Validation of OMI HCHO data and its analysis over Asia

Science of the Total Environment 490 (2014) 93–105

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Validation of OMI HCHO data and its analysis over Asia K.H. Baek a, Jae H. Kim a,⁎, Rokjin J. Park b, Kelly Chance c, Thomas P. Kurosu d a

Department of Atmospheric Science, Pusan National University, Republic of Korea School of Earth and Environmental Science, Seoul National University, Seoul, Republic of Korea c Harvard-Smithsonian Center for Astrophysics, USA d Jet Propulsion Laboratory CA Institute of Technology, USA b

H I G H L I G H T S • Corrected OMI HCHO is validated over the continental US (CONUS), and used to analyze regional sources in Northeast Asia and Southeast Asia. • The statistical approach using EOF and SVD were used to compare the spatial and temporal variability between OMI HCHO against GOME and SCIAMACHY, and against GEOS-Chem. • The corrected OMI HCHO data has realistic trends, conforms to well-known sources over United States, and has shown a stationary large concentration over polluted Asian mega-cities, and a widespread biomass burning in South Asia

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Article history: Received 2 September 2013 Received in revised form 20 April 2014 Accepted 24 April 2014 Available online xxxx Editor: Xuexi Tie Keywords: OMI HCHO EOF SVD Megacity pollution Biomass burning

a b s t r a c t OMI HCHO is validated over the continental US (CONUS), and used to analyze regional sources in Northeast Asia (NA) and Southeast Asia (SA). OMI HCHO Version 2.0 data show unrealistic trends, which prompted the production of a corrected OMI HCHO data set. EOF and SVD are utilized to compare the spatial and temporal variability between OMI HCHO against GOME and SCIAMACHY, and against GEOS-Chem. CONUS HCHO chemistry is well studied; its concentrations are greatest in the southeastern US with annual cycle maximums corresponding to the summer vegetation. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. In NA the annual cycle is super-posed by extremely large concentrations in polluted mega-cities. The other sensors generally agree with NA’s OMI HCHO regional distribution, but megacity signal is not seen in GEOS-Chem. Our study supports the findings proposed by others that the emission inventory used in GEOS-Chem significantly underestimates anthropogenic influence on HCHO emission over megacities. The persistent mega-city signal is also present in SA. In SA the spatial and temporal patterns of OMI HCHO show a maximum in the dry season. The patterns are in remarkably good agreement with fire counts, which illustrates that the variability of HCHO over SA is strongly influenced by biomass burning. The corrected OMI HCHO data has realistic trends, conforms to well-known sources over CONUS, and has shown a stationary large concentration over polluted Asian mega-cities, and a widespread biomass burning in SA. © 2014 Published by Elsevier B.V.

1. Introduction Formaldehyde (HCHO) is formed in the atmosphere as a result of the oxidation of various volatile organic compounds (VOCs), which are emitted from three main sources: combustion, biogenic activity, and biomass burning. The oxidation of methane by hydroxyl radical (OH) primarily determines a background concentration of HCHO (Palmer et al., 2006; Atkinson, 2000; Finlayson-Pitts and Pitts, 1997), which causes a number of health problems such as a respiratory disease, headache, and ⁎ Corresponding author at: Pusan National University, Dept. of Atoms. Science, Room 525-1, Pusan National University, Kumjung-gu, Jangjeondong, Busan, Republic of Korea. Tel.: +82 512 2172; fax: +82 512 1791. E-mail address: [email protected] (J.H. Kim).

http://dx.doi.org/10.1016/j.scitotenv.2014.04.108 0048-9697/© 2014 Published by Elsevier B.V.

