Observations of tropospheric NO2 using ground based MAX-DOAS and OMI measurements during the Shanghai World Expo 2010

Atmospheric Environment 119 (2015) 45e58

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

Observations of tropospheric NO2 using ground based MAX-DOAS and OMI measurements during the Shanghai World Expo 2010 K.L. Chan a, b, c, A. Hartl b, d, Y.F. Lam b, d, P.H. Xie a, *, W.Q. Liu a, H.M. Cheung b, J. Lampel e, € hler f, A. Li a, J. Xu a, H.J. Zhou a, Z. Ning b, d, M.O. Wenig c D. Po a

Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, Anhui, China School of Energy and Environment, City University of Hong Kong, Hong Kong Meteorological Institute, Ludwig Maximilian University of Munich, Munich, Germany d Guy Carpenter Asia-Pacific Climate Impact Centre, City University of Hong Kong, Hong Kong e Max-Planck Institute for Chemistry, Mainz, Germany f Institute for Environmental Physics, University of Heidelberg, Heidelberg, Germany b c

h i g h l i g h t s
We examined the reduction of NO2 during Expo 2010.
Ground based MAX-DOAS, OMI satellite and NCEP FNL meteorological data are used.
The NO2 reduction during the Expo ranged from 7.5% to 14.5%.
The NO2 reduction was mainly achieved by reduction of transportation emissions.
The emission control measures were effective only within a relatively small area.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 March 2015 Received in revised form 12 August 2015 Accepted 14 August 2015 Available online 17 August 2015

During the Shanghai World Expo 2010 ground based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of tropospheric nitrogen dioxide (NO2) were performed to investigate the effects of emission control measures during that time. In this study we measured NO2 using four identical MAX-DOAS instruments in Shanghai from April 2009 to November 2010. We combined our MAX-DOAS data, the Ozone Monitoring Instrument (OMI) satellite observations and meteorological information from the National Centers for Environmental Prediction final reanalysis data (NCEP FNL) in order to investigate the spatial distribution of NO2 over Shanghai and the effects of emission control measures during the Expo. In general, the comparison of cloud screened MAX-DOAS data and OMI observations are in good correlation (Pearson correlation coefficient between 0.67 and 0.93 for the four measurement stations). In addition, we compared the MAX-DOAS and OMI NO2 data from the Shanghai Expo in 2010 to the same time of the year in 2009. The results show that the NO2 columns were reduced up to ~ 30% in the area of central Shanghai during the Expo but no significant reduction of NO2 levels was found in the nearby industrial area. The overall NO2 reduction from May, July and September 2010 ranged from 7.5% to 14.5%, which is comparable to observations in previous studies. Our results revealed that the NO2 reduction was mainly achieved by emission control policies on transportation sources in the city rather than the controls from nearby provinces. © 2015 Elsevier Ltd. All rights reserved.

Keywords: MAX-DOAS NO2 OMI satellite Shanghai Expo

1. Introduction Nitrogen dioxide (NO2) is one of the most important trace gases

* Corresponding author. E-mail address: [email protected] (P.H. Xie). http://dx.doi.org/10.1016/j.atmosenv.2015.08.041 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

in the atmosphere playing a key role in both tropospheric and stratospheric chemistry. It participates in the catalytic formation of ozone (O3) in the troposphere, while being a catalyst for O3 destruction in the stratosphere (Crutzen, 1970). Moreover, NO2 contributes to the formation of secondary aerosols (Jang and Kamens, 2001) and acid rain. It also contributes to radiative

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warming of the atmosphere (Solomon et al., 1999). In addition, NO2 in high concentration is known to be harmful to humans health. Industrial emissions, power generation and traffic emissions are the major sources of nitrogen oxides (NOx), defined as the sum of nitric oxide (NO) and nitrogen dioxide (NO2), in China, contributing almost 90% to the total NOx emissions (Zhao et al., 2013). Recent studies show that the NOx emission over China more than doubled in the past 20 years (Zhang et al., 2007; van der A et al., 2008; Zhao et al., 2013). The increasing trend is more significant over densely populated areas such as Shanghai. NOx emissions in China are expected to increase further in the future due to economic growth and higher power consumption. Therefore, monitoring NO2 is an important tool for pollution management and control. Multi-Axis Differential Optical Absorption Spectroscopy (MAXDOAS) is a passive remote sensing technique for tropospheric aerosol extinctions and trace gas concentration measurements (Platt and Stutz, 2008). Information of aerosol and trace gases (i.e., NO2) can be obtained by applying the DOAS method to the observation of scattered sun-light in different viewing directions. In the past decade, ground based MAX-DOAS has been widely used for €nninger and atmospheric aerosol and trace gas measurements (Ho €nninger et al., 2004; Frieb et al., 2006; Irie et al., Platt, 2002; Ho mer et al., 2010; Li et al., 2013). The 2008a; Li et al., 2010; Cle MAX-DOAS experimental setup is rather simple and inexpensive. In addition, MAX-DOAS measurements have been used in previous studies to validate satellite observations (Irie et al., 2008b; Kramer et al., 2008; Ma et al., 2013). Satellite measurements provide indispensable spatial information on the distribution of atmospheric trace gases column densities (Burrows et al., 1999b; Bovensmann et al., 1999; Callies et al., 2000; Levelt et al., 2006). The column densities of NO2 are retrieved by applying the differential optical absorption spectroscopy technique to satellite measured ultraviolet and visible (UV-VIS) spectra (Boersma et al., 2004; Bucsela et al., 2006; Boersma et al., 2007, 2011). Vertical column densities of NO2 derived from satellite observations have been used to study the emissions and the dynamics of trace gases from both natural and anthropogenic sources (Beirle et al., 2003, 2004; Wenig et al., 2003; Richter et al., 2005; Zhang et al., 2007; van der A et al., 2008), as well as to determine the effects of emission control (Mijling et al., 2009; Witte et al., 2009; Wu et al., 2013). However, the temporal resolution of satellite observations are usually limited to one measurement per day in the tropic and increase towards higher latitudes. Both MAX-DOAS and satellites produce comparable datasets on the vertical column densities (VCDs), the former for the lower troposphere, the latter ideally for the whole part of the atmosphere. Therefore, it is useful to compare and combine these datasets to investigate distributions and variations of NO2. Shanghai, one of the four municipalities in China with the highest population of over 24 million, experiences serious air pollution problems due to heavy industrial activities. Shanghai and its surrounding cities in the Yantze River Delta (i.e., Jiangsu, Suzhou and Ningbo) have been ranked the top economical development area in China in terms of its Gross Domestic Products (GDPs) (National Bureau of Statistics of China (2014)). Heavy industrial activities and growing vehicle population have continued worsening air quality in their surrounding areas. Recent emission inventory from 2010 shows that NOx emission from industrial and power generation accounts for more than 66% of overall emission in the Yantze River Delta (YRD) region, while Shanghai alone has contributed nearly 19% (Huang et al., 2011). In mobile sources, Shanghai has the highest vehicle stocks and their value is as much as 70% of overall Jiangsu province and is equivalent to about 35% of overall NOx emission in the region. The transportation sector alone contributes about 30% (89.5 Gg/year) of overall anthropogenic

