Journal of Environmental & Earth Sciences

Hydrogeochemical Processes in Basement Areas Using Principal Component in Burkina Faso (West African Sahel)

Moussa Diagne Faye — 2024-10-28

The basement aquifers in Burkina Faso are increasingly exposed to groundwater pollution, largely due to socio-economic activities and climatic fluctuations, particularly the reduction in rainfall. This pollution makes the management and understanding of these aquifers particularly complex. To elucidate the processes controlling this contamination, a methodological approach combining principal component analysis (PCA) and multivariate statistical techniques was adopted. The study analyzed sixteen physicochemical parameters from 58 water samples. The primary objective of this research is to assess groundwater quality and deepen the understanding of the key factors influencing the spatial variation of their chemical composition. The results obtained will contribute to better planning of preservation and sustainable management measures for water resources in Burkina Faso. The results show that three principal components explain 72% of the variance, identifying anthropogenic inputs, with two components affected by mineralization and one by pollution. The study reveals that the groundwater is aggressive and highly corrosive, with calcite saturation. Water-rock interactions appear to be the main mechanisms controlling the hydrochemistry of groundwater, with increasing concentrations of cations and anions as the water travels through percolation pathways. PCA also revealed that the residence time of the water and leaching due to human activities significantly influence water quality, primarily through mineralization processes. These results suggest that rock weathering, coupled with reduced rainfall, constitutes a major vulnerability for aquifer recharge.

Carbon Reduction Effect of Digital New Quality Productivity: Theoretical Analysis and Empirical Evidence

Shan Yan — 2024-10-30

The continuous innovation and widespread application of digital technology have expedited the transformation of productivity and presented an opportunity to achieve carbon peak and carbon neutrality. Digital new quality productivity, characterized by the integration of advanced technologies, innovative business models, a new economic framework, and ongoing innovation, stands as a superior production factor. It plays a crucial role in fostering high-quality economic growth and leading efforts to meet the “dual carbon” objectives. Using panel data from Chinese prefecture-level cities from 2011 to 2022, this study employs various econometric models to empirically examine the impact and underlying mechanisms of digital new quality productivity on carbon emission reduction. The findings reveal that: (1) There exists a significant U-shaped nonlinear relationship between digital new quality productivity and carbon emission performance, with an inflection point at 0.2750. (2) Dual objective constraints significantly moderate the relationship between digital new productivity and carbon emission performance. Setting moderate economic growth targets positively influences the effect of digital new quality productivity on carbon emission performance. (3) The impact of digital new quality productivity on carbon emission performance varies considerably based on factors such as urban location, city size, resource endowment, and specific city characteristics. It is essential to focus on nurturing digital new quality productivity, exploring the integration of balanced economic growth objectives with environmental goals, and effectively leveraging the environmental benefits derived from the advancement of digital new quality productivity tailored to local contexts.

Sago Forests for Food Security and Handling Climate Change in Indonesia

Gun Mardiatmoko — 2024-10-31

A crucial impact of climate change is the disruption of the agricultural sector, posing a threat to food supply for the globally increasing population. In this context, prioritizing food security in each country becomes an important concern. This study aimed to explore biomass and C-Stock content of Sago forests for handling climate change and resilience. The methodology used comprised various steps including determining the type and the hydraulic conductivity of the soil, assessing biomass and C-Stock by cutting Sago at various growth stages, weighing the wet and dry weight of each fraction, calculating the Top-Root Ratio, and determining the starch yield. The results showed that there were four types of soil namely Hydric, District, and Fluvic Gleisol, as well as Oxic Cambisole. C-Stock was 26.99 tonnes per hectare with a Top-Root Ratio of 636%, implying that above-ground biomass (AGB) was six times more than below-ground biomass (BGB) and the presence of mineral soil. Sago dry starch product ranged from 490.3–571.8 kg per tree and the potential relatively varied due to differences in the structure and composition of forests, as well as habitat and environment. Although logging remained persistent on a very small scale, early signs of disturbances were observed in hydrological conditions and fluctuations in water levels or puddles in the soil profile. This implied that conversion of Sago forests to other uses for the expansion of grain crops on a large scale, would lead to the area experiencing drought.

Data to Cartography New MDE-Based Approach for Urban Satellite Image Classification

Hafsa Ouchra — 2024-10-28

Monitoring of the earth's surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT, Landsat and Sentinel-2, which produce vast datasets. The processing of this data, often referred to as Big Data, is essential for decision-making, requiring the application of advanced algorithms to analyze changes in land cover. In the age of artificial intelligence, supervised machine learning algorithms are widely used, although their application in urban contexts remains complex. Researchers have to evaluate and tune various algorithms according to assumptions and experiments, which requires time and resources. This paper presents a meta-modeling approach for urban satellite image classification, using model-driven engineering techniques. The aim is to provide urban planners with standardized solutions for geospatial processing, promoting reusability and interoperability. Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.

