Street dust is repeatedly raised by the wind as a secondary suspension, helping heavy metals therein to enter the human body through the respiratory system, harming human health. A detailed investigation was conducted to determine levels and sources of Cd (cadmium), Cr (chromium), Cu (copper), Pb (lead), Zn (zinc), Ni (nick), and Hg (mercury) contamination in street dust from Shijiazhuang, China. The average concentrations of these metals were: Cd, 1.86 mg·kg⁻¹; Cr, 131.7 mg·kg⁻¹; Ni, 40.99 mg·kg⁻¹; Cu, 91.06 mg·kg⁻¹; Pb, 154.78 mg·kg⁻¹, Hg, 0.29 mg·kg⁻¹; and Zn, 496.17 mg·kg⁻¹—all of which were greater than the local soil reference values. The concentrations of the heavy metals were mapped for the three Shijiazhuang ring roads, with the results showing significant differences between each ring. Application of enrichment factors and geoaccumulation indexes showed that there was significant enrichment and accumulation of Cd, Pb, Zn, and Hg. Multivariate statistical analyses showed that Cd, Pb, Zn, and Hg levels were mainly controlled by human activities, while Cr, Ni, and Cu levels were associated with natural sources. Absolute principal component scores with multiple linear regression (APCS-MLR) were applied to facilitate source apportionment. The results showed that the mixed (traffic and industry) group contributed 53.55%, 59.7%, and 62.25% of the Cd, Pb, and Zn concentration, respectively, while the natural sources group contributed 58.01%, 65.09%, and 66.91% of the Cu, Ni, and Cr concentration, respectively. The burning coal group was found to be responsible for 63.38% of the Hg present in the samples. These results provide a useful theoretical basis for Shijiazhuang authorities to address heavy metal pollution management.

Industrial development has brought about not only rapid economic growth, but also serious environmental pollution in China, which has led to serious health problems and heavy economic burdens on healthcare. Therefore, the relationship between the industrial air pollution and health care expenditure (HCE) has attracted the attention of researchers, most of which used the traditional empirical methods, such as ordinary least squares (OLS), logistic and so on. By collecting the panel data of 30 provinces of China during 2005–2016, this paper attempts to use the Bayesian quantile regression (BQR) to reveal the impact of industrial air pollution represented by industrial waste gas emission (IWGE) on HCE in high-, middle-, low-income regions. It was found that double heterogeneity in the influence of IWGE on HCE was obvious, which revealed that people in high-, middle-, low-income regions have significantly different understandings of environmental pollution and health problems. In addition, the BQR method provided more information than the traditional empirical methods, which verified that the BQR method, as a new empirical method for previous studies, was applicable in this topic and expanded the discussion space of this research field.