Marine plastic debris has been a pervasive issue since the last century, and research on its sources and fates plays a vital role in the establishment of mitigation measures. However, data on the quantity of plastic waste that enters the sea on a certain timescale remain largely unavailable in China. Here, we established a model using material flow analysis method based on life cycle assessment to follow plastic product from primary plastic to plastic waste with statistical data and monitoring data from accurate sources. This model can be used to estimate and forecast the annual input of plastic waste into the sea from China until 2020. In 2011, 0.547 3–0.751 5 million tons of plastic waste entered the seas in China, with a growth rate of 4.55% per year until 2017. And the amount will decrease to0.257 1 to 0.353 1 million tons in 2020 under the influence of governmental management. The amount of plastic waste discharged from coastal areas calculated in this study was much larger than that from river, thus it is suggested to strengthen the governance and control of plastic waste in coastal fishery activities in China in order to reduce the amount of marine plastic waste input. 相似文献
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
