A global estimate of multiecosystem photosynthesis losses under microplastic pollution
Edited by Lewis H. Ziska, Columbia University, New York, NY; received November 17, 2024; accepted February 9, 2025 by Editorial Board Member Alan Hastings
Significance
Our study presents a global assessment of microplastic pollution’s impact on food security. By analyzing a comprehensive dataset of 3,286 records, we quantify the reduction in photosynthesis caused by microplastics across various ecosystems. This reduction is estimated to cause an annual loss of 109.73 to 360.87 million metric tons (MT) for crop production and 1.05 to 24.33 MT for seafood production. By reducing current environmental microplastic levels by 13%, these losses could be mitigated by 14.26 to 46.91 MT in crops and 0.14 to 3.16 MT in seafood. These findings underscore the urgency for effective plastic mitigation strategies and provide insights for international researchers and policymakers to safeguard global food supplies in the face of the growing plastic crisis.
Abstract
Understanding how ecosystems respond to ubiquitous microplastic (MP) pollution is crucial for ensuring global food security. Here, we conduct a multiecosystem meta-analysis of 3,286 data points and reveal that MP exposure leads to a global reduction in photosynthesis of 7.05 to 12.12% in terrestrial plants, marine algae, and freshwater algae. These reductions align with those estimated by a constructed machine learning model using current MP pollution levels, showing that MP exposure reduces the chlorophyll content of photoautotrophs by 10.96 to 12.84%. Model estimates based on the identified MP-photosynthesis nexus indicate annual global losses of 4.11 to 13.52% (109.73 to 360.87 MT·y−1) for main crops and 0.31 to 7.24% (147.52 to 3415.11 MT C·y−1) for global aquatic net primary productivity induced by MPs. Under scenarios of efficient plastic mitigation, e.g., a ~13% global reduction in environmental MP levels, the MP-induced photosynthesis losses are estimated to decrease by ~30%, avoiding a global loss of 22.15 to 115.73 MT·y−1 in main crop production and 0.32 to 7.39 MT·y−1 in seafood production. These findings underscore the urgency of integrating plastic mitigation into global hunger and sustainability initiatives.
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Data, Materials, and Software Availability
All study data are included in the article and/or supporting information.
Acknowledgments
This work was supported by the National Key R&D Program of China (2023YFC3711400), the National Natural Science Foundation of China (42222702), the Basic Science Center Project of the National Natural Science Foundation of China (52388101), and the NSF of Jiangsu Province (BK20220092). We thank the reviewers for their valuable comments and suggestions, which have greatly improved the quality of this study.
Author contributions
R.Z., H.Z., and F.D. designed research; R.Z., Z.Z., N.Z., and F.D. performed research; Z.Z., C. Liu, F.D., and Y.Z. contributed new reagents/analytic tools; R.Z., Z.Z., and N.Z. analyzed data; H.Z. and F.D. conceptualization, project administration, supervision; R.Z., F.Z., X.Z., Y.H., and Y.Y. data collection; H.Z., C. Liu, Y.W., C. Li, H.S., M.C.R., F.D., H.R., Y.Z., and B.X. paper review and editing; and R.Z., Z.Z., H.Z., and F.D. wrote the paper.
Competing interests
The authors declare no competing interest.
Supporting Information
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References
1
FAO, World Food and Agriculture – Statistical Yearbook 2022 (Food & Agriculture Org., Rome, 2022).
2
G. V. Lowry et al., Towards realizing nano-enabled precision delivery in plants. Nat. Nanotechnol. 19, 1255–1269 (2024).
3
On the plastics crisis, Nat. Sustainability 6, 1137–1137 (2023).
4
FAO, Assessment of Agricultural Plastics and Their Sustainability (A call for action, Rome, 2021).
5
B. Speißer, R. A. Wilschut, M. van Kleunen, Number of simultaneously acting global change factors affects composition, diversity and productivity of grassland plant communities. Nat. Commun. 13, 7811 (2022).
6
J. K. Moore et al., Sustained climate warming drives declining marine biological productivity. Science 359, 1139–1143 (2018).
7
W. R. Wieder, C. C. Cleveland, W. K. Smith, K. Todd-Brown, Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).
8
S. K. Kim et al., Arctic Ocean sediments as important current and future sinks for marine microplastics missing in the global microplastic budget. Sci. Adv. 9, eadd2348.
9
R. Shi et al., Toxicity mechanisms of nanoplastics on crop growth, interference of phyllosphere microbes, and evidence for foliar penetration and translocation. Environ. Sci. Technol. 58, 1010–1021 (2023).
