Search Results (46)

Urban expansion is a major anthropogenic driver of ecosystem service degradation, and its effects differ significantly among expansion patterns and city types. This study selects Jinan, a megacity in Shandong Province, and Dongying, a resource-based city, as study areas. Based on 2000–2020 land cover data, we identified the key urban expansion patterns that lead to ecosystem service losses. We used a built-up land source matrix to analyze the land composition of newly developed built-up areas and adopted the Landscape Expansion Index (LEI) to classify urban expansion into three types: edge-expansion, infilling, and leapfrog expansion. We quantified losses of five core ecosystem services—carbon sequestration, water yield, food production, habitat quality, and soil retention—to identify which expansion pattern exerted the most significant impact on ecosystem service degradation. We further compared loss differences and underlying mechanisms to propose differentiated urban strategies. The results indicate that cultivated land was the primary source in Jinan, while Dongying’s sources were more diverse. Edge-expansion dominated both cities, with a higher proportion in Dongying. Jinan showed a greater increase in leapfrog expansion, and infilling expansion was limited. Leapfrog expansion caused the most severe losses for most services, while edge-expansion dominated food production loss via farmland occupation. This study provides a scientific basis for optimizing spatial development and coordinating urban expansion with ecological conservation. Full article

Coastal cities are located at the critical interface of land–sea interaction, and scientifically assessing their ecological importance is essential for identifying conservation priority areas. Existing assessments focus primarily on static function while neglecting dynamic system processes and resilience characteristics. To address this limitation, this study developed an innovative “State-Interaction-Resilience” (SIR) assessment framework. It integrates ecosystem services (state), ecological connectivity and network supply-demand relationships (interaction), and social-ecological system adaptive capacity (resilience) and incorporates differentiated weighting based on the unique “sea–land gradient” pattern of coastal zones. Using Dongying City in the Yellow River Delta as a case study, the results show the following: (1) The SIR framework evaluation results demonstrate balanced and significant positive correlations with all dimensional indicators (r = 0.3~0.8), showing greater comprehensiveness and scientific validity than traditional evaluation methods, with 81% spatial agreement between identified extremely important areas and existing protected areas. (2) From 2000 to 2020, the overall ecological importance of Dongying City showed an upward trend, with the proportion of extremely important areas significantly increasing from 6.03% to 10.24%, while maintaining a stable spatial gradient pattern of “high along the coast, low inland”. (3) The improvement in ecological importance in coastal core areas mainly resulted from state improvement and resilience enhancement driven by restoration projects such as “aquaculture retreat and wetland restoration”, while inland areas were constrained by both habitat fragmentation and ecological supply-demand mismatch. This study confirms that the SIR framework can accurately capture the spatial heterogeneity of coastal zones. The proposed “core protection-corridor restoration-function enhancement” hierarchical and zonal spatial governance strategy provides scientific evidence and actionable spatial guidance for coastal territorial spatial planning, ecological protection redline optimization, and targeted ecological restoration. Full article

As the central city of the Yellow River Delta, Dongying faces challenges of water scarcity and water pollution. Based on the grey water footprint theory, the paper conducted grey water footprint accounting, factor analysis, and evaluation in Dongying from 2011 to 2023, aiming to clarify the water resources situation. Results indicated that the total grey water footprint in Dongying have decreased from 1.19 billion m³ in 2011 to 235 million m³ in 2023, a reduction of 80.21%. The agricultural, industrial, and domestic grey water footprints decreased by 94 million m³, 88 million m³, and 769 million m³, respectively, with the reduction rates reaching 54.19%, 69.98%, and 86.77%, respectively. The domestic grey water footprint has a significant impact on the dynamics of the total regional grey water footprint. The technical factor, as a negative driving factor, significantly affect the total grey water footprint in Dongying. Economic and population factors, as positive driving factors, have little impact. The water pollution level has been below 100% in recent years, with the grey water footprint sustainability remaining well. The grey water footprint intensity has decreased by 58.00 m³/10,000 CNY, a reduction of 90.60%, indicating significant improvements in water resource utilization efficiency and economic benefits. The paper provides a basis for water resource protection and water environment improvement in the Yellow River Delta region. Full article

