Seasonal behavior and long-term trends of tropospheric ozone, its precursors and chemical conditions over Iran: A view from space

Accepted Manuscript Seasonal behaviour and long-term trends of tropospheric ozone, its precursors and chemical conditions over Iran: A view from space Yunsoo Choi, Amir Hossein Souri PII:

S1352-2310(15)00131-4

DOI:

10.1016/j.atmosenv.2015.02.012

Reference:

AEA 13608

To appear in:

Atmospheric Environment

Received Date: 14 July 2014 Revised Date:

24 January 2015

Accepted Date: 5 February 2015

Please cite this article as: Choi, Y., Souri, A.H., Seasonal behaviour and long-term trends of tropospheric ozone, its precursors and chemical conditions over Iran: A view from space, Atmospheric Environment (2015), doi: 10.1016/j.atmosenv.2015.02.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Seasonal behaviour and long-term trends of tropospheric ozone,

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its precursors and chemical conditions over Iran: a view from

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space

4 Yunsoo Choi and Amir Hossein Souri

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Department of Earth and Atmospheric Sciences, University of Houston, 312 Science &

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Research Building 1, Houston, TX 77204, USA

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Correspondence to: Yunsoo Choi ([email protected])

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Fax: 713-7487906

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Tel: 713-8931311

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Abstract

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To identify spatial and temporal variations over the Iranian region, this study analysed

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tropospheric formaldehyde (HCHO) and nitrogen dioxide (NO2) columns from Ozone

14

Monitoring Instrument (OMI), carbon monoxide (CO) columns from the Measurement of

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Pollution in the Troposphere (MOPITT), and tropospheric column O3 (TCO) from OMI/MLS

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(Microwave Limb Sounder) satellites from 2005 to 2012. The study discovered high levels of

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HCHO (~12×1015molec./cm2) from plant isoprene emissions in the air above parts of the

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northern forest of Iran during the summer and from the oxidation of HCHO precursors

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emitted from petrochemical industrial facilities and biomass burning in South West Iran. This

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study showed that maximum NO2 levels (~18×1015molec./cm2) were concentrated in urban

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cities, indicating the predominance of anthropogenic sources. The results indicate that

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maximum concentrations were found in the winter, mainly because of weaker local winds and

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higher heating fuel consumption, in addition to lower hydroxyl radicals (OH). The high CO

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concentrations (~2×1018molec./cm2) in the early spring were inferred to mainly originate from

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a strong continental air mass from anthropogenic CO “hotspots” including regions around

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Caspian Sea, Europe, and North America, although the external sources of CO were partly

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supressed by the Arabian anticyclone and topographic barriers. Variations in the TCO were

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seen to peak during the summer (~40 DU), due to intensive solar radiation and stratospheric

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sources. This study also examined long-term trends in TCO and its precursors over a period of

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eight years in five urban cities in Iran. To perform the analysis, we estimated seasonal

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changes and inter-seasonal variations using least-squares harmonic estimation (LS-HE),

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which reduced uncertainty in the trend by 5-15%. The results showed significant increases in

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the levels of HCHO (~0.08±0.06×1015molec./cm2yr-1), NO2 (~0.08±0.02×1015molec./cm2yr-

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1

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0.42±0.60 DUyr-1) caused by an increase in NO2 species and annual CO (~ -

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0.95±0.41×1016molec./cm2yr-1) partly resulting from the transport of reduced CO. The time

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series of the HCHO/NO2 column ratio (a proxy for the chemical conditions) indicated that

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during the last decade, the cities of Tehran, Ahvaz, and Isfahan exhibited steady chemical

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conditions while Tabriz and Mashhad exhibited a change from NOx-saturated/mixed to more

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NOx-sensitive chemical conditions.

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Keywords: tropospheric ozone; ozone precursors; chemical condition; long-term trends;

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remote sensing

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1

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In the past several decades, developing countries have witnessed dramatic growth in their

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populations accompanied by significant increases in industry and the number of vehicles, all

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of which have contributed to high concentrations of air pollutants, including tropospheric

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column ozone (TCO) and its precursors. TCO is produced by descending stratospheric ozone

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(Traub and Lelieveld 2003; Neu et al., 2014) and photochemical reactions involving its

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precursors such as NOx (NO+NO2) and volatile organic compounds (VOC) in the short term

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and carbon monoxide (CO) and methane (CH4) in the long term.

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Introduction

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), and peak annual TCO (~0.59±0.56 DUyr-1) but decreases in minimum annual TCO (~ -

NO2 produces ozone when it participates in a catalytic chemical reaction in the presence of

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sunlight. Emissions of nitrogen monoxide (NO) from the anthropogenic combustion of fossil

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fuels (Noxon, 1978), biomass burning activity (van der Werf et al., 2006), microbial activity

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in soil (Yienger and Levy, 1995), and lightning (Choi et al., 2009) are the major sources of

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NO2. The oxidation of VOC by hydroxyl radicals (OH) can lead to the conversion of NO into

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NO2, which produces ozone. Thus, ozone production can be controlled by moderating the

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emissions of either NOx or VOC, depending on which is more abundant (i.e., the VOC/NOx

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ratio) (e.g., Martin et al., 2004; Choi et al., 2012). These conditions are commonly referred to

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as NOx-saturated and NOx-sensitive regimes. Classifying locations into these regimes requires

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measurements of total VOC, OH, and NOx. However, because of a lack of such

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measurements, the formaldehyde (HCHO) level acts as a proxy for VOC reactivity. In

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addition, because of the short lifetime of NOx, NO2 concentrations are closer to the boundary

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layer and emissions sources; thus, we can use measurements of NO2 levels to determine levels

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of NOx emissions (e.g., Martin et al., 2004; Choi et al., 2012). Accordingly, the HCHO/NO2

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ratio can indicate conditions that drive ozone production (Sillman, 1995).