emissions and its contribution is expected to increase in the near future due to the emission reduction of industrial/power generation sectors from the National 12th Five-Year Plan for Environmental Protection (Li et al., 2011; Hao et al., 2011; Ministry of Environmental Protection of the People's Republic of China (2012)). Currently, Shanghai has the strictest standards of emission for factories, power plants, and cars in the YRD (Ping, 2009). The Expo 2010 Shanghai China was held in central Shanghai on both banks of the Huangpu River from 1st of May to 31st of October 2010. The Expo 2010 Shanghai China was a World Expo in the tradition of international fairs and expositions. In total 256 countries, regions and international organizations participated in the Expo, attracting over 73 million visitors from all over the world. On the one hand, one might expect an increase of traffic emissions from this large number of visitors, on the other hand, a number of emission control measure were introduced by the government to improve the air quality during the event. The regional emission control measures included temporarily reducing the productivity or shutting down heavy-polluting companies such as coal-fire power plants, iron, steel, chemical and construction industries within the YRD. The enforcement of some of these emission restrictions was applied only on those days when the next-day's air quality forecast in Shanghai area was nearly exceeding the National Ambient Air Quality Standard in China (Ping, 2009). This approach was quite different from the one used during the 2008 Beijing Olympic Games where all factories were forced to shut down for the entire period of the games. In order to control area source emissions (mainly PM2.5 and CO) from random agricultural and construction activities, waste straws burning and emission of construction dust were strictly banned or controlled in the YRD (Shanghai Environmental Monitoring Center, personal communication, 10th of April 2014). For local control measures, the government restricted the number of vehicles in the Expo area and the area close by, only vehicles with permission were allowed to drive in the area. Public transport transit hubs were set up at the border of the city during the Expo to encourage visitors using public transport to visit the Expo and reduce the number of vehicles in the city (Bureau of Shanghai World Expo Coordination (2010)). At the same time the capacity of zero-emissions public transport system such as electric buses and rapid transit rail system was increased. A number of measurement instruments were used at various locations in Shanghai to study the effect of these emission control measures. We here report on the measurements of NO2 with four identical MAX-DOAS instruments, developed by the Anhui Institute of Optics and Fine Mechanics (AIOFM), Chinese Academy of Science, from April 2009 to November 2010 covering a period prior to and during the Expo. The MAX-DOAS instruments were located at the north, northwest, southwest and southeast side of Shanghai. Three of them were installed in relatively polluted regions, another instrument was set up in a clean area. In the following, a detailed description of the MAX-DOAS instrument setup and NO2 data retrieval are presented. The MAX-DOAS measurement results are compared to the Ozone Monitoring Instrument (OMI) satellite observations along with meteorological data analysis. The MAX-DOAS and OMI datasets are used to study the distribution and variation of NO2 during the Expo 2010 Shanghai in order to investigate the influence of this large event on the NO2 pollution level and to study the effectiveness of the emission control measures. 2. The MAX-DOAS measurements 2.1. Instrument setup The MAX-DOAS instrument for measuring scattered sun-light consists of a telescope with a prism reflector and stepping motor

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and a spectrometer. Scattered sun-light collected by the telescope is redirected by the prism reflector and the quartz fiber to the spectrometer for spectral analysis. The field of view of the instrument is less than 1. An Ocean Optic HR2000 þ spectrometer with Sony ILX511-CCD detector was used for measurement at the wavelength range from 290 nm to 407 nm with spectral resolution of 0.6 nm full width half maximum (FWHM). A sketch of the MAX-DOAS instrument setup is shown in Fig. 1. The MAX-DOAS is controlled by a computer and operated automatically during measurement. A measurement cycle first starts with measuring the scattered sunlight spectra at elevation of 5 , 10 , 15 , 20 and the zenith, each with 100 scans. The exposure time of each measurement is automatically adjusted depending on the intensity of the received scattered sunlight in order to achieve similar intensities for measurement at all elevations. The offset and dark current spectrum is taken by blocking the incoming light by a shutter after taking the scattered sunlight spectra at each elevations. The offset and dark current spectrum is then automatically subtracted from the measured scattered sunlight spectra. A full measurement sequence takes about 5e15 min depending on the scattered sunlight intensity.

In this study, four identical MAX-DOAS systems were set up in Shanghai. The field measurements were performed from April 2009 to November 2010 covering a period prior to and during the Expo. Three of the MAX-DOAS were installed in relatively polluted area: BaoShan, an industrial and residential mixed-use area located ~ 17 km north of central Shanghai, JiaDing, close to an industrial area located ~ 28 km northwest of central Shanghai and SongJiang, a mixture of residential and commercial area located ~ 35 km southwest of central Shanghai. The forth MAX-DOAS instrument was installed in a relatively clean area in NanHui, ~ 35 km southeast of central Shanghai, about 10 km west from the coast and about 5 km away from the Shanghai PuDong International Airport, acting as a reference monitoring station. Some of the instruments were not measuring the entire time period due to instrumental and scheduling reasons. Details of the MAX-DOAS monitoring stations and their measurement periods are shown in Table 1. The location of all four measurement sites are marked in Fig. 2 and overlaid with the average OMI tropospheric NO2 VCD from April 2009 to November 2010 to illustrate the spatial distribution of tropospheric NO2 over Shanghai. In this study, all MAX-DOAS instruments were pointing to the north.

