Research on historical climate plays a crucial referential role in understanding the climate and its variation patterns in specific regions and periods, as well as in predicting future climate change. This study focuses on woody plants recorded in Xijin Zhi Jiyi (Collected Fragments of the Xijin Zhi), a historical document depicting Beijing’s general condition over 650 years ago. Using textual research methods, 11 out of 19 recorded woody plant names were identified to species level, 3 to genus level, 1 to family level, 1 was identified as a non-native species, and 3 remained uncertain. Based on this identification, climate-related studies were carried out on the 11 species-confirmed woody plants using data from the Atlas of Woody Plants in China: Distribution and Climate and the Coexistence Approach. Six key climate parameters were determined. Statistical analysis indicates that over 650 years ago, mean annual temperature in Beijing was 0.04 °C higher than today. However, during the hottest month, temperatures were 6.82% cooler than modern values, while in the coldest month, they were 138.14% warmer. Precipitation in Beijing was 88.49% higher overall than present levels. In the warmest season, rainfall was 313.55% greater, and in the coldest season, it was 1313.67% higher. These results suggest that Beijing’s climate over 650 years ago was slightly warmer overall with less variability in temperature compared to the modern era. Precipitation was significantly higher than today. In general, the Beijing region had a warm and humid climate during that period.

The Earth functions as an integrated system in which the ocean, land, and atmosphere are connected through complex exchanges of matter and energy [...]

The North Atlantic Oscillation (NAO) represents the dominant atmospheric mode governing climate variability across the Northern Hemisphere, particularly influencing precipitation regimes in regions such as northern Algeria. This study investigates the nonlinear linkage between monthly NAO indices and rainfall over northern Algeria for the period 1970–2009 using a cross-multiresolution analysis framework based on seven wavelet families—Daubechies, Biorthogonal, Reverse Biorthogonal, Discrete Meyer, Symlets, Coiflets, and Fejer–Korovkin—comprising a total of 106 individual mother wavelets. More than 700 cross-correlations were computed per NAO–rainfall pair to identify wavelet families that yield stable and physically coherent teleconnection structures across seven decomposition scales (D1–A7). The maximum decomposition level (27 = 128 months, ≈10.6 years) captures intra-annual to decadal variability without extending into multidecadal regimes, ensuring temporal representativeness relative to the 40-year record length. The results reveal that short-term scales (D1–D3) are dominated by noise, masking weak correlations (≤±0.20), while stronger and more coherent relationships emerge at longer scales, reaching ±0.4 at the annual and ±0.75 at the decadal bands. These findings confirm the pronounced influence of low-frequency NAO variability on regional precipitation. Unlike previous studies limited to a few Daubechies wavelets, this work systematically compares 106 wavelet forms and evaluates robustness through reproducibility across scales, consistency among wavelet families, and physical coherence with known NAO periodicities (2–4 and 8–12 years). By emphasizing stability and physical plausibility over statistical significance alone, this approach minimizes the risk of spurious correlations due to multiple testing and highlights genuine scale-dependent teleconnection patterns. The application of discrete wavelet transforms thus enhances signal clarity, isolates meaningful oscillations, and provides a robust diagnostic framework for understanding NAO–rainfall dynamics in northern Algeria.

Air pollution, a critical environmental threat, has worsened alongside urbanization and industrialization, particularly in rapidly developing economies like India. Despite efforts to curb emissions, the concurrent rise in energy consumption, industrial activity, and digitalization complicates the fight against air pollution. This study examines the interplay between air pollution, economic growth, clean energy transition, digitalization, and urbanization in India from 1990Q1 to 2020Q4. Using advanced econometric techniques, including multivariate quantile-on-quantile regression (MQQR) and the quantile ADF and quantile KPSS tests, we investigate the complex, non-linear relationships across these factors. Our findings suggest that while economic growth exacerbates air pollution, the clean energy transition can mitigate its impact, especially when integrated with digitalization. However, the effects of digitalization are nuanced, potentially increasing pollution unless paired with green energy policies. The study demonstrates that the combined strategies of promoting clean energy and digitalization can provide a sustainable pathway for reducing air pollution in India. This work offers novel insights into the role of digital technologies in enhancing environmental sustainability and highlights the need for policy interventions that balance economic growth with climate resilience. The results present a roadmap for India’s sustainable development, emphasizing the integration of clean energy, digital innovation, and urban planning.

