Long-term (2002–2014) evolution and trend in Collection 5.1 Level-2 aerosol products derived from the MODIS and MISR sensors over the Chinese Yangtze River Delta

Atmospheric Research 181 (2016) 29–43

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Long-term (2002–2014) evolution and trend in Collection 5.1 Level-2 aerosol products derived from the MODIS and MISR sensors over the Chinese Yangtze River Delta Na Kang 1, K. Raghavendra Kumar ⁎,1, Kang Hu, Xingna Yu, Yan Yin Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), International Joint Laboratory on Climate and Environmental Change (ILCEC), Collaborative Innovation Center for Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

a r t i c l e

i n f o

Article history: Received 16 March 2016 Received in revised form 10 June 2016 Accepted 13 June 2016 Available online 15 June 2016 Keywords: MODIS Aerosol optical depth Trend analysis Aerosol type HYSPLIT East China

a b s t r a c t The present study aims to investigate spatio-temporal evolution and trend in the aerosol optical properties (aerosol optical depth, AOD; Ångström exponent, AE), qualitatively identify different types and origin of aerosols over an urban city, Nanjing in the Yangtze River Delta, East China. For this purpose, the Collection 5.1 Level-2 data obtained from the Moderate resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra and Aqua satellites and the Multi-angle Imaging Spectroradiometer (MISR) instrument for the period between 2002 and 2014 have been analyzed. An inter-comparison and validation of AOD were performed against the AOD measurements obtained from the ground-based Aerosol Robotic Network (AERONET) sunphotometer. The MODIS AOD550 exhibited wide spatial and temporal distributions over East China, while MISR AOD555 was consistently lower than that of Terra and Aqua AOD550 values. The temporal variations (monthly and seasonal mean) of MODIS (Terra and Aqua) and MISR AOD values exhibited a similar pattern. The seasonal mean AOD550 (AE470–660) was found to be maximum with 0.97 ± 0.48 during summer (1.16 ± 0.33 in summer) and a minimum of 0.61 ± 0.28 during the winter season (0.80 ± 0.28 in spring). The annual mean Terra AOD550 at Nanjing showed a strong decreasing trend (−0.70% year−1), while the Aqua exhibited a slight increasing trend (+0.01 year−1) during the study period. Seasonal air mass back-trajectories obtained from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model were also computed to infer on the transport component over the study region. Different aerosol types were identified via the relationship between AOD550 and fine mode fraction, which reveals that the biomass burning/urban-industrial type aerosols (desert dust) are abundant over the region in summer (spring), apart from the mixed aerosol type. © 2016 Elsevier B.V. All rights reserved.

1. Introduction As one important component of the earth–ocean–atmosphere system, the atmospheric aerosol is considered to affect the global climate through direct interactions with solar and terrestrial radiation (direct aerosol effect), and through their effects on the optical, microphysical properties and lifetime of clouds (indirect aerosol effect) (Charlson et al., 1992). Aerosol lifetime can be just a few weeks or even shorter (Ramanathan et al., 2001), and their sources are distributed very unevenly so that the spatial and temporal distributions of the aerosol is far from homogeneous (Haywood and Boucher, 2000). The biggest uncertainty in climate change, even by the best available models, is due to uncertainties in aerosol radiative forcing (IPCC, 2013). This uncertainty ⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (K.R. Kumar). 1 Contributed equally to this work.

http://dx.doi.org/10.1016/j.atmosres.2016.06.008 0169-8095/© 2016 Elsevier B.V. All rights reserved.

arises mainly because of our poor understanding of both aerosol's spatial and temporal distributions and their associated properties (Alam et al., 2011; Sreekanth, 2013; Kang et al., 2015; Kumar et al., 2014, 2015; Mehta, 2015; He et al., 2016 and references there in). Detailed knowledge of long-term temporal changes of local, regional, and global aerosols is needed to improve our scientific understanding of their sources and sinks, and to provide evidence as a basis for policymakers (Zhang and Reid, 2010; Yoon et al., 2012; de Meij et al., 2012). Aerosols also influence air quality and therefore, affect human health and reduce visibility (Deng et al., 2012; Wang et al., 2015; Cheng et al., 2015; Yu et al., 2016b). Characterization of the aerosols becomes an increasingly challenging task due to their spatio-temporal variations in terms of abundance, optical, physical, and chemical properties. This calls for highly resolved aerosol measurements in space and time over the globe. In this regard, a number of ground-based aerosol networks were established worldwide, procuring continuous datasets on a variety of aerosol parameters over land and even over oceans (e.g., Aerosol Robotic Network

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(AERONET); Holben et al., 1998). All those efforts might often suffer manpower and proper maintenance, which eventually results in gaps in the valuable database. The emerging capability of satellite remote sensing provides an unprecedented opportunity to advance the understanding of aerosol-air-quality-climate linkages (Zhang and Reid, 2010; Yoon et al., 2012; Luo et al., 2014; He et al., 2016). Operational remote sensing of aerosols from satellites provides an efficient means to achieve a global and temporal characterization of aerosols. Several algorithms using, for example, Advanced Very High Resolution Radiometer (AVHRR), Total Ozone Mapping Spectrometer (TOMS)/Ozone Monitoring Instrument (OMI), Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) on polar-orbiting satellites (Kaufman et al., 1997; Remer et al., 2005; Mischenko and Geogdzhayev, 2007; Kahn et al., 2005, 2010) have been developed to retrieve the global aerosol optical depth (AOD hereafter). However, a few studies have shown that uncertainties still exist in current satellite aerosol products attributable to the complex surface, cloud contamination, and aerosol models used in the retrieving process (e.g., Kahn et al., 2005, 2010; Levy et al., 2010; Zhang and Reid, 2010; Yoon et al., 2012; Mehta, 2015). In order to make the best use of satellite data and reduce the uncertainty of aerosol effects on regional and global climate, satellite measurements need to be validated using ground-based observations (Xiao et al., 2009; He et al., 2010; Cheng et al., 2012). AOD values retrieved from different satellite sensors can be quite different due to the sensor characteristics and retrieval methods. Numerous comparisons and validations of the two sensors (MODIS and MISR) with the AERONET (Holben et al., 1998) have been performed (e.g., He et al., 2010; Deng et al., 2012; Cheng et al., 2012; Qi et al., 2013; Mehta, 2015; Kumar et al., 2015; Yu et al., 2016b). Except in some coastal zones, the MODIS AOD retrievals over land are well within the retrieval error, given by Δτa = ±0.05 ± 0.2(AOD) (Chu et al., 2002). The differences in AOD between MODIS and AERONET can vary significantly with region and season over China (Xiao et al., 2009; He et al., 2010; Qi et al., 2013). When the AOD is small (0–0.5), MODIS retrievals are greater than AERONET, but are lower than AERONET when the AOD is large (0.5–1.0) (Qi et al., 2013). Kahn et al. (2010) found that most MISR AOD retrievals were within either 0.05, or 20% of the AOD, of the paired validation data from AERONET. Further, Xiao et al. (2009) also indicated that MISR AOD retrievals agreed well with ground-based observations for AOD b 0.5 but were systematically underestimated for AOD N 0.5 in China. During the recent decades, China has experienced unprecedented economic development due to extensive urbanization, industrialization and an increase in population and traffic (Guo et al., 2011; Deng et al., 2012; Luo et al., 2014; Kang et al., 2015; Xu et al., 2015; He et al., 2012, 2016). Obviously, this historically unparalleled economic growth has significantly improved Chinese living standards, but it has also brought serious environmental damage and degradation (Guo et al., 2011; He et al., 2016). Satellite-derived AOD and Ångström exponent (AE) are useful parameters for the estimation of optical and physical characteristics of aerosol on the regional scale. Li et al. (2010) investigated spatial and temporal distributions of mean AOD in China between 2003 and 2006 using Aqua MODIS Level-2 aerosol products. Using TOMS and MODIS aerosol products, Guo et al. (2011) identified an upward long-term AOD trend over China between 1980 and 2008. Luo et al. (2014) used Terra MODIS Level-3 products between 2001 and 2010 to study spatio-temporal evolutions and trends in the AOD over major polluted regions of China. Further, Kang et al. (2015) have also carried out spatio-temporal distributions of AOD over 12 major urban cities in China based on the 10-year (2003−2013) long-term data derived from MODIS Level-3 onboard the Terra satellite. Recently, He et al. (2016) performed spatial and temporal analyses to map the AOD distributions over China and five typical regions, using latest Collection 006 Aqua MODIS Level-3 aerosol data obtained between 2002 and 2015. All the earlier studies on AOD using ground-based measurements and satellite-based observations were limited to either small datasets

