Traditional Chinese Medicine Syndrome Differentiation of Delta and Omicron Variants of SARS-CoV-2 Carriers in Changsha,Hunan.

Objective: To summarize and compare the main traditional Chinese medicineTCMsyndromes of Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2SARS-CoV-2 carriers to provide references for the syndrome evolution and syndrome differentiation of SARS-CoV-2 infection. Method(s):The TCM medical records of imported and local cases of infection with Delta and Omicron variants of SARS-CoV-2 in Changsha since September 23,2021 to March 27,2022 were collected,including 18 Delta variant cases and 36 Omicron variant cases. Their TCM diagnosis information and TCM pathogenesis were analyzed and compared. Result(s): The common manifestations in Delta variant cases were cough,fever,chest distress/shortness of breath,sore muscles,nausea,dry mouth,dry or sore throat,thick and greasy tongue coating,and rapid and slippery pulse. The predominant pathogenesis was dampness-heat in the upper-energizer and heat stagnation in the lesser Yang combined with dampness. The occurrence of chest distress/shortness of breath,greasy tongue coating,slippery pulse,and the proportion of dampness-heat in the upper-energizer syndrome were higher in Delta variant cases than in Omicron variant cases P<0.05. The common manifestations in Omicron variant cases were itchy and sore throat,nasal congestion,running nose,fever,mild aversion to cold,dry mouth,dizziness,slightly reddish tongue with thin white coating,and rapid or wiry pulse. The predominant pathogenesis was wind-dryness invading defensive exterior,and heat stagnation in the lesser Yang. The occurrence of white-coated tongue and the proportion of wind-dryness invading defensive exterior syndrome were higher in Omicron variant cases than in Delta variant casesP<0.05. Conclusion(s): There are certain differences in TCM syndromes and the corresponding pathogenesis between Delta variant and Omicron variant cases in Changsha,Hunan. The Delta variant of SARS-COV-2 tends to induce dampness-heat syndrome, whereas Omicron variant infection tends to elicit wind-dampness syndrome,which is expected to provide a reference for the pathogenesis evolution of SARS-COV-2 infection.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

Conclusion

The current (and largely unforeseen) COVID-19 pandemic highlights the value of scenario analysis as a complementary exercise to standard, extrapolative prediction. In this chapter, we review our main findings for geopolitical scenario analysis in general, and for Antarctic geopolitical futures in particular. We conclude that the Antarctic Treaty promotes effective governance of a region described in the Madrid Protocol as ‘a natural reserve devoted to peace and science'. We hope to have shown that a classical geopolitical lens is important and relevant to the study of Antarctic futures. On the specific topic of militarisation, we identified some key driving forces for change and equilibrium. How well the ATS responds to these driving forces will turn on its resilience as a governance system. By this, we mean ‘a capacity to prepare for, to respond to, or bounce back from problems or perturbations and disturbances, which cannot necessarily be predicted or foreseen in advance' (Chandler and Coaffee 2017). As we have seen, scenarios are useful in this zone beyond standard prediction—provided they are plausible, rigorous, and robust. It is our hope that like-minded Parties and researchers might collaborate in scenario work, to contribute to the resilience of the ATS in the challenging years ahead. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Research progress on influence of environmental and meteorological parameters on transmission of coronavirus disease 2019

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Problems-based Learning in Higher Education Under Regular and COVID19 Pandemic Conditions: An Educational Experience

Offshore wind and waves will play an important role at helping the European Union (EU) meet its sustainability goals. Among other efforts, a clear commitment is needed in the area of High Education, a field in which the European Union is also making its contribution through a miriad of initiatives. Amongst these, it is worth highlighting the Renewable Energy in the Marine Environment (REM) master (https://www.master-remplus.eu/), an European Union funded Erasmus Mundus program that started in 2018. In this work, an educational experience corresponding to one subject in the REM Msc is described. The objective of this paper is to evaluate the performance and resilience of the Problems-Based Learning Methodology (PBL) and compare the educational outcomes obtained in standard conditions first, and then, under COVID-19 pandemic conditions. © 2023 IEEE.

Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Under what circumstances would airborne transmission of SARS-CoV-2 occur?

