Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008

Atmospheric Environment 45 (2011) 4777e4788

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008 Tzu-Chin Tsai a, Yung-Jyh Jeng a, D. Allen Chu b, *, Jen-Ping Chen a, Shuenn-Chin Chang c a

Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan, No. 1, Sec. 4, Roosevelt Road, Taipei City, 10617 Taiwan, ROC Goddard Earth Sciences and Technology Center, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA c Environmental Protection Administration, No. 83, Sec. 1, Jhonghua Rd., Taipei City, 10042 Taiwan, ROC b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 June 2009 Received in revised form 7 October 2009 Accepted 7 October 2009

This study used sunphotometer, lidar, and surface particulate matter measurements to assess MODIS AOD products for air quality monitoring in Taiwan. The MODIS AOD retrievals revealed a satisfactory validation against AERONET measurements with correlation coefficient w0.91 during Terra and w0.83 during Aqua overpasses in the period of 2006e2008. The correlations in cold season (SeptembereFebruary) w0.85e0.96 appear to be slightly higher than those in warm season (MarcheAugust) w0.78e0.87. The relationships derived between PM2.5 and AOD from both MODIS and AEROENT show a strong seasonality as a result of aerosol vertical distribution. The high correlations (w0.88e0.93) obtained in autumn between PM2.5 and AOD normalized by boundary layer height (or equivalent haze layer height) are attributed to stable and wellmixed boundary layers as opposed to the summer lows (w0.12e0.67) resulted from strong convection associated with unstable weather systems. With the long-range transport of Asian dust and pollution in winter and spring under prevalent northeasterly and biomass burning from Southeast Asia in spring under prevalent southwesterly flows, better correlation is derived from the normalization by haze layer height than boundary layer height owing to abundance of aerosols aloft above boundary layer. The former is shown with correlation coefficients in the range of w0.76e0.87 and w0.77e0.80 and the latter w0.56e0.79 and w0.39e0.54 for winter and spring, respectively. The results of MODIS that uphold the relationships derived from AERONET in autumn, winter, and spring suggest MODIS AOD products have the level of quality as sunphotometer measurements for monitoring local PM2.5 in Taiwan. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: MODIS Aerosol optical depth Boundary layer height Haze layer height Particulate matter Taiwan

1. Introduction Particulate matter (PM) is one of the major air pollutants observed in the past decade in Taiwan (Taiwan Environmental Protection Administration, 2001e2008). Some particles are emitted directly from both human activities and natural events, while others are formed in the atmosphere through secondary chemical transformation. In general, particles are measured by size, known as PM2.5 and PM10 for aerodynamic diameters less than 2.5 and 10 mm, respectively. Particles small enough to penetrate deep into the lungs can cause serious health problems. For management planning strategy and policy-making, Taiwan Environmental

* Corresponding author at: Laboratory for Atmospheres, NASA Goddard Space Flight Center, Code 613.2, Greenbelt, MD 20771, USA. Tel.: þ1 301 614 6237; fax: þ1 301 614 5703. E-mail addresses: [email protected] (T.-C. Tsai), yjjeng@ webmail2.as.ntu.edu.tw (Y.-J. Jeng), [email protected] (D.A. Chu), jpchen@as. ntu.edu.tw (J.-P. Chen), [email protected] (S.-C. Chang). 1352-2310/$ e see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.10.006

Protection Administration (TWEPA) began to construct its monitoring network of PM2.5 pollutant, known as Taiwan Air Qualitymonitoring Networks (TAQN) since late 2005. However, it is difficult to obtain a complete coverage over entire Taiwan with a limited number of ground stations because of high cost of a suite of instruments and facility maintenance. Spaceborne observations overcome such limitations and provide information of aerosol particles in the lower troposphere near the surface. Chu et al. (2003) successfully applied the MODIS-derived aerosol optical depth (AOD) products to correlate with PM10 under cloud-free condition in northern Italy. Engel-Cox et al. (2004) suggested good correlation existing between MODIS AOD and PM2.5 in the eastern US but apparently more problematic in the western US over bright surfaces. A NASA pilot project IDEA (Infusing satellite Data into Environment Applications) initiated in 2003 (Al-Saadi et al., 2005) has evolved into a NASA, NOAA, and USEPA joint program in 2006 to provide daily MODIS AOD map over the continental US (http://www.star.nesdis.noaa.gov/smcd/ spb/aq/).

