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• W3212457584 endingPage "151633" @default.
• W3212457584 startingPage "151633" @default.
• W3212457584 abstract "Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution. This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides. A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method. The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm−3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas. We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors." @default.
• W3212457584 created "2021-11-22" @default.
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• W3212457584 date "2022-04-01" @default.
• W3212457584 modified "2023-10-14" @default.
• W3212457584 title "High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China" @default.
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• W3212457584 doi "https://doi.org/10.1016/j.scitotenv.2021.151633" @default.
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• W3212457584 hasPublicationYear "2022" @default.
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