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With the acceleration of urbanization, road dust poses a significant threat to ecological systems. To explore the source and ecological risks associated with heavy metals in surface dust, 36 road dust samples were collected in Beijing in May, August, and November 2018, and February 2019. The results indicated that the concentrations of Cd, Cr, Cu, Hg, Ni, Pb, and Zn exceeded their background values, and the mean concentration of Cd was nearly 10 times the background value. Three main sources were identified by Principal Component Analysis (PCA): industrial emissions, fuel combustion, and traffic exhaust. According to the Genetic Algorithm optimized Back Propagation (GA-BP) neural network, heavy metals from industrial sources and fuel combustion originate from suburban districts, while heavy metals from traffic exhaust originate from the city center. After careful screening of species and corresponding toxicity data, the concentration of pollutants corresponding to a 5% cumulative probability on the curve (HC5) of eight heavy metals was calculated by species sensitivity distribution (SSD), and the results indicate that most HC5 values are consistent with national standards. However, the HC5 values of Ni, Cu, and As differed from the national standards, which can be used to supplement existing standards. From the risk analysis based on HC5 values, the high-risk area of Ni (12.70–30.97%) was much larger than that of As (1.55–4.95%), and the proportions of high-risk areas of Ni and As reached their highest values in spring and autumn, respectively. Uncertainty existed between different geostatistical methods. The areas of uncertainty of Ni and As were small (less than 5.58%). The heavy metals in the surface dust in Beijing had potential ecological risks that vary with seasons and regions. Therefore, it is critical to implement targeted management measures based on pollution sources and risk distribution. [ABSTRACT FROM AUTHOR]

In studying the spatial, temporal, and particle size variations heavy metal sources, a source apportionment model for a four-way array of data is required. In this study, referencing two-way and three-way models, a four-way (particle fractions, elements, sites, and time) source apportionment model (FEST) was developed. Errors in the three-way models solving four-way problems verified the necessity of developing the FEST model. The results showed that the FEST model had a higher accuracy than the existing models, which was probably because of more constraints and input data in the FEST model. Based on the sampled data in Beijing, sources were apportioned for the four-way array of data using the FEST model, and the spatial, temporal, and particle size variations of sources were evaluated. The main sources of heavy metals were similar to those in our prior studies, whereas the contributions of sources to specific heavy metals differed. Traffic exhaust and fuel combustion contributed more to fine particles than coarse particles, indicating that the two should be controlled preferentially among all sources. The management of traffic exhaust should be focused on the central and northern areas in each season, and the control of fuel combustion should be strengthened in the southern area in winter. [Display omitted] • A four-way source apportionment model (FEST) was developed for the first time. • Accuracy of the FEST model was higher than that of existing models. • Tempo–spatial and particle size changes of sources were analyzed using the FEST model. • Traffic exhaust and fuel combustion impacted larger on fine particles than coarse particles. [ABSTRACT FROM AUTHOR]