Shrinking cities are generally experiencing decreases in population, economic activity, and spatial expansion. However, whether this “low-growth” trajectory leads to an actual reduction in carbon emissions or is constrained by carbon lock-in effects and the complex interaction between urban shrinkage and carbon emissions remains unclear. To address this gap, this study examines 34 shrinking cities of the three northeastern provinces in China, utilizing nighttime light data to identify the spatial–temporal patterns of carbon emissions from a multidimensional perspective. Additionally, it explores the key drivers behind these emissions. Results show the following: (1) Spatiotemporally, carbon emissions are closely linked to shrinking cities, which also exhibit spatial–temporal heterogeneity. (2) There is a significant negative spatial correlation between carbon emissions and urban shrinkage degree (SD), with HL clusters (high–low clusters) and LH clusters (low–high clusters) being the main clustering types. (3) Through population, economic, and social driving factors, this paper identifies three synergistic effects shaping spatial–temporal carbon heterogeneity: passive reduction in economic scale (scale effect), volatility effect of structural transformation (structure effect), and spatial–institutional carbon lock-in (lock-in effect). The findings offer new insights into the low-carbon transition potential of shrinking cities and provide a basis for developing targeted policy frameworks to facilitate their sustainable transformation.

Atmospheric greenhouse gases (GHGs) affect Earth’s radiation balance and are the primary drivers of climate change. This study analyzed the spatiotemporal variability of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) at domestic World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) sites located at Anmyeondo (AMY), Jeju Gosan, and Ulleungdo, examining the local influences on GHG variations and comparing the relationships between gases. Long-term records from the AMY site were used to investigate temporal changes in CO2–CH4. The results showed that short-term variations were influenced by local emissions, sink processes, and anthropogenic signals, whereas medium-to-long-term variations displayed clear seasonality driven by vegetation and meteorological changes, with continuously increasing trends. The sites strongly reflected the effects of nearby point sources, and the ∆CO2–∆CH4 relationships revealed site-specific spatiotemporal differences. At AMY, 46–49% of top-quartile CO2, CH4, and N2O enhancements occurred under easterly winds from nearby industrial and agricultural sources, whereas 14–16% under northwesterly flow indicated episodic transport from eastern China, highlighting the site’s combined exposure to domestic and foreign emissions. The observed strengthening long-term ∆CO2–∆CH4 correlation may be related to continuously increasing emissions in East Asia. However, uncertainties remain, owing to changes resulting from the 2012 instrument replacement and calibration scale. Overall, this study provides baseline insights into domestic GHG variability and offers fundamental information for understanding East Asian emissions and supporting climate policy.

Carbon dioxide (CO2) and methane (CH4) are important greenhouse gases emitted by vehicles, yet their spatial pattern within urban road networks remains poorly characterized. This study conducts mobile measurements of CO2 and CH4 along the 1st Ring Road (mean speed of the monitoring vehicle: 22 km h−1), 2nd Ring Road (22 km h−1), and Ring Expressway (90 km h−1) in Xi’an, China, to investigate their spatial distribution and influencing factors. Average CO2 and CH4 concentrations on the three ring roads exhibited a clear decline with increasing distance from the city center. Both CO2 and CH4 showed higher concentrations at lower vehicle speeds and at higher traffic volumes, but CH4 hotspots differed spatially from CO2 and exhibited weaker dependence on vehicle speed, indicating additional off-road CH4 sources (e.g., natural gas leaks, nearby livestock farms). In a tunnel case study, CO2, CH4, and CO concentrations increased concurrently from entrance to exit, resulting in strong correlations between CH4 and CO2 (R2 = 0.86) and between CO and CO2 (R2 = 0.66). This produced emission ratios (ΔCH4/ΔCO2 = 0.160 ppb ppm−1; ΔCO/ΔCO2 = 3.151 ppb ppm−1), representative of incomplete combustion under congestion. Our results provide direct observational evidence linking urban traffic dynamics to greenhouse gas emissions and emphasize the need for congestion mitigation to reduce on-road climate-relevant emissions.

