Metropolitan Area Population Research Articles

Tracking Chlamydia and Syphilis in the Detroit Metro Area by Molecular Analysis of Environmental Samples.

This paper describes one of the first studies applying wastewater surveillance to monitor Chlamydia and Syphilis and back-estimate infections in the community, based on bacterial shedding and wastewater surveillance data. Molecular biology laboratory methods were optimized, and a workflow was designed to implement wastewater surveillance tracking Chlamydia and Syphilis in the Detroit metro area (DMA), one of the most populous metropolitan areas in the U.S. Untreated composite wastewater samples were collected weekly from the three main interceptors that service DMA, which collect wastewater and discharge it to the Great Lakes Water Authority Water Resource Recovery Facility. Additionally, untreated wastewater was also collected from street manholes in three neighborhood sewersheds in Wayne, Macomb, and Oakland counties. Centrifugation, DNA extraction, and ddPCR methods were optimized and performed, targeting Chlamydia trachomatis and Treponema pallidum, the causative agents of Chlamydia and Syphilis, respectively. The limit of blank and limit of detection methods were determined experimentally for both targets. Both targets were detected and monitored in wastewater between December 25th, 2023, and April 22nd, 2024. The magnitudes of C. trachomatis and T. pallidum concentrations observed in neighborhood sewersheds were higher as compared to the concentrations observed in the interceptors. Infections of Chlamydia and Syphilis were back-estimated through an optimized formula based on shedding dynamics and wastewater surveillance data, which indicated potentially underreported conditions relative to publicly available clinical data.

Investigating urban morphological drivers of household water use in developing economies: A structural equation model approach

Urban populations are snowballing, increasing household (HH) water consumption. The growing housing density, the expanding infrastructure, and altered land use patterns place increasing pressure on the water supply and usage. This makes it harder for urban HHs to obtain clean water and worsens water scarcity, with infrastructural and environmental implications for highly populated metropolitan areas. This study aims to quantitatively evaluate the impact of urban morphology on household water use in a developing economy using a Structural Equation Model (SEM), specifically within the growing urban centres surrounding Kolkata in South Bengal. The primary hypothesis projects an increase in urban morphological indicators and HH characteristics that significantly affect water use. The findings reveal that increases in urban morphological indicators are associated with a 34.2% decrease in HH water use, highlighting the importance of urban planning strategies that emphasize compact and diverse urban forms. This work highlights new ways of building cities that consider morphological aspects and allow cities to expand in a way that conserves HH water while being resilient to rapid urban change.

Multi-scale feature fusion segmentation and lightweight ensemble deep learning approach for urban slum classification using satellite images

ABSTRACT Although there are some drawbacks to satellite imaging, such as the difficulty in identifying slums in highly populated metropolitan areas, it is nonetheless a useful tool for preliminary screening and can support on-the-ground and other data sources. Hence, we proposed the MSFF-Seg (Multi-Scale Feature Fusion-based Segmentation) model for urban slum classification using satellite images. Hence we proposed the MSFF-Seg model for urban slum classification using satellite images. Initially, three types of corrections are performed in preprocessing such as geometric correction which is corrected by Enhanced Scale Invariant Feature Transform (ESIFT), radiometric correction which is corrected by Ground-based Relative Radiometric Calibration (GRRC), and atmospheric correction which is corrected by homomorphic filter. After completion of preprocessing phase, segmentation is initiated by Tweak Mask RCNN which extracts the features in multilevel for segmentation that increases the segmentation accuracy. The segmented images are fed into an ensemble classifier for urban slum identification. Here, three types of deep learning are used for ensemble learning namely Schematic Convolutional Neural Network (SCNN), Shallow MobileNet, and Lite ShuffleNet. They extract multiple features from the segmented region, based on the extracted features. LGBM performed urban slum classification which classifies the images into two classes such as slums and non-slums. Finally, the performance of this research is evaluated based on several performance metrics like accuracy (98%), precision (96%), recall (94%), and F1 score (97%), which proved that the proposed work achieved superior performance in urban slum classification.

