Metropolitan Environment Research Articles

Planning Interventions to Develop Blue Green Infrastructure on the Verge of Water Sensitivity: A Case of Lucknow, Uttar Pradesh

This abstract emphasises the significance of planning interventions to construct blue-green infrastructure in Lucknow, Uttar Pradesh, on the border of water sensitivity. Blue-green infrastructure is a sustainable method that blends natural and engineered systems to manage water resources, boost urban resilience, and improve the quality of life in metropolitan environments. Due to urbanisation and climate change, Lucknow, the capital of Uttar Pradesh, is confronted with considerable water sensitivity challenges. Through the deployment of blue-green infrastructure, this research aims to provide planning strategies that successfully solve the water sensitivity concerns in Lucknow. The interventions emphasise the integration of green areas, water management systems, and sustainable drainage systems in order to reduce floods, improve water quality, and boost ecosystem services. The findings of this study contribute to the expanding body of information about the construction of blue-green infrastructure in water-sensitive metropolitan regions. The recommended planning interventions offer policymakers, urban planners, and local authorities with a road map for promoting resilience and sustainable development in Lucknow.

Environmental Sustainability and Territorial Competitiveness: A Comparison between Italian Provinces

The emergence of globalisation and the removal of obstacles between markets have heightened rivalry between territorial areas. To have a competitive advantage, the regions have to be unique. As one of the tactics used to boost their reputation on a territorial level, territories are progressively adopting environmental policies for sustainable and shared prosperity. Indeed, effective management of urban growth depends heavily on sustainable development. In this regard, the literature occasionally refers to the “green branding” of cities, a strategy that makes use of environmental aspects to boost the allure of metropolitan environments. There is currently little consensus in the literature on the measuring of environmental performance, and no statistical study has been done to confirm the efficacy of these measures in terms of territorial competitiveness. Therefore, it is important to determine whether there is a relationship between a territory’s level of sustainability and competitiveness in Italy. According to the statistical analysis of the Italian provinces, the Northeast, Northwest, Centre, and Islands are the four geographical regions with the highest average scores. This unquestionably indicates a basic comprehension and supports the notion that there is a relationship between the two variables. However, it also serves as a warning about how geographical disparities in Italy represent a major issue affecting the most diverse sectors. In addition, the COVID-19 pandemic problem has drastically altered consumer demands and goals, leading consumers to seek out more sustainable travel and cities that are designed with citizens’ requirements in mind. It will therefore become more and more important to research how public and private administrators, as well as policy makers, react to these changes.

TS-CAGR:Traffic sensitive connectivity-aware geocast routing protocol in internet of vehicles

High vehicular mobility, frequent path link disruption and disconnection as well as random distribution of vehicular nodes make the establishment of path link for data dissemination highly challenging and the existing ad hoc routing protocols ineffective. The presence of traffic lights in metropolitan environments has a significant impact on connectivity because changes to traffic signals can induce variations in the geographical distribution of nodes. The designing of connectivity-aware geocast routing protocol using multi-hop communicational pattern, which is sensitive to traffic signal and traffic noise is a significant and contemporary issue in urban Internet of Vehicles (IoV). In this context, this paper proposes a Traffic Sensitive Connectivity-Aware Geocast Routing (TS-CAGR) protocol to minimize the impact of a traffic signals on link connectivity among vehicles along a path. First, a system connectivity model focusing on segmentation of a road into platoon and cell formation due to traffic light is presented. Secondly, connectivity model is presented to compute and analysis the connectivity probability using both platoon and cell formation. Thirdly, the routing algorithm is proposed comprising of an adaptive path selection mechanism to find suitable path segments based on the On-the-Fly Segment Density (OFSD) approach. Next Hop Vehicle (NHV) selection using Angular Zone-based two-hop four-neighbor information (AZ-2-H-4-NI) approach, and Store-Carry-Retransmit (SCR) approach to cope with the frequent link disconnection problem. By combining both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication patterns, the total probability of connectivity in cluster and cell-based vehicular node configurations has been computed. The performance of TS-CAGR is evaluated in an urban vehicular environment by considering performance parameters: throughput, end-to-end delay, packet delivery ration and routing overhead. The comparative performance evaluation attests to the superiority of the TS-CAGR protocol over the existing protocols.

