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Hospital noise has remained a low priority problem in India. An area of not less than 100 meters around a hospital is considered a silence zone, with guidelines restricting noise levels at 50dBA during daytime and 40dBA during the night. World Health Organization (WHO) guidelines also stipulate an average of 30dBA and a maximum of 40dbA. Annoyance is a known effect of noise exposure. However, very few studies have attempted to explore the hospital soundscape and the annoyance among staff related to it. Noise data was collected from 30 sites, using a Digital Integrating Sound Level Meter, LutronSL-4035SD (ISO-9001,CE,IEC1010) meeting IEC61672 standards. Stratified random sampling of staff was done on basis of noise exposure. A pre-designed, semi-structured questionnaire collected information on sociodemographic and work profile. Annoyance was defined as a stress reaction to environmental noise and was measured using standardized general purpose noise reaction questionnaire (ISO-TS/15666). Data was analyzed in SPSS. The mean LAeq 24 h across all the sites was 69.5dBA (SD ±5.8dBA) for the weekdays and 66.2dBA (SD ±4.6dBA) for the weekends. Noise levels were higher during the day than during the night, and higher during the weekdays as compared to the weekends. 332(73.8%) study participants were found to be annoyed due to hospital noise. Annoyance among study participants was significantly associated with LAeq 24 h > 80 dBA during the weekdays [aOR = 5.08 (1.17-22.06)] and LAeq 24 h of 65-80 dBA during the weekends [aOR = 2.71 (1.46-5.01)]. Noise levels in the hospital far exceeded WHO and Central Pollution Control Board (CPCB) guidelines recommended for indoor hospital noise, as well as the national guidelines for sensitive zones. Almost three-fourth of the study participants were annoyed due to hospital noise. Significant association was found between annoyance among staff and higher noise levels of their workplace. Similar studies need to be conducted in hospitals across India to generate evidence on the current situtation and identify solutions. [ABSTRACT FROM AUTHOR]

The soundscape study of an eastern Indian coastal city (Puri) has been investigated. Acoustic data were collected at 36 sampling locations during two time intervals in and around Puri. A number of noise indices, namely, Lmin, Lmax, and Leq, were calculated to demonstrate the noise level of this city. Noise maps are generated using ARC-GIS to investigate the impact of road traffic noise on the soundscape of the city. The response of the public was appraised by a questionnaire. Due to variable traffic features, the equivalent noise level (Leq) as well as peak (L10) and background noise (L90) levels varied with location and time of the day. It was found that socio-demographic characteristics have no bearing on the amount of annoyance. However, a link was observed between age, hearing condition, and noise perception, as well as between gender and impacts of noise. [ABSTRACT FROM AUTHOR]

Noise pollution is one of the major public health problems in urban areas throughout the world. Noise is unwanted sound which produces undesirable problems in day to day life of human being (e.g., physiological and psychological problems). Rapid increase of the industrialization, urbanization, infrastructure, volume of motor vehicles, and increase in the road networks brought noise pollution to the highest level of disaster in a current situation. In urban areas, road traffic noise plays commanding role among all noise sources and affects the exposed inhabitants. The present work is done to evaluate and assess the traffic noise and its effects in Burla town. Burla, Vidyanagari of Odisha, is an emerging town in India, as it hosts national level of teaching and research institutions like IIM Sambalpur, a medical college- cum-hospital (VIMSAR), a technical university (VSSUT) and Sambalpur University. In last two decade, the road traffic volume has been increased and is facing severe noise pollution to its inhabitants. Noise pollution assessment was made at different locations of the town. This study unveiled the dismal state of noise pollution in the town. Noise contour maps were drawn to visualize the noise level at the traffic and its surroundings. The numbers of hearing impaired patients in different hospitals of the locality are increasing. That shows grim picture of the situation. Regression equations were established taking noise levels with percentage of highly annoyed people during study indicates strong correlation. [ABSTRACT FROM AUTHOR]

The major objective of the investigation was to evaluate the road traffic noise and its likely impacts on the local community of Asansol city (West Bengal, India) by monitoring and modeling. The attitudinal response of local population due to existing vehicular noise is presented in the paper. Noise and Attitudinal Survey was conducted at 25 locations. A total of 869 individuals were surveyed. The relationship between traffic noise levels and annoyance was studied using correlation, linear and multiple linear regressions analysis. The average Ldn value was 73.28 ± 8.51 dB(A) (55.1–87.3); The Traffic Noise Index (TNI) was 80.62 ± 15.88 dB(A) (49.4–115.8). The mean value of percent of population Highly Annoyed (%HA) due to road traffic noise was 26.50 ± 3.37 (19.44–33.2), whereas the mean dissatisfaction score (MDS) was 2.96 ± 0.90 (1.04–4.45). Annoyance modeling was also performed based on field data. It can be said that Noise values gives desirable annoyance predicting values in comparison to vehicular data. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for modeling noise-induced annoyance using the fuzzy technique. The fuzzy approach offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of simple IF-THEN rules. Annoyance, in the present model, is considered as a function of noise level, its duration of occurrence, and the socio-economic status of a person. The model has been implemented on the Fuzzy Logic Toolbox of MATLAB, using both Mamdani and Sugeno techniques. The results of the model are applicable to the urban areas of India. [ABSTRACT FROM AUTHOR]