Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements

2021 ◽  
pp. 111010
Author(s):  
Kerolyn K. Shairsingh ◽  
Jeffrey R. Brook ◽  
Cristian M. Mihele ◽  
Greg J. Evans
Keyword(s):  
Land Use ◽  
Metropolitan Area ◽  
Land Use Regression ◽  
Regression Modelling ◽  
Large Metropolitan Area ◽  
Mobile Measurements

Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability

2017 ◽  
Vol 224 ◽  
pp. 148-157 ◽  
Author(s):  
Chih-Da Wu ◽  
Yu-Cheng Chen ◽  
Wen-Chi Pan ◽  
Yu-Ting Zeng ◽  
Mu-Jean Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Temporal Variability ◽  
Land Use Regression ◽  
Greenness Index

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

2020 ◽  
Vol 743 ◽  
pp. 140744 ◽  
Author(s):  
Tuo Shi ◽  
Yuanman Hu ◽  
Miao Liu ◽  
Chunlin Li ◽  
Chuyi Zhang ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Spatial Variations ◽  
Land Use Regression ◽  
Regression Modelling ◽  
Different Seasons

Low-cost NO2 monitoring and predictions of urban exposure using universal kriging and land-use regression modelling in Mysore, India

2020 ◽  
Vol 226 ◽  
pp. 117395
Author(s):  
Amruta Nori-Sarma ◽  
Rajesh K. Thimmulappa ◽  
G.V. Venkataramana ◽  
Azis K. Fauzie ◽  
Sumit K. Dey ◽  
...  
Keyword(s):  
Land Use ◽  
Low Cost ◽  
Universal Kriging ◽  
Land Use Regression ◽  
Regression Modelling

scholarly journals Air Pollution Measurements and Land-Use Regression in Urban Sub-Saharan Africa Using Low-Cost Sensors—Possibilities and Pitfalls

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1357
Author(s):  
Asmamaw Abera ◽  
Kristoffer Mattisson ◽  
Axel Eriksson ◽  
Erik Ahlberg ◽  
Geremew Sahilu ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Rapid Urbanization ◽  
Final Model ◽  
Sub Saharan

Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.

Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa

2018 ◽  
Vol 610-611 ◽  
pp. 1439-1447 ◽  
Author(s):  
Sheena Muttoo ◽  
Lisa Ramsay ◽  
Bert Brunekreef ◽  
Rob Beelen ◽  
Kees Meliefste ◽  
...  
Keyword(s):  
South Africa ◽  
Land Use ◽  
Nitrogen Oxides ◽  
Land Use Regression ◽  
Regression Modelling

Predicting seasonal and spatial patterns of long-term nitrogen oxides concentration in Tehran, Iran using land use regression

2013 ◽  
Vol 2013 (1) ◽  
pp. 5223
Author(s):  
Hassan Amini ◽  
Seyed Mahmood Taghavi Shahri ◽  
Sarah B. Henderson ◽  
Kazem Naddafi ◽  
Ramin Nabizadeh ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrogen Oxides ◽  
Spatial Patterns ◽  
Land Use Regression

Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution

2014 ◽  
Vol 476-477 ◽  
pp. 378-386 ◽  
Author(s):  
Evi Dons ◽  
Martine Van Poppel ◽  
Luc Int Panis ◽  
Sofie De Prins ◽  
Patrick Berghmans ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Models ◽  
Land Use Regression
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