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The Sangam poets point out that the natural pollution mentioned in the Sangam literature. It seems very low because they live close to nature devoid of mechanical intervention like today. But in some songs there is a keen mention of noise pollution. Living and non-living organisms are greatly affected by this sound pollution. This article is designed to explore environmental definition, environmental environmental pollution, noise pollution its origins, hyperactivity, low noise, bird noise, animal noise, man's artificial noise, contemporary pollution thinking, pollution vulnerability and disease. Nature and the environment are interrelated. Environment means the ambience of life. Without that the ecology of the place will become dilapidated. The paper portrays the environment of natural environment free from any artificial pollution during the Sangam era and the corrupted modern era full of pollution, especially noise pollution. It caters possible solutions for it.

COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.