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Introduction: Policy responses to the COVID-19 pandemic, such as the NY on Pause stay-at-home order (March 22 - June 8, 2020), substantially reduced traffic and traffic-related air pollution (TRAP) in New York City (NYC). We evaluated the magnitude of TRAP decreases and examined the role of modifying factors such as weekend/weekday, road proximity, location, and time-of-day. Methods: Hourly nitrogen dioxide (NO2) concentrations from January 1, 2018 through June 8, 2020 were obtained from the Environmental Protection Agency's Air Quality System for all six hourly monitors in the NYC area. We used an interrupted time series design to determine the impact of NY on Pause on NO2 concentrations, using a mixed effects model with random intercepts for monitor location, adjusted for meteorology and long-term trends. We evaluated effect modification through stratification. Results: NO2 concentrations decreased during NY on Pause by 19% (-3.2 ppb, 95% confidence interval [CI]: -3.5, -3.0), on average, compared to pre-Pause time trends. We found no evidence for modification by weekend/weekday, but greater decreases in NO2 at non-roadside monitors and weak evidence for modification by location. For time-of-day, we found the largest decreases for 5 am (27%, -4.5 ppb, 95% CI: -5.7, -3.3) through 7 am (24%, -4.0 ppb, 95% CI: -5.2, -2.8), followed by 6 pm and 7 pm (22%, -3.7 ppb, 95% CI: -4.8, -2.6 and 22%, -4.8, -2.5, respectively), while the smallest decreases occurred at 11 pm and 1 am (both: 11%, -1.9 ppb, 95% CI: -3.1, -0.7). Conclusion: NY on Pause's impact on TRAP varied greatly diurnally. Decreases during early morning and evening time periods are likely due to decreases in traffic. Our results may be useful for planning traffic policies that vary by time of day, such as congestion tolling policies.
ABSTRACT
Ausgelöst durch die pandemiebedingten Veränderungen des Reiseverhaltens untersucht der Lehrstuhl für Humangeographie und Transformationsforschung an der Universität Augsburg in Kooperation mit dem Wissenstransferzentrum Innovative und Nachhaltige Tourismusentwicklung an der Hochschule Kempten elaborierte Formen eines möglichen low touch tourism. Im Fokus stehen dabei wirksame Strategien zur Vermeidung von Gruppenbildungen und Crowding-Effekten, Potentiale zur Minimierung physischer Kontakte zwischen Reisenden und Tourismusanbieter:innen (Techniken zur Vermeidung von near-contact services) und Möglichkeiten zur Reduzierung von physischen Kontakten mit hochfrequentierten Oberflächen. Ziel des Projektes ist die Sammlung, Strukturierung und Kategorisierung von Informationen, Daten und Erfahrungen zum low touch tourism. The corona pandemic has changed travel. The chair for human geography and transformation research at the University of Augsburg, in cooperation with the knowledge transfer center for innovative and sustainable tourism development at the University of Kempten, examined the possibilities of low-touch tourism. First, the focus is on effective strategies to avoid the formation of groups and crowding effects. Second, potentials for minimizing physical contacts between travellers and tourism providers are identified (e. g., to avoid near-contact services). And third, ways to reduce physical contact with high-traffic surfaces are analysed. The aim is to collect, structure and categorize information, data, and experiences on low touch tourism. [ FROM AUTHOR] Copyright of TW: Zeitschrift fuer Tourismuswissenschaft is the property of De Gruyter 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 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 . (Copyright applies to all s.)
ABSTRACT
INTRODUCTION: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS: Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSION: Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.