Transportation has the highest dependence on fossil fuels of any sector and accounts for 37% of carbon dioxide (CO₂) emissions. Maritime transportation is responsible for around 940 million tons of CO₂ and approximately 3% of global emissions annually. The significant increase in shipping activities around the globe has magnified the generation of toxic pollutants. In recent years, shipping emissions have received significant attention in developed countries due to global climate change, while in developing countries, researchers are making enormous efforts to tackle this catastrophic and pressing issue. This study considers Muhammad Bin Qasim Port (MBQP), Karachi, Pakistan as a case study. This study employed an activity-based or bottom-up approach with a standard procedure to estimate the various anthropogenic pollutants emissions including particular matters (PM₁₀ and PM_2.5), nitrogen oxide (NO_x), sulfur dioxide (SO₂), carbon monoxide (CO), CO₂, methane (CH₄), non-methane volatile organic compound (NMVOC), and hydrocarbon (HC) under different operational modes, i.e., hoteling, maneuvering, and reduced speed zones. The results indicated that CO₂ was the highest contributor with a proportion of 92%, NO_x 5%, and SO₂ 1.5% for all three operational modes. Moreover, the results indicated that container ships account for 64% of overall emissions, followed by tankers for 24%. Regarding the monthly trend, the findings revealed that November and December had the highest emission rates, with over 20% of the total emissions recorded. This study’s findings will assist stakeholders and policymakers to prioritize maritime emissions in developing countries.

We are in an advancing stage of data acquisition and an even greater dynamic stage of dealing with big data. Data sizes have evolved over the years from a few kilobytes to Exabyte. The transportation engineer has also been caught up in the big data era and to efficiently analyze this massive data for maximum benefits, various challenges relating to data acquisition, data storage, data cleaning, data analysis and visualization has to be overcome. In this paper, we discuss these challenges and approaches to managing them with respect to massive Radio Frequency Identification data for traffic volume count in Nanjing, China. We recommended software, use analytical and visualization techniques like aggregation, graduated circular symbols and traffic count map to overcome big data challenges to produce peak hour, offpeak hour traffic volume counts and traffic count maps showing locations of low and high volume traffic. The paper, therefore, contributes to the management of big data by transportation engineers for traffic volume and congestion analysis.

The main congestion on roads occur during peak hours, apart from incidents such as road accidents and construction works. Although there have been studies on peak period travels, these studies have only implicitly considered weekday, weekend and road type in their investigations. In this paper, it is proposed to investigate explicitly, the effect of weekday and weekend travel variability and road type on peak hour vehicular movement which leads to congestion. A study of vehicular movement patterns during these times can influence and impact on planning decisions for transportation engineers. This study utilizes structural equation model (SEM) to investigate the vehicular movements influence of weekdays, weekends, road type choice and car type on two peak hour periods 6 am to 9 am and 4 pm to 7 pm and one off-peak hour 9 am to 12 noon. Using vehicular movement data from Radio Frequency Identification for Nanjing, China, for the month of May 2014, it was revealed that in most of the cases, weekday travels influence peak hour travels more than weekends and that off-peak hour travels for both weekdays and weekends show little variations. The study also discovered that choice of road type and car type, have varying influence on peak hour travels. The high significance ratios of results prove that these chosen variables are suitable for investigations into peak hour travel pattern studies. The study has also proved the viability of this modeling method to investigate policy measures to reduce peak period congestion.

Introduction: Sustainable development, which balances economic, social, and environmental factors to meet the needs of the present and future generations, is a collective responsibility. Rapid population growth and intensification of socio-economic activities are causing significant changes in Land Use Land Cover (LULC), directly impacting ecosystem services, biodiversity, and climate. The Obuasi community is already facing environmental degradation and health problems due to gold mining. Methodology: The study assessed the spatial extent and intensity of mining activities in the Obuasi Municipality on the LULC and predicted future LULC changes due to mining activities using geospatial techniques. The LULC changes were analysed using Landsat satellite imagery from 1990 to 2024 using a Random Forest (RF) classifier. Results and Discussion: Mining Areas had a positive change of 27.655 km2 (18.846%) while tailing dams and flooded pits increased by 46.655 km2 (31.724%). Dense vegetation, less dense foliage, and built-up and bare land decreased by -34.692%, -8.024%, and -7.854%, respectively. These significant environmental changes show the need for effective land management strategies and geospatial techniques to monitor and mitigate adverse effects, promoting sustainable development policies and ecological resilience. Significance: The findings of this study provide valuable insights into the environmental impacts of mining, emphasising the need to make informed research and policy decisions in the field of environmental science.