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Many recent researches focused on examining the relationship between MODIS AOD and PM in various parts of the world with different spatial resolutions, such as in Hong Kong (1-km AOD) (Li et al., 2005a), Delhi (5-km AOD) (Kumar et al., 2007), and eastern US (5-km AOD) (Lewis et al., 2009), and modified aerosol optical properties (Li et al., 2005a). The presence of elevated aerosol layers can significantly affect the relationship. Engel-Cox et al. (2006) and He et al. (2006, 2008) pointed out that aerosol vertical profiles derived from lidar observations could improve the correlation between columnar AOD and surface measurements of PM or extinction. Thus it is important to test the assumption using collocated satellite, sunphotometer, lidar, and PM measurements. In this paper, we briefly introduce the data, including satellite and ground-based measurements, and approaches (correction of vertical height and relative humidity) used in this study in Section 2. In Section 3, we validate the collection-5 MODIS 10-km AOD products against AERONET (Aerosol Robotic Network) measurement to ensure the quality of the data applied in the Taiwan area. We also explore the relationships between MODIS AOD (Terra versus Aqua) and PM (PM2.5 and PM10) with and without relative humidity and vertical height corrections. For seasonal analysis in Section 4, we compare the results and evaluate the application of MODIS AOD on air quality in each season. Finally we summarize the results in Section 5.

2. Data and approaches 2.1. Data 2.1.1. MODIS level-2 products The MODIS collection-5 data were acquired from NASA's Goddard Earth Sciences Distributed Active Archive Center (DAAC) for the study period of 2006e2008 because of the availability of PM measurements in Taiwan. The MODIS sensors onboard the Terra and Aqua satellite overpass Taiwan at approximately 10:30 a.m. and 1:30 p.m. local solar time, respectively. AOD (also denoted as sa) is most relevant to air quality, which is a dimensionless measure of scattering and absorption of sunlight by aerosols in the total vertical column from ground to the top of atmosphere. In this study, we used AOD at 0.55 mm wavelength from the Level-2 MOD04 and MYD04 aerosol products. 2.1.2. Ground-based measurements The sunphotometer and lidar measurements are used in this study to validate the MODIS aerosol products and explore detailed information of aerosol vertical distribution, we choose TWEPAeNCU (National Central University) site (24.97 N, 121.18 E, Fig. 1) for the co-existent MPLNET (Micro-Pulse Lidar Network) and AERONET measurements at the National Central University (NCU), which is located on the hillside of Shuanglian in northern Taiwan. The TWEPAeNCU lidar is co-sponsored and maintained by TWEPA and NCU as a part of TAQN. The sunphotometer and lidar data were obtained from NASA website (http://aeronet.gsfc.nasa.gov) and (http://mplnet.gsfc.nasa. gov) for the study period of 2006e2008. Sunphotometer measures the attenuation of direct solar radiation transverse in the atmosphere, providing the direct information of total columnar AOD in 16 bands from 0.34 to 1.64 mm wavelength (Holben et al., 2003). There are three quality levels (1.0, 1.5, 2.0) of data. The level 1.0 and 1.5 data are available daily in near real-time, while the level 2.0 products require final calibration and manual inspection. Because of data availability issue of level 2.0 for the 2008 period (lagged by 9 months), level 1.5 data (cloud screened) were used in analysis. For consistency with MODIS AOD data as previously described, we

Fig. 1. The location of EPAeNCU site (24.97 N, 121.18 E) in East Asia.

derive AOD data at 0.55 mm based upon the AOD values from adjacent wavelengths. Micro-pulse lidar (Spinhirne, 1993; Spinhirne et al., 1995) developed at NASA Goddard Space Flight Center (GSFC) in the early 1990s is a compact design and eye-safe lidar system, capable of determining the range of aerosols at the wavelength of 0.523e0.527 mm. The PBL height and vertical extinction (coefficient) profile data that we used in this paper were from the version 2 level 1.5 products in correspondence to AERONET sunphotometer measurements (Campbell et al., 2008). The hourly PM2.5, PM10, and relative humidity (RH) measurements are acquired from TAQN at Pingcheng station (24.95 N, 121.20 E). Pingcheng station is located within 3 km from EPAeNCU site, equipped with Verewa F-701 BAM (b-ray Attenuation Monitor) for continuous mass concentration measurements with uncertainty of 2% (http://www.durag.com/html/ems/emsmain.html), which automatically measures the concentration of PM10 and PM2.5 with a screening device and a mass calculation system, which is commonly used in environmental monitoring as reported by Steinvil et al. (2008) and Liu et al. (2009). The intercomparison of BAM and TEOM (Tapered Element Oscillating Microbalance) measurements showed the correlation of 0.96 and standard errors of 0.02 and 0.33 for slope (w1.02) and intercept (w1.72), respectively (Schwab et al., 2006). 2.2. Approaches AOD is an integral form of aerosol extinction (scattering and absorption) with height from surface to the top of the atmosphere (TOA). Since the majority of aerosol abundance resides in the boundary layer, the thickness of boundary layer has direct impacts on the correlation between AOD and PM. Expressed below are the integral form of AOD (Eq. (1)) and an approximation of the relationship between AOD (sa) and PM (Eq. (2)).