2.2. Measurement sites

2.3. The DOAS retrieval

All the instruments were calibrated before taking measurement. Prior to the field measurements, we compared the O4 differential slant column densities (DSCDs) measured by each instrument with each other to ensure the consistency of the instruments. The comparison study was conducted on 11th March 2009 on the roof of the Institute of Optics and Fine Mechanics building, Anhui, Hefei. All four MAX-DOAS instruments were placed side by side next to each other, pointing to the north, measuring the scattered sunlight spectra at elevation of 5 and the zenith. The retrieval of the O4 DSCDs is described in section 2.3. The time series of the O4 DSCDs for all four instruments is shown in Fig. 3. The O4 DSCD measurements show excellent consistency, measurements from all instrument agree with each other with in the measurement errors which indicates there is no systematic biases between the instruments.

The Differential Optical Absorption Spectroscopy (DOAS) (Platt et al., 1979; Platt and Stutz, 2008) is used to retrieve NO2 differential slant column densities (DSCDs) from the measured scattered sun-light spectra. The spectral fit is applied for wavelength ranging from 350.2 nm to 386.6 nm which includes several strong NO2 and two O4 absorption bands. The scattered sunlight spectrum at each elevation is divided by the corresponding zenith reference spectrum and taking the logarithm to convert to optical density. The broad band structures caused by Rayleigh and Mie scattering are removed by including a fifth order polynomial in the DOAS fit. Four trace gas absorption cross sections, NO2 (Vandaele et al., 1998), O4 (Hermans et al., 1999), O3 (Voigt et al., 2001), formaldehyde (HCHO) (Meller and Moortgat, 2000) as well as the Ring spectrum are fitted to measured optical densities by a non-linear Levenberg-Marquard fit with b-spline interpolation. An example of the DOAS retrieval from a spectrum is shown in Fig. 4. Slight shift and squeeze of the wavelength is allowed in the fitting process in order to compensate small uncertainties caused by the instability of the spectrograph. The wavelength calibration is obtained from the measured sunlight spectrum by fitting to the solar atlas (Chance and Kurucz, 2010) using a robust fitting algorithm (Fischler and Bolles, 1981). By comparing to calibration mercury spectra, the uncertainty of this wavelength calibration method was found to be less than 0.1 nm. The instrument function is retrieved by using an inverse modeling method. The instrument function is assumed to be an asymmetric Gauss function. The parameters of the instrument function is then obtained by fitting the measured sun-light spectrum to a high resolution sun spectrum. The retrieved instrument function is used to convolve the literature reference cross section to the instrument resolution. The retrieval uses the zenith spectrum as the reference spectrum. Therefore, results of the DOAS analysis are the differential slant column densities (DSCDs) of trace gas, the differences between the SCDs of the measured spectra and the zenith reference spectrum. In this study, the software DOASIS (Kraus, 2005) is used to perform the DOAS analysis for all the measured data. As there is a lack of information of cloud for the radiative transfer calculations in the aerosol and trace gas retrievals. This might result in additional uncertainty if the measurement is influenced by cloud. This problem is particularly serious when there is a rapidly varying radiation transport conditions in the atmosphere, e.g. varying cloud cover, inhomogeneous cloud layer.

Fig. 1. Schematic diagram of the experimental setup of the MAX-DOAS instrument in Shanghai.

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Fig. 2. Average OMI tropospheric NO2 vertical column densities gridded with 0.02  0.02 resolution over Shanghai from April 2009 to November 2010. Locations of all four MAXDOAS measurement sites, BaoShan (NE), JiaDing (NW), SongJiang (SW) and NanHui (SE) are indicated on the map.

Fig. 3. Time series of the O4 DSCDs measured by the four MAX-DOAS instruments with elevation angle of 5 on 11th March 2009. The O4 DSCDs measured by BaoShan, JiaDing, SongJiang and NanHui instruments are indicated as blue, red, magenta and cyan colored curves, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Therefore, the measured data were undergoing an appropriate cloud screening process before further processing to the aerosol and trace gas retrieval. As the concentration profile of O4 is basically constant, the retrieved O4 DSCDs are assumed to be vary smoothly

with time when there is no rapid change of the radiation transport condition. Therefore, we applied a low pass smoothing filter with an averaging window of an hour to the O4 DSCDs time series at each elevation to filter data contaminated by clouds. Days with fast

Table 1 Details of the four MAX-DOAS monitoring stations. Station

Location (Lat, Lon)

Characteristics

Measurement period (mm/yyyy)

BaoShan (N)

31.393 N, 121.444 E

Industrial & residential

JiaDing (NW) SongJinag (SW)

31.372 N, 121.250 E 31.036 N, 121.220 E

Industrial Residential & commercial

NanHui (SE)

31.050 N, 121.790 E

Coastal station

5e7/2009, 9e11/2009, 1e2/2010 & 4e5/2010 5e11/2009 & 1e11/2010 5e11/2009, 1/2010 & 3e5/2010 6e9/2009 & 1e11/2010

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Fig. 4. Example of the DOAS retrieval from a spectrum taken by the MAX-DOAS instrument located in JiaDing on 17th of April 2010 at 9:02 local time (for elevation of 5 ). The NO2 and O4 DSCDs are (17.63 ± 0.24)  1016 molec/cm2 and (14.21 ± 0.73)  1042 molec/cm2, respectively. The differential optical densities of the scaled absorption cross sections (red curves) and the sum of scaled absorption cross sections and fit residual (blue curves) of a) NO2, b) O4, c) O3 and d) HCHO are shown. The analysis of the Ring spectrum and the fit residual are shown in panel e) and f), respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

