Enterovirga aerilata sp. nov. and Knoellia koreensis sp. nov., isolated from an automobile air conditioning system.

Two strictly aerobic and rod-shaped bacteria, labelled as DB1703T and DB2414ST, were obtained from an automobile air conditioning system. Strain DB1703T was Gram-stain-negative, while strain DB2414ST was Gram-stain-positive. Both strains were catalase-positive and oxidase-negative. Strains DB1703T and DB2414ST were able to grow at 18-42 °C. Strain DB1703T grew within a NaCl range of 0-3 % and a pH range of 6.0-8.0; while strain DB2414ST grew at 0-1 % and pH 6.5-8.5. The phylogenetic and 16S rRNA gene sequence analysis indicated that strains DB1703T and DB2414ST belonged to the genera Enterovirga and Knoellia, respectively. Strain DB1703T showed the closest phylogenetic similarity to Enterovirga rhinocerotis YIM 100770T (94.8 %), whereas strain DB2414ST was most closely related to Knoellia remsis ATCC BAA-1496T (97.7 %). The genome sizes of strains DB1703T and DB2414ST were 4 652 148 and 4 282 418 bp, respectively, with DNA G+C contents of 68.8 and 70.5 mol%, respectively. Chemotaxonomic data showed Q-10 as the sole ubiquinone in DB1703T and ML-8 (H4) in DB2414ST. The predominant cellular fatty acid in DB1703T was summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c), whereas iso-C16 : 0, C17 : 1 ω8c, and iso-C15 : 0 were dominant in DB2414ST. Overall, the polyphasic taxonomic comparisons showed that strains DB1703T and DB2414ST were distinct from their closest taxa and represent novel species within the genera Enterovirga and Knoellia, respectively. Accordingly, we propose the names Enterovirga aerilata sp. nov., with the type strain DB1703T (=KCTC 72724T=NBRC 114759T), and Knoellia koreensis sp. nov., with the type strain DB2414ST (=KCTC 49355T=NBRC 114620T).

Moderating AC Usage Can Reduce Thermal Disparity between Indoor and Outdoor Environments.

In the context of escalating urban heat events due to climate change, air conditioning (AC) has become a critical factor in maintaining indoor thermal comfort. Yet the usage of AC can also exacerbate outdoor heat stress and burden the electricity system, and there is little scientific knowledge regarding how to balance these conflicting goals. To address this issue, we established a coupled modeling approach, integrating the Weather Research and Forecasting model with the building energy model (WRF_BEP + BEM), and designed multiple AC usage scenarios. We selected Chongqing, China's fourth-largest megacity, as our study area due to its significant socioeconomic importance, the severity of extreme heat events, and the uniqueness of its energy infrastructure. Our analysis reveals that AC systems can substantially reduce indoor temperatures by up to 18 °C; however, it also identifies substantial nighttime warming (2-2.5 °C) and a decline in thermal comfort. Particularly for high-density neighborhoods, when we increase 2 °C indoors, the outdoor temperature can be alleviated by up to 1 °C. Besides, despite the limited capacity to regulate peak electricity demand, we identified that reducing the spatial cooled fraction, increasing targeted indoor temperature by 2 °C, and implementing temporal AC schedules can effectively lower energy consumption in high-density neighborhoods, especially the reduction of spatial cooled fraction (up to 50%). Considering the substantial demand for cooling energy, it is imperative to carefully assess the adequacy and continuity of backup energy sources. The study underscores the urgency of reassessing energy resilience and advocates for addressing the thermal equity between indoor and outdoor environments, contributing to the development of a sustainable and just urban climate strategy in an era of intensifying heat events.

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

Simulating Energy Use, Indoor Temperatures, and Utility Cost Impacts Amidst a Warming Climate in a Multi-family Housing Model.

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1 °C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: - $21 to - $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5 °C to - 6.2 °C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals.

The prevalence of household air conditioning in Canada.