sa;0:55 mm ¼

TOA Z

rðzÞsext 0:55 mm ðzÞdz

(1)

0

sa;0:55 mm

i PMzh f ðRHÞsext dry;0:55 mm

(2) surface

Lmix

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where sa;0:55 mm is AOD at 0.55 mm wavelength; rðzÞ is aerosol mass concentration (mg m3); sext 0:55 mm ðzÞ is aerosol extinction cross section per unit mass (m2 mg1) at 0.55 mm; f ðRHÞ is hygroscopic aerosol extinction cross section per unit growth factor; sext dry;0:55 mm mass at surface relative to dry particles at 0.55 mm; Lmix is aerosol mixing layer height (km). In this paper, we examine the effects of planetary boundary layer height and haze layer height, respectively. The former assumes homogeneously mixed aerosols in the boundary layer, and the latter takes into account aerosol layers aloft above boundary layer originated from remote sources (e.g., Asian dust outbreaks in winter and spring; springtime smoke from southeast Asia biomass burning). Secondly, we evaluate the effect of relative humidity on PM in association with AOD derived in ambient atmospheric conditions by sunphotometer and MODIS. 2.2.1. Aerosol vertical distribution Boundary layer is the lowest part of the atmosphere, directly influenced by its contact with the Earth's surface. It responds to changes in surface forcing at the time scale of an hour or less. The development of boundary layer (so-called planetary boundary layer, PBL) is related to wind velocity, temperature, and moisture. Based upon the lidar measurements at TWEPAeNCU, two types of aerosol vertical distributions are generally seen (Fig. 2). The first type assumes that aerosols are confined and mixed homogeneously within boundary layer, so the values of AOD normalized by PBL height (PBLH) could be regarded as extinction (km1) as one measured at surface. In this condition, the correlation between PM and AOD/PBLH would be high. The second type is comprised of a uniformly mixed PBL and aerosol abundance above PBL. The haze layer height is the sum of PBLH and scale height assuming extinction decreases exponentially with altitude above PBL. In other words, scale height (H) represents the height of a uniform extinction layer above PBL that aerosol extinction coefficient decreases to 1/e of that at PBLH. Mathematically, we can illustrate as follows, TOA Z

 yPBLH 0:55 mm e

zPBLH H

dz ¼ yPBLH 0:55 mm

PBLH

TOA Z

e

zPBLH H

dzzyPBLH 0:55 mm H

(3)

PBLH

1 where yPBLH 0:55 mm is extinction coefficient (km ) at PBLH at 0.55 mm wavelength. Therefore AOD can be approximated as

PBLH sa;0:55 mm zyPBLH 0:55 mm ðPBLH þ HÞ ¼ y0:55 mm HLH

(4)

where HLH is haze layer height. The presence of aerosols transported from remote source and convective dispersion of aerosols in the free atmosphere affect haze layer height. From the analysis of the EPAeNCU lidar measurements, we find that in the presence of aerosol layers aloft there can be more than

one level of extinction equal to 1/e of that at PBLH. The event of 15 October 2006 is a good example (Fig. 3) showing two possible scale heights as aerosol extinction coefficient decreases by 1/e. The first scale height (denoted as H1) is found w0.8 km and the second scale height (H2) w2.5 km above PBL, for which we define the bottom haze layer height (HLHbt ¼ PBLH þ H1) w1.4 km and top haze layer height (HLHtp ¼ PBLH þ H2) w3.1 km, respectively. In the absence of aerosol layers aloft, HLHbt would be equal to HLHtp. 2.2.2. Relative humidity correction Mass concentration of PM is measured when it is dried through a heating tube at temperature of w50  C (for preventing condensation or avoiding the interference with water vapor) while AOD derived from either MODIS or sunphotometer represents columnar aerosol abundance in ambient environments. Based upon the past 40-year measurements, the average relative humidity measured at surface is in the range of 69% and 75% in the vicinity of Taipei (Shiu et al., 2009). At Pingcheng station, the mean relative humidity is approximately 73% for the study period of 2006e2008. Relatively speaking, it is a rather moist environment in Taiwan. Scattering

b

Height

Height

a

Fig. 3. Illustration of PBLH, HLHbt and HLHtp for a long-range transport event with aerosol layers aloft above PBL on October 15, 2006. Micro-pulse lidar measurements are obtained from MPLNET website and modified by the authors.

PBL height

Extinction coefficient

Scaling height

PBL height

Haze layer height

Extinction coefficient

Fig. 2. Schematic aerosol vertical profile: (a) Type I, aerosols are well-mixed and confined in the PBL and (b) Type II, two-layer aerosol distribution characterized by aerosol wellmixed in the PBL and exponential decay of aerosol extinction coefficient with height above the top of the PBL.

