Climatological Characteristics and Aerosol Loading Trends from 2001 to 2020 Based on MODIS MAIAC Data for Tianjin, North China Plain

Abstract

The North China Plain (NCP) in East Asia has a severe air pollution problem. In this study, the long-term spatial distribution and interannual trends of aerosol optical depth (AOD) were investigated using the MODIS MAIAC (multiangle implementation of the atmospheric correction) dataset from 2001 to 2020 for Tianjin, a city on the NCP. The annual AOD in Tianjin was 0.59 from 2001 to 2020. The average AOD of Tianjin was the highest in summer (0.96), followed by spring (0.58) and autumn (0.51). The annual AOD in Tianjin increased significantly in 2008 (approximately 0.77), and the minimum annual AOD was observed in 2020 (0.41). In summer, AOD in the 11 districts of Tianjin significantly increased from 2001 to 2010 and gradually decreased from 2011 to 2020. The occurrence frequency of AOD in the range of 0.2–0.5 was high in Tianjin accounting for almost 40% of the total proportion. In Tianjin, AOD exhibited a positive trend from 2001 to 2008 and an obvious negative growth trend from 2009 to 2020 due to anthropogenic emission. The findings are valuable for analyzing the climatological characteristics of aerosol loading and their optical properties at the district level of cities on the NCP.

1. Introduction

Atmospheric aerosols can directly change the energy balance of the earth-atmosphere system by absorbing and scattering solar radiation [1,2,3]. On the other hand, atmospheric particles can also contribute to the formation of cloud condensation nuclei, thus affecting regional and global climate change [4,5]. Therefore, many studies on the optical properties of aerosols focus on revealing their impact on global climate change [6,7,8]. The ground-based monitoring network of aerosol optical properties can obtain long-term national, regional, and global observation data, and is widely used to understand the impact of aerosols on the climate. These observation networks include the Aerosol Robotic Network (AERONET [9], SKYrad Network [10], European aerosol Lidar Network [11], Global Atmosphere Watch Programmer-Precision Filter Radiometer Network [12,13], China Aerosol Remote Sensing Network, and Chinese Sun Hazemeter Network [14]. However, ground-based observations of the optical properties of aerosols are limited due to the lack of coverage of observation sites; satellite remote sensing can provide detailed information on spatial and temporal optical properties of aerosols [15,16,17,18,19]. The Moderate Resolution Imaging Spectroradiometer (MODIS) multiangle implementation of the atmospheric correction (MAIAC) algorithm was applied to provide MODIS aerosol optical depth (AOD) at 1-km-high spatial resolution. The MAIAC algorithm starts with the division of data into 1 km grids containing 16 measurement days [20]. The algorithm uses the time series analysis method and processes the pixel group to derive the surface bidirectional reflectance distribution function (BRDF) and aerosol parameters over dark and bright surfaces. The MAIAC algorithm retrieves AOD at 1 km spatial resolution over all land and ocean surfaces except for snow and ice, and greatly enhances the spatial coverage of MODIS products [21]. Some sensitivity analysis has shown that the MAIAC algorithm has a good stability [22,23].

Fine particulate matter with aerodynamic diameters of 2.5 µm and smaller (PM_2.5) is one of the major air pollutants, which have strong effects on haze events and increases visibility degradation [24,25]. PM_2.5 can directly change the energy balance between the earth-atmosphere system by scattering and absorbing solar radiation [3,26] and can indirectly affect the aerosol-cloud–precipitation system as condensation nuclei [27,28]. In the past decade, PM_2.5 has become the main pollutant in the urban areas of China during haze events; it is a consequence of rapid economic development, urbanization expansion, and energy consumption acceleration [29,30,31,32,33]. The regional visibility degradation in urban agglomerations in China that are characterized by intensive anthropogenic activities has received widespread public attention [34,35,36]. The Beijing–Tianjin–Hebei (BTH) region is the political and cultural centre of China and a crucial economic zone of northern China. In recent years, extreme haze episodes have occurred frequently in the BTH region and have resulted in considerable public concern [37,38,39,40]. Air pollution in the BTH region is mainly affected by local pollutant emissions, specific regional topography, and regional pollutant transport [41,42,43,44,45,46,47,48,49,50].