Background: Household air conditioning is one of the most effective approaches for reducing the health impacts of heat exposure; however, few studies have measured the prevalence of household air conditioning in Canada. Data and methods: Data were obtained from the 2017 Canadian Community Health Survey and the 2017 Households and the Environment Survey. Statistics Canada linked the survey respondents and created survey weights. Four heat-vulnerable populations were defined: older adults, older adults living alone, older adults with at least one health condition associated with reduced thermoregulation and older adults living alone and with a health condition associated with reduced thermoregulation. Weighted ratios and logistic regression models were used to analyze person-level air conditioning rates for national, regional and heat-vulnerable populations. Results: Approximately 61% of the national population had household air conditioning. Regional rates ranged between 32% in British Columbia and 85% in Ontario. People living alone and people who did not own a home were significantly less likely to have air conditioning in Canada and in most regions. One heat vulnerable group, older adults living alone, had significantly lower air conditioning rates compared with the national and Ontario averages, at 56% and 81%, respectively. Interpretation: This study is the first to quantify air conditioning prevalence in Canada at the person-level. The results of this study may inform heat-health policies and climate change adaptation strategies that aim to identify populations with high risks of heat-related mortality or morbidity and low access to household air conditioning.

Fuzzy Logic Controlled Simulation in Regulating Thermal Comfort and Indoor Air Quality Using a Vehicle Heating, Ventilation, and Air-Conditioning System.

Conventional heating ventilation and air-conditioning (HVAC) controllers have been designed to mainly control the temperature of a confined compartment, such as a room or a cabin of a vehicle. Other important parameters related to the thermal comfort and indoor air quality (IAQ) of the confined compartment have often been ignored. In this project, IAQ in the vehicle cabin was represented by its carbon dioxide (CO2) concentration, and the occupants' thermal comfort levels were estimated with the predicted mean vote (PMV) index. A new fuzzy logic controller (FLC) was designed and developed using the MATLAB fuzzy logic toolbox and Simulink to provide a nonlinear mapping between the measured values, i.e., PMV, temperature, CO2, and control parameters (recirculation flaps, blower's speed, and refrigerant mass flow rate) of a vehicle HVAC system. The new FLC aimed to regulate both in-cabin PMV and CO2 values without significantly increasing overall energy consumption. To evaluate the effectiveness of the proposed FLC, a cabin simulator was used to mimic the effects of different HVAC variables and indoor/outdoor environmental settings, which represented the in-cabin PMV and IAQ readings. Results demonstrated that the new FLC was effective in regulating the in-cabin PMV level and CO2 concentration, at desirable levels, by adaptively controlling the opening and closing of the recirculation flap based on in-cabin temperature and CO2 readings, while maintaining an average-to-good energy consumption level. The proposed FLC could be applied to a large variety of HVAC systems by utilizing low-cost sensors, without the need to significantly modify the internal design of the HVAC system.

Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance.

Efficiency and comfort in buildings rely on on well-functioning HVAC systems. However, system faults can compromise performance. Modern data-driven fault detection methods, considering diverse techniques, encounter challenges in understanding intricate interactions and adapting to dynamic conditions present in HVAC systems during occupancy periods. Implementing fault detection during active operation, which aligns with real-world scenarios and captures dynamic interactions and environmental changes, is considered highly valuable. To address this, utilizing the dynamic simulation system HVAC SIMulation PLUS (HVACSIM+), an HVAC fault model was developed using 194 sensor signals from each HVAC component within a single-story, four-room building. The advanced HVAC fault detection framework, leveraging simulated HVAC operational scenarios with the Gramian angular field (GAF) and two-dimensional convolutional neural networks (GAF-2DCNNs), offers a robust and proactive solution. By utilizing the GAF capacity to convert time-series sensor data into informative 2D images, integrated with 2DCNN for automated feature extraction, hidden temporal relationships within 1D signals are captured. After training on nine significant HVAC faults and normal conditions during occupancy, the effectiveness of the proposed GAF-2DCNN is evaluated through comparisons with support vector machine (SVM), random forest (RF), and hybrid RF-SVM, one-dimensional convolutional neural networks (1D-CNNs). The results demonstrates an impressive overall accuracy of 97%, accompanied by precision, recall, and F1 scores that surpass 90% for individual HVAC faults. Through the introduction of the unified approach that integrates HVACSIM+ simulated data and GAF-2DCNN, a notable enhancement in robustness and reliability for handling substantial HVAC faults is achieved.