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efficiency of a spherical particle is a function of size parameter 2pr/l (r: particle radii, l: wavelength) according to the Mie theory (Bohren and Huffman, 1983). For hygroscopic particles, a growth factor, fRH(Dp) (Dp: diameter of particle), needs to be considered so that aerosol scattering coefficients modeled would match the measurements as humidity increases (McInnes et al., 1998). Therefore the relationship of AOD retrieved in ambient condition and dehumidified PM at surface will be affected by relative humidity as approximated in Eq. (2), given a fixed Lmix. The uncertainties of AOD retrieval were discussed earlier as attributed to the assumptions of aerosol and surface properties. To account for the effect of RH in correlating PM with AOD, we use a simple function expressed as f(RH) ¼ 1/ (1  RH/100) (Li et al., 2005b). It should be noted however that the RH values matched with valid MODIS AOD retrievals within 1 h were in the range of 50e65% as opposed to the 2006e2008 average (w73%). Wang and Martin (2007) illustrated that particle scattering efficiency of hydroscopic aerosols would not change much with RH if RH is less than 70% below the deliquescence point of ammonium sulfate, a common ingredient of urban aerosols. Yet, because of the hysteresis effect (Chen, 1994) or even multiple hysteresis when additional electrolytes are present (Martin et al., 2004), the dependence of aerosol size on humidity may persist to drier conditions. We use the simple expression above to test the effect of RH when correlating with AOD. 3. Multi-year analysis MODIS AOD retrievals are at different space and time subject to progressive orbits while AERONET sunphotometer, PM, and MPL lidar measurements are taken at different time intervals. To take into account spatial and temporal variabilities, these data need to be collocated. We set the criteria for AERONET measurements within 1 h from MODIS morning and afternoon (Terra 10:30 a.m. and Aqua 1:30 p.m.) overpasses to allow more matches with hourly PM2.5 and PM10 measurements. However, the number of matches between MODIS, AERONET, and PM (Terra e 122; Aqua e 126) is reduced by the impairment of lidar measurements attributed to frequent occurrence of mist (or fog) causing condensation of water vapor on the observing window of the lidar systems because of hillside location of TWEPAeNCU and overhead sun that no measurements were taken from 11:00 a.m. to 1:30 p.m. local solar time in the summer to avoid damage on the lidar system. Furthermore, the derivation of PBLH using wavelet approach (Brooks, 2003) that requires a full sampling of AOD in each measurement period may also be partially responsible for reducing the number of matches.

Ic w0.02) (private communication, Lorraine Remer of NASA Goddard Space Flight Center, 2008). We validate MODIS aerosol products to provide the baseline for the correlation analysis between AOD and PM. For collocating MODIS AOD retrievals with AERONET measurements, we average the nearest four MODIS AOD values within the radius of 0.1 (w10 km) from the location of TWEPAeNCU in correspondence to the temporal mean of sunphotometer measurements within 1 h from MODIS overpasses. The use of 0.1 (w10 km) is primarily to accommodate the closely distributed PM network in the area that a total of seven PM stations (separated by 2e4 km) are situated within 25 km radius from TWEPAeNCU. Secondly, the mean wind speed was estimated w4.5 m s1 around TWEPAeNCU, which can be translated to the distance of 16.2 km over an hour. This setting is more suitable to resolve spatial variability of PM, given 10-km AOD data, than that of 25 km proposed by Ichoku et al. (2002) based upon the movement of Saharan dust plumes (w50 km h or 14 m s1) across the Atlantic Ocean at 3e5 km altitude. As to the temporal variation, correlation of 0.91 and 0.83 are derived with respect to Terra and Aqua 30 min, respectively, as opposed to 0.90 and 0.84 within 60 min of MODIS overpasses. Case studies in Hong Kong, China (1-km AOD) (Li et al., 2005a), Delhi, India (5-km) (Kumar et al., 2007, 2008), and eastern Virginia, USA (5-km AOD) (Lewis et al., 2009) revealed that AOD derived at finer resolutions outperforms MODIS standard 10-km AOD products in correlating with particulate matter mass concentration. Fig. 4(a) and (b) depict the scatter plots of collocated MODIS (10-km) and sunphotometer AOD means for Terra and Aqua, respectively. As shown, better agreement is derived from Terra (Sl w1.06, Ic w0.05, and correlation, R w0.91) as opposed to Aqua (Sl w0.80, Ic w0.19, and R w0.83) as a result of smaller intercept, less biased slope, and better correlation. In principle, the deviations of slope from 1 and intercept from 0 are attributed, respectively, to uncertainties of aerosol properties (when AOD is large) and uncertainties of surface reflectance (when AOD is small) if the measurements have the same calibration (Chu et al., 2002). Given different calibration from Terra and Aqua MODIS (Xiong et al., 2007, 2009), it would not be surprised to see large differences in slope and intercept when using the same aerosol retrieval algorithm owing to different degradation of solar diffuser and scan mirror in the visible as well as electronic talk and thermal leak in the shortwave infrared bands (Xiong et al., 2004, 2007, 2009). The former (visible reflectance) would affect the aerosol properties

