Sustainable Environmental Management Research Articles

Spatiotemporal changes and drivers of ecological security based on an improved ecological footprint model: the case of Hubei Province, China

The increasing contradiction between resource supply and demand has brought ecological security (ES) to the forefront. Research on the changes of ES pattern and their driving factors is crucial for coordinating the regional ecological and economic development. Hubei Province in China exhibits uneven distribution of resources and a fragile ecological environment. In this study, an improved ecological footprint (EF) model was employed to analyze the changes in the ES pattern of Hubei Province. And the logarithmic mean Divisia index model was introduced to analyze the corresponding driving factors. The results showed that (1) from 2000 to 2020, the EF per capita (ef), the ecological carrying capacity per capita (ecc), and the ecological deficit (ED) per capita (ed) of Hubei Province increased by 47.11%, 2.71%, and 51.72%, respectively. In 2020, the cities in the province were in a state of ED, and the spatial distribution was ‘high in the central part of the province and low in the east and west’. (2) Each ef type showed an increasing trend. The increase in ef mostly came from the increase in fisheries ef, fossil energy ef and cropland ef, with a contribution of 76.70%. The increase in ecc is mainly due to the increase in freshwater ecc, with a contribution of 274.87%. (3) The ecological pressure in the province continued to rise and has been in a state of being completely unsafe. The efficiency of ecological resource use has increased, and the ecological structure tends to be rationalized, but the ecological-economic coordination has weakened. (4) The overall EF growth in the province is mostly driven by economic, population and footprint structure factors, while footprint intensity suppresses EF growth. Therefore, it is advised to boost investment in eco-friendly tech, foster green economy growth, and prioritize renewable energy development. This study can provide a reference basis for policy formulation on environmental sustainability and ecosystem management.

A Multi-Temporal Analysis on the Dynamics of the Impact of Land Use and Land Cover on NO2 and CO Emissions in Argentina for Sustainable Environmental Management

This study presents an analysis of NO2 and CO emissions in Argentina, utilizing remote sensing data. This research aims to determine the spatiotemporal distribution of NO2 and CO emissions from 2019 to 2021. It examines the influence of land use and cover on NO2 and CO emissions using various climatic, anthropic, and natural indicators. The year with the highest CO and NO2 concentration was 2020. NO2 exhibited the highest concentrations in built-up urban areas and croplands, notably impacting the capital city and the northern region of Buenos Aires province. Also, CO concentration was influenced by anthropic variable distances to national route, mining extraction, power plants, airports, and urban index (UI). They were also influenced by climatic and natural variables (Palmer drought index, vapor pressure, maximum environment temperature, wind speed, DEM, humidity, and normalized difference vegetation index (NDVI)) for the different uses and land covers. NO2 concentrations were influenced by anthropic (distance to airports, service stations, open dumpsites, power plants, and factories), climatic, and natural variables (Palmer drought index, vapor pressure, wind speed, and DEM) for the different uses and land cover. This research supports sustainable environmental management by guiding the development of effective emission mitigation strategies for improved community health and well-being.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Developing stable, simplified, functional consortia from Brachypodium rhizosphere for microbial application in sustainable agriculture

The rhizosphere microbiome plays a crucial role in supporting plant productivity and ecosystem functioning by regulating nutrient cycling, soil integrity, and carbon storage. However, deciphering the intricate interplay between microbial relationships within the rhizosphere is challenging due to the overwhelming taxonomic and functional diversity. Here we present our systematic design framework built on microbial colocalization and microbial interaction, toward successful assembly of multiple rhizosphere-derived Reduced Complexity Consortia (RCC). We enriched co-localized microbes from Brachypodium roots grown in field soil with carbon substrates mimicking Brachypodium root exudates, generating 768 enrichments. By transferring the enrichments every 3 or 7 days for 10 generations, we developed both fast and slow-growing reduced complexity microbial communities. Most carbon substrates led to highly stable RCC just after a few transfers. 16S rRNA gene amplicon analysis revealed distinct community compositions based on inoculum and carbon source, with complex carbon enriching slow growing yet functionally important soil taxa like Acidobacteria and Verrucomicrobia. Network analysis showed that microbial consortia, whether differentiated by growth rate (fast vs. slow) or by succession (across generations), had significantly different network centralities. Besides, the keystone taxa identified within these networks belong to genera with plant growth-promoting traits, underscoring their critical function in shaping rhizospheric microbiome networks. Furthermore, tested consortia demonstrated high stability and reproducibility, assuring successful revival from glycerol stocks for long-term viability and use. Our study represents a significant step toward developing a framework for assembling rhizosphere consortia based on microbial colocalization and interaction, with future implications for sustainable agriculture and environmental management.

