Modeling environmental noise exceedances using non-homogeneous Poisson processes
In this work a non-homogeneous Poisson model is considered to study noise exposure. The Poisson process, counting the number of times that a sound level surpasses a threshold, is used to estimate the probability that a population is exposed to high levels of noise a certain number of times in a give...
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Published in | The Journal of the Acoustical Society of America Vol. 136; no. 4; p. 1631 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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United States
01.10.2014
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Abstract | In this work a non-homogeneous Poisson model is considered to study noise exposure. The Poisson process, counting the number of times that a sound level surpasses a threshold, is used to estimate the probability that a population is exposed to high levels of noise a certain number of times in a given time interval. The rate function of the Poisson process is assumed to be of a Weibull type. The presented model is applied to community noise data from Messina, Sicily (Italy). Four sets of data are used to estimate the parameters involved in the model. After the estimation and tuning are made, a way of estimating the probability that an environmental noise threshold is exceeded a certain number of times in a given time interval is presented. This estimation can be very useful in the study of noise exposure of a population and also to predict, given the current behavior of the data, the probability of occurrence of high levels of noise in the near future. One of the most important features of the model is that it implicitly takes into account different noise sources, which need to be treated separately when using usual models. |
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AbstractList | In this work a non-homogeneous Poisson model is considered to study noise exposure. The Poisson process, counting the number of times that a sound level surpasses a threshold, is used to estimate the probability that a population is exposed to high levels of noise a certain number of times in a given time interval. The rate function of the Poisson process is assumed to be of a Weibull type. The presented model is applied to community noise data from Messina, Sicily (Italy). Four sets of data are used to estimate the parameters involved in the model. After the estimation and tuning are made, a way of estimating the probability that an environmental noise threshold is exceeded a certain number of times in a given time interval is presented. This estimation can be very useful in the study of noise exposure of a population and also to predict, given the current behavior of the data, the probability of occurrence of high levels of noise in the near future. One of the most important features of the model is that it implicitly takes into account different noise sources, which need to be treated separately when using usual models. |
Author | Guarnaccia, Claudio Quartieri, Joseph Barrios, Juan M Rodrigues, Eliane R |
Author_xml | – sequence: 1 givenname: Claudio surname: Guarnaccia fullname: Guarnaccia, Claudio organization: Department of Industrial Engineering, University of Salerno, Salerno, Italy – sequence: 2 givenname: Joseph surname: Quartieri fullname: Quartieri, Joseph organization: Department of Industrial Engineering, University of Salerno, Salerno, Italy – sequence: 3 givenname: Juan M surname: Barrios fullname: Barrios, Juan M organization: Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Federal District, Mexico – sequence: 4 givenname: Eliane R surname: Rodrigues fullname: Rodrigues, Eliane R organization: Instituto de Matemáticas, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Federal District, Mexico |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25324067$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1007_s40857_016_0058_3 crossref_primary_10_1007_s13762_018_02186_2 crossref_primary_10_1177_1748006X221109341 crossref_primary_10_1680_jtran_16_00094 crossref_primary_10_1051_matecconf_201712505013 crossref_primary_10_1080_02664763_2021_1897972 crossref_primary_10_1088_1742_6596_1680_1_012021 crossref_primary_10_1016_j_jenvman_2016_08_053 crossref_primary_10_1016_j_apacoust_2016_07_022 crossref_primary_10_1111_2041_210X_13542 crossref_primary_10_1016_j_apacoust_2014_12_002 crossref_primary_10_1051_matecconf_201821005001 |
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