Deformation characteristics of an ultra-deep and small-scale rectangular excavation in Hangzhou soft clay

A stereogarage excavation was carried out in Hangzhou soft clay. The excavation was approximately 46.2 m deep, 10.8 m wide, and 24.4 m long. The spatial effect of this excavation was expected to be significant because of the minimum aspect ratio of B/He = 0.23, where B is the excavation width and He is the final excavation depth. This study examined the deformation characteristics of this ultra-deep and small-scale rectangular excavation through a comprehensive instrumentation program. Analyses of field data indicated that owing to the spatial effect, the maximum wall deformation.δhm and ground settlement δvm were less than 0.2% and 0.04% of the excavation depth, respectively, which were substantially smaller than those of similar excavation projects. For the ultra-deep excavation, the location of the maximum wall deformation was dominated by both excavation depth and stratum distribution. Construction of diaphragm walls and soil improvement prior to excavation caused significant building settlements, which accounted for 30%–50% of the total settlements. This project could serve as a special case study and provide insights into the design and construction of stereogarages in metropolitan environments with soft soil deposits.

Improved RRT* Algorithm-Based Path Planning for Unmanned Aerial Vehicle in a 3D Metropolitan Environment

The unmanned aerial vehicles (UAV’s) are widely used in smart logistic application. The optimal route prediction, however, is a fundamental prerequisite for UAV in commercial applications. This paper introduces an Improved Rapid random tree (IRRT*) algorithm with triangular inequality rewiring technique for finding collision free path for UAVs in a three-dimensional (3D) environment. The 3D building environments for navigation were developed using MATLAB/Simulink 2021, a virtual occupancy grid model. By considering UAV variable elements such as roll angle, air speed, flight path angle, and boundary threshold parameters, the suggested work aims to provide a comparative analysis of sampling algorithm-based optimal path. The proposed route planning control strategy is to identify the violation free path to locate the destination in 3D environment at variable altitude and air speed. Compared to the standard RRT and RRT* algorithms, the proposed IRRT* algorithm can shorten the planning time, reduce the cost distance and improve the algorithm’s applicability in the formation path planning problem. Simulation experiments with two environments and their different situations are carried out to determine the efficiency and performance of the proposed IRRT* algorithm. Statistical investigation supported the effectiveness of the IRRT* approach, which has low computational cost and a smooth travel trajectory that significantly resolves the unmanned aerial vehicle path planning issues in logistic applications.

Migratory success in the experience of poles from Berlin and London

This article investigates the issue of migration success achieved by Poles settling in Berlin and London between the 1980s and 2018. We focus on the migration wave that took place after Poland’s accession to the European Union in 2004. We show different ways in which migrants understand their new situation in the light of their integration, daily life, and well-being or satisfaction in the context of migration success. We analyse the similarities and differences of approaches to the new life in London and Berlin explicated by Polish migrants. In doing so, we pay attention to several important characteristics and processes related to the integration of Polish migrants into the metropolitan environments of Western Europe.

‘I was Born in One City, but Raised in Another’: Aretino's Perugian Apprenticeship

AbstractAccording to his apocrypha, Aretino was forced to flee his hometown of Arezzo after penning some anti‐papal verses. Similarly, it is claimed that he fled Perugia ten years later after painting a lute into the hands of a depiction of the Maddalena, which stood in one of the town's piazze. Neither anecdote is true, but they point to Aretino's early reputation for both poetry and painting. In 1512, the Opera nova del fecondissimo giovene Pietro pictore Arretino was published in Venice. It was the first work by Aretino to appear in print, and the fruit of the formative time he spent in Perugia, where he was part of an urban circle of poets, artists and scholars. It was a circle and a metropolitan environment he sought to recreate in Rome and Venice. This article draws together Aretino's letters to his lifelong Perugian friends and the poems in the 1512 volume in order to examine the correspondence between their rhetorical and discursive modes. This examination shows that Perugia was the crucible of Aretino's self‐fashioning. It was in Perugia that Aretino became a poet. Where also, it would appear, he almost became a painter (pictore); a training that would equip him to become one of the most significant writers on art of the sixteenth century. The Opera nova shows Aretino mastering the vernacular poetic traditions available to him before, ultimately, rejecting them all in favour of himself. Perugia was the city where, with a little help from his friends, Aretino became Aretino.

