remote sensing

Article Long-Term (2005–2017) View of Atmospheric Pollutants in Central Using Multiple Satellite Observations

Rong Li 1,2, Xin Mei 1,2,*, Liangfu Chen 3, Lili Wang 4, Zifeng Wang 3 and Yingying Jing 5

1 Key Laboratory of Regional Development and Environmental Response, Hubei University, 430062, China; [email protected] 2 Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China 3 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China; [email protected] (L.C.); [email protected] (Z.W.) 4 State key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics of Chinese Academy of Sciences, Beijing 100029, China; [email protected] 5 Beijing Key Laboratory of Cloud, Precipitation and Atmospheric Water Resources, Beijing Weather Modification office, Beijing 10089, China; [email protected] * Correspondence: [email protected]; Tel.: +86-133-0862-9572

 Received: 25 February 2020; Accepted: 21 March 2020; Published: 24 March 2020 

Abstract: The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO grew from ~3.2 to 5.9 1015 molecules cm 2 during 2005–2011 and 2 × − deceased to ~3.9 1015 molecules cm 2 in 2017. Unlike the steadily declining SO , variations of OMI × − 2 NO flattened out in 2016 and increased ~0.5 1015 molecules cm 2 during 2017. As result, MODIS 2 × − AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.

Keywords: air pollution; OMI; MODIS; central China; emission control

Remote Sens. 2020, 12, 1041; doi:10.3390/rs12061041 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1041 2 of 19

1. Introduction Air pollution is a dynamic and complicated mixture of particulate matter and gaseous pollutants that emitted from anthropogenic activities and natural processes into the atmosphere. During the last decades, rapid industrialization and has caused serious air quality problems [1]. Massive anthropogenic emissions in populated regions of China lead to widespread air pollution [2–5], which is characterized by high concentration and diverse sources [6–8]. With a lifetime of several days to one-week, fine particle (PM2.5, fine particulate matter with aerodynamic diameter less than 2.5 µm) is the most-watched atmospheric pollutant in air quality monitoring. Moreover, as the main gaseous precursors of PM2.5, sulfur dioxide (SO2), and nitrogen dioxide (NO2) is usually measured to monitor emission levels for combustion of fossil fuels and vehicle exhaust. Ozone (O3) pollution from photochemical reaction between NOx and volatile organic compounds (VOCs) is apt to occur under intense sunshine and high temperature conditions. Numerous epidemic studies have shown robust correlation between exposure level to atmospheric pollutants and increase in morbidity and mortality of respiratory and cardiovascular diseases [9]. Owing to adverse effects of air pollution on human health and daily life, the Chinese government has implemented continuous emission control measures. The anthropogenic emission sources and their amount in China have undergone dramatic changes during last decades due to both the rapid transition of economic structures and environmental policies [10]. There is also a large spatial difference in anthropogenic emissions sources [11], exerting considerable challenges for making timely and effective improvement measures. While ground measurements can accurately measure mass concentration of atmospheric pollutants, the national ground network for air quality monitoring in China since 2013 is only concentrated in major cities with very limited spatial coverage. Since late 1990s, advanced satellite sensors including moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) provide an unprecedented opportunity to observe atmospheric particles and trace gases from regional to global scales [12,13]. The heavy air pollution in China has drawn the attention of numerous observational studies from various perspectives. Besides ground-based measurements and chemistry model simulations, satellite studies have greatly renewed knowledge concerning spatial distribution of air pollution hotspots and variations of their major emissions in northern China [14–16], the Yangtze River Delta (YRD) [17], and (PRD) [18]. However, previous studies mostly focused on economically developed regions with extensive urban agglomeration. As an emerging economic center located between polluted northern China and clean southern China, air quality in central China has been rarely concerned under the background of rapid development. Although recent air pollution such as PM2.5 in central China (~40–60 µg/m3) is obviously lower than that in northern China (~60–80 µg/m3)[19,20], the magnitude of the atmospheric pollutants in central China is at a high level compared with health standard 3 (e.g., PM2.5: 35 µg/m ), which can further be seriously aggravated by [21]. By now, spatial patterns of major atmospheric pollutants in central China and how they change over space and time remain unclear due to few comprehensive observational studies. In this study, we first provide comprehensive insight into spatial patterns and long-term trend of primary atmospheric pollutants including aerosols, SO2, and NO2 in central China during 2005–2017 by utilizing multiple satellite products. Section2 introduces satellite products and ground measurements used in this work. A comprehensive analysis regarding spatial and temporal variations of aerosol optical depth (AOD), SO2, and NO2 in central China is given in Section3. Further, its implication of on emission control is discussed in Section4. At last, a brief summary is shown in Section5. The main purpose of this work is to reveal spatial changes of pollution hotspot and temporal trends of the primary air pollutants in central China, giving a reference for future air quality improvements. Remote Sens. 2020, 12, 1041 3 of 19

Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 19 2. Data and Methods atmospheric pollutants. Unlike the concentrated urban agglomeration in northern China, YRD, and 2.1.PRD, Study only Area the and megacity Ground of Measurments Wuhan has a population of more than 10 million in Hubei, and other cities have much smaller scales. We selected Hubei province as our study area (Figure1), which is the main part of central China. The Environmental Protection Agency of China established a national air quality monitoring Surrounded by mountains with evaluation at ~600–1000 m, Hubei is relatively isolated from other network in 2013, which releases hourly concentration of six main atmospheric pollutants (PM2.5, PM10, regions. The mountains around Hubei provide a barrier for the transport of heavy pollution from SO2, NO2, O3, and CO) in major cities with the same ambient air quality standard. Spatial distribution northern China. In the other hand, the basin-like terrain in Hubei is not conductive for dispersion of air quality monitoring sites in Hubei is shown in Figure 1. There are 57 national air quality of atmosphericmonitoring sites pollutants. in Hubei, Unlike with nine the concentrated sites in the megacity urban agglomeration of Wuhan. In inaddition, northern the China, statistical YRD, and PRD, only the megacity of Wuhan has a population of more than 10 million in Hubei, and other emission inventory data of SO2 and NOx from the statistical yearbook of China is used for comparison citiesand have reference. much smaller scales.

FigureFigure 1. Terrain1. Terrain of of central central China China and and the the ambient ambient monitoringmonitoring stations. DEM: DEM: Digital Digital elevation elevation model. model.

TheThe Environmental meteorological Protection background Agency of central of China China established is shown in a Figure national 2, utilizing air quality the monitoring National networkCenters in for 2013, Environmental which releases Prediction hourly (NCEP) concentration Final (FNL) of sixand main Operational atmospheric Global pollutantsAnalysis data (PM in2.5 , the 1° × 1° grid. Seasonal characteristics regard the height of the planetary boundary layer (PBL) with PM10, SO2, NO2,O3, and CO) in major cities with the same ambient air quality standard. Spatial distribution925 hPa winds, of air temperature, quality monitoring and relative sites humidity in Hubei (RH). is shown The PBL in Figure height1 of. There central are China 57 national is around air quality~1000 monitoring m at similar sites levels in Hubei, as in withnorthern nine sitesChina in except the megacity a little oflower Wuhan. (~800 In m) addition, in autumn. the statistical While northerly winds prevail in winter and spring within the PBL, southerly and easterly air flows are emission inventory data of SO2 and NOx from the statistical yearbook of China is used for comparison predominant in summer and autumn, respectively. In particular, wind speed over central China is and reference. obviously higher (~1–3 m/s) than adjacent areas. Central is located in the transition zone, with The meteorological background of central China is shown in Figure2, utilizing the National temperature and RH higher than in northern China and lower than in southern China. There is a high Centers for Environmental Prediction (NCEP) Final (FNL) and Operational Global Analysis data in the contrast in temperature near surface between the cold winter and hot summer, indicating distinct 1 1 grid. Seasonal characteristics regard the height of the planetary boundary layer (PBL) with ◦ ×atmospheric◦ stability. 925 hPa winds, temperature, and relative humidity (RH). The PBL height of central China is around ~1000 m at similar levels as in northern China except a little lower (~800 m) in autumn. While northerly winds prevail in winter and spring within the PBL, southerly and easterly air flows are predominant in summer and autumn, respectively. In particular, wind speed over central China is obviously higher (~1–3 m/s) than adjacent areas. Central is located in the transition zone, with temperature and RH higher than in northern China and lower than in southern China. There is a high contrast in temperature near surface between the cold winter and hot summer, indicating distinct atmospheric stability.

