AIR POLLUTION – MONITORING MODELLING AND HEALTH

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146 Air Pollution <strong>–</strong> Monitoring, Modelling and Health 2001 2002 Mean (sd) Minimum Maximun Mean (sd) Minimum Maximun pm10m pm0 pm6 pm18 dtm vvm 122,83 (58,4) 123,85 (106) 78,06 (57,9) 117,50 (83,5) 10,37 (5,3) 1,54 (0,48) 18,50 1,00 1,00 2,00 2,50 0,53 307,80 492,00 250,00 467,00 23,00 3,40 123,90 (67,2) 133,00 (126,6) 80,10 (56,9) 116,70 (83,6) 11,00 (5,1) 1,40 (0,4) 10,40 1,00 1,00 4,00 0,00 0,00 298,00 625,00 222,00 412,00 24,00 2,49 2003 2004 Mean (sd) Minimum Maximun Mean (sd) Minimum Maximun pm10m pm0 pm6 pm18 dtm vvm 127,50 (50,50) 133,60 (101,40) 86,50 (55,20) 116,10 (72,10) 11,90 (5,03) 1,42 (0,40) 28,38 1,00 1,00 6,00 2,11 0,56 260,30 450,00 263,00 385,00 22,90 2,72 101,00 (43,10) 105,50 (88,30) 68,60 (42,30) 91,40 (59,20) 11,20 (5,02) missing 20,60 1,00 1,00 1,00 1,53 missing 230,00 519,00 202,00 345,00 24,00 missing sd: standard deviation; pm0, pm6, pm18: PM10 concentration at 00, 06 and 18 previous day, respectively; dtm: temperature difference of tomorrow; vvm: wind velocity of tomorrow. Table 1. Mean and standard deviation of predictor variables used to model PM10. Years 2001 to 2004. Table No. 2 shows the successes of the class I and II by the Gamma models and the MARS models. The correlations are significant for the three models per year, the mpab remain high for all models, except the 40 fb MARS 2002 model where we can see a 19% mean absolute error similar to the Gamma Model. In general for all three years the Gamma models have better performance than MARS for the PM10 concentrations < 240 (µg/m 3 ); MARS models have better performance for PM10 exceeding 240 (µg/m 3 ). 2001 to 2002 2002 to 2003 2003 to 2004 Gamma MARS 20 bf MARS 40 bf Gamma MARS 20 bf MARS 40 bf Gamma MARS 20bf MARS 40bf mpab % r pearson Class I % Class II % 38,00 0,83 98,00 50,00 44,00 0,86 67,00 97,00 32,00 0,74 44,40 95,40 19,00 0,82 99,00 12,50 29,00 0,81 0,00 98,00 19,00 0,86 100,00 97,00 28,00 0,78 99,00 0,00 14,00 0,92 99,00 100,00 25,00 0,82 99,00 0,00 mpab: mean abolute proportion bf: basis function. Table 2. Results of MARS and Gamma modeling.
Critical Episodes of PM10 Particulate Matter Pollution in Santiago of Chile, an Approximation Using Two Prediction Methods: MARS Models and Gamma Models 147 pm10 0 100 200 300 400 0 50 100 150 Day Obs Gamma Mars 40 fb Fig. 1. Shows that the Gamma model predictions are further away from observed PM10 than MARS predictions for high PM10 concentrations for year 2003 using model of 2002, but Gamma model gives better predictions for values below 200 (µg/m 3 ). Fig. 2. Corresponds to the MARS model with 40 basis functions for 2002 and illustrates in part (a) the interaction of the basis functions max (0; pm18-137) and max (0; vvm-1, 522) that generate the basis function BF8 which represents an interaction surface (Table No. 3) with maximum value 165 (µg/m 3 ) . This implies that for PM10 concentrations at 18 pm (pm18) above 137 (µg/m 3 ) and wind speed for tomorrow of 1,522 m/s, the contribution to the PM10 tomorrow concentration due to the interaction has a maximun of 165 (µg/m 3 ). On the other hand, figure (b) shows that for concentrations above 240 (µg/m 3 ), the variable pm0 has a maximum contribution to the response of 70 (µg/m 3 ), which represents the value with which the base function BF4 contributes to the predicted value.
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AIR POLLUTION - MONITORING, MODELLI
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Contents Preface IX Chapter 1 Urban
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Preface This book provides a compre
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AIR POLLUTION – MONITORING MODELLING AND HEALTH

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