Metaphors in Child Discourse: Interpretive Research from a Participatory Perspective

This article presents the study results of an educational project focused on visual metaphor in initiated child discourse. The objective of the study was to assess the knowledge of children between the ages of 9 and 10 years old regarding the target domain of metaphorical projection (concerning togetherness) and children’s metaphorisation skills in the area of recognising similarities and differences between the target domain and the source domain of visual metaphor in an artistic picture book. In the designed didactic intervention activities, the research material originated from participant observation, focus interviews,and analysis of children’s creations (graphic visualisations). The research was conducted on two groups of third graders in selected primary schools in the metropolitan environment of Łódź, Poland. The results illustrate children’s preferences for translating one domain ofmetaphor with another, as well as the strategies for constructing children’s knowledge with respect to life in a relationship. Furthermore, they point towards the need for expanding the educational environment in Polish educational culture.

What do geriatricians think about Comprehensive Geriatric Assessment? A survey of Australian and New Zealand geriatricians.

Despite Comprehensive Geriatric Assessment (CGA) being central to the work of geriatricians, little is known about geriatricians' views on CGA. This study was designed to understand how geriatricians conceptualise and practise CGA, and whether differences exist between groups (more experienced geriatricians vs. less experienced geriatricians and registrars; those practising in rural vs. metropolitan environments; in private vs. public sectors). An anonymous 90-item electronic questionnaire was emailed to members of the Australian and New Zealand Society for Geriatric Medicine. There were 243 respondents (response rate 19%). Respondents stated that core features of CGA were multidimensional assessment (89%), implementation of a management plan (84%), and involvement of a geriatrician (70%). Important features in predicting CGA success were ability to implement health management recommendations (55%), and ease of communication with general practitioners (46%), and with the multidisciplinary team (45%). Functional decline (88%) and frailty (88%) were thought to predict patients likely to benefit from CGA, compared to age (48%) or multimorbidity (19%). Junior consultants and registrars were less likely to prioritise the importance of clinical leadership (10% vs. 30%), and more likely to report the importance of a structured format (17% vs. 7%) to CGA effectiveness. There was some agreement about the core features of CGA and who is likely to benefit, but little agreement on features that predict CGA success. Further research that explores these points of difference would be helpful.

Working Memory in Navigational and Reaching Spaces in Typically Developing Children at Increasing School Stages.

Based on studies of children with motor disabilities on topographic working memory (TWM), no influence of age was reported. The only differences were in the degree of mobility and exploration of the environment. The more active a child was in exploring the environment, the less his/her TWM was poor. However, in typically developing children (TD), exploration of the environment increases with increasing age, and age-related effects have been described. Here, we aim at investigating TWM considering age in TD with the additional question of whether WM in the reaching space differed from that in the navigational space requiring body movements. We hypothesized that WM in both spaces would improve correspondingly with increasing age, assuming that the greater the autonomy in exploring the environment, the better TWM becomes. 120 children (5-16 years old) performed the Corsi Block-Tapping test (CBT) and the Walking Corsi test (WalCT). Statistical analyses evidenced significantly increasing WalCT and CBT spans between each school stage, except in the CBT span between middle stage (MS) and upper stage (US). CBT spans were significantly higher than in the WalCT in the pre-school, lower stage, and MS, with the CBT span increasing until MS, which is sufficient for using spatial orientation strategies effectively. When navigation is gradually controlled, a child may be able to pay increasingly more attention to wayfinding and behavior in traffic. Since the US group even presented as good in the WalCT as young adults living in metropolitan environments, assuming that children may gain spatial orientation from having opportunities to move in their surroundings, this is also relevant for children with motor disabilities.

