Land-Use Regression Model for Assessing Long-Term Exposure to Ultrafine Particles in Amsterdam
Pietilä, Kari (2015)
2015
Ympäristö- ja energiatekniikan koulutusohjelma
Luonnontieteiden tiedekunta - Faculty of Natural Sciences
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Hyväksymispäivämäärä
2015-12-09
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201511301819
https://urn.fi/URN:NBN:fi:tty-201511301819
Tiivistelmä
Geographic Information Systems (GIS) and statistics based land-use regression (LUR) models are widely used for modeling small-scale spatial variation of ambient air pollution. These models have successfully been utilized in cohort studies where individual exposure of participants needs to be estimated. LUR has been used to model gases and particles alike, but to date, there are no published studies that would have utilized LUR in assessing cohort members’ long-term exposure to ultrafine particles.
Using measurement and GIS data from previous studies, two land-use regression models for UFPs were developed for the city of Amsterdam, the Netherlands. With two slightly different models it could be assessed whether model performance was sensitive to observations that had been assigned unrealistic traffic intensities. The models were validated using the holdout method. In holdout validation (HV) the original datasets were divided into several sample pairs, new models were developed with partial set of data and these models then validated with unused data. Finally, developed LUR models were utilized in a cohort of 4,683 people to estimate participants’ long-term exposure to UFPs.
The two land-use regression models performed almost equally, both explaining approximately 44% of the variability in measured particle number count, which was used as a proxy for ultrafine particles. The models incorporated inverse distance to the nearest major road as the most important predictor variable, reflecting the importance of transportation as a source of UFPs. Validation indicated that both models were stable.
Exposure estimates from applying the LUR models were fairly similar. The correlation between the estimates from the two models was 0.76. However, the estimates should be used with caution because of the limited explanatory power of the LUR models and inherent limitations of geographic data. Further to this, exposure assessment did not account for the different exposure levels that individuals may experience when they move around the city during their days.
As a way forward, developing more robust land-use regression models is important. In general, GIS and traffic data improve on a fast basis, which in turn should improve LUR models. Even then, there is a strong need to validate assigned exposures with personal monitors.
Using measurement and GIS data from previous studies, two land-use regression models for UFPs were developed for the city of Amsterdam, the Netherlands. With two slightly different models it could be assessed whether model performance was sensitive to observations that had been assigned unrealistic traffic intensities. The models were validated using the holdout method. In holdout validation (HV) the original datasets were divided into several sample pairs, new models were developed with partial set of data and these models then validated with unused data. Finally, developed LUR models were utilized in a cohort of 4,683 people to estimate participants’ long-term exposure to UFPs.
The two land-use regression models performed almost equally, both explaining approximately 44% of the variability in measured particle number count, which was used as a proxy for ultrafine particles. The models incorporated inverse distance to the nearest major road as the most important predictor variable, reflecting the importance of transportation as a source of UFPs. Validation indicated that both models were stable.
Exposure estimates from applying the LUR models were fairly similar. The correlation between the estimates from the two models was 0.76. However, the estimates should be used with caution because of the limited explanatory power of the LUR models and inherent limitations of geographic data. Further to this, exposure assessment did not account for the different exposure levels that individuals may experience when they move around the city during their days.
As a way forward, developing more robust land-use regression models is important. In general, GIS and traffic data improve on a fast basis, which in turn should improve LUR models. Even then, there is a strong need to validate assigned exposures with personal monitors.