or narrow statistical analysis over the Yangtze River Delta (YRD) in East China (e.g., Pan et al., 2010; Deng et al., 2012; He et al., 2010, 2012; Luo et al., 2014; Li et al., 2015; Kang et al., 2016). The long-term continuous observation for the spatial and temporal distributions of aerosol from multiple satellites was still limited in this region. To our knowledge, this is the first study utilizing such a long-term data set (2002–2014) obtained from the MODIS and MISR Collection 5.1 (C051) Level-2 aerosol products over Nanjing in the YRD region and discriminating the key aerosol types. The main objective of this paper is to delineate the long-term spatio-temporal characteristics of aerosol optical properties, which help people, know the distribution of pollution in recent years. This study was also carried out to present statistical estimation of long-term trends in AOD and AE using linear regression analysis following the method adopted by Weatherhead et al. (1998), and the discrimination of different aerosol types and their source regions. In this study, we also focused on the validation of MODIS AOD by using the ground-based AERONET sunphotometer measurements. 2. Site description and meteorology East China ranges from the mid-latitude to the subtropical zone, with complicated topography characterized by mostly plains in the northern part, mountains in the southern part, and coast in the eastern part (He et al., 2010, 2012; Deng et al., 2012). The observation site, Nanjing, the capital of Jiangsu province, is located in the western part of the YRD, East China. It is a highly urbanized and industrialized city, covering an area of over 6500 km2 and has a population of over 8.1 million (as of 2013 census records). With the rapid population growth and economic development, frequent occurrences of extreme haze-fog (HF) episodes were prevalent in the YRD, resulted in high concentrations of particulate matter and poor air quality with visibility degradation (e.g., Wang et al., 2015; Cheng et al., 2015; Li et al., 2015; Yu et al., 2016a). This is one of the most notable radiative effects of aerosols (Cheng et al., 2015; Wang et al., 2015; Yu et al., 2016b and references there in), and has become the major environmental problem affecting the general public. The main sources of pollution in Nanjing are the emissions coming from industries, vehicular transportation and anthropogenic pollutants released from several construction activities due to its rapid urbanization (Li et al., 2015; Yu et al., 2016a). The climate of Nanjing is characterized as humid with severely cold winters (December, January, February; DJF), extremely hot summers (June, July, August; JJA) and moderate, during spring (March, April, May; MAM) and autumn (September, October, November; SON) seasons. The city typically experiences dense HF conditions with reduced visibility during winter (Li et al., 2015; Yu et al., 2016a and references there in). In addition, the local and regional meteorology control the dynamics of the atmosphere at this location. The monthly mean (calculated from the averaged daily values) meteorological parameters such as temperature (TP in °C), relative humidity (RH in %), wind speed (WS in m s− 1), and accumulated rainfall (RF in mm) procured from http://wunderground.com during the study period, are shown in Fig. 1. July, with a monthly mean temperature of ~ 28 °C was the warmest month. January was the coldest month, with a mean temperature of ~3 °C. RH varied almost inversely with temperature. April is the driest month with mean RH of ~64%. The annual averaged WS showed a minimum in the month of November (~2 m s−1) and maximum during May (~6 m s−1). The RF pattern showed that considerable rain starts in April and peaks in July (~2100 mm) (see Fig. 1). 3. Satellite data and methods 3.1. The MODIS sensor The first MODIS instrument was launched on board the Terra satellite on 18 December 1999, with daytime equator crossing at

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Fig. 1. Monthly mean variations of meteorological parameters (except total RF) observed at Nanjing during the study period 2002–2014. The vertical bars represent the standard deviation of the mean.

10:30 local time (LT), as part of the NASA's Earth Observing System (EOS) mission; and the second one on 4 May 2002 onboard the Aqua satellite with daytime equator crossing at 13:30 LT. These are uniquely designed (wide spectral range, high spatial resolution, and daily global coverage) to observe and monitor the changes in the Earth's atmosphere. MODIS is a sensor which has 36 spectral bands covering the range between 0.415 and 14.235 μm, with a viewing swath of ~2330 km and variable spatial resolution of 1 km, 0.5 km and 0.25 km depending on the spectral band with repeat coverage of 2 days. The MODIS (dark-target) aerosol products are retrieved with special algorithms based on raw measurements following a lookup table approach in which a small set of aerosol types, loading and geometry are assumed to span the range of global aerosol conditions (Tanre et al., 1997; Kaufman et al., 1997; Levy et al., 2007, 2010; Remer et al., 2005, 2008). The retrieved products are different over land and ocean because of differences in their surface characteristics. MODIS AOD has a known accuracy of ± 0.05 ± 0.15(AOD) over land (Tanre et al., 1997) and ±0.03 ± 0.05(AOD) over the ocean (Remer et al., 2005). Orbit stability and radiometric calibration are both rigorously maintained by the MODIS characterization support team, to within ±2–3% at typical situations (Xiong et al., 2007). The MODIS data was preferred in this study due to the greater availability of valid AOD pixels, which can reduce the statistical error (Zhang and Reid, 2010; de Meij et al., 2012) and to the high (1–2 days) temporal resolution (Levy et al., 2010). MODIS C005 dataset is an improvement over earlier collections, generated with the upgraded algorithm (Remer et al., 2005, 2008). MODIS C005 AOD (Levy et al., 2010) over land retrievals use four channels centered at 0.47, 0.66, 1.24, and 2.1 μm with a nominal resolution of 0.5 km or 0.25 km at nadir. Over land, the infrared channels (1.24 and 2.1 μm) are used to estimate the surface reflectance at visible wavelengths via a linear regression model. To enhance the accuracy and reduce noise, the AOD at 0.55 μm is calculated in grids of 10 km × 10 km, averaging the 20 to 50 percentile of surface reflectance in each grid prior to the inversion. Details on the retrieval algorithms for C005 MODIS aerosol products over land and ocean targets are extensively discussed in Remer et al. (2005) and Levy et al. (2010) and hence not repeated. Daily MODIS C051 Level-2 (10 km × 10 km spatial resolution) aerosol products, which provide the best fits to surface reflectance observations (Remer et al., 2008), from the Terra and Aqua satellites for the years 2002–2014 were downloaded from the MODIS website (http:// ladsweb.nascom.nasa.gov/data/search.html) and used to prepare the analysis presented in this paper. In a 10 km × 10 km grid box, cloudfree pixels are first selected using the multi-spectral MODIS cloud mask (Chu et al., 2002). The selected cloud-free dark pixels in the grid

