*3.3. Health Risk Assessment*

The characteristics of potential non-carcinogenic risk hazard quotient (HQ) and carcinogenic risk (CR) for O3 and PM2.5 in Harbin, Shenyang, and Changchun are shown in Figure 8 and Tables 1–3. At all sites, the HQ values of PM2.5 and O3 and the sum of both were below the acceptable limit of 1.0 most of the time, indicating an acceptable risk. However, at most stations in Harbin, the maximum value of HQ for PM2.5 was more significant than 1, reaching a maximum of 2.044, indicating some non-carcinogenic risk

on a few heavy pollution days. However, the CR values of PM2.5 were all greater than 1.0 × 10<sup>−</sup>4, which had a high carcinogenic risk. In other countries, the same results were obtained by Othman et al. [26] in Selangor, Peninsular Malaysia, and in China, similar results were obtained by Wang et al. [25] in Yancheng, Jiangsu, China.

**Figure 8.** Distribution of hazard quotient (HQ) values of PM2.5 and O3 with carcinogenic risk (CR) values of PM2.5 at each monitoring station (S1–S27 are the numbers of automatic air quality monitoring stations).

**Table 1.** Non-carcinogenic risks, estimated as HQ, and the carcinogenic risks, estimated as CR, from exposure to O3 and PM2.5 in Shenyang.



**Table 2.** Non-carcinogenic risks, estimated as HQ, and the carcinogenic risks, estimated as CR, from exposure to O3 and PM2.5 in Changchun.

**Table 3.** Non-carcinogenic risks, estimated as HQ, and the carcinogenic risks, estimated as CR, from exposure to O3 and PM2.5 in Harbin.


In Shenyang, the site with the highest HQ mean for O3 was S8, reaching 0.0427; the site with the highest HQ mean for PM2.5 was S1, reaching 0.0675; the site with the highest CR mean for PM2.5 was S1, reaching 0.0145. In Changchun, the site with the highest HQ mean for O3 was S14, reaching 0.0429, and the site with the highest HQ mean for PM2.5. In Harbin, the highest HQ mean for O3 was S25, reaching 0.0387, the highest HQ mean for PM2.5 was S27, reaching 0.0739, and the highest CR mean for PM2.5 was S27 at 0.0158.

In summary, among the three cities, the highest non-carcinogenic risk for O3 was Changchun and the lowest was Harbin. The highest non-carcinogenic risk for PM2.5 was Harbin and the lowest was Changchun, and the results for the carcinogenic risk for PM2.5 were the same as those for the non-carcinogenic risk, and the carcinogenic risk for PM2.5 was high in all three eastern provinces, with the highest Harbin urgently needing to control the pollution of PM2.5, while for O3, even though the HQ values of all three cities were exceeded, Changchun, which had the highest HQ, needed to be prevented.

### **4. Conclusions**

Despite the implementation of boiler renovation projects and straw burning bans in China as well as policies and laws that have effectively reduced the concentrations of various air pollutants, there is still a gap in the cleanliness of the atmosphere in the northeast compared to other developed regions and developed countries. In 2016–2020, three provincial capitals in northeast China also experienced more severe pollution (>300 μg/m3) due to high PM2.5 concentrations caused by coal-fired heating in winter, with Harbin experiencing the highest frequency of severe pollution. The correlation between meteorological factors and PM2.5 and O3 corroborates that the external sources of O3 contribute more and the local sources contribute more of PM2.5.

The cluster analysis results show that the highest proportion of northwest-oriented trajectories was in Changchun and the lowest was in Shenyang, influenced by the surrounding topography. The city with the highest percentage of southwest trajectories was Shenyang and the lowest was Harbin, which is influenced by the Bohai Sea anticyclone and its latitude. The results of the PSCF analysis showed that the main potential source areas of PM2.5 in northeast China were concentrated in Mongolia and Inner Mongolia, and the main potential source areas of O3 were concentrated in Shandong, Jiangsu, Anhui, and the Yellow Sea and Bohai Sea. The results of the CWT analysis showed that the high concentration of PM2.5 in the northeast China contribution areas were mainly concentrated in Russia, Mongolia, Inner Mongolia, Hebei, and northeast China, and the high concentration contribution areas of O3 were mainly concentrated in Shandong Province, Jiangsu Province, and the Yellow Sea and Bohai Sea.

The results of the health risk evaluation showed that the mean HQ values of O3 and PM2.5 in all cities were below the limit values, which indicated that the non-carcinogenic risk of both air pollutants was at an acceptable level. However, the carcinogenic risk of exposure to PM2.5 was relatively high, especially in Harbin, where the highest CR value reached 0.438, indicating that PM2.5 pollution in the northeast still needs further in-depth treatment. Implementing a more stringent regional control of PM2.5 pollution in the northeast and other regions to obtain better air quality is required to implement stricter regional control of pollutants. This study can also provide some basis for future studies of atmospheric pollution characteristics, and in the future, coupled analysis can also be performed using computer techniques based on time decomposition [35], neural-based ensembles [36], nonlinear combinations method [37], and phase adjustment [38] to obtain more accurate, diverse, and informative conclusions.

**Author Contributions:** Data curation and validation, Z.L.; Methodology and directors, C.F.; Supervision, J.W.; Conceptualization, original draft writing, review and editing, H.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Ecology and Environment Department of Jilin Province. The project numbers are 2018-19 and 2019-08.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [http://www.cnemc.cn/] accessed on 17 November 2021.

**Acknowledgments:** The authors would like to thank the Ecological Environment Monitoring Center of Changchun, Jilin Province, for providing the data on pollutants. Additionally, the authors would like to thank the group members of Laboratories 537 and 142 of Jilin University.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

