Air pollution weakens global spring greening

Air pollution weakens global spring greening | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Air pollution weakens global spring greening Chaoyang Wu, Hao Hua, Jian Wang, Lingwen Dong, Constantin Zohner, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3868370/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is causing widespread land surface greening in spring1–4, but the impacts of anthropogenic air pollution on these changes remain poorly understood. Using global ground and satellite observations of fine particulate matter ≤ 2.5 μm (PM2.5) from 2000 to 2020, here we show that PM2.5 concentration offsets global spring greening as indicated by significant decreases in the normalized difference vegetation index (NDVI), leaf area index (LAI), and solar-induced fluorescence (SIF). Our experiments and meta-analyses involving up to 104 worldwide species reveal that pollution-induced greenness declines are primarily due to physical blockage and damage to leaf stomata. However, factors such as increased diffuse radiation and nitrogen deposition may occasionally enhance greening. Moreover, we observed significant variations among state-of-art terrestrial ecosystem models in replicating these greenness declines, with incorrect representation of PM2.5 effects on vegetation greening for roughly one third of global land coverage, further underscoring the importance of empirical data for benchmarking these models. This study reveals the negative feedback between anthropogenic air pollution and terrestrial carbon uptake, emphasizing the critical need for major polluting countries to mitigate air pollution and CO2 emissions. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Climate-change ecology/Phenology Figures Figure 1 Figure 2 Figure 3 Figure 4 Main The ongoing climate change is causing significant changes to the terrestrial surface of the Earth 5 . As atmospheric CO 2 concentrations rise, understanding the impact of climate change on photosynthetic activity becomes critical to comprehending and predicting the future carbon cycle 6–8 . Climate warming has increased the carbon uptake potential of terrestrial ecosystems 9 by increasing plant activity through elevated CO 2 levels and nitrogen deposition 1,2 and through inducing earlier leaf onset in spring 10–12 . Greening of the terrestrial surface has therefore been observed worldwide over recent decades 13 . However, there are still poorly understood mechanisms that might counteract global greening trends and increases in plant photosynthesis, such as air pollution, that pose significant challenges to predicting future vegetation activity and carbon uptake. One of the major challenges with air pollution is the large increase in the emissions of fine particulate matter (PM 2.5 ), particularly due to rapidly growing cities with unprecedented urbanization 14,15 as well as increased wildfires globally 16,17 . PM 2.5 pollution adversely affects human health 18 , but its effects on ecosystems, particularly on land surface greening and carbon uptake in spring, are not well understood. While it is well established that human activities have a significant impact on ecosystem processes 19 , the extent to which PM 2.5 pollutants emitted globally over the past two decades have affected vegetation activity remains unknown. Due to its properties, including scattering mass efficiency and optical hygroscopicity, PM 2.5 pollution can alter the propagation of visible light and significantly reduce atmospheric visibility 17 , making it likely to have a strong effect on spring greening and vegetation activity through both biogeophysical and biochemical paths. To investigate this, we used a large-scale monitoring network that collected ground-sourced observations of PM 2.5 emissions at 3580 sites, as well as satellite records spanning 2000-2020 20 (Extended data Fig. 1 and Supplementary Table 1 and Fig. 1). We combined this data with moderate-resolution satellite data on the normalized difference vegetation index (NDVI), leaf area index (LAI) 21 , solar-induced fluorescence (SIF) 22 , and climate to quantify the effects of PM 2.5 emissions on spring greening and vegetation activity. We then studied the underlying mechanisms of the observed trends using experimental data on leaf morphology from 15 tree species (Supplementary Table 2), meta-analytical data from 104 worldwide species (Supplementary Table 3), and global flux measurements from 187 sites representing various plant functional types and ecosystem characteristics (Supplementary Table 4). Lastly, we examined the PM 2.5 effects on spring productivity using 16 state-of-art terrestrial ecosystem models (Supplementary Table 5). Results We tested for the effects of PM 2.5 pollution and climate change on satellite-observed spring greenness (NDVI, LAI, and SIF) through rigorous analysis of ground and satellite data (Supplementary Materials and Methods). Before that, we first examined and minimized the attenuation effects of PM 2.5 pollution on the greenness signals of satellite observation (Supplementary Figs. 2 and 3). For the global scale, spatially explicit results for NDVI, LAI and SIF using satellite-based PM 2.5 pollution were provided (Fig. 1 B-D). PM 2.5 pollution led to reduced spring greening in 59.7-64.9% of the studied area, with 12.2-14.3% being significant. In comparison, PM 2.5 pollution led to increased spring greening in ~38% of regions (e.g., west Australia, North Africa, high lands in the northern Europe), with only ~4% being statistically significant. Consistent results were observed for the USA, Europe and China where ground monitoring networks have been well established. The ground-sourced observations demonstrate that PM 2.5 pollution were associated with decreased spring greening, with median standard sensitives for NDVI, LAI and SIF of -0.33, -0.36 and -0.38, respectively (Fig. 1 A). We further conducted analyses to separate the greening effects of CO 2 , temperature, precipitation, vapor pressure deficit (VPD), and PM 2.5 (Fig. 1 E-F). Stepwise regression analysis confirmed the dominant negative impacts of PM 2.5 pollution on spring greening at site and global scales, the extent of which was comparable to the