Evidence map›Paper›PMID 37258508›Full record

ArticleScientific data2023

High-resolution maps of critical loads for sulfur and nitrogen in China.

Xiaodong Ge, Qian Yu, Lei Duan, Yu Zhao, Maximilian Posch, Jiming Hao

Abstract readDataset
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaodong GeState Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
Qian YuState Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing, Jiangsu, 210023, China.
Lei DuanState Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China. lduan@tsinghua.edu.cn.
Yu ZhaoState Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing, Jiangsu, 210023, China.ORCID 0000-0002-0635-311X
Maximilian PoschInternational Institute for Applied System Analysis (IIASA), Schlossplatz 1, 2361, Laxenburg, Austria.
Jiming HaoState Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42277204
6 · The paper itself

Abstract

The critical load concept is an important scientific guideline for acid deposition control. It was not only a crucial scientific basis to determine the emission reduction targets in Europe, but also used in China's air pollution control, especially the designation of two control zones. Currently, critical loads of sulfur and nitrogen are still exceeded in Europe, America, and East Asia (mainly in China), and need to be continuously updated to meet the demands of further emission reductions. Critical loads of China were calculated and mapped in the 2000s, but are not sufficiently accurate due to methodological and data limitations. Here we present the latest high-quality critical loads for China, based on high-resolution basic data on soil, vegetation, and atmospheric base cations deposition, and up-to-date knowledge on important parameters. Our data, which is going to be included in GAINS-China, can be used to assess the ecological benefits of nitrogen and sulfur reductions in China at a regional or national scale, and to develop mitigation strategies in the future.

Identifiers

PMID37258508
PMCPMC10232513

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.