In light of the specific characteristics of the long-term record and complex sequence nature of the ocean observation data, a new method was developed based on the original Dixon detection criteria to be specifically detect and remove data outliers. This method combines the two traditional methods of data quality control and Dixon detection theory and assumes that the second-order differential sequence of parameter measurements passes an appropriate stationarity test. Thus, the measurement attributes are considered to be in the same physical state and to occupy a small range of time and space, equivalent to a parallel observation test. Provided that the observations over a small range of time and space correspond to the record of a sequence covering a short period of time, this short time sequence is treated as a sliding window in the proposed new method. Outliers are detected based on lookup-table after an index parameter Q is calculated within the sliding window. A correlation analysis and the test results show that the proposed new method can effectively instantiate a sequence of outliers characterized by different phases. Compared with other existing methods, the new method proved to be computationally efficient and easily programmable for practical implementation. Further, this method preserves the original data because the outliers are replaced by an inverse distance-weighted average of the recorded data within the window, while other data were intact.Because resources in terrestrial ecosystems are limited, the efficient use of ocean resources is extremely important for economic development and carbon-reduction policies (Pan et al. 2012). In recent years, the speed with which marine resources have been exploited is much greater than ever before. The capacity for ocean and coastal monitoring has been significantly improved to more efficiently manage human activities in marine resource development and natural marine disasters. However, the quality of ocean observations must be ensured to provide accurate information for operational management practices. The control and improvement of data quality are the backbone of meaningful analysis and information extraction in business and scientific research (Shi et al. 2000). Similarly, quality control is an important part of ocean data assimilation systems. If erroneous data are assimilated, they can cause immediate spurious disruptive overturning and error propagation because of the sparse distribution of oceanographic data (Ingleby and Huddleston 2007;Cosme et al. 2010).Presently, global ocean science has entered a multidisciplinary and three-dimensional age. An integral marine integrated monitoring system consists of three parts: marine environment integrated observation, digital communication and management, and data processing and applications. The forms of ocean observation can be remote sensing of satellites and aircrafts and in situ observation of scientific research ships, offshore fixed stations and buoy stations (Fig. 1). Offshore fixed-station obs...

Waste load allocation (WLA), as a well-known total pollutant control strategy, is designed to distribute pollution responsibilities among polluters to alleviate environmental problems, but the current policy is unfair and limited to single scale or single pollution types. In this paper, a new, alternative, multi-scale, and multi-pollution WLA modeling framework was developed, with a goal of producing optimal and fair allocation quotas at multiple scales. The new WLA modeling framework integrates multi-constrained environmental Gini coefficients (EGCs) and Delphi-analytic hierarchy process (Delphi-AHP) optimization models to achieve the stated goal. The new WLA modeling framework was applied in a case study in the Xian-jiang watershed in Zhejiang Province, China, in order to test its validity and usefulness. The results, in comparison with existing practices by the local governments, suggest that the simulated pollutant load quota at the watershed scale is much fairer than the existing policies and even has some environmental economic benefits at the pollutant source scale. As the new WLA is a process-based modeling framework, it should be possible to adopt this approach in other similar geographic areas.