Community impacts of aviation noise: a pilot survey
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Noise causes cardiovascular disease: it’s time to act
Chronic transportation noise is an environmental stressor affecting a substantial portion of the population. The World Health Organization (WHO) and various studies have established associations between transportation noise and cardiovascular disease (CVD), such as myocardial infarction, stroke, heart failure, and arrhythmia. The WHO Environmental Noise Guidelines and recent reviews confirm a heightened risk of cardiovascular incidents with increasing transportation noise levels.
Decarbonizing urban residential communities with green hydrogen systems
Community green hydrogen systems, typically consisting of rooftop photovoltaic panels paired with hybrid hydrogen-battery storage, offer urban environments with improved access to clean, on-site energy. However, economically viable pathways for deploying hydrogen storage within urban communities remain unclear. Here we develop a bottom-up energy model linking climate, human behavior and community characteristics to assess the impacts of pathways for deploying community green hydrogen systems in North America from 2030 to 2050. We show that for the same community conditions, the cost difference between the best and worst pathways can be as high as 60%. In particular, the household centralized option emerges as the preferred pathway for most communities. Furthermore, enhancing energy storage demands within these deployment pathways can reduce system design costs up to fourfold. To achieve cost-effective urban decarbonization, the study underscores the critical role of selecting the right deployment pathway and prioritizing the integration of increased energy storage in pathway designs.
Comparative analysis of nanomechanical resonators: sensitivity, response time, and practical considerations in photothermal sensing
Nanomechanical photothermal sensing has significantly advanced single-molecule/particle microscopy and spectroscopy, and infrared detection. In this approach, the nanomechanical resonator detects shifts in resonant frequency due to photothermal heating. However, the relationship between photothermal sensitivity, response time, and resonator design has not been fully explored. This paper compares three resonator types – strings, drumheads, and trampolines – to explore this relationship. Through theoretical modeling, experimental validation, and finite element method simulations, we find that strings offer the highest sensitivity (with a noise equivalent power of 280 fW/Hz1/2 for strings made of silicon nitride), while drumheads exhibit the fastest thermal response. The study reveals that photothermal sensitivity correlates with the average temperature rise and not the peak temperature. Finally, the impact of photothermal back-action is discussed, which can be a major source of frequency instability. This work clarifies the performance differences and limits among resonator designs and guides the development of advanced nanomechanical photothermal sensors, benefiting a wide range of applications.
Demand-side strategies enable rapid and deep cuts in buildings and transport emissions to 2050
Decarbonization of energy-using sectors is essential for tackling climate change. We use an ensemble of global integrated assessment models to assess CO2 emissions reduction potentials in buildings and transport, accounting for system interactions. We focus on three intervention strategies with distinct emphases: reducing or changing activity, improving technological efficiency and electrifying energy end use. We find that these strategies can reduce emissions by 51–85% in buildings and 37–91% in transport by 2050 relative to a current policies scenario (ranges indicate model variability). Electrification has the largest potential for direct emissions reductions in both sectors. Interactions between the policies and measures that comprise the three strategies have a modest overall effect on mitigation potentials. However, combining different strategies is strongly beneficial from an energy system perspective as lower electricity demand reduces the need for costly supply-side investments and infrastructure.
Predicting global distributions of eukaryotic plankton communities from satellite data
Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.
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