Effects of Mobility Restrictions on Air Pollution in the Madrid Region during the COVID-19 Pandemic and Post-Pandemic Periods

Bañuelos-Gimeno, Jorge; Sobrino, Natalia; Arce-Ruiz, Rosa María

doi:10.3390/su151712702

Cited by 6 publications

(3 citation statements)

References 62 publications

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“…The most polluting cars cannot access this ring [48]. In addition, car traffic lanes turned into new pedestrian spaces [49]. Reducing the number of cars that enter these LEZs has made the City Center more attractive for pedestrians.…”

Section: Descriptive Analysismentioning

confidence: 99%

Spatial Analysis of COVID-19 Pandemic Impacts on Mobility in Madrid Region

Al-Akioui,

Monzon

2023

Sustainability

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After three years of COVID-19 lockdown and restrictions, mobility seems to have returned to normality. However, the pandemic has left changes in the mobility patterns of the Madrid Region produced by new trends emerging from COVID-19. This paper analyzes these changes, focusing on the impacts on public transport use, the effects of telematic activities and the influence of home relocation. The basis of the analysis is a survey conducted from November to December 2022, with more than 15,000 valid responses. The results show that public transport lost 6% of trips. These trips have different transfer rates depending on their geographic location. In the City Center, the majority transferred to active modes. However, the car attracted most of these trips in the Metropolitan Ring. These changes in mobility patterns are partly a consequence of the increase in telematic activities. Teleworking has increased by more than 20% in the Madrid Region and has caused changes in trips per week and trip purpose. In addition, teleworking has caused 18% of home relocations to peripheral zones of the region. This paper investigates through statistical analysis which sociodemographic and spatial factors explain the differences in mobility impacts throughout the zones of the Madrid Region.

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Section: Descriptive Analysismentioning

confidence: 99%

Spatial Analysis of COVID-19 Pandemic Impacts on Mobility in Madrid Region

Al-Akioui,

Monzon

2023

Sustainability

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“…The data are publicly accessible through Madrid's Open Data website and Kaggle [23,24]. Analyzing this dataset holds significant practical and environmental relevance, as Madrid is among the European cities with the poorest air quality and a notably high mortality rate associated with nitrogen dioxide and carbon monoxide [25]. This underscores the imperative need to enhance air quality, beginning with a comprehensive understanding through data analysis.…”

Section: Data Collection and Cleaningmentioning

confidence: 99%

Distributed Bayesian Inference for Large-Scale IoT Systems

Vlachou,

Karras,

Karras

et al. 2023

BDCC

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In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to handle escalating data sizes without a proportional increase in computational demands. Notably, throughout the experiments, there is an observed increase in memory usage with growing test set sizes, this increment is sublinear, demonstrating the proficiency of the classifier in memory resource management. This behavior is consistent with the typical tendencies of PySpark tasks, which witness increasing memory consumption due to data partitioning and various data operations as datasets expand. CPU resource utilization, which is another crucial factor, also remains stable, emphasizing the capability of the classifier to manage larger computational workloads without significant resource strain. From a classification perspective, the Bayesian Logistic Regression Spark Classifier consistently achieves reliable performance metrics, with a particular focus on high specificity, indicating its aptness for applications where pinpointing true negatives is crucial. In summary, based on all experiments conducted under various data sizes, our classifier emerges as a top contender for scalability-driven applications in IoT systems, highlighting its dependable performance, adept resource management, and consistent prediction accuracy.

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“…Nevertheless, interest in many works has focused on the impact of COVID-19 on mobility, and few efforts have been directed at assessing how regulating mobility rates can have positive effects on controlling virus transmission. Examples of these studies have focused on Taiwan [ 6 ], Poland [ 7 ], the United States [ 8 ], India [ 9 ], Spain [ 10 , 11 ], Japan [ 12 ], the United Arab Emirates [ 13 ], Saudi Arabia [ 14 ], Greece [ 15 ], China [ 16 , 17 ], Indonesia [ 18 ], Austria [ 19 ], Italy [ 20 ], Portugal [ 21 , 22 ], South Africa [ 23 ], Costa Rica [ 24 ], and Australia [ 25 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

Aguilar-Ruiz,

Ruiz,

Giráldez

2024

Healthcare

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The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic’s upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

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Effects of Mobility Restrictions on Air Pollution in the Madrid Region during the COVID-19 Pandemic and Post-Pandemic Periods

Cited by 6 publications

References 62 publications

Spatial Analysis of COVID-19 Pandemic Impacts on Mobility in Madrid Region

Spatial Analysis of COVID-19 Pandemic Impacts on Mobility in Madrid Region

Distributed Bayesian Inference for Large-Scale IoT Systems

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

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