Land use regression modelling of community noise in Sao Paulo, Brazil

Página do item simplificado
dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorRAESS, Michelle
dc.contributor.authorBRENTANI, Alexandra
dc.contributor.authorCAMPOS, Bartolomeu Ledebur de Antas de
dc.contributor.authorFLUCKIGER, Benjamin
dc.contributor.authorHOOGH, Kees de
dc.contributor.authorFINK, Gunther
dc.contributor.authorROOSLI, Martin
dc.date.accessioned2021-08-13T15:15:14Z
dc.date.available2021-08-13T15:15:14Z
dc.date.issued2021
dc.description.abstractNoise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of Sao Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice for one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (L-den) and night sound levels (L-night). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source. Noise exposure levels in the study area were high with a site average L-den of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average Lnight of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R-2 of 0.56 for L-den and 0.63 for L-night in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (L-den) or 25 m (L-night) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available.eng
dc.description.indexMEDLINEeng
dc.description.sponsorshipEckenstein-Geigy Professorship
dc.identifier.citationENVIRONMENTAL RESEARCH, v.199, article ID 111231, 9p, 2021
dc.identifier.doi10.1016/j.envres.2021.111231
dc.identifier.eissn1096-0953
dc.identifier.issn0013-9351
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/41517
dc.language.isoeng
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCEeng
dc.relation.ispartofEnvironmental Research
dc.rightsrestrictedAccesseng
dc.rights.holderCopyright ACADEMIC PRESS INC ELSEVIER SCIENCEeng
dc.subjectNoise measurementeng
dc.subjectCommunity noiseeng
dc.subjectLand use regressioneng
dc.subjectSao pauloeng
dc.subjectNoise exposureeng
dc.subject.otherroad traffic noiseeng
dc.subject.otherair-pollutioneng
dc.subject.othertransportation noiseeng
dc.subject.otherenvironmental noiseeng
dc.subject.otherheart-diseaseeng
dc.subject.otherexposureeng
dc.subject.otheraircrafteng
dc.subject.wosEnvironmental Scienceseng
dc.subject.wosPublic, Environmental & Occupational Healtheng
dc.titleLand use regression modelling of community noise in Sao Paulo, Brazileng
dc.typearticleeng
dc.type.categoryoriginal articleeng
dc.type.versionpublishedVersioneng
dspace.entity.typePublication
hcfmusp.affiliation.countrySuíça
hcfmusp.affiliation.countryisoch
hcfmusp.author.externalRAESS, Michelle:Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, Basel, Switzerland; Univ Basel, Basel, Switzerland
hcfmusp.author.externalCAMPOS, Bartolomeu Ledebur de Antas de:Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, Basel, Switzerland; Univ Basel, Basel, Switzerland
hcfmusp.author.externalFLUCKIGER, Benjamin:Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, Basel, Switzerland; Univ Basel, Basel, Switzerland
hcfmusp.author.externalHOOGH, Kees de:Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, Basel, Switzerland; Univ Basel, Basel, Switzerland