The Yellow River Delta (YRD) is a vital agricultural and ecologically fragile zone in China. Understanding the spatial pattern and evolutionary characteristics of Rural Settlements Area (RSA) in this region is crucial for both ecological protection and sustainable development. This study focuses on Dongying, a key YRD city, and compares four advanced deep learning models—U-Net, DeepLabv3+, TransUNet, and TransDeepLab—using fused Sentinel-1 radar and Landsat optical imagery to identify the optimal method for RSA mapping. Results show that TransUNet, integrating polarization and optical features, achieves the highest accuracy, with Precision, Recall, F1 score, and mIoU of 89.27%, 80.70%, 84.77%, and 85.39%, respectively. Accordingly, TransUNet was applied for the spatiotemporal extraction of RSA in 2002, 2008, 2015, 2019, and 2023. The results indicate that medium-sized settlements dominate, showing a “dense in the west/south, sparse in the east/north” pattern with clustered distribution. Settlement patches are generally regular but grow more complex over time while maintaining strong connectivity. In summary, the proposed method offers technical support for RSA identification in the YRD, and the extracted multi-temporal settlement data can serve as a valuable reference for optimizing settlement layout in the region. Full article

To study the impact of photovoltaic facilities on the climate of aquaculture areas within the new aquaculture model (photovoltaic fishery mode, PFM), meteorological monitoring instruments were used to measure light intensity, temperature, humidity, and water environment in the PFM aquaculture areas of Dongying City and Taishan City. The experimental results showed that photovoltaic facilities (PFs) significantly affected lighting, causing a substantial decrease in light intensity above the ponds, with an annual average reduction ranging from 24.15% to 67.75%, compared to the traditional pond mode (TPM). The impact of flexible PF on lighting was less pronounced than that of fixed photovoltaic facilities, with decreases of only 24.15% and 65.06%, respectively, compared to TPM. PF influenced temperature within a small range, particularly in the Dongying City aquaculture area, where the temperature difference reached 1.48 °C. The effect of flexible PF on temperature, with a decrease of only 0.071%, was much smaller than that of fixed PF, which showed a decrease of 3.28% compared to TPM. In both Dongying City and Taishan City aquaculture areas, PF reduced environmental humidity by 4.71% to 9.62% compared to TPM. In Dongying City, the water temperature under the PFM-fixed system was 0.39 to 3.78 °C lower than that of TPM. The annual biomass variation patterns of zooplankton and phytoplankton in Dongying City and Taishan City were opposite. This study provides data to support further research on the relationship between PFM and aquaculture. Full article

Soil salinization is one of the most important factors limiting the sustainable development of global agriculture. As the core driving force of the soil carbon cycle, soil-carbon-metabolism-related enzyme activity is very important for soil ecological balance and fertility enhancement. To explore the spatial and temporal variation characteristics and coupling mechanisms of soil water, salt, nutrients and enzyme activities in different salinized wheat fields in the Yellow River Delta, field experiments were conducted in Dongying City, Shandong Province. The results showed that the soil moisture content of the low-salt wheat field was higher and that the salt content of three wheat fields was concentrated in the 0–20 cm and 80–100 cm soil layers. Here, soil nutrients and enzyme activities are concentrated in the 0–20 cm topsoil, with significant differences in different degrees among salinized wheat fields at the different growth stages of wheat. Overall, invertase activity (S-SC) and amylase activity (S-AL) presented a trend of low salt > high salt > medium salt, while cellulase activity (S-CL) presented a trend of medium salt > low salt > high salt. Redundancy analysis showed that available potassium (AK) (67.6%) and electric conductivity (EC) (21.2%) in the low-salinity wheat field, total nitrogen (TN) (48.6%) and AK (28.8%) in the medium-salinity wheat field, and EC (67%) and soil organic matter (SOM) (19%) in the high-salinity wheat field contributed the most to soil enzyme activity. This study provides a theoretical basis for the management and sustainable development of different salinized wheat fields in the Yellow River Delta. Full article