varying O4 DSCDs were filtered out and only clear sky data be considered in the further aerosol and NO2 columns retrieval. 2.4. Aerosol extinction retrieval As the effective optical path in the troposphere depends strongly on the aerosol content, a good estimation of the aerosol is necessary for the conversion of trace gas DSCDs to VCDs. Inversion of the radiative transfer equation is required to retrieve aerosol extinction parameters from MAX-DOAS observations. This inversion is commonly achieved by fitting the unknown aerosol parameters to the profile of a well known absorber, the oxygen collision complex, O4 (concentration of O4 varies with the square of the oxygen monomer (O2) concentration), to the measurements in order to retrieve the aerosol extinction (Wagner et al., 2004; €nninger et al., 2004; Sinreich et al., 2005; Frieb et al., 2006; Ho Hartl and Wenig, 2013). Previous studies show that there is a systematic error in the O4 absorption cross section (Wagner et al., mer et al., 2010). Correction for the O4 is necessary in 2009; Cle order to bring the measurements and model results into agreement. For this reason, we examined the necessity of O4 correction for our measurements by comparing radiative transfer simulation of relative intensities with the MAX-DOAS measurements for some selected clear sky days. The result shows correction of O4 DSCDs is necessary to bring relative intensity simulations and measurements into agreement. Correction factor of 0.8 was found best matching for the our selected clear sky days data, our founding mer agrees with some previous studies (Wagner et al., 2009; Cle

et al., 2010; Wagner et al., 2011). In order to account for this uncertainty, all O4 DSCDs were corrected by multiplying with a factor mer et al., 2010; Wagner et al., of 0.80 (Wagner et al., 2009; Cle 2011). Unless specified, all the O4 DSCDs stated in this paper refer to the O4 DSCDs after correction. The corrected O4 DSCDs are used as input for the aerosol extinction retrieval. In this study, we used a simple algorithm developed for the retrieval of aerosol properties of a ground layer (Hartl and Wenig, 2013). Our retrieval assumes a fix asymmetry parameter of 0.68, a single scattering albedo of 0.95 and ground albedo of 0.18. These values are chosen because according to Chen et al. (2009) they are considered to be realistic for Shanghai. In the radiative transfer model, the aerosol extinction profile is parameterize by a well mixed ground layer with layer height of HL and aerosol extinction coefficient kL with continuous transition to an exponentially decreasing profile with optical depth tE. The retrieval height zTOR is set to 5 km. We refer the reader to Hartl and Wenig (2013) for the details of the parameterization and the regularization of the inversion algorithm. The retrieved aerosol information is then used as input for the air mass factor calculation for the conversion of NO2 DSCDs to VCDs. In this study, all radiative transfer simulations were carried out using the radiative transfer model SCIATRAN (version 2.2.2) (Rozanov et al., 2005).

2.5. Retrieval of tropospheric NO2 columns The aerosol profile obtained as described above is used to calculate the differential box air mass factors (DAMF) defined as

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DSCDi ¼

X

DAMFij  Dzj  cj

(1)

j

Here the NO2 profile is parametrized by its concentration values cj in layers between heights zj and zjþ1 (Dzj ¼ zjþ1zj) and i indicates the light path. This system of linear equations is solved for the cj using the Optimal Estimate method (Rodgers, 2000). The very small number of light paths does not allow reliable retrieval of the profile shape, but we found that the tropospheric column can be reconstructed quite well, if one reduces the arbitrariness of the a priori for cj in a two-step procedure. The first step uses a fix, noncommittal a priori of exponential shape (with scale height 1 km) to retrieve the vertical columns for one day. The exponential a priori is then scaled to have the average vertical column of this day. The second run uses this scaled a priori to retrieve the final vertical columns. The details of the parametrization and retrieval are as follows. The lower most part of the troposphere up to 3 km is divided into 15 layers of 200 m. Above this retrieval height, the NO2 profile is set to the US standard atmosphere. We use a covariance matrix as shown in equation (2), where the uncertainty of the scaled a priori saj is assumed to be 50%. The correlation length hcorr (Barret et al., 2002) is 0.2 km. The error of the vertical columns given in the following are calculated from the a posteriori uncertainty of the retrieved cj.

   zi  zj  Saij ¼ sai saj exp  hcorr

(2)

3. The satellite measurements 3.1. The Ozone Monitoring Instrument The Ozone Monitoring Instrument (OMI) is a passive nadirviewing imaging spectrometer (Levelt et al., 2006) on board the Earth Observing System's (EOS) Aura satellite which was launched on 15 July 2004. OMI is equipped with two charge-coupled devices (CCDs) to measure the solar spectra reflected by the earth in the ultraviolet and visible (UV-VIS) wavelength range from 264 nm to 504 nm, providing 60 simultaneously measurements across the orbital track for every 2 s. In global observation mode, the spatial resolution of OMI is about 320 km2 (13 km  24 km) at nadir and decreases to about 6400 km2 (40 km  160 km) at both edges of the track. A scan (60 simultaneously measurements) covers approximately 2600 km across the orbital track direction. About 14.5 sunsynchronous polar orbits are scanned each day, providing daily global NO2 maps (Boersma et al., 2007). In this study, NASA's OMI tropospheric NO2 data product is used. In this product, NO2 slant column densities (SCDs) are derives from the OMI spectra by applying the DOAS technique in the wavelength range from 405 nm to 465 nm. Three trace gas reference cross sections, NO2 (Vandaele et al., 1998), O3 (Burrows et al., 1999a) and H2O (Harder and Brault, 1997), are included in the DOAS analysis. In NASA's OMI NO2 product, the AMFs are calculated based on the monthly mean NO2 profile shapes derived from the Global Modeling Initiative (GMI) chemistry transport model (Rotman et al., 2001) multiannual (2005e2007) simulation. For polluted regions, the stratospheric columns are determined using a local analysis of the stratospheric field. The tropospheric NO2 are separated from the total columns by subtracting the stratospheric columns. Detailed description of whole retrieval procedure can be found in the paper by Bucsela et al. (2013). 3.2. Spatial gridding of OMI NO2 data The ground pixels of the OMI measurements vary in size and