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FigureFigure 2. 2.Seasonal Seasonal characteristics characteristics of theof meteorologicalthe meteorological conditions conditions including including height (m)height of the (m) planetary of the

boundaryplanetary layerboundary (PBL) layer with 925(PBL) hPa with winds 925 (top), hPa winds temperature (top), temperature (◦C) (middle), (°C) and (middle), relative humidity and relative (%) (RH)humidity (bottom) (%) (RH) at 14:00 (bottom) local time at 14:00 of 2017 local in time eastern of 2017 China. in eastern China.

2.2.2.2. MODISMODIS AerosolAerosol DatasetDataset TheThe MODIS MODIS sensor sensor onboard onboard Terra Terra and and Aqua Aqua satellite satellite since since 2000 and2000 2002, and respectively, 2002, respectively, can provide can nearprovide daily near global daily observations global observations of aerosol of and aerosol cloud and properties cloud properties with a wide with swath a wide of swath ~2330 of km ~2330 and broadkm and spectrum broad spectrum between 0.4–14betweenµm 0.4–14 at high μm spatial at high resolution spatial resolution of 250–1000 of 250–1000 m. MODIS m. aerosolMODIS retrieval aerosol overretrieval land over was land firstly was implemented firstly implemented at 10 km at by 10 the km dart by targetthe dart (DT) target algorithm (DT) algorithm utilizing utilizing spectral relationshipspectral relationship between surface between reflectance surface inreflectance visible and in shortvisible infrared and short bands infrared over dense bands vegetation over dense [12]. Owingvegetation to the [12]. widely Owing application to the widely of MODISapplication aerosol of MODIS products aerosol in air products quality in research, air quality DT research, aerosol productsDT aerosol are products at a much are higher at a resolutionmuch higher of 3resolution km and are of added3 km and in the are Collection added in (C)the 6 Collection MODIS aerosol (C) 6 algorithmMODIS aerosol [22]. algorithm However, [22]. MODIS However, DT AOD MODIS is usually DT AOD not is available usually not over available urban over areas urban and heavy areas pollutionand heavy conditions pollution [ 23conditions]. To retrieve [23]. aerosols To retrieve over aerosols bright surface over bright such assurface deserts such and as urban deserts regions, and MODISurban regions, Deep Blue MODIS (DB) algorithm Deep Blue was (DB) developed algorithm to utilizewas developed pre-calculated to utilize surface pre reflectance‐calculated database, surface andreflectance externed database, to all the and cloud-free externed and to all snow the/ice-free cloud‐free areas and since snow/ice C6 [24‐free]. areas since C6 [24]. BothBoth MODISMODIS DTDT andand DBDB AODAOD atat 550550 nmnm exhibitsexhibits wellwell accuracyaccuracy compared with ground observationsobservations [23,,25].25]. Ground Ground validations validations show show that that retrieval retrieval error error of MODIS of MODIS AOD AODis generally is generally better betterthan 0.05 than + 20% 0.05 relative+ 20% relativeto Aerosol to AerosolRobotic Network Robotic Networkobservations. observations. The current The version current of MODIS version DT of MODISand DB DT aerosol and DBproducts aerosol is productsC6.1, which is C6.1, only whichconducts only some conducts slight some improvements slight improvements without changes without in changesalgorithm in algorithmprinciple. Compared principle. Comparedwith C6 products, with C6 products,the C6.1 DB the C6.1aerosol DB products aerosol products have refined have refinedinstrument instrument calibration calibration and aerosol and aerosolmodels models[26]. Quality [26]. Qualityassurance assurance flags of MODIS flags of MODISDB AOD DB depend AOD dependon number on number of retrieved of retrieved pixels and pixels their and standard their standard deviation. deviation. Usually, Usually,MODIS AOD MODIS of best AOD estimate of best estimatequality is quality suggested is suggested in application, in application, but it is found but it that is found such selection that such can selection filter out can many filter high out‐ manyAOD high-AOD(>1.0) cases (> 1.0)in eastern cases in China eastern with China only with slight only improvement slight improvement in accuracy in accuracy [23]. [Considering23]. Considering the thelimitation limitation of MODIS of MODIS DT and DT and MODIS MODIS DB products DB products of best of estimate, best estimate, we selected we selected C6.1 MODIS C6.1 MODIS DB AOD DB AODof all ofquality all quality at 10 atkm 10 to km investigate to investigate aerosol aerosol trends trends in central in central China. China.

2.3. OMI Products of Trace Gases OMI onboard Aura since 2004 is a hyperspectral instrument that measures backscattered sunlight in ultraviolet‐visible range (270 nm to 500 nm) with a spectral resolution of about 0.5 nm. As a sun‐synchronous satellite instrument, OMI crosses the equator around local time of 13:30. With a

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2.3. OMI Products of Trace Gases OMI onboard Aura since 2004 is a hyperspectral instrument that measures backscattered sunlight in ultraviolet-visible range (270 nm to 500 nm) with a spectral resolution of about 0.5 nm. As a sun-synchronous satellite instrument, OMI crosses the equator around local time of 13:30. With a 2600 km swath, OMI has a daily global coverage with a pixel size of 13 24 km at nadir. By deriving × abundance of various trace gases with their respective absorption characteristics, OMI products include vertical column density (VCD) of trace gases such as NO2 and SO2. Considering the much shorter lifetime of gaseous pollutants of SO2 and NO2 (~one hour to half day) than aerosols’, the continuous data record of OMI products has been widely used to infer anthropogenic pollution sources and verify emission inventory [27]. While NO2 is the production from combustion of fossil fuels such as vehicle emissions in urban regions, SO2 mainly originates from coal-burning sources. The OMI NO2 and SO2 data was obtained from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). OMI tropospheric NO2 VCD was retrieved by the common differential optical absorption spectroscopy (DOAS) algorithm with uncertainties of <20% in polluted regions [28]. There are three key steps in NO2 retrieval: spectral fitting of OMI backscattered radiances to laboratory absorption spectra, separating stratospheric contribution, and converting satellite observed slant column densities to VCDs. By contrast, the principal component analysis (PCA) technique was used in the current planetary boundary layer (PBL) SO2 retrieval with priori profiles and a PBL height of 3 km (700 hPa) assumed [29]. The OMP PBL SO2 derived using the PCA method registered a small bias (<0.1 DU) over background regions [30]. Unlike the common unit of OMI NO2 products (1015 molecules cm 2), Dobson units (1 DU = 2.69 1016 molecule cm 2) are usually used − × − for OMI SO2 for convenience of retrieval calculation and error estimation. The pixel size of OMI has a large variation from 13 24 km at nadir to 26 120 km at the edge. Different from satellite AOD × × derived from aerosol scattering, OMI retrieval of trace gases by their absorption is not very sensitive to cloud contamination. To ensure representativity of OMI products, NO2 retrievals with a cloud fraction >30% and for SO2 with O3 < 1500 DU in the data cell are filtered out. We selected a standard Level 3 OMI grid products at 0.25 0.25 resolution. × ◦ 3. Results and Analysis