3.1. Validation of MODIS AOD retrievals The distribution of monitoring stations and good calibration makes AERONET measurements (Holben et al., 2003) the most effective tool for validating MODIS aerosol retrievals (Chu et al., 2002; Remer et al., 2005; Levy et al., 2007). However, these kinds of measurements are still scarce in most parts of Asia as previous validation indicated (Chu et al., 2002; Remer et al., 2005). Thus general validation with estimated retrieval errors of Dsa ¼  0.05  0.15sa from globally binned data (Levy et al., 2007) may not apply to the local (hot and humid) environments in Taiwan. We use the retrieval error of Dsa ¼  0.05  0.2sa to account for much scattered AOD retrievals as opposed to those obtained from globally binned dataset (Levy et al., 2007). It is not uncommon to see larger retrieval errors from regional analyses with collection 5 data, such as in the Yangtze River Delta, China (slope, Sl w0.75 and intercept, Ic w0.12) (private communication, Chengcai Li of Peking University, 2009) and in Alberta, Canada (slope, Sl w1.2 and intercept,

Fig. 4. Comparison of MODIS- and AERONET-derived sa at 0.55 mm wavelength at TWEPAeNCU: (a) Terra (N ¼ 77), and (b) Aqua (N ¼ 80). Solid lines represent the slopes of linear regression and the dotted lines the retrieval errors of Dsa ¼  0.05  0.2sa. N is data number.

T.-C. Tsai et al. / Atmospheric Environment 45 (2011) 4777e4788

while the latter (shortwave infrared, or so-called near infrared reflectance) affect surface reflectance estimate in MODIS AOD retrieval. Though the overestimation of AOD might also be due to standing water as discussed by Chu et al. (2002) by lowering surface reflectance, however these events are most likely seasonal, such as in the summer caused by frequent thunderstorms because of stronger solar heating. The consistency of larger intercepts resulted from Aqua as opposed to Terra in each season throughout the year (Section 4) indicates that calibration is most likely the cause for the differences of linear regression. 3.2. Correlation between AOD and PM MODIS AOD data are correlated with PM2.5 and PM10 mass concentration to assess its applicability to air quality monitoring, for which MODIS AOD and PM data are averaged as previously stated in validation (i.e., within 0.05 distance from TAQN station and 1 h time interval from MODIS overpasses). Fig. 5 depicts the scatter plots between MODIS AOD and PM2.5 at Pingcheng from 2006 to 2008 for Terra and Aqua, respectively. Aqua is clearly shown with less promising results (R w0.44) compared to Terra (R w0.65), some of which may be linked to the large intercept as seen in the validation. On the other hand, however, even for Terra with nearly perfect validation (R w0.91), the correlation with PM2.5 is only considered marginal. Some critical factors are evident to be taken into account in order to improve the correlation. To test the effects of vertical distribution, we first normalize MODIS AOD by PBLH obtained from TWEPAeNCU lidar measurements. TWEPAeNCU site is located about 3 km distance from TAQN Pingcheng station so spatial variability is minimized. Fig. 6 upper panels depict the correlation between PM2.5 and AOD/PBLH. Only slight improvements are seen with the normalization, indicating that the assumption of well-mixed PBL is not totally valid. Apparently, the inclusion of RH correction does not produce better correlations (Fig. 6 lower panels). The approach of normalizing AOD by haze layer heights, especially HLHtp, results in significantly better correlations for Terra (R w0.81) and Aqua (R w0.78), respectively. Fig. 7 depicts the contrast of change in correlation with respect to haze layer heights of HLHbt and HLHtp relative to PBLH in Fig. 6. Thus it is reasonable to believe that there are a dominant number of days with elevated aerosol layers above boundary layer. The details will be illustrated in seasonal variability (Section 4). As a total, nearly 30% of lidar data are shown with elevated aerosol layers above boundary layer.