shape and often multiple pixels overlap towards the ends of the scanline. In order to better reconstruct the spatial distribution of trace gas resulting from satellite measurements, an appropriate gridding routine has to be used. We adopted the parabolic spline gridding algorithm (Kuhlmann et al., 2014) to process the OMI data for the spatial distribution analysis, as it provides more realistic continuous spatial distributions of NO2 while preserving details of emission peaks in the generated maps as can be seen in Fig. 2. For the generation of the time series and the comparison with MAXDOAS data, the OMI data was processed using an error and area weighted gridding algorithm (Wenig et al., 2008), because this approach includes a cloud screening process. Cloud contaminated pixels with a cloud cover larger than 40% are filtered out and the remaining values are weighted according to their cloud cover, favoring clear sky pixels. In this study, the overlapping product (FoV75) of the tropospheric column NO2 is reprojected on a spatial grid with a resolution of 0.01  0.01 (approximately 1.1 km (lat)  1.0 km (lon) for Shanghai) to reconstruct the distributions of tropospheric NO2 from the OMI measurements. 4. Local emissions inventories and meteorological data Sectoral emissions inventories are presented in this study in order to better understand the spatial pattern of NOx emission distribution. These emissions were originally generated by a topdown emission allocation approach using Geographical Information System (GIS) and were intended for use in Community Multiscale Air Quality (CMAQ) regional air quality model. In order to achieve a finer resolution on a regional scale (up to 3 km), supplementary land use information of Shanghai and surrounding area were used for local emissions allocation (He, 2012). Fig. 5 shows the sectoral NOx emissions in Shanghai of a) transportation source (onroad mobile) and b) sum of industrial and residential source. Summary of sectoral emissions by its administrative areas is also presented. It is clear that vehicle emission in Shanghai was concentrated along the major highways in PuDongXi, central Shanghai and JinShan districts, while industrial emission was clustered near the major industrial areas such as JiaDing, BaoShan, North of central Shanghai and North of PuDongXi district. In terms of emission contribution, central Shanghai and BaoShan have a higher vehicle emission than JiaDing; this information becomes important when we interpret MAX-DOAS and OMI outputs. For the Expo analysis, the change of NOx emission from 2009 to 2010 were unable to be analyzed through emission inventories due to the fact that inventory is normally updated every 3 years for air quality application. Nevertheless, the presented 2010 inventory was sufficient to provide valuable information in terms of its sectoral and spatial contribution in understanding our measurement outputs. Another important piece of information is the meteorological condition (i.e., wind speed, direction) that affects the movement of pollutants from its original emitted place to the receptors. Hence, it contributes to the understanding on the location/point specific measurements such as MAX-DOAS. In this study, National Centers for Environmental Prediction Final Operational Global Analysis data (NCEP FNL) was used to investigate the meteorological conditions in 2009 and 2010 prior and during the Expo (National Centers for Environmental Prediction, 2000). Wind speed, wind direction, temperature and Planetary Boundary Layer (PBL) height were extracted and analyzed for better understanding the local dispersion characteristics and the observed NO2 plume shapes from OMI products. We have realized other Shanghai Expo related studies have extensively analyzed meteorological conditions prior and during the event using weather station data and reanalysis output. For example, Hao et al. (2011) found that the weather during the Expo (May to October) was typical, compared to their reference

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Fig. 5. The sectoral NOx emissions in Shanghai: a) transportation source (on-road mobile) and b) sum of industrial and residential source. Summary of sectoral emissions characteristics by its administrative areas is shown in percentage by sector (Transportation, Industrial þ Residential, Power): 1. BaoShan (18%, 18%, 48%); 2. ChongMing (51%, 44%, 5%) 3. Central Shanghai (25%, 45%, 30%); 4. FengXian (83%, 15%, 2%); 5. JiaDing (48%, 46%, 5%); 6. JinShan (48%, 46%, 6%); 7. MinHang (23%, 39%, 38%); 8. NanHui (53%, 42%, 5%); 9. PuDongXin (13%, 51%, 36%); 6. JinShan (48%, 46%, 6%); 7. MinHang (23%, 39%, 38%); 8. NanHui (53%, 42%, 5%); 9. PuDongXin (13%, 51%, 36%); 10. QingPu (71%, 26%, 3%); 11. SongJiang (60%, 36%, 4%).

year (2007e2009) and their climatological data (1971e2000), while Lin et al. (2013) looked at the seasonal precipitation and PBL height have concluded that the meteorological conditions were stable throughout the Expo. Nevertheless, our meteorological analyses were focused on the relationship of emission location and how the emission was transported from the source region to another area that eventually impacts the MAX-DOAS measurements as well as causing particular plume patterns in OMI observations. 5. Results 5.1. MAX-DOAS and OMI NO2 VCDs comparison Time series of the monthly mean tropospheric NO2 VCDs measured by the four MAX-DOAS stations and the corresponding coinciding OMI data are shown in Fig. 6. For better comparison, the MAX-DOAS data are cloud screened and temporally averaged around the OMI overpass time (from 12:00 to 13:00 local time for Shanghai), while the OMI data are spatially averaged over the grid cells within 15 km of the ground location (approximately the average OMI pixel size) around the MAX-DOAS measurement sites. The yellow marked time periods in Fig. 6 indicate the Expo period, while the gray bar chart indicates the number of coinciding MAXDOAS and OMI measurement days in the month. Small number of sample in some months are due to cloud filtering or instrumental reasons. In total, there are 52, 98, 56 and 63 days of coinciding data for BaoShan, JiaDing, SongJiang and NanHui, respectively. Scatter plots of daily and monthly data of the MAX-DOAS measurements at different stations and the corresponding OMI observations are shown in Fig. 7. The averaged NO2 VCDs measured by MAX-DOAS instruments located at BaoShan, JiaDing, SongJiang and NanHui are 4.81, 5.33, 2.87 and 1.36 ( 1016 molec/cm2), respectively. The averaged OMI tropospheric NO2 VCDs at BaoShan, JiaDing, SongJiang and NanHui are 1.66, 1.78, 1.35 and 0.67 ( 1016 molec/cm2), respectively. All measurements show a similar annual pattern with lower NO2 VCDs during summer time and higher NO2 VCDs in the winter. Prevailing southeasterlies from the ocean as well as higher photolysis rates make the NO2 level lower in the summer. The NO2 VCDs measured by the stations located in the north (BaoShan and JiaDing) are on average higher than the stations located in the south (SongJiang and NanHui), mostly likely because most of the factories and industrial facilities are located in the north of the city. In general, both the MAX-DOAS and the OMI measurements show good correlation with each other with Pearson correlation coefficient (R) of monthly averaged NO2 VCDs ranging from 0.67