Tianjin is the largest coastal city on the North China Plain (NCP; 117.2° E, 39.13° N). This city is the central functional area of the BTH region in China’s Capital Economic Circle and a vital central city in the Bohai Economic Circle in northern China. The continual increase in anthropogenic activities as well as vehicle exhaust and coal combustion are key sources of pollutants in this region [51,52]. In addition, seasonal biomass burning substantially contributes to PM_2.5 pollution in Tianjin, thus affecting regional air quality [53,54,55]. Tianjin Port, one of the world’s largest container ports, is affected by pollutant emissions from ships. Furthermore, mesoscale atmospheric circulation substantially contributes to air pollution in coastal areas [56,57,58,59].

Most studies on pollution events in Tianjin have focused on particle concentration evolution, chemical composition, and regional transport [60,61,62], and few studies have focused on the optical properties of aerosols. Although few ground-based observation sites are located in Tianjin, the optical properties of aerosols can be determined using satellite products. Therefore, this study entailed high-resolution research on aerosol loading and on its optical properties in Tianjin; the research findings provide strong scientific support for refined regional AOD remote sensing. This study analyzed the spatial distribution and interannual variation of AOD based on MODIS MAIAC products from 2001 to 2020 in Tianjin. Moreover, the interannual trends and occurrence frequency of AOD at the district scale are discussed. The findings, based on satellite observation with high space coverage and strong time continuity, provide insights into the effects of local aerosol pollution sources on column AOD at the urban scale.

2. Materials and Methods

Tianjin is a large port city in China with a thriving chemical industry. The combined effects of local accumulation, regional transport, and secondary formation of aerosols have resulted in severe atmospheric pollution in this region [63]. Tianjin is located to the east of the Bohai Sea (Figure 1). The terrain of Tianjin is mainly a plain and basin. To the city’s north are low mountains and hills, and the altitude gradually decreases from north to south. The average altitude of the north is 1052 m. The area near the Bohai Bay in the southeast has an average altitude of only 3.5 m, which is the lowest point on the NCP; Tianjin is the lowest major city in China. Local circulation, such as a sea–land breeze, affects air quality under stable weather conditions. Under the effect of sea–land breeze circulation, the dispersion of pollutants is inhibited, leading to pollutant accumulation [64,65]. During severe pollution events, a convergence flow field appears in Tianjin and its surrounding areas, leading to high local pollution levels. This study divided Tianjin into 11 main districts, namely Jizhou (JZ), Baodi (BD), Wuqing (WQ), Ninghe (NH), Beichen (BC), Xiqing (XQ), Dongli (DL), Jinnan (JN), Jinghai (JH), Binhai (BH), and Central City (CC), from north to south.

The highest population density in Tianjin is in the CC district, which is approximately 60 (×1000 persons), followed by XQ, JH, and BH districts around CC, with a population count of approximately 30–40 (×1000 persons). The population density of the central JZ and BD districts in northern Tianjin is approximately 10–20 (×1000 persons). The district in Tianjin with the lowest population density is NH (<5 × 1000 persons). Figure 2b presents the regional distribution of SO₂ in Tianjin. The maximum concentration of SO₂ (approximately 60 tons/km²/year) typically occurs in CC and the surrounding districts of BC, DL, JN, and BH. Low SO₂ concentrations (approximately 3–5 tons/km²/year) are typically observed in NH. The distribution of sulphur dioxide in Tianjin is consistent with that of the population density, indicating the crucial influence of anthropogenic emissions on gaseous pollutants in this area.