inhibiting effects of vapor pressure deficit (Extended Data Figs. 2 and 3). Given the potential multicollinearity of driving factors, we also used partial correlation and ridge regression analyses and found consistently and dominantly negative effects of PM 2.5 pollution on spring greening (Extended Data Fig. 4 and Supplementary Fig. 4). Overall, these findings reveal that PM 2.5 pollution inhibits photosynthesis and offsets the ongoing trends in land surface greening globally. To study the underlying mechanisms that drive the observed reductions in spring vegetation activity in response to PM 2.5 pollution, we conducted experiments on the leaf morphology of 15 widely-distributed tree species. We found that PM 2.5 can adhere to the leaf surface to varying degrees (Supplementary Fig. 5), potentially causing blockages and damage to leaf stomata, as observed through scanning electron microscopy (Fig. 2A1-A15). To further investigate the effects of PM 2.5 on gas exchange and photosynthesis, including stomatal size, density and conductance, transpiration rate, chlorophyll content, maximum CO 2 assimilation rate, potential photosynthetic capacity (F v /F m ), and photosynthetic rate, we conducted a meta-analysis based on 233 records of both experimental and observational results across 104 plant species worldwide (Fig. 2B). Overall, PM 2.5 exposure caused substantial reductions in stomatal size (−0.15, P < 0.01), stomatal conductance (−0.18, P < 0.01), chlorophyll content (−0.17, P < 0.01), transpiration rate (−0.27, P < 0.01), and photosynthetic rate (−0.18, P < 0.01). Similar results were found when separately analyzing the experimental and observational data (Supplementary Fig. 6). Notably, the significant decline in stomatal conductance and photosynthetic rate was identified as the key link between plant growth and PM 2.5 pollution. Further analysis of the effects of PM 2.5 on stomatal conductance using global flux measurements showed consistent results with the meta-analysis, indicating that increased PM 2.5 caused significant ( P < 0.05) decreases in stomatal conductance and vegetation productivity accordingly (Fig. 2C and Supplementary Figs. 7 and 8). In line with flux measurements, PM 2.5 pollution could lower canopy stomatal conductance and SIF based on gridded data, supporting that PM 2.5 effects on greening trends are linked with the gas exchange between air and the interior of leaf (Supplementary Fig. 9). To gain deeper insights into the underlying mechanisms that drive the correlation between PM 2.5 pollution and spring greening, we explored potential biogeophysical and biogeochemical paths for the correlation (Fig. 3). We found that elevated levels of PM 2.5 greatly reduced the amount of photosynthetically active radiation (PAR), with 64.7% of the grids showing a negative PM 2.5 -PAR correlation (16.2% of which were significant). In line with gridded data analysis, flux measurements confirmed the negative impacts of PM 2.5 on PAR (Supplementary Fig. 10). This adverse effect on photosynthesis led to substantial declines in the maximum rate of carboxylation (VC max ), a key indicator of leaf photosynthetic capacity. This was evidenced by 64.1% negative correlations (12.3% significant) compared to only 35.9% positive correlations (3.6% significant). Since VC max generally showed a positive correlation with SIF (62.0% positive vs. 1.2% negative, P < 0.05), higher PM 2.5 levels counteracted the process of spring greening (Fig. 3A). Similar trends were observed for LAI. Nonetheless, certain regions exhibited increased spring greening with higher PM 2.5 . In these areas, PM 2.5 raised the fraction of diffuse radiation (PAR diff /PAR) and nitrogen deposition (N deposition ). Our structural equation models supported the hypothesis that PM 2.5 decreased radiation, thereby reducing VC max and LAI, but increased the fraction of diffuse radiation and N deposition (Fig. 3 C-H). Increased PM 2.5 concentration could also lower the ambient ozone (O 3 ) levels in spring due to lower atmospheric radiation, potentially undermining vegetation photosynthetic activities (Extended data Fig. 5). We also tested the impact of PM 2.5 on air temperature and found no dominant relationship (Supplementary Fig. 11), suggesting PM 2.5 pollution effect was not determined by air temperature regulation. In a last step, we used the output from the TRENDY project to test the potential of 16 state-of-art terrestrial ecosystem models to reproduce the effects of PM 2.5 on gross primary productivity (GPP), a productivity-based indicator of greening (Supplementary Table 5). Overall, the ecosystem models captured the widespread and negative effects of PM 2.5 (Fig. 4A and Supplementary Fig. 12). The standard sensitivity of GPP to PM 2.5 across the models was -0.15 ± 0.05, which is comparable with the inhibiting effects of VPD (Fig. 4B). However, the large biases of PM 2.5 effects, indicated by relatively high standard deviation of PM 2.5 sensitivity among models, were detected in the central of Europe and the eastern of America (Fig. 4C), suggesting the large inconsistency and limitation of model projections. Pixel-to-pixel comparison of PM 2.5 sensitivities between satellite observations and model projections suggests an incorrect representation of PM 2.5 effects on vegetation greening in nearly 34% of areas (Fig. 4D), highlighting the need for incorporating PM 2.5 effects into future model improvement. Discussion This study discovered and quantified the adverse effects of PM 2.5 air pollution on global spring greening trends over the past two decades. The findings reveal that ambient PM 2.5 pollution has had a negative impact on spring greenness and carbon uptake, as it has offset global change-induced greening. These results deepen our understanding of the impacts of human activities, including economic and social developments, on regional ecosystem functioning and its consequences for climate change. Given that high concentrations of PM 2.5 are closely linked with industrialization and urbanization 16,17 , these results have important implications for the disparities across population and income groups 23 and emphasize the need for urgent action to reduce air pollution and greenhouse gas emissions in