3.1. The Characteristics of SO2 and NO2 Variation during 2005–2017 As the main water-soluble components, sulfate, and nitrate together accounted for a significant fraction (~20–50%) in mass concentration of PM2.5 particles in China [4,11]. Other major particle components such as organic aerosol was generated via complicated chemistry reactions and mineral dust that originated from long-range transport and diverse local sources [4]. By contrast, emission sources of sulfate and nitrate’s gaseous precursors, SO2 and NOx, are mostly from coal-burning power plants and industry activities, which are the main control targets of the government due to the practicability for unified management. Since the short lifetime of SO2 and NO2, satellite VCD products of SO2 and NO2 are usually used to map anthropogenic emission sources as well as their emission levels [13,17]. Figure3 displays variations of OMI SO 2 in Hubei during 2005–2017. There was a notable overall increase in the amount of SO2 in 2005–2007, with the largest SO2 hotspot in the megacity of Wuhan. SO2 in Wuhan increased from ~0.4 to >0.5 DU and from ~0.25 to ~0.35–0.4 DU in other several hotspots. Then, SO2 concentration in Wuhan exhibited a large decrease by ~0.1–0.2 DU in 2008 and reached a lower level since 2009, which can be connected with implementation of the 2006 desulfurization policy in China [31]. By contrast, new SO2 hotspots emerged in 2009 in the southeastern part of Wuhan and in the southwestern part of Hubei, and reached peak values (~0.4) in 2011. In 2012, SO2 amount displayed a continuous decline and arrives at a very low level (<0.1) in the entire Hubei region by 2016 without large emission sources. Despite low concentration, it should be noted that SO2 in urban region of Wuhan got higher (~0.2) as a hotspot in 2017. RemoteRemote Sens. Sens.2020 2019, 12, 11, 1041, x FOR PEER REVIEW 66 of of 19 19

FigureFigure 3. 3.Spatial Spatial andand temporaltemporal distributiondistribution ofof annualannual ozoneozone monitoring instrument (OMI), (OMI), sulfur sulfur dioxidedioxide (SO (SO2),2), and and vertical vertical column column densitydensity (VCD)(VCD) overover Hubei province during 2005–2017.

ToTo have have a a long-term long‐term view view of of the the changes changes in in the the overall overall SOSO22 level in central China, China, spatial spatial means means ofof OMI OMI SO SO2 2compared compared withwith statisticalstatistical SO 2 emissionemission amount amount during during 2005–2017 2005–2017 (Figure (Figure 4).4). There There is isa agenerally generally consistent consistent trend trend between between OMI OMI SO SO2 concentration2 concentration and and emission emission amount. amount. Different Different from from the thepeak peak value value of SO of SO2 concentration2 concentration in 2007, in 2007, statistical statistical SO2 SOemissions2 emissions in Figure in Figure 3 show3 show the largest the largest SO2 SOamount2 amount of ~760,000 of ~760,000 tons tons in 2006. in 2006. In 2007, In 2007, the themuch much higher higher OMI OMI SO2 SOVCD2 VCD in both in both Hubei Hubei and and its itsadjacent adjacent part part to to the the north north demonstrates demonstrates that that the the bottom bottom-up‐up statistical statistical emission emission can can miss miss some some substantialsubstantial emission emission sources sources (Figure (Figure2 ),2), especially especially in in the the early early stage stage of of SO SO22 control. Considering Considering the the desulfurizationdesulfurization policy policy inin ChinaChina fromfrom 20062006 [[31],31], thethe emissionemission peak in 2006 can can be be partly partly caused caused by by statisticsstatistics of of mainly mainly large large SO SO2 sources.2 sources. On On the the other other hand, hand, OMI OMI retrievals retrievals show show a a more more rapid rapid decline decline of SOof2 SOamount2 amount (~50%) (~50%) than than statistical statistical amount amount (~10%) (~10%) since since 2008. 2008. The The increase increase of SO of2 SO(~50%)2 (~50%) in 2010–2011 in 2010– can2011 be can connected be connected with the with recovery the recovery from thefrom economic the economic downturn downturn in 2009 in 2009 [32]. [32]. Considering Considering that that SO2 mainlySO2 mainly originates originates from coal-burningfrom coal‐burning combustion combustion of fossil of fossil fuels, fuels, the low-level the low‐level SO2 (~210,000 SO2 (~210,000 tons) tons) after 2014after indicates 2014 indicates effective effective effects effects of SO 2ofcontrol SO2 control policy. policy. The strikingThe striking deviation deviation of the of annualthe annual OMI OMI SO2 showsSO2 shows large temporallarge temporal variations variations of the of SO the2 level SO2 andlevel the and variances the variances increase increase obviously obviously with thewith mean the concentration.mean concentration. Although Although there is there no coal-burning is no coal‐burning heating duringheating winter during in winter central in China central as thereChina is as in northernthere is in China, northern the formationChina, the offormation sulfate from of sulfate SO2 andfrom their SO2 and lifetime their can lifetime have can considerable have considerable seasonal diseasonalfferences differences due to variations due to variations of meteorological of meteorological conditions conditions such as such illumination, as illumination, temperature, temperature, and relativeand relative humidity humidity (Figure (Figure2). 2).

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Figure 4. Box plots of annual OMI SO22 VCD and statistical SO2 emissionsemissions (red) (red) over Hubei province during 2005–2017.2005–2017. InIn each each box, box, the the black black line line in thein the center center is the is medianthe median and theand lower the lower and upper and upper limits limitsare the are first the and first the and third the quartiles, third quartiles, respectively. respectively. The lines The extending lines extending vertically vertically from the from box indicatethe box indicatethe maximum the maximum and minimum and minimum values. The values. black The squares black indicate squares the indicate annual the means annual of OMI means SO 2of. OMI

SO2. Compared with SO2, OMI NO2 VCD during 2005–2017 exhibited distinct spatial and temporal variationsCompared (Figure with5). ThereSO2, OMI is only NO one2 VCD prominent during NO2005–20172 hotspot exhibited in Hubei distinct located spatial in the and urban temporal region variationsof the megacity, (Figure Wuhan. 5). There Despite is only one no obviousprominent emission NO2 hotspot sources in shownHubei located in the OMIin the results urban exceptregion Wuhan,of the megacity, NO2 in the Wuhan. central Despite and eastern no obvious parts of emission Hubei was sources higher shown than background in the OMI mountain results except areas. Wuhan,There was NO a2 steady in the central overall and increase eastern in NOparts2 concentrationof Hubei was inhigher central than China background during 2005–2011.mountain areas. OMI 15 2 ThereNO2 in was Wuhan a steady increased overall from increase ~10 during in NO2 2005 concentration to >14 10 in centralmolecules China cm during− in 2011. 2005–2011. The NO OMI2 in × 15 2 the whole region also reached a maximum in 2011 with an15 increase of ~4−2 10 molecules cm , NO2 in Wuhan increased from ~10 during 2005 to >14 × 10 molecules cm in× 2011. The NO2 in the− wholealmost region>100% also compared reached with a maximum that in 2005. in It2011 is worth with notingan increase that NO of 2~4in × Wuhan 1015 molecules also declined cm−2, slightlyalmost 15 2 (by>100%~2 compared10 molecules with that cm −in) 2005. as SO It2 isin worth the economic noting that downturn NO2 in periodWuhan in also 2009. declined Then, slightly NO2 in (by the × 15 2 whole east-central15 Hubei−2 began to decrease, except for a recovery of ~2 10 molecules cm in ~2 × 10 molecules cm ) as SO2 in the economic downturn period in 2009.× Then, NO2 in the whole− Wuhaneast‐central during Hubei 2013. began By 2016, to decrease, OMI NO except2 was at for a closea recovery level with of ~2 that × 10 in15 2005. molecules However, cm−2 there in Wuhan was a duringslight increase 2013. By for 2016, NO 2OMIin Wuhan NO2 was during at a 2017. close level with that in 2005. However, there was a slight increaseDiff forerent NO from2 in Wuhan the spatial during shift 2017. of SO 2 hotspot, NO2 in the megacity of Wuhan was highest in Hubei during that time, where the number of vehicles exceeded three million by 2017 with rapid increases. Figure6 gives comparison of variations of OMI NO 2 VCD and statistical NOx emissions. It can be seen that the overall NO2 in central China increased by almost ~90% to 100% from ~3.2 to 5.9 1015 molecules cm 2 in 2005–2011. Although the overall level of NO amount since 2011 had × − 2 a general downward trend as SO2, there were two notable increases in NO2 during 2013 and 2017, respectively, which were not reflected in statistical NOx emissions. The large anthropogenic sources such as power and steel plants not only emitted SO2, but also lots of NOx. By 2011, NOx emissions of ~690,000 tons were a little higher than SO2. Different from SO2 emissions mainly concentrated in coal-burning sources, NOx also originated from dispersed vehicle emissions. Since 2011, the Chinese EPA began to control NOx emissions from large sources such as coal-burning plants, but the number of 15 2 vehicles kept rapidly increasing. OMI NO in 2017 (~3.9 10 molecules cm ) indicated that NOx 2 × − emissions tended to increase under the current background. It should be noted that NOx emission of ~380,000 tons in 2017 was nearly twice of SO2 (~210,000 tons), demonstrating the more important role of NOx emission in the future control. Similar as SO2, the yearly OMI NO2 exhibited a large deviation from the mean.

