Journal of Environmental & Earth Sciences

Origins of Organic Matter in Tuo River Sediments

Man-man Wu — 2025-03-20

Ultraviolet-visible (UV-Vis) and three-dimensional excitation emission matrix fluorescence (3D-EEM) spectroscopies were conducted to investigate the structure and origin of dissolved organic matter (DOM) from soils around the Tuo river in Suzhou city in different season. The results showed that the characteristics of all samples, UV-Visible spectra were similar and the relative concentrations of DOM showed an overall increasing trend in the middle and upper reaches of the Tuo River and reached a maximum in the middle reaches of the river. In particular, the aromaticity (A₂₅₀/A₃₆₅) of DOM in sediments at the midstream point of the Tuo River and the degree of humification degree (SUVA₂₅₄) were higher than those in other river sections. The 3D-EEM fluorescence spectra showed that fulvic acid-like peaks in the visible region, fulvic acid-like peaks in the UV-visible region, and two humic acid-like peaks were reflected in the dissolved organic matter of the Tuo River sediments. Combining the three-dimensional fluorescence spectrum with the fluorescence index (fluorescence index, FI) and autochthonous index (autoch-thonous index, BIX) of DOM in the sediments of the Tuo River in different seasons, it shows that the exogenous input of DOM in the sediments of each section of the Tuo River is extremely obvious and less bioavailable. The aromaticity of DOM molecules is enhanced after the Tuo River flows through urban areas. The present study can provide a reference for the future management of the water environment of related rivers.

Enhancing Environmental Sustainability through Machine Learning: Predicting Drug Solubility (LogS) for Ecotoxicity Assessment and Green Pharmaceutical Design

Imane Aitouhanni — 2025-03-20

Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down, and the drug products are accumulated in living organisms. The ability of a drug to dissolve in water (i.e., LogS) is an important parameter for assessing a drug's environmental fate, biovailability, and toxicity. LogS is typically measured in a laboratory setting, which can be costly and time-consuming, and does not provide the opportunity to conduct large-scale analyses. This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants. We used a dataset from the ChEMBL database that contained 8832 molecular compounds. Various data preprocessing and cleaning techniques were applied (i.e., removing the missing values), we then recorded chemical properties by normalizing and, even, using some feature selection techniques. We evaluated logS with a total of several machine learning and deep learning models, including; linear regression, random forests (RF), support vector machines (SVM), gradient boosting (GBM), and artificial neural networks (ANNs). We assessed model performance using a series of metrics, including root mean square error (RMSE) and mean absolute error (MAE), as well as the coefficient of determination (R²). The findings show that the Least Angle Regression (LAR) model performed the best with an R² value close to 1.0000, confirming high predictive accuracy. The OMP model performed well with good accuracy (R² = 0.8727) while remaining computationally cheap, while other models (e.g., neural networks, random forests) performed well but were too computationally expensive. Finally, to assess the robustness of the results, an error analysis indicated that residuals were evenly distributed around zero, confirming the results from the LAR model. The current research illustrates the potential of AI in anticipating drug solubility, providing support for green pharmaceutical design and environmental risk assessment. Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.

The Role of Pigs in the Carbon Footprint of Red Meat in Canada

James A. Dyer — 2025-03-18

Global livestock production is a major driver of climate change. Lumping beef and pork together as red meat masks important differences in their carbon footprints, land uses, and social status. These two red meat choices in Canada were compared by using a meta-model of the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES). ULICEES calculated fossil CO₂, N₂O and CH₄ emissions for beef, dairy, pork, poultry, and sheep production in Canada, based on both the livestock and their supporting land base in 2001. The dynamic drivers of the meta-model were crop yields, breeding female populations, tillage practices, nitrogen fertilizer use, and the crop complex of each livestock industry. When the potential carbon sequestration in the land growing harvested perennial forage is credited to beef production, the CO₂e emissions offset does not reduce the carbon footprint of beef enough to match the lower carbon footprint of pork. Most of the land required to grow hay for beef would not be needed to feed a protein-equivalent pig population. In a hypothetical conversion of all beef production to pork production for 2021, 4.5 Mha of land under perennial forage was freed and 10.0 MtCO₂e per year was mitigated when that area was re-cultivated for annual crops—a GHG mitigation equal to 12% of the GHG emissions budget of Canadian agriculture. Leaving that area under a perennial ground cover mitigated 19.8 MtCO₂e per year, the equivalent of 23% of the sector’s GHG emissions budget.

Resource Retrieval from End-of-Life Passenger Cars in the Informal Sector of Morocco

Imane Rouichat — 2025-03-17

Sustainably managing vehicles at their end-of-life stage (ELVs) presents significant potential for resource recovery, effectively addressing resource scarcity through the closure of the material loop. While ELVs in countries like Morocco have traditionally been treated as waste rather than secondary resource material (SRM), they have the potential to reduce reliance on primary materials when used judiciously. Despite policymakers aiming for increased resource efficiency in the automobile sector, there is limited research exploring the role of the informal sector in recovering materials and parts from ELVs. This study investigates the ELV processing scenario at Salmia scrap market, recognized as one of Africa’s largest informal markets for ELVs. Using a mass-balance approach, the disposal of sedan cars is examined, and a conceptual framework illustrating the process flow and interactions among multiple stakeholders is developed. From sampled sedan cars, approximately 7% of aluminum and 76% of iron, by weight, are recovered. These findings contribute to estimating the potential for recycling and recovering materials from ELVs processed by the informal sector in Morocco. In a standard operational context, estimations suggest that the sector holds substantial potential to recover aluminum and iron by 2030. This underscores the importance of formalizing operations and integrating informal players into the value chain to effectively address resource scarcity within a circular economy.

