Microplastics (MPs) in soils have been widely studied, yet very little is known about their distribution in agricultural soils and the impact of mulching history. In this study, soil samples were taken across 3 soil layers of 60 sites with varying years of mulching history (20 years) in agricultural film-mulched cotton fields of Xinjiang, China. Microplastics were obtained from the soils using oil separation combined with density separation. Stereomicroscopy and Fourier transform infrared spectroscopy (FTIR) were used for identification. The average microplastic abundance of the sites with different years of mulching history are 538, 1484, 5812 and 9708 pieces/kg, respectively. The microplastics with sizes 1000–5000 and 200–500 μm are dominant in soils with less than 10 years and over 10 years of continuous mulching history, respectively. The results show that the abundance of microplastics increases and the size of microplastics decreases gradually as the number of years of mulching history increases. In addition, the best polynomial fitting curves were found between microplastic abundance (y) and mulching years (x) in different soil layers, and the relationship in the topsoil layer can be fitted as the following equation: y = 20.6x² = 41.39x + 198.65 (p < 0.01, R² = 0.62). The results indicate that residual agricultural mulching film is the dominating source of microplastics in cotton fields. This study provides rationale for further research on microplastics prediction in agricultural film-mulched fields.

Offshore drilling rigs are the foundation of oil and gas exploitation in water areas. Their spatial and temporal distribution, state attributes and other information directly reflect the exploitation level of regional oil and gas resources. Therefore, it is very important to build an automatic detecting method for offshore drilling rigs with good performance to accurately capture the temporal and spatial distribution and state of oil and gas exploitation activities. At present, there are two main groups of methods for offshore drilling rigs: invariant feature-based methods and nighttime firelight-based methods. Methods based on invariant location are more subjective in terms of their parameter settings and require intensive computation. Nighttime light-based methods are largely unable to identify offshore drilling rigs without associated waste gas ignition. Furthermore, multiple offshore drilling rigs in close proximity to one another cannot be effectively distinguished with low spatial resolution imagery. To address these shortcomings, we propose a new method for the automatic identification of offshore drilling rigs based on Landsat-7 ETM+ images from 2018 to 2019, taking the Caspian Sea as the research area. We build a nominal annual cloud and cloud shadow-free Normalized Difference Water Index (NDWI) composite by designing an optimal NDWI compositing method based of the influence of cloud and cloud shadow on the NDWI values of water, bare land (island) and offshore drilling rigs. The classification of these objects is simultaneously done during the compositing process, with the following rules: water body (Max_NDWI > 0.55), bare land (island) (Min_NDWI < −0.05) and offshore drilling rig (0 < Mean_NDWI < 0.4). A threshold segmentation and postprocessing were carried out to further refine the results. Using this method, 497 offshore platforms were automatically identified using a nominal annual cloud and cloud shadow-free NDWI composite image and Google Earth Engine. Validation using Sentinel-2 Multispectral Imager (MSI) and Google Earth images demonstrated that the correct rate of offshore drilling rig detection in the Caspian Sea is 90.2%, the missing judgment rate is 5.3% and the wrong judgment rate is 4.5%, proving the performance of the proposed method. This method can be used to identify offshore drilling rigs within a large water surface area relatively quickly, which is of great significance for exploring the exploitation status of offshore oil and gas resources. It can also be extended to finer spatial resolution optical remote sensing images; thus small-size drilling rigs can be effectively detected.

An increasing trend of research on microplastics (MPs) pollution in soil requires plenty of accurate data on MPs occurrence in soil samples. Efficient and economical methods of obtaining MP data are in development, especially for film MPs. We focused on MPs originating from agricultural mulching films (AMF) and presented an approach that can separate MPs in batches and identify them quickly. It mainly includes separation by ultrasonic cleaning and centrifugation, digestion of organic matter, and an AMF-MPs identification model. Adding olive oil or n-hexane to saturated sodium chloride constituted the best combination of separation solutions. Controlled experiments proved that the optimized methods improved the efficiency of this approach. The AMF-MPs identification model provides specific characteristics of MPs and can identify MPs efficiently. Evaluation results showed that the mean MP recovery rate reached 95%. The practical application demonstrated that this approach could conduct MPs analysis in soil samples in batches with less time and low cost.