Air pollution prediction with machine learning: a case study of Indian cities.

The survival of mankind cannot be imagined without air. Consistent developments in almost all realms of modern human society affected the health of the air adversely. Daily industrial, transport, and domestic activities are stirring hazardous pollutants in our environment. Monitoring and predicting air quality have become essentially important in this era, especially in developing countries like India. In contrast to the traditional methods, the prediction technologies based on machine learning techniques are proved to be the most efficient tools to study such modern hazards. The present work investigates six years of air pollution data from 23 Indian cities for air quality analysis and prediction. The dataset is well preprocessed and key features are selected through the correlation analysis. An exploratory data analysis is exercised to develop insights into various hidden patterns in the dataset and pollutants directly affecting the air quality index are identified. A significant fall in almost all pollutants is observed in the pandemic year, 2020. The data imbalance problem is solved with a resampling technique and five machine learning models are employed to predict air quality. The results of these models are compared with the standard metrics. The Gaussian Naive Bayes model achieves the highest accuracy while the Support Vector Machine model exhibits the lowest accuracy. The performances of these models are evaluated and compared through established performance parameters. The XGBoost model performed the best among the other models and gets the highest linearity between the predicted and actual data.

Morphological variations within the species Onchocerca armillata and O. gutturosa in buffaloes and cattle in India, with special reference to the male tail and cuticular ornamentation.

Morphological variations in Onchocerca armillata and O. gutturosa, from buffalo and cattle, with special reference to male tail and cuticular ornamentation, have been studied from a large collection of worms available from the infected aortae and ligamentum nuchae, procured from slaughter houses at 3 different localities in Uttar Pradesh, India. Seven variations in the disposition of caudal papillae in males in O. armillata and 14 in O. gutturosa are described. The prevalence of these two Onchocerca species in bubaline and bovine populations appears to be widespread.

Haplorchoides Chen, 1949 (Haplorchinae: Heterophyidae) in freshwater fishes.

Two metacercariae of Haplorchoides, recorded in the fingerlings of over a dozen species of freshwater fishes, are described and illustrated. Ovigerous flukes were recovered from older specimens of two species of fishes procured from the same locality. Comparison of taxonomic characters of the juveniles and adults suggests that they are conspecific. The metacercariae and adults are assigned to two new species, Haplorchoides pearsoni and H. Mehrai. The observations, presented herein, necessitated a review of the species named under this genus and a revision of the generic diagnosis. The reports on fish intermediaries of H. attenatus have been enlarged.