ArticleJournal of exposure science & environmental epidemiology2018
Noise estimation model development using high-resolution transportation and land use regression.
Article in Journal of exposure science & environmental epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- The impact of spatial distribution of noise pollution from music recreation facilities and residents' perceptions in Chongqing municipality.Scientific reports · 2025Article
- Moving beyond the noise: geospatial modelling of urban sound environments in a sub-Saharan African city.Scientific reports · 2025Article
- Advancing Exposomic Research in Prenatal Respiratory Disease Programming.Immunology and allergy clinics of North America · 2023Review
- Spatial modelling and inequalities of environmental noise in Accra, Ghana.Environmental research · 2022Article
- Reclaiming independence in spatial-clustering datasets: A series of data-driven spatial weights matrices.Statistics in medicine · 2022Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Noise pollution is a common phenomenon of the 21st century. Noise prediction models tend to estimate noise levels mainly from road traffic sources (such as cars, public transportation etc.). This paper describes the adoption of land use regression (LUR) modeling methodology to assess noise pollution in two periods of the day (rush hour and off-peak), in two major cities in Israel (Tel Aviv and Beer Sheva). For both rush hour and off-peak times, 20 min short term measurements were used to develop a LUR noise estimation model. We used GIS-based predictors alongside commonly used traffic predictors. The findings show good fits for our model, with rush hour "out of sample" ten folds cross-validated R² of 0.79 (Tel Aviv) and 0.52 (Beer Sheva). The Tel Aviv model performance was also tested with independent monitoring data in an adjacent city (Bat Yam), presenting a good performance as well (R² of 0.93). The findings demonstrate the viability of using a LUR approach for applying high-resolution spatial data to estimate and map noise pollution for environmental noise assessment.
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Registered trials
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