The Application of a Self-Made Integrated Three-in-One Microsensor and Commercially Available Wind Speed Sensor to the Cold Air Pipe of the Heating, Ventilation, and Air Conditioning in a Factory for Real-Time Wireless Measurement.

In this study, the integrated three-in-one (temperature, humidity, and wind speed) microsensor was made through the technology of the Micro-electro-mechanical Systems (MEMS) to measure three important physical quantities of the internal environment of the cold air pipe of the Heating, Ventilation and Air Conditioning (HVAC) in the factory, plan the installation positions of the integrated three-in-one microsensor and commercially available wind speed sensor required by the internal environment of the cold air pipe, and conduct the actual 310-h long term test and comparison. In the experiment, it was also observed that the self-made micro wind speed sensor had higher stability compared to the commercially available wind speed sensor (FS7.0.1L.195). The self-made micro wind speed sensor has a variation range of ±200 mm/s, while the commercially available wind speed sensor a variation range of ±1000 mm/s. The commercially available wind speed sensor (FS7.0.1L.195) can only measure the wind speed; however, the self-made integrated three-in-one microsensor can conduct real-time measurements of temperature and humidity according to the environment at that time, and use different calibration curves to know the wind speed. As a result, it is more accurate and less costly than commercially available wind speed sensors. The material cost of self-made integrated three-in-one microsensor includes chemicals, equipment usage fees, and wires. In the future, factories may install a large number of self-made integrated three-in-one microsensors in place of commercially available wind speed sensors. Through real-time wireless measurements, the self-made integrated three-in-one microsensors can achieve the control optimization of the HVAC cold air pipe's internal environment to improve the quality of manufactured materials.

Exploratory inference of the ingestive behavior of pigs in the finishing phase in an air-conditioned environment.

The objective of this research is to apply exploratory analysis and modeling associated with abiotic factors, physiological and behavioral variables of swine in the semi-arid region. The experimental design used was completely randomized, in a 3 × 3 factorial scheme, randomly distributed in nine pens, with three animals. The behavior of the animals was recorded using images and analyzed within 10-min interval. The data analysis used was multivariate, using the clustering method (tree diagram) and principal component analysis (PCA), in order to establish the main predictors of swine ingestive behavior, using multiple linear regression models. The PCA showed satisfactory results, in which the lowest eigenvalue observed was 2.82 and the accumulated variance for the treatments ranged from 69.70 to 94% for the first two principal components. Through exploratory data analysis, it was possible to identify the relationship between biotic and abiotic factors with the ingestive behavior of pigs in the finishing phase. Based on the results of the multivariate analysis, the most promising predictor variables for estimating the regression models were determined. Adiabatic evaporative cooling associated with 18 h of light was the combination of factors with the best results, presenting models for eating and drinking behavior, i.e. a complete ingestive characterization.

Optimal Control of Air Conditioning Systems by Means of CO2 Sensors in Electric Vehicles.

Considering the consistent reduction in battery range due to the operation of the Heating Ventilation and Air Conditioning (HVAC) system, this study deals with the CO2 measurement inside the cabin of an electric crane and aims to reduce the energy consumption through the control of the air recirculation. A control strategy was defined and tested through an experimental set-up where the presence of a driver was simulated as a source of CO2. The cabin was placed inside a climatic wind tunnel and the benefits of this control strategy on the HVAC system energy consumption were assessed, both in the heating and the cooling modes. In addition, we discussed the optimal position of the CO2 sensor inside the cabin by comparing the results obtained from some sensors placed around the cabin occupant with the ones logged by three sensors in the breathing zone. Finally, an investigation of the uncertainty of the indirect measurement of the leakage flow and its dependence on the number of CO2 sensors installed in the cabin was made through the Monte Carlo method.

An overview of solutions for airborne viral transmission reduction related to HVAC systems including liquid desiccant air-scrubbing.

