Long-term air pollution exposure impact on COVID-19 morbidity in China

Although previous studies have proved the association between air pollution and respiratory viral infection, given the relatively short history of human infection with the severe acute respiratory syndrome coronavirus (SARS-CoV-2), the linkage between long-term air pollution exposure and the morbidity of 2019 novel coronavirus (COVID-19) pneumonia remains poorly understood. To fill this gap, this study investigates the influences of particulate matters (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2) and carbon monoxide (CO) on COVID-19 incidence rate based on the prefecture-level morbidity count and air quality data in China. Annual means for ambient PM2.5, PM10, SO2, NO2, CO and O3 concentrations in each prefecture are used to estimate the population’s exposure. We leverage identical statistical methods, i.e., Spearman’s rank correlation and negative binomial regression model, to demonstrate that people who are chronically exposed to ambient air pollution are more likely to be infected by COVID-19. Our statistical analysis indicates that a 1 μg m-3 increase of PM2.5, PM10, NO2 and O3 can result in 1.95% (95% CI: 0.83 to 3.08% ), 0.55% (95% CI: -0.05 to 1.17% ), 4.63% (95% CI: 3.07 to 6.22% ) rise and 2.05% (95% CI: 0.51 to 3.59 % ) decrease of COVID-19 morbidity. However, we observe nonsignificant association with long-term SO2 and CO exposure to COVID-19 morbidity in this study. Our results’ robustness is examined based on sensitivity analyses that adjust for a wide range of confounders, including socio-economic, demographic, weather, healthcare, and mobility-related variables. We acknowledge that more laboratory results are required to prove the etiology of these associations

A Study on Spatial and Temporal Aggregation Patterns of Urban Population in Wuhan City based on Baidu Heat Map and POI Data

Advanced technologies and big data have brought new visions and methods to urban planning research. Based on the Baidu heat map and POI data of two typical days (a weekend day and a workday) in 2018, this paper analyses the spatial and temporal aggregation patterns of crowds in the urban centre of Wuhan using ArcGIS. Aggregation patterns are defined by the intensity of population activities and the places where crowds gather. In terms of time, the daily change of population aggregation intensity is studied by counting the heat value of 24 moments captured throughout the day. The results show that on rest days, people prefer to travel around noon and in the afternoon, reaching the highest peak of the day around 15:00, while on workdays, residents\u27 activities are affected by commuting, with obvious \u27morning rush hours\u27 and \u27evening rush hours\u27. Firstly, the spatial correlation between the density of POI distribution and the degree of population aggregation has been studied by the spatial coupling relationship between the Baidu heat map and POI data. Secondly, the index of correlation between the aggregation of different POIs and population (ICPP) are mentioned to analyse the purposes and the degrees of aggregation during weekend and workday rush hours. Based on the ICPP, we analyse activities from three aspects: the different ICPPs between the workday and the weekend; the different ICPPs between the morning, afternoon and evening; and the different ICPPs among different POIs

(Section A: Planning Strategies and Design Concepts)

Global geological hazards have brought huge losses, and the fast development in China is no exception. At present, China\u27s hazard prevention and mitigation research and construction is mostly concentrated in the cities, while the rural, mountainous regions suffering the most serious damage and loss from geological hazards are neglected. In these areas, hazard prevention planning is missing or uses the city standard, lacking scientific analysis and theoretical support. Therefore, the study of disaster prevention and mitigation in remote regions is becoming more urgent. Existing studies on geological hazard prevention mainly focus on urban areas but ignore remote and rural areas where large numbers of people live. By drawing experience from disaster prevention and reduction in urban areas and incorporating effective scientific methods, this study aims to establish a planning support system for disaster mitigation to reduce the impact of disasters in rural areas on people and their property. The most significant contributions this research and practice offers is as follows. Firstly, the high-precision data of the villages, which is usually lacking and difficult to acquire, can easily and quickly be obtained by unmanned aerial vehicles (UVA) equipped with optical sensors and laser scanners. Secondly, combining high-precision data and the disaster evaluation model, geological disaster risk assessment technology has been developed for rural areas that addresses not only the natural factors but also human activities. Thirdly, based on disaster risk assessment technology, disaster prevention planning that has been constructed specifically for villages is more quantitative than before. Fourthly, with the application of a planning support system in disaster mitigation, a scientific and effective solution for disaster rescue can be achieved automatically. Lastly, this study selects a suitable area for implementation and demonstration, which can verify the feasibility and effectiveness of the system and enrich the knowledge base through a demonstration case. Based on the above research, a scientific hazard prevention strategy is put forward, which provides a scientific basis for decision-making and a support method for disaster prevention planning in villages

