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15 pages, 4096 KiB  
Article
Yellowstone Wildfires Increased Stream Ion Concentrations and Export
by Isabella G. Sadler, Lusha M. Tronstad, Christine Fisher, Robert O. Hall and Todd M. Koel
Nitrogen 2024, 5(4), 1181-1195; https://doi.org/10.3390/nitrogen5040075 - 20 Dec 2024
Viewed by 347
Abstract
Wildfires in the western U.S. have increased in severity and duration in recent decades. Severe wildfires can enhance the rates of nutrient mineralization, causing large exports of inorganic nitrogen and other ions from forests to streams. Measuring the degree to which streams respond [...] Read more.
Wildfires in the western U.S. have increased in severity and duration in recent decades. Severe wildfires can enhance the rates of nutrient mineralization, causing large exports of inorganic nitrogen and other ions from forests to streams. Measuring the degree to which streams respond to severe, stand-replacing wildfires is critical to estimate in ecosystems prone to disturbance. In 2003, two severe crown wildfires burned in Yellowstone National Park, WY, USA. We studied the extent to which these fires increased nitrogen (ammonium, nitrate and nitrite), sulfate, chloride, and total dissolved phosphorus concentrations and export in three watersheds prior to and during the first four years post-fire. We measured higher concentrations of most ions after wildfire, and nitrate and chloride concentrations increased the most, increasing > 1000 µg/L. Concentrations of nitrate (≤146 times pre-fire concentrations), total dissolved nitrogen (≤11 times), chloride (≤9 times), and total dissolved phosphorus (≤7 times) were higher four years post-fire than before the wildfires burned. Exports of nitrate (≤1392 times), sulfate (≤14 times), and chloride (≤37 times) were also higher after wildfire, while nitrite (≤2.9 times) and ammonium (≤6.4 times) increased to a lesser degree. Stream concentrations of most ions were higher in watersheds that had a larger percent of the area burned. Comparing ion concentrations in streams before and after severe wildfires provides critical information to managers as the climate warms and the frequency of fire-conductive weather increases. Full article
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Figure 1
<p>Map of the Grizzly and East fire perimeters, which burned in the Clear, Cub, and Little Cub Creek watersheds. The wildfires burned 95% of the Cub Creek and Little Cub Creek watersheds, and 40% of the Clear Creek watershed. Samples were collected at the locations depicted with yellow diamonds.</p>
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<p>Little Cub Creek (<b>a</b>) the summer before (2003) and (<b>b</b>) the autumn after (2004) the East fire burned. The arrows show the exact tree adjacent to the stream before and after wildfire. (<b>c</b>) The forest before and (<b>d</b>) after the wildfire in the Cub Creek watershed showing the severity of the fire that burned mature trees and understory.</p>
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<p>(<b>a</b>) Ammonium, (<b>b</b>) chloride, (<b>c</b>) nitrate, (<b>d</b>) sulfate, (<b>e</b>) nitrite, and (<b>f</b>) total dissolved phosphorus (TDP) concentrations (µg N, S, P or Cl/L) before and after wildfire. The black dotted line denotes when the fires started burning and the red dotted line denotes a large precipitation event that caused a mudslide in 2004. Streams are differentiated by colors and shapes, and each point is an individual measurement.</p>
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<p>Concentrations of (<b>a</b>,<b>b</b>) nitrate, (<b>c</b>,<b>d</b>) ammonium, (<b>e</b>,<b>f</b>) nitrite, (<b>g</b>,<b>h</b>) sulfate, (<b>i</b>,<b>j</b>) chloride, and (<b>k</b>,<b>l</b>) total dissolved phosphorus (TDP) varied by (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>,<b>k</b>) Julian day and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>) year. Predicted concentrations were produced using a generalized additive model (GAM) where Julian day (day of the year) and year were two predictors. Seasonal variation and standard error in ion concentrations were observed for (<b>a</b>) nitrate, (<b>c</b>) ammonium, (<b>g</b>) sulfate, and (<b>k</b>) TDP. The annual range of concentrations before (2003; grey) and after wildfire (2004–2007; white) are shown (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>) where the bold line is the median concentration, the lower and upper edges of boxes are the 25th and 75th percentiles, and the whiskers are the minimum and maximum concentrations excluding outliers (open circles). Non-significant relationships are denoted with NS.</p>
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<p>The export of (<b>a</b>) nitrate, (<b>b</b>) nitrite, (<b>c</b>) ammonium, (<b>d</b>) sulfate, and (<b>e</b>) chloride from the Clear, Cub, and Little Cub Creek watersheds before (year 2003) and after wildfire (2004–2006). The bold line is the median export, the lower and upper edges of boxes are the 25th and 75th percentiles, and the whiskers are the minimum and maximum export excluding outliers (circles).</p>
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18 pages, 5919 KiB  
Article
Exploring the Impact of Nature-Based Solutions for Hydrological Extremes Mitigation in Small Mixed Urban-Forest Catchment
by Lina Pérez-Corredor, Samuel Edward Hume, Mark Bryan Alivio and Nejc Bezak
Appl. Sci. 2024, 14(24), 11813; https://doi.org/10.3390/app142411813 - 18 Dec 2024
Viewed by 437
Abstract
Many regions in Europe face increasing issues with flooding and droughts due to changing rainfall patterns caused by climate change. For example, higher rainfall intensities increase urban flooding. Nature-based solutions (NbS) are suggested as a key mitigation strategy for floods. This study aims [...] Read more.
Many regions in Europe face increasing issues with flooding and droughts due to changing rainfall patterns caused by climate change. For example, higher rainfall intensities increase urban flooding. Nature-based solutions (NbS) are suggested as a key mitigation strategy for floods. This study aims to address and mitigate the challenges faced in Tivoli natural park in Ljubljana regarding high peak discharges and low-flow issues in the creek entering the sewer system. The study involves setting up, calibrating and validating a Hydrologic Engineering Centre–Hydrologic Modelling System (HEC-HMS) model using available data. This study analyses NbS, such as small ponds, green roofs and permeable paving, to reduce peak discharge. Runoff was reduced by an average of 32.4% with all NbS implemented and peak discharge by 20 L/s. Permeable parking performed best, with an average runoff reduction of 6.4%, compared to 4.8% for permeable streets and 5.9% for green roofs. The ponds reduced peak discharge, although their effectiveness varied between rainfall events. Rainfall events with higher volumes and durations tended to overwhelm the proposed solutions, reducing their effectiveness. The ability of HEC-HMS to model NbS is also discussed. The curve number (CN) parameter and impervious % alterations to simulate NbS provided quantitative data on changes in runoff and discharge. Full article
(This article belongs to the Special Issue Sustainable Urban Green Infrastructure and Its Effects)
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<p>Map showing the location of the study area in Ljubljana (<b>upper right</b>), showing land use, water courses (<b>upper left</b>) and topography (<b>lower left</b>).</p>
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<p>Project methodology, including the data used, the pre-processing steps taken and the modelling conducted.</p>
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<p>Location of NbS scenarios implemented in the study area. P1 = Existing Pond; P2 = Southeast Pond; P3 = Northwest Pond.</p>
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<p>Calibration performance of the model for Events 1 and 4.</p>
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<p>Selected discharge hydrographs for Events 1, 5, 7 and 9, showing all scenarios.</p>
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<p>Reduction in peak discharge (L/s) for all events and scenarios.</p>
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18 pages, 10476 KiB  
Article
Restoration Evaluation of National Forest Park in Greater Khingan Mountains Region, China
by Yao Sun, Yunhe Ding, Miaoyi Lei and Liang Mao
Sustainability 2024, 16(24), 11022; https://doi.org/10.3390/su162411022 - 16 Dec 2024
Viewed by 422
Abstract
As an important part of ecological civilization construction and harmonious coexistence between man and nature, the importance of restorative environment construction in national forest parks is self-evident. In this paper, the national forest park in the Greater Khingan Mountains region covering a large [...] Read more.
