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Article

Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing

by
Kun Cai
1,2,
Qiushuang Zhang
2,
Shenshen Li
3,*,
Yujing Li
2 and
Wei Ge
3
1
College of Environment and Planning, Henan University, Kaifeng 475004, China
2
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China
3
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3950; https://doi.org/10.3390/s18113950
Submission received: 4 September 2018 / Revised: 3 November 2018 / Accepted: 12 November 2018 / Published: 15 November 2018
(This article belongs to the Section Remote Sensors)
Browse Figures
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Abstract

:
The Chengdu–Chongqing Economic Zone (CCEZ), which is located in southwestern China, is the fourth largest economic zone in China. The rapid economic development of this area has resulted in many environmental problems, including extremely high concentrations of nitrogen dioxide (NO2) and fine particulate matter (PM2.5). However, current ground observations lack spatial and temporal coverage. In this study, satellite remote sensing techniques were used to analyze the variation in NO2 and PM2.5 from 2005 to 2015 in the CCEZ. The Ozone Monitoring Instrument (OMI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product were used to retrieve tropospheric NO2 vertical columns and estimate ground-level PM2.5 concentrations, respectively. Geographically, high NO2 concentrations were mainly located in the northwest of Chengdu and southeast of Chongqing. However, high PM2.5 concentrations were mainly located in the center areas of the basin. The seasonal average NO2 and PM2.5 concentrations were both highest in winter and lowest in summer. The seasonal average NO2 and PM2.5 were as high as 749.33 × 1013 molecules·cm−2 and 132.39 µg·m−3 in winter 2010, respectively. Over 11 years, the annual average NO2 and PM2.5 values in the CCEZ increased initially and then decreased, with 2011 as the inflection point. In 2007, the concentration of NO2 reached its lowest value since 2005, which was 230.15 × 1013 molecules·cm−2, and in 2015, the concentration of PM2.5 reached its lowest value since 2005, which was 26.43 µg·m−3. Our study demonstrates the potential use of satellite remote sensing to compensate for the lack of ground-observed data when quantitatively analyzing the spatial–temporal variations in regional air quality.

1. Introduction

The Chengdu–Chongqing Economic Zone (CCEZ) is the fourth largest economic region in China after the Pearl River Delta, the Yangtze River Delta, and the Beijing–Tianjin–Hebei region. The rapid economic development in the CCEZ has resulted in many environmental problems [1], including air pollution. Two important constituents of air pollution are nitrogen oxides (NOx = NO + NO2) and particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5), both of which can inflict substantial harm to human health [2,3]. Studies have shown that high levels of NO2 in the air increase the risk of respiratory infections and lung damage [4]. In addition, NO2 constitutes one of the main factors that leads to the formation of acid rain photochemical smog and causes climate change; accordingly, NO2 considerably damages the atmospheric ecological environment [5]. PM2.5 particles are so small that they can reach the alveoli directly and penetrate into the bloodstream. Hence, long-term exposure to PM2.5 can lead to bronchitis and cardiovascular diseases [6]. In addition to affecting human health, PM2.5 is the primary cause of urban haze conditions [7].
Two main approaches—ground-based observation and satellite remote sensing observation—are employed to acquire long-term continuous observations of atmospheric NO2 and PM2.5 concentrations. However, current ground observation networks suffer from deficiencies in spatial and temporal coverage. By contrast, satellite remote sensing can be used to obtain data for most regions and therefore overcomes the insufficient spatial coverage associated with ground-based observations [8]. In recent years, many researchers have conducted numerous studies on tropospheric NO2 concentrations and PM2.5 mass concentrations using satellite data. For example, Liu et al. analyzed the spatial and temporal distributions of NO2 in urban agglomerations and their adjacent areas in the Pearl River Delta by using Ozone Monitoring Instrument (OMI) data; the results showed that the areas with high NO2 concentrations in the Pearl River Delta region were connected, that the urban group effect was significant, and that much higher concentrations were concentrated in the Pearl River Delta than in the surrounding areas [9]. Long et al. analyzed the effects of changes in tropospheric NO2 concentrations on human activities from 2005 to 2010 by using OMI data and found that the winter NO2 concentration is high and the summer NO2 concentration is low, while the western regions exhibit the opposite situation [10]. Krotkov et al. analyzed the changes in NO2 and SO2 concentrations in the world’s most polluted industrialized regions over the past few years (2005–2015) by using OMI data; their analysis revealed that NO2 and SO2 concentrations have declined in the past 10 years over the eastern U.S. and eastern Europe under the effects of pollution prevention and control policies, while those in China rose and then declined [11]. Guo et al. estimated the PM2.5 mass concentrations in eastern China using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product data and compared the values with monitoring data obtained from various sites (i.e., Benxi, Zhengzhou, Lushan Mountain, Guilin, and Nanning); the results showed that the locations with the most and least pollution are Zhengzhou and Nanning, respectively, which are basically consistent with ground station observations, among which the strongest correlation was found in Lushan [12].
The CCEZ was developed from the preexisting industrial base of Chengdu–Chongqing [13]. In 2007, the Chongqing Municipal Government and the Sichuan Provincial Government signed an agreement to promote Sichuan–Chongqing Cooperation and build the CCEZ together. The CCEZ was officially approved by the state council in March 2011, and the following two years witnessed substantial industrial developments. As a region with the highest urbanization level in western China, in recent years, the CCEZ has been playing an important strategic role in China’s economic and social development due to its rapid advancement of industrialization and urbanization. According to the development strategy of the CCEZ, energy is one of its leading industries. During the period between the 11th Five-Year Plan and the 12th Five-Year Plan, coal consumption was the main driving force for the development of the national economy, indicating that great challenges need to be overcome to reduce the associated nitrogen oxide and sulfide emissions [14]. Although the majority of coal-fired power plant boilers in China are currently equipped with low nitrogen combustion technology, the denitration efficiency is low [15], and the desulfurization technology is subject to resource shortages and secondary pollution in addition to the regional environment and other factors [16]. Industrial pollution and transportation pollution are the major sources of nitrogen oxides and sulfides in the CCEZ [17,18,19]. However, to the best of our knowledge, air pollution in the CCEZ region is not as widely studied compared with that in the Beijing–Tianjin–Hebei region, Yangtze River delta, and Pearl River delta in China [20,21,22]. In recent years, China has begun to pay attention to environmental monitoring. The characteristics of NO2 and PM2.5 can be measured using ground-based equipment, although the representativeness of individual sites is usually limited in a large domain. More importantly, current PM2.5 and NO2 monitoring stations in China have been established since 2013. Therefore, research on the spatial and temporal distribution and changes over long time series of pollution in the entire CCEZ is still lacking. In this paper, we used NO2 data from OMI and PM2.5 estimated by MODIS from 2005 to 2015 to statistically analyze the temporal and spatial distribution characteristics and the changes over long time series of NO2 and PM2.5 in the CCEZ as well as the main factors influencing these distributions to provide theoretical support for NO2 and PM2.5 control strategies in this area.