An AI Model to Predict Acute Hospitalization after Radiation Therapy

<h3>Purpose/Objective(s)</h3> Acute hospitalization during or after cancer treatment negatively impacts quality of care and causes significant patient morbidity. In patients receiving radiation therapy (RT) for any malignancy, we hypothesized that a machine learning approach would enable prediction of hospitalizations during or shortly after RT. <h3>Materials/Methods</h3> We analyzed 35,810 courses of RT provided to treat a cancer diagnosis at a large multisite academic department in a major metropolitan environment (all cancer patients treated with RT between 8/1999—1/2022 regardless of disease site or treatment intent). Approximately 150 clinical/treatment variables were extracted and processed into analytic format by a proprietary oncology analytics platform connected to the Electronic Medical Record System (inpatient and outpatient), Oncology Information System, and PACS. Such variables included past medical history, recent laboratory data, prior systemic cancer therapies, prior RT, recent hospitalization, and RT details. A patient was labeled as having suffered an acute hospitalization if they had an encounter classified as inpatient or emergency during RT or in the 30 days following completion of RT. A machine learning model was trained on a subset (75%) of the data reserved for training (26857 cases). Five-fold cross-validation was used to select model type and hyperparameters, using area under the ROC curve (AUC) to measure performance. The final model was evaluated for accuracy, AUC, precision, recall, and F1 score on an independent test set (25%) excluded from the training process (8953 cases). Model calibration was assessed by visual inspection of calibration plots. An AUC>0.70 was considered clinically valid. <h3>Results</h3> Among the 35,810 courses of RT, the incidence of acute hospitalization was 9.1% (9.2% training set; 9.0% test set). Model performance on the test set is shown in Table 1. Variables deemed to be significant predictors for hospitalization included recent lab values (PT INR, sodium, potassium) recent hospitalization (within 30 days prior to RT start), and patient age <h3>Conclusion</h3> In cancer patients undergoing RT, a machine learning model identified patients at risk of 30-day hospitalization. Predictive analytics may be a key tool to help providers identify high-risk patients and optimize interventions, while improving quality and value of care.

Evaluation of cloud and precipitation processes in regional and global models with ULTIMATE (ULTra-sIte for Measuring Atmosphere of Tokyo metropolitan Environment): a case study using the dual-polarization Doppler weather radars

We describe a collaborative analysis study involving numerical models and observation data for the Tokyo metropolitan area called the ULTra-sIte for Measuring Atmosphere of Tokyo Metropolitan Environment (ULTIMATE) project. It evaluates cloud microphysics schemes of numerical models using extensive observation data for the Tokyo area. We have access to various remote sensing and in situ data for the Tokyo area for operational and research purposes, particularly by enhancing observations for ground validation of the EarthCARE satellite, which is set to launch in 2023. This study focuses on using the dual-polarization Doppler weather radar, operated by the Japan Meteorological Agency. In terms of numerical models, we use and compare multi-models with single-moment (SM) and double-moment (DM) cloud microphysics schemes; the global non-hydrostatic model, Non-hydrostatic ICosahedral Atmospheric Model (NICAM) and the two regional models with A System based on a Unified Concept for Atmosphere (ASUCA) and Scalable Computing for Advanced Library and Environment (SCALE) are used. In particular, because NICAM can be used as both a global and a regional model, we can immediately test the improved scheme on a global scale for its effect on climatology and the evaluation of climate sensitivity. This paper introduces the methodology for evaluating numerical models by the dual-polarization radar using the observation simulator and compares numerical model results with observations. In particular, we evaluate the simulated rain in the lower level near the ground and the large ice particles just above the melting level. The simulation with NICAM-DM reproduces the comparable polarimetric radar characteristics of rain as the observation. However, the simulations with NICAM-SM and ASUCA-SM show larger raindrop sizes in stronger rain areas compared to the observation. For the larger ice particles just above the melting level around 4 km, NICAM-DM and ASUCA-SM overestimate particle sizes of graupel or snow, while NICAM-SM has a similar size of the ice particles. In future studies, we will use the present results to improve the cloud microphysics scheme, which will be tested on a global model.

