Near-field exposure of pathogen-laden respiratory particles based on statistical evaluation of one emitting person indoors

Risk assessment models typically assume ideal mixing, in which the pathogen-laden aerosol particles emitted by a person are evenly distributed in the room. This study points out the local deviation from this idealized assumption and a correlation between the level of pathogen concentration and the distance from the emitter. For this purpose, several numerical studies (CFD) were analyzed, and a validation experiment was performed. Statistical evaluation of the spatial pathogen distribution was used to determine the potential exposure to elevated pathogen concentrations. Compared to an ideally mixed room, at a distance of 1.5 m, the mixing ventilation cases show a 25% risk of being exposed to twice the amount of pathogens and a 5% risk to more than 5 times the assumed value. For displacement ventilation there is a 75% chance of being exposed to less pathogens than in complete mixing at a distance of 1 m. The measurement values agree with the simulation results. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

A simple dilution model of the expired jet

Short-range exposure to expired aerosols or droplet nuclei has been considered as the predominant route for SARS-CoV-2. The observed effect of mask wearing, and social distancing suggests the importance of expired jet in the spread of COVID-19. The well-known steady-state dilution model is no longer valid for the interrupted expiratory jet. We reanalysed the existing interrupted jet data and proposed a simple dilution model of expired jet using the two-stage jet model. The interrupted jet consists of two stages, i.e., the jet-like and puff-like stage. Results show dilution factor grows linearly with the distance at the jet-like stage but increases with the cubic of the increasing distance in the puff-like stage. Dilution factor at any distance for the puff-like stage decreases as the activity intensifies, which is still much larger than that estimated via the steady jet model. The findings can be further applied into the short-range airborne exposure assessment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Towards improved thermal comfort predictions and higher energy savings: building energy model of an open-plan office based on indoor CO2 and temperature controls

In recent work, a Hierarchical Bayesian model was developed to predict occupants' thermal comfort as a function of thermal indoor environmental conditions and indoor CO2 concentrations. The model was trained on two large IEQ field datasets consisting of physical and subjective measurements of IEQ collected from over 900 workstations in 14 buildings across Canada and the US. Posterior results revealed that including measurements of CO2 in thermal comfort modelling credibly increases the prediction accuracy of thermal comfort and in a manner that can support future thermal comfort prediction. In this paper, the predictive model of thermal comfort is integrated into a building energy model (BEM) that simulates an open-concept mechanically-ventilated office space located in Vancouver. The model predicts occupants' thermal satisfaction and heating energy consumption as a function of setpoint thermal conditions and indoor CO2 concentrations such that, for the same thermal comfort level, higher air changes per hour can be achieved by pumping a higher amount of less-conditioned fresh air. The results show that it is possible to reduce the energy demand of increasing fresh air ventilation rates in winter by decreasing indoor air temperature setpoints in a way that does not affect perceived thermal satisfaction. This paper presents a solution for building managers that have been under pressure to increase current ventilation rates during the COVID-19 pandemic. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Transient zonal modeling of SARS-CoV-2 airborne infection risk for incomplete mixing air distribution methods

A widely used analytical model to quantitatively assess airborne infection risk is the Wells-Riley model based on the assumption of complete air mixing in a single zone. This study aimed to extend the Wells-Riley model so that the infection risk can be calculated in spaces where complete mixing is not present. This is done by evaluating the time-dependent distribution of infectious quanta in each zone and by solving the coupled system of differential equations based on the zonal quanta concentrations. In conclusion, this study shows that using the Wells-Riley model based on the assumption of completely mixing air may overestimate the long-range airborne infection risk compared to some high-efficiency ventilation systems such as displacement ventilation, but also underestimate the infection risk in a room heated with warm air supplied from the ceiling. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Measurement of Ventilation Rate and Evaluation of Infection Risk in a Classroom

To quantitatively evaluate the effect of increasing ventilation using the immediately practicable method on infection risk, the ventilation rate in a classroom was measured by the concentration decay method using CO2. The measured value was then substituted into the Wells-Riley model to evaluate aerosol infection risk in steady and non-steady states. In the classroom, the air change rate per hour (ACH) ranged from 3.1 to 10.2, and the local mean age of air tended to be larger near the outlet. It was also shown that opening the windows increased the ventilation rate the most, resulting in a more evenly distributed local mean age of air. We also showed that the aerosol infection risk in the classroom could be significantly reduced by increasing ventilation, suppressing vocalization, and wearing a mask, compared to some outbreaks of COVID-19. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Evaluation of infection probability in a multiroom office space using computational fluid dynamics coupled with Wells-Riley model