2. Materials and Methods

2.1. Study Area

The CCEZ, which is located in the upper reaches of the Yangtze River, comprises parts of Sichuan and Chongqing, including 15 prefectures in Sichuan and 31 districts and counties in Chongqing. The entire area is located within the Sichuan Basin, which is surrounded by both high- and low-elevation areas. As shown in Figure 1, Ya’an, southern Leshan, southern Yibin, southern Luzhou, northern Mianyang, northern Dazhou, and eastern Chongqing are high-elevation areas, whereas the terrain in other areas is relatively low. The area of the CCEZ is approximately 206,000 square kilometers, and the population density is high, accounting for nearly 8% of China’s population, and its economic aggregate accounts for approximately one-third of the western region and ranks first in the west. Chengdu and Chongqing are the two central cities with the largest economic aggregate in western China.

2.2. OMI-Retrieved NO2

The OMI is one of four sensors installed on the Aura Earth Observation System satellite, which was launched by the U.S. National Aeronautics and Space Administration (NASA) on 15 July 2004. The OMI was coproduced by the Netherlands and Finland with NASA, and it is an inheritance instrument for both Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements. The orbital scanning amplitude of the OMI is 2600 km, the spatial resolution at nadir is 13 km × 24 km, and the whole world is covered once a day [23]. The OMI-retrieved NO2 data used in this paper were derived from the official Royal Dutch product, that is, the OMI tropospheric NO2 vertical column concentrations were obtained from the Derivation of OMI Tropospheric NO2 (DOMINO) data product version 2.0 [24] from 2005 to 2015. The product was retrieved by the Royal Institute of Meteorology in the Netherlands with a spatial resolution of 0.125°. Monthly average data were downloaded from http://www.temis.nl in an Environmental Systems Research Institute (ESRI) grid format and then converted into a .csv file format by using the Interactive Data Language (IDL); the units in this format are 1013 molecules·cm−2. Subsequently, Microsoft SQL Server was used to process the quarterly average data and yearly average data. Kayana et al. used a multiaxis differential optical absorption spectroscopy (MAX-DOAS) monitoring network in Russia and Asia to validate OMI-derived tropospheric NO2 vertical column concentrations. The results showed that the monthly average correlation reached an R2 value of 0.84 in urban areas and 0.74 in rural areas, indicating that OMI can better capture NO2 tropospheric levels, especially in NO2 source regions [25]. Mou et al. derived tropospheric NO2 vertical column concentrations for Hefei from ground-based MAX-DOAS observations from August 2013 to July 2014 and compared the results with OMI data; these authors reported good consistency between the data sets, with a correlation coefficient of 0.88 obtained under cloudless weather conditions [26]. Jin et al. reported that the correlation coefficient between star and ground observations in the North China Plain (Gucheng Station, Hebei, China) reached 0.945 in the absence of clouds [27]. These results show that the inversion of OMI data can reflect the spatial and temporal distributions of the tropospheric NO2 column.