The spread of the coronavirus SARS-CoV-2 affects the health of people and the economy worldwide. As air transmits the virus, heating, ventilation and air-conditioning (HVAC) systems in buildings, enclosed spaces and public transport play a significant role in limiting the transmission of airborne pathogens at the expenses of increased energy consumption and possibly reduced thermal comfort. On the other hand, liquid desiccant technology could be adopted as an air scrubber to increase indoor air quality and inactivate pathogens through temperature and humidity control, making them less favourable to the growth, proliferation and infectivity of microorganisms. The objectives of this study are to review the role of HVAC in airborne viral transmission, estimate its energy penalty associated with the adoption of HVAC for transmission reduction and understand the potential of liquid desiccant technology. Factors affecting the inactivation of pathogens by liquid desiccant solutions and possible modifications to increase their heat and mass transfer and sanitising characteristics are also described, followed by an economic evaluation. It is concluded that the liquid desiccant technology could be beneficial in buildings (requiring humidity control or moisture removal in particular when viruses are likely to present) or in high-footfall enclosed spaces (during virus outbreaks).

Assessment of university classroom ventilation during the COVID-19 pandemic.

Ventilation plays an important role in mitigating the risk of airborne virus transmission in university classrooms. During the early phase of the COVID-19 pandemic, methods to assess classrooms for ventilation adequacy were needed. The aim of this paper was to compare the adequacy of classroom ventilation determined through an easily accessible, simple, quantitative measure of air changes per hour (ACH) to that determined through qualitative "expert judgment" and recommendations from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the American Conference of Governmental Industrial Hygienists (ACGIH)®. Two experts, ventilation engineers from facilities maintenance, qualitatively ranked buildings with classrooms on campus with regard to having "acceptable classroom ventilation." Twelve lecture classrooms were selected for further testing, including a mix of perceived adequate/inadequate ventilation. Total air change per hour (ACH) was measured to quantitatively assess ventilation through the decay of carbon dioxide in the front and rear of these classrooms. The outdoor ACH was calculated by multiplying the total ACH by the outdoor air fraction. The classrooms in a building designed to the highest ASHRAE standards (62.1 2004) did not meet ACGIH COVID-19 recommendations. Four of the classrooms met the ASHRAE criteria. However, a classroom that was anticipated to fail based on expert knowledge met the ASHRAE and ACGIH criteria. Only two classrooms passed stringent ACGIH recommendations (outdoor ACH > 6). None of the classrooms that passed ACGIH criteria were originally expected to pass. There was no significant difference in ACH measured in the front and back of classrooms, suggesting that all classrooms were well mixed with no dead zones. From these results, schools should assess classroom ventilation considering a combination of classroom design criteria, expert knowledge, and ACH measurements.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Evaluation of aerosol transmission risk during home quarantine under different operating scenarios: A pilot study.

SARS-CoV-2 has been recognized to be airborne transmissible. With the large number of reported positive cases in the community, home quarantine is recommended for the infectors who are not severely ill. However, the risks of household aerosol transmission associated with the quarantine room operating methods are under-explored. We used tracer gas technique to simulate the exhaled virus laden aerosols from a patient under home quarantine situation inside a residential testbed. The Sulphur hexafluoride (SF6) concentration was measured both inside and outside the quarantine room under different operating settings including, air-conditioning and natural ventilation, presence of an exhaust fan, and the air movement generated by ceiling or pedestal fan. We calculated the outside-to-inside SF6 concentration to indicate potential exposure of occupants in the same household. In-room concentration with air-conditioning was 4 times higher than in natural ventilation settings. Exhaust fan operation substantially reduced in-room SF6 concentration and leakage rate in most of the ventilation scenarios, except for natural ventilation setting with ceiling fan. The exception is attributable to the different airflow patterns between ceiling fan (recirculates air vertically) and pedestal fan (moves air horizontally). These airflow variations also led to differences in SF6 concentration at two sampling heights (0.1 m and 1.7 m) and SF6 leakage rates when the quarantine room door was opened momentarily. Use of natural ventilation rather than air-conditioning, and operating exhaust fan when using air-conditioning are recommended to lower exposure risk for home quarantine. A more holistic experiment will be conducted to address the limitations reflected in this study.

Experimental testing of air filter efficiency against the SARS-CoV-2 virus: The role of droplet and airborne transmission.