Optimizing scope 3 emissions in the automotive manufacturing industry: a multidisciplinary approach

The acceleration of global climate change poses enormous challenges to the automotive manufacturing industry, a key sector in reducing greenhouse gas (GHG) emissions. Particularly, Scope 3 emissions, encompassing indirect emissions, often constitute the largest carbon footprint component in this sector, yet their quantification and management remain challenging. This paper proposes an interdisciplinary approach that integrates cloud computing, text analysis, and machine learning, and systematically details its implementation, key benefits, and potential applications. Through this methodology, the paper seeks to provide the automotive industry with innovative and actionable insights to tackle the complexities of Scope 3 emissions. It focuses on enhancing the accuracy of emission quantification and optimizing supply chains, aiming to reduce the overall carbon footprint. Moreover, this paper outlines the future challenges and directions for these technologies and methodologies in sustainable development and environmental management. This work underlines the critical role of interdisciplinary approaches in resolving environmental challenges, setting the stage for the automotive industry to forge paths towards a greener future.Graphical

Environmental Control and Management Continuation Through the Ruling of the State Administrative Court

Changes in environmental law provisions that have the potential to cause inconsistencies in these decisions must be harmonized with the legal system. Without consistency in decisions regarding environmental public administration disputes, it will be difficult for the government to make decisions and guideline for determining policies related to environmental protection and management. The aim of this research is to determine the consistency of environmental administration dispute decisions in state administrative courts. The writing method uses a normative juridical approach with statutory regulations, the legal sources used are primary and secondary legal sources relating to state administrative law and those relating to environmental regulations, apart from that the author uses a case study approach. The results of the research show that the implementation of regulations related to state administrative decisions in the environmental sector is still inconsistent, with the government placing more emphasis on remedial (retroactive) efforts in environmental problems which are manifested in the form of government intervention to restore environmental conditions after pollution and/or damage occurs. environment. Considering that the majority of environmental law enforcement is in the field of government administration, inconsistencies in environmental state administration decisions must be resolved immediately to realize good environmental protection and management. In fact, the decision of the state administrative court judge is not only intended to resolve a dispute but will also serve as a guideline for government agencies/officials in realizing sustainable environmental protection and management.

Harnessing the energy potential of rosehip wastes towards sustainable energy supply

Using renewable energy technologies comes to the forefront for sustainable energy, economy, and environmental management. In regions with high agricultural production, it is becoming increasingly important to contribute to sustainable energy production by using agricultural wastes as biomass. For this purpose, the evaluation of the energy potential and catalytic pyrolysis of rosehip seed (RS) and rosehip pomace (RP), which are considered the main wastes of rosehip, were investigated to obtain improved bio-oil. While C10+ hydrocarbons of alkane and alkene origin were mostly found in the composition of bio-oil, the highest ratio belongs to 50CL50RP650. Especially when only RS and RP were present, both the temperature of 650°C and the presence of catalyst were more effective in obtaining aliphatics. In the use of the blend, one of these parameters was sufficient and the volatile matter increased and fixed carbon decreased, especially as the RP ratio (70%) in the blend increased. The heating value (18.5–19 MJ/kg) and volatile matter (63–66%) of the wastes were high, while ash (0%) and moisture (2%-3%) contents were low, which is important in terms of energy potential. In the combustion profiles, the Tmax was generally higher for the blends.

Analysis of Odd-Even Policies in Overcoming Congestion and Air Pollution, Studies in Jakarta and Beijing

This study examines the effectiveness of the Odd-Even policy in Jakarta from 2017 to 2022, comparing it with similar measures in Beijing from 2013 to 2017. Both megacities face severe traffic congestion due to rapid urbanization, population growth, and increased vehicle ownership. Jakarta's congestion is exacerbated by population density, inadequate public transportation, and high private vehicle ownership, leading to prolonged travel times, air pollution, and economic losses. Similarly, Beijing struggles with traffic congestion due to its massive population and rapid economic development, resulting in gridlock, air pollution, and decreased quality of life. The research aims to understand the Odd-Even policy's impact on traffic flow, emissions, and public perception in both cities, using qualitative methods and diverse data sources. It also identifies obstacles to policy effectiveness, such as enforcement, infrastructure, and public compliance, offering recommendations for policymakers and urban planners to improve sustainable transportation and environmental management. Overall, the study contributes insights into congestion mitigation in megacities, aiding policymakers in enhancing urban life quality.

