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Article

Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data

by
Hujia Zhao
1,*,
Ke Gui
2,
Yanjun Ma
1,
Yangfeng Wang
1,
Yaqiang Wang
2,
Hong Wang
2,
Yu Zheng
2,
Lei Li
2,
Lei Zhang
2,
Yuqi Zhang
3,
Huizheng Che
2 and
Xiaoye Zhang
2
1
Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
2
State Key Laboratory of Severe Weather (LASW), Key Laboratory for Atmospheric Chemistry (LAC), Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
3
Liaoning Meteorological Service Center, Liaoning Meteorological Bureau, Shenyang 110166, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(7), 3830; https://doi.org/10.3390/ijerph19073830
Submission received: 16 February 2022 / Revised: 18 March 2022 / Accepted: 22 March 2022 / Published: 23 March 2022
(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)
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Abstract

:
With the rapid development of economy and urbanization acceleration, ozone (O3) pollution has become the main factor of urban air pollution in China after particulate matter. In this study, 90th percentile of maximum daily average (MDA) 8 h O3 (O3-8h-90per) and PM2.5 data from the Tracking Air Pollution in China (TAP) dataset were used to determine the mean annual, seasonal, monthly, and interannual distribution of O3-8h-90per and PM2.5 concentrations in Northeast China (NEC). The O3-8h-90per concentration was highest in Liaoning (>100 μg/m3), whereas the highest PM2.5 concentration was observed mainly in urban areas of central Liaoning and the Harbin–Changchun urban agglomeration (approximately 60 μg/m3). The O3-8h-90per concentrations were highest in spring and summer due to more intense solar radiation. On the contrary, the PM2.5 concentration increased considerably in winter influenced by anthropogenic activities. In May and June, the highest monthly mean O3-8h-90per concentrations were observed in central and western Liaoning, about 170–180 μg/m3, while the PM2.5 concentrations were the highest in January, February, and December, approximately 100 μg/m3. The annual mean O3-8h-90per concentration in NEC showed an increasing trend, while the PM2.5 concentration exhibited an annual decline. By 2020, the annual mean O3-8h-90per concentration in southern Liaoning had increased considerably, reaching 120–130 μg/m3. From the perspective of city levels, PM2.5 and O3-8h-90per also showed an opposite variation trend in the 35 cities of NEC. The reduced tropospheric NO2 column is consistent with the decreasing trend of the interannual PM2.5, while the increased surface temperature could be the main meteorological factor affecting the O3-8h-90per concentration in NEC. The results of this study enable a comprehensive understanding of the regional and climatological O3-8h-90per and PM2.5 distribution at distinct spatial and temporal scales in NEC.

1. Introduction

Surface ozone (O3) is a critical gaseous pollutant that affects air quality and contributes to climate change [1,2,3,4]. O3 and its interactions with precursor gaseous emissions have been widely researched in recent decades [5,6]. Surface O3 is formed by photochemical reactions of volatile organic compounds (VOCs) and nitrogen oxides; these reactions are exacerbated by sunlight [7,8,9]. The major contributions to O3 formation and accumulation emanate from natural emissions and anthropogenic sources such as long-range transport [10,11,12,13]. Worldwide, numerous studies have analyzed the relationship between surface O3 and temperature, pressure, relative humidity, wind, precipitation, cloud cover, and solar radiation, as well as the meteorological factors related to synoptic circulation patterns [14,15,16,17].
Due to several decades of rapid economic growth and urbanization, China has experienced severe fine particulate matter (PM2.5) pollution originating from anthropogenic emissions [18,19,20]. However, in recent years, O3 has become a more severe air pollutant than PM2.5 is in some Chinese cities, resulting from a substantial increase of O3 precursor emissions such as VOCs [21,22,23,24]. Numerous studies have reported the coordinated pollution of O3 and particulate matter as the primary urban pollutant in densely populated areas, such as Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Sichuan Basin [25,26,27]. Zeng et al. (2020) [28] implied that the concentrations of PM2.5 and O3 showed different seasonal trends in eastern China. Liu et al. (2021) [29] used the ground-based hyperspectral stereoscopic remote sensing network to provide a promising strategy to support management of PM2.5 and O3 and their precursors and conduct attribution of sources. In the formation of surface O3, meteorological factors play a crucial role [30,31,32,33,34]. Yang et al. (2021) [35] indicated that PM2.5 was primarily affected by wind, temperature, and rainfall, while O3 was mostly influenced by temperature, relative humidity, and sunshine duration in 284 major cities in China. Dai et al. (2020) [36] identified that the number of days with co-pollution of O3 and PM2.5 was mainly dependent on relative humidity, surface air temperature, and wind speed in the Yangtze River Delta region.
Northeast China (NEC) is a key region in Asia, bordering Siberia in the north, the Bohai and Yellow Seas and the Central Plains in the south, Mongolia to the west, and the Korean Peninsula and the Sea of Japan to the east (Figure 1a). Air quality in NEC has been improving because efforts to control PM2.5 have been intensified. However, O3 has become a new environmental problem. Due to the unique geographical location and meteorological conditions of NEC, O3 pollution originates not only from local emissions but also from those of surrounding areas. Figure 1b shows the spatial distribution of population density in NEC. The NEC has an uneven distribution of population, while southwest and central NEC are densely populated with a maximum population density of nearly 50 (×1000 persons), followed by eastern NEC with a population density of less than 15 (×1000 persons). The northern mountain of NEC is sparsely populated, with a population density of less than 0.5 (×1000 persons). The population density in NEC is mainly affected by topography, climate, and economic development, and the natural factors and social factors simultaneously affect the change of population density. From the spatial distribution of population density in the three provinces of NEC, population density is greater in Liaoning than in Jilin province, and lower in Heilongjiang province. The provincial capital cities mainly present a spatial pattern of polycentric concentrated distribution of population. These results indicate that the level of social and economic development is the main factor influencing the distribution of population density in NEC. Therefore, studies in NEC have revealed the importance of considering both local emission sources as well as the long-range transport of pollutants from regions such as the North China Plain [37,38]. Zhu and Liao (2016) [39] reported that surface O3 levels in NEC were comparable to those of BTH and the YRD and even higher than those in the PRD during spring 2000. NEC is a crucial industrial and agricultural base in China. More research on O3 pollution and its impact on ecosystem security and agriculture is needed. Understanding the spatiotemporal variation of O3 and PM2.5 can aid in providing technical support for the prevention and control of O3 and PM2.5 pollution in NEC.
This study analyzed the mean annual, seasonal, monthly, and interannual variation and distribution of O3-8h-90per and PM2.5 in NEC. The innovation in this study was its focus on the multi-year variation and distribution of O3-8h-90per and PM2.5 in NEC. The objective was to investigate the historical variation in surface O3-8h-90per (from 2013 to 2020) and PM2.5 (from 2001 to 2020) in NEC on the basis of the Tracking Air Pollution in China (TAP) dataset. The remainder of this paper is structured as follows: Section 2 describes the study area and data sources. Section 3 presents the multi-year variation in O3-8h-90per and PM2.5 at temporal (annual, seasonal, monthly, and interannual) and spatial (regional and city level) scales. Section 4 and Section 5 discusses and presents the conclusions of this study.