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Although air quality monitoring stations produce the most direct and accurate measurements,

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they are often spatially sparse.

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potentially determine spatial and temporal variations and long-term trends around the globe.

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A number of studies have examined variations in tropospheric NO2 with respect to surface

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measurements/model simulations using the Global Ozone Monitoring Experiment (GOME),

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the

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(SCIAMACHY), and the Ozone Monitoring Instrument (OMI) (e.g., van der A et al., 2008;

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Kumar et al., 2012; Bechle et al., 2013; David and Nair, 2013; Hilboll et al., 2013).

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However, satellite remote sensing of trace gases can

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As another ozone precursor, HCHO, one of the largest fractions of total VOC reactivity

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driving the production of ozone, is produced directly (primary formation) and indirectly

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(secondary formation) from both anthropogenic and biogenic sources. HCHO is supplied

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directly from combustion, biomass burning, and deteriorating plant tissue and secondarily

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from the oxidation of methane, terpenes, and alkenes. Alkenes and terpenes react quickly with

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OH during the daytime. In particular, because of its short lifetime, isoprene can produce

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HCHO within an hour (Shim et al., 2005). The amount of isoprene emissions depends on the

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Leaf Area Index (LAI), the temperature, solar radiation, and the vegetation type (Guenther et

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al., 2012; Kim et al., 2013). Studies have investigated the spatial and temporal variations of

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HCHO using GOME, SCIAMACHY, and OMI in Africa (e.g., Marais et al., 2012) and the

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United States (e.g., Martin et al., 2004).

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As another ozone precursor, CO significantly correlates with TCO (Pochanart, 2004).

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Because of the almost two-month lifetime of CO, we regard its concentration as the regional

29

background value of the ozone level, a tracer for pollution from biomass burning (Spichtinger

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et al., 2004), and the incomplete combustion of fossil fuels. MOPITT and SCIAMACHY

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have found variations among CO concentrations in urban cities around the world (e.g.,

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Edwards et al., 2004).

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As in other regions of the world, Iran has had to confront the challenge of air pollution,

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evident in the severity of air quality conditions in its major urban cities. Air pollution levels

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have reached such lethal levels that local Iranian governments have resorted to closing

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schools and imposing traffic restrictions. However, research pertaining to the problem of air

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pollution in Iran has been limited to air quality monitoring systems. Unfortunately, to the best

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of our knowledge, no one has investigated the trace gases mentioned above on a national scale

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using satellite data. Therefore, to obtain information related to the signatures of these

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chemicals, this work investigates TCO, HCHO, NO2, and total CO columns over Iran and

9

their spatial concentrations, seasonal changes, transport, and annual trends. For this purpose,

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we use HCHO and NO2 columns from OMI, CO columns from MOPITT, and TCO from

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OMI/MLS satellites from 2005 to 2012.

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With an area of 1,648,195 km2, Iran, a region of diverse populations, has a variable climate

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influenced by the Persian Gulf and the Gulf of Oman along its southern border and the

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Caspian Sea along its northern border. In the south, summers are extremely hot and humid

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while winters are mild. However, northwestern Iran faces cold winters with heavy snowfall

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during December and January and relatively dry and hot summers during June and July.

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Because of its extreme climate, energy consumption is high, particularly in northern and

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central Iran, so it exhibits seasonal and spatial variations in the previously mentioned gases.

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Thus, in addition to studying these variations over Iran (and to gather a more comprehensive

22

picture, its neighbors), this work investigates the annual trends of trace gases in the five urban

23

cities that suffer the highest levels of air pollution: Ahvaz (31.32° N, 48.67°E), Isfahan

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(32.63°N, 51.65°E), Mashhad (36.30°N, 59.60°E), Tabriz (38.07° N, 46.30° E), and Tehran

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(35.70° N, 51.42°). Figure 1 represents Iran and the five cities, each of which is unique both

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geographically and climatically.

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Case study

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3

Measurements

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3.1

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To create TCO maps and perform a long-term analysis of trends, we referred to OMI/MLS

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TCO (available at http://acdb-ext.gsfc.nasa.gov/Data_services/cloud_slice/new_data.html).

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After applying the cloud-slicing method (Ziemke et al., 2006) to make several adjustments,

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they measured TCO by subtracting the measurements of MLS stratospheric column ozone

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from OMI total column ozone and found a spatial resolution of the OMI/MLS TCO product

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of 1.25o×1o and uncertainty of 5 Dobson Units (DU) with a mean offset of 2 DU (Ziemke et

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al., 2009).

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OMI/MLS TCO

3.2

OMI NO2 and HCHO

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The OMI sensor acquires UV/Vis images that cross the equator at about 13:30 local time. For

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the spatial and temporal variations of HCHO and NO2, the KNMI (Royal Netherlands

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Meteorological Institute) provided data pertaining to monthly-averaged columns, and the

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BIRA/IASB (Belgian Institute for Space Astronomy) provided data with spatial resolutions of

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0.25o×0.25o and 0.125o×0.125o, respectively (available at temis.nl/airpollution). For a long-

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term analysis of HCHO, we chose a level-2 gridded product (v003) (Chance, 2002) with a

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spatial

18

bin/mirador/collectionlist.pl?keyword=omhcho) and then filtered our values with a cloud

19

fraction of greater than 0.4 obtained from the O2–O2 OMI cloud product (Acarreta et al.,

