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

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Arquivos
art_RAESS_Land_use_regression_modelling_of_community_noise_in_2021.PDF (4.76 MB)
Citações na Scopus
7
Tipo de produção
article
Data de publicação
2021
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
ACADEMIC PRESS INC ELSEVIER SCIENCE
Autores
RAESS, Michelle
CAMPOS, Bartolomeu Ledebur de Antas de
FLUCKIGER, Benjamin
HOOGH, Kees de
FINK, Gunther
ROOSLI, Martin
Citação
ENVIRONMENTAL RESEARCH, v.199, article ID 111231, 9p, 2021
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Noise 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.
Palavras-chave
Noise measurement, Community noise, Land use regression, Sao paulo, Noise exposure
URI
https://observatorio.fm.usp.br/handle/OPI/41517
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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