Exposure density and neighborhood disparities in COVID-19 infection risk.

Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density ([Formula: see text]) as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.

A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels.

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel's moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory.

Adsorption and leaching of novel fungicide pyraoxystrobin on soils by 14C tracing method.

Pyraoxystrobin, (E)-2-(2-((3-(4-chlorophenyl)-1-methyl-1H-pyrazole-5-yloxy)methyl)phenyl)-3-methoxyacrylate, is a newly developed strobilurin fungicide with high antifungal efficiency. It has high potential to enter soil environments that might subsequently impact surface and groundwater. Therefore, 14C-labeled pyraoxystrobin was used as a tracer to study the adsorption/desorption and migration behavior of this compound under laboratory conditions in three typical agricultural soils. The adsorption isotherms conformed with the Freundlich equation. Single factor analysis showed that organic matter content was the most important factor influencing the adsorption. The highest adsorption level was measured in soil with low pH and high organic carbon content. Once adsorbed, only 2.54 to 6.41% of the adsorbed compound could be desorbed. In addition, the mobility results from thin-layer chromatography and column leaching studies showed that it might be safe to use pyraoxystrobin as a fungicide without causing groundwater pollution from both runoff and leaching, which might be attributed to its strong hydrophobicity. High organic matter content enhanced pyraoxystrobin adsorption and desorption because of the rule of similarity (lipid solubility). In the column leaching study, 95.02% (minimum value) of the applied 14C remained within the upper 4.0-cm layer after 60 days.

Spatiotemporal analysis of urban road congestion during and post COVID-19 pandemic in Shanghai, China.

Coronavirus Disease 2019 (COVID-19) has become one of the most serious global health crises in decades and tremendously influence the human mobility. Many residents changed their travel behavior during and after the pandemic, especially for a certain percentage of public transport users who chose to drive their owned vehicles. Thus, urban roadway congestion has been getting worse, and the spatiotemporal congestion patterns has changed significantly. Understanding spatiotemporal heterogeneity of urban roadway congestion during and post the pandemic is essential for mobility management. In this study, an analytical framework was proposed to investigate the spatiotemporal heterogeneity of urban roadway congestion in Shanghai, China. First, the matrix of average speed in each traffic analysis zones (TAZs) was calculated to extract spatiotemporal heterogeneity variation features. Second, the heterogenous component of each TAZ was extracted from the overall traffic characteristics using robust principal component analysis (RPCA). Third, clustering analysis was employed to explain the spatiotemporal distribution of heterogeneous traffic characteristics. Finally, fluctuation features of these characteristics were analyzed by iterated cumulative sums of squares (ICSS). The case study results suggested that the urban road traffic state evolution was complicated and varied significantly in different zones and periods during the long-term pandemic. Compared with suburban areas, traffic conditions in city central areas are more susceptible to the pandemic and other events. In some areas, the heterogeneous component shows opposite characteristics on working days and holidays with others. The key time nodes of state change for different areas have commonness and individuality. The proposed analytical framework and empirical results contribute to the policy decision-making of urban road transportation system during and post the COVID-19 pandemic.

COVID-19 - A window of opportunity for the transition toward sustainable mobility?

The mobility sector was one of the sectors most affected by COVID-19 and its political restrictions, with, inter alia a huge drop in mobility behavior due to travel bans, lockdowns, and a reduced need to be mobile. The present study examined the potential of COVID-19 restrictions aiming at containing the spread of the virus to be a window of opportunity for the transition toward sustainable mobility by breaking up strongly habitualized daily and travel mobility behaviors through changes of behavioral contexts. We conducted an online survey in a sample representative for the German population (N = 3092) to study the consequences of the COVID-19 restrictions on Germans' daily and travel mode choices and on their wishes for future mobility. Furthermore, we examined the moderating effects of Germans' personal norms to protect the climate on changes in their mobility behavior toward sustainable mobility, both within and beyond the corona pandemic. In line with previous research, the present study shows an overall reduction of mobility across almost all modes of transport for daily and travel mobility during time periods of COVID-19 restrictions compared to pre-COVID-19-times, with different transport modes being affected differently. Our findings additionally point out the relevance of personal norms to protect the climate for the transition toward sustainable mobility behavior. Altogether, the present study provides first empirical evidence for the corona pandemic to represent a window of opportunity for the transition toward sustainable mobility. Furthermore, the study also points out relevant directions for further research.

Building back better: The COVID-19 pandemic and transport policy implications for a developing megacity.

