Evidence map›Paper›PMID 39556745›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

Increasing phosphorus loss despite widespread concentration decline in US rivers.

Wei Zhi, Hubert Baniecki, Jiangtao Liu, Elizabeth Boyer, Chaopeng Shen, Gary Shenk, Xiaofeng Liu, Li Li

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Riverine heat waves on the rise, outpacing air heat waves.Proceedings of the National Academy of Sciences of the United States of America · 2025
    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

8 authors.

Wei ZhiThe National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China.ORCID 0000-0001-5485-1095
Hubert BanieckiMI2.AI, University of Warsaw, Warsaw 00-927, Poland.ORCID 0000-0001-6661-5364
Jiangtao LiuDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802.ORCID 0000-0002-9219-8354
Elizabeth BoyerDepartment of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802.ORCID 0000-0003-4369-4201
Chaopeng ShenDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802.ORCID 0000-0002-0685-1901
Gary ShenkVirginia and West Virginia Water Science Center, United States Geological Survey, Richmond, VA 23228.ORCID 0000-0001-6451-2513
Xiaofeng LiuDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802.
Li LiDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802.ORCID 0000-0002-1641-3710

Funding

US Department of Energy DE-SC0016605US Department of Energy DE-SC0020146US National Science Foundation EAR- 2346471
6 · The paper itself

Abstract

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980-2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

Indexed as

PhosphorusRiversAgricultureEnvironmental MonitoringUnited StatesWater Pollutants, ChemicalPhosphorusWater Pollutants, Chemicalbig datachanging climatedeep learningphosphorus losswater quality

Identifiers

PMID39556745
PMCPMC11621846

What OpenQuestion holds

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Registered trials

None linked

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.