Analysis and Evaluation of Earthquake Hazard Zones Based on Spatial Models for Regency Regional Development Bantul

Abstract Earthquakes are a natural disaster that occur frequently in Indonesia. A region susceptible to earthquakes is the Bantul district. Buildings and infrastructure could be damaged by this disaster, especially in highly populated metropolitan areas where the Bantul Regency earthquake (Mw 6.4) on May 27, 2006, resulted in significant damage.The purpose of this research is to identify the distribution of the region’s level of stability against the possibility of an earthquake occurring, as well as the degree of compatibility between the vulnerability zone and the Development Territory Unit (SWP) of Bantul district. This research uses data collection techniques by direct observation with microtremors to calculate surface peak ground acceleration (PGA) with a value of Vs.30. The regional stability weighting matrix approach is used in this study, which is governed by Minister of Public Works Regulation Number 21 of 2007 covering Guidelines for Spatial Planning in Volcanic Eruptions and Earthquake-Prone Areas. The research results for Bantul Regency have a distribution of Vs.30 values between 175 - 350 m/s which is included in the Soft Rock (SC) category with surface peak ground acceleration (PGA) values between 0.112 g - 0.418 g. The final score for Bantul Regency is 20 - 43 which falls into the stable and less stable categories. The Development Area Unit (SWP) of Bantul Regency SWP, which is in the unstable zone, is dominantly distributed around the Opak Fault. SWP II, III, IV, and V require mitigation measures when used for residential areas. Spatial planning that is in accordance with built-up and non-built zones will be able to reduce risk factors.

Seismic and Wind Resillence Study of a 15 Story High-Rise Building with Floating Columns in Seismic Zones II & V

The use of floating columns in the construction of high-rise buildings, specifically in the context of G+15 structures in metropolitan cities, as per Indian standards. The structural design of a building is crucial for its durability, strength, stability, and lifespan. Floating columns play a significant role in transferring loads from the floors to the beams, affecting the overall stability of the building.Several factors impact the structural design, including bending moments, shear forces, torsion, and deflection. Due to the growing population in metropolitan areas, there is a need for high-rise buildings that can accommodate both commercial and residential spaces on a single platform. Commercial and residential floors have different load requirements. Therefore, floating columns are specifically used for residential floors to address these varying load demands. The structural analysis considers seismic and wind forces as per Indian standards (IS 1893-2016 for seismic analysis and IS 875-2015 for wind analysis) for different seismic zones (Zone- Ⅱ, Ⅲ, Ⅳ, and Ⅴ). The materials used in the construction include concrete with a grade of M40 and steel with a grade of Fe550. These material grades are commonly used in construction for their respective strength properties. The floor height for residential floors is 3 meters, while commercial floors have a height of 4 meters. This distinction in floor height is considered in the structural design. The design of high-rise buildings, especially in seismic-prone regions, requires a thorough understanding of structural engineering principles and adherence to local building codes and standards to ensure the safety and stability of the structure. The use of floating columns can be a part of a structural design strategy to address varying load requirements in different parts of the building.Safety is paramount in the design and construction of high-rise buildings, particularly in areas susceptible to seismic activity and strong winds.

Spatiotemporal transmission of SARS-CoV-2 lineages during 2020-2021 in Pernambuco-Brazil.

SARS-CoV-2 virus emerged as a new threat to humans and spread around the world, leaving a large death toll. As of January 2023, Brazil is among the countries with the highest number of registered deaths. Nonpharmacological and pharmacological interventions have been heterogeneously implemented in the country, which, associated with large socioeconomic differences between the country regions, has led to distinct virus spread dynamics. Here, we investigate the spatiotemporal dispersion of SARS-CoV-2 lineages in the Pernambuco state (Northeast Brazil) throughout the distinct epidemiological scenarios that unfolded in the first 2 years of the pandemic. We generated a total of 1,389 new SARS-CoV-2 genomes from June 2020 to August 2021. This sampling captured the arrival, communitary transmission, and the circulation of the B1.1, B.1.1.28, and B.1.1.33 lineages; the emergence of the former variant of interest P.2; and the emergence and fast replacement of all previous variants by the more transmissible variant of concern P.1 (Gamma). Based on the incidence and lineage spread pattern, we observed an East-to-West to inner state pattern of transmission, which is in agreement with the transmission of more populous metropolitan areas to medium- and small-size country-side cities in the state. Such transmission patterns may be partially explained by the main routes of traffic across municipalities in the state. Our results highlight that the fine-grained intrastate analysis of lineages and incidence spread can provide actionable insights for planning future nonpharmacological intervention for air-borne transmissible human pathogens.IMPORTANCEDuring the COVID-19 pandemic, Brazil was one of the most affected countries, mainly due its continental-size, socioeconomic differences among regions, and heterogeneous implementation of intervention methods. In order to investigate SARS-CoV-2 dynamics in the state of Pernambuco, we conducted a spatiotemporal dispersion study, covering the period from June 2020 to August 2021, to comprehend the dynamics of viral transmission during the first 2 years of the pandemic. Throughout this study, we were able to track three significant epidemiological waves of transmission caused by B1.1, B.1.1.28, B.1.1.33, P.2, and P.1 lineages. These analyses provided valuable insights into the evolution of the epidemiological landscape, contributing to a deeper understanding of the dynamics of virus transmission during the early years of the pandemic in the state of Pernambuco.