Magnetic Field Effect on Photocatalytic Dye Degradation: A Review

AbstractIn the present day, photocatalysis has become the primary and most promising technology to generate renewable energy and degrade environmental pollutants. Remarkable efforts have been made to improve photocatalytic efficiency. Among these, introducing an external magnetic field is considered a promising strategy to boost photocatalytic performance. This review explores the dynamic realm of improving photocatalytic efficiency, with a particular emphasis on the strategic integration of external magnetic fields. Examining recent breakthroughs, we reviewed the influence of magnetic fields on diverse catalyst materials, including both magnetic and nonmagnetic variants, 2D materials, and semiconductors. Comprehensive coverage is provided on the mechanisms governing photocatalytic dye degradation, materials utilized for catalysis, and the profound impact of magnetic fields on enhancing reaction kinetics. This review also focuses on recent advances in the application and effect of magnetic fields in the magnetic and nonmagnetic catalysts towards dye degradation. This review provides a forward‐looking view, highlighting the potential of magnetic field‐enhanced photocatalysis to play a central role in sustainable energy and environmental management.

Matrix-free laser desorption/ionization mass spectrometry imaging for rapid evaluation of wood biomass conversion.

This study overcomes traditional biomass analysis limitations by introducing a pioneering matrix-free laser desorption/ionization (LDI) approach in mass spectrometry imaging (MSI) for efficient lignin evaluation in wood. The innovative acetic acid-peracetic acid (APA) treatment significantly enhances lignin detection, enabling high-throughput, on-site analysis. Wood slices, softwood from a conifer tree (Japanese cypress) and hardwood from a broadleaf tree (Japanese beech), were analyzed using MSI with a Fourier transform ion cyclotron resonance mass spectrometer. The developed APA treatment demonstrated effectiveness for MSI analysis of biomass. Our imaging technique successfully distinguishes between earlywood and latewood and enables the distinct visualization of lignin in these and other wood tissues, such as the radial parenchyma. This approach reveals significant contrasts in MSI. It has identified intense ions from β-O-4-type lignin, specifically in the radial parenchyma of hardwood, highlighting the method's precision and utility in wood tissue analysis. The benefits of matrix-free LDI include reduced peak overlap, consistent sample quality, preservation of natural sample properties, enhanced analytical accuracy, and reduced operational costs. This innovative approach is poised to become a standard method for rapid and precise biomass evaluation and has important applications in environmental research and sustainable resource management and is crucial for the effective management of diverse biomass, paving the way towards a sustainable, circular society.

UAV and Deep Learning: Detection of selected riparian species along the Ganga River

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.

A social-ecological approach for identifying and mapping ecosystem service trade-offs and conservation priorities in peri-urban areas.

Considering both ecological and social dimensions in the assessment of ecosystem services (ESs) can facilitate acceptable and inclusive management strategies, especially in peri-urban areas characterized by intricate human-ecosystem interactions. A limited body of research, however, has mapped the plural values of ESs and their different types of trade-offs in such areas. This research aimed to execute an interdisciplinary analysis of the biophysical and social values of ESs in peri-urban Shanghai, China, through a social-ecological approach that integrates spatial biophysical assessment with participatory mapping. Trade-off analysis in both ES types and ES valuations were then conducted, and multicriteria decision-making was applied for conservation. Our results reveal that trade-off intensities were lower within the social values compared to the biophysical values. Within both value dimensions, relatively stronger trade-offs were found between food production and other ESs. Areas with both high biophysical and social values were infrequently observed across ESs. Based on the characteristics of diverse values, our study identified priority conservation areas and provided management implications. We argue that adopting the integrated social-ecological perspective in sustainable environmental management contributes to the realization of harmonious coexistence between people and nature in peri-urban areas.

Unraveling the relationship between coastal landscapes and sentiments: An integrated approach based on social media data and interpretable machine learning methods

AbstractCoastal landscapes exert a significant impact on the human sentimental perceptions and physical and mental well‐being of people. However, little is known about explicitly linking between the landscape characteristics and people's sentimental preferences expressed in social media data. The main objective of this study was to explore the nonlinear and interaction effects of key factors that influenced sentiments in the coastal areas of Hong Kong, considering both subjective landscape preferences and objective landscape patterns. We quantified users' sentiment polarity based on the crowdsourcing textual data of Flickr. To study users' subjective landscape preferences, we computed various visual landscape objects' proportion in images. Meanwhile, eight user clusters and nine image clusters were detected by the identified visual object labels. We quantified objective landscape patterns considering the land use pattens and the availability of public service facilities. Finally, we utilized an interpretable classification model to analyze the factors that may affect sentiments and their interplay interactions. We found that ecotourism‐related clusters exhibited the most positive sentiment. The proportion of floor and sky pixels in images exhibits the highest global relative importance when predicting sentiments. This study extends a new insight on the relationship between landscape characteristics and sentiments from both subjective and objective perspectives based on social media data and interpretable machine learning methods. This research may help decision‐makers in designing landscapes that aptly satisfy to the needs of the public and promote sustainable management of the coastal environment.