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2004), which was lower than the sensor detection limit (i.e., <1.5 1015molec./cm2) and with

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a bad quality flag. The values were normalized by HCHO observations over a remote location

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in the Pacific Ocean at the same latitude as that of the case study using a fourth order

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polynomial (Marais et al., 2013; González et al., 2015). OMI NO2 daily observations (level3)

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from

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http://mirador.gsfc.nasa.gov/cgi-bin/mirador/collectionlist.pl?keyword=omno2) and a cloud

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fraction of less than 0.2 were used for a long-term analysis. Bechle et al. (2013) found a

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strong correlation between NASA OMI NO2 and surface observations by comparing 4,138

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sets of paired data from 25 monitoring stations in the South Coast Air Basin of California,

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indicating that the OMI sensor provides reliable tropospheric NO2 measurements. HCHO

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columns measured by OMI have exhibited temporal and spatial consistency with those

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measured by aircraft over the United States, Mexico, and the Pacific Ocean (Boeke et al.,

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2011); after 2009, several problems cropped up (González et al., 2015 and the references

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therein).

3 3.3

MOPITT total column CO

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Seasonal variations of daytime CO were derived from MOP03M L3 (Edward et al., 2009),

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which contains a monthly mean gridded version of the daily L2 CO total column with a 1o×1o

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spatial resolution and an approximate 10:45 local equator crossing time. The MOPITT CO

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observations have been validated with aircraft in-situ measurements and show good quality

9

(Emmons et al., 2004). For an analysis of long-term trends, we used daily MOP03N L3 (V6)

10

because of the very low long-term drift. Deeter et al. (2014) validated the V6 MOPITT CO

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measurements with in-situ observations that effectively demonstrated the possibility of

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decadal

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http://l0dup05.larc.nasa.gov/opendap/MOPITT/.

analysis.

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CO

products

are

available

at

16

3.4

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To investigate the contributions of biomass burning activity, this study deployed the monthly-

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mean Fire Radiative Power (FRP) of the MODIS fire product (MYD14CMH) (Giglio et al.,

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2006) with a spatial resolution of 0.5 degree (available at ftp://fuoco.geog.umd.edu/modis/C5/

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cmg/monthly/hdf/). In order to probe the contributions of surface CO, a monthly mean

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gridded of surface CO mixing ratio (MOP03NM) produced by only the NIR MOPITT

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channels with a spatial resolution of 1o×1o is used. Both meteorological conditions and air

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masses are critical to locating external potential sources of air pollutants and their seasonal

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patterns. Accordingly, this study exploited half-hourly wind data from synoptic stations in the

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urban cities and retrieved their five-day backward trajectories in 2013 from the Hybrid

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Single-Particle Langrangian Integrated Trajectory (HYSPLIT, ver.4).

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contributions of isoprene emissions, we simulated MEGANv2.1 (Model of Emissions of

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Gases and Aerosols from Nature; Guenther et al., 2012) using NCEP reanalysis data from the

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summer of 2013. The data used for the investigation of tropospheric and stratospheric ozone

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contributions were nadir profiles (TL3O3D.002 product) with a spatial resolution of 2°×4°

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(available

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These ancillary data and analyses are presented in the supplementary material.

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4.1

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4.1.1

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Figure 2 depicts monthly-averaged values of HCHO smoothed by a box filter (1.25°×1.25°)

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from January to December 2005 to 2012. Figure 2 shows that the concentration of HCHO is

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noticeably higher in July than in January. From the winter to the summer, the monthly HCHO

11

values increase by a factor of more than two. The seasonal maximum HCHO, which is

12

~12×1015molec./cm2 in northern and southwestern Iran, including Ahvaz, is the result of

13

several factors: 1) Large forest regions of Iran are located in the north, where some plants tend

14

to emit isoprene to protect themselves against heat stress (Sharkey, et al., 2007), the main

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reason for the high HCHO concentration in this region, also observed from isoprene

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emissions modeled by MEGAN (Figure S1); 2) southwestern Iran (and western neighbors) is

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home to a large number of petrochemical industrial facilities and oil/gas fields that generate a

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significant quanity of VOCs, leading to higher HCHO by oxidation. Parrish et al. (2012)

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indicated that the source of HCHO (~92%) was primarily secondary formation produced

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during the atmospheric oxidation of alkenes emitted from the petrochemical facilities in

21

Houston; 3) in southwestern Iran, the considerable biomass burning of agricultural waste

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introduces a significant amount of HCHO precursors into the atmosphere, reported by the

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MODIS average FRP in 2005-2012 (Figure S2). A time series of FRP over southwestern Iran

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shows that peaks in biomass burning occurred in June, August, September, and October, but

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decreased to a minimum during the winter. A two-dimensional correlation analysis shows that

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the relationship between FRP and HCHO is the strongest in June-August (r2=0.52) over the

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landmass of Iran.

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Results and discussion

Spatial and temporal variations of TCO and its precursors

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Data analysis over Iran

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Figure 3 represents monthly-mean tropospheric NO2 for 2005-2012 in Iran and shows no

3

latitudinal variation in NO2 over Iran, which emphasizes the short lifetime of NO2 and

4

indicates that NO2 is concentrated in urban and industrial regions. High NO2 columns (10-

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20×1015molec./cm2) appear over northern Iran and low levels (0.2-1×1015molec./cm2) over

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eastern Iran. We also find maximum concentrations of NO2, mainly from anthropogenic

7

sources (van der A et al., 2008), during the winter in the most populated city in the region,

8

Tehran (~ 20×1015molec./cm2). The lowest concentrations of NO2 (~ 0.45×1015molec./cm2)

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were measured over deserts. In northern Iran, released NO2 is transported to the Caspian Sea

10

by air masses. However, because NO2 is converted to HNO3 in the presence of OH radicals, it

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has a very short lifetime (~ 1-2 days), so it is not amenable to NO2 transport out to sea.