2. Materials and Methods

NEC (120°–135° E, 40°–53° N) mainly encompasses Heilongjiang, Jilin, and Liaoning provinces. Liaoning is the most populous province and lies in the south of NEC. To the north lie Jilin and the northernmost province of Heilongjiang, which are the major agricultural provinces in the region. Precipitation is highest in summer; winter is characterized by snowfall, and surface snow usually remains for prolonged periods. NEC is the snowiest region in China. In recent years, the intensification of anthropogenic pollutants and adverse meteorological conditions have resulted in numerous processes causing air pollution. In cities with severe pollution and low air quality, particulate matter and O3 have become the primary pollutants [40,41,42].
The TAP dataset was developed at Tsinghua University as a cooperative effort among several institutions and teams [43]. The aim was to build a multiscale, near-real-time aerosol and gaseous pollutant concentration database in China and provide essential support for pollution characteristics analysis. The TAP database was generated using state-of-the-art technology involving machine learning algorithms [44,45]. The TAP data are determined based on the combination of multisource data including ground measurements, satellite aerosol optical parameter retrievals, model simulations, and meteorology field, land use information as well as population, and elevation data by multilayer machine learning models. It integrates real-time ground observations, near-real-time satellite remote sensing information, and air quality model simulation with multisource big data and provides near-real-time data while ensuring complete spatiotemporal coverage. Surface O3-8h-90per concentration data (2013–2020) and PM2.5 data (2001–2020) for China with a 10 km resolution can be downloaded from http://tapdata.org (last access: 1 July 2021). To our knowledge, this is the first study to use the pollutant concentration data from the newly released TAP dataset for investigating pollution in NEC. In this paper, we define O3-8h-90per as the average daily maximum O3-8h-90 per at the 90th percentile level. The annual, monthly, and seasonal average of O3-8h-90per data was used to analyze the temporal scale and spatial distribution of O3 (μg/m3), with the minimum value of 1.00 μg/m3.
Population data were obtained from the combined datasets of the GPWv3 (Gridded Population of the World available at 5-year intervals from 1990 to 2000) and GPWv4 (available at 5-year intervals from 2000 to 2020), from the NASA Socioeconomic Data and Applications Center (http://sedac.ciesin.columbia.edu/, last accessed on 1 July 2021), and the resolution was 2.5 arcminute.
The monthly mean tropospheric NO2 column data from 2005 to 2020 were retrieved from the Ozone Monitoring Instrument (OMI) observations with a spatial resolution of 0.125° and are available from the Tropospheric Emission Monitoring Internet Service (TEMIS; https://www.temis.nl/index.php/, last accessed 1 July 2021). Four key meteorological factors, precipitation (PPT), wind speed (WS), boundary layer height (BLH), and temperature at 2 m (T2) from 2001 to 2020, were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5; https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset/, last access: 1 July 2021) with a spatial resolution of 0.25° × 0.25°.

3. Results

3.1. Annual Distribution of O3-8h-90per and PM2.5 in NEC

The spatial distribution of the mean annual O3-8h-90per concentrations in NEC from 2013 to 2020 is presented in Figure 2a. The O3-8h-90per concentration was highest in Liaoning (>100 μg/m3); that in northern, western, and southern Liaoning and the Bohai Rim area reached 120 μg/m3. By contrast, the O3-8h-90per concentration in eastern Liaoning was lower, at approximately 90 μg/m3. The O3-8h-90per concentration in Jilin was lower than that in Liaoning and distributed in central and western Jilin. The highest O3-8h-90per concentration was approximately 90–100 μg/m3, and that in other areas of Jilin was approximately 80 μg/m3. Compared with the spatial distribution of O3-8h-90per concentrations in Liaoning and Jilin, the O3-8h-90per concentration in Heilongjiang was relatively low at approximately 80 μg/m3.
Figure 2b depicts the distribution of the multiyear mean concentrations of PM2.5 in NEC. Compared with the high O3-8h-90per concentration in western Liaoning, the high PM2.5 concentration in NEC was distributed mainly in the urban areas of central Liaoning, the Harbin–Changchun urban agglomeration, and other areas where industrial production is expanding and populations growing. Zhao et al. (2021) [46] reported that PM2.5 was concentrated mostly in central Liaoning, western Jilin, and Heilongjiang. The highest PM2.5 concentration was approximately 60 μg/m3 and occurred in Shenyang, Changchun, Harbin, and other provincial capitals of NEC. With approximately 50 μg/m3, the PM2.5 concentration in western Liaoning was higher than that in other areas. The distribution range of high PM2.5 concentrations in western Liaoning was not as large as that of O3-8h-90per concentrations. Except for the central cities, the PM2.5 concentration in Jilin was approximately 30–40 μg/m3, and the lowest PM2.5 concentration was approximately 20 μg/m3 in the eastern and western marginal areas of Jilin. At approximately 10–20 μg/m3, the PM2.5 concentration in northern Heilongjiang was relatively low.
The spatial distribution of O3-8h-90per (from 2013 to 2020) and PM2.5 (from 2001 to 2020) concentrations in NEC was closely related to the environmental background, population density, and meteorological condition. The O3-8h-90per pollution in NEC was concentrated mainly in central and western Liaoning, followed by central Jilin; the overall O3-8h-90per concentration in Heilongjiang was low. However, high levels of PM2.5 were observed throughout the densely populated areas of the three provinces. Due to rapid and geographically distinct urbanization patterns, numerous anthropogenic pollution sources are likely to have led to a regional imbalance in the distribution of PM2.5 concentrations. However, the spatial distribution of high PM2.5 concentrations was inconsistent with that of high O3-8h-90per concentrations. This indicates that although pollutants can increase O3 precursors to a certain extent, they are not the main factors affecting the distribution of O3-8h-90per concentrations in NEC. In particular, important air pollutants such as volatile organic compounds (VOCs) are also important precursors of PM2.5, and various factors affect the correlation between O3-8h-90per and PM2.5. The existence of a high concentration of O3 in the atmosphere enhances oxidation of the atmosphere, which is conducive to the formation of secondary particulate matter and thus an increase in PM2.5 pollution. The presence of a large amount of PM2.5 weakens the solar radiation reaching the near ground, reducing the photodecomposition reaction rate of O3.

3.2. Seasonal Distribution of O3-8h-90per and PM2.5 in NEC

In this study, the four seasons were spring (March, April, and May), summer (June, July, and August), autumn (September, October, and November) and winter (December, January, and February). The O3-8h-90per concentrations were higher in spring and summer, followed by autumn and winter (Figure 3a). In spring, the highest O3-8h-90per concentrations, approximately 120–130 μg/m3, were observed in central and western Liaoning. At approximately 120 μg/m3, the O3-8h-90per concentration in central Jilin was relatively high. At approximately 90–100 μg/m3, the O3-8h-90per concentration throughout Heilongjiang was relatively low. In summer, the O3-8h-90per concentrations increased considerably throughout Liaoning, and the highest O3-8h-90per concentration was observed in central Liaoning and the Bohai Rim region (150 μg/m3). The next-highest values were observed in central and western Jilin (approximately 120 μg/m3) and Heilongjiang (approximately 90–100 μg/m3). In autumn, the O3-8h-90per concentration in NEC decreased markedly. The O3-8h-90per concentration in central Liaoning was approximately 100–110 μg/m3, and that in Jilin and Heilongjiang decreased to 70–80 μg/m3. In winter, the O3-8h-90per concentration in NEC reached one of the lowest values of the whole year, approximately 60 μg/m3. The O3-8h-90per concentrations in some areas of Jilin were relatively high, approximately 70–80 μg/m3. Favorable meteorological conditions such as high temperatures and strong solar radiation can contribute to high O3-8h-90per concentrations. In addition, the abundant carbon aerosols produced by biomass burning in spring affect the generation of O3-8h-90per [47].
Unlike the distribution of O3-8h-90per concentrations, that of PM2.5 concentrations increased considerably in winter, followed by that in spring and autumn. The lowest PM2.5 concentrations were observed in summer (Figure 3b). In spring, the highest PM2.5 concentrations were observed in central Liaoning and central Jilin (approximately 60 μg/m3). The PM2.5 concentration in a typical city reaches 80 μg/m3. By contrast, the PM2.5 concentration in Heilongjiang was relatively low, approximately 20–30 μg/m3. In summer, the PM2.5 concentration in NEC decreased. Except for the PM2.5 concentrations in some central cities of Liaoning, which exhibited values of approximately 50–60 μg/m3, the PM2.5 concentration in NEC was lower than 30 μg/m3. In autumn, the PM2.5 concentration in central Liaoning increased considerably. The highest concentration was approximately 80 μg/m3, and the PM2.5 concentration in western Liaoning exhibited an increasing trend, reaching approximately 50 μg/m3. Similarly, the PM2.5 concentration was approximately 50 μg/m3 in central Jilin. In autumn, the PM2.5 concentrations in most parts of Heilongjiang were low, approximately 20–30 μg/m3. In winter, a peak in PM2.5 concentration was observed in NEC. The highest PM2.5 concentration was approximately 90 μg/m3 in central Liaoning, central Jilin, and central as well as western Heilongjiang. The PM2.5 concentration in NEC was approximately 50–60 μg/m3. By contrast, the PM2.5 concentration in northern Heilongjiang was low, approximately 20 μg/m3.
The seasonal distribution of O3-8h-90per and PM2.5 concentrations in NEC indicates that the PM2.5 concentration was the highest in winter (affected mainly by emissions from coal burning), whereas the winter O3-8h-90per concentration reached one of the lowest values of the whole year. This indicates that favorable meteorological conditions such as high temperatures and strong solar radiation were crucial factors affecting the formation of O3. Zhao et al. (2019) [48] reported that low concentrations of O3 were observed during winter (when heating is used), whereas relatively higher concentrations were observed in spring, when longer and stronger solar radiation drives the photochemical processes that lead to the formation of O3 and heating is not used. Xia et al. (2021) [49] also found that high values of daytime maximum, nighttime minimum, and diurnal difference of summer ozone concentration occurred in several city agglomerations of northern and southern China.

