Evaluating the Use of Intra-Urban Models for Characterizing Spatial Distribution of Particulate Matters in Taipei Metropolis
Date Issued
2010
Date
2010
Author(s)
Liang, You-Sin
Abstract
BACKGROUND: In recent years, many epidemiologic studies have shown that both short- and long-term exposures to traffic-related air pollutants such as PM10 (with aerodynamic diameters < 10 μm), PM2.5 (with aerodynamic diameters < 2.5 μm) and PMcoarse (with aerodynamic diameters between 2.5-10 μm) are associated with adverse effects on cardiovascular and respiratory systems. To estimate the spatial distributions of PM concentrations in Taipei metropolis, we applied five methods to predict subjects’ exposures and compared their R2 values between sampling and predicted data.
METHODS: We chose twenty-four subjects from a cohort study and put Harvard Impactors at subjects’ home balconies to collect 24-hrs PM10 and PM2.5 home outdoor micro-environmental samples during July-December, 2008. A second campaign of PM collections was conducted at the same locations. However, we measure PM10 and PM2.5 from participants home outdoor micro-environmental after six months later. LUR models were constructed using land use, road-length, and population density as predictor variables. The kriging methods use coordinates of AQM stations combining with PM monitoring results to calculate PM exposure.
RESULTS: The adjusted R2model of the final models was 0.58 for PM10, 0.94 for PM2.5, 0.65 for PMcoarse. For the validation dataset, the five methods gave R2Pred_H of 0.69-0.78 for PM10; 0.58-0.77 for PM2.5, and 0-0.19 for PMcoarse. The performance of the two kriging methods for PM10 and PM2.5 were better than those for PMcoarse. The R2Pred_H value of coarse particle was much lower than PM10 and PM2.5.
CONCLUSIONS: The R2Pred_H value of PM10 and PM2.5 were higher than 0.5, so using the nearest AQM station might be satisfied for areas with dense air monitoring network. Nevertheless, the R2Pred_H values between PMcoarse were below 0.5. Future studies should focus on increasing the model performance for PMcoarse.
Subjects
Air pollution
Exposure assessment
Land use regression
kriging
Type
thesis
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