A Characterization and Cell Toxicity Assessment of Particulate Pollutants from Road Traffic Sites in Kano State, Nigeria

Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits

This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (n = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM10, PM2.5, O3, NO2, CO, and SO2. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO2 also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O3 demonstrated mixed results. Both PM10 and PM2.5 showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.

Editorial for the Special Issue “Impacts of Transport Systems on Air Pollution and Human Health”

Transport systems (road, railway and aircraft traffic) are the main contributors to poor air quality in the major cities [...]

Correction: Sadiq et al. Characterization and Cell Toxicity Assessment of Particulate Pollutants from Road Traffic Sites in Kano State, Nigeria. Atmosphere 2022, 13, 80

Emerging African countries are characterized by explosive population growth and urbanization, which threaten environmental sustainability. This study comparatively characterized ambient aerosols and assessed cytotoxicity to facilitate improving health and environmental policy. Twenty-four air samples were collected at high and low-density traffic sites in Kano State using polysulfone and stainless steel filters attached to an automated pump. The physico-chemical properties of particulate matter were determined using scanning electron microscopy and energy dispersive X-ray analysis (SEM-EDX). In vitro, their potential toxicity was assessed using macrophages and cell fixation with staining. Results showed 51.7% of particles as PM2.5, with the highest particle concentration in mixed sites (urban and industrial). Particle classification into four groups by elemental composition and structure showed: sand particles (Si, Al, Fe, Ca, Mg, K, Na, Mo, Sr, Zr) 30–51%; other fibers 0–3%; other particles (Si, Fe, S, Mo, Zn, and other metals) 22–40%; and silicone-based fibres 23–34%. The abundant elements are: Si, Al, Ca, Ce, Ti, Fe, Cl, Pb, and Mn. The lowest viability on cytotoxicity assessment was recorded in mixed site M2. The majority of households were located within 50 m of air sampling sites. Proximity to traffic sites worsens health, as evidenced in cytotoxicity findings. We recommend improved urban planning and intensification of emissions control.