Environmental Protection Research Articles

Condition Monitoring in Marine Oil Separation Systems Using Wavelet Packet Transform and Genetic Technique

Condition Monitoring is key to predictive maintenance and especially in the operational efficiency of the Marine Oil Separation System. These systems are crucial for environmental protection and compliance with international maritime regulations. Therefore, it is necessary to design a technique capable of analyzing the signals from sensors and estimating the remaining useful life in order to avoid breakage or unnecessary replacement. This work presents an intelligent method with signal processing based on Wavelet Packets Transform that provides energy data from vibration measurements as characteristic parameters. These values can be related to its RUL, and they are used as inputs for the training process. In particular, a Genetic Neuro-Fuzzy system is proposed as an intelligent classification technique. Once the training process is completed, it can be concluded that a good classifier has been built, since it relates the energy state of the oil separation system with its remaining useful life, and therefore, the necessary information for efficient predictive maintenance is achieved. Furthermore, a mechanism to obtain the final set of fuzzy rules has been developed, showing the correspondence between these fuzzy rules and the neural network structure.

The Implementation of the Food Quality Protection Act (FQPA) in Increasing the Safety Standards for New Pesticides Used on Foods

This paper examines the Food Quality Protection Act (FQPA) and its impact on enhancing safety standards for pesticides used on food in the United States. Initially, the study explores the Delaney Clause—a strict standard prohibiting carcinogenic food additives—which FQPA replaced with a more flexible "reasonable certainty of no harm" criterion. This shift allows the Environmental Protection Agency (EPA) to assess pesticide risk with consideration of potential benefits, even if a substance is carcinogenic at high doses. The paper also critiques the EPA's implementation challenges, including reliance on quantitative risk assessment (QRA) and cumulative risk assessment, which quantify risks of multiple chemical exposures but face informational and regulatory limitations. Recommendations focus on refining judicial review and integrating cumulative risk assessment to strengthen the FQPA’s capacity to safeguard public health, especially for vulnerable populations such as children. This study underscores the importance of regulatory clarity and rigorous assessment methods in pesticide management.

Environmental public health research at the U.S. Environmental Protection Agency: A blueprint for exposure science in a connected world.

Exposure science plays an essential role in the U.S. Environmental Protection Agency's (U.S. EPA) mission to protect human health and the environment. The U.S. EPA's Center for Public Health and Environmental Assessment (CPHEA) within the Office of Research and Development (ORD) provides the exposure science needed to characterize the multifaceted relationships between people and their surroundings in support of national, regional, local and individual-level actions. Furthermore, exposure science research must position its enterprise to tackle the most pressing public health challenges in an ever-changing environment. These challenges include understanding and confronting complex human disease etiologies, disparities in the social environment, and system-level changes in the physical environment. Solutions will sustainably balance and optimize the health of people, animals, and ecosystems. Our objectives for this paper are to review the role of CPHEA exposure science research in various recent decision-making contexts, to present current challenges facing U.S. EPA and the larger exposure science field, and to provide illustrative case examples where CPHEA exposure science is demonstrating the latest methodologies at the intersection of these two motivations. This blueprint provides a foundation for applying exposomic tools and approaches to holistically understand real-world exposures so optimal environmental public health protective actions can be realized within the broader context of a One Health framework. IMPACT STATEMENT: The U.S. EPA's Center for Public Health and Environmental Assessment exposure research priorities reside at the intersection of environmental decision contexts and broad public health challenges. The blueprint provides a foundation for advancing the tools and approaches to holistically understand real-world exposures so optimal environmental protection actions can be realized. A One Health lens can help shape exposure research for maximum impact to support solutions that are transdisciplinary and must engage multiple sectors.

Decarbonization Strategies in the U.S. Maritime Industry with a Focus on Overcoming Regulatory and Operational Challenges in Implementing Zero-Emission Vessel Technologies.

