The present study provides an evaluation of noise pollution in six Urban Parks located in the city of Curitiba, Brazil. Equivalent noise levels (L(eq)) were measured in 303 points (each point measured during 3 min) spread throughout the Parks. Measured values were confronted with local legislation (Law 10625) allowed limits, and the Parks were thus classified as "acoustically polluted or unpolluted". Measured values were also evaluated according to international legislation: Decree no. 12 of the City Council of Rome, DIN 18005 for German cities, the World Health Organization, and the United States Environmental Protection Agency. Urban parks in the downtown area of Curitiba, surrounded by roads of heavy traffic and in the midst of intense commercial activities, do not satisfy any of the standards used. The most noise-polluted parks in Curitiba were the Public Walk Park and the Botanical Garden Park, with measured L(eq) of 64.8 dB(A) and 67 dB(A).

Evaluation of noise pollution in urban parks.

In India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The traffic scenario is typically different from other countries due to predominance of a variety of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous volume of vehicles. Also tendency of not following the traffic norms and poor maintenance adds to the noise generation. In the present study, Multilayer feed forward back propagation (BP) neural network has been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network (ANN) model for predicting highway traffic noise. The developed ANN model is used to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level (Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq and L10 from ANN approach and regression analysis have also been compared with the field measurement. The results show that the percentage difference is much less using ANN approach as compared to regression analysis. Further goodness-of-fit of the models against field data has been checked by statistical t-test at 5% significance level and proved the Artificial Neural Network (ANN) approach as a powerful technique for traffic noise modeling.

Vehicular traffic noise modeling using artificial neural network approach

The present study aims to compute the temporal and spatial distribution of road traffic induced noise pollution in an urban environment by monitoring and mapping. The study area was the entire Asansol city of West Bengal, India. A total of 35 locations were selected for collection of data, classified as industrial, commercial, residential, sensitive and mixed areas according to the national regulatory standards. Noise recordings were conducted during morning and night hours. Day time L eq level ranged between 51.2 and 89.0 dB(A), whereas it ranged between 43.5 and 81.9 dB(A) during night. The average L dn value was 73.28 ± 8.51 dB(A) (Range: 55.1-87.3); The traffic noise index was 80.62 ± 15.88 dB(A) (Range: 49.4-115.8). The computed data were mapped by utilization of Geographic information system methodology that allowed the visualization and identification of the extent and distribution of sound pollution across the study area. This proves to be an ideal tool for carrying out noise impact assessments in urban settings. The study reveals that present noise level in all the locations exceeds the prescribed limit. Based on the finding, it can be mentioned that the population in this industrial town are exposed to significantly high noise level, which is caused mostly due to road traffic. The study reveals that vulnerable establishments like schools and hospitals are subjected to significantly high noise level throughout the day and immediate mitigatory measures are required to alleviate the problem.

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Appraisal and mapping the spatial-temporal distribution of urban road traffic noise

Evaluation and analysis of noise pollution levels have been carried out to determine the level of noise and its sources in Ilorin metropolis. Noise measurements have been done in the morning, at noon, in the evening, and at night to determine noise pollution all over the city. The selected areas of study are commercial centers, road junctions/busy roads, passenger loading parks, and high-density and low-density residential areas. The road junctions had the highest noise pollution levels, followed by commercial centers. The results of this study show that the noise levels in Ilorin metropolis exceeded allowed values at 30 of 42 measurements points. There is a significant difference (P < 0.05) in the noise pollution levels and traffic noise index in all the locations. From the measured noise values, a map of noise pollution was developed for Ilorin. Many solutions proposed for noise abatement in the city are set out.

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Evaluation and analysis of noise levels in Ilorin metropolis, Nigeria.

The main objective of the present study was to develop an empirical noise prediction model for the evaluation of equivalent noise level (Leq) in terms of equivalent traffic density number under heterogeneous traffic flow conditions Ten commercial road networks are selected for monitoring and modeling A new factor, ie, equivalent number of light vehicles (EqLv) and for heavy vehicles (EqHv), has been used for evaluating the equivalent traffic density for each class of vehicles, and correlation graphs are plotted between equivalent traffic density with respect to EqLv and EqHv and observed equivalent noise level [Leq(o)] for the calculation of equivalent noise levels in terms of light vehicles [Leq(Lv)] and heavy vehicles [Leq(Hv)] for different identified locations as well as for the entire city Furthermore, regression noise prediction equations have been developed between Leq(o), Leq(Lv), and Leq(Hv) After comparison of the results, it can be depicted that the light motor vehicles are the main source of noise pollution in the city and gives significantly higher correlation coefficient values This model can be applied for the calculation of road traffic noise under interrupted traffic flow conditions in urban areas of Indian cities

M.G. Seshagiri Rao

Papers

Prediction of LA10T traffic noise levels in the city of Visakhapatnam, India

Community reaction to road traffic noise

Urban traffic intensity : a prediction of (Leq) noise levels

A model for computing environmental noise levels due to motor vehicle traffic in Visakhapatnam city

Study of noise levels emitted by individual motor vehicles on the roads of Visakhapatnam city