부산대학교 도서관

Abstract Particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5) is a complex mixture of chemical constituents emitted from various emission sources or through secondary reactions/processes; however, PM 2.5 is regulated mostly based on its total mass concentration. Studies to identify the impacts on climate change, visibility degradation and public health of different PM 2.5 constituents are hindered by limited ground measurements of PM 2.5 constituents. In this study, national models were developed based on random forest algorithm, one of machine learning methods that is of high predictive capacity and able to provide interpretable results, to predict concentrations of PM 2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) across the conterminous United States from 2005 to 2015 at the daily level. The random forest models achieved high out-of-bag (OOB) R2 values at the daily level, and the mean OOB R2 values were 0.86, 0.82, 0.71 and 0.75 for sulfate, nitrate, OC and EC, respectively, over 2005–2015. The long-term temporal trends of PM 2.5 sulfate, nitrate, OC and EC predictions agreed well with their corresponding ground measurements. The annual mean of predicted PM 2.5 sulfate and EC levels across the conterminous United States decreased substantially from 2005 to 2015; while concentrations of predicted PM 2.5 nitrate and OC decreased and fluctuated during the study period. The annual prediction maps captured the characterized spatial patterns of the PM 2.5 constituents. The distributions of annual mean concentrations of sulfate and nitrate were generally regional in the extent that sulfate decreased from east to west smoothly with enhancement in California and nitrate had higher concentration in Midwest, Metro New York area, and California. OC and EC had regional high concentrations in the Southeast and Northwest as well as localized high levels around urban centers. The spatial patterns of PM 2.5 constituents were consistent with the distributions of their emission sources and secondary processes and transportation. Hence, the national models developed in this study could provide supplementary evaluations of spatio-temporal distributions of PM 2.5 constituents with full time-space coverages in the conterminous United States, which could be beneficial to assess the impacts of PM 2.5 constituents on radiation budgets and visibility degradation, and support exposure assessment for regional to national health studies at county or city levels to understand the acute and chronic toxicity and health impacts of PM 2.5 constituents, and consequently provide scientific evidence for making targeted and effective regulations of PM 2.5 pollution. Highlights • First national machine learning models to predict PM 2.5 constituents in the U.S. • Random forest models provide accurate and interpretable results with full coverage. • Random forest models explained over 70% of daily variability of PM 2.5 constituents. • Predictions capture both regional and localized patterns of PM 2.5 constituents. [ABSTRACT FROM AUTHOR]