The U.S. maritime industry plays a significant role in global trade and is a substantial contributor to carbon emissions, with the sector facing increasing pressure to decarbonize in line with global climate goals. This paper discusses the challenges and strategies for decarbonizing the U.S. maritime industry, laying emphasis on the adoption of zero-emission vessel (ZEV) technologies, such as battery-electric ships, hydrogen fuel cells, wind-assisted propulsion, and alternative fuels like bio-LNG and ammonia. Despite the availability of these technologies, the transition to ZEVs faces several barriers, including high capital costs, regulatory inconsistencies, and insufficient infrastructure. Existing regulatory frameworks, including the International Maritime Organization’s (IMO) emissions targets, the U.S. Clean Air Act, and the U.S. Coast Guard requirements, offer some guidance but are often fragmented and insufficiently aligned to foster widespread adoption of decarbonization technologies. Upcoming mandates, such as the IMO's 2050 target, further brings to view the urgency of this transition. However, gaps in regulations and the lack of incentives hinder technological innovation and fleet modernization. The paper also discusses the need for enhanced governmental involvement, with organizations like the Environmental Protection Agency (EPA), the Maritime Administration (MARAD), and the U.S. Coast Guard playing a critical role in streamlining policies and supporting the adoption of zero-emission technologies. It also emphasizes the importance of addressing infrastructure gaps related to fueling, charging, and port readiness for ZEVs. Furthermore, it highlights operational challenges such as range, performance, and energy density concerns that must be overcome for ZEVs to become commercially viable. The paper advocates for stronger policy frameworks, including subsidies, tax incentives, carbon pricing, and investment in research and development, to drive the transition. Public-private partnerships and industry collaboration are essential to overcoming financial barriers and creating a sustainable, decarbonized maritime sector. By implementing these strategies, the U.S. maritime industry can significantly reduce its emissions, contribute to global climate goals, and set a precedent for sustainable maritime practices worldwide.

Evaluation of Logistics Development Under the Visual Field of Low-Carbon Environmental Protection Based on Hierarchical Methods

The development of the modern logistics industry in China is relatively backward, and the logistics industry has always shown characteristics of high emissions, high energy consumption, and high pollution. The low-carbon transformation and development of the logistics industry has become a prominent issue. At present, research on low-carbon logistics mainly focuses on its definition and characteristics. The research on its influencing factors is mostly qualitative analysis, and there is relatively little research on performance evaluation. This article establishes a performance evaluation index system from the perspective of low-carbon environmental protection, including information technology level, warehousing, transportation, packaging, etc., and constructs a comprehensive evaluation model that combines an analytic hierarchy process with data envelopment analysis for low-carbon logistics performance evaluation. By reviewing the collected data and taking five logistics enterprises in China as examples, the feasibility of low-carbon logistics performance evaluation indicators and models was verified.

Matching Is Best: Enhancing Effects of Future Orientation and Construal Level on Green Consumption

The 2024 Report on the Work of the Chinese Government promotes trade in consumer goods and green consumption. Therefore, better understanding is needed of consumer attitudes and behaviors toward environmental protection and sustainable development. The relationships among future orientation, construal level, and green consumption were explored using a delayed-effect design to conduct two surveys at one-month intervals with 160 participants (100 women and 60 men). The results showed that (1) future orientation positively predicted green consumption intention and green consumption behavior, and (2) the construal level significantly moderated the influence of future orientation on green consumption. Specifically, a high construal level and high future orientation predicted the greatest green consumption intention, green consumption behavior, and green consumption decisions, while at a high construal level, future orientation positively predicted green consumption intention and behavior, with a greater tendency to purchase environmentally friendly rather than hedonic products. At a low construal level, future orientation had no significant effect on green consumption intention, behavior, and intention to purchase environmentally friendly products. This study is important for promoting public awareness of the importance of green consumption and encouraging green consumption behaviors to achieve the goals of sustainable development and protect the environment and social well-being.