Impact Mechanism and Improvement Strategy on Urban Ventilation, Urban Heat Island and Urban Pollution Island: A Case Study in Xiangyang, China

There has been a growing interest in finding mitigation measures for urban heat islands and urban pollution islands that focus mainly on urban landscape mechanisms. However, relatively little research has considered spatial non-stationarity and temporal non-stationarity, which are both intrinsic properties of the environmental system, simultaneously. At the same time, the relevance of and differences between the thermal environment and air pollution has also been rarely discussed, and both issues are of great importance to urban planning. In this study, which is aimed at improving urban ventilation to reduce the urban heat island and urban pollution island effects, an urban ventilation potential evaluation, land surface temperature time-series clustering and air pollution source identification are comprehensively applied to identify the operational areas, compensation areas and ventilation corridors in Xiangyang, China, thus bridging the gap between academic research and urban planning. The specific research areas include: (1) defining the operational areas for urban ventilation corridor planning through an urban ventilation potential evaluation featuring urban morphology indicators, land surface temperature time-series clustering with k-means and an urban air pollution source diffusion analysis via the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and geographically weighted regression (GWR) methods; (2) identifying urban cold islands through land surface temperatures and delimiting the compensation areas in urban ventilation corridor planning; (3) designating urban ventilation corridors through an urban ventilation potential evaluation and computational fluid dynamics (CFD); and (4) improving urban ventilation corridor planning through defining operational areas, compensation areas and ventilation corridors as well as proposing corresponding control measures

(Section A: Planning Strategies and Design Concepts)

This paper introduces a comprehensive framework that assesses the urban heat environment and formulates urban wind paths. Compared with other ecosystems, the wind environment and heat environment in urban areas can be much more complicated and dynamic. Nonetheless, it is of great concern considering the agglomerated population and industries at stake. Hence, multiple computational techniques are developed to assess the contemporary heat environment, and to formulate feasible policies to improve it to a more liveable state by introducing the solution of natural wind. Three key factors are considered: solar radiation, which is the major source of heat; wind direction and wind speed, which transports heat in space; and urban land surface, which may affect radiation reflection, contain auxiliary heat sources or cause vertical air flow. Hence, mesoscale meteorological data are applied to provide information about solar radiation, and are used for simulating local wind flow; Landsat images can be translated into land surface temperature figures; and building and land use databases provide information about built-up features. These combined, the local heat environment in urban areas can be mapped and monitored in a periodic fashion, with wind path analysis providing possibilities in cooling down the hotspots. Practices in cities including Fuzhou and Wuhan have proved constructive, with some others still underway

Observation of Rydberg moir\'e excitons

Rydberg excitons, the solid-state counterparts of Rydberg atoms, have sparked considerable interest in harnessing their quantum application potentials, whereas a major challenge is realizing their spatial confinement and manipulation. Lately, the rise of two-dimensional moir\'e superlattices with highly tunable periodic potentials provides a possible pathway. Here, we experimentally demonstrate this capability through the observation of Rydberg moir\'e excitons (XRM), which are moir\'e trapped Rydberg excitons in monolayer semiconductor WSe2 adjacent to twisted bilayer graphene. In the strong coupling regime, the XRM manifest as multiple energy splittings, pronounced redshift, and narrowed linewidth in the reflectance spectra, highlighting their charge-transfer character where electron-hole separation is enforced by the strongly asymmetric interlayer Coulomb interactions. Our findings pave the way for pursuing novel physics and quantum technology exploitation based on the excitonic Rydberg states.Comment: 24 pages, including 4 figures and 6 supplementary figure

Cellular anatomy of the mouse primary motor cortex.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets