Precise and high-resolution carbon dioxide (CO₂) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO₂ Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO₂ emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO₂) retrievals. Our study on the global CO₂ emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO₂ emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO₂ emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

Understanding the climate-glacier dynamics of debris-covered glaciers is notoriously difficult given a multitude of controlling factors and feedback mechanisms involving climate forcing, debris-load properties, supraglacial water bodies, and multi-scale topographic effects. Recent studies have provided insights into controlling factors, and have reported the presence of anomalies that contradict the general consensus of the protective influence of debris loads on ablation dynamics. Nevertheless, numerous processes that regulate glacier dynamics at various spatial and temporal scales have not been adequately accounted for in statistical and numerical modeling studies. Furthermore, important feedbacks involving ablation, topography, irradiance, gravitational debris flux, and supraglacial ponding are often neglected or oversimplified in existing models, which poses a challenge to our understanding of conflicting field observations such as the accelerated mass loss of many Himalayan glaciers, and glacier-subsystem responses (ice-flow, debris flux, surface morphology, and supraglacial water bodies) to climate forcing. This paper provides insights into the complexity of debris-covered glacier systems by addressing concepts and issues associated with forcing factors and glacial processes, and highlights the importance of understanding system couplings and feedbacks. Specifically, we review recent studies on debris-covered glaciers and utilize simulation results based on the Baltoro Glacier in the central Karakoram to discuss important concepts and issues. Our results demonstrate that climate forcing, the properties and transport of debris, topography and supraglacial water bodies are the key controlling factors in a debris-covered glacier system, and that their coupled effects and positive feedbacks may increase the ice loss of a debris-covered glacier. We also recommend new research directions for future studies.

Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.

It has long been recognized that ocean gravity waves can generate microseisms through their interactions and coupling with the solid earth. Their generation mechanisms, wave types and propagation have been studied and debated intensively. In this study, we focus on the characteristics of microseisms in South China Sea (SCS) and compare them with globally observed microseisms. We use data from 6 ocean bottom seismometers (OBSs), 10 publicly available broadband land seismometers near SCS, and a reanalyzed global wave height model from May 1^st, 2012 to August 20^th, 2012 (UTC), the time span of OBSs data. We apply three techniques including power spectral density, correlation, and frequency dependent polarization analysis. Our results show that 1) microseisms observed in SCS are dominated by sources from adjacent oceans, instead of the common global sources from the Pacific, Atlantic, or Indian Ocean; 2) the split of Double Frequency Microseisms (DFMs) are observed in SCS, especially on land stations: the sources are located near the central sea basin for Long Period DFMs (0.1‐0.25 Hz) and are local near stations for Short Period DFMs (0.25‐0.5 Hz); 3) typhoons both strengthen microseisms and affect the source locations of microseisms with the biggest influence on the short period DFMs; and 4) microseisms in or near SCS are a mixture of Love and Rayleigh waves and the relative dominance of Love and Rayleigh waves changes with locations.