Continental ecosystems are crucial constituents of the worldwide carbon process, and their carbon source and sink processes are highly sensitive to human-induced climate change. However, the spatiotemporal changes and principal determinants of carbon source/sink in Shandong Province remain unclear. This study constructs six dominant control modes of carbon sources/sinks based on three carbon sink indicators (gross primary production (GPP), net primary production (NPP), and net ecosystem productivity (NEP)) and three carbon source indicators (autotrophic respiration (Ra), heterotrophic respiration (Rh), and total ecosystem respiration (Rs)), revealing the main control characteristics of the spatiotemporal dynamics of carbon source/sink in the continental ecosystems of Shandong Province. Additionally, the principal determinants of carbon sources and sinks are quantitatively analyzed using cloud models. The research findings are as follows: (1) From 2001 to 2020, the continental ecosystem of Shandong Province demonstrated a weak carbon sink overall, with both carbon sinks and sources showing fluctuating growth trends (growth rate: GPP, NEP, NPP, Rs, Ra, and Rh consist of 15.55, 6.14, 6.09, 9.59, 9.47, and 0.07 gCm⁻²a⁻¹). (2) The dominant control characteristics of carbon source/sink in Shandong Province exhibit significant spatial differentiation, which can be classified into absolute carbon sink cities (Jinan, Zibo, Rizhao, Jining, Liaocheng, Zaozhuang, Binzhou, Dezhou, Tai’an) and relative carbon source cities (Weifang, Yantai, Weihai, Linyi, Qingdao, Heze, and Dongying). GPP is the dominant control factor in carbon sink areas and is widely distributed across the province, while Rs and GPP are the dominant control factors in carbon source fields, focused on the eastern coastal and southwestern inland sites. (3) Landscape modification and rainfall are the main driving elements influencing the carbon sink and source variations in Shandong Province’s continental ecosystems. (4) The spatial differentiation of the driving factors of carbon producers and reservoirs is significant. In absolute carbon sink cities, land-use change and vegetation cover are the dominant factors for carbon sinks and sources, with significant changes in both range and spatial differentiation. In relative carbon source cities, land-use change is the leading factor for carbon source/sink, and the range of changes and spatial differentiation is most notable. The observations from this study supply scientific underpinnings and reference for enhancing carbon sequestration in continental ecosystems, urban ecological safety management, and achieving carbon neutrality goals. Full article

In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation. Full article

Globally, diverse regions are experiencing significant salinization, yet research leveraging two-dimensional spectral indices derived from fractional-order differentiated hyperspectral data remains relatively scarce. Given that the Yellow River Delta exemplifies a severely salinized area, this study employs it as a case study to advance salinization monitoring by integrating fractional-order differentiation with two-dimensional spectral indices. Compared to fractional-order differentiation (FOD) and deep learning models, integer-order differentiation and traditional detection models suffer from lower accuracy. Therefore, a two-dimensional spectral index was constructed to identify sensitive parameters. Modeling methods such as Convolutional Neural Networks (CNNs), Partial Least Squares Regression (PLSR), and Random Forest (RF) were employed to predict soil salinity. The results show that FOD effectively emphasizes gradual changes in spectral curve transformations, significantly improving the correlation between spectral indices and soil salinity. The 1.6-order NDI spectral index (1244 nm, 2081 nm) showed the highest correlation with soil salinity, with a coefficient of 0.9, followed by the 1.6-order RI spectral index (2242 nm, 1208 nm), with a correlation coefficient of 0.882. The CNN model yielded the highest inversion accuracy. Compared to the PLSR and RF models, the CNN model increased the RPD of the prediction set by 0.710 and 1.721 and improved the R² by 0.057 and 0.272, while reducing the RMSE by 0.145 g/kg and 1.470 g/kg. This study provides support for monitoring salinization in the Yellow River Delta. Full article

Suaeda salsa, a halophytic plant species, exhibits a remarkable salt tolerance and demonstrates a significant phytoremediation potential through its capacity to absorb and accumulate saline ions and heavy metals from soil substrates, thereby contributing to soil quality amelioration. Furthermore, this species serves as a critical habitat component for avifauna populations and represents a keystone species in maintaining ecological stability within estuarine and coastal wetland ecosystems. With the development and maturity of UAV remote sensing technology in recent years, the advantages of using UAV imagery to extract weak targets are becoming more and more obvious. In this paper, for Suaeda salsa, which is a weak target with a sparse distribution and inconspicuous features, relying on the high-resolution and spatial information-rich features of UAV imagery, we establish an adaptive contextual information extraction deep learning semantic segment model (ACI-Unet), which can solve the problem of recognizing Suaeda salsa from high-precision UAV imagery. The precise extraction of Suaeda salsa was completed in the coastal wetland area of Dongying City, Shandong Province, China. This paper achieves the following research results: (1) An Adaptive Context Information Extraction module based on large kernel convolution and an attention mechanism is designed; this module functions as a multi-scale feature extractor without altering the spatial resolution, enabling a seamless integration into diverse network architectures to enhance the context-aware feature representation. (2) The proposed ACI-Unet (Adaptive Context Information U-Net) model achieves a high-precision identification of Suaeda salsa in UAV imagery, demonstrating a robust performance across heterogeneous morphologies, densities, and scales of Suaeda salsa populations. Evaluation metrics including the accuracy, recall, F1 score, and mIou all exceed 90%. (3) Comparative experiments with state-of-the-art semantic segmentation models reveal that our framework significantly improves the extraction accuracy, particularly for low-contrast and diminutive Suaeda salsa targets. The model accurately delineates fine-grained spatial distribution patterns of Suaeda salsa, outperforming existing approaches in capturing ecologically critical structural details. Full article

