Seasonal dynamics of airborne biomolecules influence the size distribution of Arctic aerosols

Organic matter is crucial in aerosol-climate interactions,yet the physicochemical properties and origins of organic aerosols remain poorly understood.Here we show the seasonal characteristics of submicron organic aerosols in Arctic Svalbard during spring and summer,emphasizing their connection to transport patterns and particle size distribution.Microbial-derived organic matter(MOM)and terrestrial-derived organic matter(TOM)accounted for over 90%of the total organic mass in Arctic aerosols during these seasons,comprising carbohydrate/protein-like and lignin/tannin-like compounds,respectively.In spring,aerosols showed high TOM and low MOM intensities due to biomass-burning influx in the central Arctic.In contrast,summer exhibited elevated MOM intensity,attributed to the shift in predominant atmospheric transport from the central Arctic to the biologically active Greenland Sea.MOM and TOM were associated with Aitken mode particles(<100 nm diameter)and accumulation mode particles(>100 nm diameter),respectively.This association is linked to the molecular size of biomolecules,impacting the number concentrations of corresponding aerosol classes.These findings highlight the importance of considering seasonal atmospheric transport patterns and organic source-dependent particle size distributions in assessing aerosol properties in the changing Arctic.

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world.Therefore,timely and accurate decision-making is essential for mitigating flood-related damages.The traditional flood prediction techniques often encounter challenges in accuracy,timeliness,complexity in handling dynamic flood patterns and leading to substandard flood management strategies.To address these challenges,there is a need for advanced machine learning models that can effectively analyze Internet of Things(IoT)-generated flood data and provide timely and accurate flood predictions.This paper proposes a novel approach-the Adaptive Momentum and Backpropagation(AM-BP)algorithm-for flood prediction and management in IoT networks.The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency.Real-world flood data is used for validation,demonstrating the superior performance of the AM-BP algorithm compared to traditional methods.In addition,multilayer high-end computing architecture(MLCA)is used to handle weather data such as rainfall,river water level,soil moisture,etc.The AM-BP’s real-time abilities enable proactive flood management,facilitating timely responses and effective disaster mitigation.Furthermore,the AM-BP algorithm can analyze large and complex datasets,integrating environmental and climatic factors for more accurate flood prediction.The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%,96.4%F1-Measure,97%Precision,and 95.9%Recall.The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks,contributing to more resilient and efficient flood control strategies,and ensuring the safety and well-being of communities at risk of flooding.