order to mitigate the negative impacts of human activity on the environment. We identified specific mechanisms that explain how PM 2.5 contamination leads to a decrease in spring greening. We suggest that the key factor through which PM 2.5 affects photosynthesis appears to be the regulation of leaf stomata 24,25 . High concentrations of PM 2.5 can adsorb harmful substances such as SO 4 2- , NO 3- , and NH 4+ , which can be detrimental to leaf growth 26,27 . Our results are supported by global flux data, an analysis of leaf morphology changes in 15 tree species, and a meta-analysis on 104 worldwide species which showed that PM 2.5 exposure significantly decreases stomatal conductance and photosynthetic rates. We also observed a significant decrease in the maximum rate of carboxylation, which is the single most important driver of leaf photosynthetic capacity, and which explains the declines in spring greenness and photosynthesis under high air pollution. These findings are consistent with the reported role of stomatal conductance in regulating photosynthesis 28 . We also identified additional factors contributing to the decline in spring greening due to PM 2.5 pollution. The significant reduction in radiation is likely to further contribute to the decreased greening observed in our study 29,30 . Lower radiation is closely tied to declines in the capacity for photosynthesis, represented here by the maximum rate of carboxylation (VC max ), leading to declines in LAI and SIF. Our results also indicated that PM 2.5 pollution contributed to enhanced greening in several regions. This positive effect is likely due to the increased fraction of diffuse radiation, elevated nitrogen deposition, and reduced risk of O 3 exposure, which can boost plant photosynthesis and carbon storage 31,32 . For instance, nitrogen oxides (NO x ) associated with PM 2.5 pollution can promote nitrogen deposition 33 . Ecosystem models projected an overall negative correlation between PM 2.5 and spring Gross Primary Productivity (GPP), confirming the widespread adverse effects of PM 2.5 emissions on spring greening. Despite these findings, the limitations in accurately reproducing spatial patterns in PM 2.5 effects highlight the need for a more accurate representation of the impacts of air pollution in terrestrial ecosystem models. In summary, our research forecasts that if air pollution, particularly PM 2.5 , is not adequately managed, it will impede spring greening in the future, countering the current trends of global greening. This is especially critical for regions with high levels of PM 2.5 emissions, where immediate action to reduce air pollution is necessary. The effectiveness of these measures will be evident in the mitigation of the negative impact on spring greening, an essential factor in reducing CO 2 emissions through the photosynthesis of terrestrial ecosystems. Our results have important implications for policy design and implementation, as they highlight the synergies and trade-offs among various policies aimed at achieving sustainable development, especially in the context of social equity and climate change. Furthermore, our study underscores the impacts of aerosols, particularly PM 2.5 , on climate change and highlights the uncertainties associated with future warming 34,35 . The detrimental influence of atmospheric pollution on spring greening reveals a critical feedback mechanism through which anthropogenic pollution may reduce the terrestrial carbon sink, further accelerating climate change. This underscores the critical importance of promoting sustainable development on a global scale. Declarations Acknowledgments: This work was funded by the National Natural Science Foundation of China (42125101) and the CAS Interdisciplinary Innovation Team (JCTD-2020-05). J.P. was funded by European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. J.P. was also financially supported by the Fundación Ramon Areces grant ELEMENTAL-CLIMATE, the Spanish Government grant PID2019-110521GB-I00, and the Catalan Government grant SGR 2017-1005. C.M.Z. was funded by SNF Ambizione grant PZ00P3_193646. We also appreciate the FLUXNET in providing the valuable measurements. Author contributions: C.W. proposed the original idea. C.W. M.G. and Q.G. designed the research. C.W., J.W., C.M.Z., J.P., and P.F. wrote the first draft of the manuscript. J.W. and L.D. performed the ground and remote sensing data analysis. J.W. and H.H. performed model simulation analysis. L.D. performed the meta-analysis. H.H. performed eddy-covariance flux data analysis. D.L. performed the experimental analysis. J.W. generated the remote sensing PM 2.5 data. All authors contributed to the writing of the manuscript. 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Sci. 40 , 8–34 (2012). Rosseel, Y. lavaan : An R Package for Structural Equation Modeling. J. Stat. Soft. 48 , (2012). Additional Declarations There is NO Competing Interest. Supplementary Files PM2.5NatureSI.docx SUPPLEMENTARY INFORMATION ExtendedData.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3868370","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":270504440,"identity":"ea631019-1a23-4b8f-80d7-97190f3b718e","order_by":0,"name":"Chaoyang 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Park","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wei","suffix":""},{"id":270504451,"identity":"1f38c35b-90b1-449b-9329-89abb8dc450f","order_by":11,"name":"Wenping Yuan","email":"","orcid":"","institution":"Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenping","middleName":"","lastName":"Yuan","suffix":""},{"id":270504452,"identity":"1d9863be-fdf9-48e4-8c89-a9ca35884ea2","order_by":12,"name":"Xiuzhi Chen","email":"","orcid":"https://orcid.org/0009-0003-5688-2997","institution":"School of Atmospheric Sciences, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xiuzhi","middleName":"","lastName":"Chen","suffix":""},{"id":270504453,"identity":"87cfae38-0617-4ed6-a34b-7fd932600e7c","order_by":13,"name":"Lei Chen","email":"","orcid":"https://orcid.org/0000-0001-7011-8782","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chen","suffix":""},{"id":270504454,"identity":"a067f000-7021-462f-902c-587d027de406","order_by":14,"name":"Yongshuo