Reconstructing relationships between urban agglomeration and relevant ecosystems from an ecosystem services perspective and quantitatively assessing their interactive status holds significant implications for achieving coordinated development. Taking Shandong Peninsula Urban Agglomeration (SPUA) as our study area, we developed a Cities-ESV Coupling Index (I) serving as a composite metric for assessing city–ecosystem coupling dynamics through a multidimensional framework encompassing the following: (1) urban development level, (2) ecosystem service value (ESV), (3) ecosystem service physical quantity, and (4) spatial balance degree of ecosystem service, operationalized through 10 selected indicators. Our methodology integrates ESV quantification, biophysical assessment, correlation analysis modeling, and spatial autocorrelation modeling to comprehensively evaluate coupling relationships between cities and ecosystems across 16 cities and 78 counties. This study innovatively integrates ESV quantification with biophysical assessment methodologies in indicator selection, while strategically incorporating spatial agglomeration metrics. The multidimensional framework effectively addresses the prevalent oversimplification in existing ecosystem service measurement paradigms. The findings are as follows: the total ESV is 13,977.87 × 10⁸ CNY/a, which accounts for about 20% of the total GDP of SPUA. The Cities-ESV coupling index (I) of four cities, including Dongying, Linyi, Yantai, and Weifang, ranks among the top in SPUA, while that of seven counties, namely Weshan, Qixia, Yiyuan, Yishui, Mengyin, and Linqu, is at a relatively high-level. The conclusion is as follows: the total ESV in SPUA had been continuously decreasing. The coupling relationship between cities and ecosystems are significantly negatively correlated, and the Cities-ESV coupling index (I) of SPUA has obvious regional differentiation characteristics. Therefore, differentiated ecological land protection policies should be formulated to curb the trend of continuous decline in ESV. Full article

Understanding the correlation between PM_2.5 and O₃ is critical for complex air pollution control. This study comprehensively analyzed PM_2.5 and O₃ pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying, a typical petrochemical city in China’s Bohai Bay region. Results showed that PM_2.5–O₃ correlation in Dongying exhibited significant seasonal variations, spatial patterns, and concentration threshold effects from 2017 to 2023. PM_2.5 and O₃ showed strong positive correlations in summer, negative in winter, and weak positive in spring/autumn, with strongest links in western areas. The strongest positive PM_2.5–O₃ correlation occurred in summer when PM_2.5 ≤ 35 μg·m⁻³ and O₃ >160 μg·m⁻³, while the strongest negative correlation was exhibited in winter with PM_2.5 > 75 μg·m⁻³ and O₃ ≤ 100 μg·m⁻³. Meteorological conditions (T > 20 °C, RH < 30%, wind speed < 1.73 m/s, O_x > 125 μg·m⁻³) and non-sea-breeze periods enhanced the PM_2.5–O₃ positive correlation. During the four typical pollution episodes, the positive PM_2.5–O₃ correlation in summer was propelled by synchronous increases in O₃ and secondary components via shared precursors. In autumn, strong positivity resulted from secondary component–O₃ correlations (r > 0.7) and dominance of secondary formation in PM_2.5. In winter, the negative correlation stemmed from primary emissions inhibiting photochemistry. Random forest analysis showed that O_x, RH, and T drove positive PM_2.5–O₃ correlation via photochemistry in summer, whereas winter primary emissions and NO titration caused negative correlation. This study offers guidance for the collaborative PM_2.5 and O₃ control in the petrochemical cities of the Bay region. Full article

