Abstract:

Oxalic acid is the most abundant low-molecular-weight dicarboxylic acid in theatmosphere and plays a crucial role in the formation of new particles and cloud condensation nuclei. However, most observational studies have focused on particulate oxalate, leaving a significant knowledge gap on oxalic acid vapor. This study investigated the concentrations and formation of oxalic acid vapor and oxalate in PM2.5 at a rural tropical coastal island site in south China across different seasons, based on semi-continuous measurements using an Ambient Ion Monitor-Ion Chromatograph (AIM-IC) system. We replaced the default 25 μL sampling loop on the AIM-IC with a 250 μL loop, improving the ability to distinguish the signal of oxalic acid vapor from noise. The data revealed clear seasonal patterns in the depedent daytime and nighttime formation of oxalic acid vapor, benefiting from high signal-to-noise ratios. Specifically, concentrations were 0.059 ± 0.15 μg m-3 in February and April 2023, exhibiting consistent diurnal variations similar to those of O3, likely driven by photochemical reactions. These values decreased to 0.021 ± 0.07 μg m-3 in November and December 2023, with higher nighttime concentrations likely related to dark chemistry processes, amplified by accumulation due to low mixing layer height. The concentrations of oxalate in PM2.5 were comparable to those of oxalic acid vapor, but exhibited (3-7)-day variations, superimposed on diurnal fluctuations to varying degrees. Additionally, thermodynamic equilibrium calculations were performed on the coastal data, and independent size distributions of particulate oxalate in the upwind marine atmosphere were analyzed to support the findings.

Abstract: Orientation tools in borehole imaging logs acquire magnetic information that is currently used for spatial and geographical orientation of the images. We propose to use this magnetic field information to estimate both magnetic susceptibility and remanent magnetization of rocks inside wells. Measurements of these magnetic parameters are not often available in hydrocarbon exploration to support forward modeling of magnetic data, an interpretation tool that has played important role in the exploration risk reduction in the Pre-Salt prospects of Campos Basin, Brazil. The acquired magnetic data requires corrections for tool rotation and diurnal variation of the Earth’s magnetic field before calculation. Then, using a set of simple equations and reasonable assumptions we were able to estimate the magnetic susceptibility of carbonates and basalts, as well as the remanent magnetization of the basalts, from a Pre-Salt well in Campos Basin. When compared to susceptibility values measured in laboratory for the same rock interval, our results show a significant match. This promising result shows the importance of our methodology in providing reliable information that can minimize uncertainties in forward modeling of magnetic data, which contributes to reduction of hydrocarbon exploration risks.

Abstract: Paulownia trees represent a convergence of ecological innovation and economic opportunity. Beyond their extraordinary growth rates, adaptability to diverse climates, and capacity to produce high-value timber, Paulownia farms offer a unique solution to pressing environmental and economic challenges. However, economic sustainability is the crucial factor for any farmer to decide the introduction of this species. Therefore, the current study aimed to evaluate the profitability of seven existing paulownia farms from Romania. The economic analysis has been carried out by adopting the discounted cash flow method. The results showed that the cultivation of Paulownia for both timber and woodchip production as a dual production system with an annual gross margin between 27-55% can present a viable alternative as compared to other agricultural crops. However, the profitability highly depends on the selection of plant species according to certain environmental conditions, agricultural practices applied but also on demand and market price variations.

Abstract: A self-driving lab (SDL) is a system that automates experiment design, data collection, and analysis using robotics and artificial intelligence (AI) technologies, and its significance has grown substantially in recent years. This study analyzes the overall research trends in SDL, examines changes in specific topics, visualizes the relational structure among authors to identify key contributors and extracts major themes from extensive texts to highlight essential research content. To achieve these objectives, trend analysis, network analysis, and topic modeling are conducted on 352 research papers collected from the Web of Science from 2004 to 2023. The results revealed three key findings. First, SDL research has surged since 2019, driven by the pandemic and advancements in AI technologies, reflecting heightened activity in this field. Second, several influential researchers have been identified as central figures in the network, playing pivotal roles in collaboration and information dissemination. Third, SDL research exhibits interdisciplinary convergence, encompassing areas such as material optimization, biological processes, and AI predictive algorithms. This study underscores the growing importance of SDL as a research tool across diverse academic disciplines and provides practical insights into sustainable future research directions and strategic approaches.