hcfmusp.author.externalFINK, Gunther:Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, Basel, Switzerland; Univ Basel, Basel, Switzerland
hcfmusp.author.externalROOSLI, Martin:Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, Basel, Switzerland; Univ Basel, Basel, Switzerland
hcfmusp.citation.scopus7
hcfmusp.contributor.author-fmusphcALEXANDRA VALERIA MARIA BRENTANI
hcfmusp.description.articlenumber111231
hcfmusp.description.volume199
hcfmusp.origemWOS
hcfmusp.origem.pubmed33971126
hcfmusp.origem.scopus2-s2.0-85106356191
hcfmusp.origem.wosWOS:000663721300004
hcfmusp.publisher.citySAN DIEGOeng
hcfmusp.publisher.countryUSAeng
hcfmusp.relation.referenceAguilera I, 2015, J EXPO SCI ENV EPID, V25, P97, DOI 10.1038/jes.2014.61eng
hcfmusp.relation.referenceaiva-Vianna K.M., 2016, NOISE POLLUTION URBA, P123eng
hcfmusp.relation.referenceAllen RW, 2009, ENVIRON RES, V109, P334, DOI 10.1016/j.envres.2008.12.006eng
hcfmusp.relation.referenceBarbaresco GQ, 2019, J TOXICOL ENV HEAL A, V82, P331, DOI 10.1080/15287394.2019.1595239eng
hcfmusp.relation.referenceBasagana X, 2012, ATMOS ENVIRON, V54, P634, DOI 10.1016/j.atmosenv.2012.01.064eng
hcfmusp.relation.referenceBeelen R, 2013, ATMOS ENVIRON, V72, P10, DOI 10.1016/j.atmosenv.2013.02.037eng
hcfmusp.relation.referenceBjork J, 2006, SCAND J WORK ENV HEA, V32, P392eng
hcfmusp.relation.referenceBrentani A, 2020, INT J EPIDEMIOL, V49, P1438, DOI 10.1093/ije/dyaa129eng
hcfmusp.relation.referenceBrink M, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13111163eng
hcfmusp.relation.referenceCantuaria ML, 2018, ENVIRON INT, V117, P319, DOI 10.1016/j.envint.2018.05.002eng
hcfmusp.relation.referenceCentro de Estudos da Metropole Relat orios Favelas e Loteamentos, 2020, EST CEM SEH PMSPeng
hcfmusp.relation.referenceChang TY, 2019, ENVIRON INT, V131, DOI 10.1016/j.envint.2019.104959eng
hcfmusp.relation.referenceDe E., 2005, ENV NOISE CONTROL, V2eng
hcfmusp.relation.referencede Hoogh K, 2014, ENVIRON INT, V73, P382, DOI 10.1016/j.envint.2014.08.011eng
hcfmusp.relation.referenceEuropean Centre for Medium -Range Weather Forecasts (ECMWF), 2020, PREC DATeng
hcfmusp.relation.referenceEuropean Environment Agency, 2016, 142016 EUR ENV AGeng
hcfmusp.relation.referenceEze IC, 2017, INT J EPIDEMIOL, V46, P1115, DOI 10.1093/ije/dyx020eng
hcfmusp.relation.referenceFrei P, 2014, INT J HYG ENVIR HEAL, V217, P188, DOI 10.1016/j.ijheh.2013.04.003eng
hcfmusp.relation.referenceHarouvi O, 2018, J EXPO SCI ENV EPID, V28, P559, DOI 10.1038/s41370-018-0035-zeng
hcfmusp.relation.referenceHeritier H, 2017, EUR J EPIDEMIOL, V32, P307, DOI [10.1007/s10654-017-0234-2, 10.1007/s10654]eng
hcfmusp.relation.referenceHerve Thery, 2020, PICTURING INEQUALITIeng
hcfmusp.relation.referenceKapetanaki S., 2018, OCCUPATIONAL MED HLT, P6eng
hcfmusp.relation.referenceKloog I, 2015, ATMOS ENVIRON, V122, P409, DOI 10.1016/j.atmosenv.2015.10.004eng
hcfmusp.relation.referenceLiu Y, 2020, ENVIRON POLLUT, V256, DOI 10.1016/j.envpol.2019.113367eng
hcfmusp.relation.referenceMontes Gonz alez D., 2020, ENVIRON RES, V191eng
hcfmusp.relation.referenceMorley DW, 2015, ENVIRON POLLUT, V206, P332, DOI 10.1016/j.envpol.2015.07.031eng