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Consequently, it does not occur in high concentrations over the deep Caspian Sea.

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southwestern Iran, cities around the Persian Gulf present persistently high NO2 levels

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throughout the year that contribute largely to O3 production if the NOx-sensitive regime

15

prevails during the summer. In addition to anthropogenic sources from the oil industry, the

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soil of agricultural land appears to be a source of NO2 in this area in the summer (van der A et

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al., 2008), and the amount of NO2 emissions from soil can be amplified by burning and using

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fertilizers (Jaegle et al., 2005). Figure 3 shows that the seasonal variation of NO2 is evident

19

and consistent with that found by former studies of other regions in the world (e.g., Hilboll et

20

al., 2013). Minimum concentrations of NO2 in the summer principally result from reactions

21

with OH radicals. Since OH reacts more rapidly with NO2 than it does with VOC, the reaction

22

tends to remove NO2 (Seinfeld and Pandis, 2006). However, this photochemical reaction is

23

not the sole reason for the seasonal cycle. According to observations of synoptic stations

24

(Figure S3), local winds in the cities are usually not strong enough (<4 m/s) in the winter to

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transport air pollutants. Another factor that influences the amount of NO2 is topography.

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Mountainous regions, particularly those located to the north and east of Tehran, exacerbate

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conditions created by blocking the dispersion of air pollutants. Because of its calm weather,

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topography, and the substantial difference between radiation emitted from the surface and that

29

received from the sun during the winter, Tehran experiences frequent temperature inversion

30

(almost 250 days per year) (Atash 2007); thus, air pollutants, including NO2, accumulate

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below the inversion cap. From a more global perspective, the boundary layer height plays a

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critical role in controlling the altitude distribution of air pollutants, including NO2. As a

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lower BLH over Iran hinders the dispersion of NO2 in the winter, its concentration increases.

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Another explanation for high NO2 concentrations during the winter in the urban cities is the

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consumption of heating fuel. To compound this problem during the cold months, most

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industrial factories and power plants, to prevent gas shortages, switch from less polluting

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fuels (e.g., gas) to highly polluting fuels (e.g., fuel oil).

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• HCHO/NO2 ratio

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To classify Iran according to its chemical conditions, this work defines the boundaries of

9

NOx-saturated, -mixed and -sensitive regimes as NOx-saturated: OMI HCHO/NO2 < 1; and

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NOx-sensitive: OMI HCHO/NO2 > 2 (e.g., Martin et al., 2004; Choi et al., 2012). Ratios

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greater than two, denoting NOx-sensitive regimes prevalent over most rural regions

12

throughout the year, occur in urban areas during warm seasons. Because of a seasonal decline

13

in HCHO occurring during the fall, NOx-sensitive conditions undergo a seasonal transition to

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NOx-saturated conditions. Typically, high-speed winds blowing over Iran in the spring

15

gradually change NOx-saturated regimes to NOx-sensitive regimes. This change results from

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the introduction of more OH during warm seasons, when NO2 becomes more oxidized.

17

•

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Figure 4 depicts monthly-averaged total CO columns during the daytime. CO levels show

19

high values in the spring (~March) and low values in the summer (~July) and the fall

20

(~October). This pattern is consistent with the observed patterns of CO between 2000 and

21

2004, measured by MOPITT, showing maximum concentrations of CO in the early spring

22

over the northern hemisphere (Edwards et al., 2004). High values of CO in the eastern and

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western Caspian Sea result from peaks in biomass burning in this region (Figure S2). A two-

24

dimentional correlation analysis demonstrates a high correlation between FRP and CO during

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the spring. The noticeable gradient of CO between Iran and the neighbors is attributed to two

26

mutually dependent factors: 1) in the troposphere, a mid-latitude blocking anticyclone event

27

known as the Arabian anticyclone occurs throughout a year (Athar et al., 2013). The

28

amplitude and the location of this high pressure system varies season to season and in

29

different climatic conditions (e.g., El Niño/La Niña). Using a 950hPa wind pattern of NCEP

30

reanalysis from 2005-2012, during cold months (i.e., Jan to Mar), this blocking anticyclone is

31

found at 15-45°N and 20-70°E (Figure S4). In response, the clockwise circulation impedes

32

partly transporting CO from "hotspot" sources in Europe and North America to Iran. Also, a

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thermal inversion is formed, a result of air gradually sinking over the area and being warmed

2

by adiabatic compression (Lin et al., 2009). Additionally, the produced CO from oil industry

3

near the Persian Gulf persistently stay in the west of Iran due to co-location of the center of

4

anticyclone and this region (the most stable part). 2) Secondly, the location of Alborz (from

5

northwest to northeast) and Zagros (from northwest to south) mountains in Iran distinctly

6

contributes to trapping CO before reaching to Iran. The minimum CO in the summer is due to

7

its removal by the reaction of CO+OH, for high OH levels can be found during the summer in

8

the northern hemisphere. Explanations for the minimal CO in the autumn may relate to

9

changes in continental CO sources, or cleaner air masses, all of which could be investigated in

10

future work. As CO typically decreases with altitude, resulting from reduced vertical air

11

masses and the predominance of its sources in the lower troposphere, the Zagros Mountains

12

present minimal CO concentrations throughout the year. Although mountains dominate the

13

regions surrounding Tehran, these regions have high CO levels from anthropogenic emissions