Each of the Earth Observing System satellites operated by NASA has several sensors. Among these different sensors, the MODIS aboard Terra and Aqua satellites, belonging to the hyperspectral radiometer category, has been widely used in recent years. MODIS is a polar-orbiting sun-synchronous satellite installed on Terra (orbiting since 18 December 1999) and Aqua (orbiting since 4 May 2002). MODIS is a sensor with 36 spectral bands, covering the range of 0.415–14.235 μm with an observation range of 2330 km, a spatial resolution of 0.25 km, 0.5 km, and 1 km, and a temporal resolution of 1–2 days. MODIS sensors′ wide spectral range, high spatial resolution, and daily global coverage are more conducive to the observation and monitoring of global climate change [66]. The MODIS sensor onboard the satellites Terra (local overpass time: 10:30 AM) and Aqua (local overpass time: 1:30 PM) have been tasked with observing the earth every 1–2 days since 2000 and 2002, respectively, to collect aerosol optical data from the visible to thermal infrared spectrum band [67,68]. The Dark Target (DT) and Deep Blue (DB) algorithms have been used to retrieve MODIS AOD [18]. The DT algorithm provides AOD at 3 and 10 km resolution over dark background surfaces [69], and the DB algorithm retrieves AOD over bright surfaces at 10 km resolution [70]. Aerosol optical data were obtained by MODIS sensors and the MAIAC algorithm was used to process and improve cloud/snow detection, aerosol retrieval, and atmospheric correction of MODIS data [20,71]. The MODIS MAIAC AOD was derived from a combination of the Aqua and Terra MODIS C6 Level-2 product MCD19A2 (https://ladsweb.modaps.eosdis.nasa.gov/, accessed on 1 December 2021). The MODIS-based MAIAC algorithm can provide the AOD of the land surface (except snow/ice) and ocean at a spatial resolution of 1 km [20], and can distinguish fine aerosol features [71]. The MAIAC algorithm can separate surface elements such as aerosols and clouds that are relatively static in a short time interval from features that change rapidly over time by using the dynamic time series analysis method. The MAIAC algorithm relies on the assumption that surface reflectance changes slowly over time and shows high spatial variability. The MAIAC algorithm adopts the cloud mask method and generates surface reflectance of MODIS land and ocean bands with a spatial resolution of 1 km. Lyapustin et al. [20] indicated that MAIAC products undergo rigorous screening for pixels that are likely to be cloudy and adjacent to clouds. The MAIAC algorithm used the aerosol information and retrieved column water vapor at 0.94 μm in the near-infrared spectrum to make atmospheric correction and obtain the spectral BRDF [21]. The surface BRDF can then be used to study the cloud, cloud shadow, and snow information, as well as the aerosol type. The spatial statistical filtering of high AOD at 1 km is useful for aerosol and surface product improvement. Mhawish et al. [72] indicated that the accuracy assessment of MODIS MAIAC AOD yielded high correlation coefficients, with an AERONET AOD of approximately 0.89. In this study, MODIS MAIAC AOD products (released in May 2017) at 1 km spatial resolution were obtained from NASA (https://portal.nccs.nasa.gov/datashare/maiac/, accessed on 1 September 2019). Note that only AOD retrieval categorized as of the “Best quality” was considered in this study. To complete the spatial analysis, we resampled the MAIAC AOD products at 1 km spatial resolution onto a uniform 0.01° latitude–longitude grid.

To explore what factors drive interannual variability and trends in AOD in Tianjin, we used the tropospheric NO₂ columns (an indicator of anthropogenic emission intensity) and three key meteorological factors, including precipitation (PPT), wind speed (WS), and boundary layer height (BLH), all of which are closely associated with aerosol loading variability. Monthly mean tropospheric NO₂ data with a spatial resolution of 0.125° from 2005 to 2020 were retrieved from the Ozone Monitoring Instrument (OMI) observations [73] and are available from the Tropospheric Emission Monitoring Internet Service (TEMIS; https://www.temis.nl/index.php/, accessed on 1 December 2021). Meteorological data were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5; https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset/, accessed on 1 December 2021). These data had a spatial resolution of 0.25° × 0.25° and covered the period 2001–2020. Using Sen’s slope method of Mann [74] and Kendall [75], the Mann–Kendall (M–K) tau test was used to analyze the temporal variation trend of seasonal AOD in different aerosol types around the world. In the process of completing the M–K trend test, the sample data in the non-parametric method did not follow a certain probability distribution, therefore it wass seldom disturbed by “outliers”. Before the M–K trend analysis, each grid cell during the entire study period was subtracted from the monthly average to eliminate the effect of seasonal periodicity through de-personalization. To ensure the robustness of our analysis of the seasonal trend, the proportion of valid data in each MISR grid cell in the time series must exceed 60%. In this study, Sen’s slope was used to evaluate the strength of the trend value, and M–K statistical analysis was used to test whether the above estimated trend was significant at a given significance level.

Sen’s slope can be calculated according to Equation (1):

$$b=\mathrm{Median}\left(\frac{X_i-X_j}{\mathrm{i}-\mathrm{j}}\right)\forall j<i$$

(1)

where $b$ represents the estimated Sen’s slope, $X_i$ and $X_j$ indicate the $i$-th and $j$-th ($i<j$) values of time series $X$, respectively.

If a time series has $n$ values of $X_j$, then the $\left(\frac{X_i-X_j}{\mathrm{i}-\mathrm{j}}\right)$ value is $N=\left(\frac{n\left(n-1\right)}{2}\right)$. The slope calculated using the slope method of Sen is the median from small to large of these $\left(\frac{X_i-X_j}{\mathrm{i}-\mathrm{j}}\right)$ values.