As an important part of ecological civilization construction and harmonious coexistence between man and nature, the importance of restorative environment construction in national forest parks is self-evident. In this paper, the national forest park in the Greater Khingan Mountains region covering a large area of primary forest is taken as the research object. Based on visual perception, PRS, skin conductance level, and eye tracking technology are used as evaluation indexes to conduct restoration experiments on individuals. Among 60 participants, the PRS total scores for lawn space, shady space, dense forest space, and hard space were 166.63, 164.63, 168.43, and 158.93, respectively, indicating good restorative benefits, with hard space scoring lower. SCR decreases for dense forest space (M = 0.52) were significantly greater than for hard space (M = 0.38), suggesting better stress reduction. Eye tracking data showed that dense forest space had the longest total fixation duration (M = 42.57) and hard space the highest fixation count (M = 42.73). The results show that the national forest park is beneficial to the recovery of individual psychology and physiology, and can reduce the pressure of people’s visual perception. The psychological and physiological restorative benefits of different spatial scene types are different. Moreover, there is correlation between the eye movement index, psychological evaluation index, and physiological evaluation index, which confirms the possibility of introducing the eye movement index into the study of restorative environments. Full article
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Figure 1
<p>Comprehensive recovery evaluation framework. Note: AOI: area of interest; PRS: Perceived Restorativeness Scale; and SCR: skin conductance response.</p>
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<p>Experimental environment and equipment (informed consent was obtained from the subject to publish the image in an online open access publication). Note: The subjects depicted in the figures are graduate students. Post-experiment observations indicate that there were no discernible effects on their psychological or physiological well-being as a result of the testing procedures employed.</p>
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<p>Flow chart of experiment.</p>
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<p>Repeated measurement variance analysis of PRS total score for four spatial scene types. ***. <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Repeated measurement variance analysis of dimension score. *. <span class="html-italic">p</span> &lt; 0.05; **. <span class="html-italic">p</span> &lt; 0.01; ***. <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Repeated measurement variance analysis of SCR decreases for four spatial scene types. *. <span class="html-italic">p</span> &lt; 0.05; ***. <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Eye movement hotspot map.</p>
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<p>AOI partition (lawn, sky, road surface, landscape pieces, architecture, trees and shrubs, and water).</p>
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<p>Spatial scene type total fixation duration of all AOI.</p>
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<p>Total fixation duration and fixation count of the experimental site.</p>
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<p>Repeated measurement variance analysis of total fixation duration. ***. <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Repeated measurement variance analysis of fixation count. **. <span class="html-italic">p</span> &lt; 0.01; ***. <span class="html-italic">p</span> &lt; 0.001.</p>
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15 pages, 18466 KiB  
Article
Human Health Risk Assessment of Chlorinated Hydrocarbons in Groundwater Based on Multi-Pathway Analysis
by Yidi Wang, Guilan Li, Xiaohan Li, Ye Yang, Kaifang Ding, Shilu Xing, Yilong Zhang and Luxing Zhang
Toxics 2024, 12(12), 894; https://doi.org/10.3390/toxics12120894 - 9 Dec 2024
Viewed by 731
Abstract
The rapid development of the global chemical industry has led to widespread groundwater contamination, with frequent pollution incidents posing severe threats to water safety. However, there has been insufficient assessment of the health risks posed by chlorinated hydrocarbon contamination in groundwater around chemical [...] Read more.
The rapid development of the global chemical industry has led to widespread groundwater contamination, with frequent pollution incidents posing severe threats to water safety. However, there has been insufficient assessment of the health risks posed by chlorinated hydrocarbon contamination in groundwater around chemical industrial parks. This study evaluates the chlorinated hydrocarbon contamination in groundwater at a chemical park and conducts a multi-pathway health risk assessment, identifying the key risk pollutants. In addition, sensitivity analysis of the primary exposure pathways was performed using the Monte Carlo method. The results indicate severe exceedance of pollutant concentrations with widespread diffusion. Carcinogenic risks were mainly driven by vinyl chloride, whose oral cancer slope factor was significantly higher than that of other substances, while non-carcinogenic risks were dominated by trichloro-ethylene, which had the lowest reference dose. Both carcinogenic and non-carcinogenic risks through the drinking water pathway accounted for approximately 90% of the total risk, whereas the risk contribution from dermal contact was negligible. Although boiling water can partially reduce the risks, its effect on high-concentration pollutants is limited. Additionally, sensitivity analysis showed that pollutant concentration was the primary influencing factor for risk values, followed by exposure duration. The findings of this study provide a scientific basis for effectively formulating pollution control measures and ensuring the drinking water safety of nearby residents. Full article
(This article belongs to the Section Exposome Analysis and Risk Assessment)
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<p>Location of the study area and distribution of monitoring points.</p>
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<p>Spatial distribution map of chlorinated hydrocarbon pollutants.</p>
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<p>Risk values and hazard quotients of chlorinated hydrocarbons through different exposure pathways. (Risk values for chlorinated hydrocarbons in different exposure routes in blue; hazard quotients for chlorinated hydrocarbons in different exposure routes in green).</p>
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<p>Contribution rates of various chlorinated hydrocarbons across different pathways (including risk values and hazard quotients). (<b>a</b>,<b>b</b>) are the risk values and hazard quotients before boiling, while (<b>c</b>,<b>d</b>) are the risk values and hazard quotients after boiling.</p>
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<p>Standardized human health risk values (<b>a</b>): carcinogenic risk values, (<b>b</b>): non-carcinogenic hazard quotients).</p>
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<p>Pollutant risk values at different sampling locations. (<b>a</b>) Carcinogenic risk values for direct drinking; (<b>b</b>) Non-carcinogenic hazard quotients for direct drinking; (<b>c</b>) Carcinogenic risk values for boiled water consumption; (<b>d</b>) Non-carcinogenic hazard quotients for boiled water consumption.</p>
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<p>Sensitivity analysis of drinking water pathway based on Monte Carlo simulation ((<b>a</b>) represents the sensitivity analysis for carcinogenicity, and (<b>b</b>) represents the sensitivity analysis for non-carcinogenicity).</p>
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28 pages, 6733 KiB  
Article
Social Infrastructure During the COVID-19 Pandemic: Evaluating the Impact of Outdoor Recreation on Pandemic Dynamics in Europe
by Mahran Gamal N. Mahran, Haoying Han, Mahmoud Mabrouk and Salma Antar A. AbouKorin
Sustainability 2024, 16(23), 10343; https://doi.org/10.3390/su162310343 - 26 Nov 2024
Viewed by 644
Abstract
The COVID-19 pandemic has drastically affected mental and physical well-being, leading to significant changes in daily habits and preferences. Given that pandemics require the tear down of most social ties and interactions to limit their inevitable spread, this study delved into the extent [...] Read more.