Fu","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yongshuo","middleName":"","lastName":"Fu","suffix":""},{"id":270504455,"identity":"4d9c90f7-5e33-4e50-8641-94fdc4800413","order_by":15,"name":"Jialing Li","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Jialing","middleName":"","lastName":"Li","suffix":""},{"id":270504456,"identity":"a2e71f63-5410-4064-b798-0af6eab6534c","order_by":16,"name":"Weimin Ju","email":"","orcid":"https://orcid.org/0000-0002-0010-7401","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Ju","suffix":""},{"id":270504457,"identity":"20bd1d13-ca11-4907-b9ca-9dc0f5515e51","order_by":17,"name":"Yanlian Zhou","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yanlian","middleName":"","lastName":"Zhou","suffix":""},{"id":270504458,"identity":"075c1370-b2fa-40de-a845-84815a04db80","order_by":18,"name":"Dan Liang","email":"","orcid":"","institution":"Agricultural Information Institute of CAAS","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Liang","suffix":""},{"id":270504459,"identity":"c83f3902-c85a-459d-aa70-b9dfc0abf65d","order_by":19,"name":"Pierre Friedlingstein","email":"","orcid":"https://orcid.org/0000-0003-3309-4739","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Friedlingstein","suffix":""},{"id":270504460,"identity":"2a95cd5a-3615-4643-b36e-9715285de64a","order_by":20,"name":"Stephen Sitch","email":"","orcid":"https://orcid.org/0000-0003-1821-8561","institution":"University of Exeter","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Sitch","suffix":""},{"id":270504461,"identity":"b5637aa7-0c7e-4995-87c2-096e0abecb86","order_by":21,"name":"Yuming Guo","email":"","orcid":"https://orcid.org/0000-0002-1766-6592","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Guo","suffix":""},{"id":270504462,"identity":"5881b047-358e-4c56-bf2c-c77c47060367","order_by":22,"name":"Quansheng Ge","email":"","orcid":"https://orcid.org/0000-0001-8712-8565","institution":"[email protected]","correspondingAuthor":false,"prefix":"","firstName":"Quansheng","middleName":"","lastName":"Ge","suffix":""}],"badges":[],"createdAt":"2024-01-16 02:05:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3868370/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3868370/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50539906,"identity":"0a245bde-fbbf-4c9e-975d-19b18906e44c","added_by":"auto","created_at":"2024-02-02 06:30:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":952061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpring greening sensitivity to PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e air pollution from site to global scales. A, \u003c/strong\u003eThe standard sensitivity (Supplementary Materials and Methods) of spring greening indicators (i.e., NDVI, LAI, and SIF) to ground-observed PM\u003csub\u003e2.5\u003c/sub\u003e pollution for the globe (upper-left boxplot) and regions (i.e., Europe, USA, and China). The crosses and points with colors indicate local level of PM\u003csub\u003e2.5\u003c/sub\u003e pollution. \u003cstrong\u003eB-D,\u003c/strong\u003e The standard sensitivity of spring greening indicators (i.e., \u003cstrong\u003eB:\u003c/strong\u003e NDVI, \u003cstrong\u003eC:\u003c/strong\u003e LAI, \u003cstrong\u003eD:\u003c/strong\u003e SIF) to satellite-observed PM\u003csub\u003e2.5\u003c/sub\u003e pollution from 2000 to 2020. The right panels represent variations of standard sensitivity along with latitude gradients. \u003cstrong\u003eE, F, \u003c/strong\u003eThe standard sensitivities of spring greening indicators to driving factors, i.e., CO\u003csub\u003e2\u003c/sub\u003e, temperature, precipitation, VPD, and PM\u003csub\u003e2.5\u003c/sub\u003e pollution, at site (\u003cstrong\u003eE\u003c/strong\u003e) and global (\u003cstrong\u003eF\u003c/strong\u003e) scales.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/164a58a2fd926247729911c8.png"},{"id":50539655,"identity":"fccb5bfb-ab71-4d0a-b7ed-2d83b829b07e","added_by":"auto","created_at":"2024-02-02 06:22:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":917582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eObservational evidence of the impacts of PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e pollution on leaf morphology, gas exchange and photosynthesis.\u003c/strong\u003e \u003cstrong\u003eA1-A15\u003c/strong\u003e, Leaf morphology of 15 tree species exposed to PM\u003csub\u003e2.5\u003c/sub\u003e pollution (Supplementary Table 2). The red circle highlights the PM\u003csub\u003e2.5\u003c/sub\u003e particles adhering to the leaf surface, potentially causing blockage and damage to leaf stomata. \u003cstrong\u003eB\u003c/strong\u003e, The effect of PM\u003csub\u003e2.5\u003c/sub\u003e on plant physiological indicators related to gas exchange and photosynthesis (Supplementary Table 3). A\u003csub\u003emax\u003c/sub\u003e represents the maximum rate of carbon assimilation, and F\u003csub\u003ev\u003c/sub\u003e/F\u003csub\u003em\u003c/sub\u003e represents the maximum quantum efficiency of Photosystem Π. The values in brackets represent the mean effect size (left) and number of species (right) in each estimate. \u003cem\u003eP\u003c/em\u003e-values are shown when the effect size of a variable is significant (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). \u003cstrong\u003eC\u003c/strong\u003e, The relationship between the monthly anomalies of PM\u003csub\u003e2.5\u003c/sub\u003e and stomatal conductance derived from global flux measurements (Supplementary Table 4). The subplot shows the relationship between anomalies of stomatal conductance and gross primary productivity (GPP).