Growing crisis of urban rat infestation in Karaj city, Iran: Insights from hotspot mapping and social network analysis

To control the increasing growth of rat populations in metropolitan areas, several factors need to be considered to mitigate the risks posed by rats. Identifying rat density hotspots with more efficient methods and assessing people's experiences and attitudes toward them can be useful in prioritizing rat control efforts. We focused on the two most common species of rats (Rattus rattus and Rattus norvegicus) in the metropolis of Karaj, the fourth most populous and the largest immigrant-friendly city in Iran. The urban rat infestation is one of the problems caused by the high population growth in Karaj and the insufficient development of urban infrastructure and waste collection. It has led to discontent and public concern about rat infestations and there is a need to address the associated threats. We applied the Kernel Density Function and Optimized Hotspot Analysis to the presence points of the aforementioned types of rats and found that while Optimized Hotspot projects a macro view of hotspots at the city level, the Kernel Density Function prioritizes neighborhoods for initial actions. In addition, social network analysis with complementary regression models was conducted to determine people's experiences. The results show that fear of rats and concern about their presence form the basis of people's attitudes toward rats and lead to feelings of hatred. Moreover, economic harm reinforces people's motivation to control or eliminate this species. Despite their unpleasant feelings toward rats, people have a great influence on the growth of rat infestations by creating food sources through improper waste disposal. They prefer to use traps and poison instead of disposing of household waste and following the principles of cleanliness as preventive measures and believe that the lack of proper garbage collection by the municipality is the most important reason for the growth of rat populations. They are not sufficiently aware of their position in addressing this crisis. Our findings argue for the formation of a stronger network between the relevant authorities and residents, especially those living in the hotspots, to educate them about their role in rat control and to gain their cooperation in improving the city's waste management system.

7-day patterns in Black-White segregation in 49 metropolitan areas

While residential segregation is a persistent attribute of metropolitan areas, recent studies find segregation levels fluctuate throughout the day, reaching their lowest levels during daytime hours. This paper shows hourly variations in Black-White segregation from Monday through Sunday for the top 49 most populated metropolitan areas with Global Positioning System (GPS) data collected from mobile phones from October 2018. I find that segregation levels are higher on average over weekends compared to that of weekdays. I use models to identify the characteristics of neighborhoods with higher levels of segregation on weekends, which include all demographic variables and nearly a third of 35 sectors of businesses and organizations, such as retail, personal care, and religious organizations. I also find more than a third of the sectors are associated with higher levels of segregation during business hours on weekdays, including academic institutions, health care, manufacturing, and financial institutions. Findings from this paper display the significance in the distinction between weekdays and weekends with where people spend their time and how this relates to racial segregation. Specifically, Black people, on average, stay in their home census tracts and visit non-White neighborhoods for organizational resources more so than White people. Significant patterns of associations between racial segregation and the majority of businesses demonstrate the salience of race for more industries than previously understood.

Progressive pollen calendar to detect long-term changes in the biological air quality of cities in the Madrid Region, Spain

Rhinoconjunctivitis and allergic asthma are among the most frequent diseases in the world, and pollen is their main cause. The incidence of respiratory allergic diseases is expected to grow in the coming years as a consequence of ambient pollution, changes in land use and land cover in cities, and climate change. In this context of global change, the environmental information provided by monitoring and warning networks must be as updated as possible in order to be useful to the end users in the cities. The Madrid Autonomous Region (central Spain) has suffered a noticeable change in its land-use configuration in recent decades and a significant upward temperature trend throughout the entire area. We found that the aerobiological stations in the Madrid Region Palynological Network, covering the most populated cities of this region in central Spain, registered a marked shift towards a greater airborne pollen load of the most abundant tree pollen taxa (Cupressaceae, Platanus and Quercus, but also Pinaceae, Olea and others). These temporal changes are very evident in the pollen calendar for two periods, 1994–2004 (past) and 2012–2022 (present), pointing to changes in the phenology and intensity of the airborne pollen dynamic in cities in the Madrid Region. Based on these findings, we present a technical proposal to the Madrid Region Palynological Network to generate pollen calendars for each city using the reference period of the last ten years, and to update this information every three years. Finally, we propose the latest pollen calendar for all the aerobiological stations in the Madrid Region Palynological Network for the last ten years (2013–2022), which is considered the reference aerobiological information at present for one of the most populated metropolitan areas in Europe.