The COVID-19 pandemic has drastically affected mental and physical well-being, leading to significant changes in daily habits and preferences. Given that pandemics require the tear down of most social ties and interactions to limit their inevitable spread, this study delved into the extent to which social infrastructures have been affected, focusing on behavioral shifts in essential services such as retail, recreation, groceries, pharmacies, public transport, parks and open spaces, workplaces, and residential areas. Notably, while most social infrastructures saw a decline in public usage, parks and open spaces experienced increased visitation despite public health measures aimed at minimizing social interactions. This striking increase in park and open space visitations has captured the interest of this study to observe the impact it had on the trajectory of the COVID-19 pandemic, as well as the underlying causes behind this trend. Since Europe was heavily affected by the pandemic, this study focused specifically on European countries over a two-year period (March 2020 to March 2022), covering the severe period of the pandemic. While parks and open spaces initially showed no direct influence on the pandemic trajectory, when closely observing visitation trends, both increases and declines, opposing insights were revealed. This study found that attempts to reduce park and open space visitation were significantly unsuccessful, leading to substantial increases in both the magnitude and duration of visits once restrictions were eased. This surge in park and open space attendance corresponded to notable spikes in new infections during periods of peak visitation. Therefore, urban planning and public health authorities must prioritize safely accommodating the increased park and open space demand while effectively minimizing virus transmission. This involves considering park sizes and proximity, along with implementing a balanced set of crucial public health strategies to support community well-being and resilience. Full article
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Graphical abstract

Graphical abstract
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<p>The research methodological flow. Source: the researcher.</p>
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<p>Changes in visitation trends in social infrastructure in 15 European countries. Source: The researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>]).</p>
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<p>The average trust of people in information from the government and health authorities compared to average use of parks and open spaces. Source: the researcher (depending on [<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>]).</p>
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<p>Average change in visitations in social infrastructures across the selected 15 EU case studies. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>]).</p>
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<p>Park and open space visitation moderately impacting the COVID-19 pandemic infections in 15 European case studies during study time frame. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>]).</p>
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<p>The impact of the new COVID-19 pandemic cases and government responses on park and open space visitation across the 15 countries studied. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>,<a href="#B58-sustainability-16-10343" class="html-bibr">58</a>]).</p>
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<p>The impact of the new COVID-19 pandemic cases and government responses on park and open space visitation across the 15 countries studied. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>,<a href="#B58-sustainability-16-10343" class="html-bibr">58</a>]).</p>
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<p>The impact of the new COVID-19 pandemic cases and government responses on park and open space visitation across the 15 countries studied. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>,<a href="#B58-sustainability-16-10343" class="html-bibr">58</a>]).</p>
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<p>Relationship between park and open space visitations and the new COVID-19 pandemic cases. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>]).</p>
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<p>Correlation analysis matrix to examine influential factors in park and open space visitations and the COVID-19 pandemic. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>,<a href="#B58-sustainability-16-10343" class="html-bibr">58</a>].</p>
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<p>Impacts between study factors across park and open space visitation trends. Source: the researcher (depending on [<a href="#B55-sustainability-16-10343" class="html-bibr">55</a>,<a href="#B57-sustainability-16-10343" class="html-bibr">57</a>,<a href="#B58-sustainability-16-10343" class="html-bibr">58</a>]).</p>
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12 pages, 468 KiB  
Article
The Effects of Physical Activity and the Consequences of Physical Inactivity in Adult Patients with Congenital Heart Disease During the COVID-19 Pandemic
by Elettra Pomiato, Rosalinda Palmieri, Mario Panebianco, Giulia Di Già, Marco Della Porta, Attilio Turchetta, Massimiliano Raponi, Maria Giulia Gagliardi and Marco Alfonso Perrone
J. Funct. Morphol. Kinesiol. 2024, 9(4), 226; https://doi.org/10.3390/jfmk9040226 - 8 Nov 2024
Viewed by 724
Abstract
Background: The ongoing COVID-19 pandemic has infected more than 500 million people worldwide. Several measures have been taken to reduce the spread of the virus and the saturation of intensive care units: among them, a lockdown (LD) was declared in Italy on 9 [...] Read more.
Background: The ongoing COVID-19 pandemic has infected more than 500 million people worldwide. Several measures have been taken to reduce the spread of the virus and the saturation of intensive care units: among them, a lockdown (LD) was declared in Italy on 9 March 2020. As a result, gyms, public parks, sports fields, outdoor play areas, schools, and multiple commercial activities have been closed. The consequences of physical inactivity can be dramatic in adult patients with congenital heart disease (ACHD), in which the benefit of regular exercise is well known. In this study, we investigated the effects of reduced physical activity during the COVID-19 pandemic on ACHD’s exercise capacity. Materials and Methods: Patients who performed exercise or cardiopulmonary exercise tests from October 2019 to February 2020 and one year after lockdown with the same protocol were retrospectively enrolled in our database. Inclusion criteria: ACHD patients aged ≥ 18 years old under regular follow-up. Exclusion criteria: significant clinical and/or therapeutic changes between the two tests; significant illness occurred between the two tests, including COVID-19 infection; interruption of one of the tests for reasons other than muscle exhaustion. Results: Thirty-eight patients (55.6% males) met the inclusion criteria. Before the lockdown, 17 patients (group A) were engaged in regular physical activity (RPA), and 20 patients (group B) had a sedentary lifestyle. After LD, in group A, (a) the weekly amount of physical activity reduced with statistical significance from 115 ± 46 min/week to 91 ± 64 min/week (−21%, p = 0.03); (b) the BMI did not change; (c) the duration of exercise test and VO2 max at cardiopulmonary exercise test showed a significant reduction after the LD. In group B, BMI and exercise parameters did not show any difference. Conclusions: The COVID-19 pandemic dramatically changed the habits of ACHD patients, significantly reducing their possibility to exercise. Our data analyzed in this extraordinary situation again demonstrated that physical inactivity in ACHD worsens functional capacity, as highlighted by VO2 max. Regular exercise should be encouraged in ACHD patients to preserve functional capacity. Full article
(This article belongs to the Section Physical Exercise for Health Promotion)
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<p>VO2 max in groups A and B before (<b>a</b>) and after (<b>b</b>) the lockdown. VO2 max is displayed in mL/kg/min. LD: lockdown.</p>
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17 pages, 8018 KiB  
Article
Leverage Effect of New-Built Green Spaces on Housing Prices in a Rapidly Urbanizing Chinese City: Regional Disparities, Impact Periodicity, and Park Size
by Siqi Yu, Shuxian Hu, Yujie Ren, Hao Xu and Weixuan Song
Land 2024, 13(10), 1663; https://doi.org/10.3390/land13101663 - 12 Oct 2024
Cited by 1 | Viewed by 1001
Abstract
While newly built urban green spaces aim to address environmental concerns, the resulting green gentrification and social inequality caused by escalating property values have become critical topics of urban socio-spatial research. To prevent green initiatives from becoming unaffordable for their intended beneficiaries in [...] Read more.