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/d8f0c60095fc9b7fe5cc67f2.png"},{"id":50539651,"identity":"2e92726e-66cd-461d-a8b8-a7cf9969d18d","added_by":"auto","created_at":"2024-02-02 06:22:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":381519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential mechanisms underlying the correlation between spring photosynthesis (i.e., SIF) and PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e pollution. A, \u003c/strong\u003eCorrelation of negative effects of PM\u003csub\u003e2.5\u003c/sub\u003e emissions. The maximum rate of carboxylation (VC\u003csub\u003emax\u003c/sub\u003e), photosynthetically active radiation (PAR), and LAI were considered to link PM\u003csub\u003e2.5\u003c/sub\u003e and SIF. \u003cstrong\u003eB, \u003c/strong\u003eCorrelation of positive effects of PM\u003csub\u003e2.5\u003c/sub\u003e emissions. The fraction of diffuse radiation (PAR\u003csub\u003ediff\u003c/sub\u003e/PAR) and nitrogen deposition (N\u003csub\u003edeposition\u003c/sub\u003e) were considered to link PM\u003csub\u003e2.5\u003c/sub\u003e and SIF. \u003cstrong\u003eC-H\u003c/strong\u003e, Structural equation model describing the biogeophysical and biogeochemical relationships between PM\u003csub\u003e2.5\u003c/sub\u003e emissions and SIF for negative (\u003cstrong\u003eC-E\u003c/strong\u003e) and positive paths (\u003cstrong\u003eF-H\u003c/strong\u003e) based on three levels of AQI-based PM\u003csub\u003e2.5\u003c/sub\u003e (Supplementary Materials and Methods).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/67f72261ac62f307f469c2fd.png"},{"id":50539907,"identity":"57b9abb7-3540-431a-bb06-cf2e0f27bb61","added_by":"auto","created_at":"2024-02-02 06:30:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":514687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e pollution on spring productivity stimulated by state-of-the-art terrestrial ecosystem models. A, C, \u003c/strong\u003eThe mean (\u003cstrong\u003eA\u003c/strong\u003e) and standard deviation (SD) (\u003cstrong\u003eC\u003c/strong\u003e) of standard sensitivity of spring gross primary productivity (GPP), generated by 16 ecosystem models (Table S5), to PM\u003csub\u003e2.5\u003c/sub\u003e pollution. \u003cstrong\u003eB\u003c/strong\u003e, The standard sensitivity of spring GPP to driving factors, including CO\u003csub\u003e2\u003c/sub\u003e, temperature, precipitation, vapor pressure deficit (VPD), and PM\u003csub\u003e2.5\u003c/sub\u003e. \u003cstrong\u003eD\u003c/strong\u003e, The consistency of PM\u003csub\u003e2.5\u003c/sub\u003e effect in direction between model- (Sen\u003csub\u003emodel\u003c/sub\u003e) and satellite-observation-based (Sen\u003csub\u003eobs\u003c/sub\u003e) analyses at four levels of AQI-based PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/c4515f60eb85adf1fdd3579c.png"},{"id":67863388,"identity":"d959b0d4-fecf-4885-bbca-c30ab48d3768","added_by":"auto","created_at":"2024-10-30 13:34:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3352428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/506b84fe-315b-4d37-bf65-5de17fff9ec4.pdf"},{"id":50539657,"identity":"dfcaa66c-ee58-4c2a-8d8b-720785ef2dc7","added_by":"auto","created_at":"2024-02-02 06:22:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3850484,"visible":true,"origin":"","legend":"SUPPLEMENTARY INFORMATION","description":"","filename":"PM2.5NatureSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/8b65fc6bfdc882a008d4c0fc.docx"},{"id":50539653,"identity":"47caf84f-a291-4247-a61b-f595562bbfea","added_by":"auto","created_at":"2024-02-02 06:22:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1786507,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-3868370/v1/9355b426d874dead91bac6db.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Air pollution weakens global spring greening","fulltext":[{"header":"Main","content":"\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eongoing climate change is causing significant changes to the terrestrial surface of the Earth\u003csup\u003e5\u003c/sup\u003e. As atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentrations rise, understanding the impact of climate change on photosynthetic activity becomes critical to comprehending and predicting the future carbon cycle\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. Climate warming has increased the carbon uptake potential of terrestrial ecosystems\u003csup\u003e9\u003c/sup\u003e by increasing plant activity through elevated CO\u003csub\u003e2\u003c/sub\u003e levels and nitrogen deposition\u003csup\u003e1,2\u003c/sup\u003e and through inducing earlier leaf onset in spring\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. Greening of the terrestrial surface has therefore been observed worldwide over recent decades\u003csup\u003e13\u003c/sup\u003e. However, there are still poorly understood mechanisms that might counteract global greening trends and increases in plant photosynthesis, such as air pollution, that pose significant challenges to predicting future vegetation activity and carbon uptake.\u0026nbsp;One of the major\u0026nbsp;challenges\u0026nbsp;with air pollution\u0026nbsp;is the large increase in the\u0026nbsp;emissions of fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e),\u0026nbsp;particularly\u0026nbsp;due to\u0026nbsp;rapidly growing cities\u0026nbsp;with\u0026nbsp;unprecedented urbanization\u003csup\u003e14,15\u003c/sup\u003e as well as increased wildfires globally\u003csup\u003e16,17\u003c/sup\u003e.\u0026nbsp;PM\u003csub\u003e2.5\u003c/sub\u003e pollution adversely affects human health\u003csup\u003e18\u003c/sup\u003e, but its effects on ecosystems, particularly on land surface greening and carbon uptake in spring, are not well understood.\u003c/p\u003e\n\u003cp\u003eWhile it is well established that human activities have a significant impact on ecosystem processes\u003csup\u003e19\u003c/sup\u003e, the extent to which PM\u003csub\u003e2.5\u003c/sub\u003e pollutants emitted globally over the past two decades have affected vegetation activity remains unknown. Due to its properties, including scattering mass efficiency and optical hygroscopicity, PM\u003csub\u003e2.5\u003c/sub\u003e pollution can alter the propagation of visible light and significantly reduce atmospheric visibility\u003csup\u003e17\u003c/sup\u003e, making it likely to have a strong effect on spring greening and vegetation activity\u0026nbsp;through both biogeophysical and biochemical paths. To investigate this, we used a large-scale monitoring network that collected ground-sourced observations of PM\u003csub\u003e2.5\u003c/sub\u003e emissions at 3580 sites, as well as satellite records spanning 2000-2020\u003csup\u003e20\u003c/sup\u003e (Extended data Fig. 1 and Supplementary Table 1 and Fig. 1). We combined this data with moderate-resolution satellite data on the normalized difference vegetation index (NDVI), leaf area index (LAI)\u003csup\u003e21\u003c/sup\u003e, solar-induced fluorescence (SIF)\u003csup\u003e22\u003c/sup\u003e, and climate to quantify the effects of PM\u003csub\u003e2.5\u003c/sub\u003e emissions on spring greening and vegetation activity. We then studied the underlying mechanisms of the observed trends using experimental data on leaf morphology from 15 tree species (Supplementary Table 2), meta-analytical data from 104 worldwide species (Supplementary Table 3), and global flux measurements from 187 sites representing various plant functional types and ecosystem characteristics (Supplementary Table 4). Lastly, we examined the PM\u003csub\u003e2.5\u003c/sub\u003e effects on spring productivity using 16 state-of-art terrestrial ecosystem models (Supplementary Table 5).