The current COVID-19 pandemic has highlighted the importance of health safety assessment in various indoor scenarios. Computational fluid dynamics (CFD) combined with a modified Wells-Riley equation provides a powerful tool to analyse local infection probability in an indoor space. Compared to a single infection probability characterising the space in the traditional Wells-Riley model, the coupled approach provides a distribution of infection probability within the space. Furthermore, this approach avoids assuming a well-mixed state, usually related to Wells-Riley equation. This study compares displacement and mixing ventilation strategies with four different ventilation rates to assess the local quanta concentrations modelled using passive scalar transport approach. The simulation results are processed to also account for the effect of wearing masks and vaccinations. The result show that a well-designed displacement ventilation system can significantly reduce infection probability compared to mixing ventilation system at similar airflow rate. Additionally, the results emphasised the importance of wearing mask and getting vaccinated as a means of reducing infection probability. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Negative Sentiment Modeling and Public Legal Liability from Urban Green Space: A Framework for Policy Action in China

This paper intends to optimize the urban green space (UGS) management and implementation strategies by analyzing climate change models and reviewing economic, energy, and public health policies. This paper studies the public perception of climate change-induced public health emergency (PHE) in China by surveying online public comments. Specifically, it looks into public health perception, anxiety perception, relative deprivation, and emotional polarity from public online comments. The following conclusions are drawn through the empirical test of 179 questionnaires. The findings revealed that health risk perception has a positive predictive effect on relative deprivation and anxiety perception. The higher the health risk perception, the stronger the relative deprivation and anxiety are. Anxiety perception and relative deprivation have mediating effects in the model. In addition, the main research method adopts a questionnaire survey. The mediating effect between each variable is further studied. This paper analyzes the citizens' right to health and public health protection under climate change, and explains public risk perception and anxiety perception. Meanwhile, the evaluation cases are used to analyze the public health and UGS construction strategies to suggest climate compensation laws and improve the urban greening rate. This finding has practical reference value for promoting the deep integration of UGS and public health. It can promote the development and planning of UGS under climate change and biodiversity loss and has significant reference value for improving negative emotions and the public legal liability system.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

Implied volatility index is a popular proxy for market fear. This paper uses the oil implied volatility index (OVX) to investigate the impact of different uncertainty measures on oil market fear. Our uncertainty measures consider multiple perspectives, specifically including climate policy uncertainty (CPU), geopolitical risk (GPR), economic policy uncertainty (EPU), and equity market volatility (EMV). Based on the time-varying parameter vector autoregression (TVP-VAR) model, our empirical results show that the impact of CPU, GPR, EPU, and EMV on OVX is time-varying and heterogeneous due to these uncertainty measures containing different information content. In particular, the CPU has become increasingly important for triggering oil market fear since the recent Paris Agreement. During the COVID-19 pandemic, CPU, EPU, and EMV, rather than GPR, play a prominent role in increasing oil market fear. © 2023 Elsevier Ltd

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE

Science policy frameworks for a post-pandemic green economic recovery

As current production and consumption patterns exceed planetary boundaries, many leaders have stressed the need to adopt green economic stimulus policies in the aftermath of the COVID-19 pandemic. This paper provides an integrated multi-stakeholder framework to design an economic recovery strategy aligned with climate stabilisation objectives. We first employ quantitative energy and economic models, and then a multi-criteria decision process in which we engage social actors from government, enterprises and civil society. As a case study, we select green recovery measures that are relevant for a European Union country and assess their appropriateness with numerous criteria related to climate resilience and socio-economic sustainability. Results highlight trade-offs between immediate and long-run effects, economic and environmental objectives, and expert evidence and societal priorities. Importantly, we find that a ‘return-to-normal' economic stimulus is environmentally unsustainable and economically inferior to most green recovery schemes. © 2022 The Author(s)

Connectedness among El Niño-Southern Oscillation, carbon emission allowance, crude oil and renewable energy stock markets: Time- and frequency-domain evidence based on TVP-VAR model

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. © 2022 Elsevier Ltd

Nearly five-year continuous atmospheric measurements of black carbon over a suburban area in central France

Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017–2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 μg m−3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 μg m−3) in winter and minimum concentration (0.38 ± 0.04 μg m−3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March–10 May 2020 and third lockdown 17 March–3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October–15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well. © 2022 Elsevier B.V.