3.3. Monthly Distribution of O3-8h-90per and PM2.5 in NEC

Figure 4a depicts the monthly mean distribution of O3-8h-90per concentrations in NEC from 2013 to 2020. November, December, and January exhibited low O3-8h-90per concentrations with a monthly mean of approximately 70 μg/m3.
The monthly mean began to increase in February, and the highest means were observed in central Liaoning, central Jilin, and eastern Heilongjiang, at approximately 90 μg/m3. Beginning in March, the monthly mean O3-8h-90per concentration increased considerably. It increased to 90 μg/m3 in NEC and to 100–110 μg/m3 in central Liaoning and Jilin. In April, the monthly mean increased further, and the highest concentration (approximately 130–140 μg/m3) was observed in central Liaoning. In May and June, the spatial distributions of the monthly mean O3-8h-90per concentrations were similar. The monthly mean O3-8h-90per concentrations in Liaoning increased considerably, and the highest value (170–180 μg/m3) was observed in central and western Liaoning, followed by central and western Jilin (140–150 μg/m3) and other regions (100–110 μg/m3). Beginning in July, the monthly mean O3-8h-90per concentrations and their distribution range gradually decreased in NEC. High monthly mean O3-8h-90per concentrations (approximately 160 μg/m3) were still observed in central and western Liaoning as well as central and western Jilin (130–140 μg/m3). In August, the highest monthly mean O3-8h-90per concentration in NEC was approximately 130–140 μg/m3 in central and western Liaoning and approximately 90–100 μg/m3 in Jilin and Heilongjiang. After September, the monthly mean O3-8h-90per concentrations in NEC began to decrease, and the monthly mean in northern Heilongjiang decreased more rapidly than in other regions. From September to October, the O3-8h-90per concentration in NEC continued to decrease, and the spatial distribution of low monthly concentrations gradually increased. The highest monthly mean O3-8h-90per concentrations were observed in central and western Liaoning, approximately 130 μg/m3. The O3-8h-90per concentrations in most parts of Jilin and Heilongjiang were approximately 90 μg/m3, and the lowest O3-8h-90per concentration (approximately 70 μg/m3) was observed in northern Heilongjiang.
Figure 4b depicts the monthly mean distribution of PM2.5 concentrations in NEC. The PM2.5 concentrations in NEC were the highest in January, February, and December, and the highest monthly mean O3-8h-90per concentration was approximately 100 μg/m3.
The highest PM2.5 concentration (approximately 100 μg/m3) in NEC was observed in January in central Liaoning and the center of the Harbin–Changchun urban agglomeration. In February, the PM2.5 concentration was lower than that in January but remained high at approximately 80–90 μg/m3. Beginning in March, the mean PM2.5 concentration in NEC exhibited a declining trend, with a mean PM2.5 concentration of approximately 60–70 μg/m3 and a higher mean PM2.5 concentration of approximately 80 μg/m3 in cities. In April, the monthly mean PM2.5 concentrations in NEC began to decline gradually, with approximately 50–60 μg/m3 observed in Liaoning and 40 μg/m3 observed in other regions. From May to September, the monthly mean PM2.5 concentration in NEC decreased to approximately 20 μg/m3. In October, the monthly mean PM2.5 concentrations in central Liaoning and central Jilin began to increase to approximately 60 μg/m3. The monthly mean PM2.5 concentration in NEC increased considerably in November, with the highest PM2.5 concentration (approximately 90 μg/m3) observed in central and western Liaoning, Jilin, and Heilongjiang. In December, the monthly mean PM2.5 concentration and its distribution range continued to increase in NEC. The highest PM2.5 concentrations were observed in central Liaoning and the Harbin–Changchun urban agglomeration, with the monthly mean PM2.5 concentration reaching 100 μg/m3.
The spatial distribution of these monthly variations in O3-8h-90per and PM2.5 concentrations revealed that O3-8h-90per and PM2.5 pollution in NEC exhibit unique and time-dependent distribution characteristics. December–February is the main period of PM2.5 pollution in NEC, whereas May–July is the main period of O3 pollution. Ma et al. (2021a) [50] investigated that the temperature could alter PM2.5 and O3 through both physical and chemical processes over the North China Plain.

3.4. Interannual Distribution and Trends of O3-8h-90per and PM2.5 in NEC

We analyzed the spatial distribution of the interannual means of O3-8h-90per concentration in NEC from 2013 to 2020 (Figure 5a). From 2013 to 2015, the annual mean O3-8h-90per concentrations in NEC were low (approximately 80–90 μg/m3), and the annual mean O3-8h-90per concentrations in typical areas in central Liaoning were relatively high (approximately 110–120 μg/m3). Beginning in 2016, the annual mean distribution of O3-8h-90per in Liaoning exhibited a considerable increasing trend, in terms of both concentration and regional distribution. In 2016, high O3-8h-90per concentrations in central Liaoning and the Bohai Rim area were observed mainly in the central regions, and the annual maximal O3-8h-90per concentration reached approximately 130 μg/m3. In the same period, the annual mean O3-8h-90per concentrations in central Jilin were also high. From 2017 to 2020, the spatial distribution of the high annual O3-8h-90per concentrations in Liaoning exhibited an expanding trend, and the annual mean O3-8h-90per concentration increased to 130–140 μg/m3 in western and northern Liaoning. At the same time, the annual mean O3-8h-90per concentrations in central and western Jilin increased to 110–120 μg/m3. Compared with Liaoning and Jilin, the spatial distribution of the annual mean O3-8h-90per concentrations in Heilongjiang exhibited only little change. The annual mean spatial distribution of O3-8h-90per concentration in Heilongjiang increased from 70–80 μg/m3 in 2013 to approximately 90–100 μg/m3 in 2020.
We analyzed the interannual variation in PM2.5 concentrations in NEC from 2001 to 2020 (Figure 5b).
From 2001 to 2014, the spatial distribution and regional variation of PM2.5 concentrations in NEC were consistent. The high PM2.5 concentrations were distributed in a zonal pattern, with the highest value reaching 80 μg/m3, observed in the central Liaoning city cluster, central Jilin, and areas adjacent to southern Heilongjiang. In 2015, the areas in NEC with high PM2.5 exhibited a decreasing trend. These areas were concentrated in Liaoning, Jilin, Heilongjiang, and several typical cities, and the higher PM2.5 concentration exhibited a point-like distribution with values reaching 80 μg/m3. In 2016, the annual mean PM2.5 concentration in NEC decreased considerably, with the highest PM2.5 concentration, 50–60 μg/m3, distributed among Liaoning, Jilin, and central Heilongjiang. In 2017, the area exhibiting high PM2.5 concentrations decreased, and the highest PM2.5 concentrations (approximately 50 μg/m3) were observed in northern Liaoning as well as in central Jilin and Heilongjiang. In particular, from 2018 to 2020, the PM2.5 concentration in NEC decreased considerably and reached approximately 40 μg/m3 in typical cities such as Shenyang, Changchun, and Harbin. According to the analyzed data, the interannual characteristics of the spatial variation of O3-8h-90per and PM2.5 reveal that the O3-8h-90per concentration in NEC changed mainly regionally, exhibiting an increasing annual trend. By contrast, the PM2.5 concentration exhibited a zonal distribution characteristic from the southwest to the northeast, and the variation of PM2.5 concentrations decreased annually.
To determine the interannual variations of O3-8h-90per and PM2.5 concentrations in NEC, we investigated the monthly and annual variations of O3-8h-90per and PM2.5 concentrations in the three studied provinces.
As depicted in Figure 6A, the interannual variation of the monthly mean O3-8h-90per concentration in NEC exhibited obvious periodic variation during 2013–2020. The highest and lowest O3-8h-90per concentrations were observed from May to July and December to January, respectively. The monthly mean O3-8h-90per concentrations exhibited an increasing trend in Liaoning, Jilin, and Heilongjiang, respectively. By contrast, the interannual variation of the monthly mean PM2.5 concentration during 2001–2020 was not as substantial as the periodic variation of O3-8h-90per (Figure 6B). The highest PM2.5 concentration was observed in January and February, and the lowest was observed in August and September. The PM2.5 concentrations in Liaoning, Jilin, and Heilongjiang exhibited a small monthly decreasing trend, respectively.
We also investigated the trend in the annual mean variation of O3-8h-90per from 2013 to 2020 and PM2.5 concentrations from 2001 to 2020 in NEC. As depicted in Figure 7A, the annual mean increase of O3-8h-90per concentration exhibited slopes of 4.25, 3.13, and 2.35 (μg/m3)/yr in Liaoning, Jilin, and Heilongjiang, respectively. In 2013, the annual mean O3-8h-90per concentrations in Liaoning, Jilin, and Heilongjiang were low, approximately 91, 81, and 75 μg/m3, respectively; in 2014, they increased to approximately 100, 90, and 82 μg/m3; in 2015, they decreased to approximately 95, 87, and 78 μg/m3. In 2016, the annual mean O3-8h-90per concentrations in Liaoning, Jilin, and Heilongjiang increased rapidly to 120, 104, and 90 μg/m3, respectively, and continued increasing until 2019. In 2020, the annual mean O3-8h-90per concentrations decreased to approximately 117 μg/m3 in Liaoning and 102 μg/m3 in Jilin and increased to 92 μg/m3 in Heilongjiang.
We also investigated the interannual trend of PM2.5 concentrations in NEC from 2001 to 2020. As depicted in Figure 7B, the PM2.5 concentrations in Liaoning, Jilin, and Heilongjiang exhibited an annual declining trend with slopes of −0.90, −0.45, and −0.11 (μg/m3)/yr, respectively.
From 2001 to 2005, the annual mean PM2.5 concentrations in Liaoning, Jilin, and Heilongjiang were relatively low, namely, 65, 50, and 38 μg/m3, respectively; the highest PM2.5 concentrations were approximately 75, 57, and 46 μg/m3, respectively, and were observed in 2003. In 2007, the PM2.5 concentrations increased to approximately 85, 70, and 50 μg/m3 in Liaoning, Jilin, and Heilongjiang, respectively. Subsequently, the PM2.5 concentrations in NEC exhibited a substantial increasing trend, with the annual mean concentrations increasing to 75, 65, and 58 μg/m3 in Liaoning, Jilin, and Heilongjiang, respectively, until 2013. After 2013, the PM2.5 concentration decreased considerably in NEC, and the annual minimal PM2.5 concentration was approximately 35 μg/m3 in Liaoning, Jilin, and Heilongjiang from 2018 to 2019. After 2020, the annual mean PM2.5 concentrations in the three provinces increased, with an annual mean of approximately 40–45 μg/m3. The time series of O3-8h-90per and PM2.5 in NEC indicates that the O3-8h-90per concentrations in the three provinces increased annually, whereas the PM2.5 concentration fluctuated and decreased. The inflection point of PM2.5 concentration change was observed in 2013. This might be related to air control policies that were implemented in NEC that year. As PM2.5 emissions decreased annually, the O3-8h-90per concentrations exhibited an increasing trend. This highlights the relevance of studying the mechanisms and interaction of PM2.5 that affect the formation of surface O3. Shao et al. (2021) [51] found that the significance of ozone enhancement due to PM2.5 dropping depends on both the PM2.5 levels and optical properties of particles in many mega-cities in China.

