In order to determine the pollution sources and human health risks of metal elements in PM2.5, samples were collected by a large flow particulate matter sampler in the four seasons in 2013, 2015, and 2017 (January, April, July, and October). The mass concentrations of 10 metals (Ti, V, Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The sources of heavy metals were identified by Unmix, and the potential non-carcinogenic/carcinogenic risk was evaluated. The influences of local and regional sources were also explored during the high-carcinogenic risk period (HCRP). The wind field and 72 h backward trajectories were performed to identify the potential local and regional sources in HCRP. The results showed that the average annual concentrations of PM2.5 in the urban area of Handan city were 105.14, 91.18, and 65.85 μg/m3 in 2013, 2015, and 2017, respectively. The average daily concentrations of the metals in PM2.5 in January were higher than that of April, July, and October. The average mass concentrations of the 10 heavy metal elements in PM2.5 were 698.26, 486.92, and 456.94 ng·m−3 in 2013, 2015, and 2017, respectively. The main sources of the metals in PM2.5 were soil dust sources, vehicular emissions, coal burning, and industrial activities. The carcinogenic risks of Cr and As were above 1 × 10−6 over the three years. Wind direction analysis showed that the potential local sources were heavy industry enterprises and the economic development zone. The backward trajectory analysis indicated that PM2.5 long transported from Shandong, Henan, and the surrounding cities of Handan had quite an impact on the heavy metals contained in the atmosphere of the studied area. The health risk assessment results demonstrated that the trend for non-carcinogenic risk declined, and there was no non-carcinogenic risk in 2017. However, the carcinogenic risk levels were high over the three years, particularly in January.

Soil-heavy metals are potentially harmful to the ecosystem and human health. Quantifying heavy metals sources is conducive to pollution control. In this study, 64 surface-soil samples were collected in Handan city. Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined; then, their spatial distribution in the sampling area was drawn by ArcGIS. The pollution index (PI) method, geo-accumulation index (Igeo) method, Nemerow integrated pollution index (NIPI) and pollution load index (PLI) were used to evaluate the pollution level of heavy metals in surface soil; then, an ecological and health risk assessment of soil-heavy metals was carried out. Combined with the spatial distribution, correlation analysis, cluster analysis, PCA and PMF model, the pollution sources of heavy metals in soil were identified and apportioned. The results showed that the average content of Cd was nearly ten times that of the background limit, which was the most serious among the studied metals. In terms of non-carcinogenic risk, Cr had the highest value, followed by Pb. In terms of carcinogenic risk, Cd, Cr, and Ni had an acceptable or tolerable risk. Three pollution sources were identified by cluster analysis and PCA, including traffic sources with Cu, Pb and Cd as main loads, industrial sources with Mn, Cd and Zn as main loads, and natural sources with Cr and Ni as main loads. The PMF model analyzed three main factors: traffic source (17.61%), natural source (28.62%) and industrial source (53.77%). The source categories and the main load elements obtained from the source apportionment results were consistent with the source identification results.

Handan city as a transportation hub in North China, air quality ranks at the bottom all year round, causing environmental pollution that has aroused widespread concern. In order to explore the pollution characteristics and main sources of heavy metals in atmospheric wet deposition in the city, and comparison of the applicability of multiple traceability models, a total of 76 wet deposition samples were collected in the three functional areas from December 2017 to November 2019 by a dry and wet deposition automatic sampler. Concentrations of Cu, Zn, Cr, Ni, Pb, and As were determined and enrichment factors were calculated. Sources of these heavy metals were apportioned by PMF, Unmix, and APCS-MLR models, and analyzed using a backward trajectory analysis model. The results showed that the concentrations of these heavy metals in the atmospheric wet deposition were in order of Zn, Cr, Pb, Cu, Ni, and As, and their mean concentrations were 29.53, 14.11, 9.18, 7.03, 6.41, and 1.21 μg·L−1, respectively. According to the results of EF, the studied heavy metals were mainly affected by anthropogenic activities. The source apportionment results showed that heavy metal pollution in the wet deposition was mainly affected by traffic sources, industrial sources, and coal combustion sources, and PMF identified an additional source factor: metal smelting source. By comparing the relevant parameters of the source apportionment results of the three models, the APCS-MLR model has better accuracy results than PMF and Unmix models. The analysis of the backward trajectory of the air mass showed that the wet deposition of Handan in the study time was mainly from the southwest direction, accounting for 54.35%. In the future, more evaluation methods and models will be used to compare and analyze the different application scenarios and parameter selection requirements in order to contribute to urban atmospheric environmental pollution prevention and control work.

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Receptor models are rarely utilized in atmospheric deposition but are often used to identify pollutant sources and quantify their contributions. This article focuses on the soil in atmospheric deposition in a typical polluted city. Atmospheric deposition has become an important route for exogenous heavy metals’ input into ecosystems. In this study, the heavy metals in atmospheric deposition were determined in three monitoring points arranged in Handan City. According to the functional area, fluxes, sources, and accumulation in the soil were explored. The sources of heavy metals were identified by PMF (positive matrix factorization) and UNMIX. The accumulation of heavy metals in the soil was predicted. The results showed that the deposition fluxes in industrial areas were higher than other functional areas. The mean concentrations of 8 heavy metals (Cd, Cr, Cu, Mn, Ni, Pb, Zn, and As) in the atmospheric deposition exceed their background values. PMF identified five major sources and UNMIX analyzed four sources. Similar source apportionment results were acquired via PMF and UNMIX, which were the combustion of fossil fuels, steel-smelting emission, road dust, and industrial sources. Steel-smelter emission was the highest source contributor. Therefore, combining these two models was the most effective approach, and more attention should be paid to mitigating the pollution caused by the industrial activities. The prediction indicated that the accumulation of heavy metals from atmospheric deposition to the soil would increase in 30 years, the growth rate of Cd increased significantly. The results of this study could provide reference in reduction of heavy metal pollution in atmospheric deposition.