While newly built urban green spaces aim to address environmental concerns, the resulting green gentrification and social inequality caused by escalating property values have become critical topics of urban socio-spatial research. To prevent green initiatives from becoming unaffordable for their intended beneficiaries in rapidly urbanizing cities, it is essential to examine the spatial and temporal relationships between the construction of new green spaces and rising housing prices. This study employs a difference-in-differences methodology to analyze regional disparities, impact periodicity, and the influence of park size on housing prices, using Nanjing, China as a case study. This result reveals that the introduction of new-built parks in Nanjing significantly impacts housing prices within an 800 m radius. The premium effect of these parks is substantially higher in urban core areas compared to suburban locales, demonstrating spatial differentials. Suburban parks temporally exhibit a prolonged lag and a shorter premium impact duration. Moreover, among various park areas, medium-sized parks demonstrate the most pronounced leverage effect, approximately double that of large parks, while small parks do not significantly affect housing prices. To mitigate the exacerbation of premium effects and enhance social justice in green strategies, we advocate prioritizing the development of small parks, particularly in urban core areas, and leveraging the temporal delay in new-built park impacts for urban policy interventions. Full article
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<p>Case location and study area.</p>
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<p>Location of new parks and communities in Nanjing.</p>
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<p>Assessment of Parallel Trends.</p>
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<p>Assessment of Parallel Trends.</p>
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<p>Assessment of Parallel Trends.</p>
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27 pages, 9762 KiB  
Article
Human Physiological Responses to Sitting and Walking in Green Spaces with Different Vegetation Structures: A Seasonal Comparative Study
by Yifan Duan, Hua Bai and Shuhua Li
Forests 2024, 15(10), 1759; https://doi.org/10.3390/f15101759 - 7 Oct 2024
Viewed by 1112
Abstract
This study seeks to address the gap in knowledge regarding the varying effects of vegetation on human perception and preference, and to comprehend how green spaces can better serve community needs. The research assessed the impact of different vegetation structures on physiological responses [...] Read more.
This study seeks to address the gap in knowledge regarding the varying effects of vegetation on human perception and preference, and to comprehend how green spaces can better serve community needs. The research assessed the impact of different vegetation structures on physiological responses during two types of on-site perceptions: sitting and walking, in both winter and summer. The green spaces included single-layer grassland, single-layer woodland, tree-shrub-grass composite woodlands, and tree-grass composite woodlands, and a non-vegetated square. The findings indicated the following. (1) The physiological recovery effect of walking in green spaces is relatively greater than that of sitting; walking in green spaces with different vegetation types was found to enhance participants’ pNN50 values (p = 0). (2) During the summer, sitting and observing provided a better physiological recovery effect (p < 0.05), whereas in the winter, walking was more beneficial (p < 0.05). (3) Green spaces with vegetation were more beneficial for physiological recovery than the non-vegetated square, which could not sustain recovery effects for more than 1 min. Single-layer grassland and tree-shrub-grass composite woodlands had the most significant physiological recovery effects on health (p < 0.01). (4) Based on these conclusions, it is suggested that a combination of sitting and walking can lead to improved recovery outcomes. Therefore, when visiting parks during extreme weather conditions, individuals should adjust the duration of their sitting and walking experiences to enhance their overall experience. Full article
(This article belongs to the Section Urban Forestry)
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<p>Study Area and Research Target [<a href="#B23-forests-15-01759" class="html-bibr">23</a>,<a href="#B37-forests-15-01759" class="html-bibr">37</a>].</p>
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<p>Experimental process.</p>
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<p>Comparison of HR between two types of summer viewing and winter viewing ((<b>A</b>). summer viewing and (<b>B</b>). winter viewing). <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure.</p>
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<p>Comparison of HR between two ornamental viewing styles. <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of pNN50 between two types of summer viewing and winter viewing ((<b>A</b>). summer viewing and (<b>B</b>). winter viewing). <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of pNN50 between two ornamental viewing styles. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of RMSSD between two types of summer viewing and winter viewing ((<b>A</b>). summer viewing and (<b>B</b>). winter viewing). <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of RMSSD between two ornamental viewing styles. <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of R-R interval between two types of summer viewing and winter viewing ((<b>A</b>). summer viewing and (<b>B</b>). winter viewing). <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.01 indicates that the difference is more statistically significant, as shown by ** in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of R-R interval between two ornamental viewing styles. <span class="html-italic">p</span> &lt; 0.05 indicates that the difference is statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by *** in the figure.</p>
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<p>Comparison of SCL Between Two Observation Modes. <span class="html-italic">p</span> &lt; 0.01 indicates that the difference is more statistically significant, as shown by * in the figure.</p>
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<p>Comparison of SCL between two types of summer viewing and winter viewing ((<b>A</b>). summer viewing and (<b>B</b>). winter viewing). <span class="html-italic">p</span> &lt; 0.01 indicates that the difference is more statistically significant, as shown by * in the figure. <span class="html-italic">p</span> &lt; 0.001 indicates that the difference is very statistically significant, as shown by ** in the figure.</p>
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26 pages, 4988 KiB  
Article
Analysing Travel Patterns at Beirut Arab University, Lebanon: An In-Depth Characterization of Travel Behavior on Campus
by Rouba Joumblat, Hadi Jawad and Adel Elkordi
Sustainability 2024, 16(18), 8254; https://doi.org/10.3390/su16188254 - 23 Sep 2024
Viewed by 1056
Abstract
Understanding the travel patterns of university campus visitors is crucial for developing effective transportation strategies. Existing research predominantly focuses on student commuting within specific regions, often overlooking the diverse needs of faculty and staff and varying campus contexts. This study addresses a significant [...] Read more.