(BaoShan station, polluted site) up to 0.93 (NanHui station, clean site). The Pearson correlation coefficient (R) of daily NO2 VCDs are for BaoShan, JiaDing, SongJiang and NanHui station are 0.64, 0.62, 0.62 and 0.93, respectively. However, for most of the months the NO2 VCDs measured by the MAX-DOAS and OMI do not agree with each other within the measurement errors and the MAX-DOAS measurements are systematically higher than the OMI observations. For polluted regions (BaoShan and JiaDing), MAX-DOAS data are on average about 3 times higher than the OMI NO2 measurements, while the MAX-DOAS observations are about 2 times higher than the OMI data in the less polluted regions (SongJinag and NanHui). The discrepancy between MAX-DOAS and OMI measurements may be explained by the uncertainties in the OMI retrieval related to the aerosols and NO2 vertical profiles. Differences in the spatial coverage between the two measurements and other uncertainties in the satellite and MAX-DOAS retrieval also contribute to this discrepancy. Previous studies show that using a better estimated NO2 vertical profile in the air mass factor calculation for the NO2 column retrieval enhanced the OMI NO2 columns by 15%e20% (Chan et al., 2012; Lin et al., 2014). Including aerosol in the radiative transfer calculation of the satellite retrieval can further increase the NO2 columns by up to 20% (Lin et al., 2014). These explanations agree with our observations that the MAXDOAS measurements agree better with the OMI data over less polluted regions. In addition, we have also assessed the impact of aerosol optical depth on discerpency between the MAX-DOAS and satellite measurements. In general, the aerosol optical depth levels measured by MAX-DOAS at JiaDing (polluted site) and NanHui (non-polluted site) are similar, with overall averaged aerosol optical depth of 0.93 at JiaDing (polluted site) and 0.88 at NanHui (nonpolluted site). As the differences of aerosol optical depth are quite small and therefore cannot fully explain the discerpency between polluted and non-polluted site. Furthermore, differences in the spatial coverage between MAX-DOAS and OMI can also lead to higher discrepancy in the region with large spatial inhomogeneity. Satellite oberservations with large measurement footprint are not able to reproduce NO2 fields with large spatial gradient due to the averaging effect over the large area of the OMI pixels. Other uncertainties in the satellite and MAX-DOAS retrieval might also contribute to the discrepancy between two measurements. These factors listed above can easily explain the observed discrepancy between the MAX-DOAS measurements and OMI observations. 5.2. Diurnal analysis of MAX-DOAS measurement In urban areas, the atmospheric NO2 levels are closely related to

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Fig. 6. Time series of monthly averaged tropospheric NO2 VCDs for a) BaoShan, b) JiaDing, c) SongJiang and d) NanHui. MAX-DOAS data are cloud filtered and temporal averaged around the OMI overpass time (blue circle curves). OMI measurements are spatially averages over grid cells within 15 km around the MAX-DOAS measurement sites (red square curves). The yellow shadowed region indicates the Expo period (May to October 2010). The gray bar chart indicates the number of coinciding MAX-DOAS and OMI measurement days in the month. Small number of sample in some months are due to cloud filtering or instrumental reasons. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the anthropogenic emissions. Analyzing the diurnal cycle of atmospheric NO2 can give further information on the NO2 emission sources and the atmospheric chemistry. Fig. 8 shows the averaged diurnal cycles of tropospheric NO2 VCDs measured by the MAXDOAS at the four locations. Cloud screened MAX-DOAS data are hourly averaged in two categories, weekday (from Monday to Friday) and weekend (Saturday and Sunday). In total, 52, 98, 56 and 63 clear sky days were analyzed for BaoShan, JiaDing, SongJiang and NanHui station, respectively. Different stations show different diurnal characteristics. For the polluted sites (BaoShan and JiaDing), the NO2 levels increase in the morning and stay at a high level during daytime of the weekday. For the less polluted sites (SongJiang and NanHui), a different diurnal profile of the NO2 columns can be observed with lower NO2 values during noon time. Stronger solar irradiance at noon time leads to higher photolysis rate

resulting in lower NO2 level. The differences of the diurnal pattern between different sites are probably due to the differences in the emission composition of sources from industrial and traffic sectors, see Fig. 5. Since the polluted sites are located close to the industrial area in the north of the city, the measured NO2 levels are expected to be dominated by industrial emissions with more or less constant emission rates throughout the day. On the other hand, the less polluted sites are located further away from the industrial area in the south of the city, where traffic emissions are supposed to be the main NO2 source. Relatively lower traffic load and stronger photolysis rate reduce the NO2 levels at noon time. It is observed that NanHui has the lowest VCD values among all stations, which was likely due to the land usage in the area that is mainly agricultural application. Moreover, the positive influence from the marine boundary air also lowered the background NO2 concentration. The prevailing winds in Shanghai are SE and SSE with 73% of the time blowing from the ocean (Hao et al., 2011; Lin et al., 2013). Human activities usually fall into a 7-day weekly cycle. Reduction of industrial activities as well as traffic volume during weekend lead to lower levels of pollutant emission, this effect is known as weekend effect (Cleveland et al., 1974). The weekend effect was reported to be significant in most of the western countries, however, no weekend effect can be found over cities in China (Wenig, 2001; Beirle et al., 2003; Ma et al., 2013). We investigated the weekend effect of NO2 using our MAX-DOAS measurements in Shanghai. Fig. 8 shows the comparison of the NO2 daily patterns during weekday (from Monday to Friday) and weekend (Saturday and Sunday). Measurements from JiaDing and SongJinag do not show a significant weekend effect with about 10% reduction during weekend, while the remaining station (NanHui and BaoShan) shows a relatively pronounced weekend effect with 20e35% reduction of the NO2 levels. This observation agrees with the previous studies and indicates that industrial emissions in Shanghai do not have a significantly higher pollution level as would have be expected. Although the overall NO2 level measured at NanHui is ~60% lower than that of BaoShan and JiaDing stations, a significant reduction of NO2 levels during weekend can still be observed. The result indicates there are significant anthroprogenic NO2 sources in the area, e.g., the local traffic emissions from the PuDong airport. In BaoShan, a mixed land usage of residential and semi-industrial area, has shown an interesting bimodal diurnal profile during the weekend, but not on weekdays. The difference in VCD between weekday and weekend is about 1.50  1016 molec/cm2, which translates to about 20% of it overall value. The phenomenon observed is quite different from the emission characteristic of the heavy industrial sector (i.e., power generation and steel refinery) which is usually in full schedule all the day and shows no reduction during weekend. Our observation suggested that small factories and light duty industrial activities in the area reduced the emission as well as the work load during weekend. A significant reduction of NO2 values during weekend can also be observed at NanHui (less polluted site), the NO2 columns during weekend are in general ~30% lower compared to measurement during weekdays, which indicates there are significant anthropogenic NO2 sources in the area. NanHui station is located about 5 km away from the Shanghai PuDong International Airport, therefore the measurements can be influenced by the local traffic emissions from the airport. 5.3. Analysis of Shanghai Expo The Shanghai government implemented a series of pollutants emission control measures during the Expo, by restricting both industrial and transportation emissions in the city. In order to study the effect that hosting such an international event has on the air

