NASA/TM-2014-104606, Vol. 32 

Technical Report Series on Global Modeling and 

Data Assimilation, Volume 32 

Max J. Suarez, Editor 

Estimates of AOD Trends (2002 – 2012) over the World’s 

Major Cities based on the MERRA Aerosol Reanalysis 

Simon Provençal, Pavel Kishcha, Emily Elhacham, Arlindo M. da Silva and Pinhas Alpert 

March 2014
NASA STI Program ... in Profile

Since its founding, NASA has been dedicated 

to the advancement of aeronautics and space 

science. The NASA scientific and technical 

information (STI) program plays a key part in 

helping NASA maintain this important role.

The NASA STI program operates under the 

auspices of the Agency Chief Information Officer. 

It collects, organizes, provides for archiving, and 

disseminates NASA’s STI. The NASA STI 

program provides access to the NASA 

Aeronautics and Space Database and its public 

interface, the NASA Technical Reports Server, 

thus providing one of the largest collections of 

aeronautical and space science STI in the world. 

Results are published in both non-NASA channels 

and by NASA in the NASA STI Report Series, 

which includes the following report types:

TECHNICAL PUBLICATION. Reports of 

completed research or a major significant 

phase of research that present the results of 

NASA Programs and include extensive data 

or theoretical analysis. Includes compilations of significant scientific and technical 

data and information deemed to be of 

continuing reference value. NASA counterpart of peer-reviewed formal professional 

papers but has less stringent limitations on 

manuscript length and extent of graphic 

presentations.

TECHNICAL MEMORANDUM. 

Scientific and technical findings that are 

preliminary or of specialized interest, 

e.g., quick release reports, working 

papers, and bibliographies that contain 

minimal annotation. Does not contain 

extensive analysis.

CONTRACTOR REPORT. Scientific and 

technical findings by NASA-sponsored 

contractors and grantees.

CONFERENCE PUBLICATION. 

Collected papers from scientific and 

technical conferences, symposia, seminars, 

or other meetings sponsored or 

co-sponsored by NASA.

SPECIAL PUBLICATION. Scientific, 

technical, or historical information from 

NASA programs, projects, and missions, 

often concerned with subjects having 

substantial public interest.

TECHNICAL TRANSLATION. 

English-language translations of foreign 

scientific and technical material pertinent to 

NASA’s mission.

Specialized services also include organizing 

and publishing research results, distributing 

specialized research announcements and 

feeds, providing information desk and personal 

search support, and enabling data exchange 

services.

For more information about the NASA STI 

program, see the following:

Access the NASA STI program home page 

at http://www.sti.nasa.gov

E-mail your question to help@sti.nasa.gov

Fax your question to the NASA STI 

Information Desk at 443-757-5803

Phone the NASA STI Information Desk at 

443-757-5802

Write to:

STI Information Desk

NASA Center for AeroSpace Information

7115 Standard Drive

Hanover, MD 21076-1320
NASA/TM-2014-104606, Vol. 32

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 32 

Max J. Suarez, Editor 

Estimates of AOD Trends (2002 – 2012) over the World’s 

Major Cities based on the MERRA Aerosol Reanalysis 

Simon Provençal 

Département de géographie, Université Laval, Quebec City, Quebec, Canada 

Pavel Kishcha 

Department of Geophysical, Atmospheric and Planetary Sciences, Tel Aviv University, Tel Aviv, Israel 

Emily Elhacham 

Department of Geophysical, Atmospheric and Planetary Sciences, Tel Aviv University, Tel Aviv, Israel 

Arlindo M. da Silva

Global Modeling and Assimilation Office, NASA/GSFC, Greenbelt, Maryland, USA 

Pinhas Alpert 

Department of Geophysical, Atmospheric and Planetary Sciences, Tel Aviv University, Tel Aviv, Israel 

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

March 2014 
Available from:

NASA Center for AeroSpace Information National Technical Information Service

7115 Standard Drive 5285 Port Royal Road

Hanover, MD 21076-1320 Springfield, VA 22161 Price Code: A17

Level of Review: This material has been technically reviewed by technical 

management.

Trade names and trademarks are used in this report for identification only. Their usage 

does not constitute an official endorsement, either expressed or implied, by the 

National Aeronautics and Space Administration.