The nonparametric M–K test is calculated as shown in Equation (2):

$$S={\displaystyle \sum }_{i=1}^{n-1}{\displaystyle \sum }_{j=i+1}^{n}sgn(X_j-X_i)$$

(2)

where $n$ is the number of time series $X$ and $sgn$ is the sign function as shown in Equation (3):

$$sgn\left(X_j-X_i\right)=\{\begin{array}{c}+1 if X_j>X_i\\ 0 if X_j=X_i\\ -1 if X_j<X_i\end{array}$$

(3)

where $S$ is defined as the difference between the positive and negative values of differences and follows a normal distribution with a mean value of 0. The variance of $S$ is calculated using Equation (4):

$$Var\left(s\right)=\frac{1}{18}n\left(n-1\right)\left(2n+5\right)$$

(4)

When $n$ > 10, the standard normal test statistic $ZS$ is given as Equation (5):

$$ZS=\{\begin{array}{c}\frac{S-1}{\sqrt{\mathrm{Var}\left(S\right)}} if S>0\\ 0 if S=0\\ \frac{S+1}{\sqrt{\mathrm{Var}\left(S\right)}} if S<0\end{array}$$

(5)

where for $ZS$ a value >0 indicates an increasing trend and a value <0 indicates a decreasing trend, and $\left|ZS\right|$ values greater than 1.65, 1.96, and 2.58 indicate that the trend values pass the statistical significance tests at 90%, 95%, and 99% confidence levels, respectively. A 95% significance level was considered in this study ($p$ = 0.05).

Population data were collected using the 2015 Gridded Population of the World, Version 4 Dataset from the NASA Socioeconomic Data and Applications Center at Columbia University (http://sedac.ciesin.columbia.edu/data/collection/gpw-v4/, accessed on 1 September 2021).

Data for the air pollutant emissions of SO₂ were obtained from the inventories of Peking University in 1980–2014, and the spatial resolution was 0.1° × 0.1° (http://inventory.pku.edu.cn/, accessed on 1 September 2021).

3. Results

3.1. Spatial Distribution of Annual and Seasonal AOD in Tianjin

Figure 3 presents the spatial distributions of annual AOD in the 11 districts of Tianjin from 2001 to 2020.

The annual average value of AOD in Tianjin was 0.59 in the past two decades, and the highest value of AOD was >0.8 in coastal districts. In XQ and NH, the annual average AOD was approximately 0.6–0.7. The lowest AOD value was observed in the northern mountainous district, and the annual average AOD was approximately 0.3. In other areas, the annual average AOD was between 0.5 and 0.6, with a high value of 0.7 or so in some typical sites. At the district scale, the largest AOD in Tianjin in the past 20 years was observed in BH (average AOD value: 0.70) followed by XQ (average AOD value: 0.61). The average AOD values in DL, BC, WQ, BD, NH, and JH were close to 0.56–0.57. The AOD values were low in CC and JN (approximately 0.53); the mountainous regions in the north of JZ caused the annual mean value of AOD to be relatively low (approximately 0.50). The AOD in the land and sea interface district may be affected by the special underlying surface and climatic characteristics such as the sea–land breeze [45,76].

Figure 4 presents the seasonal distributions of AOD in Tianjin from 2001 to 2020. In this study, the year was divided into four seasons as follows, spring: March, April, and May; summer: June, July, and August; autumn: September, October, and November; and winter: December, January, and February.

The seasonal variation of AOD in Tianjin was in the following order: summer > spring > autumn > winter. The average AOD of Tianjin was the highest in summer, with an average value of 0.96, followed by spring and autumn, with average values of 0.58 and 0.51, respectively. The average AOD value in Tianjin decreased to 0.28 in winter, whereas the average AOD in summer was approximately 1.6–1.8 times that in spring and autumn and 3.4 times that in winter. In summer, high humidity had a major influence on aerosol extinction [77].

According to the seasonal spatial distribution of AOD, the highest value of AOD in Tianjin (1.4) was observed in the southeast coastal district of BH in spring. The AOD of the southern and northern parts of BH was approximately 1.0. In spring, except for BH, the AOD in most districts of Tianjin was approximately 0.6. In summer, except for the northern mountains, the overall AOD in Tianjin was high, and the highest AOD value appeared in BH (>1.4), whereas the AOD in other districts was in the range of 0.8–1.0. In autumn, the AOD in Tianjin decreased significantly. Except for BH, the AOD in coastal districts was somewhat high (approximately 0.8), and the average AOD in almost all districts was <0.6. In winter, the AOD in Tianjin decreased significantly, and a high AOD value (0.8) was observed in some coastal ports of BH, whereas the AOD in almost all districts decreased to <0.4.