3.5. Interannual Variation and Trends of O3-8h-90per at the City Level in NEC

In this section, we present a comparison of city-level interannual variation in O3-8h-90per and PM2.5 concentrations in 35 cities. In Liaoning, the following 14 cities were included: Dandong, Fushun, and Benxi in the east, Jinzhou, Huludao, Fuxin, and Chaoyang in the west, Yingkou, Dalian, and Panjin in the south, Tieling in the north, and Shenyang, Liaoyang, and Anshan in central Liaoning. In Jilin, the following nine cities were included: Yanbian and Baishan in the east, Baicheng, Songyuan, and Siping in the west, Tonghua in the south, and Changchun, Jilin, and Liaoyuan in central Jilin. In Heilongjiang, the following 12 cities were included: Yichun, Qitaihe, Mudanjiang, Jiamusi, Shuangyashan, Hegang, Jixi, Qiqihar, and Daqing in the west, Harbin in the south, Heihe in the north, and Suihua in central Heilongjiang.
As depicted in Figure 8a, the interannual variations in O3-8h-90per concentrations in different cities exhibited distinct trends in increases and increase ranges.
From 2013 to 2015, the annual fluctuation variation in O3-8h-90per concentrations in different cities in Liaoning was small, and the concentration was approximately 90–110 μg/m3. In 2016, the annual mean O3-8h-90per concentration in Panjin, Yingkou, and Dalian (southern Liaoning) began to increase considerably, whereas the O3-8h-90per concentration remained at 120–130 μg/m3. Jinzhou, Huludao, Fuxin, and Chaoyang (western Liaoning) and Tieling (northern Liaoning) exhibited the next-highest increase and an O3-8h-90per concentration of approximately 120 μg/m3. The O3-8h-90per concentration in Shenyang, Liaoyang, and Anshan (central Liaoning) was approximately 100 μg/m3. The lowest O3-8h-90per concentration was observed in Dandong, Fushun, and Benxi (eastern Liaoning) with a concentration of approximately 90–100 μg/m3 in 2019–2020. The O3-8h-90per concentrations in cities in Jilin were lower than those in Liaoning. From 2013 to 2015, the annual flat variation in O3-8h-90per concentrations in Jilin was small, and the concentration was approximately 80 μg/m3. In 2016, the O3-8h-90per concentration in Changchun, Jilin, and Liaoyuan (central Jilin) began to increase to approximately 110 μg/m3. Similarly, higher O3-8h-90per concentrations (100 μg/m3) were recorded in Baicheng, Songyuan, and Siping (western Jilin). The O3-8h-90per concentration in Tonghua (southern Jilin) was approximately 90 μg/m3 from 2019 to 2020. The O3-8h-90per concentration (approximately 80 μg/m3) was rather low in Yanbian and Baishan (eastern Jilin). The city-level O3-8h-90per concentration in Heilongjiang was lower than that in Liaoning and Jilin. From 2013 to 2015, the annual flat variation in O3-8h-90per concentrations in Heilongjiang was approximately 70–80 μg/m3. In 2016, the O3-8h-90per concentration exhibited an overall small increase. The highest O3-8h-90per concentration was approximately 100 μg/m3 in Daqing (western Heilongjiang). The O3-8h-90per concentration was approximately 90 μg/m3 in Harbin and Suihua (western and central Heilongjiang, respectively). The annual mean variation in O3-8h-90per concentrations in other areas of Heilongjiang from 2019 to 2020 was approximately 80 μg/m3.
These results reveal that the distribution of high city-level O3-8h-90per concentrations in NEC was consistent with the spatial distribution of ground temperature. Under the combined influence of high temperatures and solar radiation, the O3 formation is enhanced. In NEC, the mean temperature in various cities was closely related to distinct meteorological factors, such as the location of land and sea, latitude, topography, and altitude. Studies have reported that rising temperatures could be related to high O3 pollution in NEC [52,53]. The temperature in Liaoning increased from east to west and from north to south. Therefore, the O3-8h-90per concentrations in Huludao, Panjin, and Dalian (southwestern Liaoning) were high, whereas those in Fushun and Benxi (eastern Liaoning) were low. A similar temperature difference between east and west was observed in Jilin. The temperature was relatively high in western Jilin, especially in Baicheng and Siping (southwest Jilin); this high temperature was conducive to the formation of O3-8h-90per. The temperature in Baishan and Yanbian (eastern Jilin) was relatively low; therefore, the O3-8h-90per concentration was low. The temperature was relatively high in southern Heilongjiang; therefore, the cities with high O3-8h-90per concentration were distributed mostly in southern Heilongjiang. The northern cities exhibited low O3-8h-90per concentrations.
The city-level trends of PM2.5 in NEC were opposite to those of O3-8h-90per, and several cities exhibited a considerable decrease in PM2.5 (Figure 8b).
From 2001 to 2015, the annual PM2.5 concentrations in different regions of Liaoning exhibited high variation, and the highest PM2.5 concentration was approximately 70 μg/m3 in Shenyang, Liaoyang, and Anshan (central Liaoning). This was followed by that in Tieling (northern Liaoning), with a maximum PM2.5 concentration of approximately 60–70 μg/m3. In Jinzhou, Huludao, Fuxin, and Chaoyang (western Liaoning), the highest PM2.5 concentration decreased to 60 μg/m3. In Dandong, Fushun, and Benxi (eastern Liaoning) as well as Yingkou, Dalian, and Panjin (southern Liaoning), the PM2.5 concentration was approximately 35–55 μg/m3. In 2016, the city-level PM2.5 concentration in Liaoning began to decrease to approximately 25–35 μg/m3 until 2020. From 2001 to 2015, the city-level PM2.5 concentrations in some areas of Jilin were relatively high. The highest PM2.5 concentration in Jilin was observed in Changchun (central Jilin) at approximately 60 μg/m3. Siping and Liaoyuan (western and central Jilin, respectively) exhibited a PM2.5 concentration of approximately 55 μg/m3. Low PM2.5 concentrations (approximately 20 μg/m3) were observed in Yanbian and Baishan (eastern Jilin) and Tonghua (southern Jilin). The overall city-level PM2.5 concentration in Heilongjiang was lower than that in Liaoning and Jilin. From 2013 to 2015, the annual mean PM2.5 concentrations (approximately 50 μg/m3) were relatively large in Suihua and Harbin (central Heilongjiang). Daqing (western Heilongjiang) exhibited the next-highest value of 40 μg/m3. Qitaihe (eastern Heilongjiang) and Qiqihar (western Heilongjiang) exhibited a PM2.5 concentration of approximately 35 μg/m3. In 2016, the PM2.5 concentration decreased overall, and in 2019–2020, it was approximately 20–30 μg/m3.
Compared with the changes in O3-8h-90per concentration, the interannual variation of PM2.5 in NEC revealed PM2.5 pollution at the city level. The cities with high PM2.5 concentrations were characterized by industrial activity and large populations. Therefore, the reduction of environmental pollution caused a reduction in the contribution of PM2.5 to air pollution at the city level in NEC.
In order to discuss the potential impact of important meteorological factors and emission sources on O3-8h-90per and PM2.5, we further analyzed the annual variation of tropospheric NO2 column from 2005 to 2020, precipitation (PPT), wind speed (WS), boundary layer height (BLH), and temperature at 2 m (T2) from 2001 to 2020 in NEC. Figure 9 shows that the tropospheric NO2 column in NEC has been decreasing since 2012, which is consistent with the decreasing trend of the interannual variation of PM2.5, indicating the impact of the decreasing intensity of anthropogenic emission sources on PM2.5 concentration. From the interannual variation of meteorological elements in NEC, the higher wind speed and the increase of BLH are conducive to the diffusion of PM2.5 to a certain extent. It is worth noting that temperature at 2 m has shown a significant increasing trend since 2012. In addition to the increase of precursors, the rise of surface temperature may be the main meteorological driving factor affecting the O3-8h-90per concentration. In addition to emission and meteorological factors, the chemical mechanism affecting O3 and PM2.5 is also important and needs further study.

