scholarly journals Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia

2018 ◽  
Vol 52 (21) ◽  
pp. 12445-12455 ◽  
Author(s):  
Luke D. Knibbs ◽  
Aaron van Donkelaar ◽  
Randall V. Martin ◽  
Matthew J. Bechle ◽  
Michael Brauer ◽  
...  
Keyword(s):  
Land Use ◽  
Exposure Assessment ◽  
Land Use Regression ◽  
Continental Scale ◽  
Pm2.5 Exposure

scholarly journals Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing

2021 ◽  
Vol 18 (11) ◽  
pp. 5884
Author(s):  
Junli Liu ◽  
Panli Cai ◽  
Jin Dong ◽  
Junshun Wang ◽  
Runkui Li ◽  
...  
Keyword(s):  
Land Use ◽  
Exposure Assessment ◽  
Activity Pattern ◽  
Cell Phone ◽  
Total Population ◽  
Cell Phones ◽  
Land Use Regression ◽  
Pm2.5 Exposure ◽  
Dynamic Exposure ◽  
Pm2.5 Concentration

The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 μg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 μg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.

scholarly journals Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

2018 ◽  
Author(s):  
Seyed Mahmood Taghavi-Shahri ◽  
Alessandro Fassò ◽  
Behzad Mahaki ◽  
Heresh Amini
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Missing Data ◽  
Exposure Assessment ◽  
Expectation Maximization ◽  
Space Time ◽  
Data Imputation ◽  
Land Use Regression ◽  
Missing Data Imputation

AbstractGraphical AbstractLand use regression (LUR) has been widely applied in epidemiologic research for exposure assessment. In this study, for the first time, we aimed to develop a spatiotemporal LUR model using Distributed Space Time Expectation Maximization (D-STEM). This spatiotemporal LUR model examined with daily particulate matter ≤ 2.5 μm (PM2.5) within the megacity of Tehran, capital of Iran. Moreover, D-STEM missing data imputation was compared with mean substitution in each monitoring station, as it is equivalent to ignoring of missing data, which is common in LUR studies that employ regulatory monitoring stations’ data. The amount of missing data was 28% of the total number of observations, in Tehran in 2015. The annual mean of PM2.5 concentrations was 33 μg/m3. Spatiotemporal R-squared of the D-STEM final daily LUR model was 78%, and leave-one-out cross-validation (LOOCV) R-squared was 66%. Spatial R-squared and LOOCV R-squared were 89% and 72%, respectively. Temporal R-squared and LOOCV R-squared were 99.5% and 99.3%, respectively. Mean absolute error decreased 26% in imputation of missing data by using the D-STEM final LUR model instead of mean substitution. This study reveals competence of the D-STEM software in spatiotemporal missing data imputation, estimation of temporal trend, and mapping of small scale (20 × 20 meters) within-city spatial variations, in the LUR context. The estimated PM2.5 concentrations maps could be used in future studies on short- and/or long-term health effects. Overall, we suggest using D-STEM capabilities in increasing LUR studies that employ data of regulatory network monitoring stations.Highlights-First Land Use Regression using D-STEM, a recently introduced statistical software-Assess D-STEM in spatiotemporal modeling, mapping, and missing data imputation-Estimate high resolution (20×20 m) daily maps for exposure assessment in a megacity-Provide both short- and long-term exposure assessment for epidemiological studies

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

2018 ◽  
Vol 163 ◽  
pp. 16-25 ◽  
Author(s):  
Luke D. Knibbs ◽  
Craig.P. Coorey ◽  
Matthew J. Bechle ◽  
Julian D. Marshall ◽  
Michael G. Hewson ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrogen Dioxide ◽  
Exposure Assessment ◽  
Regression Models ◽  
Land Use Regression

Feasibility of a Canadian Land Use Regression Model for PM2.5 Exposure Assessment

Epidemiology ◽  
2009 ◽  
Vol 20 ◽  
pp. S191
Author(s):  
Perry Hystad ◽  
Eleanor Setton ◽  
Alejandro Cervantes ◽  
Karla Poplawski ◽  
Steve Deschenes ◽  
...  
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Exposure Assessment ◽  
Land Use Regression ◽  
Pm2.5 Exposure

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

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.

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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