A Time-Series Analysis on the Covid-19 Mortality, PM2.5 Levels, and Weather Variables in Denpasar City, Indonesia

The objective of this study is to explore the relationship between Covid-19 mortality cases and environmental variables, namely PM2.5 concentration and weather variables, in Denpasar City, Indonesia. Regression models were used. The response variable was the monthly Covid-19 mortality from March 2020 to December 2021 and the predictor variables were the mean concentration of PM2.5, temperature, wind speed, rainfall and duration of sunshine. All data analyzed were provided by the Indonesian Government. Simple linear regression (SLR) and dynamic regression with ARIMA error models were used. Further, of the 22 monthly data, the first 19 months data were used to train the models and the remaining data were used as the test data. It is found that both wind speed and the interaction between PM2.5 concentration and wind speed have statistically significant relationships with Covid-19 mortality. The estimates of SLR and ARIMA (0,1,1) with interaction models show that on average, in case of 0.5 m/s wind speed, an increase of 1 𝜇g/m3 in the monthly mean of daily PM2.5 concentrations associates with 17.4 and 16.3 increase in the monthly Covid-19 mortality case, respectively. Although this study is observational, its findings suggest the importance of controlling PM2.5 concentration.

The Policy Choice and Economic Assessment of High Emissions Industries to Achieve the Carbon Peak Target under Energy Shortage—A Case Study of Guangdong Province

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries. © 2022 by the authors.

Response of Natural Gas Consumption to Temperature and Projection under SSP Scenarios during Winter in Beijing

The present study investigates the response of natural gas consumption to temperature on the basis of observations during heating season (middle November–middle March) for the period 2002–2021 in Beijing, China, and then estimates temperature-related changes in the gas consumption under future scenarios by using climate model simulations from the Coupled Model Intercomparison Project Phase 6. Observational evidence suggests that the daily natural gas consumption normalized by gross domestic product is linearly correlated with the daily average temperature during heating season in the past two decades in Beijing. Hence, a linear regression model is built to estimate temperature-related changes in the natural gas consumption under future scenarios. Corresponding to a rising trend in the temperature, the natural gas consumption shows a decrease trend during 2015–2100 under both the SSP245 and the SSP585 scenarios. In particular, the temperature would increase rapidly from early 2040s to the end of 21st century under the SSP585 scenario, leading to an obvious reduction in the natural gas consumption for heating in Beijing. Relative to that in the present day (1995–2014), the natural gas consumption would show a reduction of approximately 9% (±4%) at the end of 21st century (2091–2100) under the SSP245 scenario and approximately 22% (±7%) under the SSP585 scenario.

Design Suggestion of Epidemic Prevention System for Shared Car Based on Scenario and Data

Entering the post-epidemic era, the travel demand for shared cars is increasing day by day. In the normalized epidemic prevention and control, epidemic prevention in shared cars needs to be designed systematically. This paper analyzes the existing risk of COVID-19 propagation based on two perspectives: scenario and data, and discusses the existing means of protection. Then based on the existing measures, the design suggestions are given from two aspects: scenario-based and data-based. Based on the scenario, the layout design and disinfection is implemented in regard to various ways that COVID-19 is transmitted;based on data, travel data integration should be promoted to achieve macro-structural dynamic adjustment and integrated governance from the overall transportation system. In the context of the industries, the shared car industry should response to new trend immediately and implement innovative ideas to obtain a service that is better suited for individuals in the post-epidemic era. In the end, several major functions of design in terms of developing the urban transportation system are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impacts on Global Temperature During the First Part of 2020 Due to the Reduction in Human Activities by COVID-19