4. Discussion

With increasing urbanization, environmental air quality has become an increasingly critical public concern. The pollution caused by PM2.5 and O3-8h-90per has considerably affected people and residential environments. In almost ten years, surface O3-8h-90per and PM2.5 pollution has become an environmental concern. Therefore, studying the distribution and variation of surface O3 and PM2.5 concentrations has high theoretical and practical value and aids in further investigating the meteorological and chemical factors that determine regional pollution. From the perspective of formation mechanism and meteorology, many previous studies have conducted important research on the variation characteristics of O3 and PM2.5 concentration in different regions of China. Zhao et al. (2018) [54] pointed out that the PM2.5 could directly transport from one city to another city, while Tibetan Plateau may be an important source region of high ozone in Sichuan Basin of southwest China. Ma et al. (2021b) [55] illustrated that the increase in volatile organic compounds (VOCs) along with depletions in NO2 and CO significantly boosted the ozone photochemical production in North China plain. Wang et al. (2021) [56] showed that the annual summertime ozone over Central China was significantly correlated with the springtime thermal forcing, indicated by total atmospheric energy over Tibetan Plateau in an interdecadal timescale. During the COVID-19 lockdown period, the results of Yin et al. (2021) [57] suggest that conventional emission reduction of NOx could not be sufficient to reduce surface O3 concentration, and ozone pollution needs to be controlled by a variety of pollutants in central China. The significance of this study is to improve the scientific understanding of multi-year changes of O3-8h-90per and PM2.5 concentrations due to climatological characteristics in NEC. This paper focuses on the O3-8h-90per and PM2.5 pollution at different temporal and spatial scales, especially at city level in NEC. These results could provide reference on the formation mechanism of ozone and PM2.5 in NEC for further study. Moreover, the correlation between O3 and PM2.5 is related to the reactions with VOCs. Ozone could oxidize VOCs to less volatile products that likely partition to the particle phase. Therefore, collaborative control of O3 and PM2.5 must be accelerated to reduce regional haze pollution and photochemical smog events.

5. Conclusions

In this study, TAP O3-8h-90per and PM2.5 concentration data were used to analyze the multi-year variation of O3-8h-90per and PM2.5 concentrations at distinct temporal and spatial scales in NEC.
The concentrations of O3-8h-90per were highest in northern, western, and southern Liaoning and the Bohai Rim (up to 120 μg/m3). The highest O3-8h-90per concentrations (approximately 90–100 μg/m3) in Jilin were distributed in the central and western regions. The O3-8h-90per concentration in Heilongjiang was relatively small (approximately 80 μg/m3). The highest concentrations of PM2.5 were observed in Shenyang, Changchun, Harbin, and other major provincial cities in NEC (approximately 60 μg/m3). The spatial distribution of high PM2.5 concentrations was inconsistent with that of high O3-8h-90per concentrations. The spatial distribution of O3-8h-90per and PM2.5 was closely related to the meteorological factors, population density, and environmental impact. In addition, O3 could also oxidize pollutants in the air and increase PM2.5, while PM2.5 can reduce the solar radiation reaching the ground and decrease the rate of O3 photochemical formation.
The O3-8h-90per concentrations were highest in spring and summer, followed by autumn and winter. In spring, the highest O3-8h-90per concentrations (approximately 120–130 μg/m3) were observed in central and western Liaoning. Then, in summer, the O3-8h-90per concentration increased considerably in the whole province (to approximately 150 μg/m3). In autumn, the O3-8h-90per concentration in NEC decreased markedly until, in winter, the O3-8h-90per concentration reached one of its lowest values (approximately 60 μg/m3). By contrast, the distribution of PM2.5 concentration increased considerably in winter, followed by that in spring and autumn; the lowest PM2.5 concentration was observed in summer. In spring, the highest PM2.5 concentrations (approximately 60 μg/m3) were observed in central Liaoning and central Jilin. In summer, the PM2.5 concentration in NEC decreased. Then, in autumn and winter, the PM2.5 concentration in central Liaoning increased considerably, reaching approximately 80–90 μg/m3. The highest PM2.5 concentrations in winter were affected mainly by emissions, whereas meteorological conditions such as high temperatures and strong solar radiation were conducive to the formation of O3.
The monthly mean O3-8h-90per concentration was approximately 70 μg/m3, with low concentrations observed in November, December, and January. In May and June, the spatial distribution of the monthly mean O3-8h-90per concentration increased substantially, and the highest O3-8h-90per concentrations were observed in central and western Liaoning (170–180 μg/m3), followed by central and western Jilin (140–150 μg/m3) and other regions (100–110 μg/m3). By contrast, the PM2.5 concentrations (approximately 100 μg/m3) were higher in December, January, and February. The highest PM2.5 concentration was observed in January in central Liaoning and the central part of the Harbin–Changchun urban agglomeration; the highest PM2.5 concentration was approximately 100 μg/m3. In February, the PM2.5 concentration was lower than that of January, but remained high (approximately 80–90 μg/m3).
From 2013 to 2015, the annual mean O3-8h-90per concentration was low (approximately 80–90 μg/m3). In 2016, the annual mean distribution of O3-8h-90per in Liaoning began to considerably increase, both in concentration level and in regional distribution. From 2001 to 2014, the highest PM2.5 concentrations were distributed in a zonal pattern, with the highest concentrations (80 μg/m3) observed in central Liaoning, central Jilin, and the areas adjacent to southern Heilongjiang. In 2015, the PM2.5 concentrations in Liaoning, Jilin, Heilongjiang, and other typical cities exhibited a decreasing trend, reaching approximately 80 μg/m3. The interannual variation of the monthly mean O3-8h-90per concentrations exhibited a marked periodic variation during 2013–2020. The monthly mean O3-8h-90per concentrations in Liaoning, Jilin, and Heilongjiang exhibited an increasing trend, while the PM2.5 concentrations showed a small monthly decreasing trend, respectively. The annual mean increases of O3-8h-90per concentration in the three provinces exhibited slopes of 4.25, 3.13, and 2.35 (μg/m3)/yr in Liaoning, Jilin, and Heilongjiang, respectively, and the PM2.5 concentrations in Liaoning, Jilin, and Heilongjiang exhibited annual decreasing trends with slopes of −0.90, −0.45 and −0.11 (μg/m3)/yr, respectively.
In 2016, the annual mean O3-8h-90per concentrations in Panjin, Yingkou, and Dalian (southern Liaoning) began to increase markedly (120–130 μg/m3). In 2016, the O3-8h-90per concentrations in Changchun, Jilin, and Liaoyuan (central Jilin) were relatively high (approximately 110 μg/m3). From 2013 to 2015, the annual flat variation of O3-8h-90per concentration in Heilongjiang was approximately 70–80 μg/m3. The annual mean variation in O3-8h-90per concentrations in other areas of Heilongjiang from 2019 to 2020 was approximately 80 μg/m3. The temperature distribution in Liaoning increased from east to west and from north to south. The city-level distribution of high O3-8h-90per concentrations was consistent with the spatial distribution of ground temperatures.
From 2001 to 2015, the annual PM2.5 concentration in various regions of Liaoning exhibited high variation. In 2016, the city-level PM2.5 concentration in Liaoning began to decrease, reaching approximately 25–35 μg/m3 in 2020. From 2001 to 2015, the city-level PM2.5 concentrations in some areas of Jilin were high. From 2013 to 2015, the annual mean PM2.5 concentrations were relatively high in Suihua and Harbin (central Heilongjiang), approximately 50 μg/m3; Subsequently, the PM2.5 concentration decreased overall, reaching approximately 20–30 μg/m3 in 2019–2020. The cities with high PM2.5 concentration were characterized by industrial activity and high population densities. Since 2012, the interannual variation of tropospheric NO2 has been decreasing, which is consistent with PM2.5, indicating the influence of anthropogenic emission sources on the level of PM2.5; on the contrary, the temperature at 2 m showed a significant increasing trend, which had an important impact on the increase of O3-8h-90per concentration The objective of this study was to elucidate the multi-year variation and current levels of O3 and PM2.5 pollution in NEC and provide crucial scientific support for regional air pollution prevention and control measures in China.