box may still be partially contaminated by sub-pixels clouds, snow/ice, water covered surfaces or soil types that do not fit the empirical relationship. The retrieved Terra and Aqua MODIS aerosol products (AOD, AE, and columnar water vapor (CWV)) were analyzed to study their spatio-temporal variations and long-term trends. Furthermore, we obtained the fine mode fraction (FMF) (defined as the ratio of the optical depth of small mode versus effective optical depth at 550 nm) values from the Terra satellite (due to more valid data points), in order to identify the particle-size and aerosol types at Nanjing. 3.2. The MISR instrument MISR employs nine cameras, one viewing nadir and four each viewing in forward and aft directions and has four spectral bands, i.e., blue, green, red, and near-infrared (Diner et al., 2001). The global coverage time is every 9 days with repeat coverage between 2 and 9 days depending on the latitude. The aerosol retrieval algorithm over land is dependent on the surface types within a scene i.e., dark water bodies, heavily vegetated areas or high contrast terrain (Kahn et al., 2005). We have also downloaded the MISR Terra Level-2 AOD555 global product on a daily basis with 17.6 km × 17.6 km resolution (http://wwwmisr.jpl.nasa.gov) for intercomparison during the same study period. More details about the satellite instrumentation, retrieval algorithm and methodology adopted can be found in Diner et al. (2001) and Kahn et al. (2005, 2010). The uncertainty associated with MISR data is found to be greater of 0.05 or 0.2(AOD), whichever is higher (Diner et al., 2001; Kahn et al., 2005). Though the MODIS–MISR correlation studies have been reported in the past (Kahn et al., 2010; Xiao et al., 2009; Alam et al., 2011; Cheng et al., 2012; Kumar et al., 2015; Mehta, 2015), nevertheless, a correlation analysis is carried out for MODIS and MISR datasets individually on Terra and Aqua platforms for the YRD region, East China. Monthly, seasonal, and annual composites of the aforementioned aerosol products have been made from the daily gridded datasets following the classification of seasons illustrated in Section 2. 3.3. The AERONET sunphotometer AERONET data products which can be downloaded from its website (http://aeronet.gsfc.nasa.gov/) are available at three levels: Level-1.0 has neither cloud screening nor quality control; Level-1.5 is cloudscreened data, but not quality assured; Level-2.0 is both cloudscreened and quality assured. The sunphotometer employed by the AERONET takes measurements of direct sun and diffuse sky radiances within the spectral ranges of 340–1020 nm and 440–1020 nm, respectively (Holben et al., 1998). The sunphotometer has a very narrow

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field-of-view, and is, therefore, rarely affected by surface reflectance (Holben et al., 1998). It has high AOD retrieval precision with an uncertainty of 0.01–0.02 (Smirnov et al., 2000), and is widely used to validate satellite AOD values (e.g., He et al., 2010; Deng et al., 2012; Li et al., 2015; Yu et al., 2016b, 2016c and references therein).

Weatherhead et al. (1998). Following Weatherhead et al. (1998) the following simple linear trend model has been adopted to estimate the linear trend.

3.4. Validation of MODIS and MISR against AERONET

where Yt is the geophysical variable of the trend being determined. c is the offset (y-intercept), which represents the value of Y at the beginning of the time series. X is the independent variable representing time. ω (slope/linear rate of change in the value of the geophysical variable) is the trend estimate of the geophysical variable under consideration. ε is the noise on the time series that is not represented by the linear trend model. If σω is the standard deviation in the estimated ω value, whenever the value of the ratio | ω/σω | exceeds 2, the estimated ω is considered to be significant at a 5% significance level or 95% confidence level (e.g., Weatherhead et al., 1998; Mehta, 2015; He et al., 2016). If the ratio | ω/σω | value lie in between 1.65 and 2, then the estimated ω value is considered to be at 90% confidence level (Sreekanth, 2013; He et al., 2016 and references therein).

The distribution of monitoring stations and good calibration makes AERONET measurements (Holben et al., 1998) the most effective tool for validating satellite AOD (Chu et al., 2002; Remer et al., 2005; Levy et al., 2010). In order to take into account both spatial and temporal variabilities of aerosol distribution in Nanjing, MODIS, MISR, and AERONET AODs at different intervals need to be co-located in space and time. For the AERONET data, we considered measurements during ± 30 min as the satellites (MODIS and MISR) passed over (Ichoku et al., 2002; Cheng et al., 2012). Although the AERONET Level-2.0 AOD data are the most reliable, it still cannot be provided in real time. In fact, the Level-1.5 data are also derived by cloud mask and can be used to validate the satellite aerosol products. In the present study, sunphotometer retrieved Level-1.5 AOD500 data was obtained from the AERONET_NUIST site in Nanjing for the period 2007–2014. The spatial averaged MODIS and MISR AODs were compared with the temporal averaged sunphotometer AOD data. The criteria for an acceptable AOD comparison at a site require that AERONET data should be available within a 1 h window centered on the MODIS and MISR overpass time and satellite data should be available within a 0.5° × 0.5° box (i.e., a 5 × 5 pixel region for MODIS and a 3 × 3 pixel region for MISR) centered on the AERONET site (Ichoku et al., 2002; Qi et al., 2013). The MODIS and MISR AODs are retrieved at 550 nm and 555 nm, respectively and the nearest sunphotometer AOD channel is at 500 nm. In order to perform direct comparison and validation of AOD at 550 nm, the values of AERONET AOD at 550 nm were retrieved by using AE calculated between 440 and 870 nm, to provide a common wavelength for both satellites and AERONET (Prasad and Singh, 2007; Kumar et al., 2015; Yu et al., 2016b, 2016c).  AOD550 ¼ AOD500

 550 −AE 500

ð1Þ

3.5. Air mass back-trajectory analysis Backward trajectory analysis is an effective method to clarify the transportation of air masses in the atmosphere, and to identify the remote sources of aerosols combining aerosol chemical compositions with vertical profiles associated with major emission sources. The aerosol source regions influencing Nanjing during different seasons were also examined using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (http://ready.arl.noaa.gov/HYSPLIT.php; Draxler and Rolph, 2003). NCEP/NCAR global reanalysis meteorological data was used in running the HYSPLIT model to retrieve 5-day (120 h) air mass back-trajectories arriving in Nanjing at 1500 m above ground level (AGL) for the selected year 2012. We found similar trajectories for other time periods at this altitude, but only this year has been selected and included, as it is considered to be representative of the entire time period analyzed. 3.6. Statistical significance test used for the trend analysis There are several statistical approaches to quantify the trends in the time series data of any particular geophysical variable. In the present study, linear regression analysis has been adopted to estimate the trend in AOD. The statistical significance of the estimated trend has been determined following the method discussed by

Yt ¼ c þ ωXt þ ε

ð2Þ

3.7. Discrimination of aerosol types FMF can be used in conjunction with AOD for the discrimination of different aerosol types based on the sensitivity of AOD to aerosol column density and the dependence of FMF on the aerosol particle size (Barnaba and Gobbi, 2004). We tried to provide a preliminary qualitative identification of the main aerosol types over Nanjing in the YRD region by means of AOD550 versus FMF scatter plot following the method proposed by Barnaba and Gobbi (2004). This method has been used in a large number of studies (e.g., Barnaba and Gobbi, 2004; Kaskaoutis et al., 2007) using the MODIS data. In the current classification, the following considerations were taken into account to define threshold values for a specific aerosol type: AOD550 ≤ 0.25 and FMF b 0.5 represented as the maritime (MA) aerosol type; AOD550 N 0.7 and FMF b 0.5 for desert dust (DD) aerosol type; AOD550 ≤ 0.25 and FMF N 0.6 represented as continental background (CB) aerosol type; AOD550 N 0.3 and FMF N 0.6 for biomass burning/urban-industrial (BB/UI) type of aerosols. Between the four portions, some gaps reveal where the aerosols are difficult to be discriminated and they are taken as mixed (MX) aerosol type. MX type of aerosols was chosen to bear in mind the considerable effects of the various aerosol-mixing processes in the atmosphere (e.g., coagulation, condensation, humidification, gas-to-particle conversion) (Kaskaoutis et al., 2007; Kumar et al., 2015). 4. Results and discussion 4.1. Inter-comparison of MODIS, MISR and AERONET AODs Many studies utilize MODIS and MISR data to understand the sensing capabilities of these instruments in a variety of contexts and also to improve the accuracy and coverage achievable with a single sensor (Prasad and Singh, 2007; Alam et al., 2011; Xiao et al., 2009). To facilitate the comparison of these coincident observations, MODIS is first remapped to the common MISR grid using their corresponding geographic latitude and longitudes values. Although, the MODIS Terra and MISR Terra observations are made in the morning, and the MODIS Aqua observations are made in the afternoon, they are all compared with the daytime averages. The paired data points of MODIS Terra/ Aqua AOD550 and MISR AOD555 have been fitted with 1:1 (through the origin) and regression lines and the corresponding statistical coefficients are obtained in Fig. 2a. The correlations were found to be strong with correlation coefficients of 0.96 and 0.92 for MISR versus MODIS Terra and Aqua satellite sensors, respectively. The latter correlation coefficient (between MISR and MODIS-Aqua) was observed to be less strong compared to the former. This is due to less number of data points, different overpass time, different satellite platforms, and retrieval

