Monitoring offshore oil pollution using multi-class convolutional neural networks

Evaluating the impacts of drilling and extraction activities on the marine carbonate system in the natural gas fields of Beibu Gulf, Northern South China Sea

Natural gas fields are typically located in shallow gulfs. Previous studies have predominantly focused on gas leakage and its subsequent toxic effects on marine organisms; however, the impacts of accidental CO2 leaks on carbonate dynamics during drilling and extraction remain poorly understood. In this study, we investigate carbonate parameters in two gas fields to elucidate the influences of extraction activities and natural processes on carbonate dynamics in Beibu Gulf, situated in the northern South China Sea (nSCS). Our findings indicate that Beibu Gulf acts as a CO2 source during the late spring season, with an air-sea CO2 flux ranging from 1.1 to 4.4 mmol m-2 d-1. Spatially, higher sea surface pCO2 and temperatures were recorded at 479 ± 17 μatm and 29.6 ± 0.3 °C respectively in the Dongfang gas field within the inner gulf, compared to values of 462 ± 20 μatm and 27.6 ± 0.6 °C observed in the Ledong gas field located in the outer gulf. In the Ledong gas field, carbonate dynamics are primarily influenced by mixing between offshore subsurface water and river plumes, with no significant contributions from extraction activities noted. Conversely, dissolved inorganic carbon (DIC) levels within the Dongfang gas field exhibited two extremes: a consumption of 13 μmol kg-1 in surface waters alongside an addition of 20 μmol kg-1 in subsurface waters. Although enhanced biological production may lead to decreased surface pCO2 levels, elevated surface pCO2 values observed at Dongfang can likely be attributed to higher sea surface temperatures. In subsurface layers, we quantify and determine the origin of excess DIC based on δ13CDIC analysis, attributing these carbon inputs to organic matter respiration following surface biological utilization of atmospheric CO2. This study provides the first evidence of high biological DIC consumption in surface waters and DIC generation in subsurface waters within a shallow semi-enclosed bay, a phenomenon previously seen only in river estuaries. We demonstrate that natural processes predominantly govern carbonate dynamics within these gas fields, while any potential influences from extraction activities appear negligible.

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.

A timely and accurate approach to nearshore oil spill monitoring using deep learning and GIS

Oil spill accidents are a key contributor to marine pollution worldwide. Therefore, timely and effective oil spill detection is crucial for reducing marine pollution and enhancing environmental protection. Against this backdrop, this study explored two methods for performing nearshore on-site oil spill detection and segmentation, namely the U-net and Mask region-based convolutional neural network (R-CNN) methods. The U-net and Mask R-CNN models were revealed to exhibit acceptable and favorable performance, achieving overall accuracy of 77.01 % and 89.02 %, respectively. Subsequently, a verification system based on the Geographic Information System (GIS) was developed to improve the performance of the deep-learning model. With the integration of the verification system, the Mask R-CNN model achieved higher overall accuracy of 90.78 %. The feasibility of applying deep-learning methods to nearshore on-site oil spill monitoring was demonstrated through this study. In addition, the integration of the GIS not only assisted in the provision of oil spill information but also in the improvement of the deep-learning models. The timely, accurate, and effective method for nearshore on-site oil spill monitoring that this study explored can be applied to considerably improve traditional on-site oil spill monitoring, which has received limited academic attention in the last two decades.

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Utilization of gasification slag and petrochemical incineration fly ash for glass ceramic production

This study investigated glass ceramics produced using coal gasification slag (CGS) and petrochemical incineration fly ash (PIFA) to immobilize hazardous heavy metals such as Cr and As. However, the crystallization kinetics and stabilization behavior mechanism of different heavy metals in the petrochemical incineration fly ash-derived glass-ceramics remains unclear. And X-ray diffraction, differential scanning calorimetry, scanning electron microscopy, and inductively coupled plasma mass spectrometry were used to characterize glass and crystalline products. In this paper, we reported the crystallization kinetics and chemical leaching characteristics of the glass ceramic. A low crystallization activation energy of 121.49 kJ/mol was achieved from crystallization peak of several different heating rates around 850°C, implying that it is easier to produce the glass ceramics at that temperature. The Avrami parameter of the former crystallization was determined to be 1.23 ± .12, which indicated two-dimensional crystal growth with heterogeneous nucleation. The toxicity characteristic leaching procedure results indicated that the heavy metals were well solidified, and that the leaching concentration was significantly lower than the limit specified by governmental agencies. The potentially toxic element index of the parent glass and the two glass ceramics were 11.7, 5.8, and 3.6, respectively. Therefore, the conversion of hazardous petrochemical incineration fly ash and other solid waste into environmentally friendly glass ceramics shows considerable potential and reliability.

