The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85–90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76, p < 0.01), extended work hours (>10 h/day, +40% risk), and inadequate training (β = 0.68, p < 0.05). Adaptive workload scheduling and fatigue monitoring reduced burnout prevalence by 28%. Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. The results demonstrate AI’s effectiveness in workplace safety, predicting, classifying, and mitigating risks. Reinforcement learning-based adaptive monitoring optimizes workforce well-being. Expanding predictive-driven occupational health frameworks to broader industries could enhance safety protocols, ensuring proactive risk mitigation. Future applications include integrating biometric wearables and real-time physiological monitoring to improve predictive accuracy and strengthen occupational resilience.

Background: While passive immunotherapy has been considered beneficial for patients with severe respiratory viral infections, the treatment of COVID-19 cases with convalescent plasma produced mixed results. Thus, there is a lack of certainty and consensus regarding its effectiveness. This meta-analysis aims to assess the role of convalescent plasma treatment on the clinical outcomes of COVID-19 patients enrolled in randomized controlled trials (RCTs). Methods: A systematic search was conducted in the PubMed database (end-of-search: 29 December 2022) for RCTs on convalescent plasma therapy compared to supportive care\standard of care. Pooled relative risk (RR) and 95% confidence intervals were calculated with random-effects models. Subgroup and meta-regression analyses were also performed, in order to address heterogeneity and examine any potential association between the factors that varied, and the outcomes reported. The present meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: A total of 34 studies were included in the meta-analysis. Per overall analysis, convalescent plasma treatment was not associated with lower 28-day mortality [RR = 0.98, 95% CI (0.91, 1.06)] or improved 28-day secondary outcomes, such as hospital discharge [RR = 1.00, 95% CI (0.97, 1.03)], ICU-related or score-related outcomes, with effect estimates of RR = 1.00, 95% CI (0.98, 1.05) and RR = 1.06, 95% CI (0.95, 1.17), respectively. However, COVID-19 outpatients treated with convalescent plasma had a 26% less risk of requiring hospital care, when compared to those treated with the standard of care [RR = 0.74, 95% CI (0.56, 0.99)]. Regarding subgroup analyses, COVID-19 patients treated with convalescent plasma had an 8% lower risk of ICU-related disease progression when compared to those treated with the standard of care (with or without placebo or standard plasma infusions) [RR = 0.92, 95% CI (0.85, 0.99)] based on reported outcomes from RCTs carried out in Europe. Finally, convalescent plasma treatment was not associated with improved survival or clinical outcomes in the 14-day subgroup analyses. Conclusions: Outpatients with COVID-19 treated with convalescent plasma had a statistically significantly lower risk of requiring hospital care when compared to those treated with placebo or the standard of care. However, convalescent plasma treatment was not statistically associated with prolonged survival or improved clinical outcomes when compared to placebo or the standard of care, per overall analysis in hospitalized populations. This hints at potential benefits, when used early, to prevent progression to severe disease. Finally, convalescent plasma was significantly associated with better ICU-related outcomes in trials carried out in Europe. Well-designed prospective studies could clarify its potential benefit for specific subpopulations in the post-pandemic era.

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants extensively used in industrial and consumer applications. Their accumulation in European agricultural soils through industrial discharges, biosolid applications, and contaminated irrigation water poses an unprecedented threat to food security, soil health, and water quality. Despite extensive laboratory research, no full-scale, long-term validated PFAS soil remediation study exists, leaving critical gaps in mitigation strategies. Existing approaches–including mobilization, immobilization, and degradation techniques–have demonstrated effectiveness in controlled environments but lack real-world validation in dynamic agricultural settings. This study proposes an artificial intelligence (AI)-driven remediation framework that integrates real-time detection tools, predictive modeling, and adaptive remediation technologies to overcome these challenges. Unlike static remediation strategies, the proposed AI-assisted system dynamically optimizes remediation interventions based on contamination patterns, soil composition, and environmental conditions. Machine learning algorithms and statistical models enable precise contamination tracking, predictive PFAS migration modeling, and automated remediation decision-making, offering a scalable and responsive solution for sustainable agricultural management. This study underscores the urgent need for large-scale, policy-backed field trials to validate AI-driven PFAS remediation technologies, bridging the gap between scientific advancements and real-world implementation. By transitioning AI-assisted mitigation from theory to an adaptive, field-deployable framework, this research ensures scalable solutions for sustainable food security, environmental resilience, and long-term public health protection.

The relationship between the global climate crisis, which is associated with environmental risks, and occupational hygiene has not been extensively studied. This study develops a framework for identifying how climate change and the climate crisis could impact the workplace environment, workers, and occupational morbidity, mortality, and injury. A framework is used in this paper that is based on a review of the scientific literature published from 2014 to 2024, addressing climate risks, their interaction with occupational hazards, and their effects on the workforce. Eight categories of climate-related hazards are identified: increasingly high temperatures, dust and air pollution, sun and cosmic ultraviolet exposure, pandemics and infectious diseases, diseases transmitted by insects and changes in ecosystems, industrial occupational diseases, changes and crises in the built environment, and extreme weather events. Policies need to consider the gaps in the possibility of interactions between known hazards and new conditions and the productivity of workers, especially those who are most at risk of heat-related illnesses.

The global climate crisis impacts health in numerous ways, and Public Health Inspectors (PHIs) are also addressing these issues at work. Their responsibilities have evolved in response to this pressing crisis. This study examines the effects of PHIs in classifying and assessing environmental psychosocial, organizational risks and workplace hazards exacerbated by the global climate crisis at the workplace. It highlights PHIs' challenges in adapting to changing environmental conditions and the implications for Public Health. An α mixed-methods approach was utilized, combining Quantitative Reviews, and Secondary Data Analysis in the Development model of occupational risk, and environmental health incidents reported over the past decade, by public health inspectors. PHIs. The methodology is approached from a variety of sources, including PubMed, Medline, EMBASE, CINAHL, Web of Science, ScienceDirect, alongside databases and resources provided by WHO, OSHA. By categorizing environmental risks and workplace hazards PHIs identify and classify these risks, prioritize their efforts, and develop targeted strategies for prevention and mitigation. Findings underscore the necessity for enhanced education, training and resources to effectively address the evolving landscape of environmental health risks, to handle climate-related challenges and continuous education. Public Health services and organizations must enforce regulations for sustainable practices, leading to new workplace dynamics.

Introduction: Waste handling workers are exposed to air pollutants and toxic compounds produced during waste management and processing that can cause respiratory symptoms and lung function impairment. This study aimed to evaluate the respiratory health of exposed workers in a waste management plant in Attica, Greece. Methods: 50 field workers exposed to outdoor pollutants (exposure group) and 32 office clerks with no exposure (control group) were evaluated. Upper and lower respiratory symptoms were documented and spirometry was performed. Results: There was no statistically significant difference between the exposure and the control group in forced expiratory volume in one second (FEV1)%, forced vital capacity (FVC)%, FEV1/FVC% predicted values. Workers had lower maximal mid-expiratory flow (MMEF)% predicted values compared to controls (82% vs 94%, p=0.019). No difference was observed regarding the respiratory symptoms between the two groups. Conclusion: Lower MMEF values were observed in the exposure group. Low MMEF can be indicative of small airway disease, thus smoking cessation, close follow-up, and the use of personal protective equipment are recommended.