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Fig. 2. Inter-comparison of (a) AODs obtained from MODIS (Terra and Aqua) versus MISR for the study period 2002–2014. Validation of (b) MODIS Terra, Aqua and (c) MISR AODs against AERONET AOD over Nanjing for the period 2007–2014. The solid (black) and dash (red. blue) lines in all panels represent 1:1 line and linear regression fitting lines, respectively. The corresponding regression coefficients obtained from the fitting are also shown in all panels. r is correlation coefficient and N is the number of paired data points. (For interpretation of the references to in this figure legend, the reader is referred to the web version of this article.)

algorithms (Kahn et al., 2005; Prasad and Singh, 2007; Alam et al., 2011); also due to the water contamination in the signal of MODIS Aqua AOD550 (Chu et al., 2002; Kumar et al., 2015). During the study period, MODIS Terra and Aqua AODs overestimated (with slopes greater than unity) compared to the MISR AOD by 27% and 17%, respectively (Fig. 2a). This shows a better performance of MODIS sensor in estimating and capable of producing AOD when compared to the MISR sensor at Nanjing. This better performance can only be to some extent valid to the regions of given aerosol type, underlying surfaces, and pollution events, but not globally (Kahn et al., 2010). It also depends on the range of surface albedos as well as aerosol loading in different seasons.

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The collocated AOD550 values obtained from the MODIS Terra and Aqua were validated against the AERONET AOD550 at Nanjing in Fig. 2b. It can be seen that MODIS Terra and Aqua AODs agree generally well with sunphotometer AOD (within uncertainty levels), which is consistent with the results reported by Li et al. (2015) and Kang et al. (2016) over Nanjing. The linear regression parameters (slope and intercept) obtained from the validation is of vital importance (Levy et al., 2010). In the case of Terra and Aqua MODIS, the respective correlation coefficients at Nanjing site were observed to be 0.87 and 0.81, and intercept of 0.12 and 0.21. Meanwhile, the slopes (0.71 and 0.63) deviate significantly from the ideal state (m = 1), indicating that MODIS AODs are systematically underestimated by 29% and 37%, respectively. This shows that the MODIS instrument is incapable in reproducing extremely high AODs at Nanjing. In general, the deviation of the slope of correlation plot from unity represents that MODIS aerosol inversions have systematic biases. This is induced mainly due to aerosol model assumptions (0–20%), instrument calibration (2–5%), pixels choice (0–10%) and uncertainties of aerosol properties (when AOD is large); whereas, the deviation of intercept from 0 is attributed to uncertainties of surface reflectance (when AOD is small) assumptions (Chu et al., 2002; Remer et al., 2005; Levy et al., 2010). This can also be due to point measurements (AERONET) versus 10 km and 17.6 km grid average of MODIS and MISR retrievals, respectively. It is also revealed that the correlation was found to be significant between MODIS and AERONET over land when compared to over ocean due to its low surface reflectivity characteristic (Chu et al., 2002). In high turbid conditions, MODIS retrieved AODs are less than that of the sunphotometer, is probably caused by errors in aerosol model assumptions or overestimation of single scattering albedo (Prasad and Singh, 2007; He et al., 2010). On the other hand, MODIS retrieved AODs are larger than sunphotometer in the clean atmosphere mostly for underestimating the surface reflectance. Given different calibration from Terra and Aqua MODIS (Xiong et al., 2007), it would not be surprising to see large differences in slope and intercept when using the same aerosol retrieval algorithm. This is due to different degradation of the solar diffuser and scan mirror in the visible as well as electronic talk and thermal leak in the shortwave infrared bands (Xiong et al., 2007). The former (visible reflectance) would affect the aerosol properties while the latter (shortwave infrared or so-called near infrared reflectance) affect surface reflectance estimate in MODIS AOD retrieval. These results are similar to MODIS AOD comparison investigated by previous authors over the YRD, East China (e.g., Deng et al., 2012; He et al., 2010; Cheng et al., 2012; Li et al., 2015). Deng et al. (2012) found a correlation coefficient of 0.83 for the scatter plot between MODIS and AERONET AODs over Nanjing, with slope 0.85 and intercept of 0.21 for the period 2007–2008. The observed slope and intercept of linear fit at Shanghai were 0.62 and 0.32, respectively with a correlation coefficient of 0.68 as investigated by He et al. (2010). Comparison of AODs between MISR and with those derived from AERONET is shown in Fig. 2c. MISR tends to systematically underestimate AOD compared with ground-based AERONET observations at Nanjing. The correlation coefficient was found to be r = 0.92 for the scatter plot of MISR versus AERONET, which is relatively larger than that of between MODIS and AERONET. This may be due to the relatively larger amounts of vegetation as the MODIS dark pixel algorithm may work well over this region. At Nanjing, it is observed that MODIS AOD retrievals are better than those from MISR (see Fig. 2a). The multi-angle capability of MISR enables it to distinguish the sunlight reflected by aerosols from that reflected at the Earth's surface, and it thus performs better in AOD retrieval for bright regions (highly reflective) (Diner et al., 2001; Cheng et al., 2012). MISR characterizes surface properties in the red and near-infrared bands using the clear sky (Kahn et al., 2010); whereas, MODIS uses extended dark-target approach (Remer et al., 2005; Levy et al., 2010). The inter-comparison among the AOD retrievals of a number of datasets from multiple sensors has been employed by several earlier

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researchers. Qi et al. (2013) reported that MISR performs better over the less vegetated (bright) surface (Beijing) when compared to MODIS. Cheng et al. (2012) compared MODIS and MISR aerosol products over China with AERONET data and found that MISR AODs showed higher correlation (0.77) with AERONET measurements, which can be attributed to its better viewing and spectral capabilities. Alam et al. (2011) utilized AOD data from MODIS and MISR and compared these with AERONET AOD over different cities of Pakistan for the year 2007. They revealed that MISR performed better for areas close to the ocean, while MODIS worked excellently for the vegetated regions. Recently, Kumar et al. (2015) also examined that MISR performs better over coastal regions (Durban, South Africa) when compared to the thick vegetated surface, where MODIS performs well. 4.2. Frequency distributions of aerosol properties The frequency histograms of AOD550, AE470–660, CWV and FMF are shown in Fig. 3 (a–d), respectively for all the daily gridded Terra MODIS data. It can be seen from the figures that there were single peak distributions of frequencies of occurrences for all the aerosol properties. The bin intervals in the present study for the AOD, AE, and FMF were set up to 0.1 and for the CWV, it was 0.5. The range of AOD550 at Nanjing varied from 0.15 to 2.1, accounting for 95% of the total occurrences, with a peak value of AOD550 (34%) observed between 0.4 and 0.7 (Fig. 3a). The most occurrences of AOD550 ranged between 0.2 and 1.4, accounted for 90% of the total distribution. There was less number of frequency of occurrences of lower AODs (b 0.15) indicates the fewer probability of background aerosol conditions. Meanwhile, the AE470–660 mostly varied from 0.5 to 1.6, with a modal value of ~0.7 (20% of the total) (Fig. 3b). The frequencies of occurrence of the AE470–660 values