Understanding the travel patterns of university campus visitors is crucial for developing effective transportation strategies. Existing research predominantly focuses on student commuting within specific regions, often overlooking the diverse needs of faculty and staff and varying campus contexts. This study addresses a significant gap in the literature by investigating travel behaviors at Beirut Arab University (BAU), which has not been previously studied in this context. BAU’s unique situation, with campuses in both urban and rural zones, presents distinct transportation challenges, particularly for those traveling between these areas. Through a comprehensive survey of students, faculty, and staff, this research explores differences in transportation modes, travel distances, durations, and patterns. Statistical techniques, including one-way analysis of variance (ANOVA), Chi-Squared, and McNemar-Bowker tests, reveal significant variations among traveler groups. The findings highlight specific needs, such as improvements in bus services, car-sharing programs, and parking facilities, essential for creating sustainable campus environments. By examining these travel behaviors, the study offers valuable insights into the complexities of campus transportation, contributing new perspectives to the field. The originality of this research lies in its focus on an underexplored area, providing a deeper understanding of how diverse university environments impact transportation choices. This work not only fills a critical void in campus transportation research but also offers practical recommendations for enhancing transportation systems in similar settings. Full article
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<p>Geographical boundaries of the BAU-Beirut campus.</p>
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<p>Geographical boundaries of BAU-Debbieh campus.</p>
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<p>Theoretical framework of the study.</p>
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<p>Sample breakdown by gender, age, citizenship, occupation, age, campus attending, faculty, and occupation level.</p>
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<p>Percentage of the population that owns a car, with the split shown across gender and campus.</p>
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<p>The travel modes utilized by BAU respondents.</p>
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<p>Travel mode choice, based on campus and gender.</p>
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<p>Purposes of trips to campus.</p>
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<p>Reasons for using private cars from both campuses and genders.</p>
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<p>Reasons behind using the BAU bus service, and public transportation based on campus and gender.</p>
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<p>Satisfaction levels of the respondents with the parking, bus service, and public transportation at BAU.</p>
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<p>Findings from survey recommendations to be adapted at BAU. (<b>a</b>) Strategies to improve campus parking. (<b>b</b>) Smart transportation solutions. (<b>c</b>) Transportation solutions. (<b>d</b>) Initiatives for private car users.</p>
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<p>Findings from survey recommendations to be adapted at BAU. (<b>a</b>) Strategies to improve campus parking. (<b>b</b>) Smart transportation solutions. (<b>c</b>) Transportation solutions. (<b>d</b>) Initiatives for private car users.</p>
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14 pages, 12131 KiB  
Article
Ecological Restoration Increases the Diversity of Understory Vegetation in Secondary Forests: An Evidence from 90 Years of Forest Closures
by Yuhua Ma, Fengyu Xu, Jingya Wei, Wei Wang, Zhen Wu, Duanyang Xu, Fasih Ullah Haider, Xu Li and Yan Dong
Forests 2024, 15(9), 1642; https://doi.org/10.3390/f15091642 - 18 Sep 2024
Viewed by 1163
Abstract
Ecological restoration and biodiversity are currently hot issues of global environmental concern. However, knowledge about the specific impacts of restoration duration on understory vegetation diversity remains limited. Therefore, this study comprehensive employed a spatial approach to compare the differences in understory plant diversity [...] Read more.
Ecological restoration and biodiversity are currently hot issues of global environmental concern. However, knowledge about the specific impacts of restoration duration on understory vegetation diversity remains limited. Therefore, this study comprehensive employed a spatial approach to compare the differences in understory plant diversity and species composition among secondary forests with varying ecological restoration ages (0, 10, 30, 60, and 90-year-old stands) in Huangfu Mountain National Forest Park. This methodology allowed us to clarify the key factors affecting the composition of the understory plant community and investigate the regulatory mechanisms influencing changes in understory plant diversity. The results showed that shrub Shannon’s index value, shrub evenness, herb Shannon’s index value, herb richness, and herb evenness were significantly affected by the years of restoration, with 10 years and 90 years being the highest and 60 years being the lowest. Substrate diversity was the main factor influencing plant diversity in the shrub layer. Overstory richness, soil C/N, soil C, soil N, soil bacterial Observed OTUs, soil bacterial Chao1, soil bacterial Pielou_e, and substrate diversity were the drivers of plant diversity in the herb layer. Overstory evenness had a direct effect (0.256) and an indirect effect (0.284) on herb evenness through light availability and fungal Simpson’s index value. Light availability directly negatively affected herb evenness (−0.360). In addition, 52.6% of the factors affecting the herb evenness index were from the arboreal layer evenness, light availability, and fungal Simpson’s index value. To sum up, moderate disturbance of the understory environment of natural secondary forests can be carried out after 10 years of restoration, which is more conducive to the increase of understory plant diversity. This comprehensive study provides a theoretical basis for formulating ecological restoration measures for secondary forests, particularly in understanding the optimal timing and nature of disturbance in the restoration process, reassuring the audience about the validity and reliability of the findings. Full article
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<p>Location of sample sites.</p>
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<p>Effects of different years of ecological restoration on understory plant diversity. Note: All data are presented as the mean ± SE (n = 3). Values for boxplots are medians, 75% observations in boxes, and whiskers above and below the box indicate 95th and 5th percentiles. Different letters indicate significant differences between different restoration years (<span class="html-italic">p</span> &lt; 0.05). Different lowercase letters indicate the significance of different ecological restoration years at the 0.05 level.</p>
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<p>Species composition of understory plants in natural secondary forests with different years of ecological restoration. Note: Ellipses represent the standard error of the score-weighted mean corresponding to different restoration years.</p>
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<p>Changes in vegetation community stability under different years of ecological restoration. Note: (<b>a</b>–<b>e</b>): Community stability in 0, 10, 30, 60, 90 years of restoration, <span class="html-italic">R</span><sup>2</sup>: goodness of fit, (<b>d</b>): Euclidean distance between the intersection coordinates of the community stability model (x, y) and the ideal stability coordinates (20, 80). Yellow line: ideal stable point coordinates; green line: plot stability point coordinates; red line: the fitted curve of the relative frequency of species accumulation to the inverse of the cumulative total.</p>
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<p>Effects of different years of ecological restoration on environmental factors. Note: All data are presented as the mean ± SE (n = 3). Values for boxplots are medians, 75% observations in boxes, and whiskers above and below the box indicate 95th and 5th percentiles. Different letters indicate significant differences between forest ages (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Drivers affecting species composition of understory plants. Note: Overstory Stand basal area (OSBA), Overstory Shannon index (OH), Overstory richness (OS), Overstory evenness (OJ), Soil bacterial Observed OTUs (BO), Soil bacterial Chao1 (BC), Soil bacterial Simpson (BS), Soil bacterial Pielou_e (BP), Soil fungal Observed OTUs (FO), Soil fungal Chao1 (FC), Soil fungal Simpson (FS), Soil fungal Pielou_e (FP), Light availability (LM), light heterogeneity (LSD), substrate diversity (subD), soil carbon (C), soil nitrogen (N), soil carbon to nitrogen ratio (C.N), soil pH (pH) and soil water content (WC). Vector lengths represent correlations (r) between soil bacterial communities and soil properties; red and blue vectors indicate significance <span class="html-italic">p</span> &lt; 0.05 and &gt;0.05, respectively.</p>
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<p>Structural equation modeling to analyze the regulatory mechanisms of changes in plant evenness in the herb layer. Note: Orange and gray solid arrows represent significant positive and negative effects (<span class="html-italic">p</span> &lt; 0.05), respectively. Numbers next to the variables represent the variance explained by the model (<span class="html-italic">R</span><sup>2</sup>), and numbers on the arrows represent standardized path coefficients.</p>
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10 pages, 1486 KiB  
Article
Distance Decay of Urban Park Visitation: Roles of Personal Characteristics and Visitation Patterns
by Di Shu, Yulin Peng, Ziyu Zhang, Ruirui Shi, Can Wu, Dexin Gan and Xiaoma Li
Forests 2024, 15(9), 1589; https://doi.org/10.3390/f15091589 - 10 Sep 2024
Viewed by 746
Abstract
Distance decay of urban park visitation (e.g., visitation number and visitation frequency) has been widely acknowledged and is increasingly integrated into urban park planning and management considering spatial accessibility and service equity. However, thorough understandings especially concerning the variations among visitors with different [...] Read more.
