**4. Discussion**

Previous studies have mostly discussed the spatial and temporal distribution characteristics of O3 in major urban groups in China, but there are fewer studies on long-term O3 monitoring and regional causal analysis in the NCP. Therefore, we used the ground monitoring station data to make spatial-temporal distribution maps of O3, combined with the statistical data of each administrative unit, and discussed the influencing factors of O3 pollution in a comprehensive manner. This study can provide a reference for O3 prevention and control in the NCP.

The annual variation in O3 showed that, compared with the previous two years, O3 concentrations moderately decreased by 2020, but O3 pollution was still severe compared with that in 2016. We argue that this pattern is related to high emission intensity of anthropogenic sources. The NCP is rich in mineral resources and coal, but the proportion of energy consumption is unbalanced. Due to this, the industrial structure is unreasonably unsustainable, being characterized by high energy consumption and highly polluting industries, such as metallurgy and building materials, which are common in the region. Moreover, heavily polluting enterprises are highly concentrated in the border areas of the Hebei, Shandong, and Henan provinces. Industrial production inevitably triggers the emission of pollutants, except in Beijing, where the industrial structure, similar to other cities, is low [30]. Figure 10 shows the number of civilian vehicles from 2016 to 2020. It can be seen that the number of civilian vehicles in seven provinces in the study area annually increased from 2016 to 2020. Compared to 2016, the number of civilian vehicles increased by 9.47% in Beijing, 20.36% in Tianjin, 40.24% in Hebei, 58.59% in Henan, 47.22% in Shandong, 64.20% in Anhui, and 42.73% in Jiangsu. In 2020, the number of vehicles in Shandong Province reached 25.524 million. Moreover, there is a large freight volume in the study area, and the transportation structure is mainly represented by highways. The O3 precursors

such as CO and NOx, produced by a large number of heavy diesel vehicles and other motor vehicles, exacerbate O3 pollution. The data of China's anthropogenic emission inventory in 2020 [47] indicates that the anthropogenic source emissions of VOCs and NOx in Shandong Province were the largest, while the Jiangsu and Hebei Provinces were also characterized by large emissions. The increase in precursor emissions facilitates the secondary conversion to generate O3. Therefore, anthropogenic emissions are one of the main factors affecting regional air quality.

**Figure 10.** Changes in Civil Vehicle Ownership from 2016 to 2020.

In addition to precursor-related factors, meteorological factors can also affect O3 concentrations through a series of reaction processes. Adverse meteorological conditions and high-intensity precursor emissions are often the preconditions for O3 pollution. Generally, O3 pollution events occur under high-temperature conditions, strong solar radiation, low pressure, low relative humidity, and weak winds [15,16]. Figure 11 shows the change in mean annual average temperature (MAAT) in China from 2011 to 2020, showing that the annual average temperature in China has increased significantly in the past decade, with the highest temperature in the past decade being recorded in 2015, rising by 0.94 ◦C compared to the average temperature from 1981 to 2010 (9.55 ◦C). The frequency of extreme weather in China has recently intensified and is currently higher than usual. Moreover, extreme weather conditions such as high temperatures and heavy precipitation have also intensified in China. According to the "Blue book on climate change in China 2021", released by the Climate Change Center of the China Meteorological Administration, the warming rate in China has been higher than the global trend during the same period, and the climate warming continues. Climate warming bolsters atmospheric stability, weakening the regional atmospheric convection and diffusion. In turn, the change in air quality caused by climate change is also one of the drivers behind the increase in near-ground O3 concentrations.