are equally distributed around the 1.0, which accounted 57% for AE N 1.0 and 43% for AE b 1.0. This indicates that the atmosphere of Nanjing was dominated by the mixture of fine and coarse particles during the study period. The present findings were consistent with the previous investigations of Li et al. (2015) and Cheng et al. (2015) at Nanjing and Shanghai, respectively. Li et al. (2015) showed that the distribution of AE varied in the range from 1.2 to 1.5, with a peak at 1.4 over urban Nanjing using sunphotometer measurements. Similarly, Cheng et al. (2015) found a uni-peak pattern of AE mainly centering in 1.1–1.6 over Shanghai using sunphotometer data. Also in the case of CWV, the modal value in the distribution was observed between 0.5 and 1.0 cm, with a maximum frequency of distribution that occurred from 0.5 to 2.0 cm (accounted for 64% of the total) (Fig. 3c). The CWV varied between 0.5 and 6.0, accounting for 89% of the total occurrences, with a modal value of CWV observed at 1.0 cm (24% of the total). The range of most occurrences of CWV varied from 0.5 to 2.5, accounted for 68% of the total distribution. Also in the case of FMF, it is mostly ranged from 0.05 to 0.35 (~ 60%), with a peak at 0.1 accounted for ~ 30% of the total distribution (Fig. 3d). Further, it is observed that FMF exhibited similar distribution pattern as that of AE, with more or less constant distribution with FMF N 0.5. 4.3. Spatial and temporal variations of aerosol properties 4.3.1. Spatial distribution The geographical distribution of the 13-year averaged different aerosol properties over East China is shown in Fig. 4. As a highly urbanized area with dense population and developed industries, the northern part and the YRD areas are characterized by high aerosol emission regions over East China (He et al., 2012, 2016). On the contrary, lower

Fig. 3. Annual frequency distributions (in %) of (a) AOD550, (b) AE470–660, (c) CWV, and (d) FMF obtained from Terra MODIS between 2002 and 2014. The black solid line corresponds to the Gaussian curve fitted to the obtained distribution. ‘N’ represents the number (count) of data points.

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Fig. 4. The averaged spatial distribution of (a) AOD550, (b) AE470–660, (c) CWV, and (d) FMF over East China derived from Terra MODIS Level-2 during the period 2002–2014. The open asterisk (black color) in panel (a) represents the location of Nanjing site in the YRD.

AOD (b0.3) (Fig. 4a) and higher AE (N 1.0) (Fig. 4b) can be found over the southern part and the mountainous regions of East China. The high AOD550 (N1.6) over the northern part of East China is closely related to coarse particles and is attributed to large amounts of airborne dust and sand particles rising from the natural surface of the Taklimakan Desert in the northwest of China (Luo et al., 2014; He et al., 2016; Yu et al., 2016c) and the Mongolia Desert in the north China (Yu et al., 2016c). Also, the north and east parts of East China are influenced by sea salt aerosols arriving from the Yellow Sea, the East China Sea, and the Bohai Sea. Those relatively clean air masses not only dilute urban pollution but also bring more water vapor, complicating aerosol optical properties in the north. In the other high AOD areas, the large amounts of fine particles from anthropogenic activities occupied a larger proportion of the total aerosols. Thus, it can be known that there are several factors, such as emission sources, population density, that may affect the spatial distribution of aerosols, which is consistent with the results noticed by He et al. (2012); Deng et al. (2012); Luo et al. (2014); Kang et al. (2015) and He et al. (2012, 2016) over East China. The AE470–660 depends on the size of aerosol particles. It is small for coarse particles and increases with decreasing particle size. From Fig. 4b, AE470–660 ranges from 0.4 to 1.4 for the most parts of East China, but appears to be lower over the northern part (b0.9) than the southern part (N1.0). This indicates that the decreasing AE (increasing AOD) over the northern part of East China is mainly caused by those large-sized aerosol particles like the dust particles. The smallest AE470–660 values of 0.6 were noticed in the coastal areas of East China. However, AE is more sensitive to the assumptions on the spectral dependence of the land surface than the AOD, followed by uncertainties of aerosol properties (such as particle size) (He et al., 2012). CWV varied from 1.5 to 4.0 cm for the most

parts of East China (Fig. 4c), but appears to be lower over the north part (b2.0 cm) compared to the south (2.0–4.0 cm). This clearly shows that the CWV exhibits a different spatial distribution compared to the AOD. So, the correlation between them over the pixels of the studied domain is, in general, negative. Fig. 4d presents the spatial variation of FMF over East China averaged from MODIS Level-2 products during the period 2002–2014. The distribution of FMF is similar to that of AE470–660 with lower values observed in the north part (0.2–0.3) than the higher ones in the south and east parts (N 0.6). The very small FMF (0.2–0.3) values are found in the central and north parts of East China. Thus, it is revealed that the coarse particles were more abundant over the north part, with the dominance of fine particles over the south and east parts of East China. 4.3.2. Temporal variation Fig. 5 shows the variations of monthly averaged MODIS (Terra and Aqua) and MISR AODs, AE470–660 and FMF for the study period 2002–2014 observed at Nanjing. The solid lines represent the longterm linear trend obtained from linear regression fitting of data. It can be seen that there are clear periodic variations (year-to-year variability) in all the parameters. It is also revealed that the variability in AOD derived from MODIS and MISR sensors is more or less similar with higher values noticed by MODIS at Nanjing (Fig. 5a). The aerosol measurements are expected to differ due to factors such as processing methods, calibration, and retrieval algorithms (Levy et al., 2010; Kahn et al., 2010). Other factors that influence the difference are meteorological effects, natural and anthropogenic influences, the extent of biomass burning, and sensitivity to differences in land and vegetation cover (Prasad and Singh, 2007; Kahn et al., 2010). The high values of AOD

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Fig. 5. Inter-annual variations of monthly mean (a) MODIS AOD550 and MISR AOD555, (b) AE470–660, and (c) FMF values derived from Terra (red) and Aqua (blue) MODIS for the period 2002–2014 over Nanjing. The solid straight lines represent the long-term linear fit to the data points and the corresponding regression coefficients of the linear fit are also shown in each panel. (For interpretation of the references to in this figure legend, the reader is referred to the web version of this article.)

were noticed in June compared to the other months due to increased anthropogenic activities and biomass burning. In April, the AOD was found to be enhanced attributed to aerosol loading due to dust-storm events at this site (e.g., Deng et al., 2012; Yu et al., 2016a) which coincides with the low values in AE470–660 (Fig. 5b). The FMF annual variation seems to be more complicated and not presented in an organized pattern. Despite this, a summer maximum, and winter and spring minimum may be identified (Fig. 5c). More details about the results of FMF are given in Section S3 and Fig. S2 of Supplementary Material (SM)). Fig. 6 illustrates the detailed temporal changes (monthly, seasonal and inter-annual mean) in aerosol properties (AOD, AE, and CWV) obtained from the MODIS Terra and Aqua satellites over Nanjing from 2002 to 2014. The MODIS AOD550 is also compared with the MISR AOD555 for the above study period observed at Nanjing. It is noted that the AOD derived from the MODIS (Terra and Aqua) and MISR sensors followed a similar pattern during the study period, which is consistent with the results presented in Fig. 5. The results reveal a rather significant monthly variation in the AOD and AE suggesting a difference in the aerosol load and particle-size contributed from variable source regions. Over the entire study period, the annual monthly mean AOD tends to increase gradually from February and peak in June, and then decrease with some fluctuations until December, with its lowest value. June was the highest AOD month with MODIS (Terra, Aqua) and MISR AOD550 values of around 1.36, 1.31, and 1.17, respectively during the entire study period (Fig. 6a). The respective low AOD550 values of 0.58, 0.56, and 0.32 occurred in December rather than November, as observed in other regional studies (Wu et al., 2015). The detailed monthly, seasonal and annual statistics of aerosol properties (AOD, AE, and CWV) during the period 2002–2014 obtained from the MODIS Terra satellite observed at Nanjing are presented in Table 1. The frequent alternations between the maximum and minimum monthly values indicate a noticeable seasonal variation. Seasonal

