Spatio-temporal analysis of urban heat island (UHI) and its effect on urban ecology: The case of Mekelle city, Northern Ethiopia.

The bio-geophysical effects of land cover classes are considered to be an important factor in land surface temperature variations between urban and suburban areas. This means that major cities are significantly warmer than surrounding suburban or rural areas, which is known as the Urban Heat Island (UHI) effect. The aim of this study was to assess and analyze the spatiotemporal variation, correlation and impact of UHIs on Mekelle city (1990-2020) using remote sensing techniques. The study's primary objective was accomplished using the a number of techniques, including the extraction of LULC classes, estimation of the seasonal LSTs, assessment of UHI and UTFVI, and showing the relationship between LULC and LST as well as the interactions between UHI, UTFVI, and urban LULC classes. By analyzing TIRs/OLI thermal band data after calibrating uncertainty in the images and validating it using the concept of theoretical relationships and least squares fitting method, estimates of local LST, UHI (both in Mekelle and periphery rural areas), and UTFVI were obtained. The result of standard multiple regression models showed that impervious urban land surface, built-up areas, and dry bare soil highly contribute and influence variation at LST intensity caused for the formation of UHI in the study area. The result showed that the maximum UHI value in Mekelle city was 2.73 °C during the dry season in 1990. It decreased slightly to 2.53 °C in 2000 and then increased regularly to 2.83 °C and 2.98 °C in 2010 and 2020, respectively. To determine the city's UHI status in comparison to eight selected peripheral suburban areas, a trend analysis has been done. The UHI intensity of Mekelle city was higher relatively to that of most of the periphery suburban districts, particularly in 2020 (both dry and rainy seasons); this could be due to the city's explosive growth. It's worth noting that the research area affected by the urban heat island effect has grown over time, and as a result, the study area also has severe microclimate conditions that primarily damage the quality of urban life and create the worst conditions for thermal discomfort. The results of this study provide major conceptual understandings of how improper distribution and use of urban land affects the urban environment and fuels the creation of the UHI and thermal discomfort phenomena. Hence policy makers and urban planners should consider the effects of LST and UHI and integrate UHI comprehensive mitigation strategies with urban development patterns, and current and projected local climate changes in order to create sustainable urban environments, cities, and communities. In conclusion, compared to the conventional method, satellite remote sensing provides a faster and more efficient method for researching LST and UHI.

Seismic collapse risk of RC-timber hybrid building with different energy dissipation connections considering NBCC 2020 hazard.

The 2020 National Building Code of Canada (NBCC) seismic hazard model (SHM) marks a comprehensive update over its predecessor (NBCC 2015). For different regions in Canada, this will have an impact on the design of new buildings and performance assessment of existing ones. In the present study, a recently developed hybrid building system with reinforced concrete (RC) moment-resisting frames and cross-laminated timber (CLT) infills is assessed for its seismic performance against the latest SHM. The six-story RC-CLT hybrid system, designed using the direct displacement-based method, is located in Vancouver, Canada. Along with very high seismicity, southwestern British Columbia is characterized by complex seismotectonics, consisting of subduction, shallow crustal, and in-slab faulting mechanisms. A hazard-consistent set of 40 ground motion pairs is selected from the PEER and KiK-net databases, and used to estimate the building's seismic performance. The effects of using steel slit dampers (associated with large hysteresis loops) and flag-shaped energy dissipators (associated with the recentering capability) are investigated. The results indicate that the hybrid system has good seismic performance with a probability of collapse of 2-3% at the 2475-year return period shaking intensity. The hybrid building with steel slit dampers exhibits a collapse margin ratio of 2.8, which increases to 3.5-3.6 when flag-shaped dissipators are used. The flag-shaped dissipators are found to significantly reduce the residual drift of the hybrid building. Additionally, the seismic performance of the hybrid building equipped with flag-shaped dissipators is found to improve marginally when the recentering ratio is increased.

Portfolio Seismic Loss Estimation and Risk-based Critical Scenarios for Residential Wooden Houses in Victoria, British Columbia, and Canada.

This study presents a city-wide seismic risk assessment of single-family wooden houses in Victoria, British Columbia, and Canada. The novelty and uniqueness of this study include considerations of detailed building-by-building exposure model for residential houses, current national seismic hazard model for Canada, and rigorous seismic fragility modeling of wooden houses based on nonlinear dynamic analysis of structures subjected to mainshock-aftershock sequences. A full consideration of stochastic event scenarios in probabilistic seismic risk analysis allows the identification of critical scenarios from overall regional seismic risk perspectives and provides valuable insights in informing earthquake disaster risk management actions. Outputs from the developed catastrophe model for Victoria are compared with the empirical model that was developed based on insurance claim data from the 1994 Northridge earthquake. Results of the seismic loss calculations highlight the importance of seismic resistance of the existing houses and of aftershock effects. The integrated use of the outputs from the advanced catastrophe model facilitates risk-based identification of critical earthquake scenarios, which are useful for different stakeholders for earthquake risk management purposes.