The external transmission somewhat affects the urban atmosphere. For instance, Jia et al. [48] utilized Ozone Source Apportionment Technology (OSAT) technology to analyze O3 pollution in the summer of 2015. The simulation of the O3 sources in Beijing and its surrounding areas indicated that Beijing was mainly affected by external transportation in the Hebei Province, followed by the Shandong Province and the Henan Province, while Tianjin was mainly affected by the Hebei and Shandong Provinces. Liu et al. [49] studied the transport pathways of atmospheric pollutants in Henan Province in 2017 using the WRF/CAMQ model. Their results showed that O3 concentrations in Henan Province were influenced by a combination of regional transport and natural sources, with the border between Henan Province and neighboring provinces being more significantly influenced by regional transport. Furthermore, Xing et al. [50] used the extended response surface modeling (ERSMv2.0) technique to quantify the contribution of multi-regional sources to

PM2.5 and O3 in the Beijing–Tianjin–Hebei region. The results showed that PM2.5 was more influenced by local than regional transport in most regions, while O3 showed the opposite trend, being more heavily influenced by regional transport. Through relevant articles, we know that regional transport fundamentally affects O3 pollution. The central areas in the NCP have serious O3 pollution, thereby exacerbating the pressure on the ambient air quality of the transmitted cities. Thus, joint prevention and control measures are essential for mitigating O3 pollution in the NCP.

**Figure 11.** National mean annual average temperature changes over the years.

In general, O3 pollution is the result of the combined influence of natural conditions and human factors. As the natural conditions are fundamentally unaffected by human being, the anthropogenic sources of pollutants should be primarily addressed. The key to controlling O3 pollution is to reduce the emissions of precursors. Cities in the central part of the NCP are seriously polluted by O3; we should manage the core cities with heavy industrial structures in the region, timely adjust the energy structure, promote clean energy, and adopt a long-term control strategy for NOx and VOCs. At the same time, the NCP region needs to strengthen regional cooperation to form joint prevention and control of air management mechanisms to effectively solve the problem of O3 transmission across regions. Moreover, as individuals, we should privilege green travel, which is an effective way to alleviate O3 pollution. However, due to the unavailability of detailed O3 precursor emission data for recent years, there are some methodological shortcomings in the analysis of O3 causation correlation, and the correlation between precursors and O3 should be discussed in detail in future studies.

#### **5. Conclusions**

This study investigated the characteristics, spatio-temporal distribution, and drivers of O3 pollution in the NCP from 2016 to 2020, providing an effective management reference for O3 pollution control policies. It was proven that O3 pollution in the NCP was severe from 2016 to 2018, but after 2018, O3 concentrations gradually decreased. The seasonal variation of O3 concentrations was found to be regular. The O3 concentrations were higher in spring and summer (117.89–154.20 μg/m3) and lower in autumn and winter (53.81–92.95 μg/m3). The spatial analysis revealed that O3 exhibited distinct spatial patterns from 2016 to 2020. On an interannual scale, the overall concentrations of O3 exhibited a spatial distribution trend of low concentrations in the north and south and high concentrations in the central area. This pattern was attributed to the characteristics of the regional industrial structure and the pollutants discharged by motor vehicles. Fundamentally, large-scale industrial production and frequent traffic flow trigger strong precursor emissions. In addition, the

greater the number of precursors, the worse the O3 pollution. The analysis showed that the spatial distribution of O3 exhibited certain differences being affected by precursor emissions and meteorological conditions in different seasons. Of note, high temperature and low pressure can increase O3 concentrations, and high emissions of precursors also contribute to O3 pollution. This calls for further decreasing the emissions of precursors to alleviate O3 pollution.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/atmos13050715/s1, Table S1: Names and abbreviations of cities in the study area.

**Author Contributions:** Conceptualization, X.W. and W.Z. (Wenhui Zhao); methodology, L.L.; software, X.W. and T.Z. and L.W. and D.Z.; resources, M.W.; writing—original draft preparation, X.W.; writing—review and editing, W.Z. (Wenji Zhao) and P.M. and Y.Q.; supervision, W.Z. (Wenji Zhao) All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Key Research and Development Program of China (2018YFC0706004) and National Natural Science Foundation (42071422).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The atmospheric O3 data used in this study are available at http: //106.37.208.233:20035/. Meteorological data are available at http://data.cma.cn. Vector data and DEM data are available at https://www.resdc.cn/. The statistical data from 2016 to 2020 are available at http://www.stats.gov.cn/tjsj/ndsj/ and https://data.cnki.net/NewHome/Index. The POI data are available at https://amap.com/ (accessed on 18 April 2022).

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