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In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

Three Coupled Model Intercomparison Project Phase 5 models involved in the G4 experiment of the Geoengineering Model Inter-comparison Project (GeoMIP) project were used to investigate the impact of stratospheric aerosol injection (SAI) on the mean surface air temperature and precipitation extremes in Africa.

This impact was examined under G4 and Representative Concentration Pathway (RCP) 4.5 scenarios on the total precipitation, the number of rainy days (RR1) and of days with heavy rainfall (R20 mm), the rainfall intensity (SDII), the maximum length of consecutive wet (CWD) and dry (CDD) days and on the maximum rainfall in five consecutive days (Rx5day) across four regions: Western Africa (WAF), Eastern Africa (EAF), Northern Africa and Southern Africa (SAF).

During the 50 years (2020–2069) of SAI, mean continental warming is −0.40°C lower in G4 than under RCP4.5. During the post-injection period (2070–2090), the temperature continues to increase, but at a lower rate (−0.19°C) than in RCP4.5. During SAI, annual rainfall in G4 is significantly greater than in RCP4.5 over the high latitudes (especially over SAF) and lower over the tropics. The termination of SAI leads to a significant increase of rainfall over Sahel and EAF and a decrease over SAF and Guinea Coast (WAF).

Compared to RCP4.5, SAI will contribute to reducing significantly regional warming but with a significant decrease of rainfall in the tropics where rainfed agriculture account for a large part of the economies. After the SAI period, the risk of drought over the extratropical regions (especially in SAF) will be mitigated, while the risk of floods will be exacerbated in the Central Sahel.

To meet the Paris Agreement, African countries will implement mitigation measures to contribute to keep the surface air temperature below 2°C. Geoengineering with SAI is suggested as an option to meet this challenge, but its implication on the African climate system needs a deep investigation in the aim to understand the impacts on temperature and precipitation extremes. To the best of the authors’ knowledge, this study is the first to investigate the potential impact of SAI using the G4 experiment of GeoMIP on temperature and precipitation extremes of the African continent.

Agricultural workers represent an important part of the population exposed to high heat-related health and productivity risks. This study aims to estimate the heat-related productivity loss (PL) for moderate work activities in sun and shady areas and evaluating the economic cost locally in an Italian farm and generally in the whole province of Florence. Benefits deriving by working in the shade or work-time shifting were provided. Comparisons between PL estimated in Mediterranean (Florence, Italy) and subtropical (Guangzhou, China) areas were also carried out.

Meteorological data were collected during summers 2017–2018 through a station installed in a farm in the province of Florence and by two World Meteorological Organization (WMO)‐certified meteorological stations located at the Florence and Guangzhou airports. These data were used to calculate the wet-bulb globe temperature and to estimate the hourly PL and the economic cost during the typical working time (from 8 a.m. to 5 p.m.) and by advancing of 1 h and 2 h the working time. Significant differences were calculated through nonparametric tests.

The hourly PL and the related economic cost significantly decreased (p < 0.05) by working in the shade and by work-time shifting. Higher PL values were observed in Guangzhou than in Florence. The decrease of PL observed by work-time shifting was greater in Florence than in Guangzhou.

Useful information to plan suitable heat-related prevention strategies to counteract the effects of heat in the workplace are provided. These findings are essential to quantify the beneficial effects due to the implementation of specific heat-related adaptation measures to counter the impending effects of climate change.

Understanding farmers’ perceptions of how the climate is changing is vital to anticipating its impacts. Farmers are known to take appropriate steps to adapt only when they perceive change to be taking place. This study aims to analyse how African indigenous vegetable (AIV) farmers perceive climate change in three different agro-climatic zones (ACZs) in Kenya, identify the main differences in historical seasonal and annual rainfall and temperature trends between the zones, discuss differences in farmers’ perceptions and historical trends and analyse the impact of these perceived changes and trends on yields, weeds, pests and disease infestation of AIVs.

Data collection was undertaken in focus group discussions (FGD) (N = 211) and during interviews with individual farmers (N = 269). The Mann–Kendall test and regression were applied for trend analysis of time series data (1980-2014). Analysis of variance and least significant difference were used to test for differences in mean rainfall data, while a chi-square test examined the association between farmer perceptions and ACZs. Coefficient of variation expressed as a percentage was used to show variability in mean annual and seasonal rainfall between the zones.