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe tested for the effects of PM\u003csub\u003e2.5\u003c/sub\u003e pollution and climate change on satellite-observed spring greenness (NDVI, LAI, and SIF) through rigorous analysis of ground and satellite data (Supplementary Materials and Methods). Before that, we first examined and minimized the attenuation effects of PM\u003csub\u003e2.5\u003c/sub\u003e pollution on the greenness signals of satellite observation (Supplementary Figs. 2 and 3). For the global scale, spatially explicit results for NDVI, LAI and SIF using satellite-based PM\u003csub\u003e2.5\u003c/sub\u003e pollution were provided (Fig. 1 B-D). PM\u003csub\u003e2.5\u003c/sub\u003e pollution led to reduced spring greening in 59.7-64.9% of the studied area, with 12.2-14.3% being significant. In comparison, PM\u003csub\u003e2.5\u003c/sub\u003e pollution led to increased spring greening in ~38% of regions (e.g., west Australia, North Africa, high lands in the northern Europe), with only ~4% being statistically significant. Consistent results were observed for the USA, Europe and China where ground monitoring networks have been well established. The ground-sourced observations demonstrate that PM\u003csub\u003e2.5\u003c/sub\u003e pollution were associated with decreased spring greening, with median standard sensitives for NDVI, LAI and SIF of -0.33, -0.36 and -0.38, respectively (Fig. 1 A).\u003c/p\u003e\n\u003cp\u003eWe further conducted analyses to separate the greening effects of CO\u003csub\u003e2\u003c/sub\u003e, temperature, precipitation, vapor pressure deficit (VPD), and PM\u003csub\u003e2.5\u003c/sub\u003e (Fig. 1 E-F). Stepwise regression analysis confirmed the dominant negative impacts of PM\u003csub\u003e2.5\u003c/sub\u003e pollution on spring greening at site and global scales, the extent of which was comparable to the inhibiting effects of vapor pressure deficit (Extended Data Figs. 2 and 3). Given the potential multicollinearity of driving factors, we also used partial correlation and ridge regression analyses and found consistently and dominantly negative effects of PM\u003csub\u003e2.5\u003c/sub\u003e pollution on spring greening (Extended Data Fig. 4 and Supplementary Fig. 4). Overall, these findings reveal that PM\u003csub\u003e2.5\u003c/sub\u003e pollution inhibits photosynthesis and offsets the ongoing trends in land surface greening globally.\u003c/p\u003e\n\u003cp\u003eTo study the underlying mechanisms that drive the observed reductions in spring vegetation activity in response to PM\u003csub\u003e2.5\u003c/sub\u003e pollution, we conducted experiments on the leaf morphology of 15 widely-distributed tree species. We found that PM\u003csub\u003e2.5\u003c/sub\u003e can adhere to the leaf surface to varying degrees (Supplementary Fig. 5), potentially causing blockages and damage to leaf stomata, as observed through scanning electron microscopy (Fig. 2A1-A15). To further investigate the effects of PM\u003csub\u003e2.5\u003c/sub\u003e on gas exchange and photosynthesis, including stomatal size, density and conductance, transpiration rate, chlorophyll content, maximum CO\u003csub\u003e2\u003c/sub\u003e assimilation rate, potential photosynthetic capacity (F\u003csub\u003ev\u003c/sub\u003e/F\u003csub\u003em\u003c/sub\u003e), and photosynthetic rate, we conducted a meta-analysis based on 233 records of both experimental and observational results across 104 plant species worldwide (Fig. 2B). Overall, PM\u003csub\u003e2.5\u003c/sub\u003e exposure caused substantial reductions in stomatal size (\u0026minus;0.15, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), stomatal conductance (\u0026minus;0.18, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), chlorophyll content (\u0026minus;0.17, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), transpiration rate (\u0026minus;0.27, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), and photosynthetic rate (\u0026minus;0.18, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). Similar results were found when separately analyzing the experimental and observational data (Supplementary Fig. 6). Notably, the significant decline in stomatal conductance and photosynthetic rate was identified as the key link between plant growth and PM\u003csub\u003e2.5\u003c/sub\u003e pollution. Further analysis of the effects of PM\u003csub\u003e2.5\u003c/sub\u003e on stomatal conductance using global flux measurements showed consistent results with the meta-analysis, indicating that increased PM\u003csub\u003e2.5\u003c/sub\u003e caused significant (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) decreases in stomatal conductance and vegetation productivity accordingly (Fig. 2C and Supplementary Figs. 7 and 8). In line with flux measurements, PM\u003csub\u003e2.5\u003c/sub\u003e pollution could lower canopy stomatal conductance and SIF based on gridded data, supporting that PM\u003csub\u003e2.5\u003c/sub\u003e effects on greening trends are linked with the gas exchange between air and the interior of leaf (Supplementary Fig. 9).