Search Results (587)

Analyzing vegetation coverage and land-use and land cover (LULC) characteristics helps to understand the interaction between human activities and the natural environment. The coastal regions of the Guangdong Province are economically active areas with frequent human activities, located in the advantageous natural environment of South China. This study analyzed the spatiotemporal characteristics of the normalized difference vegetation index (NDVI) and LULC from 2000 to 2020, to explore the response of NDVI changes to LULC changes. The results show that (1) the overall NDVI is relatively high, with a proportion of 85.37% to 89.48% of areas with higher coverage and above categories, mainly distributed in the east and west. Vegetation coverage showed an increasing trend. (2) The LULC in this area is mainly composed of forest land (46.5% to 47.5%) and cultivated land (30.7% to 33.4%), with forest land mainly distributed in relatively high-altitude regions and cultivated land mainly distributed in the plains. The changes in LULC from 2015 to 2020 were relatively significant, mainly due to the mutual transfer of cultivated land and forest land. In addition, built-up land continued to expand from 2000 to 2020, mainly in the Pearl River Delta. (3) The NDVI decreases come from the transfer of various types of land to built-up land, mainly in the Pearl River Delta region, while the NDVI increase comes from the stability and mutual transfer of cultivated land. The net contribution rate of forest land change to vegetation cover change is the most significant (−38.903% to 23.144%). This study has reference significance for the spatiotemporal characteristics of vegetation cover changes in coastal areas and their response to land-use changes, as well as coastal management and sustainable development. Full article

Understanding the spatial correlation of transportation carbon emissions and their influencing factors is significant in achieving an overall regional carbon emission reduction. This study analyzed the structure characteristics of the expressway carbon emission correlation network in Guangdong Province and examined its influencing factors with intercity expressway traffic flow data using social network analysis (SNA). The findings indicate that the correlation network of expressway carbon emissions in Guangdong Province exhibited a “core-edge” spatial pattern. The overall network demonstrated strong cohesion and stability, and a significant difference existed between the passenger vehicle and freight vehicle carbon emission networks. The positions and roles of different cities varied within the carbon emission network, with the Pearl River Delta (PRD) cities being in a dominant position in the carbon network. Cities such as Guangzhou, Foshan, and Dongguan play the role of “bridges” in the carbon network. The expansion of differences in GDP per capita, industrial structure, technological level, and transportation intensity facilitates the formation of a carbon emission network. At the same time, geographical distance between cities and policy factors inhibit them. This study provides references for developing regional collaborative carbon emission governance programs. Full article

To gain insights into the prevalence and antimicrobial resistance patterns of major bacterial pathogens affecting largemouth bass (Micropterus salmoides) in the Pearl River Delta (PRD) region, Guangdong, China, a study was conducted from August 2021 to July 2022. During this period, bacteria were isolated and identified from the internal organs of diseased largemouth bass within the PRD region. The antimicrobial resistance patterns of 11 antibiotics approved for use in aquaculture in China were analyzed in 80 strains of Edwardsiella piscicida using the microbroth dilution method. The results showed that 151 bacterial isolates were obtained from 532 samples, with E. piscicida (17.29%, 92/532), Aeromonas veronii (4.70%, 25/532), and Nocardia seriolae (2.26%, 12/532) being the main pathogens. Notably, E. piscicida accounted for the highest proportion of all isolated bacteria, reaching 60.92% (92/151), and mainly occurred from November to April, accounting for 68.48% (63/92) of the cases. The symptoms in largemouth bass infected with E. piscicida included ascites, enteritis, and hemorrhaging of tissues and organs. The drug sensitivity results showed that the resistance rates of all E. piscicida strains to ciprofloxacin, all sulfonamides, thiamphenicol, florfenicol, enrofloxacin, doxycycline, flumequine, and neomycin were 96.25%, 60–63%, 56.25%, 43.75%, 40%, 32.5%, 16.25%, and 1.25%, respectively. In addition, 76.25% (61/80) of these strains demonstrated resistance to more than two types of antibiotics. Cluster analysis revealed 23 antibiotic types (A–W) among the 80 isolates, which were clustered into two groups. Therefore, tailored antibiotic treatment based on regional antimicrobial resistance patterns is essential for effective disease management. The findings indicate that in the event of an Edwardsiella infection in largemouth bass, neomycin, doxycycline, and flumequine are viable treatment options. Alternatively, one may choose drugs that are effective as determined by clinical drug sensitivity testing. Full article

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods. Full article

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R² = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection. Full article