mean values obtained in each year have been averaged with the corresponding seasons during 2002–2014 to make long-term mean values for individual seasons. The seasonal change in the Terra MODIS multiyear AOD, AE and CWV observed at Nanjing during the study period is shown in Fig. 6 (d–f), respectively. Over the four seasons, the averaged MODIS Terra AOD550 reached its maximum during the summer, with a mean (± standard deviation) value of 0.97 (± 0.48), followed by the spring (0.90 ± 0.42) and autumn (0.68 ± 0.35), and then minimum during winter with its averaged value of 0.61 ± 0.28. In general, the seasonal variations of AOD in Nanjing are similar to those found at other sites in the YRD region (Pan et al., 2010; Deng et al., 2012; He et al., 2012; Cheng et al., 2015). The highest AOD occurred in summer because high temperature and RH are beneficial to the formation and hygroscopic growth of fine aerosols. Also, the higher temperature during summer favors the photochemical reactions leading to the production of secondary aerosols, mostly from the anthropogenic origin (Li et al., 2015; Cheng et al., 2015; Kang et al., 2016). Although stronger wet removal of aerosols due to intense precipitation in summer (see Fig. 1), static atmospheric conditions, aerosol hygroscopic growth, secondary aerosol formation and pollutants from agricultural residues burning in the surrounding areas cause aerosol accumulation and then enhance AODs (Kang et al., 2016). To support this, we used MODIS web fire mapper built by NASA Fire Information for Resource Management System (FIRMS; http://maps.geog.umd.edu/firms/) and downloaded the locations of fire sports over East China for different seasons. It is evident from Fig. 7 that the existence of large density of fire spots in summer compared to the other seasons, is due to a large amount of biomass/ agriculture residues burning to clear harvest and fires. Higher AODs in spring are likely associated with pollutant pooling in low atmospheric layers due to prevailing meteorological conditions, local dust emission and some dust transported from remote sources (Han et al.,

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Fig. 6. Averaged monthly (a–c), seasonal (d–f), and inter-annual (g–i) variations of aerosol properties observed over Nanjing from different satellites during 2002–2014. The values presented for CWV correspond to the Terra MODIS only. The solid and dash lines indicate the annual mean obtained from Terra and Aqua MODIS, respectively. The dotted line corresponds to the annual mean AOD derived from the MISR sensor. (For interpretation of the references to in this figure legend, the reader is referred to the web version of this article.)

2015; Kang et al., 2016; Yu et al., 2016b, 2016c). One more potential player responsible for the higher AOD values in spring is the surface wind speed. Wind speed becomes stronger during this season and reaches almost N7 m s−1 (Fig. 8), which can significantly pick up the soil, dust and biological particles (which will be mostly in the coarse particle regime) and suspends in the atmosphere. The season-wise mean synoptic wind pattern obtained at 1000 h Pa derived from NCEP/NCAR reanalysis data (http:/www.cdc.noaa.gov) for

the study period over China is shown in Fig. 8 (also refer to Section S2 of SM). Further, during spring dust from the north (Mongolia) and northwest (Taklimakan Desert) of China could be transported to Nanjing, which results in the increased aerosol loading, significantly coarse particles (low AE, Fig. 6e). This is in good agreement with the results obtained from surface measurements reported by Han et al. (2015); Li et al. (2015), and Yu et al. (2016a) measured at Nanjing. Further, the monthly mean annual variations of aerosol FMF from MODIS Terra

Table 1 Monthly, seasonal, and annual mean values (except for the count) of aerosol properties derived from Terra MODIS over Nanjing during 2002–2014. Aerosol optical depth (550 nm)

Angstrom exponent (470–660 nm)

Columnar water vapor (cm)

Month

Mean

Stdev

Max

Min

Count

Mean

Stdev

Max

Min

Count

Mean

Stdev

Max

Min

Count

January February March April May June July August September October November December Autumn Winter Spring Summer Annual

0.60 0.72 0.85 0.90 0.95 1.33 0.80 0.74 0.69 0.73 0.63 0.53 0.68 0.61 0.90 0.97 0.79

0.25 0.34 0.42 0.43 0.45 0.56 0.51 0.43 0.41 0.36 0.30 0.24 0.35 0.28 0.42 0.48 0.39

1.61 2.22 2.74 2.93 2.90 2.95 2.84 2.59 2.54 1.88 2.01 1.37 2.14 1.73 2.86 2.79 2.38

0.18 0.27 0.28 0.28 0.26 0.34 0.10 0.16 0.12 0.12 0.18 0.16 0.14 0.20 0.27 0.21 0.21

161 107 197 207 220 178 197 197 191 227 199 190 617 458 624 572 2265

0.75 0.88 0.85 0.79 0.78 0.97 1.22 1.31 1.13 0.99 0.89 0.84 1.01 0.82 0.80 1.16 0.95

0.25 0.32 0.31 0.29 0.27 0.29 0.35 0.34 0.35 0.31 0.35 0.32 0.32 0.30 0.28 0.33 0.31

1.78 1.80 1.76 1.73 1.57 1.63 1.81 1.81 1.80 1.80 1.79 1.81 1.80 1.80 1.69 1.75 1.76

0.51 0.52 0.50 0.45 0.49 0.46 0.50 0.55 0.54 0.55 0.51 0.51 0.53 0.51 0.48 0.50 0.51

161 107 197 207 220 178 197 197 191 227 199 190 617 458 624 572 2271

0.69 0.98 1.08 1.63 2.33 3.70 5.64 5.61 3.82 2.10 1.36 0.74 2.43 0.80 1.68 4.98 2.48

0.10 0.25 0.17 0.21 0.16 0.21 0.49 0.38 0.54 0.33 0.35 0.13 0.41 0.16 0.18 0.36 0.28

1.74 3.20 3.75 5.78 5.84 8.53 8.84 7.89 7.76 5.62 4.23 3.08 5.87 2.67 5.12 8.42 5.52

0.16 0.30 0.29 0.43 0.53 0.57 0.47 0.46 0.34 0.55 0.33 0.17 0.41 0.21 0.42 0.50 0.39

245 197 246 247 262 243 249 269 260 278 257 261 257 703 755 761 2476

AOD and AE are unitless quantities. Mean—Average; Stdev—Standard Deviation; Max—Maximum; Min—Minimum.

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Fig. 7. MODIS fire spots (red color solid circles) during the period 2007–2014 over East China in different seasons (a) autumn, (b) winter, (c) spring, and (d) summer. The location of site is also indicated with a blue solid asterisk in all the panels. (For interpretation of the references to in this figure legend, the reader is referred to the web version of this article.)

