**1. Introduction**

Over the past decades, China has become one of the most rapidly industrialized and urbanized countries worldwide. The growing population and intensified anthropogenic activities have led to the continuous increase in anthropogenic pollutant emissions in China. Moreover, the exacerbation of air pollution through aerosol radiative forcing has highly affected the radiation balance of the surface-atmosphere system. As an important indicator of solar radiation, sunshine duration (SD)—defined by the World Meteorological Organization (WMO) in 1989—is "the sum of the time for which the direct solar irradiance exceeds 120 W·m<sup>−</sup>2" [1]. As SD reflects the solar energy absorbed from the sun, it has become an important thermodynamic factor for large-scale atmospheric movements [2,3]. Small changes in SD may have a tremendous impact on weather and climate [4–6]. However, with the increase of anthropogenic aerosol loading in the atmosphere, the absorption

**Citation:** Chong, W.; Lyu, W.; Zhang, J.; Liang, J.; Yang, X.; Zhang, G. Effects of Air Pollution on Sunshine Duration Trends in Typical Chinese Cities. *Atmosphere* **2022**, *13*, 950. https://doi.org/10.3390/ atmos13060950

Academic Editors: Duanyang Liu, Kai Qin and Honglei Wang

Received: 13 May 2022 Accepted: 6 June 2022 Published: 10 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and scattering of solar radiation by aerosols intensifies the reduction of surface solar radiation [7]. In addition, acting as cloud condensation nuclei, aerosol particles enhance the longevity of clouds and their reflection of extraterrestrial radiation, further reducing SD [8]. Therefore, it is of great significance to study the effect of air pollution on SD.

Several studies have focused on the impact of air pollution on long-term trends in sunshine duration and solar radiation. Fu et al. found that in large cities and medium-sized cities throughout China, due to extensive anthropogenic activities and the air pollution represented by aerosol optical depth (AOD) and tropospheric column NO2 (TroNO2), sunny SD presented with a decrease of −0.13 h d−<sup>1</sup> per decade from 1960 to 2005 [9,10]. Bartoszek et al. researched the relationships between cloudiness, aerosol optical thickness, and sunshine duration in Poland from 1980 to 2018; they found a growing trend in area-average values of relative sunshine duration in each season of the year with reductions in aerosol optical thickness [11]. Wang et al. analyzed the spatiotemporal changes of surface solar radiation over East China during 2000–2016, pointing out that aerosol-induced radiation reduction could result in about a mean 6.74% reduction in rice yield over East China [12]. Chen et al. analyzed trends in global radiation and sunshine hours at 51 stations during 1961–1998 in mainland China and found decreasing trends in global radiation at 47 stations, with 42 stations showing a reduced trend in sunshine hours [13]. Song et al. reported that SD in eastern China decreased throughout the year at an annual rate of −0.132 h d−<sup>1</sup> per decade during 1961–2014, suggesting that urbanization and industrialization may be responsible [14]. Kaiser and Qian reported an average decrease of approximately 1% per decade in possible SD in China from 1954 to 1998 and proposed that the increased atmospheric anthropogenic aerosol loading due to growing fossil fuel combustion accounted for the decline in SD [15]. Liao et al. pointed out that the attenuation of visibility and SD was consistent during 1980–2012 in South China and suggested that increases in pollutants are responsible for sunlight obstruction and reductions in visibility [16]. Wang et al. investigated the long-term trends in surface solar radiation from 1960 to 2000 in China and found that surface solar radiation in most regions of China began to increase after 1990, which was attributed to decreases in cirrus and cirrostratus clouds [17]. Qi et al. reported that the interannual trends and variations of surface solar radiation decreased in both East and West China during 1961–2010, and suggested that aerosol pollutants were the main factor causing the reduction of surface solar radiation in East China [18]. Furthermore, they determined that an abrupt change occurring in the early 1990s, followed by a sustained increase—possibly due to the Chinese government's environmental protection plans [18]. The abovementioned studies improve our understanding of air pollution related to changes in SD. However, most of these studies have focused on the influence of air pollution on trends in solar radiation separately, rather than combined with SD, or the impacts have only covered limited land areas or weather conditions. Discussion of the influence of air pollution on long-term SD trends in representative cities of China under all weather conditions are rare in previous studies.

In this study, we examined SD and related solar radiation data from ten typical cities in different climate regions over China from 1981 to 2020, mainly focusing on the long-term trends in and associations between SD, the diffuse fraction (DF), annual PM2.5 concentration and the air pollution index (API)—which is related to increases in anthropogenic aerosols. This paper aims to provide an improved understanding of SD changes influenced by severe environmental issues that have occurred during the industrialization and urbanization caused by China's reform and opening policy of 1980. The present article is organized as follows: Section 2 introduces the data source and the methods. Section 3 presents interannual SD trends and seasonal SD trends from 1981 to 2020, followed by discussions of the relationships between SD changes and the DF and API. The main conclusions are presented in Section 4.