To find out the circumstances under which airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) would happen, we conducted mechanistic and systematic modelling of two Coronavirus disease 2019 (COVID-19) outbreaks, i.e., Hunan 2-bus outbreak and Luk Chuen House outbreak (the horizontal cluster). Computational fluid dynamics (CFD) simulations, multi-zone airflow modelling, multi-route mechanistic modelling, and dose-response estimation were carried out selectively according to the transmission characteristics in each outbreak. Our results revealed that poorly ventilated bus indoor environments bred the Hunan 2-bus outbreak in which airborne transmission predominates;prevailing easterly background wind and probable door opening behaviour led to the secondary infections across the corridor in Luk Chuen House outbreak. Measures to facilitate sufficient ventilation indoors and positive pressure in the housing building corridor may help minimise infection risk. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Fabrication, Testing, and 3D Comprehensive Analysis of Swept-Tip Tiltrotor Blades

This paper covers the design, fabrication, testing, and modeling of a family of Froude-scale tiltrotor blades. They are designed with the objective of gaining a fundamental understanding of the impact of a swept tip on tiltrotor whirl flutter. The goal of this paper is to describe the development of the blades needed for this purpose. The rotor is three bladed with a diameter of 4.75 ft. The blades have a VR-7 profile, chord of 3.15 inches, and linear twist of −37◦ per span. The swept-tip blades have a sweep of 20◦ starting at 80%R. The blade properties are loosely based on the XV-15 design. A CATIA and Cubit-based high-fidelity three-dimensional (3D) finite element model is developed. It accurately represents the fabricated blade and is analyzed with X3D. Experiments in a vacuum chamber were carried out to demonstrate the structural integrity of the blades. Measured frequencies and strains were validated with X3D predictions proving the fidelity of the 3D model. Thus, even though the wind tunnel facilities were closed due to COVID-19, hover and forward flight calculations for the blade stress could be performed using the high-fidelity 3D structural model. The results prove the blades have sufficient structural integrity and stress margins to allow for wind tunnel testing. © 2023 Vertical Flight Society.

The impact of meteorological factors on the spread of COVID-19.

Background: Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity. Aim: This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19). Patients and Methods: This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1st Regional Directorate. Results: The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality. Conclusion: Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

Influence of Meteorological Factors on Covid-19 Incidence in the Conditions of Ukrain

Objective: The coronavirus disease (COVID-19) is a problem for the health care systems of many countries around the world. Seasonal nature of influenza and other the respiratory viral diseases is commonly known. The nature of the relationship between the frequency of registration of cases of COVID-19 and natural factors is still being studied by researchers. The purpose is to determine the influence of air temperature, relative humidity, wind speed, and atmospheric pressure on the incidence of the coronavirus disease COVID-19 in the conditions of Ukraine. Materials and methods. Official reports of the Ministry of Health of Ukraine and data from daily monitoring of meteorological indicators conducted by the Sumy Regional Hydrometeorology Center were used in the paper. Descriptive and analytical ways of epidemiological method of investigation were applied. The search for parameters of interrelation between the frequency of registration of COVID-19 cases and meteorological cases took place using of program "Statistica", namely the relevant tools of this program: "Analysis"/ "Multiple regression". Results and Discussion: In the period under study from March 25, 2020 to December 31, 2021 in Sumy Oblast of Ukraine, three waves of rise in the incidence were registered. In the third wave of rise in the incidence, in autumn 2021 the frequency of registration of COVID-19 cases reached 1684.9 per 100 thousand of people, despite the fact that almost 70 % of the population had already recovered or were vaccinated. Meteorological factors in the conditions of Ukraine have little influence on the rate of spread of COVID-19. The value of multiple correlation coefficients was within those limits, which are considered moderate in terms of influence. A moderate inverse correlation was established between the frequency of registration of COVID-19 cases and indicators of air temperature, and a direct correlations-with indicators of relative air humidity. Conclusion(s): In the conditions of Ukraine, the studied meteorological factors (air temperature, relative humidity, wind speed, atmospheric pressure) indirectly influenced the intensity of the epidemic process of COVID-19. the strength of this influence was either weak or moderate.Copyright © 2023, Ibn Sina Trust. All rights reserved.

Ground-Based MAX-DOAS Observation of Trace Gases from 2019 to 2021 in Huaibei, China

With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2. © 2023 by the authors.

