Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Cong Men; Ruimin Liu; Qingrui Wang; Lijia Guo; Yuexi Miao; Zhenyao Shen

doi:10.1016/j.scitotenv.2018.10.212

Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Sci Total Environ. 2019 Feb 20:652:27-39. doi: 10.1016/j.scitotenv.2018.10.212. Epub 2018 Oct 17.

Authors

Cong Men¹, Ruimin Liu², Qingrui Wang¹, Lijia Guo¹, Yuexi Miao¹, Zhenyao Shen¹

Affiliations

¹ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China.
² State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China. Electronic address: liurm@bnu.edu.cn.

PMID: 30352344
DOI: 10.1016/j.scitotenv.2018.10.212

Abstract

Based on 36 road dust samples from an urbanized area of Beijing in September 2016, the information about sources (types, proportions, and intensity in spatial) of heavy metals and uncertainties were analyzed using positive matrix factorization (PMF) model, bootstrap (BS), geographic information system (GIS) and Kriging. The mean concentration of most heavy metals was higher than the corresponding background, and mean concentration of Cd was six times of its background value. Types and proportions of four sources were identified: fuel combustion (33.64%), vehicle emission (25.46%), manufacture and use of metallic substances (22.63%), and use of pesticides, fertilizers, and medical devices (18.26%). The intensity of vehicle emission and the use of pesticides, fertilizers, and medical devices were more homogeneous in spatial (extents were 1.285 and 0.955), while intensity of fuel combustion and the manufacture and use of metallic substances varied largely (extents were 4.172 and 5.518). Uncertainty analysis contained three aspects: goodness of fit, bias and variability in the PMF solution, and impact of input data size. Goodness of fit was assessed by coefficient of determination (R²) of predicted and measured values, and R² of most species were higher than 0.56. Influenced by an outlier, R² of Ni decreased from 0.59 to 0.11. Result of bootstrap (BS) showed good robust of this four-factor configuration in PMF model, and contributions of base run of factors to most species were contained in the small interquartile range and close to median values of bootstrap. Size of input data also had influence on results of PMF model. Residuals changed largely with the increase of number of site, it varied at first and then kept stable after number of site reached 70.

Keywords: Heavy metals; Positive matrix factorization; Road dust; Source apportionment; Uncertainty.