The sounds produced by humans, industries, transport and animals in the atmosphere that pose a threat to the health of humans or animals can be characterized as noise pollution. Adverse effects due to noise exposure can involve speech communication interference and declining learning skills of children. Highway traffic noise contributes to 80% of all noise. It has grown to a massive scale because of growth in population along the roads leading to a rapid change in land use and has evolved into a common reality in various Indian cities. The main objective of this work is to develop a road traffic noise prediction model using ArcGIS 10.3 for the busy corridors of Chennai. The collected data includes traffic volume, speed, and noise level in lateral and vertical directions. Noise levels were measured in 9 locations using a noise level meter. It is observed that the noise levels vary from 50 dB to 96 dB. It is found that the noise problem is severe in 18% of the area, and 6.3% of people are exposed to the traffic noise problem. The results obtained in this study show that the city is affected by severe noise pollution due to road traffic.
Air pollution in India poses a big threat to human lives. In 2017, 77% of population of India was subjected to PM2.5 (Particulate Matter) exposure resulting in mortality of 6.7 lakh throughout the country. In this study, Long Short-Term Memory (LSTM) model, a powerful deep learning technique is applied for PM2.5 prediction. Three variants of LSTM model, LSTM for regression, LSTM for regression using window and LSTM for regression with time steps are developed to predict PM2.5 concentration in India. The metrics used to evaluate the performance of the predictive models are root mean square error (RMSE) and coefficient of determination (R2). The models are applied to continuous ambient air quality data collected from 14 stations in India, for the period from May 01, 2019 to April 30, 2020 at an interval of every 15 minutes. The optimal results are obtained from the models with the tuned parameters of 64 epochs and batch size of 32. All the three variants of LSTM model performed equally well in predicting PM2.5 concentration. The experimental results revealed that the value of R2 is maintained at 0.9 consistently for all the variants of LSTM model. The low values of RMSE and high values of R2 proved the reliability of the model. Thus, the proposed model gives awareness about the air pollution level in India and alerts the society to take precautionary steps to save their lives. Further the urban planners can have idea of the pollution levels for their planning and decision making.
In recent times, air pollution has attracted the attention of policymakers and researchers as an important issue. The pollution that contaminates the air that people breathe is from pollutants such as oxides of carbon, nitrogen and sulphur as well minuscule dust particle which are smaller than 0.0025mm in diameter. The emissions contain many substances that are harmful to human health when exposed to them for a prolonged period or more than certain levels of concentration. The recent advent of technology in sensors and compact instruments to measure the concentration of pollutant levels with considerable ease. Further, this paper also predicts the air pollution for using multiple Deep Learning models that are variations of the Long Short-Term Memory (LSTM) model. In this research, only PM2.5 alone taken into consideration for prediction. Real-time air quality data were collected at selected places in the study area. It is found that the model prediction data is well matched with the other researchers' results and real-time data.
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