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Table 1 summarizes the correlation coefficients obtained from possible combinations between PM (both PM2.5 and PM10) and MODIS AOD with and without RH and vertical height (PBLH, HLHbt, HLHtp) adjustments for Terra and Aqua. The results of the threeyear data indicate that PM2.5 and PM10 have similar correlation with AOD in association with factors of RH and vertical heights. Therefore we focus on PM2.5 in analysis of seasonal variability. 4. Seasonal variability The weather in Taiwan is influenced mostly by the East-Asia monsoon systems with prevailing northeasterly flows in midautumn extending to mid-spring, and southwesterlies accompanied by tropical storms and typhoons in the summer. It is usually dry and stable under high-pressure system and wet and unstable associated with the convective systems of thunderstorms and typhoons. Stationary fronts can cause precipitation to last as long as a month, so-called the Mei-Yu, during the transition from spring to summer. Under prevalent northeasterly monsoon, long-range transported pollutants (including dust, air pollution, smoke) from northeastern Asia affect air quality over the most northern parts of Taiwan from mid-autumn to mid-spring, whereas southwesterly flows carry smoke from Southeast Asia and pollution from southern China (Pearl Rive Delta) over Taiwan (Lin et al., 2005; Chen et al., 2009). Aerosols originated from different seasonal sources transported over Taiwan (except summer) makes vertical distribution an important factor for air quality monitoring in Taiwan. Fig. 8 illustrates the mean aerosol extinction profiles observed with and without elevated aerosol layers in spring, summer, autumn, and winter. The presence of elevated aerosol layers is sorted when HLHtp is not equal to HLHbt as illustrated in Fig. 3. The large standard deviations (100% of the man) of extinction in spring, autumn, and winter reflect to a wide range of aerosol abundance aloft above PBL in association with long-range transport, whereas it is not the case in summer. The less variant mean vertical profiles obtained in summer with or without elevated aerosol layers indicate that long-range transport is not a factor when thermal convection subject to local solar heating is dominated. Table 2 depicts the seasonal mean and standard deviation values of PBLH, HLHbt, and HLHtp in corresponding to seasonal mean profiles. Clearly, spring is shown with the highest mean HLHtp (w2.89 km) accompanied by the largest standard deviation (w0.84 km) followed by winter (w2.83 km and 0.56 km, respectively) attributed to prevailing northeasterly monsoons. While autumn is ranked the third with the HLHtp altitude (2.73 km) with

Fig. 5. Correlation between MODIS AOD and PM2.5 at TAQN Pingcheng station from 2006 to 2008: (a) Terra (N ¼ 77) and (b) Aqua (N ¼ 80). Solid lines represent the slopes of linear regression and N is data number.

Fig. 6. Correlation between MODIS AOD/PBLH and PM2.5 at TAQN Pingcheng station from 2006 to 2008: Panels (a) and (b) showing without RH correction and panels (c) and (d) showing with RH correction for Terra and Aqua, respectively. Solid lines represent the slopes of linear regression.

Fig. 7. Correlation between MODIS AOD normalized by HLHbt (panels (a) and (b)) and HLHtp (panels (c) and (d)) and PM2.5 with RH correction at TAQN Pingcheng station from 2006 to 2008 for Terra and Aqua, respectively. Solid lines represent the slopes of linear regression.

T.-C. Tsai et al. / Atmospheric Environment 45 (2011) 4777e4788 Table 1 The correlation coefficients of PM2.5, PM10 and MODIS AOD separately with or without f(RH) and each vertical height corrections.

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Table 2 The mean (with standard deviation) vertical heights (km) of PBLH, HLHbt, and HLHtp, sorted with or without the presence of elevated aerosol layer in each season.

Correlation coefficient

PM2.5

PM2.5  f(RH)

PM10

PM10  f(RH)

Season

Haze aerosol

MODIS Terra (N ¼ 77)

AOD AOD/PBLH AOD/HLHbt AOD/HLHtp

0.65 0.68 0.71 0.76

0.61 0.67 0.77 0.81

0.47 0.50 0.53 0.61

0.53 0.60 0.71 0.76

Spring

AOD AOD/PBLH AOD/HLHbt AOD/HLHtp

0.44 0.53 0.62 0.75

0.38 0.50 0.66 0.78

0.41 0.55 0.56 0.70

0.38 0.56 0.68 0.81

With Without With Without With Without With Without

19 32 11 18 10 24 4 39

With Without

44 113

MODIS Aqua (N ¼ 80)