Notice for Copyrighted Information

This manuscript has been authored by employees of Universite Laval and Tel-Aviv 

University with the National Aeronautics and Space Administration. The United States 

Government has a non-exclusive, irrevocable, worldwide license to prepare derivative 

works, publish, or reproduce this manuscript, and allow others to do so, for United States 

Government purposes. Any publisher accepting this manuscript for publication 

acknowledges that the United States Government retains such a license in any published 

form of this manuscript. All other rights are retained by the copyright owner. 
Abstract

NASA’s Global Modeling and Assimilation Office has extended the Modern-Era 

Retrospective Analysis for Research and Application (MERRA) tool with five atmospheric 

aerosol species (sulfates, organic carbon, black carbon, mineral dust and sea salt). This inclusion 

of aerosol reanalysis data is now known as MERRAero. This study analyses a ten-year period 

(July 2002 – June 2012) MERRAero aerosol reanalysis applied to the study of aerosol optical 

depth (AOD) and its trends for the aforementioned aerosol species over the world’s major cities 

(with a population of over 2 million inhabitants). We found that a proportion of various aerosol 

species in total AOD exhibited a geographical dependence. Cities in industrialized regions 

(North America, Europe, central and eastern Asia) are characterized by a strong proportion of 

sulfate aerosols. Organic carbon aerosols are dominant over cities which are located in regions 

where biomass burning frequently occurs (South America and southern Africa). Mineral dust 

dominates other aerosol species in cities located in proximity to the major deserts (northern 

Africa and western Asia). Sea salt aerosols are prominent in coastal cities but are dominant 

aerosol species in very few of them. AOD trends are declining over cities in North America,

Europe and Japan, as a result of effective air quality regulation. By contrast, the economic boom 

in China and India has led to increasing AOD trends over most cities in these two highlypopulated countries. Increasing AOD trends over cities in the Middle East are caused by 

increasing desert dust.

i
ii
Table of Contents

List of Tables ........................................................................................................................ iv

List of Figures ........................................................................................................................ v

1 Introduction ................................................................................................................... 1

2 Methodology and data ................................................................................................... 3

2.1 MERRAero aerosol reanalysis ............................................................................................................................ 3 

2.2 Method ......................................................................................................................................................................... 3 

3 Results ........................................................................................................................... 4

3.1 Eastern and central United States and Canada ............................................................................................ 8 

3.2 Western United States and Canada ................................................................................................................ 10 

3.3 Mexico and Central America ............................................................................................................................. 11 

3.4 South America ......................................................................................................................................................... 13 

3.5 Northern Africa ....................................................................................................................................................... 14 

3.6 Southern Africa ....................................................................................................................................................... 16 

3.7 Western Europe...................................................................................................................................................... 17 

3.8 Eastern Europe, including Russia and Turkey ........................................................................................... 21 

3.9 Western Asia ............................................................................................................................................................ 23 

3.10 India and Bangladesh ........................................................................................................................................... 24 

3.11 China ........................................................................................................................................................................... 26 

3.12 Eastern Asia ............................................................................................................................................................. 28 

3.13 Southeastern Asia and Oceania ........................................................................................................................ 30 

4 Discussion .................................................................................................................... 32

5 References ................................................................................................................... 32

6 Appendix: Acknowledgements ..................................................................................... 37

iii
List of Tables

Table 1: Cities with the highest average total AOD values ........................................................4

Table 2: Cities with the highest average SU AOD values .........................................................5

Table 3: Cities with the highest average OC AOD values .........................................................5

Table 4: Cities with the highest average BC AOD values .........................................................6

Table 5: Cities with the highest average SS AOD values ..........................................................6

Table 6: Cities with the highest average DU AOD values .........................................................6

Table 7: List of cities in eastern and central United States and Canada, together with the 10-year mean 

total AOD and its trend for various aerosol species ............................................................8

Table 8: List of cities in western United States and Canada, together with the 10-year mean total AOD 

and its trend for various aerosol species ........................................................................11

Table 9: List of cities in Mexico and Central America, with the 10-year mean total AOD and its trend for 

various aerosol species ...........................................................................................12

Table 10: List of cities in South America, with the 10-year mean total AOD and its trend for various 

aerosol species .....................................................................................................14

Table 11: List of cities in northern Africa, together with the 10-year mean total AOD and its trend for 

various aerosol species ...........................................................................................16

Table 12: List of cities in southern Africa, together with the 10-year mean total AOD and its trend for 

various aerosol species ...........................................................................................16

Table 13: List of cities in western Europe, together with the 10-year mean total AOD and its trend for 

various aerosol species ...........................................................................................19

Table 14: List of cities in eastern Europe, together with the 10-year mean total AOD and its trend for 

various aerosol species ...........................................................................................22

Table 15: List of cities in western Asia, together with the 10-year mean total AOD and its trend for 

various aerosol species ...........................................................................................23

Table 16: List of cities in India and Bangladesh, together with the 10-year mean total AOD values and its 

trend for various aerosol species ................................................................................25