2.3. MODIS-Estimated PM2.5

We estimated the ground-level PM2.5 concentrations using satellite-retrieved AOD data with a spatial resolution of 10 km in the CCEZ for 2005–2015. The AOD data were generated using the dark target algorithm based on images retrieved from the MODIS instrument onboard the Terra satellite. The processing of the MODIS AOD data product has been described at length by its development group [28].
We followed the two-stage approach developed by Ma et al. [29] to estimate the daily PM2.5 concentrations using satellite-retrieved AOD. The PM2.5 data used for model fitting were obtained from the website of the China Environmental Monitoring Center (CEMC) for all available PM2.5 sites located inside the CCEZ. We matched the AOD values to ground-level measurements of the PM2.5 concentrations (µg·m−3) within the same 10 km grid cell acquired on the same day that the PM2.5 data were collected. The first stage of the statistical model adopted a linear mixed-effect model, which was adjusted with the monitored PM2.5 concentrations and the same grid cell’s AOD as well as meteorological factors collected from weather stations near the grid cell. The second stage adopted a generalized additive model, which used a smoothing function to optimize the land use parameters and geographical coordinates to improve the model’s performance in predicting the PM2.5 concentration. This model also used meteorological parameters, such as the planetary boundary layer (PBL) height, relative humidity (RH), wind speed, and surface pressure, from the Goddard Earth Observing System Data Assimilation System (GEOS-5) as well as 300 m resolution land use data from the European Space Agency Land Cover data [30].
To further illustrate the applicability of the near-surface PM2.5 mass concentration in the CCEZ estimated by the two-stage statistical model method, we calculated the monthly average between the PM2.5 mass concentrations released by the China Environmental Monitoring Center (CEMC) during 2013–2015 and estimated the PM2.5 mass concentrations. Until 2013, the Ministry of Environmental Protection (MEP) of China greatly expanded its air pollution monitoring network to measure hourly ground-level mass concentrations of PM2.5. Thus, we statistically analyzed the correlation between these concentrations, and Figure 2 shows that the correlation coefficient reached 0.74. Thus, the PM2.5 concentrations estimated by this method are consistent with near-surface observations. The historical data estimated by this method can be used to study the concentration level of near-surface PM2.5 in the study area.

