Abstract Quantifying changes in flowering phenology is a well-established method of determining the impact of climate change on an ecosystem, as changes in flowering timing are often the first and most visible indicator of environmental shifts. However, previous assessments of flowering timing have been limited to historical climate records. Here we assess the estimated response of flowering for 19 tropical species to moderate and high future carbon dioxide emissions using ten fully coupled global climate models and demonstrate the utility of this framework for additional taxa. Our calculations reveal robust flowering responses to both moderate and high emissions scenarios, with average projected changes in flowering of roughly one month (absolute change in date) by the end of this century, with significant diversity of this response across taxa.

Abstract Polycyclic Aromatic Hydrocarbons (PAHs) were quantified in the surface sediments at various sites along the Kenyan coast, namely Mwache Creek, Tudor Creek, Mida Creek and Sabaki river estuary. Total parent PAH concentrations in the sediments ranged from 0.94 to 99.02 ng g -1 across the 36 studied stations. Alkylated PAHs, mostly methyl-phenanthrenes, were only detected in sediments from Mwache Creek with concentrations ranging from 1.06 to 5.56 ng g -1 . Highest PAH levels were found in Mwache Creek and Tudor Creek reflecting their proximity to pollution sources from Kilindini harbor, the highly populated city of Mombasa and nearby amenities like the airport and surrounding roads experiencing heavy car traffic. Lower PAH levels were observed in Mida Creek as expected from its status as Marine National Park and Reserve with slow urbanization and limited emission sources of PAHs. Similarly, low levels were detected in the Sabaki river estuary. Diagnostic parent PAH ratios, Fl/(Fl+Py), Ph/(Ph+An), BbF/bkF, BaA/BaA+Chr and IP/BghiP point to a dominant pyrolytic fingerprint across sites. Positive Matrix Factorization (PMF) was applied to our dataset for a detailed PAHs source apportionment. The first four factors indicate petroleum contamination at some stations in Mwache Creek and major coal and wood combustions with a contribution of maritime and traffic emission sources.

Abstract The prediction of suspended sediment concentration (SSC) is of critical importance for the management of aquatic ecosystems, flood control, and erosion assessments. However, the continuous measurement of SSC is technically and logistically challenging. Consequently, reliable prediction models are of great value in replacing or supplementing field measurements. In this study, a range of regression models, as well as artificial neural networks (ANNs) and Kolmogorov–Arnolds networks (KANs) models, were employed to predict SSC data using USGS #01357500 Mohawk River at Cohoes, NY data. The data underwent a process of normalisation and scaling, after which they were divided into training, validation, and test subsets. The ANN and KAN models were trained using the following optimisation algorithms: stochastic gradient descent (SGD), ADAM, and Broyden–Fletcher–Goldfarb–Shanno (LBFGS). In the context of time series prediction, the incorporation of one, two, and three time-step lagged features within the data set is essential for capturing the impact of past values. The findings demonstrate that, in addition to the efficacy of the ANN model, the KAN model yielded the most accurate SSC prediction, with regard to both general data and peak values. The LBFGS algorithm, a prominent tool in the field of optimisation, yielded particularly successful outcomes when employed in the enhancement of the models. The incorporation of lagged values within the data set has been observed to exert a favourable influence on the prediction performance. It is important to note that processing the data in chronological order did not cause any decline in model performance. The findings indicate that the KAN method is a reliable and applicable model for SSC prediction.

