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The rapid expansion of aquaculture ponds at the end of the last century raised concerns about their environmental and ecological impacts in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This study aims to comprehensively investigate the spatiotemporal evolutionary characteristics and explore the influencing factors of aquaculture development in the GBA. An automated approach was established to extract the aquaculture pond areas on the Google Earth Engine (GEE) platform using Landsat images and showed high accuracy. The aquaculture development in the GBA underwent three phases of rapid expansion (1986–1996), constant transformation (1996–2010), and drastic contraction (2010–2020), and was closely related to the important aquaculture policies in China and Guangdong Province. The local aquaculture ponds showed three evolutionary patterns of continuous shrinkage, earlier shrinkage, and later shrinkage, which were mainly influenced by the economic development level and urbanization in each city. Rapid urbanization created great demands for aquaculture products and intense land use competition, which drove the conversion of aquaculture ponds into impervious land and migration from the central GBA towards the surrounding coast and upstream areas. Government policies promoted the initial rapid increase and regulated the aquaculture ponds to develop towards a more environment-friendly and intensive mode, which led to a steady increase of aquaculture products despite the drastic decrease in aquaculture pond areas in the past decade. These evolutionary features are of significance for policymakers and farmers to maintain the sustainable development of aquaculture in the GBA and Guangdong Province.

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas.

Both oil spill and microplastics (MPs) are major issues in marine environments. After a spill, the oil film may disperse into the water column as droplets under the function of sea waves. The oil dispersion may be affected due to the attachment of oil to MPs. In this paper, the impacts of salinity and temperature on the dispersion of spilled oil in the presence of MPs were individually studied by batch conical flask oscillation experiments. The results indicated that the rise in salinity or temperature displayed a more significant effect on promoting, rather than inhibiting, oil dispersion with MPs. When the salinity rose from 15% to 35%, the oil dispersion efficiency (ODE) with the 13 μm polyethylene (PE) and polystyrene (PS) MPs was increased by 10.4% and 12.9%, respectively; when the temperature rose from 10 °C to 25 °C, the corresponding ODE was increased by 15.6% and 12.7%, respectively. In addition, the volumetric mean diameter (VMD) of the dispersed oil droplets decreased with an increase in salinity or temperature. Furthermore, the 13 μm MPs showed a higher impact on the oil dispersion than 106 μm of MPs, and the ODE with PE MPs was greater than that with PS MPs. The findings of this study expanded the understanding of the migration of spilled oil in seawater in the presence of MPs and may further improve the capability of predicting the impact of oil spills by marine environment managers.

Synthetic aperture radar (SAR), with all-day and all-weather observation capabilities, can capture the phenology of crops with short growth cycles to improve land cover classification results. The present study carried out a land cover classification using multitemporal RADARSAT-2 fully polarimetric SAR (PolSAR) images that are 24 days apart. The objectives of this study were 1) examining the land cover classification capacity of multitemporal fully PolSAR data processed with multiple polarimetric decomposition methods, 2) investigating the contribution of multi-type polarimetric decomposition methods to multitemporal PolSAR image classification, 3) and determining optimal image acquisition dates and polarimetric parameters for land cover classification. Overall accuracy and kappa coefficient attained using the multitemporal PolSAR data were 96.55% and 0.96, respectively, which were improved by as much as 16.77% and 0.20, respectively, compared with those obtained with a single scene. Compared with the multitemporal PolSAR image classification using coherency matrices alone, the use of polarimetric decomposition methods improved overall accuracy and kappa value by 2.22% and 0.03, respectively. Using decision tree algorithms for feature selection, we found that April 14, May 8, June 1, and June 25 and Pauli, Cloude, Neumann3, An&Yang4, Freeman3, Barnes2, and MCSM5 decomposition methods were optimal for the land cover classification.