#### **2. Methodology**

## *2.1. Data*

The SD and solar radiation data were collected from ten China Meteorological Radiation Data International Exchange Stations located in seven climate regions in mainland China [19] (Figure 1), with station information shown in Table 1. Daily SD, global solar radiation and diffuse radiation were measured simultaneously from 1981 to 2020—except for Shanghai, from 1991 to 2020. SD was measured using a Jordan Sunshine Recorder with an absolute error of ±0.1 h and global solar radiation and diffuse radiation were measured horizontally using a thermopile-based pyranometer with a relative error of ±2%. The daily data were archived at the National Meteorological Information Center of China Meteorological Administration (http://data.cma.cn/, accessed on 31 August 2021). The annual and seasonal averages of SD and solar radiation deduced from daily data were analyzed. In this study, spring, summer, autumn and winter corresponded to March–May, June–August, September–November and December–February, respectively.

**Figure 1.** Locations of the ten investigated stations and their climate conditions. NC for North China, NE for Northeast China, CC for Central China, SC for South China, SW for Southwest China, EA for Eastern arid regions, WA for Western arid/semi-arid regions, TP for the Tibetan plateau.


**Table 1.** Geographical information of the investigated stations used in this study.

The DF and clearness index (CI) are introduced in this article. The DF is defined as the ratio of diffuse radiation to global solar radiation and is an important indicator of the global transmissivity of the atmosphere. The clearness index, which varies primarily due to

cloud cover and cloud type, is the ratio of global solar radiation to extraterrestrial radiation. To obtain the clearness index, the equation for calculating the extraterrestrial radiation (MJ/m2) on a horizontal surface, *H*0, is as follows [20,21]:

$$H\_0 = \frac{24 \times 3600 \times I\_{\rm sc}}{\pi} \left[ 1 + 0.033 \cos \left( \frac{360 \imath}{365} \right) \right] \times \left[ \cos \varphi \cos \delta \sin w\_s + \frac{\pi w\_s}{180} \sin \varphi \sin \delta \right] \tag{1}$$

where *Isc* is the solar constant (1367 W m<sup>−</sup>2), *n* is the day number of the year—starting from January 1—*ϕ* is the latitude of the station in degrees, *δ* is the declination of the sun in degrees and *ws* is the sunrise hour angle in degrees. *δ* and *ws* can be obtained by Equations (2) and (3) [20,21]:

$$\delta = 23.45 \sin\left[\frac{360(n+284)}{365}\right] \tag{2}$$

$$w\_s = \arccos[-\tan\delta \tan\varphi] \tag{3}$$

To ensure the quality of the SD and solar radiation data, some quality control criteria were applied to the daily data. The criteria included: (1) daily collections were rejected if either SD or solar radiation was missing; (2) only daily collections meeting 0 < DF < 1 and 0 < CI < 1 simultaneously were used; (3) the month was rejected if more than ten days of SD or solar radiation were missing in that month.

API is a dimensionless parameter describing the comprehensive conditions of urban environmental air quality. The higher the API, the more serious the comprehensive pollution. API includes six pollutants: SO2, NO2, PM10, PM2.5, CO and O3; it is calculated using the sub-index of the six pollutants derived from daily or monthly average concentrations. The equations are as follows:

$$I\_i = \frac{\mathcal{C}\_i}{\mathcal{S}\_i} \tag{4}$$

$$\text{API} = \sum\_{i=1}^{6} I\_i \tag{5}$$

where *Ii* is the sub-index of the *i*th pollutant of the six pollutants, *Ci* is the concentration of the *i*th pollutant of the six pollutants; when the *i*th pollutant is either SO2, NO2, PM10 or PM2.5, *Ci* is the monthly mean concentration; when the *i*th pollutant is either CO or O3, *Ci* is the concentration at a specific percentile. *Si* is the secondary standard of the *i*th pollutant; when the *i*th pollutant is either SO2, NO2, PM10 or PM2.5, *Si* is the secondary standard for the annual mean; when the *i*th pollutant is CO, *Si* is the secondary standard for the daily mean; and when the *i*th pollutant is O3, *Si* is the secondary standard for the 8 h mean.

The monthly API data used in this study were obtained from the China National Environmental Monitoring Centre (http://www.cnemc.cn/jcbg/kqzlzkbg/, accessed on 31 August 2021); however, the data consistent with the SD and global solar radiation stations are only available from January 2013 to August 2020 because the observation starting time and data were unavailable for this study. In addition, the annual PM2.5 concentration data from 2012 to 2020 were used in this study, which were obtained from the Tracking Air Pollution in China website: http://tapdata.org.cn/ (accessed on 11 May 2022).
