Land use regression model: Difference between revisions
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==Application examples== |
==Application examples== |
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===Health impact assessments=== |
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LUR Models were originally developed to assess the exposure resulting from air pollution as a result of vehicular traffic, but they have since been expanded to cover air pollution [[epidemiology]]. The EPA has an ongoing grant for these types of assessments, where they collect hourly updates across three major U.S. cities to study how pollution concentrations change over time, and tracking health effects reported by those who live there.<ref>{{cite web |last1=Robinson |first1=Allen |title=Next Generation LUR Models: Development of Nationwide Modeling Tools for Exposure Assessment and Epidemiology |url=https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/10668/report/0 |publisher=EPA |accessdate=2018-11-26}}</ref> |
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===== Liaoning Province, China ===== |
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A study incorporating annual satellite aerosol optical depth (AOD) observations and 5 specific canyon indicators (building height, coverage ratio, shape coefficient, floor area ration, and skyscraper building ratio) was used to successfully enhance the LUR's modeling accuracy. The area picked for this study has been affected by rapid urbanization which resulted in serious environmental atmospheric pollution and measured a few key pollutants, in particular (PM2,5, PM10, SO2, NO2, NOx, CO, and O3). |
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==== Ontario, Canada ==== |
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Worked on developing the application of LUR to predict the concentration of benzene, toluene, ethylbenzene, m/p-xylene, and o-xylene (BTEX) concentrations in Ontario. 39 locations were monitored for 2 weeks in order to develop LUR models to have predictor variables and best estimate BTEX concentrations. |
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<ref>{{cite journal |last1=Atari |first1=Dominic |title=Assessing the distribution of volatile organic compounds using land use regression in Sarnia, Ontario, Canada |journal=Environmental Health |volume=8 |pages=16 |doi=10.1186/1476-069X-8-16 |pmid=19371421 |pmc=2679013 |year=2009 }}</ref> |
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==== Gothenburg, Sweden ==== |
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A 20-year pollution study on urban pollution in Gothenburg (an urban area in Sweden) about the NO2 concentration. The results were accurate in the effects of altitude and traffic intensity on pollution in a certain region. It is used to estimate outdoor concentrations in urban areas but not accurate in less populated regions, such as islands or rural areas.<ref>{{cite journal |last1=Habermann |first1=Mateus |title=Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden |url=https://www.sciencedirect.com/science/article/pii/S1877705815016331 |journal=Procedia Engineering |volume=115 |pages=21–28 |accessdate=2018-10-26|doi=10.1016/j.proeng.2015.07.350 |year=2015 }}</ref> |
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==Further development== |
==Further development== |
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LUR Models were originally developed to assess the exposure resulting from air pollution as a result of traffic but has since been expanded to cover air pollution. In addition to long scale studies, LUR can be expanded to encapsulate less studied areas that have similar characteristics. |
LUR Models were originally developed to assess the exposure resulting from air pollution as a result of traffic but has since been expanded to cover air pollution. In addition to long scale studies, LUR can be expanded to encapsulate less studied areas that have similar characteristics. |
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Revision as of 22:39, 19 January 2019
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A land use regression model (LUR model) is an algorithm often used for pollution analysis, particularly in densely populated areas.
The model is based on the properties of pollution, where it spreads in a predictable pattern making it so that its concentration in an area can be estimated. This requires some linkage to the environmental characteristics of an area's surrounding, especially characteristics that influence the emission intensity and dispersion efficiency of pollutants. LUR modeling is a useful approach for screening studies and can substitute dispersion models when there is insufficient input data or dispersion models. To achieve these models multiple regression equations are used to describe the relationship between sample locations and relevant environmental variables, often relying on geographic information systems (GIS) to collect measurements. This results in an equation that can predict the pollution concentration at unmeasured locations based on the predictor variables in both specific locations or a fine grid, for which a raster graphics of the area is generated and intersected with area-level population data to formulate the exposure distribution.
Application examples
Further development
LUR Models were originally developed to assess the exposure resulting from air pollution as a result of traffic but has since been expanded to cover air pollution. In addition to long scale studies, LUR can be expanded to encapsulate less studied areas that have similar characteristics.
Mobile monitoring
This is an alternative to the traditional fixed-site measuring system, which is complicated by the sheer volume of data points required for LUR modeling. Mobile monitoring enables good spatial coverage even with limited monitoring devices. This type of monitoring enables the investigation of pollutants that may be limited by monetary constraints. The drawback of this method comes from temporal variability that the researcher may face due to time-of-day variability that may not be measured properly. [1]
Inclusion of Additional Prediction Variables
To more accurately develop LUR models, there are more variables being introduced that account for variations in pollution concentrations. These include some like holiday pollution pattern variants, where data collected on certain days are known to be outliers so as not to negatively interfere with the LUR. Other variables to consider include meteorological variations which are a result of seasonal changes.
Geographically Weighted Models
The incorporation of Geographically Weighted Regression (GWR) into LURs involves applying a spatial weighting function to the spatial coordinates which divide the study area into various local neighborhoods. This is of particular interest as it can reduce the effects of spatial non-stationarity, a defect that occurs when variables form inconsistent relationships over large areas, essentially misrepresenting the data points as unchanging when in reality there is a variation. [2]
Alternatives
Alternative approaches to LUR include kriging, atmospheric dispersion modeling, and Bayesian Maximum Entropy modeling.[3]
References
- ^ Hankey, Steve (2015). "Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring". Environmental Science & Technology. 49 (15): 9194–9202. doi:10.1021/acs.est.5b01209. PMID 26134458.
- ^ Bertazzon, Stefania (2015). "Accounting for spatial effects in land use regression for urban air pollution modeling" (PDF). Spatial and Spatio-Temporal Epidemiology. 14–15: 9–21. doi:10.1016/j.sste.2015.06.002. PMID 26530819. Retrieved 2018-10-26.
- ^ Adam-Poupart, Ariane (2014). "Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches". Environmental Health Perspectives. 122 (9): 970–976. doi:10.1289/ehp.1306566. PMC 4153742. PMID 24879650.
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