Retrospective Prediction of PM2.5 Levels：Comparison of Empirical Models
Date Issued
2011
Date
2011
Author(s)
Chen, Yu-Ju
Abstract
Many epidemiological studies have found increased mortality as well as disease, such as lung cancer and cardiopulmonary morbidity were associated with long-term exposure to ambient fine particulate matters, PM2.5 (particulate matters with aerodynamic diameter ≦2.5 μm). However, ambient monitoring programs for PM2.5 did not exist in early years, posing difficulties on analyzing health effects from long-term PM2.5 exposure. This thesis developed site-specific models for retrospective prediction of PM2.5 levels using available data on air pollutants and meteorological variables.
The Taiwan Environmental Protection Administration has complete PM2.5 data at each monitoring site since 2005. In order to predict values during periods when PM2.5 data were not available, the dataset from air quality monitoring and central weather bureau stations were divided into two groups with data in 2005-2009 and in 1993-2004 used for model building and verification, respectively. The data were used to model PM2.5/PM10 ratio with daily 24-hour average levels of CO, NOx, SO2, and/or O3. In addition to air pollutants, visibility and meteorological variables including daily temperature, wind speed, and relative humidity were also considered. Models were developed separately for each air monitoring site.
PM2.5 estimation results for each air monitoring site from 2005 to 2009 matched well with the actual PM2.5 data (R2=0.62-0.92), except for the Taitung site (R2=0.32-0.40). For Guting, Chungming, Fengshan and Linyuan sites which had complete PM2.5 data from 1997, comparison of the PM2.5 estimation for 1997-2004 with the measured PM2.5 also shows moderate association (R2=0.66-0.72). With only PM10, visibility and the meteorological data, but not gaseous pollutants, included in the analysis shows strong association (R2=0.66-0.71). Our study results show that it is feasible to use PM2.5/PM10 ratio to predict historical PM2.5 exposure levels from existing data of air pollutants, visibility and meteorological variables. The variability of pollution concentration in different spatial scales could affect the modeling results. Thus, establishing empirical models separately for different types of monitoring sites may be necessary.
Subjects
PM2.5
Exposure
Model
Retrospective
SDGs
Type
thesis
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