Risk Associations between Vehicular Traffic Noise Exposure and Cardiovascular Diseases: A Residential Retrospective Cohort Study.

Environmental noise can induce detrimental health effects such as cardiovascular disease (CVD). The relationship between vehicular traffic noise pollution and CVD was investigated through a retrospective residential cohort study in the city of Pisa. Four exposure classes were defined for noise pollution, using noise propagation maps. The association between noise exposures and cause-specific mortality or hospitalization of the subjects of the cohort was calculated using the hazard ratio (HR) for night and day through a multiple time-dependent and sex-specific Cox regression adjusting for age, the socio-economic deprivation index, and traffic air pollution. Mortality excess for CVD and risk trends for a 1 decibel noise increment were observed among the most exposed women (mortality: HRnightclass4 1.15 (1.03-1.28); Trendnight 1.007 (1.002-1.012); HRdayclass4 1.14 (1.02-1.27); Trendday 1.008 (1.003-1.013)), particularly for ischaemic disease (mortality: Trendnight 1.008 (0.999-1.017); Trendday 1.009 (0.999-1.018)) and cerebrovascular disease (mortality: HRnightclass3 1.23 (1.02-1.48), HRdayclass3 1.24 (1.03-1.49)). Hospitalization analyses confirm mortality results. A decreased risk for hospitalization was also observed among the most exposed men (HRdayclass4 0.94 (0.88-1.01), particularly for ischaemic disease (HRnightclass4 0.90 (0.80-1.02); HRdayclass4 0.86 (0.77-0.97)) and cerebrovascular disease (HRnightclass4 0.89 (0.78-1.01)). Authors recommend the adoption of prevention measures aimed at mitigating noise and the activation of a monitoring of the risk profile in the Pisa population updating both the residential cohort and health data.

An emotional artificial neural network for prediction of vehicular traffic noise.

Road traffic is a leading source of environmental noise pollution in large cities, which greatly affects the health and well-being of people. A reliable method for the prediction of road traffic noise is required for monitoring and assessment of traffic noise exposure. This study presents the first application of the Emotional Artificial Neural Network (EANN), as a new generation of neural network method for modeling the road traffic noise in Nicosia, North Cyprus. The efficiency of the EANN model was validated in comparison with the classical feed-forward neural network (FFNN) using two different scenarios with different input combinations. In the first scenario, vehicular classification (the number of cars, medium vehicles, heavy vehicles) and average speed were considered as the models' inputs. In the second scenario, the total traffic and percentage of heavy vehicles were used instead of the classification where the input parameters were total traffic volume, average speed and percentage of heavy vehicles. Application of the EANN model in the prediction of road traffic noise could improve the efficiency of the FFNN, MLR and empirical models at the verification stage up to 14%, 35% and 37%, respectively. Classifying the traffic volume into sub-classes (in scenario 1) before feeding them into the models improved the performance of the EANN and FFNN models at the verification stage by 8% and 12%, respectively. Sensitivity analysis of the input parameters indicated that total traffic volume is the most relevant factor influencing road traffic noise in the study area followed by the number of cars, medium vehicles, heavy vehicles, average speed and percentage of heavy vehicles, respectively.

Vehicular traffic noise prediction using soft computing approach.

A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data.