\u003c/p\u003e\n\u003cp\u003eTo gain deeper insights into the underlying mechanisms that drive the correlation between PM\u003csub\u003e2.5\u003c/sub\u003e pollution and spring greening, we explored potential biogeophysical and biogeochemical paths for the correlation (Fig. 3). We found that elevated levels of PM\u003csub\u003e2.5\u003c/sub\u003e greatly reduced the amount of photosynthetically active radiation (PAR), with 64.7% of the grids showing a negative PM\u003csub\u003e2.5\u003c/sub\u003e-PAR correlation (16.2% of which were significant). In line with gridded data analysis, flux measurements confirmed the negative impacts of PM\u003csub\u003e2.5\u003c/sub\u003e on PAR (Supplementary Fig. 10). This adverse effect on photosynthesis led to substantial declines in the maximum rate of carboxylation (VC\u003csub\u003emax\u003c/sub\u003e), a key indicator of leaf photosynthetic capacity. This was evidenced by 64.1% negative correlations (12.3% significant) compared to only 35.9% positive correlations (3.6% significant). Since VC\u003csub\u003emax\u003c/sub\u003e generally showed a positive correlation with SIF (62.0% positive vs. 1.2% negative, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), higher PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003elevels counteracted the process of spring greening (Fig. 3A). Similar trends were observed for LAI. Nonetheless, certain regions exhibited increased spring greening with higher PM\u003csub\u003e2.5\u003c/sub\u003e. In these areas, PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eraised the fraction of diffuse radiation (PAR\u003csub\u003ediff\u003c/sub\u003e/PAR) and nitrogen deposition (N\u003csub\u003edeposition\u003c/sub\u003e). Our structural equation models supported the hypothesis that PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003edecreased radiation, thereby reducing VC\u003csub\u003emax\u003c/sub\u003e and LAI, but increased the fraction of diffuse radiation and N\u003csub\u003edeposition\u003c/sub\u003e (Fig. 3 C-H). Increased PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003econcentration could also lower the ambient ozone (O\u003csub\u003e3\u003c/sub\u003e) levels in spring due to lower atmospheric radiation, potentially undermining vegetation photosynthetic activities (Extended data Fig. 5). We also tested the impact of PM\u003csub\u003e2.5\u003c/sub\u003e on air temperature and found no dominant relationship (Supplementary Fig. 11), suggesting PM\u003csub\u003e2.5\u003c/sub\u003e pollution effect was not determined by air temperature regulation.\u003c/p\u003e\n\u003cp\u003eIn a last step, we used the output from the TRENDY project to test the potential of 16 state-of-art terrestrial ecosystem models to reproduce the effects of PM\u003csub\u003e2.5\u003c/sub\u003e on gross primary productivity (GPP), a productivity-based indicator of greening (Supplementary Table 5). Overall, the ecosystem models captured the widespread and negative effects of PM\u003csub\u003e2.5\u003c/sub\u003e (Fig. 4A and Supplementary Fig. 12). The standard sensitivity of GPP to PM\u003csub\u003e2.5\u003c/sub\u003e across the models was -0.15 \u0026plusmn; 0.05, which is comparable with the inhibiting effects of VPD (Fig. 4B). However, the large biases of PM\u003csub\u003e2.5\u003c/sub\u003e effects, indicated by relatively high standard deviation of PM\u003csub\u003e2.5\u003c/sub\u003e sensitivity among models, were detected in the central of Europe and the eastern of America (Fig. 4C), suggesting the large inconsistency and limitation of model projections. Pixel-to-pixel comparison of PM\u003csub\u003e2.5\u003c/sub\u003e sensitivities between satellite observations and model projections suggests an incorrect representation of PM\u003csub\u003e2.5\u003c/sub\u003e effects on vegetation greening in nearly 34% of areas (Fig. 4D), highlighting the need for incorporating PM\u003csub\u003e2.5\u003c/sub\u003e effects into future model improvement.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study discovered and quantified the adverse effects of PM\u003csub\u003e2.5\u003c/sub\u003e air pollution on global spring greening trends over the past two decades. The findings reveal that ambient PM\u003csub\u003e2.5\u003c/sub\u003e pollution has had a negative impact on spring greenness and carbon uptake, as it has offset global change-induced greening. These results deepen our understanding of the impacts of human activities, including economic and social developments, on regional ecosystem functioning and its consequences for climate change. Given that high concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e are closely linked with industrialization and urbanization\u003csup\u003e16,17\u003c/sup\u003e, these results have important implications for the disparities across population and income groups\u003csup\u003e23\u003c/sup\u003e and emphasize the need for urgent action to reduce air pollution and greenhouse gas emissions in order to mitigate the negative impacts of human activity on the environment.\u003c/p\u003e\n\u003cp\u003eWe identified specific mechanisms that explain how PM\u003csub\u003e2.5\u003c/sub\u003e contamination leads to a decrease in spring greening. We suggest that the key factor through which PM\u003csub\u003e2.5\u003c/sub\u003e affects photosynthesis appears to be the regulation of leaf stomata\u003csup\u003e24,25\u003c/sup\u003e. High concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e can adsorb harmful substances such as SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e, NO\u003csup\u003e3-\u003c/sup\u003e, and NH\u003csup\u003e4+\u003c/sup\u003e, which can be detrimental to leaf growth\u003csup\u003e26,27\u003c/sup\u003e. Our results are supported by global flux data, an analysis of leaf morphology changes in 15 tree species, and a meta-analysis on 104 worldwide species which showed that PM\u003csub\u003e2.5\u003c/sub\u003e exposure significantly decreases stomatal conductance and photosynthetic rates. We also observed a significant decrease in the maximum rate of carboxylation, which is the single most important driver of leaf photosynthetic capacity, and which explains the declines in spring greenness and photosynthesis under high air pollution. These findings are consistent with the reported role of stomatal conductance in regulating photosynthesis\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe also identified additional factors contributing to the decline in spring greening due to PM\u003csub\u003e2.5\u003c/sub\u003e pollution. The significant reduction in radiation is likely to further contribute to the decreased greening observed