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FigureFigure 5. 5.Spatial Spatial and and temporal temporal distribution distribution ofof annualannual OMI nitrogen dioxide dioxide (NO (NO2)2 VCD) VCD over over Hubei Hubei Remoteprovince Sens.province 2019 during, 11during, x FOR 2005–2017. 2005–2017. PEER REVIEW 9 of 19

Different from the spatial shift of SO2 hotspot, NO2 in the megacity of Wuhan was highest in Hubei during that time, where the number of vehicles exceeded three million by 2017 with rapid increases. Figure 6 gives comparison of variations of OMI NO2 VCD and statistical NOx emissions. It can be seen that the overall NO2 in central China increased by almost ~90% to 100% from ~3.2 to 5.9 × 1015 molecules cm−2 in 2005–2011. Although the overall level of NO2 amount since 2011 had a general downward trend as SO2, there were two notable increases in NO2 during 2013 and 2017, respectively, which were not reflected in statistical NOx emissions. The large anthropogenic sources such as power and steel plants not only emitted SO2, but also lots of NOx. By 2011, NOx emissions of ~690,000 tons were a little higher than SO2. Different from SO2 emissions mainly concentrated in coal‐burning sources, NOx also originated from dispersed vehicle emissions. Since 2011, the Chinese EPA began to control NOx emissions from large sources such as coal‐burning plants, but the number of vehicles kept rapidly increasing. OMI NO2 in 2017 (~3.9 × 1015 molecules cm−2) indicated that NOx emissions tended to increase under the current background. It should be noted that NOx emission of ~380,000 tons in 2017 was nearly twice of SO2 (~210,000 tons), demonstrating the more important role of NOx emission in the future control. Similar as SO2, the yearly OMI NO2 exhibited a large deviation from the mean.

FigureFigure 6. 6. TheThe samesame asas FigureFigure3 3 but but for for NO NO22..

To further examine spatial patterns of NO2 pollution, seasonal distribution of OMI tropospheric NO2 VCD is shown in Figure 7. The NO2 concentration was highest in winter and then in Autumn, with the lowest NO2 in summer. Since chemical reactions of NOx highly depend on ambient temperature [10], the hot and cold weather in summer and winter were the main drivers of this great seasonal contrast in NO2 levels. Moreover, the more stagnant weather in winter favored accumulation of NOx emissions (Figure 2). It can be seen that NO2 hotspots (~12–13 × 1015 molecules cm−2) existed over several smaller cities of Hubei during winter and exceeded ~14 × 1015 molecules cm−2 in the entire Wuhan region. There is a notable connection for high NO2 between Hubei and adjacent areas, indicating potential transport from northern China [14,21]. By contrast, the NO2 hotspots in central China were independent in other seasons. Compared with very low SO2 concentration that was close to the background level in region scale by 2017, the considerable NOx emission with a slight increase can have a much larger contribution to PM2.5.

Figure 7. Seasonal OMI tropospheric NO2 and VCD during 2013–2017.

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Remote Sens. 2020, 12, 1041 9 of 19 Figure 6. The same as Figure 3 but for NO2.

To further examine spatial patterns of NO2 pollution,pollution, seasonal seasonal distribution distribution of of OMI OMI tropospheric tropospheric NO22 VCDVCD is is shown shown in in Figure Figure 77.. TheThe NONO22 concentrationconcentration was was highest highest in in winter winter and and then then in in Autumn, Autumn, with the lowest NONO22 in summer.summer. Since Since chemical chemical reactions reactions of of NOxx highlyhighly depend depend on ambient temperaturetemperature [10], [10], the hot and cold weather in in summer summer and winter were were the the main drivers of this this great great seasonalseasonal contrast in NO2 levels.levels. Moreover, Moreover, the the more more stagnant stagnant weather weather in in winter winter favored favored accumulation accumulation 1515 −2 of NO x emissionsemissions (Figure 2 2).). It It can can be be seen seen that that NO NO2 hotspotshotspots (~12–13 (~12–13 × 1010 molecules cmcm )) existed existed 2 × − over several several smaller smaller cities cities of of Hubei Hubei during during winter winter and and exceeded exceeded ~14 ~14 × 1015 molecules1015 molecules cm−2 in cm the2 entirein the × − Wuhanentire Wuhan region. region. There There is a notable is a notable connection connection for forhigh high NO NO2 between2 between Hubei Hubei and and adjacent adjacent areas, areas, indicatingindicating potential transport transport from from northern China [14,21]. [14,21]. By contrast, the NO2 hotspots in central China were independent in other seasons. Compared with very low SO 2 concentrationconcentration that that was was close close toto the the background level in region scale by 2017, the considerable NO x emissionemission with with a a slight slight increase increase can have a much larger contribution to PM 2.5..

Figure 7. SeasonalSeasonal OMI tropospheric NO 2 andand VCD VCD during during 2013–2017. 2013–2017. 3.2. Spatial and Temporal Distribution of Particle Pollution in 2005–2017

The spatial variations of annual MODIS AOD at 550 nm over Hubei during 2005–2017 are displayed in Figure8. There were dramatic changes in aerosol loading over Hubei at both temporal and spatial scales. The aerosol particles were concentrated over the non-mountain areas with large spatial differences. Unlike gaseous precursors of SO2 and NO2 concentrated around Wuhan, particle pollution was mainly in the central part of Hubei and communicated with polluted regions in other provinces to the north and south. The high-AOD area (>0.8) grew and extended to the eastern part of Hubei in 2005, and in 2011 nearly covered the entire non-mountain area, including Wuhan. Then, the high-AOD area retreated to the central part of Hubei, which implies large changes in emission sources as well as control policies. It is worth noting that the high AOD in central part of Hubei have rapidly decreased since 2014. By 2017, the aerosol loading in non-mountain areas became homogeneous with an AOD around ~0.6. Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 19

3.2. Spatial and Temporal Distribution of Particle Pollution in 2005–2017 The spatial variations of annual MODIS AOD at 550 nm over Hubei during 2005–2017 are displayed in Figure 8. There were dramatic changes in aerosol loading over Hubei at both temporal and spatial scales. The aerosol particles were concentrated over the non‐mountain areas with large spatial differences. Unlike gaseous precursors of SO2 and NO2 concentrated around Wuhan, particle pollution was mainly in the central part of Hubei and communicated with polluted regions in other provinces to the north and south. The high‐AOD area (>0.8) grew and extended to the eastern part of Hubei in 2005, and in 2011 nearly covered the entire non‐mountain area, including Wuhan. Then, the high‐AOD area retreated to the central part of Hubei, which implies large changes in emission sources as well as control policies. It is worth noting that the high AOD in central part of Hubei have rapidly decreased since 2014. By 2017, the aerosol loading in non‐mountain areas became Remote Sens. 2020, 12, 1041 10 of 19 homogeneous with an AOD around ~0.6.