3. Results

3.1. Spatial Distributions of NO2 and PM2.5 in the CCEZ

Figure 3 shows the 11-year average distributions of the tropospheric NO2 vertical column concentration and PM2.5 mass concentration in the CCEZ from 2005 to 2015. The mass concentration distribution of PM2.5 was similar but not identical to that of NO2. In addition, as shown in Figure 3b, the middle- and high-value areas of PM2.5 are more widely distributed than the low-value areas. The NO2 high-value areas (NO2 > 750 × 1013 molecules·cm−2) were mainly distributed in Chengdu, Deyang, and the main city of Chongqing, and the median-value (500 × 1013 molecules·cm−2 < NO2 < 750 × 1013 molecules·cm−2) areas were mainly distributed in Mianyang, Neijiang, and northern Yibin. According to traffic department statistics, at the end of 2014, motor vehicle ownership in Chengdu reached 3,857,000, representing a 13.9% increase from the end of the previous year; in addition, the resident population reached 13,653,000, ranking first in the province [31,32].
In Chengdu, which is located in the Sichuan Basin, the RH is very high; consequently, PM suspended in the air undergoes hygroscopic growth, thereby accelerating the accumulation of pollutants. Moreover, the weather conditions in Chengdu are fairly calm and stable, under which pollutants are not easily diluted or dispersed, leading to an increase in air pollution; additionally, inversion layers are common in Chengdu and can be relatively thick [33]. These conditions constitute important reasons why the air pollution in the CCEZ does not readily dissipate. The permanent residents in Chongqing’s main urban area account for nearly 28% of the total population in Chongqing, and they occupy an area of less than 6.7% of Chongqing. Furthermore, Chengdu and the main cities of Chongqing have a relatively well-developed traffic industry, and Deyang is a heavy industry base in China. Each of the abovementioned factors places enormous pressure on the environment, thereby resulting in serious air pollution throughout the CCEZ.
According to the 2015 Yearbook of Sichuan Province and Chongqing Municipality, the number of practitioners in the secondary industry is linearly related to air pollution. The Sichuan Province Environmental Protection Agency released a bulletin on the ambient air quality in June 2017; the ambient air quality was measured in the first half of the year, and the results regarding the magnitude of the variation in the air quality were ranked. This information showed that within the CCEZ, the cities of Sichuan Province (i.e., Chengdu, Deyang, and Neijiang) are the most polluted [34]. This result is consistent with the multiyear average mean from 2005 to 2011. The CCEZ is centered around Chengdu and Chongqing, which drive the development of the surrounding cities. According to the development of the central cities in the regional report of the CCEZ, Deyang is an important manufacturing base for heavy equipment, Zigong is a modern industrial city where machinery is manufactured, and Meishan comprises mainly locomotive manufacturing and constitutes an important transport node. The main industries of Neijiang, which is an important transport node and port city, are metallurgical building materials and automotive parts production. All of these cities exhibit serious pollution. Leshan and Ya’an form part of the clean energy industrial base; appropriately, these two cities are the least polluted areas in the CCEZ [35]. As shown in Tables S1 and S2, Ya’an has the lowest 11-year annual average NO2 and PM2.5 concentrations (i.e., 232.66 × 1013 molecules·cm−2 and 53.26 µg·m−3, respectively) whereas Chengdu displays the highest NO2 concentration (i.e., 849.46 × 1013 molecules·cm−2) and Zigong presents the highest PM2.5 concentration (i.e., 91.34 µg·m−3). The pollution in these cities is inseparable from their industrial development.

