Air pollution weakens global spring greening

doi:10.21203/rs.3.rs-3868370/v1

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Air pollution weakens global spring greening

https://doi.org/10.21203/rs.3.rs-3868370/v1

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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

The ongoing climate change is causing significant changes to the terrestrial surface of the Earth⁵. As atmospheric CO₂ 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 ecosystems⁹ by increasing plant activity through elevated CO₂ 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 decades¹³. 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 health¹⁸, 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 processes¹⁹, 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 visibility¹⁷, 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 2000-2020²⁰ (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).

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 CO₂, 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 CO₂ 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_2.5levels 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_2.5raised the fraction of diffuse radiation (PAR_diff/PAR) and nitrogen deposition (N_deposition). Our structural equation models supported the hypothesis that PM_2.5decreased radiation, thereby reducing VC_max and LAI, but increased the fraction of diffuse radiation and N_deposition (Fig. 3 C-H). Increased PM_2.5concentration could also lower the ambient ozone (O₃) 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.

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 groups²³ 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 SO₄^2-, NO^3-, and NH⁴⁺, 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 photosynthesis²⁸.

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_2.5pollution 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 O₃ 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 deposition³³. 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_2.5effects 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 CO₂ 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.

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.

Competing interests: Authors declare that they have no competing interests.

Data and materials availability: All data are available in the supplementary materials. The specific link for each dataset can be found in the Supplementary Table 1. All the data analyses were performed using MATLAB or R.

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