in our study\u003csup\u003e29,30\u003c/sup\u003e. Lower radiation is closely tied to declines in the capacity for photosynthesis, represented here by the maximum rate of carboxylation (VC\u003csub\u003emax\u003c/sub\u003e), leading to declines in LAI and SIF. Our results also indicated that PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003epollution contributed to enhanced greening in several regions. This positive effect is likely due to the increased fraction of diffuse radiation, elevated nitrogen deposition, and reduced risk of O\u003csub\u003e3\u003c/sub\u003e exposure, which can boost plant photosynthesis and carbon storage\u003csup\u003e31,32\u003c/sup\u003e. For instance, nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e) associated with PM\u003csub\u003e2.5\u003c/sub\u003e pollution can promote nitrogen deposition\u003csup\u003e33\u003c/sup\u003e. Ecosystem models projected an overall negative correlation between PM\u003csub\u003e2.5\u003c/sub\u003e and spring Gross Primary Productivity (GPP), confirming the widespread adverse effects of PM\u003csub\u003e2.5\u003c/sub\u003e emissions on spring greening. Despite these findings, the limitations in accurately reproducing spatial patterns in PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eeffects highlight the need for a more accurate representation of the impacts of air pollution in terrestrial ecosystem models.\u003c/p\u003e\n\u003cp\u003eIn summary, our research forecasts that if air pollution, particularly PM\u003csub\u003e2.5\u003c/sub\u003e, is not adequately managed, it will impede spring greening in the future, countering the current trends of global greening. This is especially critical for regions with high levels of PM\u003csub\u003e2.5\u003c/sub\u003e emissions, where immediate action to reduce air pollution is necessary. The effectiveness of these measures will be evident in the mitigation of the negative impact on spring greening, an essential factor in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions through the photosynthesis of terrestrial ecosystems. Our results have important implications for policy design and implementation, as they highlight the synergies and trade-offs among various policies aimed at achieving sustainable development, especially in the context of social equity and climate change. Furthermore, our study underscores the impacts of aerosols, particularly PM\u003csub\u003e2.5\u003c/sub\u003e, on climate change and highlights the uncertainties associated with future warming\u003csup\u003e34,35\u003c/sup\u003e. The detrimental influence of atmospheric pollution on spring greening reveals a critical feedback mechanism through which anthropogenic pollution may reduce the terrestrial carbon sink, further accelerating climate change. This underscores the critical importance of promoting sustainable development on a global scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e This work was funded by the National Natural Science Foundation of China (42125101) and the CAS Interdisciplinary Innovation Team (JCTD-2020-05). J.P. was funded by European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. J.P. was also financially supported by the Fundaci\u0026oacute;n Ramon Areces grant ELEMENTAL-CLIMATE, the Spanish Government grant PID2019-110521GB-I00, and the Catalan Government grant SGR 2017-1005. C.M.Z. was funded by SNF Ambizione grant PZ00P3_193646. We also appreciate the FLUXNET in providing the valuable measurements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eC.W.\u0026nbsp;proposed the original idea.\u0026nbsp;C.W. M.G. and Q.G. designed the research.\u0026nbsp;C.W., J.W., C.M.Z., J.P., and P.F. wrote the first draft of the manuscript.\u0026nbsp;J.W. and L.D. performed the ground and remote sensing data analysis. J.W. and H.H. performed model simulation analysis. L.D. performed the meta-analysis. H.H. performed eddy-covariance flux data analysis. D.L. performed the experimental analysis. J.W. generated the remote sensing PM\u003csub\u003e2.5\u003c/sub\u003e data. All authors contributed to the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e All data are available in the supplementary materials. The specific link for each dataset can be found in the Supplementary Table 1. 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Using global ground and satellite observations of fine particulate matter ≤ 2.5 μm (PM2.5) from 2000 to 2020, here we show that PM2.5 concentration offsets global spring greening as indicated by significant decreases in the normalized difference vegetation index (NDVI), leaf area index (LAI), and solar-induced fluorescence (SIF). Our experiments and meta-analyses involving up to 104 worldwide species reveal that pollution-induced greenness declines are primarily due to physical blockage and damage to leaf stomata. However, factors such as increased diffuse radiation and nitrogen deposition may occasionally enhance greening. Moreover, we observed significant variations among state-of-art terrestrial ecosystem models in replicating these greenness declines, with incorrect representation of PM2.5 effects on vegetation greening for roughly one third of global land coverage, further underscoring the importance of empirical data for benchmarking these models. This study reveals the negative feedback between anthropogenic air pollution and terrestrial carbon uptake, emphasizing the critical need for major polluting countries to mitigate air pollution and CO2 emissions.","manuscriptTitle":"Air pollution weakens global spring greening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 06:22:06","doi":"10.21203/rs.3.rs-3868370/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"249a30b8-0176-4ad2-a538-e29cf0a0d35a","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28516424,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":28516425,"name":"Earth and environmental sciences/Ecology/Climate-change ecology/Phenology"}],"tags":[],"updatedAt":"2024-10-30T13:26:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 06:22:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3868370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3868370","identity":"rs-3868370","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}