Table 17: List of cities in China, together with the 10-year mean total AOD and its trend for various 

aerosol species .....................................................................................................26

Table 18: List of cities in eastern Asia, together with the 10-year mean total AOD and its trend for various 

aerosol species .....................................................................................................30

Table 19: List of cities in southeastern Asia and Oceania, together with the 10-year mean total AOD and 

its trend for various aerosol species .............................................................................31

iv
v
List of Figures

Figure 1: Proportion of aerosol species in the MERRAero 10-year mean total AOD for the world’s 

megacities ............................................................................................................7

Figure 2: Cities in eastern and central United States and Canada with proportion of aerosol species in the 

MERRAero 10-year mean total AOD ...........................................................................9

Figure 3: Cities in western United States and Canada with proportion of aerosol species in the 

MERRAero 10-year mean total AOD ..........................................................................10

Figure 4: Cities in Mexico and Central America with proportion of aerosol species in the MERRAero 10-

year average total AOD ..........................................................................................12

Figure 5: Cities in South America with proportion of aerosol species in the MERRAero 10-year mean 

total AOD ..........................................................................................................13

Figure 6: Cities in northern Africa with proportion of aerosol species in the MERRAero 10-year mean 

total AOD ..........................................................................................................15

Figure 7: Cities in southern Africa with proportion of aerosol species in the MERRAero 10-year mean 

total AOD ..........................................................................................................17

Figure 8: Cities in western Europe with proportion of aerosol species in the MERRAero 10-year mean 

total AOD ..........................................................................................................18

Figure 9: SO2 emissions in western European countries .........................................................19

Figure 10: Evolution of MERRAero yearly mean OC AOD for a selection of cities in western Europe ..20

Figure 11: Cities in eastern Europe with proportion of aerosol species in the MERRAero 10-year mean 

total AOD ..........................................................................................................21

Figure 12: Evolution of MERRAero yearly mean OC AOD in Moscow .......................................22

Figure 13: Cities in western Asia with proportion of aerosol species in the MERRAero 10-year mean total 

AOD ................................................................................................................24

Figure 14: Cities in India and Bangladesh with proportion of aerosol species in the MERRAero 10-year 

mean total AOD ...................................................................................................25

Figure 15: Cities in China with proportion of aerosol species in the MERRAero 10-year mean total AOD 

........................................................................................................................27

Figure 16: MERRAero yearly mean SU AOD for Chengsha, Zhengzhou, Xi'an and Wenzhou ............28

Figure 17: Cities in eastern Asia with proportion of aerosol species in the MERRAero 10-year mean total 

AOD ................................................................................................................29

Figure 18: Cities in southeastern Asia and Oceania with proportion of aerosol species in the MERRAero 

10-year mean total AOD .........................................................................................31

v
1 Introduction

Microscopic airborne aerosols have long been a prominent topic of study in the field of 

environmental science, predominantly in the atmospheric sciences. A considerable amount of 

literature has emerged in order to better understand the nature of these particles, but more 

specifically, to assess their impact on various spheres of life and the environment. Aerosols are 

found in highly variable space and time distribution, size and chemical composition, and they 

originate from many sources, both natural and anthropogenic [Poschl, 2005].

Aerosols affect considerably the environment and its living organisms. It is well 

documented that aerosols are a serious health hazard to humans, fauna and flora. They are linked 

to cardiovascular, respiratory and allergic diseases, as well as enhanced mortality [Poschl, 2005; 

Tager, 2013]. Aerosols affect weather and climate. Acting as cloud condensation nuclei, 

aerosols are an essential element of cloud formation. As such, they play an indirect role in 

increasing clouds' and the Earth's albedo as a whole [Haywood and Boucher, 2000; Lohmann 

and Feichter, 2005]. They also affect the Earth's radiation budget as absorbers of radiation, 

contributing to a warming of the atmosphere, and as reflectors of radiation, in which case they 

act as a cooling agent [Haywood and Boucher, 2000]. Finally, in high enough concentration, 

they can significantly reduce visibility [Charlson, 1969; Cheng and Tsai, 2000]. This is often 

associated with episodes of haze and smog.