cancer (Olsen et al., 1984; Malaka and Kodama, 1990). In addition, it is an important source of tropospheric ozone, which is a greenhouse gas and a major pollutant, and affects atmospheric chemistry by its reaction with OH. Because HCHO has a short lifetime of a few hours, its spatial distributions are significantly affected by the local VOC emissions rather than the atmospheric transport. Previous studies have used the satellite measured HCHO columns to constrain the VOC emissions, particularly for isoprene emissions from vegetation (Carslaw et al., 2000; Chance et al., 2000; Palmer et al., 2001; Abbot et al., 2003; Palmer et al., 2003, 2006, 2007; Wittrock et al., 2006; Millet et al., 2006, 2008; Fu et al., 2007; Stavrakou et al., 2009a, 2009b). There are a number of recent satellite based HCHO measurement sensors: Global Ozone Monitoring Experiment (GOME), aboard European Remote Sensing-2 (ERS-2), SCanning Imaging Absorption SpectroMeterfor

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Atmospheric CHartographY (SCIAMACHY) aboard Environment Satellite (ENVISAT), and Ozone Monitoring Instrument (OMI) aboard National Aeronautics and Space Administration's (NASA) Aura satellite. Among them, OMI has the highest spatial resolution of 13 × 24 km2 with daily global coverage. Retrieval of total HCHO column is based on non-linear least-squares fitting in the spectral window 327.5–356.5 nm, within the Ultraviolet-2 (UV-2) channel of the OMI instrument (Chance, 2002). This algorithm was originally developed for GOME (Chance et al., 2000) and later applied to the SCIAMACHY and OMI instruments. The results from the spectral fitting provide HCHO slant columns, which are eventually converted to vertical columns based on air mass factors (AMFs) (Palmer et al., 2001; Kurosu et al., 2007). The retrieval of HCHO suffers from the largest error among trace gas measurements from satellites,

which typically range from 40 to 105% (Palmer et al., 2006; Kurosu et al., 2007). Even taking into account these large errors, Kim et al. (2009) found that OMI HCHO showed unrealistic trends, and suggested the need for improved OMI HCHO data. Since then, corrected OMI HCHO was produced by the methodology discussed by Kim et al. (2011). The purpose of this study is to evaluate corrected HCHO data, and analyze the characteristic of the regional source of the HCHO based on the statistical methods of Empirical Orthogonal Function (EOF) and Singular Value Composition (SVD). These approaches suggested by Kim et al. (2008, 2011) enable us to evaluate the satellite HCHO measurements based on morphology and seasonality of HCHO emission sources and chemical process from various tropospheric constituents. Similarly, Barkley et al. (2009) used the EOF approach to analyze a

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Fig. 1. The EOF analyses of (a) GOME, (b) SCIAMACHY, (c) uncorrected OMI and (d) corrected HCHO over US. The left and right columns in the figure show the maps of spatial patterns and the coupled time series of the expansion coefficients with squared covariance fractions in mode one, respectively. The black line represents time series of the expansion coefficients and the red line represents the monthly average over entire measurement period, which is repeats. The blue dots represent the January and the black dots represent the July. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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merged GOME and SCIAMACHY data set and showed that the variation of vegetation is the major source of HCHO variability over the globe. In order to evaluate the reliability of corrected OMI HCHO we compare the coherency of the spatial and temporal variability between HCHO and various emission proxies. We begin by selecting OMI HCHO data over the CONUS, where a number of studies of VOC emission and HCHO were performed and thereby readily available for validation. Then, we use the same HCHO analyses over Northeast Asia and Southeast Asia, where HCHO related studies have been very limited. Finally, we evaluate a VOC emission inventory used in a 3-D global chemical transport model (GEOS-Chem) by comparing it with satellite measurements. 2. Material and methods 2.1. Methodology The methods of EOF and SVD analyses have been applied to HCHO data sets over CONUS, Northeast and Southeast Asia. The method of EOF analysis has been widely used in climate research to decompose a data set in terms of orthogonal basis functions, which are determined from the data (Lian and Chen, 2012; http://www.wikipedia.org). These analyses provide a statistical method for identifying coupled relationships with spatial and temporal patterns of individual parameters. The method of SVD analysis examines the coupled variables of two fields. Each pair of singular vectors describes a fraction of the square covariance (SCF) between the two variables. The leading SVD of coupled mode shows the largest SCF, and each succeeding pair describes the maximum remaining SCF that is unexplained by the previous pairs (Venegas et al., 1997). If corrected OMI HCHO is properly improved, the spatial–temporal variability of OMI HCHO should be more consistent with those of HCHO measurements from other satellites as well as various species related to HCHO. The