have been analyzed for the study period shown in Fig. S2 of SM. FMF can be used as a proxy for delineating fine mode aerosols from coarse mode particles. It is observed that the FMF decreased to reach a value of ~0.2 in April (spring), resulted in a significant contribution of coarse particles. The maximum AE appeared in summer and minimum during winter and spring (Fig. 6e). 90% of the monthly mean values of AE were observed to be N1.0 (Fig. 6b) during the study period, which indicates that the aerosol particle size is small and the proportion of fine particles is larger than that of coarse particles. The monthly (Fig. 6c) and seasonal (Fig. 6f) changes of CWV followed similar patterns as those of AOD. 4.4. Inter-annual variations of aerosol properties Fig. 6 also shows the inter-annual mean variations of aerosol properties obtained from the Terra and Aqua MODIS at Nanjing for the study period. The inter-annual mean values of AOD550 were found to be high and low for the years 2003, 2014, and 2004, respectively with more or less similar values during 2007–2012 (Fig. 6g). For the first half period (2002–2007), the AOD showed increasing trend at + 6.7% year−1 and the rest of the years (2008–2014) noticed decreasing trend at −5.6% year−1. Luo et al. (2014) interpreted that the reduction in anthropogenic aerosol emissions could be the dominant factor in aerosol reduction over China. Similar type of decadal trend has been observed over other urban sites in the world, such as Seoul, South Korea (Panicker et al., 2012), Bangalore, India (Sreekanth, 2013), and Pretoria and Durban, South Africa (SA) (Kumar et al., 2014, 2015) using the recent MODIS data. It is depicted from Fig. 6h that the AE

values increased from 2007 compared to the previous years of study, indicating the dominance of fine particles over Nanjing due to increased anthropogenic activities (Pan et al., 2010; Luo et al., 2014; Kang et al., 2015, 2016; He et al., 2016). In the case of CWV, a constant mean value of ~2.5 cm was observed in all the years during the study period (Fig. 6i). 4.5. Statistical long-term trends in AOD and AE To compute the long-term trends in AOD, we performed the general linear regression analysis following Weatherhead et al. (1998) for the MODIS (Terra and Aqua) AOD and AE on annual and seasonal basis retrieved during the study period. The detailed statistics including the slope and standard deviation obtained from the regression fitting are tabulated in Table 2. The respective annual mean Terra and Aqua AODs over Nanjing exhibited decreasing and increasing trends of − 0.0055 year− 1 (− 0.70%) and + 0.0001 year− 1 (+ 0.01%). However, the observed trends were highly dependent on the daily and mean values of AOD, which can introduce biases in the trends (Weatherhead et al., 1998). The reduction of error and uncertainty in the trend analysis of cloud-free AOD can be achieved through a variety of approaches. This includes optimization of instrument calibration and the refinement of retrieval algorithms (Mischenko and Geogdzhayev, 2007). The uncertainty factor was cloud occurrence, which prevents the retrieval of cloud-free AOD using passive visible sensors, and therefore, influences the calculation of monthly AOD means with statistical representativeness. Yoon et al. (2012) suggested

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Fig. 8. Mean synoptic wind pattern at 1000 hPa for four seasons. Background color indicates the magnitude of the wind speed and the direction of arrow head represents the wind direction. Black color solid circle represents the location of study region (Nanjing). The respective seasons are the same as that mentioned in Fig. 7.

an approach to minimize this effect by the use of weighted leastsquares regression and multiple satellite-derived AODs from the space-born instruments, and thereby, showed the significant improvements in the trend estimates for atmospheric aerosols during the past decade.

Also, the AOD trend has been estimated using the seasonal mean values and the corresponding statistics are also presented in Table 2. The confidence level of the observed long-term trend and associated uncertainty has been estimated adopting the methodology described in Section 3.6. The observed significant increasing trends in the AOD

Table 2 Annual and seasonal statistical trends in AOD550 and AE440–670 derived from Terra and Aqua MODIS over Nanjing during 2002–2014. The numerals in bold and italic are significant at 95% confidence level whereas, italic is significant at 90% confidence level and the rest are less significant. Parameter Terra satellite AOD

AE

Aqua satellite AOD

AE

Season

Mean

Standard deviation

Slope (ω, year−1)

Standard deviation of ω (σω)

Ratio ω/σω

Variation in trend (%year−1)

Autumn Winter Spring Summer Annual Autumn Winter Spring Summer Annual

0.68 0.61 0.90 0.97 0.79 1.01 0.82 0.80 1.16 0.95

0.35 0.28 0.42 0.48 0.39 0.32 0.30 0.28 0.33 0.31

−0.0151 +0.0157 −0.0214 −0.0012 −0.0055 +0.0199 +0.0172 +0.0187 −0.0051 +0.0138

0.7650 0.5283 1.0188 0.9793 0.8211 0.0087 0.0070 0.0067 0.0076 0.0053

0.02 0.03 0.02 0.12 0.01 0.01 2.46 2.79 0.67 2.60

−2.22 +2.57 −2.38 −0.12 −0.70 +1.97 +2.10 +2.34 −0.44 +1.45

Autumn Winter Spring Summer Annual Autumn Winter Spring Summer Annual

0.70 0.65 0.94 0.99 0.81 0.98 0.72 0.74 1.27 0.92

0.32 0.25 0.39 0.43 0.37 0.34 0.26 0.28 0.38 0.22

−0.0137 +0.0203 −0.0099 −0.0035 +0.0001 −0.0013 +0.0058 +0.0077 +0.0045 +0.0079

0.7740 0.5373 0.9895 0.9673 0.8170 0.0092 0.0046 0.0053 0.0049 0.0038

0.02 0.04 0.01 0.01 0.01 0.14 1.26 1.45 0.92 2.08

−1.96 +3.13 −1.06 −0.35 +0.01 −0.13 +0.81 +1.04 +0.35 +0.86

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values during winter is mainly due to a consistent increase in AOD produced from various anthropogenic activities associated with biomass and fossil fuel burning. Further, the frequent fireworks (Cheng et al., 2015), more usage of heating systems to get warmth against the cold winters, and industrial emissions, results in the frequent formation of HF events over the YRD (Wang et al., 2015). This is true in the case of both Terra and Aqua AODs (see Table 2). In contrast, the negative (decreasing) trends were observed during the spring and summer seasons suggest an increase in the relative abundance of anthropogenic fine aerosols or reduction in natural coarse particles. While positive (increasing) trends in AE implies steepening of AOD spectra due to increasing abundance of fine particles, linked to anthropogenic sources. In addition to this, the Chinese Government has introduced several environmental policies to cut-down emissions from industries and introducing new environment-friendly technologies in the construction field, which could contribute to a decrease in anthropogenic aerosol emissions (Luo et al., 2014). Along with these, proper maintenance of vehicular emissions, strict observation of benzene standards, and enhancement of natural gas vehicles instead of petrol/diesel vehicles could be other factors for the reductions in aerosol loading (He et al., 2016). 4.6. Impact of long-range aerosol transport Apart from the local emissions and modulations of the aerosol concentrations with respect to the synoptic meteorological conditions, long-range transport also contributes to the columnar aerosol load. The transport of natural and anthropogenic aerosols critically depends on the synoptic scale wind circulation pattern (see Fig. 8). The season-

wise isentropic back-trajectory analysis computed from the HYSPLIT model derived at 1500 m AGL for the selected year 2012 is shown in Fig. 9. During autumn, the study region is influenced by mixed air masses originated from the north, east, and south of China (Fig. 9a). During this season, the AOD values slightly dropped off, but the FMF values increased considerably due to more anthropogenic activities. In winter, the specific local conditions prevailing over the study region like calm winds, increased RH (Fig. 1), and higher levels of anthropogenic water-soluble aerosols, favor the formation of HF conditions (Fig. 9b). The same has been examined by several authors over urban regions of China (Wang et al., 2015; Cheng et al., 2015; Yu et al., 2016b). During the spring season, most of the air mass trajectories were originated from the north (Mongolia Desert) and northwest (Taklimakan Desert) of China (Fig. 9c), enhanced aerosol loading composed of coarse particles (low AE). The same has been derived from the seasonal variations of aerosol properties demonstrated in the previous Section 4.3.2. Also, the FMF values obtained over Nanjing during spring were relatively low (with a seasonal mean of ~ 0.22) supporting the above argument (Fig. S2 of SM). The air masses reaching Nanjing during spring are characterized by higher wind speeds (Fig. 1), which is evident by the lengthier trajectories. The remaining trajectories have a long continental history over Russia and Kazakhstan, resulting in the moderate AOD during spring. During summer, the air masses have both oceanic and continental history before reaching the study location (Fig. 9d). This leads to enhanced aerosol loading with increased concentrations of fine aerosols (high AE; FMF ~ 0.64) over the study region. Further, the observed high value of AOD at the study region is the consequence of the seasonal agriculture residues burning and fires. In addition, the winds from the north and northwest directions of the YRD can produce a significant

Fig. 9. HYSPLIT back trajectories deriving air mass tracks ending at 0600 UTC analyzed at 1500 m AGL arriving in Nanjing for the year 2012 during different seasons. The cyan color asterisk in all panels represents the location of Nanjing site. (For interpretation of the references to in this figure legend, the reader is referred to the web version of this article.)

