Greenhouse Gas Emissions in the Industrial Processes and Product Use Sector of Saudi Arabia—An Emerging Challenge

The Kingdom of Saudi Arabia has been experiencing consistent growth in industrial processes and product use (IPPU). The IPPU’s emission has been following an increasing trend. This study investigated time-series and cross-sectional analyses of the IPPU sector. Petrochemical, iron and steel, and cement production are the leading source categories in the Kingdom. In recent years, aluminum, zinc, and titanium dioxide production industries were established. During the last ten years, a significant growth was observed in steel, ethylene, direct reduce iron (DRI), and cement production. The growth of this sector depends on many factors, including domestic and international demand, socioeconomic conditions, and the availability of feedstock. The emissions from IPPU without considering energy use was 78 million tons of CO2 equivalent (CO2eq) in 2020, and the cement industry was the highest emitter (35.5%), followed by petrochemical (32.3%) and iron and steel industries (16.8%). A scenario-based projection analysis was performed to estimate the range of emissions for the years up to 2050. The results show that the total emissions could reach between 199 and 426 million tons of CO2eq in 2050. The Kingdom has started initiatives that mainly focus on climate change adaptation and economic divergence with mitigation co-benefits. In general, the focus of such initiatives is the energy sector. However, the timely accomplishment of the Saudi Vision 2030 and Saudi Green Initiative will affect mitigation scenarios significantly, including in the IPPU sector. The mitigation opportunities for this sector include (i) energy efficiency, (ii) emissions efficiency, (iii) material efficiency, (iv) the re-use of materials and recycling of products, (v) intensive and longer use of products, and (vi) demand management. The results of this study will support the Kingdom in developing an appropriate climate change mitigation roadmap

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context

Sustainable water use in construction

Water is needed in direct construction activities and also for the production of construction materials as embodied water. Water use during the life time of an infrastructure also needs to be considered in the planning and design phase of the infrastructure to save and conserve water in the long run. A typical building is associated with water use in direct and indirect forms. Direct forms include water consumed by workers, water used in washing aggregates, preparing raw concrete, curing concrete, dust suppression and washing of hard surfaces and equipment. Indirect use is related to embodied water, which has been used in production of construction materials. Use of water-efficient devices can save significant volumes of water during the operation phase of a building. It is expected that a greater environmental awareness, favorable government policy and continuing education will enhance the water use efficiency in the construction industry

Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents. In this study, using Al-Ahsa city in Saudi Arabia’s Eastern Province as a case study, a Bayesian belief network (BBN) model was established by incorporating an expectation–maximization algorithm. The model examines the relationships between indicator variables with a special focus on driving behavior to measure the uncertainty associated with accident outcomes. The BBN was devised to analyze intentional and unintentional driving behaviors that cause different types of accidents and accident severities. The results showed when considering speeding alone, there is a 26% likelihood that collision will occur; this is a 63% increase over the initial estimate. When brake failure was considered in addition to speeding, the likelihood of a collision jumps from 26% to 33%, more than doubling the chance of a collision when compared to the initial value. These findings demonstrated that the BBN model was capable of efficiently investigating the complex linkages between driver behavior and the accident causes that are inherent in road accidents

Greenhouse Gas Emissions from Solid Waste Management in Saudi Arabia—Analysis of Growth Dynamics and Mitigation Opportunities

The continuous growth in population, urbanization, and industrial development has been increasing the generation of solid waste (SW) in the Kingdom of Saudi Arabia. Consequently, the associated greenhouse gas (GHG) emission is also following an increasing trend. The collection and use of greenhouse gases emitted from solid waste management practices are still limited. A causality analysis examined the driving factors of the emissions from solid waste management. The methane (CH4) emissions from municipal solid waste (MSW) increased with an increase in gross domestic product (GDP) per capita and urban population, and an increase in foreign direct investment (FDI) inflows and literacy rate was likely to reduce CH4 emissions from municipal solid waste and vice versa. The CH4 emission generated from industrial solid wastes was found to be positively related to GDP per capita, urban population, and FDI inflows. However, a decrease in the unemployment rate was likely to increase CH4 emissions from industrial solid wastes. The future greenhouse gas emissions were projected under different possible socio-economic conditions. The scenario analysis based on different variations of population and GDP growth revealed that methane emission from total waste would increase at an average annual rate of 5.13% between 2020 and 2050, and is projected to reach about 4000 Gg by the end of the year 2050. Although the Kingdom has been taking some initiatives towards climate change mitigation, it has significant opportunities to adopt some of the best practices in solid waste management including reduction, recycling, composting and waste-to-energy, and carbon capture and utilization. This study also put emphasis on developing appropriate policy approaches for climate change mitigation based on the circular economy which is gaining momentum in the Kingdom

Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model

Abstract The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5–2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety