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Article

The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China

by
Zhixiang Yu
1 and
Xia Li
2,*
1
Urumqi Meteorological Satellite Ground Station, Urumqi 830011, China
2
Institute of Desert Meteorology, Chinese Meteorological Administration, Urumqi 830002, China
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(10), 1533; https://doi.org/10.3390/atmos13101533
Submission received: 5 August 2022 / Revised: 7 September 2022 / Accepted: 15 September 2022 / Published: 20 September 2022
(This article belongs to the Special Issue Remote Sensing and Multiple Observations of Air Quality in China)
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Abstract

:
This paper investigates the temporal–spatial characteristics of column NO2 concentration and influence factors in Xinjiang based on the Tropospheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The findings indicate that there is a high linear correlation between TROPOMI NO2 data and ground-based data, with Yining having the highest correlation (R2 = 0.8132) and Aksu having the lowest correlation (R2 = 0.7307). The TROPOMI NO2 data can be used to approximate the characteristics of near-surface atmospheric NO2 concentration. NO2 VCD in the troposphere varies greatly geographically, with a noticeable ‘island’ pattern. The high-value zones are mostly found on the northern slope of Tianshan Mountain, in the capital cities of several prefectures, and occasionally in the industrial parks. Urumqi has the highest annual average NO2 VCD of 553.9 × 10−6 mol·m−2. The NO2 VCD is characterized by seasonal shifts and cyclical swings of “low in spring, high in winter, and transition in summer and autumn”. The monthly mean value is highest in December (27.14 × 10−6 mol m−2) and lowest in March (12.66 × 10−6 mol m−2). Meteorological factors can influence the temporal and spatial distribution of NO2 VCD. The GRA in Urumqi is 0.774 between the monthly mean of NO2 VCD and relative humidity. The main causes of the increase in NO2 VCD are man-made emissions. The annual GDP of the secondary industry and its annual average NO2 VCD in fifteen key cities in Xinjiang have a correlation coefficient of 0.78. TROPOMI NO2 data can provide strong support for the fine control of air pollution and air quality in early warning forecast in Xinjiang.

1. Introduction

Nitrogen dioxide (NO2) is one of the most important and prominent air pollutants affecting human health and ecosystems [1,2,3]. It not only influences the concentration of particulate matter near the surface as a precursor to secondary particle production [4,5,6,7], but also plays a critically important role in tropospheric and stratospheric atmospheric chemistry [7]. As one of the reasons for acid rain and heavily polluted events, NO2 can bring cascading environmental effects, such as producing a greenhouse effect, destroying the ozone layer, reducing atmospheric visibility, forming photochemical smog, and causing acidification and eutrophication of surface water [8,9]. The sources of NO2 in the atmosphere are generally subdivided into natural and anthropogenic sources. Natural source emissions include atmospheric flash oxidation, microbial nitrate decomposition, natural combustion of biomass, etc., and anthropogenic sources are mainly fossil fuel combustion and automobile emissions [10].
The high tropospheric NO2 column concentration areas are mainly located in industrialized and densely populated cities and marine ship routes, vertical column density (NO2 VCD) has generally declined by more than 30% in Western countries over the past two decades. In the last decade, NO2 emissions in China have shown an increase in the industrialization process, and despite the adoption of strict emission reduction measures, it is still a global NO2 high value area [11]. China has become a high incidence area for NOx pollution in addition to a hot spot for nitrogen deposition, which has a large negative impact on air quality [12,13], climate change [14], human health [15,16,17], and atmospheric pollution [18,19]. Atmospheric NO2 concentration has become one of the indicators to assess the intensity of atmospheric pollution, and the study of NO2 concentration distribution and variation characteristics has been a popular research direction. A comprehensive understanding of the tropospheric NO2 column concentration distribution characteristics will provide a more thorough reference basis for NO2 pollution management.
NO2 studies are built on ground-based data and space-based (satellite) remote sensing data. Ground-based monitoring, as the traditional monitoring method for air pollutants, mainly includes air quality monitoring stations [20,21], vehicle-borne (ship-borne, airborne) [22,23,24,25], and LIDAR [26,27]. Its advantage is that it can obtain high-precision measurement data on a small spatial scale. The disadvantage is that the monitoring stations are unevenly distributed and mainly concentrated in urban areas. NO2 is largely present in the troposphere and stratosphere, and near-surface NO2 concentrations do not fully characterize atmospheric NO2 levels [28]. Ground-based monitoring cannot monitor changes in NO2 concentrations over long periods of time and large spatial scales. Compared with ground-based monitoring, satellite remote sensing has the advantages of long time series, wide coverage and detection of the whole atmosphere, which makes up for the shortage of ground-based monitoring methods and lays the foundation for studying the spatial and temporal variation, diffusion and transfer change patterns of NO2. The satellite remote sensing detection of NO2 began in the 1990s, mainly GOME, SCIAMACHY, OMI, GOME-2, and TROPOMI [18,19,29,30,31]. The Tropospheric Monitoring Instrument (TROPOMI) is the world’s most technologically advanced and highest spatial resolution atmospheric monitoring spectrometer. Studies have shown that TROPOMI data have high accuracy in NO2 monitoring and can directly monitor the air pollution in urban built-up areas [32,33,34,35,36].
In recent years, scholars in China have had a large number of results based on ground monitoring and satellite remote sensing means to study NO2. From the study regions, there are the Beijing–Tianjin–Hebei Urban Agglomeration, Yangtze River Delta Urban Agglomerations, Guangdong–Hong Kong–Macao–Great Bay Area, and Sichuan Basin [37,38,39]. The study shows that the NO2 VCD has been decreasing in recent years. The spatial distribution varies greatly, forming a pattern of “high in the east and low in the west” with the Hu line as the boundary. Elevated concentrations are mainly distributed in Beijing–Tianjin–Hebei, most of Shandong, northern Henan, southern Jiangsu, northern Zhejiang, Yangtze River Delta, the Sichuan Basin and local areas in Xinjiang [28]. There are considerable seasonal differences and cyclical fluctuations in NO2 column concentrations [40]. For example, NO2 VCD in the Sichuan Basin in winter (2.15 × 1015 molec/cm2) > spring (1.72 × 1015 molec/cm2) > autumn (1.65 × 1015 molec/cm2) > summer (1.49 × 1015 molec/cm2) [41]. In terms of research content, studies on NO2 column concentrations include spatial and temporal distribution [38], remote sensing estimation of concentrations [39], and analysis of influencing factors [42]. It is noted that the spatial and temporal distribution characteristics of NO2 VCD vary with socio-economic [43,44], meteorological elements [45], topographic factors, and pollution management in different regions, seasons, and stages.
Xinjiang is situated in the northwest border of China and belongs to the arid and semi-arid region (Figure 1), with a total area of 1.66 million km2, accounting for about 1/6, with complex topography and substratum. Numerous studies based on ground monitoring data have shown that the share of NO2 emissions in Xinjiang in the country is on the rise with economic development [46]. In the urban clusters of Kuitun–Dushanzi–Wusu and Urumqi–Changji–Shihezi in Xinjiang, NO2 column concentrations appear in a spatially hierarchical structure, gradually decreasing from the urban area to the periphery, with seasonal changes characterized by high concentrations in winter and low in summer. NO2 pollution is dominated by stationary source emissions from factories and the power sector, and traffic source emissions also have an impact [47,48]. In 2019, only four cities met the national secondary standard for air quality among the fourteen key cities in Xinjiang [49], accounting for 28.57%, much lower than the national rate of 59.9% [50]. At present, there are only thirty-seven state—controlled air environment monitoring stations in Xinjiang (Figure 2). The overall distribution characteristics of NO2 in Xinjiang cannot be fully revealed by using NO2 ground monitoring data. It is necessary to apply the high-resolution TROPOMI satellite data to the study of NO2 VCD. Not only can we further understand the distribution characteristics of NO2 in Xinjiang, but also offer a scientific basis for air pollution monitoring and management.

2. Data and Methods

2.1. Data

In this study, we employed four data sources (Table 1), TROPOMI Level2 geophysical products Nitrogen Dioxide from the European Space Agency (ESA), NO2 ground-based data from Department of Ecology and Environment of Xinjiang Province, Meteorological Elements data from CMA, and GDP data from Statistic Bureau of Xinjiang Uygur Autonomous Region (http://tjj.xinjiang.gov.cn/) (accessed on 1 August 2022). The time series of all data is from 2019 to 2021. On 13 October 2017, ESA launched the Sentinel-5P satellite, with an orbital altitude of 824 km. It carries TROPOMI, with an imaging width of 2600 km and an imaging resolution of 7 km × 3.5 km [28], it makes a daily observation with global coverage that can effectively fill the shortcomings of the lack of NO2 ground-based data in Xinjiang. The TROPOMI instrument combines the strength of SCIAMACHY, OMI and state-of-the-art technology to provide observations with performances that cannot be met by the current instruments in space. Performance of current in-orbit instruments is surpassed in terms of sensitivity, spectral resolution, spatial resolution and temporal resolution. It has been proven that TROPOMI is stable and reliable for NO2 VCD in west-central China, Helsinki, and India [12,51,52].
This study utilized the level2 offline products for NO2 from 2019 to 2021. Level2 products which stored in netCDF4 format can be downloaded from Sentinel-5P Pre-Operations Data Hub (https://s5phub.copernicus.eu/dhus/#/home) (accessed on 1 August 2022). NO2 products are based on the DOMINO-2 and the EU QA4ECV NO2. After further optimization, it incorporates the global 3D, 1° × 1° chemical transport model TM5-MP, which improved accuracy than the 2° × 3° of previous sensors [19]. The national benchmark weather station in Urumqi, China, provides meteorological data such as pressure, temperature, precipitation, relative humidity, wind direction, and wind speed.

2.2. Methods

Delaunay [53,54] and nearest neighbor search were used to process the daily TROPOMI NO2 data on a regional scale in Xinjiang. The correlation analysis is used to evaluate the correlation between TROPOMI NO2 data and ground-based data. In order to evaluate the applicability of TROPOMI NO2 VCD in Xinjiang region, NO2 ground-based data of six cities, namely, Urumqi, Shihezi, Yining, Korla, Aksu and Hotan, were selected and correlated with their own TROPOMI NO2 data. Sentinel-5P imaging time is about 1:30 p.m. local time. To make the results more accurate, both NO2 ground-based data and meteorological element data are the average of the data at 14:00, 15:00 and 16:00 Beijing time during the correlation analysis. In analyzing the influence of meteorological elements on NO2 VCD, we utilized the gray relational analysis (GRA) method with reference to the literature [55,56].

3. Results

3.1. Applicability Calibration of TROPOMI NO2 VCD in Xinjiang

It has been shown that NO2 VCD based on Sentinel-5P TROPOMI data can approximate the NO2 concentration characteristics in the global surface atmosphere [52,53,57]. Although the NO2 VCD and the NO2 ground-based data are different, they have a linear relationship. Performance evaluation of Sentinel-5P NO2 product shows the TROPOMI NO2 VCD to have a 7 –19 % low bias, airborne data are more correlated with TROPOMI measurements (R2 = 0.96) than Pandora measurements are with TROPOMI (R2 = 0.84) [36]. To evaluate the reliability of TROPOMI NO2 VCD in the Xinjiang region, correlation analysis was conducted in the cities of Urumqi, Shihezi, and Yining in the north of Xinjiang and Korla, Aksu, and Hotan in the south of Xinjiang, using monthly average NO2 ground-based data and monthly average NO2 VCD during January 2019 to December 2021 (Figure 3). As shown in the figure, NO2 ground-based data and NO2 VCD data in six cities had good linear correlations with an average coefficient of determination R2 > 0.7772, and northern Xinjiang cities outperform southern Xinjiang cities in terms of correlation. The highest correlation was noted in Yining city with R2 of 0.8132 and lowest in Aksu city with R2 of 0.7307. Overall, NO2 ground-based data correlated well with TROPOMI NO2 VCD, the Sentinel-5P NO2 product could reflect the ground NO2 pollution status in Xinjiang. Therefore, the TROPOMI tropospheric NO2 VCD were used for the subsequent analysis.

3.2. Spatial and Temporal Distribution Characteristics of NO2 VCD in Xinjiang

3.2.1. Spatial Distribution Characteristics

Figure 4 shows the spatial distribution of annual mean tropospheric NO2 VCD in Xinjiang from 2019–2021. There are significant differences in the distribution of NO2 VCD in the troposphere, including both a circular structure and “island” characteristics. First, with 43 degrees north latitude as the dividing line, NO2 VCD levels are generally higher in most of the northern Xinjiang than in the southern Xinjiang; secondly, the NO2 VCD in the Urumqi–Changji–Shihezi city cluster (Wu–Chang–Shi) shows a clear circular structure, decreasing from the center to the periphery. The highest value in the whole Xinjiang is found in Urumqi, with an annual average value of 553.9 × 10−6 mol·m−2, compared with the NO2 VCD values over the cities in eastern China [37,38,39,40], for example Beijing (300 × 10−6 mol·m−2), Tianjin (433 × 10−6 mol·m−2), Shanghai (367 × 10−6 mol·m−2) [40], NO2 VCD value in Urumqi is high. Third, except for the central Tianshan North Slope Economic Zone, the NO2 VCD values in other regions of Xinjiang show ‘island’ distribution characteristics, it means that high NO2 VCD areas correspond to the cities where the people’s governments are located, just like an island. In general, high NO2 VCD areas are concentrated almost entirely in the economically developed core areas of Xinjiang, which are densely populated, industrially developed, and have increasingly serious air pollution problems [47,48]. It has been emphasized that secondary industry and motor vehicle emissions are the main contributors to the elevated NO2 concentration [58].
The NO2 VCD is abnormally high in the northeastern part of the Wu–Chang–Shi high value area (location 1 in the figure), in the western part of the Turpan basin (location 2 in the figure), and in the area north of the Tianshan Mountains in Hami (location 3 in the figure). The value are 90.1 × 10−6 mol·m−2, 60.8 ×1 0−6 mol·m−2, 47.2 × 10−6 mol·m−2, respectively. Corresponding areas are unimportant towns in Xinjiang. Observed through Google Earth HD satellite map, the above locations 1–3 correspond to Zhundong–Xinjiang Economic and Technological Development Zone (89.06 E, 44.71 N), Tuokexun Industrial Park (88.59 E, 42.75 N), and Naomaohu Comprehensive Energy Industry Zone (95.03 E, 43.66 N), respectively. This further demonstrates that the TROPOMI NO2 column concentration inversion product is capable of seamlessly and finely monitoring the full picture of NO2 concentration in Xinjiang.
Table 2 shows the annual mean tropospheric NO2 VCD across Xinjiang prefectures and key cities for 2019–2021. The NO2 VCD is higher in Changji Hui Autonomous Prefecture (55.5 × 10−6 mol·m−2), Tarbagatay Prefecture (23.5 × 10−6 mol·m−2) and Turpan (23.4 × 10−6 mol·m−2). The lowest values were found in Hotan Prefecture and Mongolian Autonomous Prefecture of Bayingolin, with NO2 VCD below 10 × 10−6 mol·m−2. Among the key cities, Urumqi, Changji and Shihezi ranked in the top three with NO2 VCD of 553.9 × 10−6 mol·m−2, 411.1 × 10−6 mol·m−2 and 205.7 × 10−6 mol·m−2, respectively, the lowest value occurred in Tacheng City, where the NO2 VCD was 15.2 × 10−6 mol·m−2. The NO2 VCD in Urumqi is significantly higher than other cities in northwest China, for example Lanzhou and Yinchuan [40].

3.2.2. Monthly Variation Characteristics

From Figure 5, it can be seen that the monthly average values of tropospheric NO2 VCD in Xinjiang from 2019 to 2021 show a significant cyclical variation, with one peak and two troughs each year cycle. The average monthly value in the northern Xinjiang is larger than that in the south. The highest value of NO2 VCD occurs in December with a monthly average value of 27.14 × 10−6 mol·m−2, the lowest in March with a monthly average value of 12.66 × 10−6 mol·m−2. The monthly average NO2 VCD was at a low value during March–October, roughly stable at 13–15 × 10−6 mol·m−2. The monthly average NO2 VCD between November and December showed a rapid upward trend and reached the annual peak in December, from January to February it showed a rapid downward trend and dropped back to a low value.
The monthly average NO2 VCD in the fifteen key cities showed a consistent trend, with a “U” shaped cyclical variation on a yearly basis, the same trend as ground-based NO2 concentration, most of the monthly average maximum values occurred in December. The monthly average maximum values in northern Xinjiang cities occurred in 2020, for example, Shihezi, Changji and Urumqi had the maximum monthly average NO2 VCD in December 2020, which are 968.8 × 10−6 mol·m−2, 2389.1 × 10−6 mol·m−2 and 2830.7 × 10−6 mol·m−2, respectively. The monthly average maximum values in southern Xinjiang cities occurred in 2021, such as the maximum monthly average NO2 VCD in Aksu, Kashgar and Hotan in December 2021, which are 82.3 × 10−6 mol·m−2, 80.7 × 10−6 mol·m−2 and 59.9 × 10−6 mol·m−2, respectively.
The reasons for the higher NO2 VCD in January and December are, on the one hand, because of the heating period, industrial emissions, motor vehicles, and the fact that other emissions increase more than during the non-heating period, on the other hand, solar radiation is weaker during this time, photochemical reactions are slower, and NO2 has a longer lifetime, coupled with a thicker inversion layer that tends to occur at low altitudes, meaning that pollutants are not easily diffused, resulting in high values of NO2 VCD.
Figure 6 showed the spatial distribution of monthly average NO2 VCD in Xinjiang from 2019–2021. Spatial distribution of NO2 monthly average VCD varied significantly. The NO2 VCD high value area was large in January. The area with NO2 VCD values above 60 × 10−6 mol·m−2 covered most of the economic zone on the northern slope of Tianshan Mountain, the high values area for about 6.25% of the total area of Xinjiang, that have developed economies, concentrations of industry and agriculture, and a high intensity of human activity and industrial production, which is responsible for significantly higher NO2 VCD than in other regions. The range of the high concentration zone gradually decreased from February to April. Areas where NO2 VCD exceeded 60 × 10−6 mol·m−2 essentially vanished from May to August. From September to December, areas of high NO2 VCD gradually expanded.

3.2.3. Seasonal Variation Characteristics

Figure 7 gave the spatial distribution of NO2 VCD for four seasons (spring, March–May; summer, June–August; autumn, September–November; winter, January, February, December) in Xinjiang from 2019 to 2021. The tropospheric NO2 VCD showed the characteristics of “high in winter and low in spring, middle in summer and autumn”, the NO2 VCD value was winter (21.65 × 10−6 mol·m−2) > autumn (15.62 × 10−6 mol·m−2) > summer (15.16 × 10−6 mol·m−2) > spring (13 × 10−6 mol·m−2). High-value areas in different seasons were distributed over the economic zone of the northern slope of Tianshan and the key cities. NO2 VCD showed an increasing trend in four seasons except summer for three years, and the increasing trend was especially obvious in winter. Because NOX is typically influenced by temperature and solar radiation, the intensity of solar radiation, temperature and precipitation in Xinjiang are significantly lower in winter than in summer, making the NO2 gathered over the city difficult to transform, decompose, and dissipate, resulting in NO2 VCD rising in winter and decreasing in spring and summer.
Analysis of the seasonal mean NO2 VCD in the key cities found that the NO2 VCD varied greatly among the cities in four seasons, with higher seasonal mean values in Urumqi, Changji and Shihezi, and lower values in Tacheng and Altai. Figure 8 showed the percentage of four season average NO2 VCD in the key cities. It can be seen that the NO2 VCD in winter contributes the most to the whole year, especially in Shihezi, Changji and Urumqi, where the winter percentage reached 65.2, 78.1 and 75%, respectively. The highest NO2 VCD in all cities occurred in winter, the lowest values occur in summer except for Tacheng and Atushi, which occurred in spring. This seasonal variation is primarily due to the fact that winter is unfavorable for atmospheric dispersion, and pollutants can remain in the troposphere for extended periods of time.
The NO2 VCD in Urumqi, Changji and Shihezi were 1662.25 × 10−6 mol·m−2, 1284.16 × 10−6 mol·m−2 and 536.83 × 10−6 mol·m−2 in winter, its NO2 VCD had reached or exceeded the pollution level of Beijing-Tianjin-Hebei Urban Agglomeration, Yangtze River Delta Urban Agglomerations, Guangdong-hong Kong-macao Bay area and other developed regions in the same period [37,38,39,40,41]. With 4.1% of Xinjiang’s land area carrying nearly half of Xinjiang’s coal consumption and about one-third of NOx emissions, total coal consumption in the region increased from 65 million tons in 2015 to 77.29 million tons in 2020, resulting in higher NO2 VCD in Urumqi, Changji, and Shihezi relative to other cities.

3.3. Analysis of Factors Affecting NO2 VCD

NO2 VCD distribution is closely related to other factors in addition to the direct relationship with the emission of pollution sources. In this paper, we analyzed the influencing factors affecting NO2 VCD with three aspects, meteorological elements (taking Urumqi as an example), economic development and industrial layout, and motor vehicle exhaust emissions [45].

3.3.1. Meteorological Factors

More previous studies have discussed the effect of meteorological conditions on the concentration of atmospheric pollutants, meteorological elements such as wind direction, wind speed and precipitation have a constraining effect on atmospheric pollutants, but not a simple linear relationship [41,58,59]. NO2 VCD were found to be negatively correlated with three meteorological elements, precipitation, relative humidity, and wind speed, and to have varying correlations with temperature and barometric pressure throughout the seasons [60]. This paper focused on six meteorological elements, namely wind direction, wind speed, precipitation, air pressure, air temperature and relative humidity, to examine the relationship between NO2 VCD and each meteorological element in Urumqi. Gray correlation coefficients and GRA between NO2 VCD with each meteorological element in Urumqi were given in Figure 9. The GRA was high, the highest GRA of 0.774 with relative humidity and the lowest gray correlation of 0.581 with temperature.
Wind direction and wind speed dominates the direction and speed of NO2 diffusion in the atmosphere. The higher the wind speed, the faster the pollutant diffusion, and the wind speed is negatively correlated with the NO2 VCD [59]. As can be seen from Figure 10, the two most frequent wind directions from 14:00 to 16:00 in Urumqi in 2019–2021 were northeasterly and southeasterly, with frequencies of 14.6 and 13.26%, and average wind speeds of 2.18 m/s and 2.78 m/s, respectively. It indicated that NO2 is transported from northeast and southeast to west in Urumqi. The dominant wind direction in Urumqi was northeast in spring with a wind speed of 2.56 m/s, southeast in summer with a wind speed of 2.88m/s, northeast in autumn with a wind speed of 2.38 m/s, and northeast in winter with a wind speed of 2 m/s. Higher wind speeds were mainly in the northeast, with lower wind speeds in the south and northwest, atmospheric diffusion and transport capacity were weak because of the high frequency and duration of lower wind speed, which was not conducive to long-range diffusion of pollutants, easily leading to the high NO2 VCD over Urumqi.
When the relative humidity in the atmosphere rises, the likelihood of precipitation rises. The effect of precipitation on NO2 VCD is mainly reflected in the scavenging and flushing. Distribution of precipitation in Urumqi was extremely uneven in four seasons, with 45.7% of the annual precipitation in summer and only 12.4% in winter. GRA between the monthly mean of NO2 VCD and precipitation was 0.696. The maximum gray correlation coefficients were 0.841, 0.834 and 0.836 in the summer months of June, July, August, respectively. Precipitation has a negative impact on the NO2 VCD, so the NO2 VCD decreased significantly in summer.
Sinking airflow inhibits the upward diffusion of atmospheric pollutants under high pressure control, which is likely to cause the accumulation of atmospheric pollutants and contributes to an increase in NO2 concentration. Urumqi is monitored by Mongolian high pressure in winter, it is strong and stable with little movement, and the city is located behind the cold high-pressure center. Significant heat island effect took place in urban areas in winter, with an average intensity of 2.5 °C during the day and night. Due to prolonged snow cover time, high relative humidity of the air, cloudy and foggy weather, caused the thickness of the inverse layer reached 1100–1400 m [61]. GRA of monthly average NO2 VCD with air temperature, air pressure and relative humidity in Urumqi were 0.581, 0.722 and 0.774, respectively. Fluctuation characteristics of NO2 VCD were negatively correlated with air temperature and positively correlated with air pressure and relative humidity. The higher the temperature, the more O3 is produced and NO2 participates in photochemical reactions, accelerating NO2 consumption.

3.3.2. Economic Development and Industrial Layout

Gross domestic product (GDP) is the most important indicator of a region’s economic development level. Based on data from the Statistical Bulletin of National Economic and Social Development published by the Statistic Bureau of Xinjiang Uygur Autonomous Region for 2019–2021, the analysis found that GDP of Xinjiang had increased in the past three years, with an average annual increase of 5.2%, and the secondary industry [58], which contributes most to NOx emissions, had also increased with an average annual increase of 7.25%. The correlation coefficient between the secondary sector of economy GDP and annual average value of NO2 VCD in key cities in Xinjiang from 2019 to 2021 was 0.78. Table 3 shows the statistics of nitrogen oxide emissions in Xinjiang in 2019–2020 [62,63]. Industrial NOx emissions were 138,200 and 118,700 tons, 46.27 and 46.97% of the total NOx emissions. In response to the above statistics, Xinjiang should actively adjust its energy industry structure to reduce industrial emissions of nitrogen oxides.

3.3.3. Motor Vehicle Exhaust Emissions

Motor vehicle ownership is an important factor affecting vehicle emissions. Large amounts of vehicle exhaust fumes being released into the air greatly increase the NO2 VCD. With the economic development of Xinjiang in recent years, the number of motor vehicles has grown rapidly. The Statistical Bulletin of National Economic and Social Development [56,57] shows that Xinjiang had 4,380,400 civilian vehicles (including three-wheeled vehicles and low-speed trucks) at the end of 2019, an increase of 6.0% over the previous year, of which 3,636,100 were private vehicles, an increase of 6.6%, and 4,711,300 civilian vehicles at the end of 2020, an increase of 7.6% over the previous year, of which 3,914,000 were private vehicles, an increase of 7.6%. As shown in Table 2, motor vehicle NOx emissions in Xinjiang in 2019 and 2020 were 140,300 tons and 114,900 tons, accounting for 47.37% and 45.85% of total NOx emissions, respectively, which shows that motor vehicle exhausts are one of the important anthropogenic emission sources of NO2.

4. Conclusions

In this study, TROPOMI NO2 VCD data were utilized to investigate the temporal–spatial characteristics of NO2 over Xinjiang. The major conclusions are summarized below.
The TROPOMI Level2 NO2 data agree well with ground-based NO2 data in Xinjiang, with an average coefficient of determination R2 > 0.7772. Sentinel-5P NO2 product could reflect the level of ground NO2 pollution status in Xinjiang. Spatial distribution of NO2 VCD in Xinjiang is characterized by a combination of circles and islands. The circles are concentrated in the Urumqi–Changji–Shihezi area, and the islands are scattered over the key cities. The annual average NO2 column concentration over Urumqi is the highest, reaching 553.9 × 10−6 mol·m−2. The seasonal variation characteristics NO2 VCD shows the characteristics of high in winter and low in spring, middle in summer and autumn, the NO2 VCD value was winter (21.65 × 10−6 mol·m−2) > autumn (15.62 × 10−6 mol·m−2) > summer (15.16 × 10−6 mol·m−2) > spring (13 × 10−6 mol·m−2), the highest monthly average value occurred in January, reaching 22.98 × 10−6 mol·m−2.
In Xinjiang, the spatial and temporal distribution of NO2 VCD is caused by a combination of socioeconomic and natural factors. Meteorological elements can affect the spatial and temporal distribution of NO2 VCD. The highest GRA is 0.774 between NO2 VCD with relative humidity, the lowest GRA of 0.581 with temperature in Urumqi. Meteorological elements such as wind direction, wind speed and precipitation affect the NO2 VCD to different degrees in different seasons, for example, the effect of precipitation on the flushing and wet deposition of NO2 is particularly significant in summer. GDP and motor vehicle ownership in Xinjiang increased year by year from 2019 to 2021, in which the correlation coefficient between the annual GDP of the secondary industry and the annual mean NO2 VCD reached 0.78. Anthropogenic emission sources are the fundamental influencing factors for the increase in NO2 VCD in Xinjiang in recent years. As precursors to other pollutants, the high NO2 VCD is an important cause of air pollution, this study can offer a scientific basis for air pollution monitoring and management in Xinjiang.

