The cartography of lineaments across a territory can be optimized using geophysical potential field data. In this study, land gravity and EMAG2 (Earth Magnetic Anomaly Grid) data were simultaneously used to identify and characterize the major lineaments that spread across Cameroon. The data were filtered using a multi-scale approach including horizontal and vertical gradient analyses. The Euler Deconvolution method was later applied to the filtered data to estimate the extension and depth of the identified lineaments. Results show that the main lineaments across Cameroon are laterally extended with a dominant N45°E orientation. Some of these lineaments correlated well with the geographical location of some known major tectonic structures found across the country. The depth of these lineaments varies between 1 and 35 km. Some of the identified faults are still active as their location correlated with the location of some recent earthquakes that occurred in Cameroon. This work, therefore, highlights some hidden tectonic features which knowledge generally precedes exploration for subsurface resources. Graphical Abstract

Manufacturing and mining sectors are serious pollution sources and risk factors that threaten air quality and human health. We analyzed pollutants at two study sites (Talcher and Brajrajnagar) in Odisha, an area exposed to industrial emissions, in the pre-COVID-19 year (2019) and consecutive pandemic years, including lockdowns (2020 and 2021). We observed that the annual data for pollutant concentration increased at Talcher: PM2.5 (7–10%), CO (29–35%), NO2 and NOx (8–57% at Talcher and 14–19% at Brajrajnagar); while there was slight to substantial increase in PM10 (up to 11%) and a significant increase in O3 (41–88%) at both sites. At Brajrajnagar, there was a decrease in PM2.5 (up to 15%) and CO (around half of pre-lockdown), and a decrease in SO2 concentration was observed (30–86%) at both sites. Substantial premature mortality was recorded, which can be attributed to PM2.5 (16–26%), PM10 (31–43%), NO2 (15–21%), SO2 (4–7%), and O3 (3–6%). This premature mortality caused an economic loss between 86–36 million USD to society. We found that although lockdown periods mitigated the losses, the balance of rest of the year was worse than in 2019. These findings are benchmarks to manage air quality over Asia’s largest coalmine fields and similar landscapes.

This work investigates the effect of low frequency vibratory processing for cleaning and washing various machine components parts from rusts and old paints deposits. The experimental investigation was carried out with special prepared samples that were weighted and exposed to paints and rust contaminants. These samples were treated in universal horizontal vibration machine UVHM 4 × 10 with different combination of instrumental processing medium, process fluid, machine amplitude and frequency of oscillations. They were periodically reweighted after processing and compared to etalon with control of quantity of dust that have been removed, sample cleanliness and also other functional parameters. Statistical analysis has been used to characterize ongoing process and full factorial analysis to establish experimental parameters dependency. The result is showing the complex dependence of samples cleanliness to each processing parameters like processing time, amplitude of oscillations, frequency of oscillations, process fluid parameters, instrumental medium, etc. Between this parameters although the most important successively the amplitude of oscillations, the frequency of oscillations the processing medium and the processing fluid depending to his considered composition, the optimal processing time can be reach only by complex combination of all this parameters every of them carry an amplify coefficient. Low frequency oscillations can be used to monitor and optimize washing and cleaning operations of paints and rusts contaminations. That guarantees process automation, its effectiveness for a large industrial application.

In this study, Douala, Cameroon was used as a case study to analyze the characteristics of sustainable energy for road transport from 2010 to 2019. Douala, being the national capital and entry point to Central Africa, served as a major hub for the movement of people and goods. However, the road transport sector was plagued by a number of problems, including traffic congestion, the use of fossil fuels, air pollution, and global warming associated with road traffic. The objective of this work was to evaluate a set of indicators that would allow monitoring the evolution of trends in the interactions between the energy component and sustainable development. The DPSIR (Driving Force, Pressure, State, Impact, and Response) model was used to select a set of indicators. According to the results, the energy intensity of the fuel used for transport decreased from 9.93 to 15.9 toe/M€. This increase in energy intensity reflected the energy-intensive nature of the road industry. Additionally, from 2010 to 2019, the energy efficiency of road transport vehicles in the city of Douala fluctuated between 20 and 22%. This indicates a significant potential for improving energy efficiency. Therefore, decision-makers need to implement sustainable transport planning to address these issues.

The city of Douala in Cameroon is facing great challenges in terms of its demographic growth, economic development and urbanization, especially in relation to environmental and economic factors. However, there has been significant growth in its road transport sector, which has led to an excessive demand for the consumption of fossil fuels and an increase in greenhouse gas emissions in recent decades within this sector. However, no concrete policy has yet been put in place to improve the energy efficiency of the transport sector. This work aims to identify the driving factors and determine their contributions to the variation in energy consumption. In this study, a decomposition analysis via the Logarithmic Mean Divisia Index (LMDI) method is used for the period of 2010–2019 to quantify the respective effects of the driving factors on the variation in energy consumption. Based on the study of the literature, we classified four main driving factors in the road transport sector that contributes to the total variation in energy consumption, such as vehicle energy intensity, vehicle intensity, gross domestic product (GDP) by capita, and population scale, with each contributing 13.06%, 31.30%, 12.85%, and 42.76%, respectively. In particular, we note that the energy intensity coefficient of the vehicles from 2013 to 2016 and that of the intensity of the vehicles coefficient from 2010 to 2011 and 2012 to 2013 are the two factors that have, nevertheless, led to a slight decrease in the variation in energy consumption. This implies that an improvement in these two factors would contribute to enhancing the energy efficiency of the road transport sector of the city of Douala. It will therefore be necessary to put in place several energy-saving strategies that would lead to a rationalization of energy consumption in order to reduce greenhouse gas emissions by road transports. Policymakers should take this study into account to achieve a balance between energy consumption and economic growth to better integrate the notion of sustainable road transport.