Abstract: Microplastics are ubiquitous in our environment and significantly threaten ecosystems and human health. This paper presents the design and fabrication of an innovative prototype sensor capable of detecting microplastics by employing multiple wavelengths. Our system combines mechanical, electronic, and optical components, providing an efficient and cost-effective solution for environmental monitoring.

Abstract:

The planting of fruit trees on sloping land can bring significant benefits to the local economy, but it also causes different degrees of soil and water erosion problem. In this study, we studied the difference of nutrient migration in the slope ditch runoff. In the 39 scouring tests, grass ditch could reduce the loss of carbon(C), nitrogen(N) and phosphorus(P) by intercepting runoff. There was a positive correlation between runoff and the loss rate of N and P. Flow affected the retention time of runoff in the ditch, and then changed dissolved organic carbon (DOC) loss rate in runoff. The concentration of N and P wouldn't affect N and P loss rate, but would affect N and P total loss amount and DOC loss rate in runoff. The addition of organic fertilizer would significantly increase the N loss rate in runoff, and the change rule of P and DOC loss rate was similar, the co-migration may occurs. To sum up, the importance of the four factors on the migration and loss of C, N and P in ditch runoff was as follows: organic fertilizer (100%) > fertilizer concentration (74.8%) > ditch type (12.6%) > initial flow (10%).

Abstract: We strongly encourage authors to use the following style of structuredAbstract: Polymers can be used as soil water-retention agents to slow down fertilizer nutrient release and improve soil properties. However, the mechanism of combined application of polymer and nitrogen fertilizer influencing soil biocommunity characteristics and nitrogen transformation is still unclear. In this study, the polymer PPM with water retention and slow-release functions was combined with nitrogen fertilizer (N100 treatment: 300 kg/hm2 of nitrogen fertilizer (traditional nitrogen application rate); PN100 treatment: PPM + 300 kg/hm2 of nitrogen fertilizer; PN80 treatment: PPM + 240 kg/hm2 of nitrogen fertilizer) to investigate soil biodiversity, enzyme activities, and metabolomics. The objective was to clarify the effects of combined application of polymer and nitrogen fertilizer on soil characteristics, biocommunity structure, and metabolites in drip-irrigated wheat fields in arid areas. The results showed that under the application of PPM, the contents of soil total nitrogen (TN), alkali hydrolyzable nitrogen (ANS), nitrate nitrogen, organic carbon (SOC), and microbial biomass nitrogen (MBN) increased with the decrease of nitrogen application rate, while soil bulk density, pH, and EC (electrical conductivity) showed an opposite change. The Chao 1 index of soil bacterial and nematode communities of the PN80 treatment increased by 30.63% and 10.68%, respectively, and the Shannon index increased by 2.72% and 2.64%, respectively, compared with those of N100 treatment, but there was no difference in the Pielo e index. The results of RDA (redundancy analysis) and co-occurrence network showed that combined application of PPM and nitrogen fertilizer improved soil physicochemical properties, and soil ammonium nitrogen, TN, SOC, and MBN contents were the key factors affecting the distribution of soil bacterial communities. In addition, in the short term, the addition of PPM affected the structure and composition of soil bacterial and nematode communities. Especially, the relative abundances of omnivorous (Aporcelaimellus) and bacterivorous (Prismatolaimus) nematodes increased significantly compared with those of N100 treatment, actively responding to changes in the soil environment. This regulated the soil microbial communities and metabolites, thereby promoting soil nitrogen transformation. This study will provide a scientific basis for nitrogen reduction, farmland environment protection, and agricultural production efficiency improvement in drip-irrigated wheat planting in arid regions.