Abstract To accurately predict the variation trends of atmospheric pollutant concentrations, this study analyzed the spatiotemporal evolution characteristics of major air pollutants in Beijing from 2021 to 2025 using hourly monitoring data from 35 ambient air quality automatic monitoring stations between January 1, 2021, and August 31, 2025. The analysis incorporated spatial visualization techniques and temporal conversion processing. Building on existing prior knowledge, a hybrid Graph Neural Network-Convolutional Neural Network (GNN-CNN) model was developed to estimate time series concentrations of major air pollutants in Beijing. Additionally, the CNN model and GNN-CNN model were employed to predict the spatiotemporal distribution of major air pollutant concentrations in the study area for 2025. The results indicate the following: (1) Beijing’s air quality showed gradual improvement from 2021 to 2024. The monthly variations of major air pollutants PM 2.5 , PM 10 , NO 2 , and SO 2 exhibited a ‘U-shaped’ trend, with significantly lower concentrations in summer and autumn compared to winter and spring. O 3 pollutant concentrations were notably higher from late spring to early autumn than during other periods, while CO pollutant concentrations showed a relatively stable yet fluctuating monthly distribution. (2) Monitoring data indicates that the distinct pollution areas in Beijing are located in the urban districts. The spatial distribution of major pollutants PM 2.5 , PM 10 , NO 2 , and CO displayed a ‘higher in the south, lower in the north’ pattern across Beijing. (3) The constructed GNN-CNN model demonstrated superior prediction accuracy for major air pollutants compared to the GNN model, with a significant improvement in the R 2 metric. For the six pollutants, the correlation coefficient between predicted and observed values exceeded 0.9 at more than 90% of the sites, except for PM 10 .

Abstract Variations in the thermal forcing of the Qinghai–Tibet Plateau (QTP) constitute an important precursor for downstream climate prediction. Especially for its downstream population, the Sichuan Chongqing region with dense economy and frequent extreme high temperatures in summer has a significant impact. Based on meteorological observations from the QTP and the Sichuan–Chongqing Region (SCR) during 1981–2023, this study investigates the spatiotemporal characteristics of the winter Land–Air Temperature Difference (ΔT) over the QTP and its predictive relevance for summer high temperatures in the SCR. The results show that winter ΔT over the QTP has exhibited a significant upward trend over the past 43 years, with an interdecadal shift occurring in the early 21st century. The spatial enhancement is mainly concentrated in the eastern and southern parts of the plateau. Singular value decomposition (SVD) analysis reveals a dominant coupled mode between pre-winter ΔT over the QTP and summer high temperatures in the SCR, explaining 91.74% of the covariance and passing the 99% confidence level in Monte Carlo tests. The corresponding time coefficients are significantly positively correlated, indicating a stable cross-seasonal statistical linkage between winter thermal anomalies over the plateau and downstream summer heat. Composite circulation analysis further supports this relationship. On this basis, a statistical prediction framework using only pre-winter ΔT over the QTP as a predictor is constructed. Hindcast experiments for summer high temperatures during 2021–2023 demonstrate that the method effectively reproduces the primary spatial patterns of summer heat in the SCR, with high spatial similarity between predicted and observed fields. These results suggest that pre-winter ΔT over the QTP can serve as an important precursor signal for summer high temperatures in the SCR and has potential value for cross-seasonal heat prediction.

Abstract The study evaluates atmospheric aerosol properties at five sites across the Warsaw Metropolitan Area, Poland, on New Year's Eve 2022-23, by comparing number and mass size distributions of ambient particulate matter (PM). The measurements were conducted using three optical aerosol spectrometers: Palas Fidas 200, Palas AQ Guard, and Grimm 11D. Site selection was meticulous, encompassing a diverse mix of urban and suburban locations within a 30-kilometer radius of the city center. The number size distributions of PM had a dominant peak near 0.2 µm and shoulder at 0.25 µm with total number concentration in range 1000-1500 #/cm3 at sites distal to local fireworks launches, while in proximal sites the peak dominance was reversed, with higher total number concentrations (3000-3500 #/cm3). Statistical tests identified significant differences in number median diameters and geometric standard deviations at four of the five sites, while mass median diameters differed at only one site. The findings suggest that emissions from fireworks have a substantial impact on the number, mass concentration and spectrum width of PM, particularly for the particles <1 µm. The study underscores the need for high temporal resolution and sufficient sampling range to capture the rapid fluctuations in aerosol properties during episodic emission events.