3.2. Monthly and Seasonal Patterns of NO2 and PM2.5 in the CCEZ

This paper presents the monthly average spatial distributions of the NO2 column concentration and PM2.5 mass concentration for the 11 years between 2005 and 2015. The spatial and temporal distributions of NO2 are shown in Figure 4. Highly polluted areas were distributed in the center of Chengdu and the main urban area of Chongqing. Along the border of the CCEZ (Ya’an, northern Mianyang, southern Leshan, northern Dazhou, and northeastern Chongqing), the pollution concentration was very low (NO2 < 250 × 1013 molecules·cm−2) throughout the year, and slight pollution (250 × 1013 molecules·cm−2 < NO2 < 500 × 1013 molecules·cm−2) was observed in December and January in southern Leshan and northeastern Chongqing. The highest levels of pollution were recorded in November, December, and January, and high-value areas were concentrated in the western and southern CCEZ and in most of the areas in and around the main urban area of Chongqing. The extent of highly polluted areas decreased considerably from February to March, and those of both highly and moderately polluted areas decreased on a monthly basis from April to August. However, the extents of areas with high and moderate pollution increased in September and October. The monthly mean distribution of PM2.5 is shown in Figure 5, which indicates that the overall pollution along the border area of the CCEZ was relatively low (PM2.5 < 60 μg·m−3) throughout the year. The highest levels of pollution (PM2.5 > 90 μg·m−3) were recorded in January, and even the border area pollution level, which was relatively low compared with those in other areas, was approximated high. The high-value areas in February were similar to those in January, and the pollution along the Chengdu–Chongqing boundary decreased to moderate levels. The areas with high levels of pollution decreased in March and December, and the high- and median-value areas (60 μg·m−3 < PM2.5 < 90 μg·m−3) overlapped. Higher levels of pollution were recorded in the border area in December than in February. In April and May, the extents of the high- and median-value areas decreased sharply, and areas with medium and high levels of pollution around Zigong and Chengdu intersected. From June to August, the entire area was slightly polluted; from September to November, the extents of the high- and median-value areas began to gradually expand to include most of the CCEZ, and the extents of the high-pollution areas in December were similar to but slightly heavier than those in March.
The seasonal variations in the NO2 columnar concentrations and PM2.5 mass concentrations varied among the different areas as shown in Figure 6. The months were divided into seasons according to the climate and phenology as follows: spring comprised March, April, and May; summer comprised June, July, and August; fall comprised September, October, and November; and winter comprised December, January, and February. The NO2 column concentrations in Ya’an, southern Leshan, northern Mianyang, northern Dazhou, and northeastern Chongqing were very low; this result is closely related to the low elevations of these geographic locations. The NO2 concentrations in the center of Chengdu and in Chongqing’s downtown area were very high, and heavy pollution was most obvious during the winter. The extents of the high- and median-value areas were large, and the overall spatial and temporal distributions in the spring and fall were fairly similar. However, the extent of the high- and median-value areas in spring was obviously larger than that in fall; moreover, the lowest values were observed in summer, during which most areas were only slightly polluted. Several prominent low-value areas, namely, Ya’an, northern Mianyang, southern Leshan, and the northeastern part of Dazhou, Chongqing, were observed in terms of the PM2.5 concentration. The highest pollution occurred in winter; some of the borders of Chengdu and Chongqing were moderately polluted, while others were heavily polluted. The lowest pollution was observed in the summer, during which the entire CCEZ was only slightly polluted. However, differences were observed between spring and fall: the border area was more polluted in spring than in fall but the central area had relatively higher levels of pollution in fall than in spring.
To analyze the seasonal variation characteristics of the tropospheric NO2 column concentration and the PM2.5 mass concentration in the CCEZ, we statistically calculated the quarterly average NO2 column concentrations and the PM2.5 mass concentrations from 2005 to 2015. Figure 7 shows the quarterly average for each season spanning the 11 years from 2005 to 2015, and the error bars represent the standard deviation of the mean for a particular season over the 11-year period. The NO2 concentrations in the various seasons were ranked from high to low as follows: winter, fall, spring, and summer. An analysis of the sizes of the error bars in the graph indicates that the difference in the average value for a particular season over the 11-year period from 2005 to 2015 was consistent with the NO2 concentration. The PM2.5 concentration was highest in winter and lowest in summer, and it was slightly higher in spring than in fall. Within a season, the mean difference over the 11-year period was ranked as follows: winter > fall > spring > summer. In the wintertime, cold weather is dominated by downward air flow; thus, pollutants do not easily disperse, which is one of the main reasons for the higher NO2 and PM2.5 concentrations during the winter season [36]. Moreover, the CCEZ is located in the Sichuan Basin, which receives a considerable amount of rain and is consequently referred to colloquially as the “Huaxi rain screen”. In the CCEZ, dry weather occurs in winter, droughts occur in spring, floods occur during summer, and substantial rainfall occurs in fall. The annual rainfall distribution is uneven, that is, 70~75% falls from June to October, which plays a role in reducing the amount of pollution. According to research results, precipitation has little ability to remove atmospheric pollutants when the daily precipitation is less than 5 mm. However, when the daily precipitation exceeds 5 mm, the efficiency with which pollutants are removed increases with increasing precipitation. The greater the amount of daily precipitation is, the greater the clearance rate and the better the air quality. In addition, the maximum clearance rate can reach 48.55% [37,38,39]. Rainfall affects the concentration of pollutants in spring, summer, and fall, although air pollution is also affected by industrial and human factors [38].
In this study, we further processed the Multiresolution Emission Inventory for China (MEIC, http://meicmodel.org) data with a spatial resolution of 0.25° × 0.25° to estimate the anthropogenic emissions in different seasons over the CCEZ. Table 1 and Table 2 show four sources of anthropogenic NOx and PM2.5 emissions (i.e., industrial, power, residential, and transportation) in different seasons of 2012. For NOx, industry, power plants, and transportation were the major sources of NOx emissions, with industry accounting for approximately a half of the total NOx emissions. In addition, Table 1 clearly shows that the emissions throughout the four seasons are similar for industry, power, and transportation, while residents’ emissions are significantly higher in winter than in the other three seasons. Compared with NOx, anthropogenic PM2.5 emissions are mainly contributed by industrial and residential sources, which together accounted for more than 90% of the emissions in 2012. Similar to NOx, industrial emissions accounted for more than half of the total PM2.5 emissions. As shown in Table 2, residential sources contributed more than 64% of the PM2.5 emissions in winter, and these emissions are approximately three times higher than those of the other three seasons.