3.2. Spatial Distribution of Monthly AOD in Tianjin

Figure 5 depicts the monthly mean distribution of AOD in Tianjin from 2001 to 2020 in north China.

In January, the mean AOD value in Tianjin was 0.26. BH was an exception, with a monthly mean value of AOD significantly lower than 0.2, but the AOD in all other districts was <0.4. In February, the mean AOD value in Tianjin was 0.30, which was slightly higher than that in January, and the lowest value was observed in BH. The monthly mean value of AOD in other districts was approximately 0.5. In March, the monthly mean AOD in Tianjin showed an increasing trend, with its value being 0.47 in the whole region. The highest monthly mean value of AOD was observed in the BH coastal district (approximately 1.4), whereas the monthly mean value of AOD in other districts was approximately 0.6. In April, the monthly mean AOD in Tianjin increased continuously and reached 0.63. Furthermore, the monthly average maximum value of AOD was observed in the coastal district of BH (approximately 1.4). The monthly average value of AOD in other coastal districts was approximately 1.2, whereas that in other inland districts was approximately 0.7. In May, the mean AOD in Tianjin was 0.63. The spatial distribution of AOD was similar to that in April. Particularly in BH, the monthly average value range of AOD increased, and the monthly average value of AOD in the whole coastal district was high (1.2–1.4). In June, the mean AOD in Tianjin increased significantly and reached 1.07. The average monthly AOD value in BH was >1.5, whereas that in other districts was approximately 1.0. Similarly, in July, the mean value of AOD in Tianjin was 1.08. The monthly maximum value of AOD was observed in coastal districts. The monthly mean value of AOD in BH remained at a high level of >1.5, whereas that in other districts was in the range of 1–1.2. From August onwards, the monthly mean AOD in Tianjin exhibited a decreasing trend and reached 0.72. The monthly mean value of AOD in coastal districts decreased slowly, with a value of approximately 1.5 in BH and approximately 0.6 in other districts of Tianjin. In September, the monthly mean value of AOD in Tianjin was 0.58. The range of the AOD value in BH narrowed to approximately 1.0–1.2, whereas the AOD in other districts of Tianjin decreased to approximately 0.5. In October, the monthly mean value of AOD in Tianjin was 0.58. Except for the coastal districts, the monthly mean value of AOD in other districts exhibited little change. The AOD in the coastal districts decreased to 0.7 in October, and in November, the monthly AOD decreased significantly in Tianjin to 0.39. In December, the monthly mean value of AOD in Tianjin decreased to 0.27.

As evident from the monthly variation and distribution characteristics of AOD in Tianjin, as a typical coastal urban area, the monthly variation in AOD is significantly different depending on geographical location (i.e., whether near the sea–land boundary or an inland area).

3.3. Spatial Distribution of Interannual AOD in Tianjin

Figure 6 presents the interannual variation and distribution of AOD in Tianjin from 2001 to 2020.

In 2001, the annual average AOD in Tianjin was 0.54, which was relatively low. The highest AOD was >1.2 in BH, a coastal district, whereas the AOD in other districts was approximately 0.4–0.5. In 2002, the annual value of AOD in Tianjin was 0.57. The highest values of AOD were approximately 1.2 in BH and approximately 0.6 in other districts. In 2003, the annual AOD in Tianjin increased further to an average of 0.67. Except for the coastal districts, the value of annual AOD in other districts increased to approximately 0.8. In 2004, the annual AOD in Tianjin exhibited a decreasing trend, with the annual mean AOD being 0.47. In 2005, the annual AOD in Tianjin exhibited an increasing trend, with an average of 0.58. The highest annual AOD value appeared in the BH coastal district—the value was approximately 1.4. In 2006, the annual average value of AOD in Tianjin increased to 0.74. The highest value of AOD appeared in BH—approximately 1.2, and AOD in other districts increased to approximately 0.8. In 2007, the AOD in Tianjin decreased slightly, with an annual average of 0.65. In 2008, the AOD increased considerably, with the annual mean value being 0.77. In BH, the annual AOD was approximately 1.2, whereas annual AOD in the other districts of Tianjin increased significantly to approximately 0.8–1.0. In 2009, the AOD in Tianjin decreased significantly, with the annual mean value of AOD being 0.59. The value of AOD in BH was approximately 1.2, whereas that in other districts decreased significantly to approximately 0.6. In 2010, the annual mean value of AOD in Tianjin increased to 0.65. Except for the value of AOD in BH, the value of AOD in other districts increased to approximately 0.8. Similar to the annual distribution of AOD in Tianjin in 2010, the annual mean value of AOD in Tianjin in 2011 was approximately 0.68, which decreased to 0.57 in 2012. From 2013, the annual mean value of AOD in Tianjin exhibited a significant downward trend, with values of 0.63 in 2013, 0.60 in 2014, 0.55 in 2015, and 0.54 in 2016. From 2017 to 2019, the annual mean value of AOD in Tianjin further decreased from 0.48 to 0.46 and 0.44, and the annual minimum value of AOD was observed in 2020, which was 0.41.

