Source Apportionment of Particulate Matter by Application of Machine Learning Clustering Algorithms

Size-resolved source apportionment of particulate matter

Introduction Conclusions References

The application of carbon - 14 analyses to the source apportionment of atmospheric carbonaceous particulate matter

Application of Machine Learning Clustering Algorithms to Thematic Map Design

The application of data mining and machine learning technology and especially machine learning clustering algorithms can aid in the design of thematic maps portraying structural characteristics of geographical distributions. However, a review of the literature indicates that such applications are relatively rare. In order to stimulate greater use of clustering algorithms for thematic map design...

Development and application of a robotic chemical mass balance model for source apportionment of atmospheric particulate matter

An advanced computational procedure is presented for the source apportionment (SA) of airborne particulate matter (PM) using chemical mass balance (CMB) receptor modeling. The so-called “Robotic Chemical Mass Balance” model (RCMB) minimizes personal judgment, by leading straight-forwardly to the bestefit combination of the source profiles that are included in a set of input data. RCMB involves ...

Workgroup Report: Workshop on Source Apportionment of Particulate Matter Health Effects—Intercomparison of Results and Implications

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 microm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency's Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The...