The COVID-19 pandemic has affected human mobility via lockdowns, social distancing rules, home quarantines, and the full or partial suspension of transportation. Evidence-based policy recommendations are urgently needed to ensure that transport systems have resilience to future pandemic outbreaks, particularly within Global South megacities where demand for public transport is high and reduced access can exacerbate socio-economic inequalities. This study focuses on Metro Manila - a characteristic megacity that experienced one of the most stringent lockdowns worldwide. It analyzes aggregated cell phone and GPS data from Google and Apple that provide a comprehensive representation of mobility behavior before and during the lockdown. While significant decreases are observed for all transport modes, public transport experienced the largest drop (-74.5 %, on average). The study demonstrates that: (i) those most reliant on public transport were disproportionately affected by lockdowns; (ii) public transport was unable to fulfil its role as public service; and, (iii) this drove a paradigm shift towards active mobility. Moving forwards, in the short-term policymakers must promote active mobility and prioritize public transport to reduce unequal access to transport. Longer-term, policymakers must leverage the increased active transport to encourage modal shift via infrastructure investment, and better utilize big data to support decision-making.

It's All Up to My Fellow Citizens. Descriptive Norms as a Decisive Mediator in the Relationship Between Infrastructure and Mobility Behavior.

Following the implementation of temporary pop-up bike lanes in Berlin, traffic counts by the city administration show an increased number of cyclists. This present paper aims to understand reasons behind this observation. To this end, we focus on the role of mobility-related descriptive social norms as mediators of this effect. Results from one correlational and two experimental online studies are reported. The correlational study confirms the expected association of mobility-related descriptive social norms and self-reported mobility behavior. Moreover, it demonstrates that, as expected, mobility-related descriptive social norms reliably reflect differences in cities' objective transport structure and mediate the impact of these infrastructural differences on mobility behavior. Results from two online experiments provide additional causal evidence that participants use the visual cues provided by manipulated photos to form their perceived mobility-related descriptive social norms. Furthermore, the second online experiment provides evidence that the combination of infrastructural cues and observable mobility behavior has the strongest impact on perceived mobility-related descriptive social norms.

Potential toxic trace element (PTE) contamination in Baoji urban soil (NW China): spatial distribution, mobility behavior, and health risk.

Rapid urbanization and industrialization may cause increased exposure levels to potential toxic trace elements (PTEs) and associated health risks for population living in cities. The main objectives of this study are to investigate systematically the occurrence, source, fate, and risk of PTE contamination from industrial influence in Baoji urban soil. Seven PTE levels (Pb, Zn, Cu, Cr, V, Sb, and As) were surveyed in 50 composite samples from Baoji urban soil by wavelength dispersive X-ray fluorescence spectrometry. Results reveal that the long-term industrial activities have increased PTEs Pb (409.20 mg/kg mean value), Cu (107.19 mg/kg mean value), Zn (374.47 mg/kg mean value), and Sb (26.00 mg/kg mean value) to enrich in urban soil at the different extents. The same results concur with the significant similarity of spatial distribution patterns of Pb, Zn, Cu, and Sb (slightly similar distribution) interpolated by GIS, implying a considerable Pb, Zn, Cu, and Sb contamination pool in urban soil disturbance from local metallic industrial activities. Whereas As in study area mainly controls parent material leaching and therefore has natural sources. Cr and V with the heterogeneous spatial distributions are possibly inclined to coal combustion sources. Those conclusions are also confirmed by the results of multivariate analysis. The chemical forms of PTEs fractionated by BCR three-stage sequential extraction procedure show that Pb and Cu are highly associated to the reducible phase (62.55 and 36.41%, respectively). However, Zn is highly associated to the oxidizable phase (33.68%), and a significant concentration is associated to acid and water extractable fractionation of 15.93% for Zn and 34.40% for Pb. In contrast, As, Cr, V, and Sb are mainly bound to the residual phase (>65% for all elements) with low concentrations retained to water extractable fractionation. The health risk assessed by a new classification Modified Integrate Risk Assessment Code (MI-RAC) reveals that the Pb poses the extremely high risk for human health than others. The results of PTE leaching in organic acids (artificial chelating agent and LMMOAs) indicate that low pH and more carboxyl groups of organic acid can quickly increase the PTEs release from soil and induce more mobility. By comparison, DTPA and EDTA are the effective extractant for Pb and Sb. The leaching kinetics of most PTEs are best described with the Elovich equation model and which involve the ligand exchange (LE) and ligand-enhanced dissolution (LED) two major process. It is a conclusion that long-term metallic industrial activities would accelerate the PTE accumulations in Baoji urban soil and enhance their mobility in a local scale. The considerable mobility and extremely high risk of Pb in Baoji ecoenvironment should be paid more attentions, and the phytoremediation with organic acid leaching assistant could be used to reduce total metal content of multiPTE contaminants in Baoji soils. The research will give the scientific knowledge for controlling the pollution of PTEs in urban soil and can be used as guidance to control the soil pollution in similar cities worldwide.