Evaluating the Interaction of Mycorrhizal Fungi, Azotobacter, and Biochar in Enhancing Cucumber Productivity and Soil Health

Noor A. J. K. Al-Silmawy — 2024-11-18

This experiment evaluated the effects of the mycorrhizal fungus Glomus mosseae, Azotobacter chroococcum bacteria, and Biochar on the characteristics of the root system, and yield of the cucumber plant, Cucumis sativus L.; for this purpose, experiment designed: the first factor is a combination of Mycorrhizae (M) at 35 g plant^–¹, Azotobacter (A) 15 ml plant^–¹ with a microbial density of 2.2, and three concentrations (0, 5, 10%) of Biochar sprayed on the plant. The results of the research demonstrated that using mycorrhizae, Azotobacter bacteria, and phosphate rock with half the mineral recommendation (MAR) and spraying Biochar at a concentration of 10% gave the highest rate of infection of the roots with mycorrhizae, amounting to 80%, and the highest dry weight of the root system reached 84.53 g. The highest number of total bacteria was 8.74 log Cfu g m^–¹ of soil, the highest plant height reached 375.0 cm, the highest dry weight of the shoot reached 101.66 g plant^–¹, and the highest yield for the greenhouse was 4.501 ton greenhouse^–¹, followed by the treatment of adding Mycorrhiza with phosphate rock and half the mineral recommendation (MR) with Biochar at a concentration of 10%, then treatment with the addition of mycorrhizae with Azotobacter bacteria with half the mineral recommendation (AR) with 10% of Biochar. It is possible to eliminate half of the mineral recommendation by using these fertilizers, reduce the harmful impact of pollution on the environment and enhance sustainability in agriculture.

Capital Matching, Environmental Regulation and Carbon Emission Performance

Shan Yan — 2024-10-29

Under the “dual carbon” goal, local governments in China have strategically focused on enhancing capital utilization efficiency and enforcing environmental regulations to improve carbon emission performance. This dual approach targets the intertwined challenges of economic development and environmental protection. Utilizing data from 266 prefecture-level cities in China from 2007 to 2019, this study systematically investigates the effects of capital matching and environmental regulation on carbon emission performance through the spatial Durbin model and the instrumental variable method. The results indicate that both capital matching and environmental regulation significantly enhance carbon emission performance. Capital matching demonstrates positive spatial spillover effects; whereas environmental regulation exhibits negative spatial spillover effects. Furthermore, there are synergistic effects between capital matching and environmental regulation that jointly enhance carbon emission performance. To address potential biases caused by endogenous environmental regulation, the study uses the proportion of environment-related words in provincial government work reports as an instrumental variable for environmental regulation. Additionally, to capture the heterogeneity in the environmental governance willingness and intensity of prefecture-level municipal governments, the study constructs heterogeneous instrumental variables. These variables are derived by multiplying the proportion of a prefecture-level city’s total industrial output value to the province’s total industrial output value with the proportion of environment-related words in the provincial government work reports. Analyses based on these instrumental variables reveal that endogenous issues in environmental regulation lead to an overestimation of its positive impact on carbon emission performance.

New MDA Transformation Process from Urban Satellite Image Classification to Specific Urban Landsat Satellite Image Classification

Hafsa Ouchra — 2024-10-31

In a context where urban satellite image processing technologies are undergoing rapid evolution, this article presents an innovative and rigorous approach to satellite image classification applied to urban planning. This research proposes an integrated methodological framework, based on the principles of model-driven engineering (MDE), to transform a generic meta-model into a meta-model specifically dedicated to urban satellite image classification. We implemented this transformation using the Atlas Transformation Language (ATL), guaranteeing a smooth and consistent transition from platform-independent model (PIM) to platform-specific model (PSM), according to the principles of model-driven architecture (MDA). The application of this IDM methodology enables advanced structuring of satellite data for targeted urban planning analyses, making it possible to classify various urban zones such as built-up, cultivated, arid and water areas. The novelty of this approach lies in the automation and standardization of the classification process, which significantly reduces the need for manual intervention, and thus improves the reliability, reproducibility and efficiency of urban data analysis. By adopting this method, decision-makers and urban planners are provided with a powerful tool for systematically and consistently analyzing and interpreting satellite images, facilitating decision-making in critical areas such as urban space management, infrastructure planning and environmental preservation.

Innovative Approaches in Water Decontamination: A Critical Analysis of Biomaterials, Nanocomposites, and Stimuli-Responsive Polymers for Effective Solutions

Rakesh Namdeti — 2024-11-08

In recent years, smart materials have emerged as a groundbreaking innovation in the field of water filtration, offering sustainable, efficient, and environmentally friendly solutions to address the growing global water crisis. This review explores the latest advancements in the application of smart materials—including biomaterials, nanocomposites, and stimuli-responsive polymers—specifically for water treatment. It examines their effectiveness in detecting and removing various types of pollutants, including organic contaminants, heavy metals, and microbial infections, while adapting to dynamic environmental conditions such as fluctuations in temperature, pH, and pressure. The review highlights the remarkable versatility of these materials, emphasizing their multifunctionality, which allows them to address a wide range of water quality issues with high efficiency and low environmental impact. Moreover, it explores the potential of smart materials to overcome significant challenges in water purification, such as the need for real-time pollutant detection and targeted removal processes. The research also discusses the scalability and future development of these materials, considering their cost-effectiveness and potential for large-scale application. By aligning with the principles of sustainable development, smart materials represent a promising direction for ensuring global water security, offering both innovative solutions for current water pollution issues and long-term benefits for the environment and public health.