Author Contributions

Conceptualization, Z.Y. and X.L.; methodology, Z.Y. and X.L.; software, Z.Y.; formal analysis, Z.Y.; data curation, Z.Y.; writing-original draft preparation, Z.Y.; writing-review and editing, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by China Desert Meteorological Science Research Fund (NO. Sqj2021011), Special Funds for Basic Research Operations of Central Public Welfare Research Institutes (No. IDM2020001) and Nature Foundation of Xinjiang Uygur Autonomous Region, China (2020D01A99).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The study did not report any data.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of Xinjiang showing its key cities.
Figure 1. Map of Xinjiang showing its key cities.
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Figure 2. Map of air environment monitoring stations and Urumqi national weather station.
Figure 2. Map of air environment monitoring stations and Urumqi national weather station.
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Figure 3. Correlation analysis of the ground-based NO2 concentration and NO2 VCD in (a) Urumqi, (b) Shihezi, (c) Yining, (d) Korla, (e) Aksu, and (f) Hotan.
Figure 3. Correlation analysis of the ground-based NO2 concentration and NO2 VCD in (a) Urumqi, (b) Shihezi, (c) Yining, (d) Korla, (e) Aksu, and (f) Hotan.
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Figure 4. Spatial distribution of annual mean tropospheric NO2 VCD in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2) (left). (Right 1) Zhundong-Xinjiang Economic and Technological Development Zone. (Right 2) Tuokexun Industrial Park. (Right 3) Naomaohu Comprehensive Energy Industry Zone.
Figure 4. Spatial distribution of annual mean tropospheric NO2 VCD in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2) (left). (Right 1) Zhundong-Xinjiang Economic and Technological Development Zone. (Right 2) Tuokexun Industrial Park. (Right 3) Naomaohu Comprehensive Energy Industry Zone.
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Figure 5. Monthly average changes of NO2 VCD in 2019–2021 for Xinjiang, Northern Xinjiang, Southern Xinjiang and Urumqi.
Figure 5. Monthly average changes of NO2 VCD in 2019–2021 for Xinjiang, Northern Xinjiang, Southern Xinjiang and Urumqi.
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Figure 6. Spatial distribution of monthly average tropospheric NO2 VCD in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2). (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, (l) December.
Figure 6. Spatial distribution of monthly average tropospheric NO2 VCD in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2). (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, (l) December.
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Figure 7. Spatial distribution of seasonal average tropospheric NO2 VCD in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2). (a) Spring, (b) Summer, (c) Autumn, (d) Winter.
Figure 7. Spatial distribution of seasonal average tropospheric NO2 VCD in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2). (a) Spring, (b) Summer, (c) Autumn, (d) Winter.
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Figure 8. Percentage of seasonal averages NO2 VCD in key cities in 2019–2021.
Figure 8. Percentage of seasonal averages NO2 VCD in key cities in 2019–2021.
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Figure 9. Gray correlation coefficients and GRA between monthly average NO2 VCD and meteorological elements in Urumqi.
Figure 9. Gray correlation coefficients and GRA between monthly average NO2 VCD and meteorological elements in Urumqi.
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Figure 10. Wind rose map for 2019–2021 at 14:00–16:00 h at Urumqi meteorological station.
Figure 10. Wind rose map for 2019–2021 at 14:00–16:00 h at Urumqi meteorological station.
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Table
Table 1. The main data sets obtained for this paper.
Table 1. The main data sets obtained for this paper.
VariablesData SetsSourceUnit
Remote SensingNO2 VCDEuropean Space Agencymol·m−2
Ground-basedNO2 concentrationDepartment of Ecology and Environment of Xinjiang Provinceug·m−3
MeteorologicalpressureThe national benchmark weather station in Urumqi, ChinahPa
temperature°C
precipitationmm
relative humidity%
wind direction−
wind speedm/s
Socio-economicGDPXinjiang Uygur Autonomous Region Bureau of Statisticsbillion
Nitrogen oxide emissions10,000 tons
Motor vehicle exhaust emissions−
Table
Table 2. Average values of NO2 VCD in various prefecture and key cities in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2).
Table 2. Average values of NO2 VCD in various prefecture and key cities in Xinjiang in 2019–2021 (unit: 10−6 mol·m−2).
NO2 VCD of PrefecturesNO2 VCD of Key Cities
Altay Prefecture12.3Aletai City19.4
Tarbagatay Prefecture23.5Tacheng City15.2
Bortala Mongol Autonomous Prefecture19.8Bole41.5
Ili Kazak Autonomous Prefecture20.0Yining City60.0
Changji Hui Autonomous Prefecture55.5Changji City411.1
Turpan23.4Turpan City50.2
Hami14.3Hami City38.0
Mongolian Autonomous Prefecture of Bayingolin9.9Korla28.3
Aksu Prefecture14.5Aksu City45.6
Kizilsu Kirghiz Autonomous Prefecture12.2Atushi29.0
Kashgar Prefecture12.9Kashgar City41.4
Hotan Prefecture9.4Hotan City32.0
Urumqi553.9
Shihezi City205.7
Karamay39.7
Table
Table 3. Nitrogen oxide emissions statistics in Xinjiang in 2019–2020 (unit: 10,000 tons).
Table 3. Nitrogen oxide emissions statistics in Xinjiang in 2019–2020 (unit: 10,000 tons).
Nitrogen Oxide Emissions20192020
Industrial emissions13.8211.87
Urban domestic emissions2.021.69
Motor vehicle emissions14.0311.49
Total 29.8725.06
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Yu, Z.; Li, X. The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China. Atmosphere 2022, 13, 1533. https://doi.org/10.3390/atmos13101533

AMA Style

Yu Z, Li X. The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China. Atmosphere. 2022; 13(10):1533. https://doi.org/10.3390/atmos13101533

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Yu, Zhixiang, and Xia Li. 2022. "The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China" Atmosphere 13, no. 10: 1533. https://doi.org/10.3390/atmos13101533

APA Style

Yu, Z., & Li, X. (2022). The Temporal–Spatial Characteristics of Column NO2 Concentration and Influence Factors in Xinjiang of Northwestern Arid Region in China. Atmosphere, 13(10), 1533. https://doi.org/10.3390/atmos13101533

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