Printing and dyeing wastewater is characterized with complex water quality and poor biodegradability. In this study, a pilot-scale anaerobic baffled reactor (ABR) with packing was verified to effectively degrade the complex organic pollutants in the wastewater through the hydrolysis and acidification of anaerobic microorganisms. At a hydraulic retention time (HRT) of 12 h and an organic loading rate (OLR) of 2.0–2.5 kg COD/(m3·d), the ABR stabilized the fluctuation range of pH and achieved an average colority removal rate of 10.5%, which provided favorable conditions for subsequent aerobic treatment. During the early operation period, the reactor increased the alkalinity of the wastewater; after 97 days of operation, the volatile fatty acid (VFA) content in the wastewater decreased. To demonstrate the suitability of the support vector regression (SVR) technology in predicting the performance of the reactor, two SVR algorithms with three kernel functions were employed to relate the chemical oxygen demand (COD) removal rate to its influencing factors, and the predictions of both the training and validation groups agreed with the measurements. The results obtained from this study can contribute to the design and optimal operation of the anaerobic treatment project of the industrial wastewater treatment plant.

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

This research aims to advance our understanding towards forest transition, which is about the shift from net deforestation to net reforestation over a given area during certain period, through a case study of a western city in China from 1990 to 2015. Three main contributions are made to the theory and practice of forest land management. First, this research enriches forest transition theory with a meso-level exploration on forest land change in subtropic Chongqing, which echoes the “U” shape transition rules widely observed in the Euro-American context but was found a time lag of the turning point until 2000s. Second, it intentionally identifies the subtype of forests—the artificial plantation, which is considered influential on performance of forest’s carbon sequestration but not paid sufficient attention to. Third, it adds knowledge on forest transition pathway at an intra-urban scale through the identified significance degree of forest transition dynamics, which implies that economic development matters but is less important than topography in a mountainous city like Chongqing, and different dimensions of economy impact differently on forest transition.

Agricultural land resources have been the central issue for the Chinese government in its attempts to secure food and agricultural sustainability. Yet strict land use control does not protect the agricultural land from erosion by urban expansion. Identifying the specific patterns and mechanisms of the agricultural land conversion, thus, is critical for land management and related decision making. Based on the annual nominal 30 m land use/land cover datasets (called CLUD-A), this study goes below the national/regional level to examine agricultural land conversion in Chongqing from a multiscale perspective. At the metropolis and its subdivision’s scales, the volume of the conversion area has been generally increasing, from 122.40 km2 in 1980–1990, 162.26 km2 in 1990–2000, and 706.14 km2 in 2000–2010, to 684.83 km2 in 2010–2015. Such a conversion in the main city area and its surroundings far outweighed that in the rural outskirts, as 68.9% (1990–2000), 92.2% (2000–2010), and 82.7% (2010–2015) of the conversion happened in the former. Moreover, values of Gini coefficients and coefficient of variation (CV) based on the county/district scale (Gini [0.46, 0.64], CV [0.69, 0.99] throughout the four periods) are much lower than those based on the town/village scale (Gini [0.88, 0.94], CV [3.18, 4.47] throughout the four periods), suggesting the uneven extent of spatial distribution of the agricultural land conversion trickles down along with the downscale of administration: the lower the administrative level, the more severe the unbalance. The policy rationale behind this transition is also discussed. This research argues for tangible approaches to a sustainable rural-urban transformation.

Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.

The formation of sunken oils is mainly dominated by the interaction between spilled oils and sediments. Due to their patchiness and invisibility, cleaning operations become difficult. As a result, sunken oils may cause long-term and significant damage to marine benthonic organisms. In the present study, a bench experiment was designed and conducted to investigate the quantitative distribution of polycyclic aromatic hydrocarbons (PAHs) in sunken oils in the presence of chemical dispersant and sediment. The oil sinking efficiency (OSE) of 16 priority total PAHs in the sediment phase was analyzed with different dosages of dispersant. The results showed that the synergistic effect of chemical dispersant and sediment promoted the formation of sunken oils, and the content of PAHs partitioned in the sunken oils increased with the increase of dispersant-to-oil ratios (DORs). Furthermore, with the addition of chemical dispersant, due to the solubility and hydrophobicity of individual PAHs, the high molecular weight (HMW) PAHs with 4−6 rings tended to partition to sediment compared with low molecular weight (LMW) PAHs with 2−3 rings. The synergistic effect of chemical dispersant and sediment could enhance the OSE of HMW PAHs in sunken oils, which might subsequently cause certain risks for marine benthonic organisms.