In summary, the interannual variation in AOD in Tianjin indicates that AOD in Tianjin exhibited an increasing trend of fluctuation from 2001 to 2010 in both coastal BH and other districts, whereas AOD in Tianjin decreased rapidly in the last 10 years from 2011 (0.68) to 2020 (0.41), by 39.7%.

3.4. Interannual Variation in AOD at the District Level

We further analyzed the interannual variation of AOD in 11 districts of Tianjin from 2001 to 2020 (Figure 7). In terms of seasonality, AOD at the district scale in Tianjin exhibited a significant interannual variation trend.

In the spring, the annual mean value of AOD in the 11 districts displayed a significantly high value in 2003 and 2008. In 2003, the AOD values of JZ and JN were 0.79 and 0.71, respectively, and the AOD of other districts was >0.80, with the value in BH particularly notable (1.0). In 2001, the AOD values of BH and XQ were >0.91 and >0.69, respectively. The annual AOD in the remaining nine districts was low, varying from 0.47 to 0.60, and the minimum values were observed in JZ (0.47) and JH (0.50). In 2008, AOD was lower than that in 2003, and the maximum AOD value in most regions was approximately 0.8. By contrast, the AOD values in the 11 districts of Tianjin were low in 2002, with the lowest AOD of 0.33 appearing in JZ, JN, and CC. From 2004 to 2007, the AOD in the 11 districts of Tianjin fluctuated between 0.4 and 0.6; of the districts, BH exhibited a high peak value of 1.00 in 2006. After 2009, AOD had a decreasing trend.

In summer, the AOD in the 11 districts of Tianjin significantly increased from 2001 to 2010 and gradually decreased from 2011 to 2020. In 2001, the annual AOD in BH reached its highest value of 1.23, and that of XQ reached 0.97. From 2002 to 2003, the annual AOD increased significantly in the 11 districts of Tianjin, with the highest values (>1.1) observed in XQ, DL, and CC. In 2004, AOD decreased significantly in the 11 districts, with the lowest AOD in JZ (0.50), DL (0.51), and CC (0.49). In 2005, the AOD in the 11 districts of Tianjin increased, with the highest AOD being 1.30 in BH. The variation in the range of AOD in the remaining 10 districts was approximately 0.8–1.0. From 2006 to 2007, the highest value of AOD appeared in JH, BH, BC, JN, and XQ, with the AOD values up to approximately 1.3. In 2008, the AOD in the 11 districts of Tianjin was >1.0, with the highest value observed in BH (2.01), followed by NH (1.61), BC (1.62), and XQ (1.59), and the other districts had a range of 1.4–1.5. In 2009, the AOD in Tianjin e a decreasing trend, with the highest value of 1.09 in BH, and the variation in other districts was <1.0. In 2010, the AOD in Tianjin increased to >1.1 in all districts except for JZ (0.96). In 2011, the AOD values continued to be high— approximate values of 1.0, and the highest value of >1.2, were observed in BH, JN, and XQ. Since 2012, the AOD in the 11 districts has decreased significantly, with the AOD in most districts being in the range of 0.8–1.0 and the highest value appearing in XQ (approximately 1.03). In 2013, the maximum value of AOD was observed in BH (approximately 1.21), and the AOD in the remaining 10 districts varied within the range of 0.7–1.0. From 2014 to 2017, the variation range of AOD in the 11 districts decreased to approximately 0.7, with the highest value in BH. In 2018, AOD ranged from 0.6 to 0.7, with a maximum value of 0.58 in CC. From 2019 to 2020, the AOD in the 11 districts in Tianjin decreased significantly, with the highest value of 0.80 in BH and a range of 0.5–0.66 in the other districts.

