Ministry of Science

Electronic display-related organic materials (DROMs) are widely used in modern displays but remain largely overlooked as environmental contaminants. Here, we constructed the first comprehensive inventory of Chinese-produced DROMs by integrating data from public registries, corporate disclosures, patents, and Chemical Book listings, identifying 1,551 compounds and mapping their nationwide production distribution. In silico screening with T.E.S.T. and OPERA models across eight toxicological end points prioritized 117 substances with high predicted hazard potential. Of these, 46 were tested in zebrafish embryos encompassing diverse developmental end points, revealing that 24 (52.2%) induced significant toxicity at concentrations as low as 0.1 μM. These toxicants fell into six structural classes: fluorobenzenes, bromobenzenes, cyanobiphenyls, fluorenes, anthracenes, and bathocuproine, each displaying characteristic hazard profiles. Fluorobenzenes and cyanobiphenyls triggered broad developmental disruptions, whereas bromobenzenes primarily impaired pigmentation and behavior. Bathocuproine caused concentration-dependent lethality and severe multiorgan edema, while fluorenes and anthracenes heightened tactile responsiveness and reduced larval body length. Notably, 22 of these compounds have been detected in environmental matrices or human biomonitoring studies at levels approaching those causing toxicity in vivo, underscoring their environmental relevance. Collectively, this study reveals unrecognized developmental hazards associated with DROMs, providing critical preliminary data to inform future risk assessment and safer chemical design efforts.

Understanding the interdecadal periodicity of large-scale seismic activities is critical for improving long-term earthquake forecasting, yet it remains constrained by the limited duration of instrumental records. Here we apply high-resolution spectral analysis to a millennia-long catalog of historical earthquakes in China, extending to 1831 BC. The most pronounced seismic activities occurred during the 1620–1630 s AD, a period that coincided with a major regime shift that preceded the collapse of the Chinese Ming Dynasty. High-resolution spectral analysis was performed on the historical dataset to identify robust ~ 10- and ~ 50-year periodicities in seismic frequency. The ~ 10-year periodicity exhibits a significant lagged correlation with solar (sunspot) activity, while the ~ 50-year periodicity aligns with multidecadal variability in solar irradiance, sea level fluctuations, and tropical sea surface temperatures (SSTs). We propose that enhanced solar irradiance modulates the mean state and variability of tropical Pacific climate modes, particularly the El Niño–Southern Oscillation (ENSO), which is associated with changes in inter-basin sea-level gradients and crustal stress regimes. These findings reveal a potential coupling between external solar forcing and internal climate variability in shaping seismic cyclicity at interdecadal scales, offering a novel framework for assessing long-term earthquake risks.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-26103-z.

Demographic transitions are reshaping global patterns of residential energy consumption and associated carbon emissions, yet the implications of population aging remain underexplored. Here we use nationally representative survey data from over 24,000 U.S. households to quantify the impact of aging on residential energy demand and emissions from 1990 to 2020. We find that aging contributed approximately 2.2% to the rise in total residential energy use and 1.8% to the increase in associated emissions over this period. Due to aging, per capita energy consumption and emissions rose by 11.3% and 10.4%, respectively. Projections suggest that continued population aging could raise total residential energy consumption and emissions by 3.1–5.6% and 2.5–4.5%, respectively, by 2100 relative to 2020 levels, with per capita increases of 4.2–8.1% and 3.5–6.7%. These findings highlight the need for demographic-sensitive energy policies, such as promoting housing downsizing and reducing solo-living rates among older populations, to mitigate long-term energy demand and carbon emissions in aging societies.

<strong class="journal-contentHeaderColor">Abstract.</strong> Understanding future changes in compound climate extremes (CCEs) is critical for climate risk assessment. However, existing research have relied on stationary assumptions, overlooking the dynamic evolution of CCEs under non-stationary climate change. Therefore, based on an enhanced Generalized Additive Models for Location, Scale, and Shape (GAMLSS), this study provides novel perspectives into the non-stationary characteristics of hot-wet (HW), hot-dry (HD), cold-wet (CW), and cold-dry (CD) extremes under future climate scenarios, focusing on the Minjiang River Basin (MRB), located in Southeast China. The high-resolution dataset employed for CCEs detection is generated through dynamical downscaling of a bias-corrected CMIP6 dataset, utilizing the Weather Research and Forecasting (WRF) model. The results show that (1) CCEs increase significantly at a rate of 3.55d/10a under the SSP5-8.5 scenario, with hot extremes (HW and HD) playing a dominant role. The spatial distribution exhibits a distinct west to east increasing gradient, peaking in the MRB downstream areas. (2) Under the SSP5-8.5 scenario, CCEs exhibit a marked transition from stationary to non-stationary characteristics, with non-stationarity detected in 95.20 % of grid cells. Mean warming, not variability, served as the dominant factor behind this transition, explaining 80.81 % of the changes. (3) The non-stationary results demonstrate that the severity and recurrence risks of CCEs are systematically underestimated. Most CCEs (except for CD) exhibit increasing recurrence risks under the SSP5-8.5 scenario, with a trend of 3.12d/10a in the 100-year return period, showing a stronger increase. This study emphasizes the necessity of updating the risk changes of CCEs under a non-stationary framework.

<strong class="journal-contentHeaderColor">Abstract.</strong> The relationship between net primary productivity (NPP) and forest age varies among forest species, yet there were no available NPP&ndash;age relationships established for various forest species in subtropical China for use in forest carbon modeling. This study explored the NPP&ndash;age relationships for seven forest species in subtropical China using data from the Strategic Priority Project of Carbon Budget (SPPCB), National Forest Inventory (NFI) Type I (NFI-I), and Type II (NFI-II) data using the Semi-Empirical Model (SEM). Forest species included Pinus massoniana (P. massoniana), Cunninghamia lanceolata (C. lanceolata), Eucalyptus robusta (Eucalyptus), Other Coniferous Forests (OCF), Softwood Broadleaf (SWB), Hardwood Broadleaf excluding Eucalyptus (HWB), and Mixed Forests (MF). Based on these three datasets, we were able to derive subtropical forest species-specific NPP&ndash;age relationships. Compared with China-wide NPP&ndash;age relationships previously derived from the SPPCB dataset, these species-specific relationships derived in this study resulted in improved simulations of aboveground biomass for subtropical forests using a process-based InTEC model, suggesting that these species-specific NPP&ndash;age relationships are valuable for forest carbon modeling and management in subtropical China.