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Air pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due to roadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essential because it permits the experts and the citizens to take appropriate actions to restrict their exposure and execute protective measures to improve air quality. This study aimed to develop a specialized computational intelligence methodology that uses principal component (PC) based artificial neural networks (ANN). The model was used to forecast PM10 in ambient air using meteorological data. This application is demonstrated for monitoring data from the urban area of Belagavi city of Karnataka state, India. Principal component analysis (PCA) was applied to understand the interactions between PM10 concentration and meteorological data. The analysis found that the PCANN model is better than the principal component regression (PCR) model, based on using various performance indexes (MAE, MSE, MAPE, RMSE, R, and R2). The PM10 predictive model performance was satisfactory, with a MAPE of 0.069. The overall predictive capability of PM10 was 89.59% in terms of R. [ABSTRACT FROM AUTHOR]

It is not so common to aspirate foreign body in normal adults without any predisposing factors as compared to children and those with the altered neurological state. Endobronchial foreign bodies are one of the causes of obstructive pneumonia and difficult to diagnose as signs and symptoms are often nonspecific. However, once they are diagnosed, they can generally be removed, leading to rapid and drastic resolution of symptoms. Bronchoscopy is the gold standard in the identification and localization of an airway foreign body and also for better management of the ailment. However with the help of virtual bronchoscopy one can decide the location of the foreign body before any invasive intervention and being noninvasive it can be performed in follow-up easily to check the patency of airways. It is not possible to detect the exact size of foreign body with the virtual bronchoscopy. In this article, we report a case of unnoticed foreign body aspiration in a 49-year-old female patient who was initially treated for pneumonia. However, due to nonresolution of opacity contrast enhanced computed tomography thorax with virtual and flexible bronchoscopy were performed, which revealed a foreign body in the right lower lobe bronchus that was removed with biopsy forceps in piecemeal. In her follow-up visit, she underwent virtual broncoscopy that revealed clear airways. Thus, detailed history and high index of suspicion is required for nonresolving pneumonias that may occur due to unnoticed foreign body/ies in an adult. [ABSTRACT FROM AUTHOR]