The interannual variation of AOD at the district scale during autumn and spring in Tianjin was similar. In 2006, the highest annual AOD was observed in BH (approximately 0.82), followed by XQ and DL (approximately 0.71), and ranged from 0.5 to 0.7 in the other districts. The annual maximum value of AOD in autumn was approximately 0.71 and 0.73 in BC and approximately 0.7 in the other districts. Since 2016, the annual variation of AOD in autumn in the 11 districts has exhibited a decreasing trend, and the annual mean value of AOD in 2020 decreased to approximately 0.4–0.5. The interannual variation in the AOD of Tianjin in winter was not significant, and the annual mean of AOD increased from approximately 0.2 in 2001 to approximately 0.3–0.4 in 2020. These results indicate that the interannual variation in AOD at the district scale in Tianjin presents an obvious characteristic of seasonal variation.

3.5. Occurrence Frequency and Interannual Trend of AOD

We analyzed the interannual variation in the occurrence frequency of different AOD levels at the district level in Tianjin from 2001 to 2020 (Figure 8). We divided AOD values into 13 levels as follows: 0.0 ≤ AOD < 0.1, 0.1 ≤ AOD < 0.2, 0.2 ≤ AOD < 0.3, 0.3 ≤ AOD < 0.4, 0.4 ≤ AOD < 0.5, 0.5 ≤ AOD < 0.6, 0.6 ≤ AOD < 0.7, 0.7 ≤ AOD < 0.8, 0.8 ≤ AOD < 0.9, 0.9 ≤ AOD < 1.0, 1.0 ≤ AOD < 1.5, 1.5 ≤ AOD < 2.0, and AOD > 2.0.

Except for BH, 0.0 ≤ AOD < 0.1 in 10 districts of Tianjin indicated that the occurrence frequency of extreme cleaning conditions was <1%. The occurrence frequency of 0.0 ≤ AOD < 0.1 in BH was significantly higher than that in other districts, at approximately 4%. This result indicates that the frequency of extreme cleaning conditions in BH in the coastal region is higher than that in inland areas. For 0.1 ≤ AOD < 0.2, the occurrence frequencies in BH and JN were 18% and 14%, respectively. The occurrence frequency for CC, XQ, and NH was 6–8%, and that for JH, BD, BC, and WQ was 4–6%. For 0.2 ≤ AOD < 0.3, the occurrence frequency was high (approximately 17%) mainly in XQ and JZ, whereas in the other districts, it was between 12% and 15%; particularly in BH, the occurrence frequency decreased significantly. With a further increase in AOD, for 0.3 ≤ AOD < 0.4, the occurrence frequency in BH and JN rapidly decreased to approximately 10% and 12%, whereas the occurrence frequency in other districts increased significantly. The occurrence frequency in JZ was approximately 18%, and that of other districts was approximately 14–16%. For 0.4 ≤ AOD < 0.5, the occurrence frequency in BH further decreased to <10%, whereas that in JN increased to 13%, and the occurrence frequency was higher in WQ, JZ, BD, and NH, which was approximately 16–18%. For BC, XQ, and DL, the occurrence frequency was approximately 15%. For AOD > 0.5, the occurrence frequency of AOD decreased with an increase in AOD. For 0.5 ≤ AOD < 0.6, the occurrence frequency in BH was the lowest (approximately 8%) and approximately 12–14% in the other districts. For AOD > 0.6, the occurrence frequencies in coastal BH and other inland districts were close. For 0.6 ≤ AOD < 0.7, 0.7 ≤ AOD < 0.8, 0.8 ≤ AOD < 0.9, and 0.9 ≤ AOD < 1.0, the occurrence frequencies decreased to <10%, <8%, <6%, and approximately 4%, respectively, in all districts. For 1.0 ≤ AOD < 1.5, the occurrence frequency in all districts significantly increased, which may be related to the influence of pollution events in Tianjin on aerosol loading and optical extinction. The occurrence frequency of AOD > 1.5 in Tianjin was low, specifically <1%. The aforementioned research results indicate that AOD occurrence frequency in Tianjin was high (range: 0.2–0.5), accounting for almost 40% of the total proportion.

Figure 9a clearly indicates that the average AOD in Tianjin from 2001 to 2020 has significantly decreased since 2008. We focus on the variation of tropospheric NO₂ columns from 2005 to 2020 and the annual variation of important meteorological factors such as precipitation (PPT), wind speed (WS), and boundary layer height (BLH) in Tianjin from 2001 to 2020. These results found that the interannual variation of NO₂ columns in Tianjin was consistent with that of AOD (Figure 9b), while the interannual variation trend of meteorological elements were less consistent with that of AOD (Figure 9c–e). Therefore, the interannual variation of AOD in Tianjin could be mainly dominated by anthropogenic emission intensity and was not significantly affected by meteorological factors. In addition to emission and meteorological factors, the contribution of regional transport to aerosol loading in Tianjin also showed a decreasing trend due to the emission reduction policies of the surrounding areas including Beijing and Hebei province in the past 10 years, which needs further study.