Advanced Quantitative Precipitation Information: Improving Monitoring and Forecasts of Precipitation, Streamflow, and Coastal Flooding in the San Francisco Bay Area

Abstract Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide.

Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning.

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.

Unleashing Urban Technology Dynamics: The Interplay of AI Patents, Metropolitan Area Population, and R&D Expenditures in Sustainable Urban Development

ABSTRACT In light of escalating population growth in many metropolitan areas, there is an increasing need for sensibly managed technological innovations pertaining to the urban environment. To facilitate the development of such innovations in the realm of urban technology, it is imperative to explore the migration of patented artificial intelligence (AI) applications into the urban environment as well as to discuss the degrees of, prevalence of, and types of AI. However, scholarly research on urban technology tends to neglect the research and development (R&D) expenditures linked to this expansion. This present study focuses on AI patents as a fundamental driver for the proliferation of AI applications within cities, taking factors into account, such as the concentration of the population in urban centers and the levels/volume of allocation of R&D expenditure. By conducting a panel data analysis on 18 countries that have demonstrated internationally noted practices in smart city development, the study reveals compelling findings that underscore the positive impact of augmented R&D expenditures on the generation of AI patents, surpassing the effects of corresponding expenditure increases. These findings suggest that policymakers can reasonably view R&D investment as a favorable policy approach to foster advancements and applications in commercially underpinned but societally beneficial AI.

A Linear Regression Study Relating Stray Animal Management with the Importance of Relative Funding. A Case Study in a Small Greek City

Cities all around the world face the constant difficulty of regulating stray animal populations in urban environments. Abandoned or lost dogs and cats find refuge on urban streets, where they face a variety of challenges. This essay dives into the complex subject of managing stray animal populations in metropolitan areas. Stray animals on the streets are malnourished, exposed to harsh weather conditions, and infected, creating a huge public health concern by spreading illnesses such as rabies. Collaboration among communities, local governments, and animal welfare groups is critical in combating this issue efficiently. Implementing large spay and neuter programs, building animal shelters, and encouraging ethical pet ownership are all popular solutions for addressing this issue. To create safer and more compassionate urban settings for both humans and animals, it is critical to invest in vaccination and disease prevention programs, strengthen animal welfare regulations, and encourage community involvement. This study will use data from a municipality in Greece's Attica area to evaluate the link between greater spending and improved stray animal management. The examination of yearly veterinary care costs demonstrates an increase in financing but no commensurate increase in the number of animals seeking veterinary treatment. As a result, state and local authorities must take a more targeted and efficient approach to addressing this disparity. Although the state is willing, the challenge of reducing stray animal numbers persists, requiring ongoing attention and financial support. Cities can strive towards creating inclusive and healthier urban environments for all residents by addressing root causes and implementing viable solutions. This paper proposes a more targeted approach to tackling this complex issue.

Urban Heat Island Vulnerability in the City of Recife, Pernambuco, Brazil

Abstract This study introduces the urban heat island vulnerability index (UHIVI) for Recife, Brazil, the center of the most populated metropolitan area in the Northeast region. The index, encompassing sensitivity, adaptive capacity, and exposure, integrates demographic data through factor analysis to derive a social vulnerability index (SVI). Urban heat island (UHI) intensity data addresses exposure, enabling a comprehensive analysis of both the physical and social dimensions of the city. Results reveal heightened UHI exposure in the city center and coastal areas, correlating with higher urbanization density. However, populations in most areas of these regions demonstrated higher adaptive capacities, translating to lower UHI vulnerability. Conversely, less-discussed areas in traditional UHI approaches, with limited adaptive capacity and heightened sensitivity, emerge, shedding light on previously overlooked urban vulnerabilities. Regions near the city center featuring irregular settlements prove most susceptible to UHI. Illiteracy, aging demographics, and local environmental conditions emerge as the three main factors contributing to UHIVI. The index’s application unveils spatial complexities and inequalities, offering urban planners a nuanced understanding of the city. This comprehensive insight aids in policy development and decision-making, empowering planners to address urban disparities effectively. The UHIVI thus emerges as a valuable tool for understanding the challenges of urban planning, fostering more resilient and equitable urban development.