Summer Autumn Winter All

smallest standard deviation (w0.37 km) indicates stratification of aerosol layers subject to stable atmospheric conditions. The scatter plots in Fig. 9(a) and (b) show the validation of AOD derived from Terra and Aqua for each season over the period from 2006 to 2008. Spring and summer are shown with slopes greater than 1 and autumn and winter are shown with slopes less than 1. The former is most likely caused by overestimated and the latter by underestimated aerosol properties (e.g., single scattering albedo) (Zhang et al., 2001; Chu et al., 2002). The intercepts derived from Aqua are consistently larger than those from Terra in four seasons that sensor calibration in the shortwave infrared spectral band of 2.1 mm between Terra and Aqua most likely caused such differences as previously stated in Section 3. Overall, the validation is considered marginal. Further investigations are needed to improve aerosol model properties and surface estimates for AOD retrievals in Taiwan area. Nevertheless, despite the biases derived from linear regression, correlation coefficients are derived better than 0.9 in autumn (0.94e0.96) and 0.85 in winter (0.85e0.95), respectively, followed by summer (R w0.81e0.83), and spring (R w0.78e0.87). An important aspect of examining the relationship between AOD and PM2.5 is to see whether the biases affect the correlation. Table 3 summaries the correlation coefficients calculated for each season between PM2.5 and MODIS AOD with and without corrections of RH and vertical heights (i.e., PBLH, HLHbt, and HLHtp). It is evident that better correlations are derived during morning hours with an exception in summer season. Fig. 10 depicts scatter plots of PM2.5 vs. MODIS AOD corresponding to the correlation coefficients

N

PBLH (km) 0.93 0.95 0.98 1.19 0.97 0.93 1.02 1.03

       

0.22 0.33 0.36 0.42 0.24 0.24 0.23 0.53

0.96  0.26 1.01  0.41

HLHbt (km) 1.42 1.79 1.25 1.71 1.28 1.33 1.55 1.39

       

0.39 0.85 0.39 0.73 0.17 0.37 0.40 0.60

1.36  0.35 1.54  0.69

HLHtp (km) 2.89 1.79 2.55 1.71 2.73 1.33 2.83 1.39

       

0.84 0.85 0.99 0.73 0.37 0.37 0.56 0.60

2.76  0.77 1.54  0.69

listed in Table 3. Standing water (resulted from late afternoon or evening hour thunderstorms with precipitation >20 mm) is the most likely cause for the two outliers of AOD (w0.6) in the summer season. We would like to emphasize that though the number of seasonal events is limited (as previously stated) the large standard deviation (100%) of vertical extinction profile is sufficient to represent the variabilities in spring, autumn, and winter attributed to the prevalent northeasterly monsoon, and that the mean HLHtp and large standard deviation in altitude in winter (2.83 and 0.56 km, respectively) and spring (2.89 and 0.84 km, respectively) is evident for the long-range transport events. The change in slope towards diagonal is clearly seen with improved correlation. Since autumn and winter have the best correlations possibly obtained from both Terra and Aqua, it is worth noting that the slopes are aligned diagonally over the same ranges of PM2.5*f(RH) and AODMODIS/HLHtp. Fig. 11 shows the scatter plots of PM2.5 vs. AERONET AOD for a direct comparison with those shown in Fig. 10. The results of agreement in correlation and slope between Figs. 10 and 11 validate the use of MODIS AOD for PM2.5 monitoring in autumn, winter, and spring seasons. Fig. 12 summaries the linear regressions between PM2.5 and AOD that MODIS upholds the relationships obtained by AERONET for spring, autumn, and winter seasons for both Terra and Aqua. In Fig. 13, we plot the results of PM2.5 and MODIS AOD over the range of 1s and 2s (standard deviation) of linear regressions obtained

Fig. 8. Mean aerosol vertical extinction profile sorted by the presence with and without elevated aerosol layers in spring, summer, autumn, and winter seasons. The error bar is shown as the standard deviation.

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Fig. 9. Comparison of MODIS and AERONET-derived sa at 0.55 mm wavelength each season (a) Terra and (b) Aqua. Solid lines represent the slopes of linear regression and the dot lines the retrieval errors of Dsa ¼  0.05  0.2sa.

from PM2.5 and AERONET AOD of autumn and winter in an attempt to standardize the quality of MODIS AOD retrievals since autumn and winter have the best correlations possibly obtained from both Terra and Aqua as discussed earlier. As shown, more than 85%, 90%, and 95% of AODMODIS data fall within 1s of the linear regressions (p-values of 95% confidence interval w0.062 and 1.42E-30 and 1.40E-08 and 1.28E-21 with respect to Terra and Aqua versus AERONET; the former is corresponding to intercept and the latter to the slope) shown in Eqs. (5) and (6) including MODIS morning and afternoon overpasses,

Y1 ¼ 158X þ 19; Y2 ¼ 252X þ 12; Y3 ¼ 262X þ 19

for AM (5)

Y1 ¼ 103X þ 41; Y2 ¼ 195X þ 29; Y3 ¼ 231X þ 27

for PM (6)

Table 3 The correlation coefficients of PM2.5 with f(RH) correction against MODIS AOD (Terra and Aqua) with different vertical height corrections each season. Correlation coefficient (R)

Spring (MAM)

Summer (JJA)

Autumn (SON)

Winter (JFD)

MODIS Terra

Data numbers (N) PM2.5  AOD PM2.5*f(RH)  AOD/PBLH PM2.5*f(RH)  AOD/HLHbt PM2.5*f(RH)  AOD/HLHtp

