change. The paper examines oil spill incidents worldwide and addresses climate change&rsquo;s possible influences on oil spill risk. Moreover, available oil spill modeling and decision support tools for oil spill response are reviewed considering climate change. The paper signals the need for emerging decision and modeling tools considering climate change effects, which can help decision-makers to evaluate the risk on time and provide early warnings to adapt or prevent the unforeseen impacts on the oil industry partially resulting from global warming, including oil spill accidents

Diversity indices and distribution of brachyuran crabs found in the lagoon waters of Sonmiani, Balochistan

712-720A total of 8 species of Brachyuran crabs was recorded,crabs belong to the family Portunidae were dominant and represented by 6 species. Portunus pelagicus and Portunus sanguinolentus were found throughout the years.Various ecological indices were calculated through the obtained data. The diversity ranged from 0.63 - 1.64, the richness varied from 0.84-1.76, the evenness ranged from 0.1- 0.35, the index of dominance varied from 0.36 - 0.73. Maximum diversity was recorded during Pre monsoon (yr. 2005-06). The ecological indices were positively correlated with environmental parameters