A database of atmospheric nitrogen concentration and deposition from the nationwide monitoring network in China

Abstract

Atmospheric nitrogen (N) deposition has increased substantially across China since 1980; however, data for N deposition fluxes since the 2000s has been very limited. Understanding and mitigating the impacts of N deposition requires long-term quantification of dry as well as wet deposition of key reactive nitrogen (Nr) species. Here we present a dataset for inorganic N concentrations and deposition for the period 2010–2015 in China, compiled from the nationwide deposition monitoring network. The dataset comprises information from 32 monitoring sites on concentrations and bulk deposition (wet plus part of dry deposition) fluxes of NH 4 + -N and NO 3 − -N, air concentrations and dry deposition fluxes of the major Nr species NH 3 , NO 2 , HNO 3 , and particulate NH 4 + and NO 3 − . This unique database is available inter alia to advance understanding of the spatial patterns of inorganic N concentrations and deposition in China and its associated effects, constrain primary Nr (e.g., NH 3 , NO x ) emission inventories, and validate outputs of atmospheric chemistry and transport models.

Chemical compositions of fog and precipitation at Sejila Mountain in the southeast Tibetan Plateau, China

“…Fog samples were collected with the CASCC when fog interception events were observed at the site and continued until the fog disappeared. After sampling, all collected fog water samples were mixed as one sample for each individual fog event, (Xu et al, 2015(Xu et al, , 2018(Xu et al, , 2019a…”

A high-resolution map of reactive nitrogen inputs to China

“…N fertilizer) is less uncertain than the data of N input to water system, since N input to cropland is calculated directly from governmental statistical data while N input to water is calculated from data of other subsystems (e.g., cropland, livestock, forest, human, wastewater and industry). The stability of CHANS model has been validated by international peers and we have published many papers using the data and methods of this model 6,[20][21][22] , and the data of CHANS model show similar spatial distribution with other studies 23,24 . Overall, CHANS-SD 1.0 database provides high-quality open-access information on N input to China's land and water.…”

Long-term trends of ambient nitrate (NO<sub>3</sub><sup>−</sup>) concentrations across China based on ensemble machine-learning models

“…The last cluster featured the opposite temporal trend to the second cluster during 2005-2010, which included many southern provinces such as Fujian, Guangdong, Zhejiang, and Guangxi. Although the central government proposed the emission reduction goal in 2006, the ambient NO − 3 concentrations in most provinces did not display pronounced decreases, which was totally different from the decrease in PM 2.5 since 2007 (Xue et al, 2019). Especially in the provinces of northeast China (e.g.…”