A Low-Cost Sensor Network for Real-Time Thermal Stress Monitoring and Communication in Occupational Contexts

Climate projections of human thermal comfort for indoor workplaces

Climate models predict meteorological variables for outdoor spaces. Nevertheless, most people work indoors and are affected by heat indoors. We present an approach to transfer climate projections from outdoors to climate projections of indoor air temperature (Ti) and thermal comfort based on a combination of indoor sensors, artificial neural networks (ANNs), and 22 regional climate projections. Human thermal comfort and Ti measured by indoor sensors at 90 different workplaces in the Upper Rhine Valley were used as training data for ANN models predicting indoor conditions as a function of outdoor weather. Workplace-specific climate projections were modeled for the time period 2070-2099 and compared to the historical period 1970-1999 using the same ANNs, but ERA5-Land reanalysis data as input. It is shown that heat stress indoors will increase in intensity, frequency, and duration at almost all investigated workplaces. The rate of increase depends on building and room properties, the workplace purpose, and the representative concentration pathway (RCP2.6, RCP4.5, or RCP8.5). The projected increase of the mean air temperature in the summer (JJA) outdoors, by + 1.6 to + 5.1 K for the different RCPs, is higher than the increase in Ti at all 90 workplaces, which experience on average an increase of + 0.8 to + 2.5 K. The overall frequency of heat stress is higher at most workplaces than outdoors for the historical and the future period. The projected hours of indoor heat stress will increase on average by + 379 h, + 654 h, and + 1209 h under RCP2.6, RCP4.5, and RCP8.5, respectively.

Measuring Heat Stress for Human Health in Cities: A Low-Cost Prototype Tested in a District of Valencia, Spain

Nowadays, the measurement of heat stress indices is of principal importance due to the escalating impact of global warming. As temperatures continue to rise, the well-being and health of individuals are increasingly at risk, which can lead to a detrimental effect on human performance and behavior. Hence, monitoring and assessing heat stress indices have become necessary for ensuring the safety and comfort of individuals. Thermal comfort indices, such as wet-bulb globe temperature (WBGT), Tropical Summer Index (TSI), and Predicted Heat Strain (PHS), as well as parameters like mean radiant temperature (MRT), are typically used for assessing and controlling heat stress conditions in working and urban environments. Therefore, measurement and monitoring of these parameters should be obtained for any environment in which people are constantly exposed. Modern cities collect and publish this relevant information following the Smart City concept. To monitor large cities, cost-effective solutions must be developed. This work presents the results of a Heat Stress Monitoring (HSM) system prototype network tested in the Benicalap-Ciutat Fallera district in Valencia, Spain. The scope of this work is to design, commission, and test a low-cost prototype that is able to measure heat stress indices. The Heat Stress Monitoring system comprises a central unit or receiver and several transmitters communicating via radiofrequency. The transmitter accurately measures wind speed, air temperature, relative humidity, atmospheric pressure, solar irradiation, and black globe temperature. The receiver has a 4G modem that sends the data to an SQL database in the cloud. The devices were tested over one year, showing that radio data transmission is reliable up to 700 m from the receiver. The system's power supply, composed of a Photovoltaic panel and Lithium-ion batteries, provided off-grid capabilities to the transmitter, with a tested backup autonomy of up to 36 days per charge. Then, indicators such as WBGT, TSI, and MRT were successfully estimated using the data collected by the devices. The material cost of a 12-point network is around EUR 2430 with a competitive price of EUR 190 per device.

ECG-Based Stress Detection and Productivity Factors Monitoring: The Real-Time Production Factory System

Productivity and production quality have become primary goals for the success of companies in all industrial and manufacturing sectors. Performance in terms of productivity is influenced by several factors including machinery efficiency, work environment and safety conditions, production processes organization, and aspects related to workers' behavior (human factors). In particular, work-related stress is among the human factors that are most impactful and difficult to capture. Thus, optimizing productivity and quality in an effective way requires considering all these factors simultaneously. The proposed system aims to detect workers' stress and fatigue in real time using wearable sensors and machine learning techniques and also integrate all data regarding the monitoring of production processes and the work environment into a single platform. This allows comprehensive multidimensional data analysis and correlation research, enabling organizations to improve productivity through appropriate work environments and sustainable processes for workers. The on-field trial demonstrated the technical and operational feasibility of the system, its high degree of usability, and the ability to detect stress from ECG signals exploiting a 1D Convolutional Neural Network (accuracy 88.4%, F1-score 0.90).