3D Simulation of Battery Fire on a Large Steel Frame Structure due to Depleted Battery Piles

Lithium ion batteries (LIB) can rupture and result in thermal runaway and battery fires. In the process of transporting lithium ion batteries using trains, the massive collection of batteries can cause train fire and pose significant danger to the public. This is especially critical when the fire occurs amid a heavily populated metropolitan environment. This paper reports the 3D analysis of a warehouse with possible train fire due to LIB rupture and the fire propagation at a rail yard. Six critical fire cases with the battery train in close vicinity to the warehouse were considered. The six fire cases are the worst-case scenarios of a Monte Carlo simulation of different fire cases that may occur to an actual steel storage facility at the Capital Railyard, Raleigh, North Carolina. A 3D finite element (FE) frame model was constructed for the steel warehouse and the most critical fire cases were simulated. The results indicated that several structural components of the warehouse would experience large stresses and deflections during the simulated battery fires and resulting in instability to the structure. Specifically, members of the roof frame represent the most critical elements and that the members can result in large deformations as early as 4 minutes after the fire starts. Furthermore, effective utilization of fire protection can delay somewhat the fire effects and extend time to failure to 45 minutes and in one of the simulated cases, prevent structural instability. Thus, fire from LIB waste transport using train is a very realistic problem due to the thermal runaway, and the analysis performed in current study can be used as a preventive investigation technique for buildings that may be exposed to the train fire risk.

Sex and Age Characteristics of Thunderstorm Asthma Emergency Department Visits in a Midwest Metropolitan Environment (2007-2018)

Background Severe asthma has been shown to occur in the combined presence of high pollen and thunderstorm conditions, also known as ‘thunderstorm asthma.’ Studied as severe epidemic events, recent work has explored less dramatic, but more persistent chronic occurrences. We explore thunderstorm asthma-related emergency department (ED) visits in the Minneapolis-St. Paul region and evaluate unknown risk differences by sex and age groups. Methods We define a thunderstorm asthma exposure event as daily occurrence of 2 or more lightning strikes during high pollen periods. We use daily counts of asthma-related ED visits from the Minnesota Hospital Association (2007-2018). We estimate relative and absolute risk of severe asthma during thunderstorm asthma events for the full population and sex and age subgroups. Results The overall population had a 1.055 (95% CI: 1.019, 1.093) relative risk of asthma-related ED visits during thunderstorm asthma conditions. We observe a greater relative risk on the day of the exposure for males (RR 1.07; 95% CI: 1.02, 1.12) compared to females (RR 1.04; 95% CI: 1.00, 1.09), and observed age 18-44 years and 45 and up to be most vulnerable. Children have no significant increase in ED visits during thunderstorm asthma events. Absolute risk counts show similar patterns to these age and sex results, but age-sex subgroups show more variation in absolute vs relative risk for males compared to females. Conclusions Our results support an association between ED visits and thunderstorm asthma, and provide evidence of varying risk by sex across the life course. Thunderstorm asthma shows a unique pattern where middle-aged males and older females show the greatest risk, unlike baseline asthma incidence that is greatest in male children and middle-aged females. These patterns have implications for unrecognized at-risk groups and provide clinical information for this unique asthma exposure. Keywords Climate change, thunderstorm asthma, pollen, ED visits

The Battles around Urban Governance and Active Citizenship: The Case of the Movement for the Caracol da Penha Garden

Building a civil society that can act as a collaborative voice in the processes of change that take place in the territories does not always come about naturally and peacefully but is often something that needs to be won, based on concrete episodes in daily life. These changes are framed by the ongoing trend of shift from a form of power carried out in accordance with the old values of opacity, autonomy and imposition to one dominated by transparency, informality and sharing. The consolidation of governance processes in line with the legitimate exercise of local, national, regional or metropolitan government therefore makes sense. A descriptive methodology is adopted here of the process of affirmation of an organic movement of citizens, identifying step by step the interactions between actors that led to the reversal of the initial decision taken by the municipality. This emblematic case is framed theoretically by the ongoing paradigm shift related to the modes of exercising power at the local scale. This analysis of the case of the Movement for the Caracol da Penha Garden in Lisbon, Portugal is a contribution to understanding how this slow and difficult transformation takes place in urban and metropolitan environments and how the learning that can be taken from these processes can be of great benefit to all urban stakeholders.

Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.

Evaluating the public acceptance of sustainable mobility interventions responding to Covid-19: The case of the Great Walk of Athens and the importance of citizen engagement.

COVID-19, the most wide-spread and disruptive pandemic in over a century, enforced emergency urban design responses meaning to recalibrate transport provision globally. This is the first work that systematically evaluates the ‘public acceptance’ as a proxy for ‘policy success’ and ‘potential for longer-term viability’ of the high-profile sustainable transport intervention package introduced in 2020 in the capital city of Greece known as the Great Walk of Athens (GWA). This is achieved through a twin statistical analysis of an e-survey that looked into the attitudes and urban mobility experiences of Athenians accessing the area of the trial daily. The research enabled a comparison between the pre- and post-implementation traffic situations and provided details about specific measures packaged in the GWA project. Our results suggest that walking and cycling uptake were only marginally improved. Traffic delays for car users were considerable. Car usage declined somewhat, with the exception of ride-sharing. Public transport ridership numbers suffered a lot because of concerns about sharing closed space with many others during a pandemic. Men and people on low income were more likely to agree with the ‘change’. Naturally this was the case for people identified as primarily cyclists and pedestrians. The most impactful package elements in terms of car lane sacrifices (i.e., the redevelopment of Panepistimiou Street) had the lowest acceptability rates. A key reason that underpinned people's hesitation to approve the GWA initiative was the lack of public consultation in the decision-making that shaped the project. Our study provides evidence-based generalisable lessons for similar metropolitan environments looking to implement more or evaluate for possibly making permanent ‘rushed’ anti-Covid street redevelopment measures.

Drawing the Highways in the Sky for Urban Air Mobility Operations

Urban air mobility (UAM) is a new mode of intracity transportation that promises to free commuters of ground congestion by providing air-taxi services within dense metropolitan environments. The introduction of UAM operations into the airspace, however, brings challenges related to the efficient and equitable management of UAM air traffic, particularly with regard to trajectory assignment and optimization. Near-term UAM missions are anticipated to operate within structured airspace corridors, leading to the interesting problem of corridor formation. In this paper, we describe a minimum-cost, network flow optimization framework that provides global optimal corridors for a given set of origin-destination pairs, subject to airspace metrics such as airspace density, complexity, and flexibility. The framework is applied to both small-scale scenarios that demonstrate the behavior of the framework and a full-scale scenario representative of the Atlanta metropolitan area. This work contributes to understanding how to effectively integrate UAM into the airspace.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus’ effect on human health. During the entire analyzed period 1 January 2020–21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO2, sulfur dioxide-SO2, carbon monoxide-CO) and radon - 222Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments

Various applications of unmanned aerial vehicles (UAVs) in urban environments facilitate our daily life and public services. However, UAV operations bring third party risk (TPR) issues, as UAV may crash to pedestrians and vehicles on the ground. It may also cause property damages to critical infrastructures and noise impacts to the public. Path planning is an effective method to mitigate these risks and impacts by avoiding high-risk areas before flight. However, most of the existing path planning methods focus on minimizing flight distance or energy cost, rarely considered risk cost. This paper develops a novel flight path optimization method that considers an integrated cost assessment model. The assessment model incorporates fatality risk, property damage risk, and noise impact, which is an extension of current TPR indicators at modeling and assessment levels. To solve the proposed integrated cost-based path optimization problem, a hybrid estimation of distribution algorithm (EDA) and CostA* (named as EDA-CostA*) algorithm is proposed, which provides both global and local heuristic information for path searching in cost-based environments. A downtown area in Singapore is selected for the case study. Simulation results demonstrate the effectiveness of the developed cost-based path optimization model in reducing the risk cost. The statistical analysis for 100 sampled environments also shows the reliability of the proposed method, which reduced the cost by [42.64%, 44.15%] at 95% confidence level.