In addition to the climatological variation of AOD in Tianjin, we further investigated the decadal trend at the 95% confidence level for 2001–2008 and 2009–2020.

AOD in Tianjin exhibited different interannual variation trends in the past two decades (Figure 10). AOD in Tianjin showed a positive trend from 2001 to 2008. The AOD of BD, NH, and BC increased most rapidly at approximately 0.5/decadal followed by the AOD of WQ, DL, JN, and JH at approximately 0.3/decadal and the AOD of JZ, CC, XQ, and BH at approximately 0.2/decadal. AOD in Tianjin exhibited an obvious negative growth trend from 2009 to 2020. The negative growth trend of AOD in coastal BH was the fastest at approximately −0.4/decadal followed by BD, WQ, and XQ at approximately −0.3/decadal and JZ, NH, BC, DL, JN, and JH at approximately −0.2/decadal. The negative growth trend in CC was the slowest at approximately −0.1/decadal.

4. Conclusions

Aerosol optical properties have a major effect on the earth–atmosphere energy balance and global climate change. In the NCP, many source emissions and adverse meteorological conditions due to economic and industrial development as well as its dense population lead to severe pollution. In this study, the spatial distribution, interannual variation, and occurrence frequency of AOD at the regional scale in Tianjin from 2001 to 2020 were analyzed, revealing the significant long-term variation of aerosol optical properties of NCP in coastal areas at the district scale with high temporal and spatial resolutions.

The annual average value of AOD in Tianjin was 0.59, and the highest value of AOD was >0.8 in the coastal district. The average AOD of Tianjin was the highest in summer (0.96) followed by that in spring (0.58) and autumn (0.51). The average value of AOD in Tianjin decreased to 0.28 in winter. The average AOD in summer was approximately 1.6–1.8 times that in spring and autumn and 3.4 times that in winter. The monthly mean AOD in Tianjin increased significantly in June (1.07), whereas it decreased in December (0.27). As a typical coastal urban area, the monthly variation range of AOD in Tianjin is significantly different depending on geographical locations, such as the sea–land boundary and inland areas. The innovation of this conclusion is that the long-term variation of aerosol optical properties with high spatial and temporal resolution at the sea-land boundary area in NCP is presented, which is helpful to study the climate change of different underlying surfaces.

Since 2013, the annual mean value of AOD in Tianjin has exhibited a significantly downward trend. The annual value of AOD was reached a minimum in 2020 (0.41). The interannual variation of AOD in Tianjin showed an increasing fluctuation trend from 2001 to 2010, whereas it decreased rapidly from 2011 (0.68) to 2020 (0.41) by 39.7%. In the spring, the annual mean value of AOD in the 11 districts was very high in 2003 and 2008. In the summer of 2008, AOD values in the 11 districts of Tianjin were >1.0. The interannual variations in AOD at the district scale in autumn and spring in Tianjin were similar. The interannual variation of AOD in Tianjin in winter was nonsignificant, and the annual mean of AOD increased from approximately 0.2 in 2001 to approximately 0.3–0.4 in 2020. The long-term variation of aerosol optical properties on seasonal scale is conducive to further study of the influencing factors of air pollution in the land-sea boundary region.

For 0.2 ≤ AOD < 0.3, the largest occurrence frequency of 17% was mainly in XQ and JZ, and in the other districts, it was between 12% and 15%. The AOD occurrence frequency in Tianjin was high (range: 0.2–0.5), accounting for almost 40% of the total proportion. AOD in Tianjin exhibited a positive trend from 2001 to 2008. The AOD of BD, NH, and BC increased the most rapidly at approximately 0.5/decadal. AOD in Tianjin showed an obvious negative growth trend from 2009 to 2020 due to the emission reduction over the last 10 years. The negative growth trend of AOD in coastal BH was faster (approximately −0.4/decadal) than in BD, WQ, and XQ (approximately −0.3/decadal). At the district scale with high spatial and temporal resolution, AOD occurrence frequency is significantly different, and these results can specifically describe the climatological change of aerosol optical properties in different areas of coastal cities in North China.

These findings provide crucial information for assessing interannual variations in aerosols, long-term trends, and climatological characteristics based on regional refinement satellite aerosol optical products.