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Fig. 7. Correlation of tropospheric NO2 VCDs measured by the MAX-DOAS (x-axis) and OMI satellite (y-axis) over a) BaoShan, b) JiaDing, c) SongJiang and d) NanHui. MAX-DOAS data are cloud filtered and temporally averaged around the OMI overpass time. OMI measurements are spatially averaged over pixels within 15 km of ground location around the MAX-DOAS measurement sites. Daily coinciding data are indicated in gray circle dots, while monthly averaged data are shown in the plots as blue square dots. The red curves indicate the linear regression fit of the monthly mean data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

quality, we compare our data before the Expo and during the Expo for the same month of the year. Previous research has shown that the average emission reductions from the Expo ranged from 8% to 30% for NOx, 28% for SO2 and 14%e30% for Aerosol Optical Depth (AOD)/Aerosol Optical Thickness (AOT) (Hao et al., 2011; Zhao et al., 2013). In a recent report from the Shanghai Environmental Protection Bureaus (2011), it stated that the overall NOx emissions from mobile sources is estimated to be 10% (Shanghai Environmental Monitoring Center, 2011). For aerosol, banning waste straws and controlling construction dust seems to be an effective control of local PM2.5 since the AOD/AOT has reduced much more than NO2. For SO2, reduction came from the fuel switching (i.e., to lower Sulfur coal) from power plant sector on 4th of April, 19th of May, 14th of August, 17th of October and 29th of October. It is observed that the correlation between PM2.5 and SO2 (R ¼ 0.5) was much lower than NOx (R ¼ 0.7) in Shanghai, which suggests that marine and power generation sectors were not the key contributor to the urban Shanghai air quality since the major sources of SO2 are supposedly coming from those two sectors (Lin et al., 2013). As a result, it is logical to believe that the remaining sectors (i.e., transportation and industrial) would be the key source of NOx pollution in Shanghai. For our analysis, Fig. 9 shows the averaged OMI tropospheric NO2 VCDs over Shanghai from 1st of May to 31st of October 2009 and for the same period in 2010 (period of Expo

2010 Shanghai). For better comparison, the differences of the NO2 VCDs between these two periods are also shown in Fig. 9c. Negative values indicate a reduction of NO2 levels during the Expo 2010 Shanghai when compared to the same time period in 2009. A significant reduction of NO2 levels (around 30%) can be observed in central Shanghai during the Expo period. However, the reduction effect is not obvious in the surrounding area. Fig. 10 shows the comparison of monthly MAX-DOAS and OMI NO2 values over JiaDing and NanHui during the Expo and the same time period in 2009. In general, the MAX-DOAS data shows similar variation trend with the OMI satellite observations. The overall averages of both MAX-DOAS and OMI data for both stations measured during the Expo and in 2009 agree within the measurement errors. The seasonal trend has a curved pattern with lower concentration in the mid-term (July, August and September) with the high values at the beginning and the end of the Expo. Lin et al. (2013) showed the highest correlations were found in May, June and October between PM2.5 and NOx, which reflects the traffic volume reached the highest peak at the beginning and the end of the event. In JiaDing, reduction of NO2 is significant compared to 2009 during July and September. However, this effect was compensated by the enhancement of NO2 values in other months, especially in June and October. It is reasonable to wonder what may have caused the increase of NO2 in the JiaDing station, particularly when various

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Fig. 8. Averaged diurnal cycles of tropospheric NO2 VCDs measured by the MAX-DOAS located at a) BaoShan, b) JiaDing, c) SongJinag and d) NanHui. Cloud screened MAX-DOAS data are hourly averaged into two categories, weekday (from Monday to Friday, red circle curves) and weekend (Saturday and Sunday, blue square curves). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

emission control measures had been implemented during the Expo. Therefore, we further evaluated the meteorological factors (mainly wind speed, wind direction and PBL height) on those sampling days in 2009 and 2010 for both MAX-DOAS and OMI to search for potential reasons. It is observed that the dispersion characteristics for 2009 and 2010 in June and October were in fact inconsistent, which has resulted in negative biases (increase in concentration from 2009 to 2010) in the analysis. In terms of PBL, both years showed a similar pattern for all months with not much variability throughout the season. The mean PBL height for the period from May to October is 520 m for 2009 and 540 m for 2010. The largest difference was observed in July, where PBL in 2010 was higher than in 2009 by about 300 m that coincided with the largest observed difference in NO2 VCD (3.5  1016 molec/cm2) of MAX-DOAS measurement in July 2009 and 2010. In terms of wind direction, significant differences in the prevailing wind were found in June and October are shown in Fig. 11. The difference in the wind patterns have significantly impacted the analysis of MAX-DOAS point measurements. In June 2009, the direction of wind was predominantly from the South with a relative high wind speed, which transported a relative clean marine air to the Shanghai area and forced local emissions in Shanghai to move up to the North. On the other hand, in June 2010, the wind direction was predominantly from the East with very calm and low wind speed, the local emissions were unable to disperse further to the North and remained in

the nearby area that ultimately caused high concentration in the Shanghai area. The October case has also shown the different wind pattern, where prevalent wind in 2009 was coming from the North pushing local emissions to the South, while in 2010 Easterly wind was observed. Hence, it is clear that June and October should not be included in the analysis for our final conclusion. In terms of the size of the sample in OMI, we performed a t-test for June and October and have also found that the NO2 levels are insignificant due to small sample size. On the other hand, there is only few measurement data in August 2009 and therefore not representative for the month when compared to 2010. Based on the valid measurements in the remaining months, the overall NO2 level at JiaDing was reduced by ~15% during the Expo. In NanHui, the NO2 variation during the Expo follows a similar trend as 2009, monthly data from both years agree with each other within the measurement errors. Hence, no significant reduction of NO2 level was observed at NanHui. Since NanHui is located at the coastal area southeast from all major sources, no significant change of dispersion effects has been observed from May to October. Reduction of NO2 levels mainly occurred in the central Shanghai where the Expo 2010 Shanghai took place. The outer areas further away from central Shanghai seem not to have benefited from the emission control measures. An enhancement of NO2 levels (about 20% up to 50%) can be observed for some cities nearby Shanghai due to the Expo chain effect where more tourism activities were