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The data sets used in this study are GOME, SCIAMACHY (http://www.temis.nl/airpollution/ch2o.html), OMI HCHO Version 2.0 (Collection 3) (uncorrected OMI HCHO) from the NASA Data and Information Services Center (http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/ OMI/ omhchov003.shtml), and corrected OMI HCHO. The corrected OMI HCHO columns used in this study were obtained after correcting an apparent increase of background values that likely reflects degradation of the instrument (http://www.knmi.nl/omi/research/product/ rowanomaly-background.php). The corrected HCHO data was processed by Kuruso according to the methodology in Kim et al. (2011). Additional data sets to analyze the major sources for HCHO variability are OMI NO2 (http://mirador.gsfc.nasa.gov), and regional population data (http://sedac.ciesin.columbia.edu/gpw), Along-Track Scanning Radiometer (ATSR) firecount data (http://www.atsr.rl.ac.uk/), and NDVI (http://gdata2.sci.gsfc.nasa.gov/). The data periods are from October 2004 to December 2009 for OMI HCHO and OMI NO2, from April 1996 to Dec 2001 for GOME HCHO, and from January 2003 to December 2009 for SCIAMACHY. GOME data after 2002 was excluded because of the degradation of GOME sensor (Palmer et al., 2007). ATSR fire count data was used for the period of 2004–2006. NDVI was used for the period of 2004–2006.

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species used in this analysis are OMI NO2 and regional population data as proxies of anthropogenic source, fire count data as a proxy of biomass burning, and Normalized Difference Vegetation Index (NDVI) as a proxy of biogenic source. The same approach is used to characterize the major sources affecting the HCHO variability over Northeast Asia and Southeast Asia. In order to evaluate the VOC emission inventory used in GEOS-Chem over Northeast and Southeast Asia, simulated HCHO columns are compared with the corrected OMI HCHO columns.

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All HCHO data with cloud cover greater than 40% were rejected by suggestion of Millet et al. (2006). The time period differences among various measurements are not significant because the study's objective is to learn the major regional sources of HCHO. CONUS is rich with ground based measurements as well as model analyses relative to Asia studies to evaluate satellite measurements including the corrected OMI HCHO data. The Northeast and the southeast regions of Asia were chosen for separate study because the amount of pollutants in these regions is significantly increased as a result of the rapid economic development and increased energy use, but there is scant ground measurements for analysis. The two regions of Asia have different geography and land use policies to warrant their separate study.

The GEOS-Chem global 3D Chemistry and Transport Model (CTM) (http://geos-chem.org) has been used to evaluate the VOC emission inventory by comparing simulated HCHO with the satellite observations in the U.S. and Asia (Millet et al., 2006; 2008; Fu et al., 2007), and the detailed description of the model can be found elsewhere (Bey et al., 2001; Park et al., 2006). Here we briefly discuss a few updates from the previous application of the model focusing on Asia. We use the version 8-0301 with anthropogenic emissions in Asia from the Streets et al. (2006). Because this inventory was based on 2000, we applied the scaling factor of Regional Emission Inventory of Asia by Ohara et al. (2007) to calculate 2004–2006 emissions. Biogenic isoprene emission is calculated locally in GEOS-Chem using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) inventory (Guenther et al., 2006). The emissions

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Fig. 3. The EOF analyses of (a) GOME, (b) SCIAMACHY (c) corrected OMI and (d) uncorrected OMI HCHO over Northeast Asia. The left and right columns in the figure show the maps of spatial patterns and the coupled time series of the expansion coefficients with squared covariance fractions in mode one, respectively.