One of the major events transpiring in the 21st century is the unforeseen outbreak due to COVID-19. This pandemic directly altered human activities due to the forced confinement of millions of inhabitants over the world. It is well known that one of the main factors that affect global warming is human activities;however, during the first part of 2020, they were severely reduced by the spread of the coronavirus. This study strives to investigate the possible impact of quarantine initiation worldwide and the linked outcomes on a global scale related to the temperatures since the worthwhile. To achieve this goal, the evaluation of the short-term temperature status at the continental scale was conducted in two particular forms: (i) concerning the short-term comparing the data from 2016, 2017, 2018, and 2019;and, assessing the long-term differences comprising 30 years of data (1981-2010). The data employed in this study were obtained from the respective NASA and Copernicus databases. The temperature maps and temperature differences of different years before the pandemic was compared to the Coronavirus onset (winter and spring) data with the aid of Python programing language. Continental temperature mapping results showed that the temperature difference of the American continent had attained its maximum value in January 2016, and yet, the temperature is observed to be warmer than in 2016. The largest difference in the short-term temperature in terms of comparison to 2020 referred to the months when the maximum quarantine began, that is, February and March, and the temperature was cooler in comparison to the prior years. The long-term mean study denoted that the temperatures throughout the South American continent remained consistent during the first part of 2020 in comparison to the 30-year average data, but temperatures in North America declined from February to April. Similarly, the temperatures in Eurasia in April is observed to be lower compared to the 30 years average in February and March. Accordingly, the average temperature of the Earth has dropped about 0.3 degrees C compared to 2019. We concluded that temperature could show some specific changes and hypothesize that under the COVID-19 pandemic, it could manifest different trends. The next step would be to conduct further analysis to observe at the regional scale if under unforeseen phenomena are or not affecting global warning during the coming years.

Convection-Permitting Models for Climate Research

4th International Convection-Permitting Modeling Workshop for Climate Research What: The purpose of the workshop was to discuss the performance of convection-permitting models (<4-km horizontal grid spacing) at global and local scales and also to discuss the potential of CPMs data for hazard and impact studies. Because of the rapid development of the convection-permitting modeling (CPM) field, we felt the need to host a virtual workshop this year to maintain community interactions and to provide a forum where scientific advances are presented and discussed. [...]the use of satellite observations and targeted model experiments that make use of field campaign data were discussed for evaluating global CPMs. High-resolution and high-quality observations were identified as crucial for a better understanding of processes and phenomena that cause extreme events and for supporting the development of parameterization schemes. Since rainfall is expected to intensify at small spatial and temporal scales in future climates, the impact of precipitation on the initiation of landslides in small river catchments becomes increasingly important.

Harmonization and Verification of Three National European Icing Forecast Models Using Pilot Reports

The Single European Sky Air Traffic Management Research (SESAR) program aims at modernizing and harmonizing the European airspace, which currently has a strongly fragmented character. Besides turbulence and convection, in-flight icing is part of SESAR and can be seen as one of the most important meteorological phenomena, which may lead to hazardous flight conditions for aircraft. In this study, several methods with varying complexities are analyzed for combining three individual in-flight icing forecasts based on numerical weather prediction models from Deutscher Wetterdienst, Météo-France, and Met Office. The optimal method will then be used to operate one single harmonized in-flight icing forecast over Europe. As verification data, pilot reports (PIREPs) are used, which provide information about hazardous weather and are currently the only direct regular measure of in-flight icing events available. In order to assess the individual icing forecasts and the resulting combinations, the probability of detection skill score is calculated based on multicategory contingency tables for the forecast icing intensities. The scores are merged into a single skill score to give an overview of the quality of the icing forecast and enable comparison of the different model combination approaches. The concluding results show that the most complex combination approach, which uses iteratively optimized weighting factors for each model, provides the best forecast quality according to the PIREPs. The combination of the three icing forecasts results in a harmonized icing forecast that exceeds the skill of each individual icing forecast, thus providing an improvement to in-flight icing forecasts over Europe.

Urban climate changes during the COVID-19 pandemic: integration of urban-building-energy model with social big data

The changes in human behaviour associated with the spread of COVID-19 infections have changed the urban environment. However, little is known about the extent to which they have changed the urban climate, especially in air temperature (T), anthropogenic heat emission (QF) and electricity consumption (EC). We quantitatively evaluated these effects using a unique method that integrates real-time human population data (social big data) with an urban climate model. The results showed that in an office district in the city centre of Tokyo, the biggest metropolis in the world, under a significantly reduced population, EC (CO2 emissions) would be 30% and QF would be 33% of pre-COVID levels (without the stay-at-home advisories). This resulted in a T decrease of about 0.2 °C, representing about 20% of the past greenhouse gas-induced warming (about 1.0 °C) in Tokyo. This method can be benchmarked and then applied to worldwide. The results suggest that changes in human behaviour can represent an adaptation and decarbonising strategies to climate change in cities.