*3.3. Connections between API and SD Trends*

Figure 7 displays the time series of the annual mean API from 2013 to 2020. A negative correlation was observed between SD and API, which confirms that light pollution will result in high SD. Furthermore, sharp decreases in API were observed after 2015 for all stations, along with a slight improvement in SD during this period. This might correlate with pollution controls such as supersessions in polluting industrial equipment and enterprises, and reductions in emissions of sulfur and nitrogen oxides from large plants.

Using only the available API data, the correlation coefficients between SD and the API were calculated and listed in Table 3, ranging from −0.12 to −0.58. Correlation coefficients of less than −0.3 could be found in the urban agglomerations in North China Plain, Yangtze River delta, Northeast Plain, and Northwest Plateau, this is supported by the distribution of annual PM2.5 concentration across China.


**Table 3.** The correlation coefficients between SD and API.

**Figure 6.** The spatial distributions of annual PM2.5 concentrations from 2012 to 2020 (Source: http://tapdata.org.cn, accessed on 11 May 2022).

**Figure 7.** *Cont*.

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**Figure 7.** Interannual changes in averaged SD, DF and API for the ten cities from 1981 to 2020. (**a**) For Beijing; (**b**) For Shenyang; (**c**) For Harbin; (**d**) For Shanghai; (**e**) For Wuhan; (**f**) For Guangzhou; (**g**) For Chengdu; (**h**) For Kunming; (**i**) For Urumqi; (**j**) For Lanzhou.

#### **4. Conclusions**

In this study, the SD, surface solar radiation, PM2.5 concentration and API data from ten China Meteorological Radiation Data International Exchange Stations in ten representative cities were collected to examine the trends between DF and SD from 1981 to 2020, PM2.5 concentration and SD from 2012–2020, and API and SD from 2013–2020. Our analysis indicates that solar radiation and SD are associated closely with aerosol pollutants due to urbanization and industrialization.

Overall, SD decreased in seven of the ten selected cities' stations from 1981 to 2020, with a decreasing rate of −0.03 h d−<sup>1</sup> per decade to −0.36 h d−<sup>1</sup> per decade—notable in Beijing, Shanghai and Wuhan, where trend coefficients were lower than −0.5. By contrast, SD increased in Kunming, Guangzhou and Shenyang, with the largest trend coefficient of 0.54 and the largest increasing rate of 0.38 h d−<sup>1</sup> per decade in Kunming. Seasonal trends in SD showed a fluctuating decrease in SD from 1981 to 2010 and increases from 2011 to 2020. In contrast to the seasonal trends in SD, the DF trend coefficients suggested that diffuse radiation increased continuously from 1981 to 2010, peaking in the 2010s and decreasing after 2010. The correlation coefficients between DF and SD ranged from −0.04 to −0.62, validating the negative relationship between DF and SD—this was supported by the improvement in annual PM2.5 concentrations due to the stringent pollution controls in place since 2013 throughout China. Furthermore, the correlation coefficients ranging from −0.12 to −0.58 demonstrated a negative relationship between SD and API; sharp decreases in API were observed after 2015 for the ten typical cities' stations and slight improvements

in SD during this period were found which accounted for the pollution impact on SD trends from the other side.

Because many factors can affect the transmission of solar radiation in the atmosphere, some factors might not have been accounted for in this study. SD and diffuse radiation are not only affected by air pollution, but also by clouds. As cloud coverage data are not available for this study, we only adopted the clearness index related to the cloud cover, without analyzing the relationship between SD and cloud cover. We will further explore the influence of clouds on SD in subsequent studies.

**Author Contributions:** Conceptualization, W.C.; methodology, W.C.; software, W.C.; validation, W.L. and G.Z.; investigation, X.Y.; resources, J.L.; data curation, W.C.; writing—original draft, W.C.; writing—review and editing, J.Z.; supervision, W.L.; project administration, W.L.; funding acquisition, J.Z and G.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Youth Fund of the National Natural Science Foundation of China, grant number 61805027; the Jilin Scientific and Technological Development Program, grant number 20190302124GX; and the Innovation Fund of the Changchun University of Science and Technology, grant number XJJLG-2018-02.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The daily SD and solar radiation data were collected from ten China Meteorological Radiation Data International Exchange Stations, located in Mainland China; the data were archived at the National Meteorological Information Center of China Meteorological Administration (http://data.cma.cn/, accessed on 31 August 2021); The monthly API data used in this study were provided openly and freely by the China National Environmental Monitoring Centre (http://www.cnemc.cn/jcbg/kqzlzkbg/, accessed on 31 August 2021). The annual PM2.5 concentration data were obtained from Tracking Air Pollution in China (http://tapdata.org.cn/, accessed on 11 May 2022).

**Acknowledgments:** The authors are grateful to the staff at the investigated stations for collecting the sunshine duration and air pollution data used in our research, and the National Meteorological Information Centre of China Meteorological Administration and National Environmental Monitoring Centre of China for providing these archived data. Figure 6 is cited from http://tapdata.org.cn, accessed on 11 May 2022; the authors also appreciate the support of the TAP team (Tracking Air Pollution in China) for producing Figure 6.

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

#### **References**