FigureFigure 8. 8.Spatial Spatial and and temporal temporal distribution distribution ofof annual moderate resolution resolution imaging imaging spectroradiometer spectroradiometer (MODIS)(MODIS) aerosol aerosol optical optical depth depth (AOD) (AOD) at at 550 550 nm nm over over HubeiHubei province during during 2005–2017. 2005–2017.

Moreover,Moreover, there there were were drastic drastic temporal temporal changes changes in aerosolin aerosol loading loading over over all theall the non-mountain non‐mountain areas of Hubeiareas of (Figure Hubei8 (Figure). Compared 8). Compared with the with persistent the persistent heavy aerosol heavy aerosol loading loading ( >0.8) (>0.8) in central in central Hubei Hubei during 2005–2011,during 2005–2011, there was there an annual was an AOD annual increase AOD increase from ~0.6–0.7 from ~0.6–0.7 to ~1.0. to In ~1.0. contrast, In contrast, AOD inAOD the in eastern the parteastern (i.e., Wuhan) part (i.e., exhibited Wuhan) largeexhibited yearly large variations yearly (variations>0.3), which (>0.3), indicates which the indicates obvious the influence obvious of regionalinfluence transport. of regional In addition, transport. consistent In addition, or even consistent larger or increases even larger in the increases magnitude in the of magnitude aerosol loading of in adjacentaerosol loading areas of in other adjacent provinces areas of contributed other provinces to part contributed of the heavy to part particle of the pollution heavy particle in central pollution Hubei. The overall decrease of AOD in Hubei began in 2011 wherein AOD declined from >1.0 to ~0.5–0.7.

In 2017, there was more than a ~30–50% decrease in AOD over most non-mountain areas. Considering that there was no aerosol retrieval when land surface was covered by clouds, sampling frequency of available satellite AOD within one year is significant for whether its annual mean value can well represent the true variations. As shown in the available frequency of MODIS AOD (Figure9), the annual sampling frequency of MODIS DB AOD of all quality is ~120–150 days/per year, which is statistically adequate to capture the AOD gradient [33], even with some yearly difference. Moreover, long-term observations show that there are no significant changes for clouds during the last decade, except in 2013 [20]. Thus, the long-term trend of aerosol loading derived from MODIS AOD is generally representative of central China. As shown in numerous studies, annual or seasonal mean maps of satellite AOD is usually used to denote particle pollution [19,33]. Different from the consistent distribution between gaseous precursors (NO2 and SO2) and AOD in northern China [15], the hotspots of gaseous pollutants and particles in central China are concentrated in completely different spatial locations of central China. Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 19

in central Hubei. The overall decrease of AOD in Hubei began in 2011 wherein AOD declined from >1.0 to ~0.5–0.7. In 2017, there was more than a ~30%–50% decrease in AOD over most non‐mountain Remote Sens. 2020, 12, 1041 11 of 19 areas. Considering that there was no aerosol retrieval when land surface was covered by clouds, sampling frequency of available satellite AOD within one year is significant for whether its annual Although sulfate and nitrate only account for at most about half of PM2.5 in major cities of China [11], mean value can well represent the true variations. As shown in the available frequency of MODIS anthropogenic emissions of SO2 and NOx have a much larger amount than organic Carbon (OC) AOD (Figure 9), the annual sampling frequency of MODIS DB AOD of all quality is ~120–150 and black Carbon (BC) [31]. In particular, as the major control targets of the government, the overall days/per year, which is statistically adequate to capture the AOD gradient [33], even with some reduction of SO and NO emission throughout China is approximately ~5–10 times of that for OC and yearly difference.2 Moreover,x long‐term observations show that there are no significant changes for BC [4clouds]. Thus, during despite the thelast spatial decade, inconsistency, except in 2013 the [20]. substantial Thus, the variabilitylong‐term trend of SO of2 andaerosol NO loadingx in central Chinaderived can cause from a MODIS considerable AOD is contribution generally representative to overall of variations central China. of overall aerosol loading.

FigureFigure 9. Annual 9. Annual retrieval retrieval frequency frequency of of MODIS MODIS AOD over over Hubei Hubei province province during during 2005–2017. 2005–2017.

SpatialAs shown mean in of numerous aerosol loading studies, annual is usually or seasonal used tomean evaluate maps of variations satellite AOD of is regional usually used emission levelsto [ 34denote]. The particle annual pollution average of[19,33]. MODIS Different AOD valuesfrom the within consistent Hubei distribution province duringbetween 2005–2017 gaseous are shownprecursors in Figure (NO 10.2 Consistentand SO2) and with AOD spatial in northern variations, China there [15], wasthe hotspots a slow increasing of gaseous trendpollutants (0.55–0.61) and in AODparticles during 2005–2008in central China with are an concentrated obvious decrease in completely (0.56) in different 2009. This spatial indicates locations significant of central China. changes in Although sulfate and nitrate only account for at most about half of PM2.5 in major cities of China [11], anthropogenic emissions or regional meteorological conditions. The increase of AOD from 2010 (0.61) anthropogenic emissions of SO2 and NOx have a much larger amount than organic Carbon (OC) and was much larger and annual AOD of the entire Hubei region reached a peak value of ~0.7 in 2011. black Carbon (BC) [31]. In particular, as the major control targets of the government, the overall Unlike northern China’s growing AOD in 2013 [30], aerosol loading in Hubei continuously decreased reduction of SO2 and NOx emission throughout China is approximately ~5–10 times of that for OC fromand ~0.7 BC in [4]. 2011 Thus, to the despite lowest the value spatial of inconsistency, ~0.38 in 201. the This substantial was largely variability due toof spatialSO2 and di NOfferencex in in emissioncentral trends. China Although can cause a the considerable annual mean contribution of MODIS to overall AODs variations was at similar of overall levels aerosol around loading. 0.5 in most years, their daily values had very large fluctuations from ~0.1 to ~1.3, which was consistent with the frequent heavy pollution events and their cleanups in eastern China [20,21]. The particle pollution in central China is obviously lower than in northern China [19]. However, it should be noted that regional AOD exceeds 0.8 for about more than one quarter of the days during 2005–2014. After the clean air actions taken by the Chinese government in 2013, there has been a rapid decline in particle pollution in eastern China [35]. While there has been a continuous decrease in SO2 and particle pollutants in central China, the notable increase in NOx indicates a transition from air pollution, i.e., a larger fraction of OC and enhanced O3 pollution. Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 19

