Exposure of humans to high concentrations of PM_2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM_2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM_2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

This study investigates organizational culture typology and strategic implementation process in the local governments of a low middle income country in order to reveal the mix of culture types and implementation patterns that are needed for the success of local governments. Based on rich secondary datasets and in-depth qualitative study of six Nigerian local governments involving 42 participants, the findings highlights that local governments adopt different combinations of organizational cultures and those combinations can have varying implications for the way strategic actors approached their implementation process. As governments and funding bodies, such as the World Bank, continue to seek ways to meet the infrastructural and social needs of low middle income countries, particularly within local governments, this article presents opportunities and a platform for researchers and policy makers to explore the interrelationships between different organizational culture types and strategic implementation process for the successful delivery of developmental projects.

The link between air pollution and health burden in urban areas has been well researched. This has led to a plethora of effective policy-induced monitoring and interventions in the global south. However, the implication of pollutant species like PM_2.5 in low middle income countries (LMIC) still remains a concern. By adopting a positivist philosophy and deductive reasoning, this research addresses the question, to what extent can we deliver effective interventions to improve air quality at a building structure located at a busy road node in a LMIC? This study assessed the temporal variability of pollutants around the university environment to provide a novel comparative evaluation of occupational shift patterns and the use of facemasks as risk control interventions. The findings indicate that the concentration of PM_2.5, which can be as high as 300% compared to the WHO reference, was exacerbated by episodic events. With a notable decay period of approximately one-week, adequate protection and/or avoidance of hotspots are required for at-risk individuals within a busy road node. The use of masks with 80% efficiency provides sufficient mitigation against exposure risks to elevated PM_2.5 concentrations without occupational shift, and 50% efficiency with at least ‘2 h ON, 2 h OFF’ occupational shift scenario.