Air Quality Characterization and Trend Analysis in a Brazilian Industrialized Metropolitan Area in the Period from 1995 to 2022

The Brazilian population grew approximately 9% from 2006 to 2016, and the number of deaths caused by air pollution increased by 14% in Brazil in the same period. Facing the lack of studies on air quality in the Metropolitan Area of Belo Horizonte (MABH)—the third most populous Brazilian metropolitan area—this study aimed to investigate the air quality and the trends of air pollutant concentrations in the MABH between 1995 and 2022, using data from the air quality monitoring network. The methodology consisted of checking MABHs air quality trends following the WHO air quality guidelines. The Mann–Kendall test was used to check statistically for the possibility of tendencies. The results showed a trend of stability in the concentrations of air pollutants in the MABH without any trend of improvement or worsening. However, the time series of the MABH exposed the challenge of ensuring better air quality that protects human health. Furthermore, the results reinforced the importance of focusing on pollutant sources and exposed the need for improvements in air quality management. Thus, it is essential to reverse the current dismantling scenario of the public environmental agencies in Brazil.

Disparities in access to high-volume parathyroid surgeons in the United States: A call to action

BackgroundParathyroidectomy by a high-volume surgeon is associated with a reduced risk of perioperative complications and of failure to cure primary and secondary hyperparathyroidism. There are limited data on disparities in access to high-volume parathyroid surgeons in the United States. MethodsWe used publicly available 2019 Medicare Provider Utilization and Payment data to identify all surgeons who performed >10 parathyroidectomies for Medicare fee-for-service beneficiaries, anticipating that fee-for-service beneficiaries likely represent only a subset of their high-volume practices. High-volume parathyroid surgeon characteristics and geographic distribution were evaluated. Inequality in the distribution of surgeons was measured by the Gini coefficient. The association between neighborhood disadvantage, based on the Area Deprivation Index, and proximity to high-volume parathyroid surgeons was evaluated using a one-way analysis of variance with Bonferroni-corrected pairwise comparisons. A sensitivity analysis was performed restricting to high-volume parathyroid surgeons within each hospital referral region, evidence-based regional markets for tertiary medical care. ResultsWe identified 445 high-volume parathyroid surgeons who met inclusion criteria with >10 parathyroidectomies for Medicare fee-for-service beneficiaries. High-volume parathyroid surgeons were 71% male sex, and 59.8% were general surgeons. High-volume parathyroid surgeons were more likely to practice in a Metropolitan Statistical Area with a population >1 million than in less populous metropolitan or rural areas. The number of high-volume parathyroid surgeons per 100,000 fee-for-service Medicare beneficiaries in the 53 most populous Metropolitan Statistical Areas ranged from 0 to 4.94, with the highest density identified in Salt Lake City, Utah. In 2019, 50% of parathyroidectomies performed by high-volume parathyroid surgeons were performed by 20% of surgeons in this group, suggesting unequal distribution of surgical care (Gini coefficient 0.41). Patients in disadvantaged neighborhoods were farther from high-volume parathyroid surgeons than those in advantaged neighborhoods (median distance: disadvantaged 27.8 miles, partially disadvantaged 20.7 miles, partially advantaged 12.1 miles, advantaged 8.4 miles; P < .001). This association was also shown in the analysis of distance to high-volume parathyroid surgeons within the hospital referral region (P < .001). ConclusionOlder adults living in disadvantaged neighborhoods have less access to high-volume parathyroid surgeons, which may adversely affect treatment and outcomes for patients with primary and secondary hyperparathyroidism. This disparity highlights the need for actionable strategies to provide equitable access to care, including improved regionalization of high-volume parathyroid surgeon services and easing travel-related burdens for underserved patients.

The phenological response of European vegetation to urbanisation is mediated by macrobioclimatic factors