14

transported by prevailing southwesterly winds in the city that create a basin of CO in the

15

northeast. Maximum concentrations of CO occur over southwestern Iran, the region in which

16

most biomass burning activity takes place. These maximum concentrations of CO persist

17

longer than those of NO2 mainly because of the longer lifetime of CO against of the oxidation

18

with OH. Maps of the monthly averages of MOPITT observations of surface CO is provided

19

in the supplement (Figure S5). As expected, high concentrations of surface CO are found for

20

areas of biomass burning in southwest of Iran, and areas with high CO emission from fossil

21

fuel in Tehran. The seasonal variations in surface CO distribution show its peak in winter

22

because of higher fuels consumed and the minimal solar radiation which is a primary factor

23

controlling OH. The difference between peaks in total CO columns and surface mixing ratio

24

might be caused by continental air flows that prevail during the spring, transporting CO from

25

Europe and North America to the troposphere, both of which have high levels of CO.

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Figure 5 shows monthly-averaged TCO. A significant spatial and temporal variation in O3 can

29

be identified during the course of one year. The results show evidence of a seasonal cycle of

30

O3 supported by the fact that high temperature, intensive solar radiation, and a dominant high

31

pressure system during the summer favor the production of ozone. Regardless of location, the

32

concentration of O3 in the spring increases, peaks in July (~40 DU), then decreases during the

TCO

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fall, and gradually declines to a minimum in January (~20 DU). Although O3 changes

2

dramatically in space because of various concentrations of precursors and photochemical

3

conditions, it shows a background value associated with solar radiation (a latitudinal

4

dependence), dynamic transport processes, and concentrations of long lifetime CO. Normally,

5

the Zagros Mountains show minimal O3 (~15 DU), which may be due to reduced vertical air

6

masses, lower CO levels, and other anthropogenic sources, and cooler air that leads to cleaner

7

air. Maximum concentrations of O3 occur over southwestern Iran in the summer (~55 DU).

8

When high VOCs such as HCHO are dominant during the summer in this region (i.e., an

9

NOx-sensitive regime), a relatively high constant amount of NO2 (e.g., the average is

10

3×1015molec./cm2 and the standard deviation 1×1015molec./cm2 in Ahvaz during the period)

11

contributes to the production of ozone. Another explanation for the high production of ozone

12

relates to the transport of air pollutants from west of Iran, including the Mediterranean Basin,

13

Europe, the central Middle East, East Africa, and the United States, identified by the

14

backward trajectory analysis. Such long-range transport is more likely to affect central,

15

western, and northern Iran (Figure S6). Furthermore, over the Indian region, intense lightning

16

coupled with the summer monsoon generates large amounts of NOx in the upper troposphere

17

(Li et al., 2001). The resultant ozone is transported to southern Iran by the tropical easterly jet

18

as part of the anticyclonic circulation over southern Asia.

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In addition to boundary layer O3 sources, summertime stratospheric ozone, which moves

21

downward over the Mediterranean and Middle East, is in higher concentration than

22

springtime stratospheric ozone. In the summer, when the Arabian heat low and the Asian

23

monsoon surface trough couple with anticyclones in the upper troposphere, the tropical

24

easterly jet stream on the southern flank of the monsoonal anticyclone is redirected toward the

25

Arabian anticyclone (Barret et al., 2008). This flow converges with the polar front jet stream,

26

which enhances the horizontal wind and leads to an increase in horizontal and vertical wind

27

shear, creating a jet streak and upper troposphere folds (Traub and Lelieveld, 2003). The

28

convergence causes stratospheric ozone to descend on regions in western Iran. To compute

29

the contribution of stratospheric sources to ozone, Liu et al. (2009) employed a tagged ozone

30

simulation using the GEOS-Chem model and found that they account for only 5 to 15% of

31

TCO. To examine the relative contributions pertaining to various portions of the troposphere,

32

we use TES O3 profiles from the summer of 2005. According to the profiles (Figure S7),

33

tropospheric folding associated with high ozone concentrations in the middle and lower

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troposphere is located at 42-46°E, 31-37°N. In this region, considering pressure levels higher

2

than 200hPa as the troposphere, nearly 49% of TCO is contributed by the boundary layer

3

height (<4km), and more than 90% of tropospheric ozone density is located at altitudes lower

4

than 9km. Approximately 51% of ozone density is found in the upper and middle troposphere,

5

a finding that coincides with that of Li et al. (2001).

6

Please insert figure5 here

4.2

9

4.2.1

Data analysis of the urban cities of Iran Long-term analysis of trends

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Investigations of long-term trends in tropospheric O3 and its precursors in urban cities are

11

important in the context of its role in atmospheric chemistry. As previously mentioned, a

12

seasonal cycle of O3 and its precursors has been observed because of its strong dependence

13

on solar radiation. However, sporadic biomass burning activities and metrological conditions

14

can lead to large interannual variability. Thus, to reduce uncertainty that results from not

15

account for non-seasonal cycles, we use spectal analysis to determine unidentified harmonics.

16

However, using ordinary Fourier spectral analysis is useless, for observations that are not

17

evenly spaced because of the cloud fraction test. Instead, this work uses a method proposed

18

by Amiri-Simkooei and Tiberius (2007), least squares harmonic estimation (LS-HE), which

19

does not require evenly spaced input. Estimation can be performed by finding the spectral

20

value P( i) for a set of discrete frequencies

21

spectral values P( i) for total discrete frequencies, we need to perform only one test to see

22

whether the frequency is indeed considerable. We assume that the original observations were

23

approximately normally distributed and their noise was only white. The method yielded a

24

resulting estimation with a central chi-square distribution of two degrees of freedom

25

(Teunissen, 2002). After finding the main frequencies within a 95% confidence

j

of a sinusoidal function. By estimating the

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indicator, we performed a least-squares fitting on the vector of observations with a set of

27

significant frequencies and fit the following model to the time series:

28

.

