Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment.

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Psychosocial risk and job satisfaction in professional drivers.

Psychosocial work factors are implicated in the development of stress and job satisfaction. This relationship has been scarcely studied in so-called singular activities, as is the case of professional drivers. This cross-sectional study of 601 professional drivers assesses psychosocial risks and job satisfaction using Copenhagen Psychosocial Questionnaire (COPSOQ) and Overall Job Satisfaction questionnaire models. The values of the psychosocial scales of professional drivers were compared with thresholds values of the Spanish working population. The relationships of the psychosocial scales with the level of job satisfaction were examined using logistic regression models, adjusted for age, gender, length of driving license and years of experience. In general, professional drivers have an unfavorable psychosocial environment compared to the average Spanish workers. The relationship between psychosocial scales and job satisfaction is observed, with the most influential variables being the meaning and development of work, integration in the company, social relations, esteem and level of psychological demands.