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of anthropogenic VOCs and biogenic isoprene are 18.6 and 36.5 TgC y−1 over 90–150°E and 5–50°N. The GEOS-Chem is driven by Goddard Earth Observing System (GEOS-4) assimilated meteorological data from the NASA Global Modeling and Assimilation Office. The data include winds, convective mass fluxes, temperature, clouds, and precipitation at 6-h frequencies with a horizontal resolution of 1° × 1° and 55 hybrid pressure-sigma levels up to 0.01 hPa. We degraded these meteorological fields to a horizontal resolution of 2° × 2.5° and 30 vertical levels for computational expediency. We conduct model simulations for 2004–2006 with three years of spin up time. 3. Results Fig. 1 shows the EOF analyses of (a) GOME, (b) SCIAMACHY, (c) uncorrected and (d) corrected OMI HCHO over the CONUS. The first EOF modes of all data sets explain 53%, 23%, 74% and 81% of the total variance, respectively. The differences in total variance of HCHO among satellites are likely due to the difference in spatial and temporal resolutions. (OMI has better spatial resolution of 13 × 24 km2 and temporal resolution of 1-day coverage than SCIAMCHY with spatial resolution of 60 × 30 km2 and temporal resolution of 6-day coverage and GOME with spatial resolution of 40 × 320 km2 and temporal resolution of 3-day coverage.) Because the higher EOF modes explained negligible amounts of variance, this paper only focuses on the analyses of the first mode. The spatial and temporal patterns of the first EOF modes from OMI, GOME and SCIAMACHY HCHO show a strong signal in the southeastern USA with a distinctive annual cycle of maximum between June and July and minimum between December and January. These spatial

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and temporal patterns are consistent with previous studies determined from ground-based, aircraft-based measurements and modeling, which shows that isoprene is the dominant source over the southeastern USA (Guenther et al., 2006; Millet et al., 2006; 2008; Palmer et al., 2003, 2006). Detailed comparison has been performed between the corrected OMI HCHO and the simulated HCHO from GEOS-Chem model with MEGAN version 2.1 for the June–July–August (Fig. 2). The spatial distribution between the two products shows excellent agreement with maximum over Southeast US with correlation coefficient (R2) of 0.94, but the modeled HCHO columns are biased higher than the OMI. The previous studies have found lower correlations (R2 = 0.69) between GOME HCHO and simulated by GEOS-Chem (Palmer et al., 2001; Shim et al., 2005) and correlations (R2 = 0.84 and 0.92) between OMI HCHO and simulated by GEOS-Chem over North America in summer (Millet et al., 2008). Among them, the high correlation from our analysis is likely due to the improvement of corrected OMI HCHO data. The patterns of the first EOF mode for corrected OMI HCHO show consistency with those for GOME and SCIAMACHY. However, the pattern of the first EOF mode for uncorrected OMI HCHO shows a strong signal over the middle western USA with a sudden increasing trend after 2008. These features are different from the ones seen from the other three sensors and the general understanding of the region's sources and illustrate the errors with the uncorrected OMI HCHO data. The same analysis is made over Northeast Asia, where anthropogenic emissions are the largest and there are less reliable ground measurements (Streets and Waldhoff, 2000). Previous HCHO studies over this region were performed by Shim et al. (2005) and Fu et al. (2007). Shim et al. (2005) evaluated global isoprene emission obtained from Bayesian inverse modeling analysis with the GOME