The floating oil can drift to the coastal areas and interact with the shoreline substrates after oil spill accidents. This process is demonstrated to be the cause of the formation of oil-contaminated sediments, which has attracted much attention. However, no systematic study has concerned the desorption process of oil from oil-contaminated sediments when the coastal hydrodynamic conditions change. This work determines the effects of wave conditions and particle size on the release of oil from artificially prepared sediments in a wave tank. Nonlinear fitting results show that the oil release kinetic curves can be correctly estimated with the Lagrangian first-order (LFO) first-order equation. Under different test conditions. The oil concentration in the water increases rapidly within 6 h. However, the oil desorption is inhibited thereafter and the process of sorption occurs dominantly. Under higher wave energy, the process of desorption is significantly enhanced and more large oil droplets release from sediments. Under the same wave condition, small oil droplets firstly release from the sediments. Besides, more oil especially with a larger size can release from larger sediment while oil releases more quickly from smaller sediment.

A pilot-scale six-compartment hybrid anaerobic baffled reactor (HABR) with effective volume of 18 m3 was used to treat dyeing wastewater. The HABR system was able to treat the wastewater efficiently after FeSO4 pretreatment, as indicated by removal efficiencies of 33.7% for chemical oxygen demand (COD), 39.9% for suspended solid (SS), and 22.5% for sulfate (SO42−) during steadily operational period. Gas chromatography–mass spectrometry (GC-MS) showed that the concentrations of alkanes, amides, organic acids, ketones, phenols, and esters were much lower in the effluent than those in the influent; many high-molecular-weight compounds such as cyclanes, quinolines, and phenols were successfully transformed to low-molecular-weight ones. As illustrated from the results of generalized grey relational analysis (GGRA), COD removal efficiency was more closely associated with flow rate, organic loading rate (OLR), water temperature, and influent SS among the whole selected possible factors. Based on the overall treating effectiveness and the GGRA study, the optimized operation strategy of the dyeing wastewater treatment by HABR was obtained as the hydraulic retention time (HRT) of 12 h for steady-state operation with an up-flow velocity of 1.7 m/h as well as OLR of 1.5–2.0 kg COD/(m3·d).

Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

A coal mine dual prevention construction evaluation model based on association rule mining and combined weighting cloud model is proposed. Firstly, based on association rule mining, the evaluation indicators for the effectiveness evaluation system of the dual prevention mechanism construction in coal mines are determined. Secondly, the analytic hierarchy process and anti entropy weight method are used to obtain the subjective and objective weights of the indicators. Then, the composite normalization method is used to obtain the combined weights of the indicators, and a cloud theory model is introduced to construct an evaluation model for coal mine dual prevention construction based on the cloud model. Finally, by analyzing a coal mine as a sample, the evaluation results of the model are compared with those of the analytic hierarchy process cloud model and the anti entropy weight method cloud model. The results indicate that the evaluation model is more scientific and accurate in the evaluation of dual prevention construction in coal mines, which helps to assist coal mines in establishing a scientific and reasonable “dual prevention” mechanism.

Abstract A ground-breaking roadmap of III-nitride solid-state deep-ultraviolet light emitters is demonstrated to realize the wafer-scale fabrication of devices in vertical injection configuration, from 2 to 4 inches. The epitaxial device structure is stacked on a GaN template instead of conventionally adopted AlN, where the primary concern of the tensile strain for Al-rich AlGaN on GaN is addressed via an innovative decoupling strategy, making the device structure decoupled from the underlying GaN template. Moreover, the strategy provides a protection cushion against the stress mutation during the removal of substrates. As such, large-sized wafers can be obtained without surface cracks, even after the removal of the sapphire substrates by laser lift-off. Wafer-scale fabrication of 280 nm vertical injection deep-ultraviolet light-emitting diodes is eventually demonstrated, where a light output power of 65.2 mW is achieved at a current of 200 mA, largely thanks to the significant improvement of light extraction. This work will definitely speed up the application of III-nitride solid-state deep-ultraviolet light emitters featuring high performance and scalability.