10
Y. Wang et al., Positively charged microplastics induce strong lettuce stress responses from physiological, transcriptomic, and metabolomic perspectives. Environ. Sci. Technol. 56, 16907–16918 (2022).
11
X. D. Sun et al., Differentially charged nanoplastics demonstrate distinct accumulation in Arabidopsis thaliana. Nat. Nanotechnol. 15, 755–760 (2020).
12
M. S. Bank, D. M. Mitrano, M. C. Rillig, C. Sze Ki Lin, Y. S. Ok, Embrace complexity to understand microplastic pollution. Nat. Rev. Earth Environ. 3, 736–737 (2022).
13
M. Helal, N. B. Hartmann, F. R. Khan, E. G. Xu, Time to integrate “One Health Approach” into nanoplastic research. Eco-Environ. Health 2, 18–20 (2023).
14
L. Chen et al., A machine learning model that outperforms conventional global subseasonal forecast models. Nat. Commun. 15, 6425 (2024).
15
S. Zhong et al., Machine learning: New ideas and tools in environmental science and engineering. Environ. Sci. Technol. 55, 12741–12754 (2021).
16
X. Jia, T. Wang, H. Zhu, Advancing computational toxicology by interpretable machine learning. Environ. Sci. Technol. 57, 17690–17706 (2023).
17
S. Wang et al., Estimation of leaf photosynthetic capacity from leaf chlorophyll content and leaf age in a subtropical evergreen coniferous plantation. J. Geophys. Res. Biogeosci. 125, e2019JG005020 (2020).
18
A. Porcar-Castell et al., Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. Nat. Plants 7, 998–1009 (2021).
19
P. H. Raven, R. F. Evert, S. E. Eichhorn, Raven Biology of Plants. 8th edn. 7, 122–155 (W.H. Freeman and Company, 2013).
20
E. Prikaziuk, M. Migliavacca, Z. Su, C. van der Tol, Simulation of ecosystem fluxes with the SCOPE model: Sensitivity to parametrization and evaluation with flux tower observations. Remote Sens. Environ. 284, 113324 (2023).
21
D. Treutter, Significance of flavonoids in plant resistance: A review. Environ. Chem. Lett. 4, 147–157 (2006).
22
R. Matyssek et al., Growth and parasite defence in plants; The balance between resource sequestration and retention: In lieu of a guest editorial. Plant Biol.4, 133–136 (2002).
23
J. R. Jambeck et al., Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015).
24
G. G. Deme et al., Macro problems from microplastics: Toward a sustainable policy framework for managing microplastic waste in Africa. Sci. Total Environ. 804, 150170 (2022).
25
E. S. Okeke et al., Microplastic burden in Africa: A review of occurrence, impacts, and sustainability potential of bioplastics. Chem. Eng. J. Adv. 12, 100402 (2022).
26
S. Peng et al., Rice yields decline with higher night temperature from global warming. Proc. Natl. Acad. Sci. U.S.A. 101, 9971–9975 (2004).
27
D. B. Lobell, W. Schlenker, J. Costa-Roberts, Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
28
C. Lesk, P. Rowhani, N. Ramankutty, Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).
29
J. Fu et al., Extreme rainfall reduces one-twelfth of China’s rice yield over the last two decades. Nat. Food 4, 416–426 (2023).
30
Q. Zhang et al., Divergent effectiveness of irrigation in enhancing food security in droughts under future climates with various emission scenarios. npj Clim. Atmos. Sci. 6, 40 (2023).
31
FAO, Ifad, UNICEF, WFP and WHO, The State of Food Security and Nutrition in the World, Urbanization, Agrifood Systems Transformation and Healthy Diets across The Rural–urban Continuum (FAO, Rome, 2023).
32
F. Dang, Q. Wang, Y. Huang, Y. Wang, B. Xing, Key knowledge gaps for one health approach to mitigate nanoplastic risks. Eco-Environ. Health 1, 11–22 (2022).
33
L. J. W. van der Laan, T. Bosker, W. J. G. M. Peijnenburg, Deciphering potential implications of dietary microplastics for human health. Nat. Rev. Gastroenterol. Hepatol. 20, 340–341 (2023).
34
L. Cao et al., Vulnerability of blue foods to human-induced environmental change. Nat. Sustainability 6, 1186–1198 (2023).