Membangun Kesadaran Lingkungan Melalui Pendidikan Kewarganegaraan di Sekolah Dasar

Citizenship education plays an essential role in developing children's character, in line with moral, character and character education. This program aims to produce good citizens based on the values ??of Pancasila, which is the culture of the Indonesian nation. Building environmental awareness through citizenship education in elementary schools is very important, because it helps students understand their responsibilities towards the environment. The five principles of Pancasila are basic values ??that have developed into instrumental and rational values, creating a generation that cares about others and preserving nature. This research method uses a qualitative approach with literature study, collecting data from various relevant literature such as books, journals and official documents related to education citizenship in Indonesia. The data collection process began with a literature review through academic databases such as Google Scholar and JSTOR, followed by literature selection based on inclusion and exclusion criteria. The data is then categorized according to themes to produce an in-depth understanding of this topic. Civic education lessons have an important role in building environmental awareness by introducing the concepts of citizens' rights and obligations and environmental protection. Although there are challenges in integrating environmental education, there are opportunities to develop environmentally caring characters in students. Therefore, it is important to instill environmental awareness from an early age in everyday life, both at school and outside school.

Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets.

DEVELOPMENT AND OPTIMIZATION OF HPLC METHODS FOR THE CHIRAL SEPARATION OF ARYLPYRAZOLE, CHLOROACETANILIDE AND ORGANOCHLORINE PESTICIDES: CASE STUDIES OF BENZOBICYCLON, ACETOCHLOR AND CHLORDANE.

This work is centered on the development and optimization of High-Performance Liquid Chromatographic methods for the separation of chiral pesticides, particularly benzobicyclon, chlordane and acetochlor employing Agilent Technologies Infinity II chiral HPLC system equipped with a UV – 2000 detector. The stereoisomers were successively separated using a standard analytical column of dimensions 250 mm X 4.6 mm. The Cis– stereoisomers of chlordane exhibited partial baseline separation while the Trans – enantiomers were fully separated. Benzobicyclon and acetochlor enantiomers attained full baseline separation under optimized conditions. Chlordane and benzobicyclon were analyzed using normal phase chromatography [NP]c with a mobile phase of 90% Normal hexane and 10% Isopropanol, while acetochlor was analyzed by reverse phase chromatography [RP] c using 70% methanol and 30% water. Benzobicyclon was later spiked into soil sample and subsequently extracted using Environmental Protection Agency (EPA) methods and the extracts were further analyzed with a shorter analytical column of dimensions 75 mm X 4.6 mm. The HPLC separation revealed that the longer column provided more efficient separation for the benzobicyclon stereoisomers than the shorter column as reflected by high separation factors (?). The high recovery rate of the stereoisomers of benzobicyclon and the internal standard from the soil extracts confirmed the effectiveness of the method of extraction and the HPLC system employed.

Research on Microgrid Optimal Scheduling Based on an Improved Honey Badger Algorithm

As global energy demands continue to grow and environmental protection pressures increase, microgrids have garnered widespread attention due to their ability to effectively integrate distributed energy sources, improve energy utilization efficiency, and enhance grid stability. Due to the complexity of internal structure, variety of energy sources, and uncertainty of load demand, the optimal scheduling problem of microgrids becomes extremely complicated. Traditional optimization methods often perform poorly in complex and dynamic microgrid environments, and it is assumed that the complexity is low or that more simplification is needed, which leads to poor convergence and local optimality when dealing with uncertainty and nonlinear problems, making intelligent optimization algorithms a crucial solution to this problem. To address the shortcomings of the traditional honey badger algorithm, such as the slow convergence speed and a tendency to fall into local optima in complex microgrid optimal scheduling problems, this paper proposes a multi-strategy improved honey badger algorithm. During the population initialization phase, a combined opposition-based learning strategy is introduced to enhance the algorithm’s exploration and exploitation capabilities. Additionally, the introduction of variable spiral factors and a linearly decreasing strategy for parameters improves the overall efficiency of the algorithm and reduces the risk of local optima. To further enhance population diversity, a hunger search strategy is employed, providing stronger adaptability and global search capabilities in varying environments. The improved honey badger algorithm is then applied to solve the multi-objective optimal scheduling problem in grid-connected microgrid modes. The simulation results indicate that the improved honey badger algorithm effectively enhances the economic and environmental benefits of microgrid operations, improving system operational stability.

