Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Visualization and Analysis of Global Vision Zero Studies and Policy Orientation in China.

As a policy that is widely used worldwide, Vision Zero is a long-term strategic goal for road safety in China. The aim of this paper is to examine the literature in the field of Vision Zero retrieved by the Web of Science (WOS) Core Collection database from 1997 to 2021 based on mapping knowledge domain (MKD) and bibliometric methods. In this paper, we analyzed the overall development level of Vision Zero at different stages using the statistical analysis of the distribution of literature years. Based on the analysis, four major research directions of Vision Zero are obtained through keywords co-occurrence analysis, including "Vision Zero in road safety", "vulnerable road users' risks", "effect of speed on severity", and "responsibility management for crashes". Furthermore, we explore the influence and development potential of each country and region of Vision Zero based on the number and citation frequency of the literature, and the distribution of time dimensions. Among the research topics, all countries and regions are clustered into four clusters, and the current status of Vision Zero for countries in the most influential clusters, which include Sweden, the United States, Australia, China, and Norway, have been presented. Finally, an analysis of road safety in China is carried out, which includes displaying the changes in the number of road accidents, fatalities, and serious injuries in China between 2009 and 2018, comparing the fatalities per 10,000 vehicles and the fatalities per 100,000 people between China and other countries, and describing China's strategic routes toward Vision Zero.