Sources and trends of oxidized and reduced nitrogen wet deposition in a typical medium-sized city of eastern China during 2010-2016

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.

Wet Inorganic Nitrogen Deposition at the Daheitin Reservoir in North China: Temporal Variation, Sources, and Biomass Burning Influences

Atmospheric nitrogen deposition is of great concern to both air quality and the ecosystem, particularly in northern China, which covers one-quarter of China’s cultivated land and has many heavily air polluted cities. To understand the characteristics of wet N deposition at rural sites in northern China, one-year wet deposition samples were collected in the Daheitin reservoir region. Due to the intense emissions of gaseous nitrogen compounds from heating activities during cold seasons and distinct dilution effects under different rainfall intensities and frequencies, the volume weighted mean concentrations of wet N deposition showed higher levels in dry seasons but lower levels in wet seasons. In contrast, the wet N deposition rates varied consistently with precipitation, i.e., high during the wet season and lower during the dry season. The annual wet deposition rate of total inorganic ions (the sum of NO3−–N and NH4+–N) at the rural site in North China from July 2019 to June 2020 was observed at 18.9 kg N ha−1 yr−1, still remained at a relatively high level. In addition, biomass burning activities are ubiquitous in China, especially in northern China; however, studies on its impact on wet N deposition are limited. Non-sea salt potassium ion (nss-K+) was employed as a molecular tracer to investigate the characteristics of biomass burning activities as well as their impact on the chemical properties of wet N deposition. Three precipitation events with high nss-K+ levels were captured during the harvest season (June to July). The variations in the patterns of nss-K+, deposited N species, and ratios of nss-K+ to nitrogen species as well as their relationships all indicated that biomass burning emissions contributed remarkably to NO3−–N but had a minor influence on NH4+–N.