Author Contributions

Conceptualization, writing—original draft preparation, H.Z. and K.G.; data curation, Y.M. and Y.W. (Yangfeng Wang); investigation, Y.W. (Yaqiang Wang) and H.W.; writing—review and editing, Y.Z. (Yu Zheng), L.L., L.Z. and Y.Z. (Yuqi Zhang); conceptualization, writing—review and editing, H.C. and X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work is financially supported by grant from National Natural Science Foundation of China (41875157, 41825011 & 42030608), Basic Scientific Research Fund of Shenyang Institute of Atmospheric Environment (2021SYIAEZD3, 2020SYIAEZD1), Science and Technology Development Fund of Chinese Academy of Meteorological Sciences (2019KJ001), Key R&D Program of Science and Technology Department of Liaoning Province (2019JH8/10300094, 2019JH8/10300095). Tsinghua University’s Tracking Air Pollution team is gratefully acknowledged for making the fine particulate matter concentration data and 10 km resolution near-surface ozone concentration data publicly accessible. The authors would like to thank the anonymous reviewers and the editor for their constructive suggestions and comments.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lefohn, A.S.; Malley, C.S.; Smith, L.; Wells, B.; Hazucha, M.; Simon, H.; Naik, V.; Mills, G.; Schultz, M.G.; Paoletti, E.; et al. Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research. Elem. Sci. Anthr. 2018, 6, 28. [Google Scholar] [CrossRef] [Green Version]
  2. Lelieveld, J.; Evans, J.S.; Fnais, M.; Giannadaki, D.; Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 2015, 525, 367–371. [Google Scholar] [CrossRef] [PubMed]
  3. Monks, P.S.; Archibald, A.T.; Colette, A.; Cooper, O.; Coyle, M.; Derwent, R.; Fowler, D.; Granier, C.; Law, K.S.; Mills, G.E.; et al. Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer. Atmos. Chem. Phys. 2015, 15, 8889–8973. [Google Scholar] [CrossRef] [Green Version]
  4. Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. Climate Change 2013: The Physical Science Basis; Cambridge University Press: New York, NY, USA, 2013. [Google Scholar] [CrossRef] [Green Version]
  5. Sillman, S. The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos. Environ. 1999, 33, 1821–1845. [Google Scholar] [CrossRef]
  6. NRC. Rethinking the Ozone Problem in Urban and Regional Air Pollution; National Academy Press: Washington, DC, USA, 1991. [Google Scholar]
  7. Trainer, M.; Parrish, D.D.; Goldan, P.D.; Roberts, J.; Fehsenfeld, F.C. Review of observation-based analysis of the regional factors influencing ozone concentrations. Atmos. Environ. 2000, 34, 2045–2061. [Google Scholar] [CrossRef]
  8. Vingarzan, R. A review of surface ozone background levels and trends. Atmos. Environ. 2004, 38, 3431–3442. [Google Scholar] [CrossRef]
  9. Fiore, A.M.; Jacob, D.J.; Field, B.D.; Streets, D.G.; Fernandes, S.D.; Jang, C. Linking ozone pollution and climate change: The case for controlling methane. Geophys Res. Lett. 2002, 29, 21–25. [Google Scholar] [CrossRef]
  10. Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef] [Green Version]
  11. Fiore, A.M.; Naik, V.; Spracklen, D.V.; Steiner, A.; Unger, N.; Prather, M.; Bergmann, D.; Cameron-Smith, P.J.; Cionni, I.; Collins, W.J.; et al. Global air quality and climate. Chem. Soc. Rev. 2012, 41, 6663–6683. [Google Scholar] [CrossRef] [Green Version]
  12. Atkinson, R. Atmospheric chemistry of VOCs and Nox. Atmos. Environ. 2000, 34, 2063–2101. [Google Scholar] [CrossRef]
  13. Roelofs, G.J.; Lelieveld, J.; van Dorland, R. A threedimensional chemistry/general circulation model simulation of anthropogenically derived ozone in the troposphere and its radiative climate forcing. J. Geophys. Res. 1997, 102, 23389–23401. [Google Scholar] [CrossRef]
  14. Bloomer, B.J.; Stehr, J.W.; Piety, C.A.; Salawitch, R.J.; Dickerson, R.R. Observed relationships of ozone air pollution with temperature and emissions. Geophys. Res. Lett. 2009, 36, 269–277. [Google Scholar] [CrossRef] [Green Version]
  15. Doherty, R.M.; Wild, O.; Shindell, D.T.; Zeng, G.; MacKenzie, I.A.; Collins, W.J.; Fiore, A.M.; Stevenson, D.S.; Dentener, F.J.; Schultz, M.G.; et al. Impacts of climate change on surface ozone and intercontinental ozone pollution: A multi-model study. J. Geophys. Res. 2013, 118, 3744–3763. [Google Scholar] [CrossRef] [Green Version]
  16. Dayan, U.; Levy, I. Relationship between synoptic-scale atmospheric circulation and ozone concentrations over Israel. J. Geophys. Res. Atmos. 2002, 107, 4813. [Google Scholar] [CrossRef]
  17. Camalier, L.; Cox, W.; Dolwick, P. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmos. Environ. 2007, 41, 7127–7137. [Google Scholar] [CrossRef]
  18. Che, H.; Gui, K.; Xia, X.; Wang, Y.; Holben, B.N.; Goloub, P.; Cuevas-Agulló, E.; Wang, H.; Zheng, Y.; Zhao, H.; et al. Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth. Atmos. Chem. Phys. 2019, 19, 10497–10523. [Google Scholar] [CrossRef] [Green Version]
  19. Gui, K.; Che, H.; Zeng, Z.; Wang, Y.; Zhai, S.; Wang, Z.; Luo, M.; Zhang, L.; Liao, T.; Zhao, H.; et al. Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model. Environ. Int. 2020, 141, 105801. [Google Scholar] [CrossRef]
  20. Huang, R.J.; Zhang, Y.L.; Bozzetti, C.; Ho, K.F.; Cao, J.J.; Han, Y.M.; Daellenbach, K.R.; Slowik, J.G.; Platt, S.M.; Canonaco, F.; et al. High secondary aerosol contribution to particulate pollution during haze events in China. Naturie 2014, 514, 218–222. [Google Scholar] [CrossRef] [Green Version]
  21. Lu, X.; Hong, J.Y.; Zhang, L.; Cooper, O.R.; Schultz, M.G.; Xu, X.B.; Wang, T.; Gao, M.; Zhao, Y.H.; Zhang, Y.H. Severe surface ozone pollution in China: A global perspective. Environ. Sci. Tech. Lett. 2018, 5, 487–494. [Google Scholar] [CrossRef]
  22. Ling, Z.H.; Guo, H.; Cheng, H.R.; Yu, Y.F. Sources of ambient volatile organic compounds and their contributions to photochemical ozone formation at a site in the Pearl River Delta, southern China. Environ. Pollut. 2011, 159, 2310–2319. [Google Scholar] [CrossRef]
  23. Wang, T.; Xue, L.K.; Brimblecombe, P.; Lam, Y.F.; Li, L.; Zhang, L. Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects. Sci. Total Environ. 2017, 575, 1582–1596. [Google Scholar] [CrossRef] [PubMed]
  24. Wang, T.; Dai, J.; Lam, K.S.; Nan Poon, C.; Brasseur, G.P. Twenty-five years of lower tropospheric ozone observations in tropical East Asia: The influence of emissions and weather patterns. Geophys. Res. Lett. 2019, 46, 11463–11470. [Google Scholar] [CrossRef]