Redefining human–nature relations: decolonial perspectives on Inter-American Court environmental jurisprudence

ABSTRACT Debates surrounding environmental protection and its intersection with human rights have gained significant traction in the past decade, coinciding with the advancement of decolonial thought across political, academic and social spheres. These discussions and agendas have become intertwined as humanity faces unprecedented threats to essential global ecosystems while indigenous peoples and traditional black communities, often the primary stewards of the environment, seek emancipation from historical violence. This dynamic struggle between resilience and violence, along with the link between nature and human rights, is also evident within the Inter-American Court of Human Rights. Drawing upon decolonial insights and employing a dialectical method, our analysis aims to scrutinize the Court’s jurisprudence to understand its approach concerning the connection between human and environmental rights.

Dilodia sapinea as the pathogen causing trunk canker on Pinus bungeana trees in China

Pinus bungeana belonging to the genus Pinus is a pine tree species endemic to China. It plays vital roles in afforestation, forest regeneration, landscaping, forest ecosystems, and environmental protection. However, P. bungeana has been listed as a rare and endangered species in recent years due to population fragmentation. The occurrence of lethal diseases on the trees has made the situation even worse. This study aimed to identify the pathogen causing the trunk canker on P. bungeana trees in Qingdao, China. The morphological identification of the pathogen relied primarily on the morphology of conidia. Molecular identification was performed using the combined sequences of ITS, Tef and Tub2 genes. The eight isolates were all identified as Diplodia sapinea. The pathogenicity of the eight isolates was examined both in vitro and in vivo. The results showed that all the fungal isolates caused severe canker symptoms on the inoculated P. bungeana branches. Moreover, D. sapinea was re-isolated from the inoculated branches. This study is novel in reporting D. sapinea causing trunk canker on P. bungeana trees in China.

Construction and optimization of ecological corridors in coastal cities based on the perspective of "structure-function".

The contradiction between the demand of urban economic development and ecological environmental protection is becoming more and more prominent, and the reasonable construction of ecological corridors is of great significance for the protection of urban ecosystems. Taking Changle District of Fuzhou coastal city as an example, this study proposes an ecological source identification method from the perspective of "structure-function" by combining Morphological Spatial Pattern Analysis (MSPA) and Remote Sensing Ecological Index (RSEI) methods, and constructs and optimizes ecological corridors by means of the model of Minimum Cumulative Resistance (MCR) and Circuit Theory. The results show that: (1) a total of 20 ecological sources were extracted based on MSPA-RSEI; (2) a total of 31 ecological corridors were extracted based on Linkage Mapper, including 8 Level 1 corridors, 13 Level 2 corridors, and 10 Level 3 corridors; (3) the Pinch Point and Barrier Mapper software was utilized to identify 6.01 km2 and 2.59 km2, respectively, as Level 1 "pinch points" and barrier points within the study area. The land use types of these areas were as follows: The majority of "pinch points" were forested (60.72%), while the majority of barrier points were composed of construction land (55.27%), bare land (17.27%), and cultivated land (13.90%). (4) the optimal width of the corridor was determined by using the buffer zone method and gradient analysis: 30m for the Level 1 corridor, 60m for the Level 2 and Level 3 corridors, and the average current density before and after the construction of the eco-corridor was increased from 0.1881 to 0.4992. The results of the study can provide a reference for the decision-making of the ecological construction of the Changle District and the coastal urban areas of eastern China.