Verifying the capacity of different types of air filters to stop the propagation of the SARS-CoV-2 virus has become a strategic element to contain viral spreading in enclosed spaces. This paper shows the results of experimental tests about the capacity of different commercial filter grades to stop SARS-CoV-2 propagation using inactivated virions. In the first test, the obtained results showed that the F8 filter blocks SARS-CoV-2 propagation if it encounters a flow devoid of liquid phase, i.e., a biphasic flow that can wet the filtering material. On the contrary, as shown in the second test, the SARS-CoV-2 virus propagates through the F8 filter if the droplet content in the air flow is enough to wet it. In these operational conditions, i.e., when the filter is wet by a flow with a high droplet content, the absolute H14 filter was also shown to fail to stop the transmission of the SARS-CoV-2 virus. Lastly, in the third test, the viral load was shown to be stopped when the pathway of the infected droplet is blocked.

Applicability evaluation of a demand-controlled ventilation system in livestock.

The distribution of agricultural and livestock products has been limited owing to the recent rapid population growth and the COVID-19 pandemic; this has led to an increase in the demand for food security. The livestock industry is interested in increasing the growth performance of livestock that has resulted in the need for a mechanical ventilation system that can create a comfortable indoor environment. In this study, the applicability of demand-controlled ventilation (DCV) to energy-efficient mechanical ventilation control in a pigsty was analyzed. To this end, an indoor temperature and CO2 concentration prediction model was developed, and the indoor environment and energy consumption behavior based on the application of DCV control were analyzed. As a result, when DCV control was applied, the energy consumption was smaller than that of the existing control method; however, when it was controlled in an hourly time step, the increase in indoor temperature was large, and several sections exceeded the maximum temperature. In addition, when it was controlled in 15-min time steps, the increase in indoor temperature and energy consumption decreased; however, it was not energy efficient on days with high-outdoor temperature and pig heat.

[Antibiotic-resistant bacteria contamination in the air of different departments in hospital].

OBJECTIVE: To investigate the contamination of antibiotic-resistant bacteria in air of different departments in hospital. METHODS: From 2018.07 to 2021.06, 191 samples of the air-conditioning filter dust in three hospitals were collected. Antibiotic-resistant bacteria were isolated from the accumulated dust. The drug sensitivity test was conducted for Staphylococcus aureus, Acinetobacter baumannii and Enterobacteriaceae. RESULTS: A total of 119 samples were detected antibiotic-resistant bacteria from 191 samples, and the detection rate was 62.30%. The detection rate of different departments from high to low was surgical ward(68.29%) >intensive care unit(ICU)(59.62%) >medical ward(57.92%). A total of 362 strains of antimicribial-resistant organisms were isolated, mainly were Acinetobacter(28.73%), Pseudomonas(22.10%), Bacillus(22.10%), Staphylococcus(9.12%), etc. Among them, 72 strains of target organisms were detected, and the detection rate was 19.89%(72/362), the detection rate of different target bacteria from high to low was Acinetobacter baumannii(12.71%)>Enterobacteriaceae(4.72%)>Staphylococcus aureus(2.76%)(P<0.05). The drug sensitivity test showed that 41 strains of antimicribial-resistant organisms were detected, and the detection rate was 56.94%(41/72), including carbapenem-resistant Acinetobacter baumannii(CR-ABA), methicillin-resistant Staphylococcus aureus(MRSA), carbapenem-resistant Enterobacteriaceae(CRE), etc.24 strains of multidrug-resistant organisms(MDROs) were detected and the detection rate was 58.54%(24/41). The detection rate of different departments from high to low was ICU(80.00%)>medical ward(60.00%)>surgical ward(46.15%). CONCLUSION: There was contaminated by Acinetobacter baumannii, Staphylococcus aureus, Enterobacteriaceae in the air of hospitals, some of them were MDROs, mainly were detected in neurological ward, respiratory medical ward, hyroid and breast surgery ward, neurosurgery ward, cardiothoracic surgery ward, gallideulous surgical ICU and general ICU.

Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs.