The impact of industrial co-agglomeration (ICA) on green total factor productivity (GTFP) has garnered considerable academic attention. However, there remains a gap in research systematically investigating how ICA affects China’s GTFP within the framework of green development, specifically by analyzing transmission mechanisms, regulatory mechanisms, and spatial spillover effects. To address this gap, this study utilizes panel data from 283 Chinese cities, spanning the years 2006 to 2020, and conducts both theoretical and empirical analyses to examine ICA’s influence on GTFP through these three mechanisms. Our findings indicate that ICA significantly enhances GTFP by alleviating the mismatch of capital and energy factors but does not improve GTFP by addressing labor mismatches. Furthermore, when the intensity of local government competition exceeds a threshold of 14.3825, the positive impact of ICA diminishes, whereas an environmental regulation intensity above 0.4381 strengthens ICA’s positive effect on GTFP. ICA was found to substantially increase local GTFP and generate positive spatial spillover effects on surrounding cities within a 100 km radius. Co-agglomeration of both high-end and low-end producer services with manufacturing boosts local GTFP, while co-agglomeration of low-end producer services with manufacturing also enhances GTFP in adjacent cities. In megacities, ICA positively influences both local and nearby GTFP, whereas in large cities, ICA tends to suppress GTFP in neighboring areas. Additionally, with the exception of the Middle Yangtze River and Pearl River Delta city clusters, ICA in urban clusters enhances local GTFP; ICA in the Middle Yangtze River cluster promotes GTFP in neighboring areas, whereas ICA in the Chengdu–Chongqing cluster inhibits neighboring GTFP. Full article

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions. Full article

China aims to reduce its carbon emissions to achieve carbon peaking and neutrality. Measuring the carbon emission efficiency of three urban agglomerations in China, exploring their spatiotemporal characteristics, and investigating the main influencing factors are crucial for achieving regional sustainable development and dual carbon goals. Using the super-slack-based measurement (super-SBM) model, we calculated the carbon emission efficiency of the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2011 to 2021 and explored the spatiotemporal non-equilibrium characteristics of carbon emission efficiency and its influencing factors. The results indicated that: (1) Overall, the carbon emission efficiency showed an N-type trend, with the PRD having the highest average efficiency. Regional differences between the YRD and BTH regions gradually increased. (2) The efficiency hotspots shifted from the PRD to the YRD, whereas the cold spots were mainly concentrated in the BTH region. The variation in the standard deviation ellipse radius of carbon emission efficiency in the urban agglomerations was clear, and the spatial disequilibrium was significant. (3) Economic level and opening up had positive impacts on carbon emission efficiency, whereas energy intensity and industrial structure had negative impacts. The effects of population size, government intervention, and technological level varied among the regions. Full article

Black carbon (BC) aerosols have attracted wide attention over the world due to their significant climate effects on local and global scales. BC extinction aerosol optical thickness (AOT), scattering AOT, and single scattering albedo (SSA) over China are systematically studied based on the MERRA-2 satellite reanalysis data from 1983 to 2022 in terms of the spatial, yearly, seasonal, and monthly variations. The extinction and scattering AOTs of BC show similar spatial distribution, with high values in eastern and southern China, generally as opposed to BC SSA. A decrease in BC extinction and scattering AOTs has been documented over the last decade. The mean BC extinction AOT, scattering AOT, and SSA over China are 0.0054, 0.0014, and 0.26, respectively. The BC SSA showed small variations during 1983–2022, although a high BC extinction AOT and scattering AOT have been seen in the last two decades. During different decades, the seasonal patterns of BC extinction and scattering AOTs may differ, whereas the BC SSA shows seasonal consistency. Significant monthly variations in the BC SSA are seen over four decades, which are in agreement with their seasonal patterns. The mean BC extinction AOTs are 0.037, 0.033, 0.023, and 0.0054, whereas the average BC scattering AOTs are 0.0088, 0.0082, 0.0060, and 0.0014 in the Pearl River Delta (PRD), Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH) region, and Tarim Basin (TB), respectively. It is interesting to see that BC SSA values in the TB region are generally higher than those over the PRD, YRD and BTH areas, whereas the reverse is true for BC extinction and scattering AOTs. This study provides references for further research on black carbon aerosols and air pollution in China. Full article

Carbonyl compounds are important oxygenated volatile organic compounds (VOCs) that play significant roles in the formation of ozone (O₃) and atmospheric chemistry. This study presents comprehensive field observations of carbonyl compounds during an unusual wintertime ozone pollution event at a suburban site in Guangzhou, South China, from 19 to 28 December 2020. The aim was to investigate the characteristics and sources of carbonyls, as well as their contributions to O₃ formation. Formaldehyde, acetone, and acetaldehyde were the most abundant carbonyls detected, with average concentrations of 7.11 ± 1.80, 5.21 ± 1.13, and 3.00 ± 0.94 ppbv, respectively, on pollution days, significantly higher than those of 2.57 ± 1.12, 2.73 ± 0.88, and 1.10 ± 0.48 ppbv, respectively, on nonpollution days. The Frame for 0-D Atmospheric Modeling (F0AM) box model simulations revealed that local production accounted for 62–88% of observed O₃ concentrations during the pollution days. The calculated ozone formation potentials (OFPs) for various precursors (carbonyls and VOCs) indicated that carbonyl compounds contributed 32.87% of the total OFPs on nonpollution days and 36.71% on pollution days, respectively. Formaldehyde, acetaldehyde, and methylglyoxal were identified as the most reactive carbonyls, and formaldehyde ranked top in OFPs, and it alone contributed 15.92% of total OFPs on nonpollution days and 18.10% of total OFPs on pollution days, respectively. The calculation of relative incremental reactivity (RIR) indicates that ozone sensitivity was a VOC-limited regime, and carbonyls showed greater RIRs than other groups of VOCs. The model simulation showed that secondary formation has a significant impact on formaldehyde production, which is primarily controlled by alkenes and biogenic VOCs. The characteristic ratios and backward trajectory analysis also indicated the indispensable impacts of local primary sources (like industrial emissions and vehicle emissions) and regional sources (like biomass burning) through transportation. This study highlights the important roles of carbonyls, particularly formaldehyde, in forming ozone pollution in megacities like the Pearl River Delta region. Full article