Abstract:

This paper presents estimates of activity data, emission factors and emissions and removals for Malawi’s three REDD+ activities: deforestation, forest degradation, and enhancement. Calculations are based on data from Malawi’s National Forest Inventory (NFI) and plantation data for the reference period 2010 to 2020. Deforestation occurred at a rate of 11,565ha ± 1,067ha per year, representing a 0.66% annual change. Notably, deforestation was higher in non-protected areas (0.69%) than protected areas (0.66%). Forest degradation resulted in an annual loss of 14,192 ha, equivalent to 0.77% per annum, with 54% degradation occurring in protected areas, mainly within the dense canopy class (>60%). These protected areas had an area weighted carbon stock of 57.26 tC/ha while forests outside the protected areas had an average carbon stock of 45.03 tC/ha. The estimated annual emissions from deforestation and forest degradation were 1,008,600 and 543,511 tCO2e/yr, respectively. Annual removal due to forest enhancement averaged 61,070 tCO2e/yr. The mean net forest emissions for this reference period were 1,491,041 tCO2e/yr. Malawi has the potential to enhance its forests and mitigate its emissions through industrial plantations while also abating forest and biodiversity loss through avoided deforestation and forest degradation. By using Collect Earth, a free and open source platform, this analysis provides a model for emissions and removals assessments that are transparent, fully replicable, and cost-effective for governments.

Abstract: Soil moisture (SM) is intricately connected to various components of the Earth's system and plays a critical role in human survival and development. Although significant efforts have been made in soil moisture retrieval and validation, the monitoring and assessment of high-precision, high-resolution SM networks remain limited. This study focuses on a long-term SM network (QLB-NET) located in a high-altitude plateau with significant topographic variability. Utilizing more than 20 soil moisture-related indices derived from Landsat data, along with elevation and its derivatives, we estimate soil moisture at a 30-meter resolution with the aid of ensemble Learning Models. Soil moisture is treated as the dependent variable, while the other data serve as independent variables, integrated into a dataset which is subsequently divided into training, validation, and testing sets. Four ensemble Learning Models—Random Forest (RF), Extremely Randomized Trees (ERT), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were evaluated for their performance in soil moisture retrieval. The CatBoost model demonstrated the best performance, surpassing the other models across the training, validation, and test sets. In the test set, it achieved a correlation coefficient (R² = 0.83), a root mean square error (RMSE) of 0.052 m³/m³, a bias of 0.003 m³/m³, and a mean square error (MSE) of 0.003. We also used the four models to generate 30 m soil moisture maps for three different dates, providing more detailed insights into the spatial distribution of soil moisture. SHAP was employed to assess the contribution of different features to soil moisture predictions, revealing that elevation had the greatest impact. Finally, we assessed the overall heterogeneity of QLB-NET, using terrain complexity to represent local heterogeneity. Our findings indicate that the northern-central region exhibits significant local heterogeneity. Moreover, areas with higher heterogeneity also show greater uncertainty, making them a key source of model prediction error. The findings of this study contribute to more accurate retrieval of soil moisture, enhancing both new and existing methods.

Abstract:

This study examines historical water management practices as a means of addressing climate change, focusing on the Tyski Stream catchment in Upper Silesia, Poland a region marked by ex-tensive urbanization and industrial evolution. It investigates the effectiveness of historical hydro-technical systems, such as bead-like ponds from the 15th to 18th centuries, in enhancing water retention, reducing flood risks, and supporting ecosystem services. in a critical period, over 54 years (1827-1881), the number of reservoirs decreased from 142 to 31 (the area of ponds decreased from 161 to 32 ha). This was related to progressive industrialization, urban sprawl, and changes in legal frameworks, such as land ownership laws, leading to ecosystem degradation, loss of biodiversity, and altered hydrological processes. The research emphasizes the potential of reviving historical water management systems on natural processes to mitigate the impacts of climate change. By analyzing historical cartographic resources, the study assesses the feasibility of reconstructing lake systems in areas with similar topographic and hydrological features. It also stresses the necessity of community engagement and securing widespread social and political support to ensure public acceptance and the success of renaturation initiatives. The findings indicate that restoring these systems can offer diverse benefits, including improved water management, enhanced biodiversity, and greater urban resilience, while addressing the socio-political complexities of implementing large-scale environmental restoration projects.