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Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

In a rapidly shifting market, organizations seek more diverse and innovative employee development interventions. Yet, these initiatives may have limited impact without employees’ engagement. This conceptual paper aims to propose self-leadership as a value-added strategy for promoting both individual and organizational development.

The authors conducted a conceptual analysis with three case examples. The cases were purposefully selected, aiming to comprehend how the concept of self-leadership has been applied within organizations and to identify real-life examples where self-leadership has been adopted as an organizational strategy.

This study demonstrates that self-leadership plays a significant role in facilitating human resource development (HRD) initiatives. Specifically, the authors illustrate how self-leadership interventions in companies empower individuals to take charge of their development, aligning personal and organizational goals. When effectively applied, self-leadership strategies positively impact HRD practices in the areas of training and development, organization development and career development, yielding benefits for both employees and employers.

This study addresses knowledge gaps in the emerging field of self-leadership in HRD by providing three companies’ examples of how self-leadership can add value to HRD. The findings offer unique insights into the synergy between self-leadership and HRD, benefiting academics interested in this line of inquiry and HRD practitioners seeking innovative approaches to employee and organizational development.