In the context of pursuing high-quality development, the coupling and coordination of the ecosystem and economy has become the fundamental goal and inevitable choice for achieving the sustainable development of urban agglomerations. Based on remote sensing and statistical data for the Pearl River Delta (PRD) region from 2005 to 2020, in this paper, we construct an index system of the ecological and economic levels to assess the ecosystem service value (ESV). We use the equivalent factor method, entropy method, coupling coordination model, and relative development model to systematically grasp the spatial pattern of the levels of the two variables, analyse and evaluate their spatial and temporal coupling and coordination characteristics, and test the factors influencing their coupling and coordination using the geographical and temporal weighted regression (GTWR) model. The results show that ① the ESV in the PRD exhibited a fluctuating decreasing trend, while the level of the economy exhibited a fluctuating increasing trend; ② the coordination degree of the ESV and economy in the PRD exhibited a fluctuating increasing trend, and the region began to enter the basic coordination period in 2007; ③ in terms of the spatial distribution of the coordination degree, there was generally a circular pattern, with the Pearl River Estuary cities as the core and a decrease in the value towards the periphery; ④ the coordinated development model is divided into balanced development, economic guidance, and ESV guidance, among which balanced development is the major type; ⑤ the results of the GTWR reveal that the influencing factors exhibited significant spatial–temporal heterogeneity. Government intervention and openness were the dominant factors affecting the coordination, and the normalised difference vegetation index was the main negative influencing factor. Full article

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 10⁶ Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 10⁶ Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA. Full article

The advancement of urbanization has led to a decline in the ecological function and environmental quality of cities, seriously reducing the services and sustainable development capacity of urban ecosystems. The construction of the National Forest Urban Agglomeration of China is conducive to alleviating the ecological and environmental problems brought about by rapid urbanization and promoting sustainable urban development. A time series analysis of ecological network changes can quickly and effectively explore the development and changes of ecological spatial patterns over time. Identifying ecological protection and restoration areas in urban agglomerations is an important way to promote ecosystem restoration and optimize ecological networks. This paper takes the Pearl River Delta forest urban agglomeration as the research area, uses multi-source remote sensing data from 2000 to 2020 (every 5 years), identifies ecological sources based on the morphological spatial pattern analysis (MSPA) method, generates ecological corridors based on the minimum cumulative resistance (MCR) model, constructs a time series ecological network pattern in the Pearl River Delta region, and analyzes the evolution process of the ecological network pattern over time. The results indicate that over time, the core green area in the ecological network pattern of the Pearl River Delta first decreased and then increased, and the complexity of ecological corridors first decreased and then increased. The main reason is that the urbanization process in the early 21st century led to severe ecological fragmentation. Under the promotion of the national forest urban agglomeration construction, the ecological network pattern of the Pearl River Delta was restored in 2015 and 2020. The time series analysis of the ecological network pattern in the Pearl River Delta region of this research confirms the effectiveness of the construction of forest urban agglomerations, providing a scientific reference for the identification of ecological networks and optimization of spatial patterns in forest urban agglomerations. Full article

Current agriculture intensifies crop cultivation to meet food demand, leading to unsustainable use of chemical fertilizers. This study investigates a few physiological and agronomic responses of common wheat following the inoculation with plant growth-promoting bacteria to reduce nitrogen inputs. A field trial was conducted in 2022–2023, in Legnago (Verona, Italy) on Triticum aestivum var. LG-Auriga comparing full (180 kg ha⁻¹) and reduced (130 kg ha⁻¹) N doses, both with and without foliar application at end tillering of the N-fixing bacterium Methylobacterium symbioticum. Biofertilization did not improve shoot growth, while it seldom increased the root length density in the arable layer. It delayed leaf senescence, prolonged photosynthetic activity, and amplified stomatal conductance and PSII efficiency under the reduced N dose. Appreciable ACC-deaminase activity of such bacterium disclosed augmented nitrogen retrieval and reduced ethylene production, explaining the ameliorated stay-green. Yield and test weight were unaffected by biofertilization, while both glutenin-to-gliadin and HMW-to-LMW ratios increased together with dough tenacity. It is concluded that Methylobacterium symbioticum can amplify nitrogen metabolism at a reduced nitrogen dose, offering a viable approach to reduce chemical fertilization under suboptimal growing conditions for achieving a more sustainable agriculture. Further research over multiple growing seasons and soil types is necessary to corroborate these preliminary observations. Full article

With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R² of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil. Full article