Spatial mean of aerosol loading is usually used to evaluate variations of regional emission levels [34]. The annual average of MODIS AOD values within Hubei province during 2005–2017 are shown in Figure 10. Consistent with spatial variations, there was a slow increasing trend (0.55–0.61) in AOD during 2005–2008 with an obvious decrease (0.56) in 2009. This indicates significant changes in anthropogenic emissions or regional meteorological conditions. The increase of AOD from 2010 (0.61) was much larger and annual AOD of the entire Hubei region reached a peak value of ~0.7 in 2011. Unlike northern China’s growing AOD in 2013 [30], aerosol loading in Hubei continuously decreased from ~0.7 in 2011 to the lowest value of ~0.38 in 201. This was largely due to spatial difference in emission trends. Although the annual mean of MODIS AODs was at similar levels around 0.5 in most years, their daily values had very large fluctuations from ~0.1 to ~1.3, which was consistent with the frequent heavy pollution events and their cleanups in eastern China [20,21]. The particle pollution in central China is obviously lower than in northern China [19]. However, it should be noted that regional AOD exceeds 0.8 for about more than one quarter of the days during 2005–2014. After the clean air actions taken by the Chinese government in 2013, there has been a rapid decline in particle pollution in eastern China [35]. While there has been a continuous decrease in SO2 and particle pollutants in central China, the notable increase in NOx indicates a transition from air pollution, i.e., a larger fraction of OC and enhanced O3 pollution. Remote Sens. 2020, 12, 1041 12 of 19

FigureFigure 10. Box 10. Box plots plots of of annual annual MODIS MODIS AODAOD at 550 550 nm nm over over Hubei Hubei province province during during 2005–2017. 2005–2017.

Seasonal mean of MODIS AOD at 550 nm showed variations of particle pollution in different meteorologicalSeasonal conditions mean of MODIS (Figure AOD 11). at Similar 550 nm to showed gaseous variations pollution, of particle MODIS pollution AOD hadin different the highest AODmeteorological in winter and conditions autumn. (Figure MODIS 11). AOD Similar in centralto gaseous Hubei pollution, exceeded MODIS ~0.8–1.0 AOD had and the influenced highest the northernAOD part in winter by adjacent and autumn. transport MODIS under AOD the in prevailing central Hubei northerly exceeded winds ~0.8–1.0 in winter and [influenced2,21]. By contrast,the MODISnorthern AOD part reached by adjacent moderate transport levels under (~0.6) the inprevailing the spring northerly and summer. winds in winter Different [2,21]. from By contrast, the regional particleMODIS hotspot AOD in reached winter, moderate high AOD levels values (~0.6) were in the only spring concentrated and summer. in Different certain parts from ofthe central regional Hubei duringparticle the other hotspot seasons. in winter, Moreover, high AOD values these were AOD only hotspots concentrated exhibited in certain notable parts spatial of central variations Hubei in differentduring seasons, the other which seasons. implies Moreover, a significant these AOD intra-transport hotspots exhibited due to notable higher spatial wind speedsvariations in centralin different seasons, which implies a significant intra‐transport due to higher wind speeds in central ChinaRemote (Figure Sens.2 2019). The, 11, x mountains FOR PEER REVIEW around Hubei have an obvious blocking e ffect on outside transport13 of 19 of China (Figure 2). The mountains around Hubei have an obvious blocking effect on outside transport particle pollutants, even in the polluted winter. On the other hand, the limited spatial coverage of of particle pollutants, even in the polluted winter. On the other hand, the limited spatial coverage of aerosolaerosol hotspots hotspots (except (except in in winter) winter) can can partly partly explainexplain the the lack lack of of gaseous gaseous pollution pollution hotspots hotspots in central in central HubeiHubei from from OMI OMI products products with with a coarsea coarse resolution. resolution.

FigureFigure 11. 11.Seasonal Seasonal MODIS MODIS Dobson Dobson unit (DU) (DU) AOD AOD at at 550 550 nm nm during during 2013–2017. 2013–2017.

3.3. The Implication of Changing Air Pollution in Central China

Spatial variations of gaseous precursors, SO2 and NOx, can provide a visible view on their major emission sources and the effects of control measures [13]. Moreover, comparison of MODIS AOD and OMI trace gases that exhibit a significant correlation in main global pollution hotspots [36]. Although other components such as OC and mineral dust account for a considerable fraction (~30%–60%) in PM2.5 of China [11], sulfate and nitrate had a much larger variability in the last decade due to specific and strict control of their emissions [4]. Variations of regional AOD in Hubei showed consistent trends with SO2 and NO2 (Figure 12). In particular, there was almost a linear relationship between regional mean value of MODIS AOD and OMI SO2 with a high correlation coefficient (R), 0.84. Despite a much lower R (0.366) between AOD and NO2, it can be seen that variations of AOD and NO2 had a close relation. Conversely, the connection between MODIS AOD and OMI SO2 and NO2 was weaker in seasonal scales. The yearly variations of seasonal meteorological conditions as well as their influence on the amount of atmospheric pollutants and satellite sampling frequency had considerable magnitude compared with the actual trends of air pollution [20,21]. The MODIS AOD in Hubei had slow changes from 2005 to 2008, with a decrease in SO2 and increase in NO2. While the reduction of SO2 emission in Hubei was ~60,000 tons and growth of NOx emission approximately 150,000 tons—inferred from comparison with OMI NO2—it is clear that the effect of desulfurization was exceeded by the increase of NOx and other emissions.

Remote Sens. 2020, 12, 1041 13 of 19

3.3. The Implication of Changing Air Pollution in Central China

Spatial variations of gaseous precursors, SO2 and NOx, can provide a visible view on their major emission sources and the effects of control measures [13]. Moreover, comparison of MODIS AOD and OMI trace gases that exhibit a significant correlation in main global pollution hotspots [36]. Although other components such as OC and mineral dust account for a considerable fraction (~30–60%) in PM2.5 of China [11], sulfate and nitrate had a much larger variability in the last decade due to specific and strict control of their emissions [4]. Variations of regional AOD in Hubei showed consistent trends with SO2 and NO2 (Figure 12). In particular, there was almost a linear relationship between regional mean value of MODIS AOD and OMI SO2 with a high correlation coefficient (R), 0.84. Despite a much lower R (0.366) between AOD and NO2, it can be seen that variations of AOD and NO2 had a close relation. Conversely, the connection between MODIS AOD and OMI SO2 and NO2 was weaker in seasonal scales. The yearly variations of seasonal meteorological conditions as well as their influence on the amount of atmospheric pollutants and satellite sampling frequency had considerable magnitude compared with the actual trends of air pollution [20,21]. The MODIS AOD in Hubei had slow changes from 2005 to 2008, with a decrease in SO2 and increase in NO2. While the reduction of SO2 emission in Hubei was ~60,000 tons and growth of NOx emission approximately 150,000 tons—inferred from comparison with OMI NO2—it is clear that the effect of desulfurization was exceeded by the increase ofRemote NO Sens.x and 2019 other, 11, x emissions. FOR PEER REVIEW 14 of 19

Figure 12. Correlation of annual (top) MODIS AOD atat 550550 nm,nm, withwith SOSO22 and VCD (left), andand NONO22 and VCD (right) over Hubei, in winter andand summersummer (bottom),(bottom), respectively.respectively.

