Mapping global soil acidification under N deposition

Author:

Chen Chen1ORCID,Xiao Wenya2,Chen Han Y. H.1ORCID

Affiliation:

1. Faculty of Natural Resources Management Lakehead University Ontario Thunder Bay Canada

2. School of the Environment and Safety Engineering Jiangsu University Jiangsu Zhenjiang China

Abstract

AbstractSoil pH is critically important in regulating soil nutrients and thus influencing the biodiversity and ecosystem functions of terrestrial ecosystems. Despite the ongoing threat of nitrogen (N) pollution especially in the fast‐developing regions, it remains unclear how increasing N deposition affects soil pH across global terrestrial ecosystems. By conducting a global meta‐analysis with paired observations of soil pH under N addition and control from 634 studies spanning major types of terrestrial ecosystems, we show that soil acidification increases rapidly with N addition amount and is most severe in neutral‐pH soils. Grassland soil pH decreases most strongly under high N addition while wetlands are the least acidified. By extrapolating these relationships to global mapping, we reveal that atmospheric N deposition leads to a global average soil pH decline of −0.16 in the past 40 years and regions encompassing Eastern United States, Southern Brazil, Europe, and South and East Asia are the hotspots of soil acidification under N deposition. Our results highlight that anthropogenically amplified atmospheric N deposition has profoundly altered global soil pH and chemistry. They suggest that atmospheric N deposition is a major threat to global terrestrial biodiversity and ecosystem functions.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

General Environmental Science,Ecology,Environmental Chemistry,Global and Planetary Change

Reference66 articles.

1. Global Estimates of Inorganic Nitrogen Deposition Across Four Decades

2. Nitrogen alters carbon dynamics during early succession in boreal forest

3. Bates D. Bolker B. Walker S. Christensen R. H. B. Singmann H. Dai B. &Grothendieck G.(2019).lme4: Linear mixed‐effects models using Eigen and S4. R package Version 1.1‐23.https://cran.r‐project.org/web/packages/lme4/index.html

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