Correlation networks of air particulate matter ( PM 2.5 ): a comparative study.

Over the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations' time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014-2020). We learn that the use of hourly PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks' complexity through node subsampling. The end result separates the temporal series of PM 2.5 in set of regions that are similarly affected through the year.

Mobile phone location data for disasters: A review from natural hazards and epidemics

Rapid urbanization and climate change trends, intertwined with complex interactions of various social, economic, and political factors, have resulted in an increase in the frequency and intensity of disaster events. While regions around the world face urgent demands to prepare for, respond to, and to recover from such disasters, large-scale location data collected from mobile phone devices have opened up novel approaches to tackle these challenges. Mobile phone location data have enabled us to observe, estimate, and model human mobility dynamics at an unprecedented spatio-temporal granularity and scale. The COVID-19 pandemic, in particular, has spurred the use of mobile phone location data for pandemic and disaster management. However, there is a lack of a comprehensive review that synthesizes the last decade of work and case studies leveraging mobile phone location data for response to and recovery from natural hazards and epidemics. We address this gap by summarizing the existing work, and point to promising areas and future challenges for using mobile phone location data to support disaster response and recovery.

A sustainable strategy for Open Streets in (post)pandemic cities

Cities world-wide have taken the opportunity presented by the COVID-19 pandemic to improve and expand pedestrian infrastructure, providing residents with a sense of relief and pursuing long-standing goals to decrease automobile dependence and increase walkability. So far, due to a scarcity of data and methodological shortcomings, these efforts have lacked the system-level view of treating sidewalks as a network. Here, we leverage sidewalk data from ten cities in three continents, to first analyse the distribution of sidewalk and roadbed geometries, and find that cities present an unbalanced distribution of public space, favouring automobiles at the expense of pedestrians. Next, we connect these geometries to build a sidewalk network –adjacent, but irreducible to the road network. Finally, we compare a no-intervention scenario with a shared-effort heuristic, in relation to the performance of sidewalk infrastructures to guarantee physical distancing. The heuristic prevents the sidewalk connectivity breakdown, while preserving the road network's functionality. While public space is traditionally shared by pedestrian and cars, the distancing measures imposed by the Covid-19 pandemic have underlined the need for wider pedestrian spaces. Here, the authors take a complex network approach to analyze sidewalk data from cities across the world to evidence a strong unbalance in the space distribution between cars and pedestrian, and propose a strategy to improve urban walkability within a socially distancing context and without compromising road traffic. [ABSTRACT FROM AUTHOR] Copyright of Communications Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)