The high correlation coecoefficientfficient (R(R >> 0.8) between AODAOD andand SOSO22 shows that variation ofof SOSO22 can explain most changes of particle pollution inin centralcentral China.China. Despite the slight increase ofof NONO22 in 2009 (Figure5 5),), MODIS MODIS AOD AOD in in Hubei Hubei exhibited exhibited consistent consistent decline decline in in 2009 2009 and and then then rose rose in in 2010–2011 2010–2011 as as SO2 (Figures(Figures 44 andand6 6).). There There was was a a persistent persistent and and rapid rapid decline decline in in MODIS MODIS AOD AOD with with the the decrease decrease of of both SO2 and NOx from 2012. Although OMI NO2 became slightly higher (~0.5 × 1015 molecules cm−2) and SO2 did not change, MODIS AOD fell by ~0.03 during 2017 as SO2 and NOx emissions. It has to be stated that other anthropogenic emission sources as well as natural dust also make important contributions to aerosol loading [3]. Different from the notable gaseous hotspot in Wuhan, high‐AOD (>0.8) that converge in central Hubei mainly originated from emissions of primary aerosols and small local sources. To have an examination on reliability and representativity of satellite observations, annual variation of the main atmospheric pollutants during 2013–2017 was analyzed based on ground measurements in Hubei (Figure 13). It can be seen that there was a rapid decline of PM2.5 concentration from ~100 μg/m3 in 2013 to ~52 μg/m3 during 2017, which was consistent with variations of MODIS AOD. An annual average of SO2 concentration from the ground network exhibited nearly the same trends with PM2.5 and declined from ~53 μg/m3 to ~13 μg/m3. Moreover, NO2 concentration decreased rapidly in 2013−2015 by ~22 μg/m3, but flattened out during 2016, exhibiting an obvious rise in NO2 concentration by ~5 μg/m3 in 2017 with similar trends as OMI NO2. The high consistency with ground measurements demonstrated that satellite observations well captured temporal variations of the overall anthropogenic emission in central China.

Remote Sens. 2020, 12, 1041 14 of 19

15 2 both SO and NOx from 2012. Although OMI NO became slightly higher (~0.5 10 molecules cm ) 2 2 × − and SO2 did not change, MODIS AOD fell by ~0.03 during 2017 as SO2 and NOx emissions. It has to be stated that other anthropogenic emission sources as well as natural dust also make important contributions to aerosol loading [3]. Different from the notable gaseous hotspot in Wuhan, high-AOD (>0.8) that converge in central Hubei mainly originated from emissions of primary aerosols and small local sources. To have an examination on reliability and representativity of satellite observations, annual variation of the main atmospheric pollutants during 2013–2017 was analyzed based on ground measurements in 3 Hubei (Figure 13). It can be seen that there was a rapid decline of PM2.5 concentration from ~100 µg/m in 2013 to ~52 µg/m3 during 2017, which was consistent with variations of MODIS AOD. An annual average of SO2 concentration from the ground network exhibited nearly the same trends with PM2.5 and 3 3 declined from ~53 µg/m to ~13 µg/m . Moreover, NO2 concentration decreased rapidly in 2013 2015 3 − by ~22 µg/m , but flattened out during 2016, exhibiting an obvious rise in NO2 concentration by 3 ~5 µg/m in 2017 with similar trends as OMI NO2. The high consistency with ground measurements demonstrated that satellite observations well captured temporal variations of the overall anthropogenic emissionRemote Sens. in 2019 central, 11, x FOR China. PEER REVIEW 15 of 19

Figure 13. Annual variations of ground-levelground‐level PM PM2.52.5, SO2,, NO2,, and and O O33 observedobserved by by ambient ambient monitoring stations located in Hubei during 2013–2017.

Despite the large overalloverall decreasedecrease of of PM PM2.52.5,, SO22, and NO x duringduring 2013–2015, O 3 pollutionpollution became more serious inin earlyearly 2014.2014. The precursors of O3 includedincluded NO x andand VOCs, VOCs, but but there there was also a notable rise in O 3,, as as NO NO22 decreaseddecreased in in 2014–2016. 2014–2016. Considering Considering the the nonlinear nonlinear relationship relationship between between O3 O and3 and its itsprecursors, precursors, the the decrease decrease of NO of NO2 in2 highin high NO NOx/VOCx/VOC ratio ratio conditions conditions promoted promoted O3 Oformation3 formation [37]. [37 In]. Inaddition, addition, it was it was found found that that the the large large reduction reduction of of aerosol aerosol loading loading enhanced enhanced O O33 productionproduction rate to stronger photolysisphotolysis [ 38[38].]. By By now, now, NO NOx emissionx emission has has become become the mainthe main gaseous gaseous pollutant pollutant in central in central China andChina increases and increases with continuous with continuous growth in growth the number in the of number motor vehicles. of motor Therefore, vehicles. it Therefore, is significant it tois strengthensignificant theto strengthen control measures the control for NO measuresx emissions for NO as wellx emissions as VOCs as in well the as eastern VOCs and in the central eastern regions and ofcentral Hubei regions in the of future. Hubei in the future.

4. Discussion

The spatial and temporal variations ofof satellitesatellite AOD,AOD, SOSO22, and NO2 reveal dramatic changes of 15 anthropogenic pollutantspollutants inin centralcentral ChinaChina and and their their emission emission levels. levels. OMI OMI NO NO2 2(~15 (~15 × 101015 molecules 2 × cm−2) )and and SO SO22 (~0.5(~0.5 DU) DU) in in the the hotspot hotspot of of Wuhan Wuhan in in central central China China is is much lower than the high concentrations of NO2 (~20 × 1015 molecules cm−2) and SO2 (~1.0 DU) in northern China [15,16]. Unlike the concentrated air pollution in northern China, pollution hotspots in central China have smaller scales and larger spatial variations. Temporal changes (~30%–50%) in the amount of main atmospheric pollutants in central China is similar to northern China, demonstrating that the control measure of the government is the main driver for changes of air quality in China. In particular, the large spatial difference of MODIS AOD has reached a close level after 2016 in all the non‐mountain areas (Figure 8). For instance, in 2017, MODIS AOD ranged between 0.5–0.6 in both central and eastern Hubei. Ground measurements from the air quality monitoring network showed similar spatial patterns of atmospheric pollutants (Figure 14). While PM2.5 hotspots are concentrated in central China as high values of MODIS AOD, NO2 is mainly distributed in Wuhan. Despite the low concentration <~35 μg/m3 in central Hubei, small SO2 and NO2 hotspots indicate the existence of emission sources in these small cities, which cannot be detected in OMI trace gases due largely to the small scale and limited amount. On the other hand, new problems of air pollution begin to emerge in central China due to different reduction amounts of the anthropogenic emissions. There have been great improvements in particle pollution and its gaseous precursors. Recently, rapid increases in motor vehicles has led to more NO2 in urban regions of central China, especially in Wuhan. Spatial distribution of O3 pollution covers both central and eastern Hubei, implying that O3 pollution is a regional air quality problem. The photochemical pollution has become the primary air pollution in

Remote Sens. 2020, 12, 1041 15 of 19 concentrations of NO (~20 1015 molecules cm 2) and SO (~1.0 DU) in northern China [15,16]. 2 × − 2 Unlike the concentrated air pollution in northern China, pollution hotspots in central China have smaller scales and larger spatial variations. Temporal changes (~30–50%) in the amount of main atmospheric pollutants in central China is similar to northern China, demonstrating that the control measure of the government is the main driver for changes of air quality in China. In particular, the large spatial difference of MODIS AOD has reached a close level after 2016 in all the non-mountain areas (Figure8). For instance, in 2017, MODIS AOD ranged between 0.5–0.6 in both central and eastern Hubei. Ground measurements from the air quality monitoring network showed similar spatial patterns of atmospheric pollutants (Figure 14). While PM2.5 hotspots are concentrated in central China as high values of MODIS AOD, NO2 is mainly distributed in Wuhan. Despite the low concentration 3 <~35 µg/m in central Hubei, small SO2 and NO2 hotspots indicate the existence of emission sources in these small cities, which cannot be detected in OMI trace gases due largely to the small scale and limited amount. On the other hand, new problems of air pollution begin to emerge in central China due to different reduction amounts of the anthropogenic emissions. There have been great improvements in particle pollution and its gaseous precursors. Recently, rapid increases in motor vehicles has led to more NO2 in urban regions of central China, especially in Wuhan. Spatial distribution of O3 pollution Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 19 covers both central and eastern Hubei, implying that O3 pollution is a regional air quality problem. The photochemical pollution has become the primary air pollution in most cities, which is characterized 3 g 3 most cities, which is characterized3 by high O centration (>160 μ /m ) in the daytime of summer. by high O3 centration (>160 µg/m ) in the daytime of summer. Besides NOx, VOCs are another type of Besides NOx, VOCs are another type of complicated precursor for O3, as they consist of numerous complicated precursor for O , as they consist of numerous volatile gases. By now, only few VOCs volatile gases. By now, only 3few VOCs can be measured by satellite observations with much lower can be measured by satellite observations with much lower accuracy than SO2 and NO2 [27], exerting accuracy than SO2 and NO2 [27], exerting large challenges for constraining their spatial and temporal largedistributions. challenges for constraining their spatial and temporal distributions.