Impact of COVID-19 lockdown on particulate matter (PM₂.₅) concentration in Kathmandu, Nepal

This paper reports a study on the statistics for particulate matter pollution (PM₂.₅) and the COVID-19 lockdown in the Kathmandu valley. The PM₂.₅ decreased during the COVID-19 pandemic lockdown periods 2020 compared to the average value of the previous three years (2017, 2018, and 2019). Further, analysis of active fire and air mass trajectory for April and May in 2019 and 2020 shows that the particulate matter trend associated with Kathmandu is not directly influenced by the long-range transport of wind carrying aerosols from the active fire regions. Statistical tests indicate a reduction of particulate matter pollution during the period.

THREE-DIMENSIONAL ANALYTICAL SOLUTION OF THE ADVECTION-DIFFUSION EQUATION FOR AIR POLLUTION DISPERSION

We develop a new analytical solution of a three-dimensional atmospheric pollutant dispersion. The main idea is to subdivide vertically the planetary boundary layer into sub-layers, where the wind speed and eddy diffusivity assume average values for each sub-layer. Basically, the model is assessed and validated using data obtained from the Copenhagen diffusion and Prairie Grass experiments. Our findings show that there is a good agreement between the predicted and observed crosswind-integrated concentrations. Moreover, the calculated statistical indices are within the range of acceptable model performance.

Sustainability assessment of crops' production in India: empirical evidence from ARDL-ECM approach

PurposeThe study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.Design/methodology/approachThe study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.FindingsThe ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.Research limitations/implicationsThe study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.Originality/valueFor a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.

Standalone hybrid PV/wind/diesel-electric generator system for a COVID-19 quarantine center.

This work is motivated by the need in overcoming the electricity crisis in Gaza, which is initiated due to political reasons and the spread of COVID-19. Building quarantine centers is one of the most important means used in combating the COVID-19, but connecting these centers to the electricity distribution network at the appropriate time is not always possible and increases the burden on the local utility company. This article proposed a hybrid off-grid energy system (HES) to effectively energize the quarantine COVID-19 center in Gaza economically and environmentally. To achieve this aim, the estimated load profile of the quarantine center is fed to the HOMER-Pro program. In addition, the various systems components are introduced to the program, then modeled, and optimized. The developed approach was tested using a real case study considering realistic input data. HOMER-Pro program is used to simulate and optimize the system design. The results revealed the potential of the HES to provide environment-friendly, cost-effective, and affordable electricity for the studied quarantine center, as compared to just the diesel generators system. For the considered case study, it is found that the PV-wind-diesel generators HES can cover the connected load with the lowest cost ($ 0.348/kWh) in comparison to other possible HES structures. Taking into consideration the price of harmful emissions, the wining system shows a reduction of 54.89% of the cost of energy (CoE) compared to other systems. For the considered case study, it is found that a combination of 150 kW PV, 200 kW wind, and two diesel generators with capacities of 500 and 250 kW can hold 100% of the electrical load required to keep the quarantine COVID-19 center in operation. The initial capital cost of this HES is $510,576 where the share of wind energy, solar PV, inverter, and diesel-electric generators are $320,000, $83,076, $25,000, and $82,500, respectively. The replacemen cost ($55,918) is due to diesel generators. The total operation and maintainance cost (O&M) is $268,737, that is, 25.6% for wind turbines, 1.2% for inverters, and 70.7% for diesel electric generators. The PV/wind/diesel generators HES generate 1,659,038 kWh of electricity. The total energy requirement of 1,442,553 kWh, which means a surplus of 212,553 kWh of energy/year. The total energy (kWh) is an integration of energy sources which are 427,276 (25.8%), 274,500 (16.5%), and 857,263 (57.7%), due to wind, solar and diesel generators respectively. The cost of yearly consumed fuel is $437,828.769. The payback period for the winning system is 1.8 years. Finally, it is proved that the developed approach gives a reasonable solution to the decision-makers to find a fast, economic and reliable solution to energize the quarantine centers.