End-user perspective of low-cost sensors for urban stormwater monitoring: a review

The large-scale deployment of low-cost monitoring systems has the potential to revolutionize the field of urban hydrology monitoring, bringing improved urban management, and a better living environment. Even though low-cost sensors emerged a few decades ago, versatile and cheap electronics like Arduino could give stormwater researchers a new opportunity to build their own monitoring systems to support their work. To find out sensors which are ready for low-cost stormwater monitoring systems, for the first time, we review the performance assessments of low-cost sensors for monitoring air humidity, wind speed, solar radiation, rainfall, water level, water flow, soil moisture, water pH, conductivity, turbidity, nitrogen, and phosphorus in a unified metrological framework considering numerous parameters. In general, as these low-cost sensors are not initially designed for scientific monitoring, there is extra work to make them suitable for in situ monitoring, to calibrate them, to validate their performance, and to connect them with open-source hardware for data transmission. We, therefore, call for international cooperation to develop uniform low-cost sensor production, interface, performance, calibration and system design, installation, and data validation guides which will greatly regulate and facilitate the sharing of experience and knowledge.

Wireless Temperature, Relative Humidity and Occupancy Monitoring System for Investigating Overheating in Buildings

With the climate change we are experiencing today, the number and intensity of heatwaves are increasing dramatically, significantly impacting our buildings' overheating. The majority of the prefabricated concrete panel buildings in Hungary are considered outdated from an energy point of view. These buildings may be at greater risk from extreme weather events. To examine this, long-term monitoring measurements are needed. Therefore, we developed a unique, reliable, and cost-effective wireless monitoring system, which can track in real time the indoor air quality data (temperature, relative humidity, CO₂) of the investigated apartment building, as well as users' habits, such as resident presence, window opening, and blind movement. The data were used to analyse and quantify the summer overheating of the dwelling and user habits. The measurements showed that the average temperature in all rooms was above 26 °C, and there were several occasions when the temperature exceeded 30 °C. Overheating in apartment buildings in summer is a significant problem that needs to be addressed. Further investigation of ventilation habits will help develop favourable ventilation strategies, and using these measurements in dynamic simulations will also help improve the models' validity for further studies.

Influence of the Thermal Environment on Occupational Health and Safety in Automotive Industry: A Case Study

Considering thermal environment aspects have a major impact not only on occupational health and safety (OH&S) performance but also on the productivity and satisfaction of the workers, the aim of the case study was to assess the thermal comfort of a group of 33 workers in an automotive industry company, starting with collecting data about the thermal environment from different workplaces, continuing with the analytical determination and interpretation of thermal comfort using the calculation of the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indices, according to provisions of the standard ISO 7730:2005, and comparing the results with the subjective perception of the workers revealed by applying individual questionnaires. The results of the study represent an important input element for establishing the preventive and protective measures for the analysed workplaces in correlation with the measures addressing other specific risks and, also, could serve as a model for extending and applying to other similar workplaces in future studies. Moreover, the mathematical model and the software instrument used for this study case could be used in further similar studies on larger groups of workers and in any industrial domain.

Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments

Heat stress in many industrial workplaces imposes significant risk of injury to individuals. As a means of quantifying these risks, a comparison of four rationally developed thermoregulatory models was conducted. The health-risk prediction (HRP) model, the human thermal regulation model (HuTheReg), the SCENARIO model, and the six-cylinder thermoregulatory model (SCTM) each used the same inputs for an individual, clothing, activity rates, and environment based on previously observed conditions within the Portuguese glass industry. An analysis of model correlations was conducted for predicted temperatures (°C) of brain (TBrain), skin (TSkin), core body (TCore), as well as sweat evaporation rate (ER; Watts). Close agreement was observed between each model (0.81-0.98). Predicted mean ± SD of active phases of exposure for both moderate (TBrain 37.8 ± 0.25, TSkin 36.7 ± 0.49, TCore 37.8 ± 0.45 °C, and ER 207.7 ± 60.4 W) and extreme heat (TBrain 39.1 ± 0.58, TSkin, 38.6 ± 0.71, TCore 38.7 ± 0.65 °C, and ER 468.2 ± 80.2 W) were assessed. This analysis quantifies these heat-risk conditions and provides a platform for comparison of methods to more fully predict heat stress during exposures to hot environments.