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HCHO data between September 1996 and August 1997. They suggested that biogenic emission for the growing season (May–August) accounts for about half of HCHO column in that season, while biomass burning is the most significant source in spring over East Asia. Fu et al. (2007) compared the GOME HCHO observations with simulated HCHO columns from GEOS-Chem using bottom-up emission inventories to obtain Non-Methane Volatile Organic Compounds (NMVOC) emissions over Asia using top-down constraints. They concluded that summer maximum of GOME HCHO resulted from biogenic activity with some addition from agriculture burning, whereas the influence of anthropogenic activity on HCHO is marginally observed only in winter. Fig. 3 shows the first EOF modes for (a) GOME, (b) SCIAMACHY, (c) uncorrected OMI HCHO and (d) corrected OMI HCHO, which explains 57%, 37%, 74%, 68% of the total variance, respectively. The spatial and temporal patterns show remarkably good agreement between corrected OMI, GOME and SCIAMACHY HCHO; all have a strong signal over eastern China and an annual cycle with its maximum in summer (June–July) and minimum in winter (December– January). These spatial and temporal patterns are similar to the population density distribution shown in Fig. 4, which are associated with anthropogenic activity. Note the uncorrected OMI HCHO in Fig. 3c does not show a high coincidence with corrected OMI, GOME and SCIAMACHY HCHO and does not have a pronounced signal over megacities such as Beijing, Shanghai, and Seoul. The spatial pattern of the EOF analysis cannot reveal what the leading source of the HCHO emission is over Northeast Asia, but the seasonal variability of emission suggests that anthropogenic activity is most likely

responsible for causing the variability. In order to support this suggestion, we have performed SVD analysis between HCHO and NO2, for which fuel combustion is its major source. Fig. 5 shows the first mode of the SVD between OMI NO2 and OMI HCHO. This mode explains 79% of total square covariance with correlation coefficient −0.68. The spatial pattern of SVD illustrates that NO2 has strong signal correspondence to megacities in the same manner as HCHO. The temporal pattern of NO2 has a distinct annual cycle of the maximum in winter (December– January) and the minimum in summer (June–July), and well anticorrelated with that of HCHO. The distinct seasonal variation of NO2 is explained by the variation of the lifetime of NOx in the boundary layer, related variations in meteorological conditions, and possibly also by higher winter emission (Richter et al., 2005). According to its emission aspect, the seasonal maximum of NO2 contributed to higher heating fuel combustion in the winter (Lee et al., 2008). In its chemical aspect, as OH increases in the summer, NO2 is converted to nitric acid through the reaction with OH (Platt et al., 1984; Dentener and Crutzen, 1993). Both NO2 emission and OH related reaction result in NO2 minimum in summer and maximum in winter (Richter et al., 2005, Jacobs). On the other hand, seasonal variation of HCHO strongly depends on the oxidation power of VOC through its reaction with OH, which has a maximum in summer and minimum in winter seasons (Benning and Wahner, 1998; Palmer et al., 2007; Stavrakou et al., 2009a, 2009b). The anthropogenic VOC emission is greater than that of biogenic sources in the polluted mega-cities but does not vary with seasons mainly because its primary sources are industry and transportation and thus the production of HCHO is limited by the concentrations of oxidants. Therefore, it is likely that the OH-related

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chemistry may be the cause of the anti-correlated seasonality between NO2 and HCHO. The strong spatial and temporal coherence between NO2 and HCHO suggests that anthropogenic activity is the main driving variability for HCHO over Northeast Asia.

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Fig. 6. Seasonal distribution of (a) GEOS Chem HCHO, (b) NDVI, (c) corrected OMI HCHO, and (d) OMI NO2 over Northeast Asia for 2004–2006 period. The background of HCHO from GEOS-Chem is (5.3 × 1015 molecules/cm2) and HCHO from OMI is (2.6 × 101 molecules/cm2) in all seasons over the domain.

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by almost 5–10 times. In Asian countries, particularly in China, such emission inventories are unavailable or are highly uncertain (Zhang and He, 2008). Because of the absence of a reliable database in China, the vehicle emission inventory is difficult to establish with high

temporal or spatial resolution (Gue et al., 2004). Therefore, in order to evaluate the emission inventory used in GEOS-Chem over Northeast Asia, GEOS-Chem model runs were compared with corrected OMI HCHO.