Farmers perceived that higher temperatures, decreased rainfall, late onset and early retreat of rain, erratic rainfall patterns and frequent dry spells were increasing the incidences of droughts and floods. The chi-square results showed a significant relationship between some of these perceptions and ACZs. Meteorological data provided some evidence to support farmers’ perceptions of changing rainfall. No trend was detected in mean annual rainfall, but a significant increase was recorded in the semi-humid zone. A decreasing maximum temperature was noted in the semi-humid zone, but otherwise, an overall increase was detected. There were highly significant differences in mean annual rainfall between the zones. Farmers perceived reduced yields and changes in pest infestation and diseases in some AIVs to be prevalent in the dry season. This study’s findings provide a basis for local and timely institutional changes, which could certainly help in reducing the adverse effects of climate change.

This is an original research paper and the historical trends, farmers’ perceptions and effects of climate change on AIV production documented in this paper may also be representative of other ACZs in Kenya.

The ecological environment of the Loess Plateau, China, is extremely fragile under the context of global warming. Over the past two decades, the vegetation of the Loess Plateau has undergone great changes. This paper aims to clarify the response mechanisms of vegetation to climate change, to provide support for the restoration and environmental treatment of vegetation on the Loess Plateau.

The Savitsky–Golay (S-G) filtering algorithm was used to reconstruct time series of moderate resolution imaging spectroradiometer (MODIS) 13A2 data. Combined with trend analysis and partial correlation analysis, the influence of climate change on the phenology and enhanced vegetation index (EVI) during the growing season was described.

The S-G filtering algorithm is suitable for EVI reconstruction of the Loess Plateau. The date of start of growing season was found to gradually later along the Southeast–Northwest direction, whereas the date of the end of the growing season showed the opposite pattern and the length of the growing season gradually shortened. Vegetation EVI values decreased gradually from Southeast to Northwest. Vegetation changed significantly and showed clear differentiation according to different topographic factors. Vegetation correlated positively with precipitation from April to July and with temperature from August to November.

This study provides technical support for ecological environmental assessment, restoration of regional vegetation coverage and environmental governance of the Loess Plateau over the past two decades. It also provides theoretical support for the prediction model of vegetation phenology changes based on remote sensing data.

This study aims to provide a comprehensive framework for the study of indoor air quality (IAQ) in hospitality premises. The goal is to identify the drivers of air pollution, both at the exogenous and endogenous level, to generate insights for facility managers.

The complexity of hospitality premises requires an integrated approach to properly investigate IAQ. The authors develop an overarching framework encompassing a monitoring method, based on real-time sensors, a technological standard and a set of statistical analyses for the assessment of both IAQ performance and drivers, based on correlation analyses, analysis of variance and multivariate regressions.

The findings suggest that the main drivers of IAQ differ depending on the area monitored: areas in contact with the outdoors or with high ventilation rates, such as halls, are affected by outdoor air quality more than guestrooms or fitness areas, where human activities are the main sources of contamination.

The results suggest that the integration of IAQ indicators into control dashboards would support management decisions, both in defining protocols to support resilience of the sector in a postpandemic world and in directing investments on the premises. This would also address guests’ pressing demands for a broader approach to cleanliness and safety and support their satisfaction and intention to return.

To the best of the authors’ knowledge, this is the first study developing a comprehensive framework to systematically address IAQ and its drivers, based on a standard and real-time monitoring. The framework has been applied across the longest period of monitoring for a hospitality premise thus far and over an entire hotel facility.

The adverse impacts of climate change coupled with rapid informal urbanization in the Southern African region are increasing the vulnerability of already sensitive population groups. Consequently, these urban regions are highly vulnerable to urban heat island effects and heatwaves due to exogenous and endogenous factors. While the dynamic interplay between the built environment, climate and response strategies is known, this paper highlights the lived experience of informal settlement residents. It presents work from a project undertaken in Melusi, an informal settlement in Tshwane, South Africa, as a multi-disciplinary project focusing on improving the local resilience to climate change associated heat stress.

Following a mixed method approach, a semi-structured observational analysis of the spatial layout and material articulation of selected dwellings along with the continuous monitoring and recording of their indoor environments were undertaken.

The paper presents the research results in terms of the dwelling characteristics, as spatial and material-use strategies and documented heat stress exposure in these structures. The findings highlight that informal dwellings perform poorly in all cases due to endogenous factors and that inhabitants experience extreme heat stress conditions for between 6 and 10 h daily during the peak summer period.

Currently, there are little empirical data on the heat stress residents living in informal settlements in Southern Africa are experiencing. This article provides insight into the indoor environments of informal dwellings and hopes to contribute future guidelines or heat health policies.