Oil spill identification in X-band marine radar image using K-means and texture feature

Marine oil pollution poses a serious threat to the marine ecological balance. It is of great significance to develop rapid and efficient oil spill detection methods for the mitigation of marine oil spill pollution and the restoration of the marine ecological environment. X-band marine radar is one of the important monitoring devices, in this article, we perform the digital X-band radar image by "Sperry Marine" radar system for an oil film extraction experiment. First, the de-noised image was obtained by preprocessing the original image in the Cartesian coordinate system. Second, it was cut into slices. Third, the texture features of the slices were calculated based on the gray-level co-occurrence matrix (GLCM) and K-means method to extract the rough oil spill regions. Finally, the oil spill regions were segmented using the Sauvola threshold algorithm. The experimental results indicate that this study provides a scientific method for the research of oil film extraction. Compared with other methods of oil spill extraction in X-band single-polarization marine radar images, the proposed technology is more intelligent, and it can provide technical support for marine oil spill emergency response in the future.

Dynamic pollution emission prediction method of a combined heat and power system based on the hybrid CNN-LSTM model and attention mechanism

Combined thermal power (CHP) production mode plays a more important role in energy production, but the impact of its pollutant emission on the natural environment is still difficult to eradicate. Traditional pollutant control adopts post-treatment process to degrade the generated pollutants, but there is little research on controlling the generation of pollutants from the source. Therefore, starting from the source, this paper predicts the pollutants through the prediction model, so as to provide countermeasures for production regulation and avoiding excessive emission. In this paper, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network, long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data

Reliable long-horizon PM_2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM_2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM_2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM_2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM_2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM_2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Facile Preparation of Hydrophobic PLA/PBE Micro-Nanofiber Fabrics via the Melt-Blown Process for High-Efficacy Oil/Water Separation

Polylactic acid (PLA) micro-nanofiber fabrics with a large specific surface area and excellent biodegradability are commonly used in oil/water separation; however, challenges remain due to their poor mechanical properties. Herein, a thermoplastic polylactic acid/propylene-based elastomer (PLA/PBE) polymer was prepared by blending PLA with PBE. Then, PLA/PBE micro-nanofiber fabrics were successfully prepared using a melt-blown process. The results show that the PLA/PBE micro-nanofiber fabric has a three-dimensional porous structure, improving the thermal stability and fluidity of the PLA/PBE blended polymers. The PLA/PBE micro-nanofiber fabric demonstrated a significantly reduced average fiber diameter and an enhanced breaking strength. Moreover, the water contact angle of the prepared samples is 134°, which suggests a hydrophobic capacity. The oil absorption rate of the fabric can reach 10.34, demonstrating excellent oil/water separation performance. The successful preparation of PLA/PBE micro-nanofiber fabrics using our new method paves the way for the large-scale production of promising candidates for high-efficacy oil/water separation applications.

A novel lightweight bilateral segmentation network for detecting oil spills on the sea surface

Accidental oil spills from pipelines or tankers have posed a big threat to marine life and natural resources. This paper presents a novel lightweight bilateral segmentation network for detecting oil spills on the sea surface. A novel deep-learning semantic-segmentation algorithm is firstly created for analyzing the characteristics of oil spill images. A Bilateral Segmentation Network (BiSeNetV2) is then selected as the basic network architecture and evaluated by using experimental comparison of the current mainstream networks on detection accuracy and real-time performances for oil samples. Furthermore, the Gather-and-Expansion (GE) layer of the semantic branch in the traditional network is redesigned and the parameter complexity is reduced. A dual attention mechanism is deployed in the two branches of the BiSeNetV2 to solve the problem of inter-class similarity. Finally, experimental results are given to show the good detection accuracy of the proposed network.