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Fig. 10. Seasonal scatter plots between MODIS Terra AOD550 and FMF to identify different aerosol types over Nanjing during 2002–2014. Areas dominated by different aerosol types are qualitatively drawn in the figure. MA—Maritime; CB—Continental Background; BB/UI—Biomass Burning/Urban-Industrial; DD—Desert Dust; MX—Mixed.

fraction of anthropogenic fine aerosols. This may be due to highly urbanization and industrialization that may be transported over long distances before they settle down due to gravity (Deng et al., 2012). 4.7. Qualitative analysis to identify aerosol types The seasonal scatter plot of AOD550 against FMF is shown in Fig. 10 and can be used to physically interpret various aerosol types based on their size and density of aerosols in the atmospheric column for a particular geographical location. It is evident that the MX aerosol type exhibited the maximum occurrence in all seasons, except during summer. During all the seasons, the BB/UI aerosol type was more pronounced during the summer at Nanjing attributed to local anthropogenic activities (including agricultural residues burning) and meteorological conditions. Moreover, the study region is densely populated, highly urbanized, and more industrialized in the YRD significantly contribute to BB/UI aerosol type. While the DD type of aerosols was more dominated during the spring season contributed to high columnar aerosol load (low FMF). This is in agreement with the results observed in the previous sections during which most of the air mass has been transported from the north and northwest of China, attributed to the dominance of large-sized (coarse) particles. Contributions of MA and CB aerosol types were less prominent during the spring season whereas, these aerosol types contribute to columnar aerosol load in the rest of the seasons. Thus, it can be concluded that all the aerosol types contributed with varying magnitudes during different seasons. 5. Conclusions The present study provides an analysis of spatio-temporal variability and trend of columnar aerosol optical properties over Nanjing in the

YRD region, East China. To accomplish this objective, we used an extensive long-term (2002–2014) aerosol data derived from multiple satellite sensors (MODIS and MISR). We compared MODIS and MISR AOD values with those of the AERONET sunphotometer data measured at Nanjing. For a better understanding of the results, the aerosol source regions and transport pathways were identified via the HYSPLIT backtrajectories, while a preliminary qualitative assessment of the dominant aerosol types was also performed based on the relationship between Terra MODIS AOD550 and FMF. The major conclusions drawn out of the study are as follows: 1. At Nanjing, MODIS and MISR AOD retrievals have been compared and showed strong positive correlations between the two sensors, with overestimation of MODIS compared to the MISR. Further, AOD obtained from MODIS and MISR have been validated against the AERONET measurements, indicating that satellite AOD tend to underestimate at Nanjing. This has been attributed to errors in the selection of aerosol type and surface reflectance assumptions. 2. Columnar aerosol optical properties (AOD and AE) experienced an obvious seasonal pattern in Nanjing during the study period. The lowest AOD values were observed during the winter (0.61 ± 0.28) and the highest during the summer season (0.97 ± 0.48) followed by the spring (0.90 ± 0.42). In addition, maximum AE appeared in summer and minimum during the spring and winter seasons. 3. Higher AOD (high AE) in summer is attributed to aerosol hygroscopic growth, secondary aerosol formation and pollutants from agricultural biomass burning after harvest in surrounding areas, causing a build-up of pollutants in this region. In spring-time, the aerosol is affected by dust originating from the far distant continental remote locations (north and northwest of China), thus, the AOD is higher and AE is lower.

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4. The annual mean AOD showed an increasing (+0.01% year−1) and a decreasing (− 0.70% year− 1) trend for Aqua and Terra MODIS, respectively at Nanjing during the study period. Whereas, the seasonal mean Terra and Aqua MODIS AODs observed decreasing trends in all seasons, except during winter. In contrast to this, the annual AE470–660 showed significant fair increasing trends in both Terra (+ 1.45% year− 1) and Aqua (+ 0.86% year− 1). 5. The HYSPLIT model back-trajectory results showed different air masses reaching the location. Apart from the far distant and local sources (e.g., vehicular and industrial emissions, resuspended road dust, and biomass burning), the observed increase in aerosol loading may be attributed to the prevailing meteorological conditions in the study region. 6. According to the classification scheme from Terra MODIS AOD550 versus FMF values, the small-sized (i.e., BB/UI) and coarse (i.e., DD) particles were clearly dominated during summer and spring seasons, respectively over Nanjing. Apart from this, MX type aerosols were contributed more to the aerosol load in all seasons, except summer. Acknowledgments This work was supported by the Natural Science Foundation of Jiangsu Province (grant no. BK20140996), the Key Laboratory for AerosolCloud-Precipitation of China Meteorological Administration, NUIST (grant no. KDW1404), the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (grant no. XDB05030104), the National Natural Science Foundation of China (grant nos. 91544229, 41475142), and the Qing Lan Project. We gratefully acknowledge the MODIS and MISR science data support team for processing data. The authors owed thanks to the NOAA ARL for computing back trajectories using HYSPLIT model, the MODIS FIRMS for providing fire data and NCEP/NCAR for providing reanalysis data which are used in this paper. Thanks are also due to PIs of AERONET_NUIST site, in particular, Dr. Jing Wang for her help in retrieving the AOD used for validation. The authors would like to acknowledge Prof. Jose Luis Sánchez, the Editor-in-Chief of the journal and the two anonymous reviewers for their helpful comments and constructive suggestions towards the improvement of an earlier version of the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.atmosres.2016.06.008. References Alam, K., Qureshi, S., Blaschke, T., 2011. Monitoring spatio-temporal aerosol patterns over Pakistan based on MODIS, TOMS and MISR satellite data and a HYSPLIT model. Atmos. Environ. 45, 4641–4651. Barnaba, F., Gobbi, G.P., 2004. Aerosol seasonal variability over the Mediterranean region and relative impact of maritime, continental and Saharan dust particles over the basin from MODIS data in the year 2001. Atmos. Chem. Phys. 4, 2367–2391. Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, J.A., Hansen, J.E., Hofmann, D.J., 1992. Climate forcing by anthropogenic aerosols. Science 255, 423–430. Cheng, T., Chen, H., Gu, X., Yu, T., Guo, J., Guo, H., 2012. The inter-comparison of MODIS, MISR and GOCART aerosol products against AERONET data over China. J. Quant. Spectrosc. Radiat. Transf. 113, 2135–2145. Cheng, T., Xu, C., Duan, J., Wang, Y., Leng, C., Tao, J., Che, H., He, Q., et al., 2015. Seasonal variation and difference of aerosol optical properties in columnar and surface atmospheres over Shanghai. Atmos. Environ. 123, 315–326. Chu, D.A., Kaufman, Y.J., Ichoku, C., Remer, L.A., Tanre, D., Holben, B.N., 2002. Validation of MODIS aerosol optical depth retrieval over land. Geophys. Res. Lett. 29 (12,1617). http://dx.doi.org/10.1029/2001GL013205 (MOD2–1-MOD2–4). de Meij, A., Pozzer, A., Lelieveld, J., 2012. Trend analysis in aerosol optical depths and pollutant emission estimates between 2000 and 2009. Atmos. Environ. 51, 75–85. Deng, X., Shi, C., Wu, B., Chen, Z., Nie, S., He, D., Zhang, H., 2012. Analysis of aerosol characteristics and their relationships with meteorological parameters over Anhui province in China. Atmos. Res. 109–110, 52–63. Diner, D.J., Abdou, W.A., Bruegge, C.J., Conel, J.E., Crean, K.A., Gaitley, B.J., et al., 2001. MISR aerosol optical depth retrievals over southern Africa during the SAFARI-2000 dry season campaign. Geophys. Res. Lett. 28, 3127–3130.

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