(4) 12

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In the above equation, r0 is the mean term,

2

and

3

(harmonics), respectively. The difference between the lower and upper confidence boundaries

4

of the computed trends (95%) is equal to the standard deviation (1.96σ) for the normal

5

distribution. Similar to Hilboll et al. (2013), we perform a bootstrap technique with 2,000

6

iterations on the trend estimator (

7

accelerated confidence interval, we find a statistically significant trend, if and only if the

8

confidence interval does not include zero.

9

The annual trends of HCHO, NO2, and CO for each city are listed in Table1. It should be

10

noted that the trend analysis of NO2 and HCHO (short-lived gases) can be inferred for near

11

surface emission sources. On the other hand, that of total CO columns (a relatively long-lived

12

gas) reflects not only the changes in local sources, but also in those from other parts of the

13

globe (here the Northern Hemisphere; Worden et al., (2013)). Regarding HCHO species, all

14

of the trends are statistically significant. A spatially uniform trend of enhanced HCHO

15

concentrations occurred over all five cities in Iran. The great annual trend, which indicates

16

that an increase in anthropogenic sources occurred in Tehran; particularly abundant

17

formaldehyde compounds result from the combustion (and incomplete combustion) of diesel

18

fuel used in heavy-duty vehicles (Rodrigues et al., 2011). We found the highest average

19

concentrations of HCHO (6.95×1015molec./cm2) for Ahvaz, where oil industry and biomass

20

burning activity is prevalent.

21

Regarding NO2 species, all the cities exhibit a statistically significant upward trend. The

22

results for Tehran strongly agree with those reported by van der A et al. (2008) and Hilboll et

23

al. (2013), who cite measurements of 0.260 ± 0.050 ×1015molec./cm2yr for 1996 to 2006 and

24

0.208 ± 0.068×1015molec./cm2yr, measured by GOME and SCIAMACHY instruments,

25

respectively, for 1996 to 2011. The slight difference may be the result of two factors: First,

26

the period and spatial resolutions differ; second, an offset between the two instruments was

27

not considered in van der A et al. (2008). The results indicate that the increase in population

28

and the number of automobiles in Tehran are larger than they are in the other cities. The high

29

seasonal amplitude in Tehran demonstrates that local winds, temperature inversion, and fuel

30

changes in power plants can lead to various concentrations of NO2 during the winter and other

the amplitude, the phase, and the frequency of the ith component

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. Afterwards, using a 95% bootstrap biased-corrected and

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the trend, m the number of periodic components,

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We obtain the highest mean NO2 values in Tehran (4.30×1015molec./cm2).

seasons.

2

According to the results obtained from the HCHO and NO2 time series, roughly 17%, 27%,

3

23%, 16%, and 28% of the selected days during the 2005-2012 period are NOx-saturated (i.e.,

4

the ratio < 1) for Ahvaz, Isfahan, Mashhad, Tabriz, and Tehran, respectively. The annual

5

values of the ratios demonstrate a steady chemical condition (NOx-saturated) in Tehran,

6

Ahvaz, and Isfahan resulting from an almost simultaneous increase in HCHO and NO2;

7

however, Tabriz and Mashhad exhibit a change from an NOx-saturated/mix to an NOx-

8

sensitive condition.

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9

Concerning the annual trend in CO, some observations between 2009 and 2012 are filtered

11

out because of a cooler (or noisier) anomaly. The results indicate a decrease in species. Using

12

four different nadir-viewing thermal infrared instruments, Worden et al. (2013) showed

13

decreasing trends in the total CO column over the last decade in the eastern United States (~-

14

3.14± 0.44×1016molec./cm2yr-1), Europe (~-3.03±0.46×1016molec./cm2yr-1), and even China (-

15

4.56±1.37×1016molec./cm2yr-1). These downward trends over the cities partly result from the

16

transport of reduced CO from these regions (high background impacts for a long-lived gas). It

17

is not certain whether the local CO emissions of the power and transportations sectors have

18

led to this decline. Accordingly, with more empirical studies, we could shed light on both

19

long-range transport of CO and the implications of emissions policies on the long-term trends.

20

The highest number of CO sources (1.76×1018molec./cm2), which likely originate from

21

biomass burning and anthropogenic emissions from industry can be found in Ahvaz.

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To present a more accurate picture of the impact of precursors on ozone levels, we calculate

25

the long-term trends of average, maximum, and minimum ozone column values (Figure S8).

26

The five cities present an upward trend in their annual peaks. Ahvaz (0.60 ± 0.24 DUyr-1),

27

Isfahan (0.62 ± 0.44 DUyr-1), Mashhad (0.57 ± 0.35 DUyr-1), Tabriz (0.82 ± 1.1 DUyr-1), and

28

Tehran (0.34 ± 0.7 DUyr-1) annual peak trends mainly resulting from an increase in the

29

quantity of precursors. However, such trends are not statistically significant for Tabriz and

30

Tehran. In most of the urban cities in the winter, O3 decreases, the largest decrease observed

31

over Tehran. Annual minimum trends are statistically significant only in Tabriz (-0.50± 0.45

32

DUyr-1) and Tehran (-0.85 ± 0.64 DUyr-1), which may be the result of rising NO2

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concentrations in low HCHO/NO2 ratio conditions that lead to less production of ozone. Since

2

Tehran has the highest positive NO2 annual trend, the largest decrease over Tehran supports

3

this assumption. None of the annual trends are found meaningful because of the minimal

4

number of observations, instrument errors, and high temporal variations of TCO. Using

5

satellite-based measurements to analyze long-term trends of TCO, unlike that of other trace

6

gases, still sometimes remains impractical. It is worth mentioning that, although the trends of

7

TCO precursors control its levels, other relevant agents such as solar radiation, transport

8

sources, convective activities, and stratospheric contributions affect the long-term trends of

9

TCO. Future work could develop a model for quantifying these effects.