Distance decay of urban park visitation (e.g., visitation number and visitation frequency) has been widely acknowledged and is increasingly integrated into urban park planning and management considering spatial accessibility and service equity. However, thorough understandings especially concerning the variations among visitors with different personal characteristics and visitation patterns are still scarce. Taking Changsha, China as an example, we collected data on visitation distance (i.e., the distance between urban parks and visitor’s homes) and visitation frequency of 2535 urban park visitors, modeled the distance decay of visitation density and visitation frequency, and investigated their variations among visitors with different personal characteristics and visitation patterns. The results show that: (1) The median visitation distance was 1.3 km and the median visitation frequency was 24 times per season. (2) Both visitation density and visitation frequency showed clear spatial patterns of distance decay and can be effectively modeled using common distance decay functions (e.g., power function, exponential function, and logarithmic function). (3) Visitors’ characteristics (e.g., gender and age) and visitation patterns (e.g., duration time, transportation modes, and visitation purposes) significantly impact visitation distance, visitation frequency, and the characteristics of distance decay (i.e., the rate of distance decay). These findings extend our understanding of the distance decay of urban park visitation which can help better urban park planning and management. Full article
(This article belongs to the Special Issue Urban Green Infrastructure and Urban Landscape Ecology)
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<p>Location of the study area and spatial distribution of the selected eight urban parks.</p>
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<p>Distance decay of visitation density (person per km<sup>2</sup>) (columns 1 and 2) and visitation frequency (times per season) (columns 3 and 4) with the increase in Euclidean distance (km) (columns 1 and 3) and travel distance (km) (columns 2 and 4) to urban parks based on logarithmic function (<b>first row</b>), exponential function (<b>second row</b>), and power function (<b>third row</b>).</p>
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25 pages, 1252 KiB  
Article
Does Parking Type Preference Behavior Differ According to Whether It Is Paid or Free? A Case Study in Istanbul, Türkiye
by Gürcan Sarısoy and Hüseyin Onur Tezcan
Sustainability 2024, 16(17), 7269; https://doi.org/10.3390/su16177269 - 23 Aug 2024
Viewed by 1150
Abstract
Parking behavior depends on drivers’ choice of parking type and willingness to pay for parking. Generally, the parking type refers to off-street and on-street parking facilities. The main factors affecting the preference for parking types are driver, vehicle, travel, and parking characteristics. Understanding [...] Read more.
Parking behavior depends on drivers’ choice of parking type and willingness to pay for parking. Generally, the parking type refers to off-street and on-street parking facilities. The main factors affecting the preference for parking types are driver, vehicle, travel, and parking characteristics. Understanding drivers’ parking type preference behavior and accurately modeling drivers’ tendencies helps develop sustainable parking management policies. This study examines the parking preferences of drivers in Istanbul with binary logit models according to whether they pay for parking. The results of the models show that the number of factors influencing parking type preference is higher for free parking than for paid parking, including driver, vehicle, travel, and parking characteristics. Moreover, some factors in the models affect drivers’ parking type preferences differently for paid and free parking. Namely, low-income individuals tend to use on-street parking when parking is free and off-street parking when it is paid. Conversely, individuals who drive small-size vehicles prefer off-street parking for free parking and on-street parking for paid parking. Individuals who prefer off-street parking for free parking expect shorter walking distances to the final destination and parking duration. On the contrary, individuals who choose on-street parking for paid parking anticipate shorter walking distances to the final destination and parking duration. Full article
(This article belongs to the Section Sustainable Transportation)
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<p>Flowchart used in this study on parking type preference behavior.</p>
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<p>Heatmap of travel and parking characteristics according to parking types.</p>
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15 pages, 3614 KiB  
Article
Visual Aesthetic Quality of Qianjiangyuan National Park Landscapes and Its Spatial Pattern Characteristics
by Zhiqiang Gao, Chunjin Wu, Nan Li, Peng Wang and Jiang Li
Forests 2024, 15(8), 1289; https://doi.org/10.3390/f15081289 - 24 Jul 2024
Viewed by 883
Abstract
This paper conducts a scientific assessment of aesthetic quality to provide intuitive and scientific planning strategies for national park construction. Focusing on Qianjiangyuan National Park, the study used the scenic beauty evaluation (SBE) method to subjectively assess landscape photos from 16 sample sites. [...] Read more.
This paper conducts a scientific assessment of aesthetic quality to provide intuitive and scientific planning strategies for national park construction. Focusing on Qianjiangyuan National Park, the study used the scenic beauty evaluation (SBE) method to subjectively assess landscape photos from 16 sample sites. Objective eye movement indicators describing visual behavior were also analyzed. A national park landscape visual quality assessment model was derived through multiple linear regressions correlating subjective evaluations with objective indicators. Spatial technologies like ArcGIS were used to analyze the visual quality and its spatial distribution. Key findings include (1) subjective evaluations showed higher SBE scores for wetland landscapes, followed by recreational, village, and forest landscapes, (2) eye movement behavior varied across landscape types, with the forest landscape having the shortest first fixation time and the lowest saccade frequency, while recreational landscapes had the lowest average saccade speed, (3) strong correlations were found between SBE and indicators such as average fixation time and saccade frequency, with fixation duration ratio being the leading factor influencing visual aesthetic quality, and (4) visual aesthetic quality was highest in the north and south areas of the park, with significant differences between sample sites in these regions compared to the central area. Among different functional zones, the ecological protection area had the highest quality, while the Suzhuang management area excelled in aesthetic quality compared to the Hetian management area. Full article
(This article belongs to the Section Urban Forestry)
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<p>Geographical location of Qianjiangyuan National Park [<a href="#B22-forests-15-01289" class="html-bibr">22</a>].</p>
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<p>SBE scores of various sample sites in Qianjiangyuan National Park.</p>
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<p>Visual representation of eye movement data for different landscape types.</p>
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<p>Correlation between SBE and eye movement indicators.</p>
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<p>Effectiveness of the predictive model.</p>
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<p>Spatial Distribution of Landscape Visual Aesthetic Quality in Qianjiangyuan National Park.</p>
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<p>Comparison of landscape visual aesthetic quality in different geographic locations.</p>
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22 pages, 13447 KiB  
Article
Understanding Outdoor Cold Stress and Thermal Perception of the Elderly in Severely Cold Climates: A Case Study in Harbin
by Xiaoyun He, Long Shao, Yuexing Tang and Liangbo Hao
Land 2024, 13(6), 864; https://doi.org/10.3390/land13060864 - 15 Jun 2024
Viewed by 1446
Abstract
This study collected data through microclimate monitoring, surface temperature measurements, and questionnaire surveys, and used indicators, such as the universal thermal climate index (UTCI), surface temperature (Ts), and wind chill temperature (tWC), to determine the thermal comfort [...] Read more.
