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Multiple linear regression model to estimate PM <inf>1</inf> concentration

Abstract:

During 2014-2015, in the Sartenejas Valley, Greater Caracas, Venezuela, samples of particulate matter (PM) were collected using a cascade impactor that segregates PM in six ranges of particle sizes: > 7.2 µm, 3.0-7.2 µm, 1.5-3.0 µm, 0.95-1.5 µm, 0.49-0.95 µm, and < 0.49 µm, together with local weather data. As a complement, we investigated the occurrence of forest fires and rains for the sampling period, as well as the monthly historical accumulated precipitation for the Greater Caracas. The objective of this investigation was to obtain a linear multivariate model for the pbkp_rediction of PM1 from environmental, meteorological and physical eventualities in an inter-tropical region in the center-north of Venezuela. Making use of the information from sampling and information from secondary sources, a data matrix was constructed with environmental, meteorological and eventualities variables capable of pbkp_redicting the behavior of fine particles (PM 1 ) based on other PM sizes, temperature, historical precipitation, occurrence of fires and rains. Finally, a multiple linear regression model was constructed to estimate average concentrations of PM1 from the occurrence of forest fires, concentration of PM in the range of 3.0-0.95 µm, and the historical average of monthly-accumulated precipitation. The variance of PM 1 is explained in more than 75% from these variables (R 2 = 0.759, p <0.000). The model was validated using the average bias error indicator, which underestimates the pbkp_redicted values.