35
T. Marshall, Dietary guidelines for Americans, 2010. J. Am. Dental Assoc. 142, 654–656 (2011).
36
WHO, Fao, UNU, Protein and Amino Acid Requirements in Human Nutrition in Report of A Joint FAO/WHO/UNU Expert Consultation Technical Report Series No 935 (WHO, Geneva, 2007).
37
J. Z. Koehn, J. P. Leape, E. H. Allison, Aquatic foods at the nutrition–environment nexus. Nat. Sustainability 6, 1497–1498 (2023).
38
R. Manu et al., Response of tropical forest productivity to seasonal drought mediated by potassium and phosphorus availability. Nat. Geosci. 17, 524–531 (2024).
39
C. Plaza et al., Ecosystem productivity has a stronger influence than soil age on surface soil carbon storage across global biomes. Commun. Earth Environ. 3, 233 (2022).
40
L. Yang, X. He, S. Ru, Y. Zhang, Herbicide leakage into seawater impacts primary productivity and zooplankton globally. Nat. Commun. 15, 1783 (2024).
41
E. Y. Kwon et al., Nutrient uptake plasticity in phytoplankton sustains future ocean net primary production. Sci. Adv. 8, eadd2475 (2022).
42
A. Ortega et al., Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).
43
R. M. Thompson et al., Food webs: Reconciling the structure and function of biodiversity. Trends in Ecol. Evol. 27, 689–697 (2012).
44
A. P. K. Tai, M. V. Martin, C. L. Heald, Threat to future global food security from climate change and ozone air pollution. Nat. Clim. Change 4, 817–821 (2014).
45
R. C. Henry et al., Global and regional health and food security under strict conservation scenarios. Nat. Sustainability 5, 303–310 (2022).
46
L. Jia, S. Evans, S. v. d. Linden, Motivating actions to mitigate plastic pollution. Nat. Commun. 10, 4582 (2019).
47
C. Morales-Caselles et al., An inshore–offshore sorting system revealed from global classification of ocean litter. Nat. Sustainability 4, 484–493 (2021).
48
UN, What is the Triple Planetary Crisis? (2022), https://unfccc.int/news/what-is-the-triple-planetary-crisis. Accessed 17 August 2024.
49
C. Li, H. Zhong, How to clear the obstacles blocking uptake of a global plastic treaty. Nature, (2025) in press.
50
J. Shi et al., Optimally estimating the sample standard deviation from the five-number summary. Res. Synth. Methods 11, 641–654 (2020).
51
J. Shi et al., Detecting the skewness of data from the five-number summary and its application in meta-analysis. Stat. Methods Medical Res. 32, 1338–1360 (2023).
52
F. Dang et al., Threats to terrestrial plants from emerging nanoplastics. ACS Nano. 16, 17157–17167 (2022).
53
W. Viechtbauer, Publication bias in meta-analysis: Prevention, assessment and adjustments. Psychometrika 72, 269–271 (2007).
54
M. B. Mathur, T. J. VanderWeele, Sensitivity analysis for publication bias in meta-analyses. J. R. Stat. Soc. Series C. Appl. Stat. 69, 1091–1119 (2020).
55
L. V. Hedges, J. Gurevitch, P. S. Curtis, The meta-analysis of response ratios in experimmental ecology. Ecology 80, 1150–1156 (1999).
56
J. Gurevitch, J. Koricheva, S. Nakagawa, G. Stewart, Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).
57
M. J. Lajeunesse, Bias and correction for the log response ratio in ecological meta-analysis. Ecology 96, 2056–2063 (2015).
58
D. Hu, C. Wang, A. M. O’Connor, A likelihood ratio test for the homogeneity of between-study variance in network meta-analysis. Syst. Rev. 10, 310 (2021).
59
J. Yan, X. Yan, S. Hu, H. Zhu, B. Yan, Comprehensive interrogation on acetylcholinesterase inhibition by ionic liquids using machine learning and molecular modeling. Environ. Sci. Technol. 55, 14720–14731 (2021).
60
Plastic Europe, Plastics – the fast facts 2023. https://plasticseurope.org/knowledge-hub/plastics-the-fast-facts-2023/. Accessed: July 2024.
61
C. van der Tol, W. Verhoef, A. Rosema, A model for chlorophyll fluorescence and photosynthesis at leaf scale. Agricul. Forest Meteorol. 149, 96–105 (2009).
62
C. van der Tol et al., A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops. Remote Sens. Environ. 186, 663–677 (2016), https://doi.org/10.1016/j.rse.2016.09.021.
63
C. van der Tol, J. A. Berry, P. K. E. Campbell, U. Rascher, Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence. J. Geophys. Res. Biogeosci. 119, 2312–2327 (2014).