This study collected data through microclimate monitoring, surface temperature measurements, and questionnaire surveys, and used indicators, such as the universal thermal climate index (UTCI), surface temperature (Ts), and wind chill temperature (tWC), to determine the thermal comfort threshold of the elderly in severely cold climates and evaluate their cold stress. The results indicated that (1) the neutral UTCI (NUTCI) for elderly individuals in winter was 13.3 °C, and the NUTCI range was from 1.4 to 25.2 °C; (2) the intensity of elderly individuals’ physical activity affected the magnitude of risk of whole-body cooling, with duration-limited exposures corresponding to 0.5, 3.3, and over 8 h for light, moderate, and vigorous activity levels, respectively; (3) the tWC in all four spaces was below −10 °C, potentially inducing discomfort or even frostbite in the elderly; (4) for a 10 s touch, the maximum Ts (−17.2 °C) of stone was lower than the numbness threshold (−15.0 °C), while that (−15.1 °C) of steel materials remained below the frostbite threshold (−13 °C), posing risks for the elderly during physical activity. This study’s results will provide valuable insights and theoretical references for the landscape design of urban park activity spaces for elderly individuals in cold climate regions. Full article
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<p>Monthly mean/maximum/minimum air temperature (<span class="html-italic">T<sub>a</sub></span>) and mean relative humidity (<span class="html-italic">RH</span>) in Harbin from 1991 to 2021.</p>
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<p>Site location and measurement spaces: (<b>a</b>) site location; (<b>b</b>) photos of open spaces; (<b>c</b>) winter fisheye photos.</p>
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<p>Meteorological measurements.</p>
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<p>An infrared thermal image taken in area FA (17 January 2022).</p>
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<p>Simplified garment checklist (clo).</p>
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<p>Meteorological parameter preference votes.</p>
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<p>Pearson correlation statistics of thermal sensation votes and meteorological parameters.</p>
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<p>Pearson correlation statistics of thermal sensation votes and personal parameters.</p>
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<p>Thermal perception vote distribution for different intensities of physical activity.</p>
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<p>Correlation between UTCI and MTSV: (<b>a</b>) total, (<b>b</b>) women and men.</p>
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<p>Daytime variation of wind chill temperature in four open spaces.</p>
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<p>Different materials’ <span class="html-italic">T<sub>s</sub></span> (°C) and cold risk in the sun or shadow.</p>
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<p>Preferred thermal adaptation behavior: (<b>a</b>) first choice, (<b>b</b>) last choice.</p>
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25 pages, 16782 KiB  
Article
Mean Field Game-Based Algorithms for Charging in Solar-Powered Parking Lots and Discharging into Homes a Large Population of Heterogeneous Electric Vehicles
by Samuel M. Muhindo
Energies 2024, 17(9), 2118; https://doi.org/10.3390/en17092118 - 29 Apr 2024
Viewed by 1161
Abstract
An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy [...] Read more.
An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy available in a parking lot between vehicles where the statistics of their arrival states of charge are dictated by an aggregator. Then, we develop a vehicle-to-grid strategy so that vehicle owners with a satisfactory level of energy in their batteries could help to decongest the grid when they return by providing backup power to their homes at an aggregate level per vehicle based on a duration proposed by an aggregator. Both strategies, with concepts from Mean Field Games, would be implemented to reduce the standard deviation in the states of charge of batteries at the end of charging/discharging vehicles while maintaining some fairness and decentralization criteria. Realistic numerical results, based on deterministic data while considering the physical constraints of vehicle batteries, show, first, in the case of charging in a parking lot, a strong to slight decrease in the standard deviation in the states of charge at the end, respectively, for the sunniest day, an average day, and the cloudiest day; then, in the case of discharging into the grid, over three days, we observe at the end the same strong decrease in the standard deviation in the states of charge. Full article
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<p>Diagram of the MFG inverse Nash algorithm for charging or discharging a BEV <span class="html-italic">i</span> at time <span class="html-italic">t</span>.</p>
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<p>(<b>Top</b>)—The SOCs of 400 BEVs’ upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) ordered according to their capacities, <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mi>k</mi> </msub> <mo>,</mo> <mspace width="0.166667em"/> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>10</mn> </mrow> </semantics></math>, with <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mi>k</mi> </msub> <mo>=</mo> <mrow> <mo>[</mo> <mn>16</mn> <mo>,</mo> <mn>22</mn> <mo>,</mo> <mn>31</mn> <mo>,</mo> <mn>40</mn> <mo>,</mo> <mn>54</mn> <mo>,</mo> <mn>62</mn> <mo>,</mo> <mn>70</mn> <mo>,</mo> <mn>80</mn> <mo>,</mo> <mn>93</mn> <mo>,</mo> <mn>100</mn> <mo>]</mo> </mrow> </mrow> </semantics></math> kWh; (<b>bottom</b>)—mean SOCs per battery capacity upon arrival (<math display="inline"><semantics> <mrow> <msub> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mrow> <mn>0</mn> <mo>,</mo> <msub> <mi>β</mi> <mi>k</mi> </msub> </mrow> </msub> <mo>=</mo> <mrow> <mo>[</mo> <mn>0.125</mn> <mo>,</mo> <mn>0.193</mn> <mo>,</mo> <mn>0.166</mn> <mo>,</mo> <mn>0.140</mn> <mo>,</mo> <mn>0.126</mn> <mo>,</mo> <mn>0.152</mn> <mo>,</mo> <mn>0.134</mn> <mo>,</mo> <mn>0.143</mn> <mo>,</mo> <mn>0.170</mn> <mo>,</mo> <mn>0.156</mn> <mo>]</mo> </mrow> </mrow> </semantics></math>) and number of BEVs per battery capacity (<math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <msub> <mi>β</mi> <mi>k</mi> </msub> </msub> <mo>=</mo> <mrow> <mo>[</mo> <mn>44</mn> <mo>,</mo> <mn>38</mn> <mo>,</mo> <mn>42</mn> <mo>,</mo> <mn>36</mn> <mo>,</mo> <mn>27</mn> <mo>,</mo> <mn>55</mn> <mo>,</mo> <mn>35</mn> <mo>,</mo> <mn>42</mn> <mo>,</mo> <mn>40</mn> <mo>,</mo> <mn>41</mn> <mo>]</mo> </mrow> </mrow> </semantics></math>). Note that more detailed data are reported in <a href="#app3-energies-17-02118" class="html-app">Appendix C</a> in the case of charging.</p>
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<p>(<b>Top</b>)—daily solar energies (<span class="html-italic">W</span>) in 2020; (<b>bottom</b>)—daily solar power curves (<math display="inline"><semantics> <msub> <mi>u</mi> <msub> <mi>W</mi> <mi>t</mi> </msub> </msub> </semantics></math>) for three days in 2020 (sunniest, cloudiest, average) and the yearly average solar power curve.</p>
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<p>Sunniest day in the case of charging: (<b>top left</b>)—daily solar power curve (<math display="inline"><semantics> <msub> <mi>u</mi> <mi>W</mi> </msub> </semantics></math>); (<b>bottom left</b>)—mean target SOC trajectory (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>); (<b>top right</b>)—pressure field (<math display="inline"><semantics> <msubsup> <mi>q</mi> <mi>t</mi> <mi>y</mi> </msubsup> </semantics></math>); (<b>bottom right</b>)—400 BEVs’ optimal SOC trajectories (<math display="inline"><semantics> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>∗</mo> </msubsup> </semantics></math>) and empirical average SOC per BEV (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mo>∗</mo> </msubsup> </semantics></math>), dotted line.</p>