Plant phenology is a crucial component of ecosystem functioning and is affected by multiple elements of global change; we therefore need to quantify the current phenological changes associated to human activities and understand their impacts on ecosystems. Urbanisation and the intensification of anthropogenic activities alter meteorological conditions and cause phenological changes in urban vegetation worldwide. We used remote sensing data to evaluate the phenological response (start of season date SOS, length of season LOS and end of season date EOS) of five main vegetation types (evergreen forests, deciduous forests, mixed forests, sparse woody vegetation and grasslands) to urbanisation in the 69 most populated pan-European metropolitan areas (i.e., those that include cities with a population over 450,000 inhabitants) for the period 2002–2021. In general, SOS advanced and LOS increased with urbanisation intensity across European metropolitan areas. We found that macrobioclimatic factors strongly determined the strength and direction of the phenological response to urbanisation intensity. The greatest advances in SOS with increasing urbanisation were registered in metropolitan areas in the Mediterranean region, where there was also more uncertainty in this relationship. The EOS advanced with urbanisation in metropolitan areas in the Mediterranean macrobioclimate, whereas in areas with higher precipitation during summer the opposite trend was observed suggesting water availability mediates the response between urbanisation and autumn phenophases. Our results suggest that macrobioclimatic constraints operating at the continental scale are crucial to understand the relationship between plant phenology and urbanisation intensity.

Equity of bike infrastructure access in the United States: a risky commute for socially vulnerable populations

This study examines the bike access risk gap (BARG) for commuting in the 50 most populated metropolitan areas in the United States and equips bike advocates with the knowledge and tools necessary to identify the priority areas that need bike infrastructure improvements and the well-connected low-stress bike infrastructure. The analysis (i) examines the average BARGs of metropolitan areas for twelve travel time thresholds, (ii) considers the temporal and spatial disparities of slightly and extremely risky bike infrastructure, and (iii) reveals the disproportionate exposure of socially vulnerable populations to extremely risky bike infrastructure for a journey to work. The results indicate that (i) few metropolitan areas are associated with slightly risky bike infrastructure, (ii) the exposure to extremely risky bike infrastructure becomes more likely as commute travel time increases, and (iii) African Americans, Hispanics, low-income, and carless households are disproportionally exposed to extremely risky bike infrastructure and yet are the least prioritized in urban planning and bike infrastructure investments. The findings offer insights for identifying areas in which constructing low-stress bike infrastructure on or near high-stress bike infrastructure narrows the BARG.

Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.

Mobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture. In Japan, the de facto population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments. Combined with this data, we conducted a time series linear regression analysis to explore the relationship between daily reported case counts of COVID-19 in Tokyo, Osaka, and Aichi, and night-time de facto population in downtown areas estimated from mobile phone location data, from February 2020 to May 2022. As an approximation of the effective reproduction number, the weekly ratio of cases was used. Models using night-time population with lags ranging from 7 to 14 days were tested. In time-varying regression analysis, the night-time population level and the daily change in night-time population level were included as explanatory variables. In the fixed-effect regression analysis, the inclusion of either the night-time population level or daily change, or both, as explanatory variables was tested, and autocorrelation was adjusted by introducing first-order autoregressive error of residuals. In both regression analyses, the lag of night-time population used in best fit models was determined using the information criterion. In the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time. Our results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.

Seismic reflection stratigraphy of Lac Simard, Quebec, Canada: mass flow sedimentation in glacial Lake Barlow-Ojibway

Lac Simard is a glaciated Shield basin situated in the Western Quebec Seismic Zone, an area of recurrent intraplate seismicity encompassing several densely populated areas. Previous works nearby have demonstrated that sediments preserved in lake basins hold the potential to serve as valuable archives of paleoseismicity. Correspondingly, a high-resolution chirp seismic reflection survey was conducted in Lac Simard, with results showing four acoustic facies (Af-1 to Af-4) within two stratigraphic successions (SS-1 and SS-2). The lowermost SS-1 is dominated by high-amplitude, laminated Af-2, interpreted as lateglacial varves deposited by suspension from primarily overflows and interflows during glacial lake ponding following deglaciation. Suspension deposition of Af-2 was episodically interrupted by stacked, chaotically bedded, weakly graded to opaque, channelled Af-3 interpreted as either debris flows or high-density turbidity currents. Presence of silt-clast breccias in Af-3 also suggests downslope collapse and reworking of varves from basin sidewalls. The overlying SS-2 is dominated by weakly reflective, laminated, high-frequency varved Af-4, suggesting a continuing seasonal control on postglacial sedimentation but with relative sediment starvation. A basin-wide erosional unconformity separating SS-1 and postglacial SS-2 records the abrupt drainage of glacial Lake Barlow-Ojibway around 8 000 years ago. Considering the history of lake floor disturbance in the region, mass flow facies Af-3 may be seismogenic, and thus, have regional tectonic significance. However, we cannot rule out the possibility of mass flows triggered by abrupt inflows of meltwater and sediment from a highly dynamic retreating Laurentide Ice Sheet prone to surging.