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seasons over five biggest megacities: Beijing (116°E, 39°N), Tianjin (117°E, 39°N), Shanghai (121°E, 31°E) and Chongqing (106°E, 29°N) as well as Hong Kong (114°E, 22°N). Even in winter, the elevated OMI HCHO signal is observed in Beijing and Tianjin over Northern China, which is not shown in the model. The simulated seasonal pattern is in part due to the biogenic isoprene emission in the model. For example, in Shanghai, seasonal anthropogenic VOC emission is 120 GgC for each season, whereas biogenic isoprene flux varies greatly depending on seasons (3 GgC for DJF versus 162 GgC for JJA), although the annual emission of anthropogenic VOCs (480 Gg yr−1) is higher than that of biogenic isoprene (247 GgC yr−1). In addition, the model appears to be higher than the corrected OMI HCHO especially in south China for JJA and SON (Fig. 6a versus Fig. 6c). This might be due to the biogenic isoprene emissions. However, the previous study using the MEGAN

Fig. 6a shows the simulated seasonal mean of HCHO for 2004–2006 from GEOS-Chem. The model illustrates that HCHO over southern China is higher than northern China throughout the year, and the HCHO concentrations starts to increase and progressively extends to northern Asia peaking in the summer. The spatial and seasonal patterns are similar to those of NDVI shown in Fig. 5b, indicating that the biogenic activity plays a leading role in controlling the background level of HCHO over China. Biomass burning is also known as one of the main driving mechanisms in the spring over southern China (Chan et al., 2003; Duncan et al., 2003). However, the seasonal distribution of corrected OMI HCHO in Fig. 6c shows a somewhat different result from the model. The simulated HCHO shows progressive northward movement from winter to summer, whereas the OMI HCHO shows notable enhancements of all

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Fig. 7. The EOF analyses of (a) GOME, (b) SCIAMACHY and (C) corrected OMI and (d) uncorrected OMI HCHO over Southeast Asia.

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inventory showed an underestimate of HCHO in south China relative to the GOME observations (Fu et al., 2007). Therefore, a further study is needed to examine the uncertainty of the biogenic emission estimate as well as the satellite observations in that region. Although the magnitudes of the satellite measured HCHO concentrations bear large uncertainty, the observed spatial distribution is more reliable. We have compared spatial and temporal distribution of HCHO with those of NO2 by seasons. NO2 is inversely correlated with corrected OMI HCHO in each season, and shows expansion and contraction of emission centered at megacities in Fig. 5d. This high coherency in the spatial and seasonal distribution is robust evidence that anthropogenic source has strongly influenced HCHO emission over this region. It indicates that the background of HCHO from GEOS-Chem (5.3 × 1015 molecules/cm2) is about 2 times greater than HCHO from satellite (2.6 × 1015 molecules/cm2) in all seasons over the domain. Our study cannot determine which of the two values is correct, but it is clear that HCHO derived from the GEOS-Chem model underestimates anthropogenic emission relative to HCHO from OMI. Therefore, the difference in HCHO distribution between GEOS-Chem and OMI HCHO are likely due to the underestimate of anthropogenic VOC emission in the model. Fig. 7 shows the mode 1 EOF of HCHO over Southeast Asia of (a) GOME, (b) SCIAMACHY, (c) uncorrected OMI, and (d) corrected OMI HCHO. They explain 53%, 37%, 62% and 60% of the total variance, respectively. The spatial and temporal patterns of corrected OMI HCHO are consistent with GOME, SCIAMACHY, which show a persistent maximum over the northern part of Southeast Asia occurring in March–April corresponding to the dry season and a minimum in June–July corresponding to the wet season. However, uncorrected OMI HCHO shows weak HCHO spatial pattern and different temporal patterns having secondary

