scholarly journals Estimation of Spatial Variations in Urban Noise Levels with a Land Use Regression Model

2014 ◽  
Vol 3 (4) ◽  
Author(s):  
Sophie Goudreau ◽  
Céline Plante ◽  
Michel Fournier ◽  
Allan Brand ◽  
Yann Roche ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Spatial Variations ◽  
Noise Levels ◽  
Land Use Regression ◽  
Urban Noise

scholarly journals Community daytime noise pollution and socioeconomic differences in Chicago, IL

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254762
Author(s):  
Yu-Kai Huang ◽  
Uchechi A. Mitchell ◽  
Lorraine M. Conroy ◽  
Rachael M. Jones
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Public Transportation ◽  
Noise Level ◽  
Noise Pollution ◽  
Environmental Noise ◽  
Noise Levels ◽  
Land Use Regression ◽  
Spatial Autoregressive ◽  
Per Capita

Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (Leq, 5-min) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (Leq, 5-min) was 60.6 dBA. The adjusted R2 and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

An innovative land use regression model incorporating meteorology for exposure analysis

2008 ◽  
Vol 390 (2-3) ◽  
pp. 520-529 ◽  
Author(s):  
Jason G. Su ◽  
Michael Brauer ◽  
Bruce Ainslie ◽  
Douw Steyn ◽  
Timothy Larson ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Land Use Regression ◽  
Exposure Analysis

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto, Canada

2010 ◽  
Vol 44 (29) ◽  
pp. 3529-3537 ◽  
Author(s):  
Jason G. Su ◽  
Michael Jerrett ◽  
Bernardo Beckerman ◽  
Dave Verma ◽  
M. Altaf Arain ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Compound ◽  
Regression Model ◽  
Volatile Organic Compound ◽  
Land Use Regression ◽  
Volatile Organic

A description of methods for deriving air pollution land use regression model predictor variables from remote sensing data in Ulaanbaatar, Mongolia

2016 ◽  
Vol 60 (3) ◽  
pp. 333-345 ◽  
Author(s):  
Weiran Yuchi ◽  
Anders Knudby ◽  
Joanna Cowper ◽  
Enkhjargal Gombojav ◽  
Ofer Amram ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Remote Sensing Data ◽  
Predictor Variables ◽  
Land Use Regression ◽  
Sensing Data ◽  
Model Predictor

scholarly journals A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach

2018 ◽  
Vol 630 ◽  
pp. 1324-1334 ◽  
Author(s):  
O. Naughton ◽  
A. Donnelly ◽  
P. Nolan ◽  
F. Pilla ◽  
B.D. Misstear ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Spatial Variation ◽  
Land Use Regression ◽  
Pollution Levels
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