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10 5

Conclusion

12

In this study, we examined spatial and temporal variations and conducted a long-term analysis

13

of satellite-based HCHO, NO2, CO, and TCO data from 2005 to 2012 to determine changes in

14

their concentrations over the cities of Ahvaz, Isfahan, Mashhad, Tabriz, and Tehran, all

15

located in Iran. From the discussion above, we can draw the following conclusions:

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1) The results showed high HCHO concentrations in northern Iran resulting from

17

biogenic isoprene emissions and in southwestern Iran resulting from petrochemical

18

and biomass burning activities. Maximum concentrations of NO2 occurred in the

19

wintertime over populated cities mainly because of local weak winds, thin BLH, and

20

peaks in anthropogenic sources in addition to the low amount of OH radicals.

21

Maximum CO columns, the results of both continental air masses transporting high

22

CO (compared with that of other seasons) from Europe and North America and

23

contributions of biomass burning in areas around the Caspian Sea, occurred in the

24

spring, although the external CO emissions were partially blocked by the Arabian

25

anticyclone and the topographic barriers. TCO patterns observed in this study showed

27 28 29

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both latitudinal dependences and their precursors. The most polluted region was located over the Persian Gulf, the result of high NO2 emissions during the summer

when NOx-sensitive conditions prevailed, the contribution of stratospheric sources increased and long-range transport.

30

2) By classifying the Iranian region into NOx-saturated and NOx-sensitive regimes, the

31

study found that most rural areas were persistently NOx-sensitive regimes while

15

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1

populated cities exhibited a chemical transition from a NOx-saturated to NOx-sensitive

2

condition in the spring. 3) An analysis of long-term trends showed an increasing trend in HCHO, NO2, and

4

maximum TCO concentrations in the urban cities but negative (annual minimum O3)

5

trends were found over Tehran due to rising NO2 in winter. Results showed downward

6

annual trends of CO for all cities, partly resulting from a reduction in CO emissions in

7

North America and Europe (and the transport of low CO from there). The difference

8

between annual trends of HCHO and NO2 concentrations gradually changed the

9

chemical conditions of Mashhad and Tabriz from NOx-saturated/mixed to more NOx-

10

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sensitive. Acknowledgements

12

We thank all the mission scientists and the principal investigators who prepared for and

13

provided the satellite data (from OMI, MOPITT, TES, and MODIS) used in this study. We

14

would also like to thank M. Deeter for his constructive suggestion that we use an appropriate

15

MOPITT product. We gratefully acknowledge the two anonymous reviewers whose

16

comments significantly improved the quality of the paper.

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Table 1. Annual trends of HCHO, NO2, and CO and the chemcial conditions over the urban

3

cities of Iran for 2005-2012.

HCHO

NO2

CO

HCHO/NO2 ratio

(×1015molec./cm2yr-1)

(×1015molec./cm2yr-1)

(×1016molec./cm2yr-1)

(unitless)

Ahvaz

0.10 ± 0.05

0.06 ± 0.01

-1.04 ± 0.47

0.02 ± 0.01

Isfahan

0.06 ± 0.05

0.10 ± 0.02

-0.94 ± 0.35

-0.03 ± 0.02

Mashhad

0.04 ± 0.06

0.02 ± 0.01

-0.94 ± 0.46

0.06 ± 0.02

Tabriz

0.08 ± 0.07

0.04 ± 0.01

-0.62 ± 0.46

0.07 ± 0.03

Tehran

0.12 ± 0.05

0.19 ± 0.04

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-1.22 ± 0.34

-0.03 ± 0.01

Gas

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Figure 1. The location of Iran and selected cities used in this study.

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Figure 2. Monthly-averaged OMI HCHO concentrations for 2005-2012

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Figure 3. Monthly-averaged OMI NO2 concentrations for 2005-2012.

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Figure 4. Monthly-averaged MOPITT CO column concentrations during the daytime

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for 2005-2012.

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Figure 5. Monthly-averaged OMI/MLS tropospheric column ozone (TCO) for 2005-

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Highlights:

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Spatial and temporal variations of TCO and its precursors over the Iranian region. Role of transport in air pollutants over Iran. Satellite-based trend analysis of TCO and its precursors in urban cities.

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Seasonal behaviour and long-term trends of tropospheric ozone, its

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precursors and chemical conditions over Iran: a view from space Yunsoo Choi and Amir Hossein Souri

Department of Earth and Atmospheric Sciences, University of Houston, 312 Science & Research

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Building 1, Houston, TX 77204, USA

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Figure S1.Isoprene emissions simulated by MEGANv2.1 for summer of 2013.

Figure S2.Averaged MODIS fire radiative power for 2005-2012.

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Figure S3. Wind rose pattern at urban cities in 2013 for (a) Ahvaz, (b) Isfahan, (c) Mashhad, (d) Tabriz and (e) Tehran.

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Figure S4. Air flows at 950ha in m/s during cold months for 2005-2012.

Figure S5. Monthly-averaged surface CO concentrations for 2005-2012.