  25. Zheng, J.Y.; Zhong, L.J.; Wang, T.; Louie, P.K.K.; Li, Z.C. Ground-level ozone in the Pearl River Delta region: Analysis of data from a recently established regional air quality monitoring network. Atmos. Environ. 2010, 44, 814–823. [Google Scholar] [CrossRef]
  26. Zhao, S.; Yu, Y.; Qin, D.; Yin, D.; Dong, L.; He, J. Analyses of regional pollution and transportation of PM2.5 and ozone in the city clusters of Sichuan Basin, China. Atmos. Pollut. Res. 2018, 10, 374–385. [Google Scholar] [CrossRef]
  27. Sun, L.; Xue, L.; Wang, Y.; Li, L.; Lin, J.; Ni, R.; Yan, Y.; Chen, L.; Li, J.; Zhang, Q.; et al. Impacts of meteorology and emissions on summertime surface ozone increases over central eastern China between 2003 and 2015. Atmos. Chem. Phys. 2019, 19, 1455–1469. [Google Scholar] [CrossRef] [Green Version]
  28. Zeng, J.; Zhang, L.; Yao, C.; Xie, T.; Rao, L.; Lu, H.; Liu, X.; Wang, Q.; Lu, S. Relationships between chemical elements of PM2.5 and O3 in Shanghai atmosphere based on the 1-year monitoring observation. J. Environ. Sci. 2020, 95, 49–57. [Google Scholar] [CrossRef]
  29. Liu, C.; Xing, C.; Hu, Q.; Li, Q.; Liu, H.; Hong, Q.; Tan, W.; Ji, X.; Lin, H.; Lu, C.; et al. Ground-based Hyperspectral Stereoscopic Remote Sensing Network: A Promising Strategy to Learn Coordinated Control of O3 and PM2.5 over China. Engineering 2021, in press. [Google Scholar] [CrossRef]
  30. Lee, Y.C.; Shindell, D.T.; Faluvegi, G.; Wenig, M.; Lam, Y.F.; Ning, Z.; Hao, S.; Lai, C.S. Increase of ozone concentrations, its temperature sensitivity and the precursor factor in South China. Tellus B 2014, 66, 23455. [Google Scholar] [CrossRef]
  31. He, J.; Gong, S.; Yu, Y.; Yu, L.; Wu, L.; Mao, H.; Song, C.; Zhao, S.; Liu, H.; Li, X.; et al. Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities. Environ. Pollut. 2017, 223, 484–496. [Google Scholar] [CrossRef]
  32. Wang, T.; Cheung, V.T.F.; Li, Y.S.; Anson, M. Ozone and relatedgaseous pollutants in the boundary layer of eastern china: Overview of the recent measurements at a rural site. Geophys. Res. Lett. 2001, 28, 2373–2376. [Google Scholar] [CrossRef] [Green Version]
  33. Gong, C.; Liao, H. A typical weather pattern for ozone pollution events in North China. Atmos. Chem. Phys. 2019, 19, 13725–13740. [Google Scholar] [CrossRef] [Green Version]
  34. Yin, Z.; Cao, B.; Wang, H. Dominant patterns of summer ozone pollution in eastern China and associated atmospheric circulations. Atmos. Chem. Phys. 2019, 19, 13933–13943. [Google Scholar] [CrossRef] [Green Version]
  35. Yang, Z.; Yang, J.; Li, M.; Chen, J.; Ou, C. Nonlinear and lagged meteorological effects on daily levels of ambient PM2.5 and O3: Evidence from 284 Chinese cities. J. Clean. Prod. 2021, 278, 123931. [Google Scholar] [CrossRef]
  36. Dai, H.; Zhu, J.; Liao, H.; Li, J.; Liang, M.; Yang, Y.; Yue, X. Co-occurrence of ozone and PM2.5 pollution in the Yangtze River Delta over 2013–2019: Spatiotemporal distribution and meteorological conditions. Atmos. Res. 2020, 249, 105363. [Google Scholar] [CrossRef]
  37. Wu, R.; Xie, S.D. Spatial distribution of ozone formation in China derived from emissions of speciated volatile organic compounds. Environ. Sci. Technol. 2017, 51, 2574–2583. [Google Scholar] [CrossRef]
  38. Ding, A.J.; Wang, T.; Xue, L.K.; Gao, J.; Stohl, A.; Lei, H.C.; Jin, D.Z.; Ren, Y.; Wang, X.Z.; Wei, X.L.; et al. Transport of north China air pollution by midlatitude cyclones: Case study of aircraft measurements in summer 2007. J. Geophys. Res. Atmos. 2009, 114, D08304. [Google Scholar] [CrossRef] [Green Version]
  39. Zhu, J.; Liao, H. Future ozone air quality and radiative forcing over China owing to future changes in emissions under the Representative Concentration PathFuture ozone air quality and radiative forcing ovways (RCPs). J. Geophys. Res. Atmos. 2016, 121, 1978–2001. [Google Scholar] [CrossRef] [Green Version]
  40. Zhao, H.; Che, H.; Zhang, L.; Gui, K.; Ma, Y.; Wang, Y.; Wang, H.; Zheng, Y.; Zhang, X. How aerosol transport from the North China plain contributes to air quality in northeast China. Sci. Total. Environ. 2020, 738, 139555. [Google Scholar] [CrossRef]
  41. Li, Y.; Zhao, H.; Wu, Y. Characteristics of particulate matter during haze and fog (pollution) episodes over Northeast China, Autumn 2013. Aerosol Air. Qual. Res. 2015, 15, 853–864. [Google Scholar] [CrossRef]
  42. Liu, N.; Ma, Y.; Ma, J.; Wang, Y.; Yang, S.; Li, L. Atmospheric extinction properties in Shenyang, China. Particuology 2015, 18, 120–126. [Google Scholar] [CrossRef]
  43. Geng, G.; Xiao, Q.; Liu, S.; Liu, X.; Cheng, J.; Zheng, Y.; Xue, T.; Tong, D.; Zheng, B.; Peng, Y.; et al. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion. Environ. Sci. Technol. 2021, 55, 12106–12115. [Google Scholar] [CrossRef] [PubMed]
  44. Xiao, Q.; Geng, G.; Cheng, J.; Liang, F.; Li, R.; Meng, X.; Xue, T.; Huang, X.; Kan, H.; Zhang, Q.; et al. Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models. Atmos. Environ. 2021, 244, 117921. [Google Scholar] [CrossRef]
  45. Xiao, Q.; Zheng, Y.; Geng, G.; Chen, C.; Huang, X.; Che, H.; Zhang, X.; He, K.; Zhang, Q. Separating emission and meteorological contribution to PM2.5 trends over East China during 2000−2018. Atmos. Chem. Phys. 2021, 2021, 9475–9496. [Google Scholar] [CrossRef]
  46. Zhao, H.; Gui, K.; Ma, Y.; Wang, Y.; Wang, Y.; Wang, H.; Zheng, Y.; Li, L.; Zhang, L.; Che, H.; et al. Climatology and trends of aerosol optical depth with different particle size and shape in northeast China from 2001 to 2018. Sci. Total Environ. 2021, 763, 142979. [Google Scholar] [CrossRef] [PubMed]
  47. Wu, Y.; Zhu, J.; Che, H.; Xia, X.; Zhang, R. Column-integrated aerosol optical properties and direct radiative forcing based on sun photometer measurements at a semi-arid rural site in Northeast China. Atmos. Res. 2015, 157, 56–65. [Google Scholar] [CrossRef]
  48. Zhao, H.; Ma, Y.; Wang, Y.; Wang, H.; Sheng, Z.; Gui, K.; Zheng, Y.; Zhang, X.; Che, H. Aerosol and gaseous pollutant characteristics during the heating season (winter–spring transition) in the Harbin-Changchun megalopolis, northeastern China. J. Atmos. Solar-Terr. Phys. 2019, 188, 26–43. [Google Scholar] [CrossRef]
  49. Xia, N.; Du, E.; Guo, Z.; Vries, W. The diurnal cycle of summer tropospheric ozone concentrations across Chinese cities: Spatial patterns and main drivers. Environ. Pollut. 2021, 286, 117547. [Google Scholar] [CrossRef]