Optimizing Enhanced Heavy Metal Detoxification by Novel Hybrid Fungal Hyphae-Nano-Biocomposite Functionalized with Graphene Oxide: Unravelling Process Parameters & Adsorption Modelling

Heavy metal pollution poses significant environmental and health risks, demanding efficient novel remediation strategies. This paper presents a fungal hyphae-based nano-biocomposite integrated with graphene oxide (FHGO) for the efficient removal of heavy metals from aqueous solutions. The successful formation of nanoparticles was investigated by morphological characterization using Fourier Transform Infrared Spectroscopy (FTIR), Scanning electron microscopy (SEM) & X-ray diffraction (XRD). Through comprehensive exploration of process parameters, including pH, initial concentration, and contact time, the nano-biocomposite demonstrated exceptional removal efficiencies exceeding 90% for both heavy metals i.e. chromium (Cr6+) & nickel (Ni2+). The optimum pH conditions for Cr (VI) removal was 4,whereas it was 9 for Ni (II) removal. The equilibrium data were well-fitted to Langmuir isotherm models, suggesting monolayer adsorption onto a homogeneous surface with adsorption capacities of 38.89mg/g & 42.88mg/g for Ni (II) & Cr (VI), respectively, with value of R2=0.997. Additionally, biocompatibility assessments via (3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide) MTT assays on Henrietta Lacks (HeLa) cell line cultures revealed a low IC50 value of FHGO nano-biocomposite, affirming the efficiency of the composite for biomedical applications. Furthermore, seed germination assays indicated comparatively minimal hindrance to plant growth in the treated sample, highlighting the composite's potential for environmental remediation without compromising plant health. These findings underscore the promising potential of the fungal hyphae-based nano-biocomposite with GO as an effective and environmentally safe adsorbent for heavy metal removal, with implications for sustainable environmental management and public health protection.

Highly efficient Cu-Fe containing prussian blue analogs (PBAs) for excellent electrocatalytic activity towards overall water splitting

Hydrogen generation by water electrolysis is considered a clean, green, and renewable approach for sustainable energy demands and environmental management. The efficient use of earth-abundant transition metal catalysts is cost-effective and limits the usage of noble metal catalysts like IrO2, RuO2, and Pt/C. The PBAs (Prussian Blue Analogs) are a class of transition metal hexacyanoferrates that can be tailored for specific electrochemical reactions. Both iron and copper are well-proven transition metals for catalytic activities owing to their aptitude to achieve much-pronounced reactant transformations under minor circumstances, as well as their environmental friendliness, cyclability, and long-term durability at 10 mA cm−2 in 1.0 M KOH, the greenly synthesized Fe8Cu2CN (PBAs) on NF (Nickel foam) displays a low overpotential of 150 mV (118 mV dec−1) for HER (Hydrogen Evolution Reaction) and 290 mV (112 mV dec−1) for OER (Oxygen Evolution Reaction). Fe8Cu2CN displays ultra-stable behavior (150 h) with a slight current loss of 4.2 % for OER and 3.4 % for HER, respectively. The interaction of Fe, Cu, and CN (cyanide) enhances the rate of reaction kinetics. The designing of a water electrolyzer that delivers 10 mA cm−2 at 1.60 V is made possible with the help of the improved bifunctional catalyst Fe8Cu2CN/NF. The Fe8Cu2CN/NF//Fe8Cu2CN/NF exhibits long-last durability (over 180 h) with a negligible current reduction of 5.4 %. The improved effectiveness of the synthesized catalyst is demonstrated by the solar water splitting at 1.66 V. These findings suggest that Fe8Cu2CN/NF//Fe8Cu2CN/NF can be used to produce large quantities of H2 at very affordable prices.