Figure 14. Annual mean of interpolated PM2.5, SO2, NO2, and O3 values from ambient monitoring Figure 14. Annual mean of interpolated PM2.5, SO2, NO2, and O3 values from ambient monitoring stationsstations with with the the Kriging Kriging methodmethod inin HubeiHubei during 2017.

TheThe high high consistency consistency with with ground ground measurements measurements indicates indicates that satellitethat satellite observations observations can provide can reliableprovide information reliable information regarding regarding the overall the overall trends trends of SO of2 SO, NO2, NO2,2 and, and particles particles in regional regional scales. scales. TheThe distinct distinct seasonal seasonal patterns patterns of of satellite satellite AOD,AOD, SOSO22, and NO22 givegive further further insight insight into into the the main main factors factors ofof air air pollutionpollution in central central China. China. Unlike Unlike the the close close connection connection between between the annual the annual mean mean of AOD of AODand andSO2 SOand2 andNO2 NO(Figure2 (Figure 12), their 12), lower their lowercorrelation correlation in seasonal in seasonal scales indicates scales indicates that other that factors other such factors as outside transport can also have important influence on air quality in central China during certain periods. Our results demonstrate that different control measures should be taken in view of the large spatial discrepancy of gaseous and particle pollutants in central China. It should be stated that spatial resolution of MODIS AOD and OMI gaseous products we used is 10 × 10 km and 0.25 × 0.25°, respectively, which can be too coarse to reveal point sources such as steel plants and small cities in central Hubei. Other than SO2 and NOx emissions, trace gases such as ammonia (NH3) and formaldehyde (HCHO) also have important contribution to air pollution in central China. Although satellite products of HCHO and NH3 are available, their accuracy is much lower compared with NO2 and SO2 [27,35]. Despite much smaller variations in emissions than sulfate and nitrate [4], the long‐ term changes of other major components in PM2.5 including OC and mineral dust have to be considered in central China. They cannot by be quantified by current satellite observations and have no corresponding ground measurements. As an enhanced version of OMI, the TROPOspheric monitoring instrument (TROPOMI) onboard Sentinel‐5 satellite since 2017 has a higher spatial resolution of 7 × 3.5 km, which can capture much finer emission sources [39]. Additionally, satellite observations can provide an observational constraint in emission sources for chemistry transport models [40,41]. To improve air quality on the regional scale, it is necessary to monitor spatial and temporal variations of emission sources and make detailed control measures timely.

Remote Sens. 2020, 12, 1041 16 of 19 such as outside transport can also have important influence on air quality in central China during certain periods. Our results demonstrate that different control measures should be taken in view of the large spatial discrepancy of gaseous and particle pollutants in central China. It should be stated that spatial resolution of MODIS AOD and OMI gaseous products we used is 10 10 km and 0.25 0.25 , × × ◦ respectively, which can be too coarse to reveal point sources such as steel plants and small cities in central Hubei. Other than SO2 and NOx emissions, trace gases such as ammonia (NH3) and formaldehyde (HCHO) also have important contribution to air pollution in central China. Although satellite products of HCHO and NH3 are available, their accuracy is much lower compared with NO2 and SO2 [27,35]. Despite much smaller variations in emissions than sulfate and nitrate [4], the long-term changes of other major components in PM2.5 including OC and mineral dust have to be considered in central China. They cannot by be quantified by current satellite observations and have no corresponding ground measurements. As an enhanced version of OMI, the TROPOspheric monitoring instrument (TROPOMI) onboard Sentinel-5 satellite since 2017 has a higher spatial resolution of 7 3.5 km, which can capture much × finer emission sources [39]. Additionally, satellite observations can provide an observational constraint in emission sources for chemistry transport models [40]. To improve air quality on the regional scale, it is necessary to monitor spatial and temporal variations of emission sources and make detailed control measures timely.

5. Conclusions During the last few decades, air pollution in China has drawn wide attention due to high-level anthropogenic emissions and drastic changes. However, most studies focus on economically developed and densely populated regions such as northern China, the YRD, and PRD. As an emerging pollution hotspot with diverse emission sources, air pollution in central China has been of less concern. To have a long-term insight into the spatial pattern, variations, and influence factors of atmospheric pollutants in central China, we utilized ground measurements and long-term multiple satellite observations including MODIS AOD, OMI SO2, and NO2 during 2005–2017. Satellite observation showed large spatial and temporal variations in AOD, SO2, and NO2 over central China. The air quality exhibited notable seasonal differences, which was heaviest in winter and lightest in summer. There was a large overall decrease in atmospheric pollutants in central China due to continuous control measures. Different from the common collocation of gaseous and particle pollution, eastern Hubei was mainly influenced by gaseous pollutants including NOx and O3, whereas particle pollution was predominant in central Hubei. Despite slight recovery in 2011, OMI SO2 in Hubei declined by more than 90%, which was close to the background level. By contrast, with a later emission control, OMI NO2 reached its peak value in 2011 and fell back to the level of 2005 with a ~50% reduction in 2017. Thus, before 2011, MODIS AOD slowly changed due to the offset of decreased SO2 and increased NO2 and then sharply declined as the combined reduction of both SO2 and NO2. It should be noted that there was a slight rise in OMI NO2 during 2017, which was consistent with an increase in vehicle usage. High consistency with ground measurements shows that satellites can reliably reflect the overall trends of atmospheric pollutants in regional scales. Annual concentration of PM2.5, SO2, and NO2 in ground network declines ~45 (45%), ~40 (75%), and ~17 (29%) µg/m3 during 2013–2017, respectively. There is a high correlation (R > 0.8) between satellite SO2 and AOD, demonstrating that reduction of SO2 emissions has a large contribution to decrease of PM2.5. Despite the large overall decrease of PM2.5 and its gaseous precursors, increased O3 has become the main air pollution problem in eastern Hubei with growth of NOx emissions. For air quality improvement in central Hubei, primary emission of particle pollutants should be controlled. To improve air quality on a regional scale, it is necessary to monitor the distinct spatial and temporal variations of atmospheric pollutants in central China via satellite observation at finer resolution in the future. Remote Sens. 2020, 12, 1041 17 of 19

Author Contributions: Conceptualization, R.L. and X.M.; methodology, R.L.; software, L.C.; validation, L.W. and Z.W.; formal analysis, Y.J.; investigation, R.L.; resources, L.C.; data curation, L.W.; writing—original draft preparation, R.L.; writing—review and editing, L.C.; visualization, R.L.; supervision, X.M.; project administration, L.C.; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Key R and D Program of China (Grant NO. 2017YFB0503901 and 2016YFA0600203) and the National Natural Science Foundation of China (Grant No.41601472).

Acknowledgments: We appreciate MODIS and OMI team for the aerosol, and SO2 and NO2 products used. Conflicts of Interest: The authors declare no conflict of interest.

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