Air quality and meteorological patterns of an early spring heatwave event in an industrialized area of Attica, Greece

Evaluating the influence of ambient conditions in the cooking space of railway pantry car using selected thermal indices and physiological parameter

BACKGROUND

Hot and humid indoor environment of the kitchen affects worker performance. The Indian Railway's pantry car culinary is one of them that cooks food for the on-board passengers, which could be bothered by the hot indoor climate.

OBJECTIVE

The current study aimed to identify the indoor working environment of the railway "pantry car" using heat stress indices such as "Universal Thermal Climate Index-UTCI," "Wet-Bulb Globe Temperature-WBGT," "Discomfort Index-DI," "Tropical Summer Index-TSI," "Heat Index-HI," and Heart Rate-HR with clothing insulation.

METHODS

The study was performed in 2018 (August-summer season) to collect field survey data on 6 railway pantry cars. Measurements were carried out during peak cooking times such as morning "7 : 00 am", day "11 : 30 am", evening "4 : 00 pm," and night "6 : 30 pm". This study's descriptive and Pearson's correlation analysis was accomplished using SPSS version 2016 software.

RESULTS

The analysis results revealed that the average values were for UTCI (37.77±5.26°C), WBGT (30.42±2.28°C), DI (30.05±2.70°C), TSI (33.21±2.90°C), HI (48.53±4.86°C), correspondingly. During analysis, the strongest correlation association was observed between "TSI and DI" (r = 0.985, p < 0.000) and WBGT and TSI (r = 0.958, p < 0.000). A "significant correlation" was found between UTCI and HI (r = 0.637, p < 0.05). While no signification correlation was found between "heat stress indices and physiological parameters (p > 0.05)".

CONCLUSION

In this study, all the heat stress index limit values showed highly harsh working conditions inside the pantry car, which created unfavorable circumstances for the culinarians. Inappropriate "ventilation design" could be a reason for discomfort in the railway pantry car.

Machine Learning-Based Simulation of the Air Conditioner Operating Time in Concrete Structures with Bayesian Thresholding

Efficient energy use is crucial for achieving carbon neutrality and reduction. As part of these efforts, research is being carried out to apply a phase change material (PCM) to a concrete structure together with an aggregate. In this study, an energy consumption simulation was performed using data from concrete mock-up structures. To perform the simulation, the threshold investigation was performed through the Bayesian approach. Furthermore, the spiking part of the spiking neural network was modularized and integrated into a recurrent neural network (RNN) to find accurate energy consumption. From the training-test results of the trained neural network, it was possible to predict data with an R2 value of 0.95 or higher through data prediction with high accuracy for the RNN. In addition, the spiked parts were obtained; it was found that PCM-containing concrete could consume 32% less energy than normal concrete. This result suggests that the use of PCM can be a key to reducing the energy consumption of concrete structures. Furthermore, the approach of this study is considered to be easily applicable in energy-related institutions and the like for predicting energy consumption during the summer.

Impact assessment of air pollutants and greenhouse gases on urban heat wave events in the Beijing-Tianjin-Hebei region

The production and quality of human life have been impacted by the extreme heat wave events caused by global warming and urbanization. This study analyzed the prevention of air pollution and the strategies of emission reduction based on decision trees (DT), random forests (RF), and extreme random trees (ERT). Additionally, we quantitatively investigated the contribution rate of atmospheric particulate pollutants and greenhouse gases to urban heat wave occurrences by combining numerical models and big data mining technology. This study focuses on changes in the urban environment and climate. The main findings of this study are as follows. The average concentrations of PM2.5 in the northeast of Beijing-Tianjin-Hebei in 2020 were 7.4%, 0.9%, and 9.6% lower than those in the corresponding years of 2017, 2018, and 2019, respectively. The carbon emissions in the Beijing-Tianjin-Hebei region showed an increasing trend during the previous 4 years, which was consistent with the spatial distribution of PM2.5. In 2020, there were fewer urban heat waves, which was attributable to a reduction of 75.7% in emissions and an improvement of 24.3% in the prevention and management of air pollution. These results suggest that the government and environmental protection agencies need to pay attention to changes in the urban environment and climate to diminish the negative effects of heatwaves on the health and economic growth of the urban population.