Seawater Desalination System Driven by Sustainable Energy: A Comprehensive Review

Seawater desalination is one of the most widely used technologies for freshwater production; however, its high energy consumption remains a pressing global challenge. Both the development and utilization of sustainable energy sources are anticipated to mitigate the energy shortages associated with seawater desalination while also effectively addressing the environmental issues linked to fossil fuel usage. This study provides a comprehensive overview of the classification and evolution of traditional desalination technologies, emphasizing the advancements, progress, and challenges associated with integrating various sustainable energy sources into the desalination process. Then, the cost, efficiency, and energy consumption of desalination systems driven by sustainable energy are discussed, and it is found that even the most widely used reverse osmosis (RO) technology driven by fossil fuels has CO2 emissions of 0.3–1.7 kgCO2/m3 and the lowest cost of desalinated water as high as 0.01 USD/m3, suggesting the necessity and urgency of applying sustainable energy. A comparison of different seawater desalination systems driven by different sustainable energy sources is also carried out. The results reveal that although the seawater desalination system driven by sustainable energy has a lower efficiency and a higher cost than the traditional system, it has more potential from the perspective of environmental protection and sustainable development. Furthermore, the efficiency and cost of desalination technology driven by a single sustainable energy source is lower than that driven by multi-sustainable energy sources, while the efficiency of desalination systems driven by multi-sustainable energy is lower than that driven by hybrid energy, and its cost is higher than that of desalination systems driven by hybrid energy. Considering factors such as cost, efficiency, consumption, economic scale, and environmental impact, the integration of various seawater desalination technologies and various energy sources is still the most effective strategy to solve water shortage, the energy crisis, and environmental pollution at present and in the future.

Insights into relationship of environmental inequalities and multimorbidity: a population-based study.

Substantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators. This ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency. The overall prevalence of multimorbidity was 33.4% [32.6%-34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02-1.03; OR = 1.01, 95% CI 1.00-1.02 and OR = 1.03, 95% CI 1.02-1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA. There is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships.

Thermal Oxidation Degradation Characteristics and Kinetic Analysis of Waste Shell Powders for Efficient Utilization and Environmental Protection

ABSTRACT The thermal oxidation degradation and kinetic behavior of four waste shell powders: Hyriopsis cumingii, abalone, scallop, and clam were investigated. The thermal oxidation degradation process was elucidated using thermogravimetric analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and the Coats-Redfern method. This process typically occurs in four stages, with the decomposition of CaCO₃ (Stage III) being pivotal. Notably, clam and scallop powders exhibited the highest CaO yields of approximately 55.1% and 55.0%, respectively. Although abalone powder had a lower yield of 50.2%, it demonstrated the lowest activation energy of 118.08 kJ/mol in Stage III, suggesting potential energy and cost savings during CaO conversion. High-temperature calcination altered the chemical composition and microstructure of the shell powders, resulting in rough and porous CaO surfaces conducive to heavy metal ion and pollutant adsorption. The research provides a theoretical framework and experimental validation for the sustainable utilization of calcined shell powders, fostering efficient shell resource management and environmental protection.

The impact of digital technology and technological innovation on environmental attitudes——insights from a cross-country survey

ABSTRACT The evolving landscape of digital technologies and technological innovations is reshaping our relationship with the environment. While empirical studies at the micro level have extensively examined the separate impacts of these advancements on environmental attitudes, a significant gap persists in understanding their collective influence at the macro level. To address this gap, our study investigates the impact of digital technology and technological innovation on environmental attitudes using data from 44,622 surveys across 28 countries, sourced from the International Social Survey Program. Our analysis reveals that country-level digital technology and technological innovation significantly influence individuals’ environmental attitudes. At the individual level, factors such as air pollution, the quality of the individual’s living environment, and education level play crucial roles in shaping these attitudes. Additionally, public trust in the media plays a significant moderating role in the relationship between digital technology and environmental attitudes. Our research contributes to the literature by simultaneously examining these factors at both individual and national levels. To gain citizens’ attitudes toward environmental protection, there is a need to strengthen the integration of technological innovation and environmental protection and to improve the authenticity of mass media materials while focusing on environmental governance.