  50. Ma, S.; Shao, M.; Zhang, Y.; Dai, Q.; Xie, M. Sensitivity of PM2.5 and O3 pollution episodes to meteorological factors over the North China Plain. Sci. Total Environ. 2021, 792, 148474. [Google Scholar] [CrossRef]
  51. Shao, M.; Wang, W.; Yuan, B.; Parrish, D.; Li, X.; Lu, K.; Wu, L.; Wang, X.; Mo, Z.; Yang, S.; et al. Quantifying the role of PM2.5 dropping in variations of ground-level ozone: Inter-comparison between Beijing and Los Angeles. Sci. Total Environ. 2021, 788, 147712. [Google Scholar] [CrossRef]
  52. Sun, F.H.; Yuan, J.; Lu, S. The change and test of climate in Northeast China over the last 100 years. Clim. Environ. Res. 2006, 11, 101–108. [Google Scholar] [CrossRef]
  53. Wang, L.; Wang, W.J.; Wu, Z.; Du, H.; Shen, X.; Ma, S. Spatial and temporal variations of summer hot days and heat waves and their relationships with large-scale atmospheric circulations across Northeast China. Int. J. Climatol. 2018, 38, 5633–5645. [Google Scholar] [CrossRef]
  54. Zhao, S.P.; Yu, Y.; Yin, D.Y.; Qin, D.H.; He, J.J.; Dong, L.X. Spatial patterns and temporal variations of six criteria air pollutants during 2015 to 2017 in the city clusters of Sichuan Basin, China. Sci. Total Environ. 2018, 624, 540–557. [Google Scholar] [CrossRef] [PubMed]
  55. Ma, M.; Yao, G.; Guo, J.; Bai, K. Distinct spatiotemporal variation patterns of surface ozone in China due to diverse influential factors. J. Environ. Manag. 2021, 288, 112368. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, Y.; Yan, Y.; Duan, K.; Kong, S.; Lin, J.; Zheng, H.; Song, A.; Zhang, Z. Effect of springtime thermal forcing over Tibetan Plateau on summertime ozone in Central China during the period 1950–2019. Atmos. Res. 2021, 261, 105723. [Google Scholar] [CrossRef]
  57. Yin, H.; Liu, C.; Hu, Q.; Liu, T.; Wang, S.; Gao, M.; Xu, S.; Zhang, C.; Su, W. Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China. Environ. Pollut. 2021, 289, 117899. [Google Scholar] [CrossRef]
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Figure 1. (a) Geography and city distribution and (b) population density in NEC.
Figure 1. (a) Geography and city distribution and (b) population density in NEC.
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Figure 2. Spatial distribution of mean annual concentrations of (a) O3-8h-90per averaged from 2013–2020 and (b) PM2.5 averaged from 2001–2020 in NEC.
Figure 2. Spatial distribution of mean annual concentrations of (a) O3-8h-90per averaged from 2013–2020 and (b) PM2.5 averaged from 2001–2020 in NEC.
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Figure 3. Spatial distribution of mean seasonal concentrations of (a) O3-8h-90per averaged from 2013–2020 and (b) PM2.5 averaged from 2001–2020 in NEC.
Figure 3. Spatial distribution of mean seasonal concentrations of (a) O3-8h-90per averaged from 2013–2020 and (b) PM2.5 averaged from 2001–2020 in NEC.
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Figure 4. Spatial distribution of mean monthly concentrations of (a) O3-8h-90per averaged from 2013–2020 and (b) PM2.5 averaged from 2001–2020 in NEC.
Figure 4. Spatial distribution of mean monthly concentrations of (a) O3-8h-90per averaged from 2013–2020 and (b) PM2.5 averaged from 2001–2020 in NEC.
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Figure 5. Spatial distribution of mean annual concentrations of (a) O3-8h-90per from 2013–2020 and (b) PM2.5 from 2001–2020 in NEC.
Figure 5. Spatial distribution of mean annual concentrations of (a) O3-8h-90per from 2013–2020 and (b) PM2.5 from 2001–2020 in NEC.
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Figure 6. Interannual trend of mean monthly concentrations of (A) O3-8h-90per from 2013–2020 and (B) PM2.5 from 2001–2020 in (a) Liaoning, (b) Jilin and (c) Heilongjiang province over NEC.
Figure 6. Interannual trend of mean monthly concentrations of (A) O3-8h-90per from 2013–2020 and (B) PM2.5 from 2001–2020 in (a) Liaoning, (b) Jilin and (c) Heilongjiang province over NEC.
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Figure 7. Interannual trend of mean annual concentrations of (A) O3-8h-90per from 2013–2020 and (B) PM2.5 from 2001–2020 in (a) Liaoning, (b) Jilin and (c) Heilongjiang province over NEC. Red solid lines indicate slopes with a linear trend and statistical significance at the 95% confidence level (p < 0.05).
Figure 7. Interannual trend of mean annual concentrations of (A) O3-8h-90per from 2013–2020 and (B) PM2.5 from 2001–2020 in (a) Liaoning, (b) Jilin and (c) Heilongjiang province over NEC. Red solid lines indicate slopes with a linear trend and statistical significance at the 95% confidence level (p < 0.05).
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Figure 8. Interannual trends of (a) O3-8h-90per from 2013–2020 and (b) PM2.5 from 2001–2020 in 35 cities of NEC.
Figure 8. Interannual trends of (a) O3-8h-90per from 2013–2020 and (b) PM2.5 from 2001–2020 in 35 cities of NEC.
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Figure 9. Interannual variation of region-averaged tropospheric NO2 columns, precipitation (PPT), wind speed (WS), boundary layer height (BLH), and temperature at 2 m (T2) in (a) Liaoning, (b) Jilin, and (c) Heilongjiang in NEC. Note that tropospheric NO2 columns data are available for the years 2005–2020.
Figure 9. Interannual variation of region-averaged tropospheric NO2 columns, precipitation (PPT), wind speed (WS), boundary layer height (BLH), and temperature at 2 m (T2) in (a) Liaoning, (b) Jilin, and (c) Heilongjiang in NEC. Note that tropospheric NO2 columns data are available for the years 2005–2020.
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Zhao, H.; Gui, K.; Ma, Y.; Wang, Y.; Wang, Y.; Wang, H.; Zheng, Y.; Li, L.; Zhang, L.; Zhang, Y.; et al. Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data. Int. J. Environ. Res. Public Health 2022, 19, 3830. https://doi.org/10.3390/ijerph19073830

AMA Style

Zhao H, Gui K, Ma Y, Wang Y, Wang Y, Wang H, Zheng Y, Li L, Zhang L, Zhang Y, et al. Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data. International Journal of Environmental Research and Public Health. 2022; 19(7):3830. https://doi.org/10.3390/ijerph19073830

Chicago/Turabian Style

Zhao, Hujia, Ke Gui, Yanjun Ma, Yangfeng Wang, Yaqiang Wang, Hong Wang, Yu Zheng, Lei Li, Lei Zhang, Yuqi Zhang, and et al. 2022. "Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data" International Journal of Environmental Research and Public Health 19, no. 7: 3830. https://doi.org/10.3390/ijerph19073830

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