24 0.56 0.55 0.79 0.80

13 0.04 0.17 0.12 0.12

17 0.84 0.93 0.94 0.93

23 0.72 0.79 0.80 0.83

MODIS Aqua

Data numbers (N) PM2.5  AOD PM2.5*f(RH)  AOD/PBLH PM2.5*f(RH)  AOD/HLHbt PM2.5*f(RH)  AOD/HLHtp

27 0.43 0.39 0.56 0.77

16 0.79 0.65 0.52 0.67

17 0.85 0.88 0.89 0.88

20 0.39 0.56 0.76 0.77

where X is equal to PM2.5*f(RH) and Y1, Y2, and Y3 are equal to AODAERONET/PBLH, AODAERONET/HLHbt, and AODAERONET/HLHtp, respectively. It is clear that haze layer height of HLHtp would be best used to correlate AOD with PM2.5 for conditions with abundance of aerosols, including elevated aerosol layers, above boundary layer.

5. Conclusions The coincident measurements of sunphotometer, lidar, and PM at the research and operational site TWEPAeNCU of TAQN provide the baseline to assess the MODIS-derived AOD products for air quality monitoring in Taiwan. The statistics of the three-year study period from 2006 to 2008 are unique to evaluate the relationship between AOD and PM in the vicinity of the capital city, Taipei, of Taiwan and surrounding urban and industrial regions since the development of TAQN in late 2005. It is evident that aerosol vertical height is critical to the relationship between PM2.5 and AOD. The strong seasonality of correlation reflects to high correlations (w0.88e0.93) obtained in autumn by normalizing AOD with boundary layer height (or equivalent haze layer height) as attributed to a stable and wellmixed boundary layer compared to the summer low values (<0.67) resulted from strong convective mixing associated with unstable weather systems. As frequently influenced by long-range transport of Asian dust and pollution in winter and spring under prevalent northeasterly and biomass burning from Southeast Asia in spring under southwesterly monsoon flows, respectively, haze layer height has shown larger impacts on correlation than boundary layer height owing to abundance of aerosols above the boundary layer, for which the correlations are found between 0.77 and 0.83.

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Fig. 10. The linear regression correlation of MODIS AOD and PM2.5 by each season. The comparisons are shown horizontally from left to right for PM2.5 vs. AOD, PM2.5*f(RH) vs. AOD/ PBLH, PM2.5*f(RH) vs. AOD/HLHbt, and PM2.5*f(RH) vs. AOD/HLHtp and vertically from top to bottom for spring, summer, autumn, and winter, respectively. Triangles indicate Terra data and circles indicate Aqua data. Solid and dashed lines represent the linear regression of Terra and Aqua MODIS AOD, respectively.

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Fig. 11. The linear regression correlation of AERONET AOD and PM2.5 by each season. The comparisons are shown horizontally from left to right for PM2.5 vs. AOD, PM2.5*f(RH) vs. AOD/PBLH, PM2.5*f(RH) vs. AOD/HLHbt, and PM2.5*f(RH) vs. AOD/HLHtp and vertically from top to bottom for spring, summer, autumn, and winter, respectively. Triangles indicate Terra data and circles indicate Aqua data. Solid and dashed lines represent the linear regressions of AM and PM of sunphotometer AOD corresponding to Terra and Aqua overpasses, respectively.

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Fig. 12. The scatter plot of (a) MODIS AOD, (b) AERONET AOD corresponding to the corrections of vertical heights for Terra, AM (closed) and Aqua, PM (open) including autumn (triangle), winter (square), and spring (circle). The solid line and long-dashed line separately represent the linear regression of Terra, AM and Aqua, PM for all data.

The results of this analysis are consistent with earlier publications by He et al. (2008) who applied haze layer height correction to sunphotometer AOD values in order to derive better correlation with surface extinction measurements in Hong Kong as both are affected by the same seasonal long-range transport events. MODIS AOD products uphold the relationships as derived by AERONET for spring, autumn, and winter seasons, which suggest MODIS AOD

products have the level of quality as sunphotometer measurements (except summer) for use in monitoring local PM2.5 in Taiwan. Future work of near real-time tracking aerosols emitted from remote source region and analysis of chemical composition together with model simulation of entrainments would enable us to better understand the processes involved in the relationship between PM and AOD for policy making and management.

Fig. 13. The scatter plot of AODMODIS (closed spots for Terra and open ones for Aqua) corresponding to the vertical height corrections and PM2.5*f(RH), including autumn (triangle), winter (square), and spring (circle). The thick-solid (for AM) and long-dashed (for PM) lines represent the linear regression results of AODAERONET as Fig. 12 panel (b) and the thin and dotted lines separately represent the linear regression with 1 and 2s (standard deviation).

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