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Accurate spatial information of PM is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM. However, the predicted PM spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM in Beijing, and then seasonal PM maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM concentration at seasonal level (R ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R ≥ 0.81 in model fitting and testing). PM maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing.
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Name: Environmental pollution (Barking, Essex : 1987)
Due to the rapid urbanization and increasing energy consumption, air pollution, especially some fine particulates like PM rise in the context of fast urbanization. PM pollution has been given consider...
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Methods used to take into account and incorporate spatial autocorrelation and regional variation into regression analysis models of data that has spatial dependency, and also to provide information on the spatial relationships among the variables.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
A return to earlier, especially to infantile, patterns of thought or behavior, or stage of functioning, e.g., feelings of helplessness and dependency in a patient with a serious physical illness. (From APA, Thesaurus of Psychological Index Terms, 1994).
Integration of spatial information perceived by visual and/or auditory CUES.