MFMamba: A Mamba-Based Multi-Modal Fusion Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images is a fundamental task in computer vision, holding substantial relevance in applications such as land cover surveys, environmental protection, and urban building planning. In recent years, multi-modal fusion-based models have garnered considerable attention, exhibiting superior segmentation performance when compared with traditional single-modal techniques. Nonetheless, the majority of these multi-modal models, which rely on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) for feature fusion, face limitations in terms of remote modeling capabilities or computational complexity. This paper presents a novel Mamba-based multi-modal fusion network called MFMamba for semantic segmentation of remote sensing images. Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). To capitalize on the distinct attributes of the multi-modal remote sensing data from both branches, a feature fusion block (FFB) is designed to synergistically enhance and integrate the features extracted from the dual-branch structure at each stage. Extensive experiments on the Vaihingen and the Potsdam datasets have verified the effectiveness and superiority of MFMamba in semantic segmentation of remote sensing images. Compared with state-of-the-art methods, MFMamba achieves higher overall accuracy (OA) and a higher mean F1 score (mF1) and mean intersection over union (mIoU), while maintaining low computational complexity.

Research on the efficiency and spatiotemporal evolution characteristics of agricultural green development in central cities of the Yangtze River Delta

IntroductionAgricultural green development (AGD) prioritizes sustainable growth by integrating economic, ecological, and social dimensions, aiming to harmonize agricultural economic development with environmental protection and social progress.MethodsThis study integrates the three-stage super-efficiency DEA-SBM model with the BP algorithm, creating an advanced DEA-SBM-BP model to overcome the shortcomings of traditional DEA in evaluation and management processes. The study further applies the Dagum Gini coefficient, kernel density estimation (KDE), and Moran’s index to assess and forecast the efficiency and spatiotemporal evolution patterns of green agricultural development in key cities within the Yangtze River Delta.ResultsThe analysis shows that AGD in the central city of the Yangtze River Delta is overall balanced; however, substantial variations exist among cities within individual provinces. Factors like macroeconomic conditions, workforce quality, and policy support play a crucial role in promoting the efficiency of AGD. Among these, macroeconomic development level has a negative impact, while labor quality and policy support exhibit bidirectional effects. Infrastructure construction, digitalization of agricultural economy, and rural security have become key factors in the green development of modern agriculture. The green advancement of agriculture in the central Yangtze River Delta region typically exhibits a marked clustering effect; however, the local clustering reveals a trend toward dispersed development.DiscussionDespite the emergence of new characteristics in agricultural production in China within the context of high-quality development, differences in resource endowments and economic structures among cities continue to be significant factors contributing to regional imbalances and changes in the agglomeration patterns of agricultural development.

Sustainable Nitrogen Management in Rice Farming: Spatial Patterns of Nitrogen Availability and Implications for Community-Level Practices

Sustainable nitrogen management is crucial for long-term food security and environmental protection in rice farming systems. However, the spatial patterns of nitrogen availability at the community level remain poorly understood, hindering the development of effective sustainable management strategies. This study introduces a novel application of spatial autoregressive analysis to investigate available nitrogen distribution in paddy soils across a rice farming community in Kyoto, Japan. Soil samples from 61 plots, including organically farmed ones, were analyzed for available nitrogen and various physicochemical properties. Contrary to the hypothesis of high variability between adjacent plots, significant positive spatial autocorrelation in available nitrogen was observed, revealing previously unrecognized community-level patterns. The spatial Durbin model outperformed traditional regression approaches and revealed complex spatial interactions in soil properties. Water-soluble organic carbon and humus content showed strong but opposing effects, with a positive direct impact but negative spatial interaction, suggesting topography-driven accumulation processes. Water-soluble nitrogen exhibited reverse patterns with negative direct effects but positive spatial interaction, indicating potential nutrient transport through water movement. These findings highlight the importance of considering both direct and indirect spatial effects in understanding soil fertility patterns, challenging the conventional plot-by-plot management approach. This methodological advancement provides new perspectives for more effective, community-scale soil management strategies in rice farming systems. Moreover, it demonstrates an innovative approach to maximizing the value of outsourced soil analysis data, providing a model for more comprehensive utilization of such data in agricultural research. By enabling more targeted and efficient nitrogen management practices that consider both plot-level processes and landscape-scale interactions, this study potentially contributes to the development of more sustainable and resilient rice production systems.