This study maps how the scholarly literature examines government actions in relation to carbon emissions, rather than estimating the impact of specific policies. We conduct a bibliometric science-mapping using the Web of Science Core Collection (2010–25 June 2025). After de-duplication, the corpus comprises 2212 publications. Using CiteSpace, VOSviewer, and Bibliometrix, we address three questions: (i) How have outputs, actors, and collaborations evolved? (ii) Which journals, authors, and works structure the field? (iii) How have research themes developed over time? We find sustained growth in publications, persistent cores around carbon pricing and regulatory approaches, and emerging clusters on carbon neutrality, innovation/green finance, and urbanization. To contextualize quantity with quality, we report age-adjusted citations (citations per year) and journal-quartile summaries for key units. A calibration subsample of citation contexts suggests that highly cited works are referenced both in substantive analytical ways and as passing mentions. Scope: This is a descriptive mapping; we do not present primary empirical data or causal estimates of policy effects. Implications: This review identifies concentrations, gaps, and research fronts to guide future empirical evaluations of policy effectiveness and cross-disciplinary work.

Volatile sulfur compounds (VSCs) are known to cause characteristic—and sometimes unpleasant—body odour. Human presence may influence the odour of indoor air; however, the contribution of skin-derived VSCs has not been thoroughly evaluated. This study aimed to elucidate the regional and whole-body dermal emission rates of VSCs—diallyl disulfide (DADS), allyl methyl sulfide (AMS), ethyl mercaptan (EMT), allyl mercaptan (AMT) and dimethyl trisulfide (DMTS)—by conducting simultaneous and multi-point measurements of dermal emissions from the human skin surface to assess their potential impact on indoor air quality. Dermal emission fluxes of VSCs were measured at 14 anatomical regions of 12 healthy young subjects using a passive flux sampler coupled with gas chromatography/mass spectrometry. These fluxes were converted to emission rates using regional body surface area, and the whole-body emission rates were subsequently used to estimate indoor air concentrations for comparison with the odour thresholds of each VSC. The results showed that although some regional differences in emission rates were observed among subjects, the large inter-individual variability ultimately led to no significant differences in whole-body emission rates of VSCs between males and females. Using the average whole-body emission rates across 12 subjects, the estimated indoor air concentrations of VSCs followed the descending order: EMT > AMT >> DMTS > AMS > DADS. The odour quotient was used to evaluate the impact of skin-derived VSCs on indoor air quality and indicated that EMT consistently contributes to indoor odour.

Black carbon (BC), as a potent light-absorbing aerosol, is mainly removed from the atmosphere through wet deposition. The efficiency of this process depends on the capacity of BC particles to serve as cloud condensation nuclei (CCN) or ice nuclei (IN). Newly emitted BC particles are typically small in size and highly hydrophobic, which limits their activation potential. However, atmospheric aging processes involving interactions with sulfates, nitrates, or organic matter enhance their hydrophilicity and nucleation capacity. Particle size serves as the critical link between aging and removal processes. Larger or coated BC particles are more readily activated and removed, while smaller particles require higher supersaturation levels. Both observations and models indicate that uncertainties in BC particle size distribution and aging processes lead to significant discrepancies in lifetime and transport estimates. This paper reviews recent research on the size dependence of wet removal of BC, evaluates current observational and modeling results, and proposes key research priorities to more accurately constrain its role in the climate system.