3.3. Eleven-Year Variations in the NO2 and PM2.5 Concentrations in the CCEZ

Figure 8 shows the quarterly average for each year of the 2005–2015 period. The error bars represent the standard deviation of the mean of all months within a season. From 2005 to 2015, the NO2 concentration in winter exhibited the largest fluctuation with an increase of 348.86 × 1013 molecules·cm−2 and a decrease of 184.95 × 1013 molecules·cm−2. The NO2 concentration increased linearly from 2006 to 2010 and reached a peak in 2010, followed by a decrease and then an increase from 2011 to 2013 and subsequently a linear decrease from 2013 to 2015. The NO2 concentration in spring decreased slightly from 2005 to 2008 but continued to increase from 2008 to 2013 and reached a maximum increase of 129.13 × 1013 molecules·cm−2. Over the 11-year period, the NO2 concentration fluctuated slightly during the summer but remained fairly stable. The NO2 concentration in fall increased from 2006 to 2011 and exhibited an increase of 164.32 × 1013 molecules·cm−2. After 2011, the NO2 concentration began to decline annually and exhibited a decrease of 111.39 × 1013 molecules·cm−2. Based on the error bars in the figure, we can conclude that the mean monthly fluctuation was typically highest in winter and then fall, spring, and summer. The fluctuations in the PM2.5 mass concentration during the fall and winter were relatively large with no regularity. The difference between the highest and lowest concentrations was 51.93 µg·m−3 in winter and 41.11 µg·m−3 in fall. The PM2.5 concentration was fairly stable in spring and summer from 2005 to 2013 but exhibited a steady decline from 2013 to 2015; this result is related to the “Suggestions on Haze Pollution in the Sichuan Basin and Suggestions for Countermeasures” formulated by the Sichuan Provincial Department of Environmental Protection in 2013 and the “Chongqing City Blue Sky” action implementation plan (2013–2017) proposed by Chongqing Municipality. The average monthly fluctuation in the PM2.5 mass concentration was highest in winter and lowest in summer, while the fluctuation in spring was unstable and greater than that in fall.
We used the monthly average data of 2005–2015 and the regression model developed by Lamsal et al. to compare the interannual changes over the 11-year period in the CCEZ [40,41,42]. The time series of the monthly average consists of three parts: the seasonal trend, linear trend, and irregular fluctuation:
Ω ( t ) = S ( t ) + T ( t ) + I ( t )
where t represents time (in months) [43]. In general, the seasonal trend is composed of sine and cosine functions [44]:
S ( t ) = c 0 + ∑ j = 1 3 ( c 1 j sin ( 2 π jt / 12 ) +   c 2 j cos ( 2 π jt / 12 ) )
where c0, c1j, and c2j are constant coefficients. The monthly average value changes with the month; therefore, we used the 12-month moving average method to calculate the monthly index. We identified and extracted the seasonal trend components by exploiting changes in the measured seasonal pattern (amplitude and phase) for individual years. Eleven regression lines (2005–2015) are based on annual observations plus six-month observations in adjacent years. Comparing the 11 regression lines with high and low amplitudes, two seasonal terms (S(t) = s1 + s2 Equation (1)) were identified and isolated. In this paper, we calculated the changes in the deseasonalized trends of the NO2 columns (Figure 9a) and PM2.5 (Figure 9b) from 2005 to 2015. The error bars show that the monthly fluctuation in NO2 was greater than that in PM2.5. The black-filled circles in the figure show that the NO2 and PM2.5 concentrations generally exhibited upward trends from 2005 to 2011, followed by downward trends after 2013. The annual mean NO2 concentration was fairly similar from 2011 to 2013 but exhibited a subsequent downward trend, which was attributed to the environmental strategy formulated for the regional planning requirements of the CCEZ. The final year for the total emission reduction of major pollutants in the 12th Five-Year Plan was 2015, during which all cities in Sichuan and Chongqing Municipalities accelerated the key emission reduction projects pertaining to the total amount of major pollutants, and they achieved remarkable results. By the end of 2015, the NO2 concentration decreased by 15.4% compared with 2011, and the PM2.5 concentration decreased by 38%. In 2011, the concentrations of NO2 and PM2.5 reached their highest values of 450.71 × 1013 molecules·cm−2 and 78.96 µg·m−1, respectively, and in 2015, they reached their lowest values of 381.19 × 1013 molecules·cm−2 and 48.98 µg·m−3, respectively.
The monthly average NO2 and PM2.5 concentrations over the CCEZ are shown in Figure 10. The monthly average concentrations of NO2 and PM2.5 showed cyclical variations throughout the year. The troughs of NO2 and PM2.5 in each cycle generally occurred in July and August (i.e., summer); NO2 reached a minimum value of 230.15 × 1013 molecules·cm−2 in August 2007, and PM2.5 reached a minimum value of 26.43 µg·m−3 in August 2015. The peaks usually occurred in December and January (i.e., winter); NO2 reached a maximum value of 975.18 molecules.cm−2 in December 2012, and PM2.5 reached a maximum of 157.64 µg·m−3 in February 2010. From 2005 to 2008, the tropospheric NO2 columnar concentration and PM2.5 mass concentration were fairly stable and exhibited few fluctuations. From 2008 to 2010, the NO2 concentration continued to increase, and the PM2.5 mass concentration fluctuated slightly. The concentrations of NO2 and PM2.5 dropped sharply in 2011 compared with those in previous years, and the concentrations in 2012 were unchanged from the 2010 levels. Between 2013 and 2015, the NO2 and PM2.5 concentrations continued to decrease periodically, and in August 2015, they reached the lowest values in the 11-year period.