Deep learning-based multistep ahead wind speed and power generation forecasting using direct method

Long-term effective and accurate wind power potential prediction, especially for wind farms, facilitates planning for the sustainable development of renewable energy. Accurate wind speed forecasting enhances wind power generation planning and reduces costs. Wind speed time series has nonlinearity, intermittence, and fluctuation, which makes the prediction difficult. Deep learning techniques can be beneficial when there is no specific structure to data. These techniques can predict wind speed with reasonable accuracy and reliability. In this study, four different algorithms, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolu-tional Neural Network (CNN), and CNN-LSTM, for three different long-term horizons (6 months, 1 year, and 5 years) are successfully developed using the direct method. GRU method showed a higher degree of accuracy compared to other methods. In addition, it is confirmed that using a multivariate data set increases the model's accuracy compared to the univariate model. A computational cost analysis is also conducted to compare the proposed algorithms. Finally, the power production capacity of the wind farm at a given location, Zabol city, is calculated for the next five years, which is indispensable for planning, management, and economic analysis. The reasonable conformance between the real data and predicted ones is shown to confirm the capability of the proposed model to use in long-term wind speed forecasting.

The transformation of microclimate adaption in public spaces by smart ventilation approach: a case study of Eastern Banlieue memory industrial Park, China

With the normalization of the COVID-19 pandemic prevention and control, there is an urgent need to develop a healthy urban public space. However, because of the fast urbanization process with a series of problems, such as PM2.5 air pollution, the Urban Heat Island, and the relatively high frequency of static winds under the influence of its topography, the ventilation problem in the public spaces of Chengdu is of great importance. Along these lines, in this work, the history of theoretical research on urban ventilation is summarized and reviewed first to evaluate the urban wind environment. Second, so far, qualitative methods are mainly adopted for the evaluation methods of microclimate adaptation. However, the practical application has achieved few results. Meanwhile, there is still a lack of comprehensive and unified research on the multi-element of human microclimate comfort in public space. For this reason, the urban ventilation assessment system was established in this work according to the physical, physiological, and psychological aspects, with 9 indices selected and ranked. Then, an optimization strategy for rebuilding the urban public space was proposed for improving the wind environment microclimate adaption on three levels: macro city-regional level, meso block linear space, and micro space node. By taking Eastern Banlieue Memory Industrial Park as an example, the statistical data were systematically investigated on the spot from the results of 249 wind environment questionnaires, and 30 Delphi expert consultation questionnaires. Combined with the Computational Fluid Dynamics (CFD) simulation, the results reveal that most public spaces in the study area were below 0.6 m/s in more than 80% of the public space, and wind-based environmental problems obviously exist without any ventilation improvement measures. Combined with the background of the carbon peak era, the ventilation environment of the urban public space is not conducive to using active ventilation equipment. The solution of a complete set of regional intelligent ventilation systems was thoroughly discussed here, while some innovative sustainable systematic solutions and urban ventilation furniture combined with a geothermal heat pump and cloud data platform were formulated.

Influence of natural ventilation design on the dispersion of pathogen-laden droplets in a coach bus.

Natural ventilation is an energy-efficient design approach to reduce infection risk (IR), but its optimized design in a coach bus environment is less studied. Based on a COVID-19 outbreak in a bus in Hunan, China, the indoor-outdoor coupled CFD modeling approach is adopted to comprehensively explore how optimized bus natural ventilation (e.g., opening/closing status of front/middle/rear windows (FW/MW/RW)) and ceiling wind catcher (WCH) affect the dispersion of pathogen-laden droplets (tracer gas, 5 µm, 50 µm) and IR. Other key influential factors including bus speed, infector's location, and ambient temperature (Tref) are also considered. Buses have unique natural ventilation airflow patterns: from bus rear to front, and air change rate per hour (ACH) increases linearly with bus speed. When driving at 60 km/h, ACH is only 6.14 h-1 and intake fractions of tracer gas (IFg) and 5 µm droplets (IFd) are up to 3372 ppm and 1394 ppm with ventilation through leakages on skylights and no windows open. When FW and RW are both open, ACH increases by 43.5 times to 267.50 h-1, and IFg and IFd drop rapidly by 1-2 orders of magnitude compared to when no windows are open. Utilizing a wind catcher and opening front windows significantly increases ACH (up to 8.8 times) and reduces IF (5-30 times) compared to only opening front windows. When the infector locates at the bus front with FW open, IFg and IFd of all passengers are <10 ppm. More droplets suspend and further spread in a higher Tref environment. It is recommended to open two pairs of windows or open front windows and utilize the wind catcher to reduce IR in coach buses.