Membrane technology is widely used in various aspects of wastewater treatment; however, single membrane technology has a series of disadvantages, such as high selectivity, poor recycling performance, and susceptibility to contamination. In this study, a treatment method combining an advanced oxidation process and membrane separation technology was proposed, and a geopolymer-based Fenton-like catalytic tubular membrane (GFM) was prepared by using H₂O₂ as a blowing agent by the direct foaming method. It was shown that the optimum conditions for the preparation of the membrane were a water glass modulus of 1.8 M, the addition of foaming agent of 1 mL, and a thickness of the membrane of 6.5 mm, with a flux of 6942 L·m⁻²·h⁻¹. Due to the characteristics of the tubular membrane, the possibility of adding hydrogen peroxide directly inside the membrane allows an optimal Fenton-like removal, which is better than outside the membrane, thus reducing the consumption of hydrogen peroxide. The tubular membrane has a multi-stage porous structure, high flux, and a high specific surface area (68.74 m²/g). The GFM/H₂O₂ Fenton-like system formed is capable of almost completely degrading all kinds of synthetic dyes under various stringent conditions, and the XRD, FTIR, and TG analyses and cycling tests showed that the GFM has excellent stability and a significant advantage in terms of reusability. Full article

Sapovirus (SaV) is a major pathogen responsible for acute gastroenteritis (AGE), and its incidence has been increasing in recent years. This study investigates the prevalence and the genetic characteristics of SaV in Shandong Province during 2022–2023, based on a surveillance network covering all age groups. Samples were obtained from a viral diarrhea surveillance network in Shandong Province during 2022–2023. SaVs were identified through quantitative reverse-transcription polymerase chain reaction (RT-qPCR). PCR amplification and Sanger sequencing were performed on positive samples, and whole-genome sequencing was conducted using metagenomic sequencing technology. Sequence analysis was conducted using BioEdit 7.0.9.0 and MEGA X, while statistical analysis was performed with SPSS 26.0. A total of 157 SaV-positive cases were identified, resulting in a positivity rate of 1.12%. The positivity rate for SaV was 0.75% in 2022 and it increased significantly to 1.42% in 2023. The highest positivity rates for both 2022 and 2023 were observed in November. The highest positivity rate was observed in the 3–5-year-old age group. In 2022, Dongying City had the highest positivity rate, while Zaozhuang City exhibited the highest rate in 2023. The incidence of vomiting in SaV-positive patients was significantly higher compared to SaV-negative patients (P = 0.002). Eight genotypes were identified in both the VP1 and RdRp regions. The complete genome sequence analysis of a GI.3 strain showed that NS1 (5.88%, 4/68) was the region most prone to amino acid variation, followed by VP2 (5.45%, 9/165) within the same genotype. SaV infections are more prevalent in cold weather, with young children being particularly susceptible. The SaV positivity rate in 2023 increased significantly accompanied by an increased diversity of genotypes, compared to that of 2022. The NS1 region exhibits the biggest variation within the same genotype, indicating that more attention should be paid to other regions besides VP1 in the future study. Ongoing surveillance of SaV is recommended for effective prevention and control. Full article

The ambient levels of NO₂ in urban areas in China in recent years have generally shown a downward trend, but high NO₂ concentrations still exist under certain conditions, and the causes for such phenomenon and its impact on air quality remain unclear. Taking Dongying, a typical petrochemical city in the Bohai Bay of China, as an example, this paper analyzed the influence of NO₂ on urban air quality and investigated the causes for the formation of NO₂ with high concentrations. The results indicated that higher daily NO₂ concentrations (>40 μg/m³) mainly occurred during January-April and September-December each year, and higher hourly NO₂ concentrations mainly occurred during the nighttime and morning rush hour in Dongying from 2017 to 2023. With the increase in daily NO₂ concentrations, the daily air pollution levels showed a general increasing trend from 2017 to 2023. The occurrence of high NO₂ values in Dongying was affected by the combination of unfavorable meteorological conditions, local emissions and regional transports, and localized atmospheric chemical generation. High-pressure and uniform-pressure weather patterns in 2017–2022, along with land–sea breeze circulation in 2022, contribute to high NO₂ concentrations in Dongying. Boundary layer heights (BLH) in spring (−0.43) and winter (−0.36), wind direction in summer (0.21), and temperature in autumn (−0.46) are the primary meteorological factors driving NO₂-HH (High hourly NO₂ values), while BLH (−0.47) is the main cause for NO₂-HD (High daily NO₂ values). The titration reaction between NO with O₃ is the main cause for NO₂-HH in spring, summer and autumn, and photochemical reactions of aromatics have a significant influence on NO₂-HD. NOx emissions from the thermal power and petrochemical industry in Dongying and air pollution transports from western and southwestern Shandong Province (throughout the year) and from the Bohai Sea (during spring and summer) had serious adverse impact on high NO₂ values in 2022. The results of the study could help to provide a scientific basis for the control of NO₂ and the continuous improvement of air quality in Dongying and similar petrochemical cities. Full article