64
Programme of the European Union, Climate data store. ERA5 hourly data on single levels from 1940 to present. (1940), https://doi.org/10.24381/cds.adbb2d47. Accessed: August 2024.
65
Copernicus, CLMS portfolio vegetation. https://land.copernicus.eu/global/products/lai. Accessed: August 2024.
66
M. Xu et al., Retrieving global leaf chlorophyll content from MERIS data using a neural network method. ISPRS J. Photogram. Remote Sens. 192, 66–82 (2022).
67
Y. Liu et al., Global photosynthetic capacity of C3 biomes retrieved from solar-induced chlorophyll fluorescence and leaf chlorophyll content. Remote Sens. Environ. 287, 113457 (2023).
68
C. van der Tol, W. Verhoef, J. Timmermans, A. Verhoef, Z. Su, An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences 6, 3109–3129 (2009).
69
M. Lu et al., A cultivated planet in 2010 – Part 1: The global synergy cropland map. Earth Syst. Scientif. Data 12, 1913–1928 (2020).
70
B. Ping, Y. Meng, C. Xue, F. Su, Oceanic primary production estimation based on machine learning. J. Geophys. Res. Oceans 128, e2022JC018980 (2023).
71
M. J. Behrenfeld, P. G. Falkowski, A consumer’s guide to phytoplankton primary productivity models. Limnol. Oceanogr. 42, 1479–1491 (1997).
72
L. Song et al., On the spatial and temporal variations of primary production in the South China Sea. IEEE Transac. Geosci. Remote Sens. 61, 1–14 (2023).
73
K. W. Lan, L. J. Lian, C. H. Li, P. Y. Hsiao, S. Y. Cheng, Validation of a primary production algorithm of vertically generalized production model derived from multi-satellite data around the waters of Taiwan. Remote Sens. 12, 1627 (2020).
74
Ocean Productivity, Standard VGPM http://orca.science.oregonstate.edu/npp_products.php.
75
D. Pauly, V. Christensen, Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).
76
E. Chassot et al., Global marine primary production constrains fisheries catches. Ecol. Lett. 13, 495–505 (2010).
77
Sea Around Us, Tools & data. Net parimary production of Large marine ecosystems. https://www.seaaroundus.org/. Accessed: July 2024.
78
FAO, Fisheries and aquaculture. FishStat. https://www.fao.org/fishery/en/fishstat. Accessed: June 2024.
79
FAO, Global perspectives studies. Food and agriculture projections to 2050. https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en. Accessed: June 2024.
80
UN, World population prospects 2024. https://population.un.org/wpp/. Accessed: June 2024.
81
FAO. FAOSTAT, Production value of agricultural production. https://www.fao.org/faostat/en/#data/QV. Accessed: July 2024.
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Copyright © 2025 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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Received: November 17, 2024
Accepted: February 9, 2025
Published online: March 10, 2025
Published in issue: March 18, 2025
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Acknowledgments
This work was supported by the National Key R&D Program of China (2023YFC3711400), the National Natural Science Foundation of China (42222702), the Basic Science Center Project of the National Natural Science Foundation of China (52388101), and the NSF of Jiangsu Province (BK20220092). We thank the reviewers for their valuable comments and suggestions, which have greatly improved the quality of this study.
Author contributions
R.Z., H.Z., and F.D. designed research; R.Z., Z.Z., N.Z., and F.D. performed research; Z.Z., C. Liu, F.D., and Y.Z. contributed new reagents/analytic tools; R.Z., Z.Z., and N.Z. analyzed data; H.Z. and F.D. conceptualization, project administration, supervision; R.Z., F.Z., X.Z., Y.H., and Y.Y. data collection; H.Z., C. Liu, Y.W., C. Li, H.S., M.C.R., F.D., H.R., Y.Z., and B.X. paper review and editing; and R.Z., Z.Z., H.Z., and F.D. wrote the paper.
Competing interests
The authors declare no competing interest.
Notes
This article is a PNAS Direct Submission. L.H.Z. is a guest editor invited by the Editorial Board.
Although PNAS asks authors to adhere to United Nations naming conventions for maps (https://www.un.org/geospatial/mapsgeo), our policy is to publish maps as provided by the authors.
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A global estimate of multiecosystem photosynthesis losses under microplastic pollution, Proc. Natl. Acad. Sci. U.S.A.
122 (11) e2423957122,
https://doi.org/10.1073/pnas.2423957122
(2025).
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