Full article ">Figure 5
<p>Average day in the case of charging: (<b>top left</b>)—daily solar power curve (<math display="inline"><semantics> <msub> <mi>u</mi> <mi>W</mi> </msub> </semantics></math>); (<b>bottom left</b>)—mean target SOC trajectory (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>); (<b>top right</b>)—pressure field (<math display="inline"><semantics> <msubsup> <mi>q</mi> <mi>t</mi> <mi>y</mi> </msubsup> </semantics></math>); (<b>bottom right</b>)—400 BEVs’ optimal SOC trajectories (<math display="inline"><semantics> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>∗</mo> </msubsup> </semantics></math>) and empirical average SOC per BEV (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mo>∗</mo> </msubsup> </semantics></math>), dotted line.</p>
Full article ">Figure 6
<p>Cloudiest day in the case of charging: (<b>top left</b>)—daily solar power curve (<math display="inline"><semantics> <msub> <mi>u</mi> <mi>W</mi> </msub> </semantics></math>); (<b>bottom left</b>)—mean target SOC trajectory (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>); (<b>top right</b>)—pressure field (<math display="inline"><semantics> <msubsup> <mi>q</mi> <mi>t</mi> <mi>y</mi> </msubsup> </semantics></math>); (<b>bottom right</b>)—400 BEVs’ optimal SOC trajectories (<math display="inline"><semantics> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>∗</mo> </msubsup> </semantics></math>) and empirical average SOC per BEV (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mo>∗</mo> </msubsup> </semantics></math>), dotted line.</p>
Full article ">Figure 7
<p>Sunniest day in the case of charging: (<b>top left</b>)—400 BEVs’ SOCs upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>top right</b>)—400 BEVs’ maximum charging rates (<math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>i</mi> <mo>,</mo> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>∗</mo> </msubsup> </semantics></math>); (<b>bottom left</b>)—400 BEVs’ energies upon arrival (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>bottom right</b>)—fractions of total energy per BEVs’ capacity upon arrival (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>).</p>
Full article ">Figure 8
<p>Average day in the case of charging: (<b>top left</b>)—400 BEVs’ SOCs upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>top right</b>)—400 BEVs’ maximum charging rates (<math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>i</mi> <mo>,</mo> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>∗</mo> </msubsup> </semantics></math>); (<b>bottom left</b>)—400 BEVs’ energies upon arrival (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>bottom right</b>)—fractions of total energy per BEVs’ capacity upon arrival (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>).</p>
Full article ">Figure 9
<p>Cloudiest day in the case of charging: (<b>top left</b>)—400 BEVs’ SOCs upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>top right</b>)—400 BEVs’ maximum charging rates (<math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>i</mi> <mo>,</mo> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>∗</mo> </msubsup> </semantics></math>); (<b>bottom left</b>)—400 BEVs’ energies upon arrival (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>bottom right</b>)—fractions of total energy per BEVs’ capacity upon arrival (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>).</p>
Full article ">Figure 10
<p>Sunniest day in the case of discharging: (<b>top left</b>)—400 BEVs’ roundtrip commute and range distances; (<b>bottom left</b>)—mean target SOC trajectory (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>); (<b>top right</b>)—pressure field (<math display="inline"><semantics> <msubsup> <mi>q</mi> <mi>t</mi> <mi>y</mi> </msubsup> </semantics></math>); (<b>bottom right</b>)—400 BEVs’ optimal SOC trajectories (<math display="inline"><semantics> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>∗</mo> </msubsup> </semantics></math>) and empirical average SOC per BEV (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mo>∗</mo> </msubsup> </semantics></math>), dotted line.</p>
Full article ">Figure 11
<p>Average day in the case of discharging: (<b>top left</b>)—400 BEVs’ roundtrip commute and range distances; (<b>bottom left</b>)—mean target SOC trajectory (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>); (<b>top right</b>)—pressure field (<math display="inline"><semantics> <msubsup> <mi>q</mi> <mi>t</mi> <mi>y</mi> </msubsup> </semantics></math>); (<b>bottom right</b>)—398 BEVs’ optimal SOC trajectories (<math display="inline"><semantics> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>∗</mo> </msubsup> </semantics></math>) and empirical average SOC per BEV (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mo>∗</mo> </msubsup> </semantics></math>), dotted line.</p>
Full article ">Figure 12
<p>Cloudiest day in the case of discharging: (<b>top left</b>)—400 BEVs’ roundtrip commute and range distances; (<b>bottom left</b>)—mean target SOC trajectory (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mrow> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>t</mi> </mrow> </msubsup> </semantics></math>); (<b>top right</b>)—pressure field (<math display="inline"><semantics> <msubsup> <mi>q</mi> <mi>t</mi> <mi>y</mi> </msubsup> </semantics></math>); (<b>bottom right</b>)—320 BEVs’ optimal SOC trajectories (<math display="inline"><semantics> <msubsup> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>∗</mo> </msubsup> </semantics></math>) and empirical average SOC per BEV (<math display="inline"><semantics> <msubsup> <mover> <mi>x</mi> <mo>¯</mo> </mover> <mi>t</mi> <mo>∗</mo> </msubsup> </semantics></math>), dotted line.</p>
Full article ">Figure 13
<p>Sunniest day in the case of discharging: (<b>top left</b>)—400 BEVs’ SOCs upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>top right</b>)—400 BEVs’ maximum discharging rates (<math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>i</mi> <mo>,</mo> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>∗</mo> </msubsup> </semantics></math>); (<b>bottom left</b>)—400 BEVs’ energies upon arrival (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>bottom right</b>)—fractions of total energy per BEVs’ capacity upon arrival (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>).</p>
Full article ">Figure 14
<p>Average day in the case of discharging: (<b>top left</b>)—398 BEVs’ SOCs upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>top right</b>)—398 BEVs’ maximum discharging rates (<math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>i</mi> <mo>,</mo> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>∗</mo> </msubsup> </semantics></math>); (<b>bottom left</b>)—398 BEVs’ energies upon arrival (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>bottom right</b>)—fractions of total energy per BEVs’ capacity upon arrival (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>).</p>
Full article ">Figure 15
<p>Cloudiest day in the case of discharging: (<b>top left</b>)—320 BEVs’ SOCs upon arrival (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>top right</b>)—320 BEVs’ maximum discharging rates (<math display="inline"><semantics> <msubsup> <mi>U</mi> <mrow> <mi>i</mi> <mo>,</mo> <mo movablelimits="true" form="prefix">max</mo> </mrow> <mo>∗</mo> </msubsup> </semantics></math>); (<b>bottom left</b>)—320 BEVs’ energies upon arrival (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>0</mn> </mrow> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>); (<b>bottom right</b>)—fractions of total energy per BEVs’ capacity upon arrival (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mn>0</mn> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>) and upon departure (<math display="inline"><semantics> <msub> <mi>f</mi> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mo>,</mo> <mi>β</mi> </mrow> </msub> </msub> </semantics></math>).</p>
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