Most of India's current electricity demand is met by combustion of fossil fuels, particularly coal. But the country has embarked on a major expansion of renewable energy and aims for half of its electricity needs to be met by renewable sources by 2030. As climate change-driven temperature increases continue to threaten India's population and drive increased demand for air conditioning, there is a need to estimate the local benefits of policies that increase renewable energy capacity and reduce cooling demand in buildings. We investigate the impacts of climate change-driven temperature increases, along with population and economic growth, on demand for electricity to cool buildings in the Indian city of Ahmedabad between 2018 and 2030. We estimate the share of energy demand met by coal-fired power plants versus renewable energy in 2030, and the cooling energy demand effects of expanded cool roof adaptation in the city. We find renewable energy capacity could increase from meeting 9% of cooling energy demand in 2018 to 45% in 2030. Our modeling indicates a near doubling in total electricity supply and a nearly threefold growth in cooling demand by 2030. Expansion of cool roofs to 20% of total roof area (associated with a 0.21 TWh reduction in cooling demand between 2018 and 2030) could more than offset the city's climate change-driven 2030 increase in cooling demand (0.17 TWh/year). This study establishes a framework for linking climate, land cover, and energy models to help policymakers better prepare for growing cooling energy demand under a changing climate.

Transitioning to low-GWP alternatives with enhanced energy efficiency in cooling non-residential buildings of China.

The electricity demand for space cooling in the non-residential building (NRB) sector of China is growing significantly and is becoming increasingly critical with rapid economic development and mounting impacts of climate change. The growing demand for space cooling will increase global warming due to emissions of hydrofluorocarbons used in cooling equipment and carbon dioxide emissions from the mostly fossil fuel-based electricity currently powering space cooling. This study uses the Greenhouse Gas and Air Pollution Interaction and Synergies (GAINS) model framework to estimate current and future emissions of hydrofluorocarbons and their abatement potentials for space cooling in the NRB sector of China and assess the co-benefits in the form of savings in electricity and associated reductions in greenhouse gas (GHG), air pollution, and short-lived climate pollutant emissions. Co-benefits of space cooling are assessed by taking into account (a) regional and urban/rural heterogeneities and climatic zones among different provinces; (b) technical/economic energy efficiency improvements of the cooling technologies; and (c) transition towards lower global warming potential (GWP) refrigerants under the Kigali Amendment. Under the business-as-usual (BAU) scenario, the total energy consumption for space cooling in the NRB sector will increase from 166 TWh in 2015 to 564 TWh in 2050, primarily due to the rapid increase in the floor space area of non-residential buildings. The total GHG mitigation potential due to the transition towards low-GWP refrigerants and technical energy efficiency improvement of cooling technologies will approximately be equal to 10% of the total carbon emissions from the building sector of China in 2050. Supplementary Information: The online version contains supplementary material available at 10.1007/s11027-022-10021-w.

Schlegelella koreensis sp. nov., isolated from evaporator core of automobile air conditioning system.

A white-coloured, aerobic, and rod-shaped bacterium, designated strain ID0723T was isolated from evaporator core of automobile air conditioning system. The strain was Gram-stain-negative, catalase positive, oxidase negative, and grew at pH 5.5-9.5, at temperature 18-37 °C, and at 0-2.0% (w/v) NaCl concentration. The phylogenetic analysis and 16S rRNA gene sequence data revealed that the strain ID0723T was affiliated to the genus Schlegelella, with the closest phylogenetic member being Schlegelella brevitalea DSM 7029 T (98.1% sequence similarity). The chemotaxonomic features of strain ID0723T were diphosphatidylglycerol, phosphatidylglycerol, and phosphatidylethanolamine as the main polar lipids; Q-8 as an only ubiquinone; and summed feature 3 (C16:1ω7c and/or C16: 1ω6c), C16:0, and summed feature 8 (C18:1ω7c/or C18:1ω6c) as the major fatty acids. The average nucleotide identity (ANI) and in silico DNA-DNA hybridization values between strain ID0723T and S. brevitalea DSM 7029 T were 74.8% and 20.0%, respectively, which were below the cut-off values of 95% and 70%, respectively. The DNA G + C content was 69.9 mol%. The polyphasic taxonomic data clearly indicated that strain ID0723T represents a novel species in the genus Schlegelella for which the name Schlegelella koreensis sp. nov. is proposed, with the type strain ID0723T (= KCTC 72731 T = NBRC 114611 T).