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•

Backward trajectory analysis

To determine transport regimes and subsequently to locate external potential source of air pollutants, one should compute backward trajectories. We generate five-day backward

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trajectories arriving at 0:00, 6:00, 12:00, and 18:00 UT at 2000-4500m above mean sea level depending on the boundary layer and surrounding mountains, in the urban cities during all four seasons in 2013 by HYbrid Single-Particle Langrangian Integrated Trajectory (HYSPLIT, ver.4), developed by the USA NOAA Air Resources Laboratory (Draxler and Hess, 1998). The meteorological data used in this model come from an NCEP/NCAR reanalysis with 2.5o×2.5o

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horizontal resolution, 17 vertical resolutions, and a six-hour time resolution. After locating all of the backward trajectories for a city, we use a cluster analysis in the model to classify the

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trajectories into major regimes. According to the cluster means, the origin of transport can be categorized into two main groups; the Central Middle East (CME, 25-40oN, 40-50oE) and the Mediterranean Basin (MB, 30-40oN, 10-35oE).

The MB region, referred to as a “pressure-cooker,” is the crossroad of air pollutants from Europe and Africa, as well as the basin surrounded by megacities, including Cairo, Istanbul, and Athena

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(Kanakidou et al., 2011). Particularly during the summer, on the eastern flank of the Azores High, the dry anticyclonic descent extends across the Mediterranean, transporting European air pollutants to the basin and subsequently to the Middle East (Lelieveld et al., 2002). Tropospheric O3, the major source of air pollution in the area, has been addressed in detail in Kanakidou et al.

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(2011). The MB trajectories intersect the CME region at a higher altitude (>3km) and prevail

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throughout the year.

As seen before, the CME region suffers severely from air pollutants, including O3, NO2, and CO. According to Lelieveld et al. (2009), in the summer, a heat low with cyclonic flow over the southern Arabian Peninsula is generated by hot desert conditions strengthened by the summer monsoon, and it transports air from East Africa (a source of CO). This circulation converges with the northeasterly flow from the Mediterranean over the region. CME trajectories linger a considerable amount of time over western and southwestern Iran during their short-range transport. The regime prevails during the winter and the spring. They generally move through in the lower troposphere (<3km) before subsiding.

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To present a clearer picture of the trajectories, we compute a relative frequency map depending on a grid (0.25o×0.25o) that increases as each trajectory intersects with its cell. Figure S6, which presents the relative frequency maps of all of trajectories arriving in the urban cities, accurately

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depicts the mentioned origins. According to mean clusters, both regimes are dominant for Ahvaz, Isfahan, and Tehran; however, in Tabriz, MB trajectories are more frequent (~70%) than CMEs. Apparently, because of the remote location of Mashhad with respect to the origins, it is subject to

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fewer contributions of direct air pollutants transport.

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Figure S6. Gridded relative frequency maps (0.25o×0.25o) of five-day backward trajectories arriving in the urban cities of Iran in 2013.

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TES O3 profile TES is a nadir and limb viewing infrared Fourier transform spectrometer (FTS) with an equator crossing of near 13:45 local time. The TES instrument and data acquisition modes are well

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discussed by (Beer et al., 2001; Beer, 2006). The data used for the investigation of stratospheric ozone contribution are nadir profiles (TL3O3D.002 product) with a spatial resolution of 2°×4°. Based on the comparison of TES O3 and ozonesondes profiles (Worden et al., 2007), TES is capable of distinguishing between high and low ozone levels in both the lower and upper troposphere; furthermore it can detect large-scale features in ozone profiles.

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Figure S7 depicts the ozone contributions from upper-troposphere/stratosphere to lower troposphere using latitude-pressure and longitude-pressure cross maps of TES O3 for spring and

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summer of 2005. As expected, higher ozone can be seen in springtime in upper-troposphere, while a significant descending can be observed over the region with a folding located at (4246°E, 31-37°N). Moreover, the hotspot is nicely observed from TCO in July in Figure S7, which

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also has been originated from photochemical reactions of TCO precursors.

Figure S7. Latitude/longitude-pressure cross maps retrieved from TES O3 profiles in the summer and the spring of 2005.

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Figure S8. Observed TCO values (blue dots), annual peak (red circle), peak fitted (red line),

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line)

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minimum

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annual average (green circle), average fitted (green line), annual minimum (blue circle), values

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Iran.

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References Beer, R., 2006. TES on the Aura Mission: Scientific Objectives, Measurements and Analysis Overview. IEEE Trans. Geosci. Remote Sens., 44(5), 1102.

System’s Aura satellite. Appl. Opt., 40, 2356–2367.

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Beer, R., T. A. Glavich, and D. M. Rider., 2001. Tropospheric Emission Spectrometer for the Earth Observing

Draxler, R.R., and G.D. Hess, 1998. An overview of the HYSPLIT_4 modeling system of trajectories, dispersion, and deposition. Aust. Meteor. Mag., 47, 295-308.

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Kanakidou, M., et al. 2011. Megacities as hot spots of air pollution in the East Mediterranean. Atmospheric Environment 45(6): 1223-1235.

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Lelieveld, J., et al., 2002. Global air pollution crossroads over the Mediterranean. Science, 298, 794–799. Lelieveld, J., et al., Severe ozone air pollution in the Persian Gulf region. Atmos. Chem. Phys., 9, 1393-1406, doi:10.5194/acp-9-1393-2009.

Worden, H. M., et al., 2007. Comparisons of Tropospheric Emission Spectrometer (TES) ozone profiles to

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ozonesondes: Methods and initial results. J. Geophys. Res., 112, D03309, doi:10.1029/2006JD007258.