Potential of Plant Based Metallophytes for Phytoremediation in Agriculture

Metallophytes are the unique group of plants that have evolved to thrive in metal rich environments and have drawn plenty of attention. Remediating heavy metal contaminated places with plants is an effective choice due to phytoremediation, an environmentally friendly method that uses plants to mitigate the pollution. Metallophytes are ideal options for phytoremediation applications due to their inherent traits such as hyper-accumulation, efficient metal absorption and tolerance mechanisms impacting both the plant and soil. These plants absorb and translocate heavy metals, detoxifying the soil while accumulating them in tissues. This reduces metal toxicity in soil and holds potential for resources recovery. The role of metallophytes in phytoremediation is analysed in this review with particular focus given to their ways of metal absorption, translocation and detoxification. Metallophytes have high metal tolerance and accumulation capacities due to their unique physiological and biochemical adaptations including enhanced metal sequestration in vacuoles, metal chelation by phytochelatins and activation of anti-oxidant defence systems. This review also highlights the significance of metallophytes in enhancing the soil health, reducing metal bioavailability, and promoting the ecological sustainability as well as their potential for restoring contaminated ecosystems. Utilizing the unique capabilities of metallophytes obtained from plants possesses enormous possibilities to minimise the negative effects of heavy metal pollution, protect ecosystems, and promote sustainable development for future generations. Eventually, it outlines future research approaches that aim to enhance metallophytes based phytoremediation strategies, widen their implementation and include them in holistic approaches for environmental restoration and sustainable land management.

Detection and Screening of Organic Contaminants in A Riverine System of Georgia Using Non-Targeted Analysis

Numerous organic chemicals exist within aquatic environments, yet effectively screening and prioritizing them is a huge challenge. This study provides a comprehensive investigation into the ecological dynamics of the North Oconee River within Athens-Clarke County, Georgia, with a specific focus on the distribution of 33 identified compounds, including a prominent pesticide. The research, conducted in the riverine ecosystems proximal to the Firefly trail, employs advanced analytical techniques to elucidate potential contamination sources arising from agricultural and urban runoff. Intriguingly, the study reveals North Oconee River near the Firefly Trail as a notable site for heightened pesticide contamination, warranting a meticulous exploration of its origins. Furthermore, the investigation unveils the intricate microbial degradation processes of malathion within the North Oconee River, elucidating the pivotal role played by microbial activity in river water. The detection of degradant byproducts prompts the considerations of bioavailability and toxicity, associating potential implications for the river’s overall ecological health. Ongoing research endeavors to precisely quantify environmental risks and unravel indigenous microbial degradation pathways, presenting pivotal contributions to the scientific community’s understanding of complex riverine ecosystems. This research serves as a foundational piece in informing sustainable environmental management practices and emphasizes the urgency of comprehensive stewardship in safeguarding aquatic ecosystems.

Application of isotope as a nuclear technique in groundwater pollution assessment due to human activities

Clean and sustainable groundwater is an important asset for human life and ecosystems. However, the threat of groundwater pollution is increasing as a result of human activities such as industrial, agricultural, and municipal. The objective of this review article is to examine how isotope techniques, as one of the nuclear techniques serve as an effective tool in studying, detecting, and understanding the issue of groundwater pollution stemming from anthropogenic activity on the surface. This article covers the description and discussion of the types of isotopes used in the detection of groundwater pollution and factors that influence the variation in the composition of isotopes in groundwater based on the findings of previous studies. In addition, this article also comments on the study of groundwater pollution using isotopes in the context of groundwater pollution assessment in Malaysia. This article serves as a guide or reference to researchers, environmental professionals, and water resources managers who want to understand and implement nuclear techniques in the monitoring and assessment of groundwater pollution. This article also outlines a holistic view on the resolution of groundwater pollution issues through the application of nuclear techniques and isotopes as a significant tool in ensuring sustainable environmental management.

WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach

Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.

Harnessing Ash for Sustainable CO2 Absorption: Current Strategies and Future Prospects.

This review explores the potential of using different types of ash, namely fly ash, biomass ash, and coal ash etc., as mediums for CO2 capture and sequestration. The diverse origins of these ash types-municipal waste, organic biomass, and coal combustion-impart unique physicochemical properties that influence their suitability and efficiency in CO2 absorption. This review first discusses the environmental and economic implications of using ash wastes, emphasizing the reduction in landfill usage and the transformation of waste into value-added products. Then the chemical/physical treatments of ash wastes and their inherent capabilities in binding or reacting with CO2 are introduced, along with current methodologies utilize these ashes for CO2 sequestration, including mineral carbonation and direct air capture techniques. The application of using ash wastes for CO2 capture are highlighted, followed by the discussion regarding challenges associated with ash-based CO2 absorption approach. Finally, the article projects into the future, proposing innovative approaches and technological advancements needed to enhance the efficacy of ash in combating the increasing CO2 levels. By providing a comprehensive analysis of current strategies and envisioning future prospects, this review aims to contribute to the field of sustainable CO2 absorption and environmental management.