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peak in October relative to corrected OMI HCHO as well as a distinct increasing trend that was not observed from other satellite. In Fig. 8, the ATSR fire-count seasonal distribution shows that many fires occur in spring over the northern part of Southeast Asia due to mostly forest burning (Streets et al., 2003) and the distribution is in remarkably good agreement with seasonal HCHO variability. Because the location of the maximum variability of HCHO over the northern part of Southeast Asia corresponds to tropical forest regions with little human activity, it is not likely that the variability HCHO over this region is related with anthropogenic activities. The agreement between the fire count and satellite HCHO distribution maps suggest that the variability of HCHO is strongly influenced by biomass burning during the dry season, and is consistent with previous studies in this region (Chan et al., 2003; Duncan et al., 2003; Fu et al., 2007). Then, the question arises: Why didn't the seasonal HCHO EOF analysis in Fig. 7(d) show a HCHO signal from biogenic activity. The region is located in a broad leaf evergreen forest which normally results in elevated HCHO emission from isoprene oxidation, but has warm temperature throughout the whole year, suppressing the variability of isoprene. The growing season is relatively uniform throughout the year. Therefore the high background level of HCHO with low variability originates from isoprene (Marbach et al., 2008) but the annual variability of HCHO over Southeast Asia is due to biomass burning. Fig. 9 shows the seasonal distributions of corrected OMI and GEOSChem for the period between 2004 and 2006. The overall seasonal distribution of GEOS-Chem agrees with OMI HCHO showing elevated HCHO during biomass growing season between March and May. However, the average value of HCHO from GEOS-Chem (9.55 × 1015 molecules/cm2) is greater than from OMI (3.41 × 1015 molecules/cm2) in all seasons over the domain. The heavily polluted megacities such as Hanoi (105°E,

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21°N), Bangkok (100°E, 13°N), and Hong Kong (114°E, 20°N) show elevated HCHO from OMI, but not from the GEOS-Chem model. This difference becomes clearer when HCHO averaged over 2004–2006 for the two data sets (Fig. 10), which show elevated OMI HCHO over the megacities and the northern part of Southeast Asia. This illustrates that HCHO emissions over megacities are strong enough to be compatible magnitude

in signal strength with the biomass burning emissions. Just as we found over Northeast Asia, GEOS-Chem HCHO does not show a noticeable signal over megacities of Hanoi and Bangkok as is seen in the corrected OMI HCHO. Overall, the comparison suggests that GEOS-Chem significantly underestimates anthropogenic influence on HCHO emission over megacities.

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4. Conclusions

Acknowledgments

In order to evaluate the corrected OMI HCHO data, and analyze the characteristic of the regional sources of the HCHO, we have performed EOF and SVD over CONUS, Northeast Asia, and Southeast Asia. The EOF analysis with corrected OMI HCHO over CONUS shows a strong signal in southeastern US with a distinctive annual cycle of summertime maximum, which was not seen in the uncorrected OMI HCHO. These spatial and temporal patterns are consistent with previous studies showing that biogenic activity is the dominant source over CONUS. The EOF analysis of HCHO over Northeast Asia shows differences from that over CONUS because along with an annual cycle of maximum in summer and minimum in winter, there is a strong persistent signal over the megacities. Further SVD analysis between HCHO and NO2 clearly shows the same spatial pattern as of EOF analysis, but inversely correlated. The seasonal variation is due to HCHO production by the oxidation of VOC through reactions with OH, whereas NO2 is removed from the reaction with OH. These analyses suggest that the variability of HCHO over Northeast Asia is controlled by anthropogenic sources through its reaction with OH. However, GEOS-Chem HCHO does not show any noticeable signal over megacities. This contradiction suggests that the emission inventory used by GEOS-Chem underestimates anthropogenic VOC sources over Northeast Asia. The EOF analysis for HCHO over Southeast Asia shows that the strongest signal is observed over the northern part of Southeast Asia during the dry season (February–April), which is consistent with the seasonality of fire counts. The variability of HCHO over Southeast Asia is strongly influenced by biomass burning occurring in the dry season. In addition, the heavily polluted megacities show also elevated HCHO from OMI, but not from GEOS-Chem. This difference becomes clear in HCHO distribution averaged over 2004–2006 between OMI and GEOS-Chem data set. Overall, the comparisons suggest that GEOS-Chem significantly underestimates anthropogenic influence on HCHO emission over megacities in Asia.

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012R1A1A2040757).

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