Mapping safety culture attributes with integrity management program to achieve assessment goals: A framework for oil and gas pipelines industry.

INTRODUCTION: The safety of oil and gas pipelines is an increasing concern for the public, government regulators, and the industry. A safety management system cannot be efficient without having an effective integrity management program (IMP) and a strong safety culture. IMP is a formal document (policies, planning, scheduling, and technical processes) while safety culture is a measure of views, beliefs, and traditions about safety. For regulatory authorities and O&G companies, assessing the effectiveness of both the IMP and safety culture through regulatory audits is a daunting task with indistinct findings. METHOD: An integrated framework based on regulatory audits is developed to assess the maturity of safety culture based on IMP efficacy through risk-based approach by using failure mode and effect analysis (FMEA). The framework focuses on three distinct aspects, the probability of failure occurrence in case of the non-compliance of regulatory and program requirements, severity of non-compliance, and effectiveness of the corrective actions. RESULTS: Program requirements and performance indicators are translated into assessment questions which are grouped into 18 IMP components. Subsequently, these components are linked with four safety culture attributes. Sensitivity analysis revealed that four IMP components, i.e., organizational roles and responsibilities, policy and commitment, risk assessment, and training and competency, significantly affect the safety culture maturity level. CONCLUSIONS: Individual assessment of IMP and safety culture in O&G sector consumes extensive time and efforts in the auditing process. The framework facilitates the process by pursuing common criteria between IMP and safety culture. The O&G companies and regulator can prioritize the improvement plans and guidelines using the framework's findings. Practicalapplications: The integrated framework developed in this research will improve the existing assessment mechanism in O&G companies. The framework has been effectively implemented on a case of 17 upstream O&G pipeline-operating companies in the province of British Columbia, Canada.

Environmental risk assessment of acid rock drainage under uncertainty: The probability bounds and PHREEQC approach.

Acid rock drainage (ARD) is a major environmental problem that poses significant environmental risks during and after mining activities. A new methodology for environmental risk assessment based on probability bounds and a geochemical speciation model (PHREEQC) is presented. The methodology provides conservative and non-conservative ways of estimating risk of heavy metals posed to selected endpoints probabilistically, while propagating data and parameter uncertainties throughout the risk assessment steps. The methodology is demonstrated at a minesite located in British Columbia, Canada. The result of the methodology for the case study minesite shows the fate-and-transport of heavy metals is well simulated in the mine environment. In addition, the results of risk characterization for the case study show that there is risk due to transport of heavy metals into the environment.

Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.

Acid rock drainage (ARD) is a major pollution problem globally that has adversely impacted the environment. Identification and quantification of uncertainties are integral parts of ARD assessment and risk mitigation, however previous studies on predicting ARD drainage chemistry have not fully addressed issues of uncertainties. In this study, artificial neural networks (ANN) and support vector machine (SVM) are used for the prediction of ARD drainage chemistry and their predictive uncertainties are quantified using probability bounds analysis. Furthermore, the predictions of ANN and SVM are integrated using four aggregation methods to improve their individual predictions. The results of this study showed that ANN performed better than SVM in enveloping the observed concentrations. In addition, integrating the prediction of ANN and SVM using the aggregation methods improved the predictions of individual techniques.

Selection of remedial alternatives for mine sites: a multicriteria decision analysis approach.

The selection of remedial alternatives for mine sites is a complex task because it involves multiple criteria and often with conflicting objectives. However, an existing framework used to select remedial alternatives lacks multicriteria decision analysis (MCDA) aids and does not consider uncertainty in the selection of alternatives. The objective of this paper is to improve the existing framework by introducing deterministic and probabilistic MCDA methods. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods have been implemented in this study. The MCDA analysis involves processing inputs to the PROMETHEE methods that are identifying the alternatives, defining the criteria, defining the criteria weights using analytical hierarchical process (AHP), defining the probability distribution of criteria weights, and conducting Monte Carlo Simulation (MCS); running the PROMETHEE methods using these inputs; and conducting a sensitivity analysis. A case study was presented to demonstrate the improved framework at a mine site. The results showed that the improved framework provides a reliable way of selecting remedial alternatives as well as quantifying the impact of different criteria on selecting alternatives.

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques.

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.

Decision making under uncertainty--an example for seismic risk management.

Decision-making techniques are used to select the "best" alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision-making process. The major thrust of this article is to extend the framework proposed by Yager for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step-by-step formulation of the proposed approach is illustrated using a hypothetical example and a three-story reinforced concrete building.