Climatic Variables and Food Security of Villagers during the COVID-19 Pandemic in the Districts of Huancayo, Peru

Yesenia Antonieta Villalva Castellanos — 2025-03-19

The study investigated the relationship between climatic variables and food security in households in the districts of Huancayo (Chongos Alto, Viques, Pucará, and Huancayo) during the COVID-19 pandemic. A cross-sectional observational study was conducted with a sample of 272 households out of 36,453. Food security data were collected through questionnaires, and climatic variables (temperature, humidity, and precipitation) were obtained from CEPREANDES weather stations between September 2020 and February 2021. The results showed that 44.49% of households experienced mild food insecurity, while 55.5% experienced moderate food insecurity. Recorded climatic conditions included maximum temperatures of 28°C in Pucará and 27°C in Huancayo, and a minimum of -8°C in Chongos Alto. Relative humidity reached 89% in Pucará and 87% in Chongos Alto and Huancayo, while maximum rainfall was 28 mm in Chongos Alto and 23 mm in Huancayo. Multivariate analysis revealed that relative humidity had a significant association with moderate food insecurity (B=16.406; 95% CI: -64735 to 64768), increasing the risk 16 times under high humidity conditions. No significant relationships were found with temperature (B=-7.107; 95% CI: -77320 to 77306) or precipitation (B=-7.831; 95% CI: -25690 to 25674). It was concluded that relative humidity is a key factor in food security, particularly during the pandemic, while other climatic variables showed no significant impacts. These findings highlight the need for urgent adaptations to climatic challenges in vulnerable contexts.

Environmental-Based Supply Chain Integration for the Development of Carrageenan Production Centers in Laikang Village Takalar Regency, South Sulawesi Province

Leffy Hermalena — 2025-03-21

The rapidly growing seaweed farming contributes to food, feed, biofuel, and biochemicals, with Indonesia being the largest exporter of raw seaweed (53.35%) but low in carrageenan derivative products (4.49%). Sustainable farming supports SDG 13 and SDG 14 through emission reduction, marine ecosystem conservation, and increased biodiversity. However, its supply chain faces challenges in raw material availability, such as fluctuating harvests, inconsistent quality, limited logistics infrastructure, and market price volatility. Therefore, good environmental management is needed to optimize raw materials in carrageenan production through resource efficiency, environmentally friendly products, environmental regulations, and market and stakeholder awareness. This study identifies factors in the environment-based supply chain that influence the development of carrageenan production centers and analyzes their ecological impacts using quantitative SEM analysis. SEM analysis reveals that resource efficiency, waste management, environmental regulations, and stakeholder awareness and commitment are the main influencing factors. shows significant factors including raw material efficiency, waste management, and environmental regulations. Environmentally based supply chain integration for the development of carrageenan production center areas can be done by; focusing on sustainability and minimal environmental impact, support for achieving SDG 12, 13, and 14, strategies for sustainable raw material management, circular economy, sustainability certification, renewable energy and environmentally friendly technology, and stakeholder collaboration and awareness.

Copula Method and Neural Networks for X-Band Polarimetric Radar Rainfall Retrieval in West Africa

Sahouarizié Adama Ouattara — 2025-03-19

In the context of climate change, countries in West Africa are faced with recurrent flooding with catastrophic consequences, that makes it imperative to have access to rainfall information on fine spatial and temporal scales for better monitoring and prediction of these phenomena, as could be provided by weather radars. Based on an extensive archive of data from the X-band polarimetric radar and rain gauges observations gathered during the intensive AMMA campaigns in 2006–2007 and the Megha-Tropiques satellite measurement validation programme in 2010 in West Africa, we (i) simulated jointly realistic data for polarimetric radar variables and rain intensity using copula, and (ii) assessed rain rate estimation methods based on neural network (NN) inversion techniques and non-linearly calibrated parametric algorithms. The assessment of rainfall rate retrieval by these estimators is carried out using the part of the observations database not employed for calibration steps. The multiparametric algorithms R(Z_H,K_DP) and R(Z_DR,K_DP) perform better than R(Z_H,Z_DR) and R(Z_H,Z_DR,K_DP), especially since they are calibrated using copulas with upper tail dependencies, with KGE ranging in 0.68-0.75 and 0.79-0.82, respectively versus ranges of 0.40-0.64 and 0.20–0.51, for the two latter estimators. The neural network-based estimators R_NN(Z_DR,K_DP) and R_NN(Z_H,K_DP), show KGE score characteristics comparable to those obtained from the best parametric relations, specifically optimized for the synthetic copula-based dataset. However, the neural network-based estimators were shown to be more robust when applied to a specific rainfall event. More specifically, neural network-based estimators trained on synthetic data are sensitive to the copulas' ability to capture the dependence between the variables of interest over the entire distribution of joint values. This leads to a near-cancellation of sensitivity to variability in the raindrop size distribution, as shown the coefficients of correlation near 1, especially for R_NN(Z_DR,K_DP), and for less extent R_NN(Z_H,K_DP).