hcfmusp.relation.referenceMoura-de-Sousa C., 2002, NOISE HEALTH, V4, P57eng
hcfmusp.relation.referencenstituto Brasileor de Geografia e Estatistica, 2010, POP CENS 2010eng
hcfmusp.relation.referenceOpenStreetMap contributors, 2020, NTReng
hcfmusp.relation.referencePassam A.G, 2021, LAB ENV ANAL AIR POLeng
hcfmusp.relation.referenceRagettli MS, 2016, J EXPO SCI ENV EPID, V26, P597, DOI 10.1038/jes.2015.82eng
hcfmusp.relation.referenceRedo A, 2016, ENVIRON RES, V150, P383, DOI 10.1016/j.envres.2016.06.014eng
hcfmusp.relation.referenceSarantopoulos G, 2014, SCI TOTAL ENVIRON, V482, P493, DOI 10.1016/j.scitotenv.2013.09.010eng
hcfmusp.relation.referenceSchlittmeier SJ, 2015, NOISE HEALTH, V17, P148, DOI 10.4103/1463-1741.155845eng
hcfmusp.relation.referenceSecretaria Municipal da Saude, 2018, PREF CID SAO PAUL 20eng
hcfmusp.relation.referenceSeidler A, 2017, ENVIRON RES, V152, P263, DOI 10.1016/j.envres.2016.10.017eng
hcfmusp.relation.referenceSeidler A, 2016, INT J HYG ENVIR HEAL, V219, P749, DOI 10.1016/j.ijheh.2016.09.012eng
hcfmusp.relation.referenceSheng N, 2011, INT J ENV RES PUB HE, V8, P1977, DOI 10.3390/ijerph8061977eng
hcfmusp.relation.referenceSieber C, 2017, INT J ENV RES PUB HE, V14, DOI 10.3390/ijerph14101262eng
hcfmusp.relation.referenceSingh Narendra, 2004, Journal of Human Ecology, V16, P181eng
hcfmusp.relation.referenceSorensen M, 2013, ENVIRON HEALTH PERSP, V121, P217, DOI 10.1289/ehp.1205503eng
hcfmusp.relation.referenceSorensen M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0039283eng
hcfmusp.relation.referenceTang UW, 2007, ENVIRON MODELL SOFTW, V22, P1750, DOI 10.1016/j.envsoft.2007.02.003eng
hcfmusp.relation.referencetewart C.M., 1999, J ACOUST SOC AM, V105eng
hcfmusp.relation.referenceTiesler CMT, 2013, ENVIRON RES, V123, P1, DOI 10.1016/j.envres.2013.01.009eng
hcfmusp.relation.referenceU.S. Geological Survey, 2020, LANDS 8 MISSeng
hcfmusp.relation.referenceuropean Environment Agency, 2020, 222019 EEAeng
hcfmusp.relation.referencevan Kempen E, 2010, ENVIRON HEALTH-GLOB, V9, DOI 10.1186/1476-069X-9-25eng
hcfmusp.relation.referenceVienneau D, 2015, ENVIRON RES, V138, P372, DOI 10.1016/j.envres.2015.02.023eng
hcfmusp.relation.referenceWorld Health Organization, 2018, ENV NOIS GUID EUR RE ENV NOIS GUID EUR REeng
hcfmusp.relation.referenceWorld Health Organization, 2011, BURDEN DIS ENV NOISE, P126eng
hcfmusp.relation.referenceXie D, 2011, ENVIRON SCI TECHNOL, V45, P7358, DOI 10.1021/es200785xeng
hcfmusp.relation.referenceZijlema WL, 2016, INT J HYG ENVIR HEAL, V219, P212, DOI 10.1016/j.ijheh.2015.11.006eng
hcfmusp.scopus.lastupdate2024-05-10
relation.isAuthorOfPublication2a610711-6cad-4575-bec0-1d5c7cb663f7
relation.isAuthorOfPublication.latestForDiscovery2a610711-6cad-4575-bec0-1d5c7cb663f7
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
art_RAESS_Land_use_regression_modelling_of_community_noise_in_2021.PDF
Tamanho:
4.76 MB
Formato:
Adobe Portable Document Format
Descrição:
publishedVersion (English)
Baixar
Coleções
Artigos e Materiais de Revistas Científicas - FM/MPE
Artigos e Materiais de Revistas Científicas - ODS/03
Artigos e Materiais de Revistas Científicas - ODS/04