4. Conclusions

This study analyzed the spatial–temporal distributions in NO2 and PM2.5 using satellite remote sensing data in the CCEZ over 11 study years. Geographically, the area with the highest level of column NO2 is Chengdu, which presented an average value of 848.55 × 1013 molecules·cm−2 in the 11-year study period. The area with the highest near-surface PM2.5 concentration is Zigong, which presented an average value of 91.34 µg·m−3 over the 11-year study period. The area with the lowest concentration of NO2 and PM2.5 is Ya’an, which presented average values of 91.34 µg·m−3 and 53.26 µg·m−3 over the 11-year study period, respectively.
Seasonally, the tropospheric NO2 column concentration and near-surface PM2.5 mass concentration in the CCEZ all showed obvious monthly and seasonal variation characteristics. The concentrations of NO2 and PM2.5 reach their highest annual values usually in December and January in winter and their lowest annual values in July and August in summer. For anthropogenic emissions, industrial emissions contributed approximately 50% of NOx and PM2.5 emissions. For PM2.5, residential emissions contributed more than 64% in winter, and these emissions were approximately three times higher than those in the other three seasons.
During our study period, NO2 presented relatively greater fluctuations. From 2005 to 2011, the concentration of column NO2 showed annual increases and reached a high value of 953.33 × 1013 molecules·cm−2 in January 2011, after which the column NO2 concentration showed a downward trend, although it increased in 2013 and then continued to decline. From 2005 to 2011, the concentration of PM2.5 was basically unchanged and presented an annual average of 76.52 µg·m−3. After 2011, the concentration of PM2.5 began to decline sharply, and the lowest value of 26.43 µg·m−3 was found in August of 2015.

Supplementary Materials

The following are available online at https://www.mdpi.com/1424-8220/18/11/3950/s1: Table S1: The mean (plus standard deviation) NO2 concentrations (1013 molecules·cm−2) for the sixteen cities in the CCEZ during 2005–2015; Table S2: The mean (plus standard deviation) PM2.5 concentrations (µg·m−3) for the sixteen cities in the CCEZ during 2005–2015.

Author Contributions

K.C. and S.L. conceived the study; K.C. and Q.Z. performed the data analyses and wrote the manuscript; Q.Z. and Y.L. conducted the validation; K.C., S.L. and W.G. contributed to the revisions.

Funding

This research was financially supported by the National Key R&D Program of China (2016YFC0201507) and the National Natural Science Foundation of China (grant Nos. 41801281 and U1704122).