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Fig. 9. Averaged OMI tropospheric NO2 vertical column densities over Shanghai a) from 1st of May 2009 to 31st of October 2009 and b) from 1st of May 2010 to 31st of October 2010 (period of Expo 2010 Shanghai). The differences of tropospheric NO2 VCDs between a) and b) is shown in c), so that negative value indicate a reduction of NO2 levels during the Expo 2010 Shanghai.

happening in the surrounding provinces such as Jiangsu (Lin et al., 2013). This might also result from some industrial activities which were moved to the nearby cities due to the emission control measures introduced in Shanghai during the Expo. Our observation implies that the emission control measures were effective only within a small area, mainly from the mobile emission control policy. Indeed, we only focused on NO2 and NOx in this study and the same conclusion may not apply to other atmospheric pollutants. 6. Summary and conclusions In this paper, we present measurements of tropospheric NO2 using four MAX-DOAS instruments in Shanghai. The MAX-DOAS

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Fig. 10. Comparison of monthly averaged NO2 VCDs during Expo 2010 Shanghai and the same month in the year before. Both MAX-DOAS and OMI satellite data measured at a) JiaDing and b) NanHui are shown. Overall averages are also show for reference.

measurements were performed from April 2009 to November 2010 covering a period prior to and during the Shanghai World Expo 2010. Details of the instrumental setup and the NO2 VCDs retrieval are presented. In order to investigate the spatial distribution of NO2 over Shanghai, we compare our MAX-DOAS data with the OMI satellite observations. The OMI data correlate well with the MAX-DOAS measurements with Pearson correlation coefficient (R) ranging from 0.67 over a polluted area to 0.93 over a clean region. In general, the MAX-DOAS measurements are 2e3 times higher than the OMI data. The discrepancy can be explained by the uncertainties in the satellite retrieval in the AMFs calculation related to the uncertainties of aerosol and NO2 vertical profiles. Furthermore, satellite measurements are not able to reconstruct NO2 fields

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Fig. 11. Monthly averaged NO2 VCD overlaid with average prevailing wind pattern for a) June 2009, b) June 2010, c) October 2009 and d) October 2010.

with large spatial gradient due to the averaging effect over the large area of OMI pixels, which matches with our observations that the MAX-DOAS data agree better with the OMI observations over the less polluted site. The MAX-DOAS NO2 VCDs data show strong temporal variability. Analysis of the diurnal patterns of tropospheric NO2 VCDs measured by the MAX-DOAS at different locations in Shanghai shows that the diurnal behavior of NO2 levels is given by the composition of the emission source. In addition, we compared the averaged NO2 columns measured at different monitoring stations during weekends and weekdays. The result shows no significant weekend reduction effect of NO2 over some of the monitoring sites located in heavy industrial area and sub-urban area. However, obvious reduction of NO2 level during weekend (~20e35%) is observed at two sites from the semi-industrial area and sub-rural area that are near the coast. The observation implies that the heavy industrial emissions in Shanghai appear not to have a significant weekly pattern while the traffic emissions and emissions from small factories are reduced during weekend. In order to investigate whether there is any impact on the local air quality while hosting the Expo, we have examined and compared the MAX-DOAS and OMI NO2 data with the consideration of meteorological patterns during the Expo 2010 Shanghai and the same time period in 2009. Overall averaged OMI observations show that the NO2 columns were reduced by ~30% in Shanghai center during the Expo but no significant reduction of NO2 levels can be observed in the surrounding areas further away from Shanghai center. As the meteorological conditions of June and October 2010 were significantly different from 2009 and there is a lack of valid measurements in August 2009, therefore, we did not consider MAX-DOAS measurement data in these months in the analysis of reduction effect during Expo. By comparing the measurements over

JiaDing station before and during Shanghai Expo, the reduction of NO2 level could be determined to be ~12e15% using MAX-DOAS and ~13% using OMI measurements. On the other hand, an enhancement of NO2 levels is observed over some cities nearby Shanghai due to the consequential effects of the Expo. Our observations show that the emission control measures implemented during the Expo were effective only within a relatively small area in the central Shanghai. Acknowledgments The authors would like to thank the Shanghai Environmental Protection Bureau for their support on this project. The work described in this paper was jointly supported by the National Natural Science Foundation of China (Project No. 41275038), the National High-Technology Research and Development Program of China (Project No. 2014AA06A508), the Scientific and Technological Project of Anhui province (Project No. 1301022083), the Guy Carpenter AsiaePacific Climate Impact Centre, City University of Hong Kong (Project No. 9360126), the Research Grants Council of Hong Kong (Project No. CityU 102912 and 9041479) and the Marie Curie Initial Training Network of the European Seventh Framework Programme (Grant No. 607905). References Barret, B., De Maziere, M., Demoulin, P., 2002. Retrieval and characterization of ozone profiles from solar infrared spectra at the Jungfraujoch. J. Geophys. Res. Atmos. 107 (D24). ACH 19e1 e ACH 19e15. Beirle, S., Platt, U., Wenig, M., Wagner, T., 2003. Weekly cycle of NO2 by gome measurements: a signature of anthropogenic sources. Atmos. Chem. Phys. 3 (6), 2225e2232. Beirle, S., Platt, U., Wenig, M., Wagner, T., 2004. NOx production by lightning estimated with GOME. Adv. Space Res. 34 (4), 793e797.

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