Acknowledgments

We would like to acknowledge the use of the tropospheric NO2 column data from http://www.temis.nl, and we are grateful for the use of the MODIS aerosol data that are freely available at https://ladsweb.modaps.eosdis.nasa.gov/search/.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The geographic location and terrain of the study area (Chengdu–Chongqing Economic Zone) in China.
Figure 1. The geographic location and terrain of the study area (Chengdu–Chongqing Economic Zone) in China.
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Figure 2. Scatter plots of the monthly PM2.5 (µg·m−3) mass concentrations over CEMC observations and AOD-estimated PM2.5 from 2013 to 2015.
Figure 2. Scatter plots of the monthly PM2.5 (µg·m−3) mass concentrations over CEMC observations and AOD-estimated PM2.5 from 2013 to 2015.
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Figure 3. Spatial distributions of the 11-year average NO2 and PM2.5 concentrations in the CCEZ during 2005–2015: (a) NO2 columnar concentration based on the OMI DOMINO product at a resolution of 0.125° × 0.125° and (b) MODIS-estimated PM2.5 mass concentration at a resolution of 0.1° × 0.1°.
Figure 3. Spatial distributions of the 11-year average NO2 and PM2.5 concentrations in the CCEZ during 2005–2015: (a) NO2 columnar concentration based on the OMI DOMINO product at a resolution of 0.125° × 0.125° and (b) MODIS-estimated PM2.5 mass concentration at a resolution of 0.1° × 0.1°.
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Figure 4. Monthly average NO2 distributions in the CCEZ from 2005 to 2015.
Figure 4. Monthly average NO2 distributions in the CCEZ from 2005 to 2015.
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Figure 5. Monthly average PM2.5 distributions in the CCEZ from 2005 to 2015.
Figure 5. Monthly average PM2.5 distributions in the CCEZ from 2005 to 2015.
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Figure 6. Seasonally averaged NO2 (upper panel) and PM2.5 (lower panel) concentration distributions in the CCEZ from 2005 to 2015 (Spring: March, April, and May; Summer: June, July, and August; Fall: September, October, and November; and Winter: December, January, and February).
Figure 6. Seasonally averaged NO2 (upper panel) and PM2.5 (lower panel) concentration distributions in the CCEZ from 2005 to 2015 (Spring: March, April, and May; Summer: June, July, and August; Fall: September, October, and November; and Winter: December, January, and February).
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Figure 7. Seasonal variations in the NO2 and PM2.5 concentrations in the CCEZ from 2005 to 2015; error bars represent standard deviations.
Figure 7. Seasonal variations in the NO2 and PM2.5 concentrations in the CCEZ from 2005 to 2015; error bars represent standard deviations.
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Figure 8. Seasonally averaged NO2 (1013 molecules·cm−2) (upper panel) and PM2.5 (µg·m−3) (lower panel) concentration changes in the CCEZ from 2005 to 2015; vertical bars represent standard deviations.
Figure 8. Seasonally averaged NO2 (1013 molecules·cm−2) (upper panel) and PM2.5 (µg·m−3) (lower panel) concentration changes in the CCEZ from 2005 to 2015; vertical bars represent standard deviations.
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Figure 9. Changes in the annual average NO2 (a) and PM2.5 (b) concentrations in the CCEZ during 2005–2015; gray circles represent deseasonalized monthly columns, while black-filled circles show the annual means, and vertical bars show the standard deviations.
Figure 9. Changes in the annual average NO2 (a) and PM2.5 (b) concentrations in the CCEZ during 2005–2015; gray circles represent deseasonalized monthly columns, while black-filled circles show the annual means, and vertical bars show the standard deviations.
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Figure 10. Changes in the monthly average NO2 (1013 molecules·cm−2) and PM2.5 (µg·m−3) concentrations in the CCEZ from 2005 to 2015.
Figure 10. Changes in the monthly average NO2 (1013 molecules·cm−2) and PM2.5 (µg·m−3) concentrations in the CCEZ from 2005 to 2015.
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Table
Table 1. Four sources of anthropogenic NOx emissions (i.e., industrial, power, residential, and transportation) during all seasons of 2012 in the CCEZ (Unit: 104 t·yr−1).
Table 1. Four sources of anthropogenic NOx emissions (i.e., industrial, power, residential, and transportation) during all seasons of 2012 in the CCEZ (Unit: 104 t·yr−1).
TypeSpringSummerFallWinter
Industry15.45 (49.9%)15.59 (51.9%)16.05 (52.2%)15.41 (43.9%)
Power5.17 (16.7%)4.07 (13.5%)4.29 (13.9%)6.85 (19.5%)
Residential1.49 (4.8%)1.49 (5.0%)1.47 (4.7%)3.9 (11.1%)
Transportation8.92 (28.7%)8.91 (29.6%)8.92 (29.2%)8.94 (25.5%)
Table
Table 2. Four sources of anthropogenic PM2.5 emissions (i.e., industrial, power, residential, and transportation) during all seasons in 2012 in the CCEZ (Unit: 104 t·yr−1).
Table 2. Four sources of anthropogenic PM2.5 emissions (i.e., industrial, power, residential, and transportation) during all seasons in 2012 in the CCEZ (Unit: 104 t·yr−1).
TypeSpringSummerFallWinter
Industry9.21 (53.4%)8.96 (53.3%)9.2 (54.1%)9.08 (31.7%)
Power0.53 (3.1%)0.41 (2.4%)0.44 (2.6%)0.70 (2.5%)
Residential7.04 (40.8%)6.99 (41.6%)6.91 (40.7%)18.36 (64.2%)
Transportation0.47 (2.7%)0.46 (2.7%)0.47 (2.8%)0.47 (1.6%)

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Cai, K.; Zhang, Q.; Li, S.; Li, Y.; Ge, W. Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing. Sensors 2018, 18, 3950. https://doi.org/10.3390/s18113950

AMA Style

Cai K, Zhang Q, Li S, Li Y, Ge W. Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing. Sensors. 2018; 18(11):3950. https://doi.org/10.3390/s18113950

Chicago/Turabian Style

Cai, Kun, Qiushuang Zhang, Shenshen Li, Yujing Li, and Wei Ge. 2018. "Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing" Sensors 18, no. 11: 3950. https://doi.org/10.3390/s18113950

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