The seriousness of the impacts listed in the previous paragraph is dependent on the aerosol 

concentration and size, but particularly on its chemical composition. It is therefore relevant to 

distinguish between different aerosol species:

Sulfate (SU) aerosols are the product of fossil fuel and, to a much smaller extent, biomass 

burning, and therefore are vastly considered anthropogenic. However, small natural 

contributions originate from volcanoes and the oceans [Haywood and Boucher, 2000; Forster 

et al., 2007, sect. 2.4.4.1];

Organic carbon (OC) is the result of fossil fuel and biomass burning. The former is an 

anthropogenic source while the latter can be both a natural and an anthropogenic source. As a 

whole, sources of OC are widely considered to be anthropogenic [Haywood and Boucher, 

2000; Forster et al., 2007, sect. 2.4.4.3];

Black carbon (BC) originates from the same sources as OC [Haywood and Boucher, 2000; 

Forster et al., 2007, sect. 2.4.4.2];

Sea salt (SS) aerosols originate from the oceans. The release of salt particles from the oceans 

depends on meteorological factors such as surface wind speed [Denman et al., 2007, sect. 

7.5.1.2];

Mineral dust (DU) is the product of wind erosion predominantly in arid environments. 

Therefore, sources are in majority considered natural. However, agricultural and industrial 

practices, as well as deforestation, are responsible for a portion of anthropogenic dust aerosols 

in the atmosphere [Haywood and Boucher, 2000; Forster et al., 2007, sect. 2.4.4.6; Denman et 

al., 2007, sect. 7.5.1.1].

There is scientific interest in studying aerosol pollution in cities because associated 

industries and urban road traffic are major sources of particulate matter. Cities do offer a wide 

1
variety of opportunities, such as better employment, education, health care, entertainment and 

other services, which stimulate an ongoing and accelerating urbanization movement around the 

world [Moore et al., 2003]. In 1950, the United Nations estimated that 29% of the world’s 

population lived in an urban area (http://esa.un.org/unup/CD-ROM/Urban-RuralPopulation.htm). This proportion grew to 47% in 2000 and is expected to reach 67% in 2050. 

Particularly in developing countries, where the rate of urbanization is the greatest [Subbotina, 

2004, chap. 10], cities are lacking the means to adjust fast enough to fulfill the demand of its 

rapidly growing population and economic development. In this respect, urbanization comes with 

its fair share of environmental consequences, among other problems, such as degradation of air 

quality [Shao et al., 2006]. Indeed, atmospheric aerosol concentration is significantly higher in 

populated cities as opposed to rural or remote areas [Cheng and Tsai, 2000], and the cities' 

population growth in developing countries tend to correlate with an increase of aerosol 

concentration [Kishcha et al., 2011]. Rapid urbanization and development in India and China 

resulted in a sharp increase of air pollutant emissions during the last decade [Lu et al., 2011,

Kishcha et al.,2014] and frequently recurring episodes of air pollution and haziness. On the 

other hand, urbanization in developed countries, albeit at a slower rate, hasn't had such a negative 

impact. Developed countries did indeed struggle with severe air pollution issues in the past, but 

their economical and democratic situation provides them with the means to enforce clean air 

regulations and develop green technologies. As a result, air quality has significantly improved 

over the last decades in the United States [Hand et al., 2013], Europe [Vestreng et al., 2007] and 

Japan [Kanada et al., 2013], even though their population and GDP kept on growing.

Alpert et al. [2012] estimated aerosol optical depth (AOD) trends over the 189 largest 

cities in the world during the period from 2002 to 2010. They used three remote sensing 

instruments of the National Aeronautics and Space Administration (NASA): the two Moderate 

Resolution Imaging Spectroradiometers (MODIS) and the Multiangle Imaging 

SpectroRadiometer (MISR) on board the Terra and Aqua satellites. They found that AOD trends 

exhibit some geographical dependence: trends were increasing in Indian, Middle Eastern and 

Chinese cities, while they were decreasing in European, north-eastern American and southeastern Asian cities. The clear advantage of using satellites to study aerosols, compared to 

ground-based measurements, is the global coverage. The major deficiency, however, is the 

inability to distinguish between aerosol species.

Several years ago, NASA’s Global Modeling and Assimilation Office (GMAO) introduced 

the Modern-Era Retrospective Analysis for Research and Application (MERRA), a reanalysis 

tool incorporating satellite data to reproduce spatially consistent observations for many 

environmental variables. While the original MERRA included only meteorological parameters 

(winds, temperature, humidity, etc.), it has recently been extended to include assimilation of 

AOD from the MODIS sensor on both Aqua and Terra, which let to its rebranding now known as 

MERRAero. Although only the total AOD is constrained by MODIS observations, the data 

assimilation algorithm in MERRAero provides speciated monthly average, with the relative 

contribution from all major aerosol species (dust, sea salt, sulfates and carbonaceous).

The current study is aimed at estimating the contribution of these aerosol species to total 

AOD and their trends over the world’s largest agglomerations (with population over 2 million 

people, hereafter simply referred to as “cities”) using the obtained ten-year (July 2002 – June 

2
2012) assimilated aerosol dataset. We focus our attention on dominant aerosol species and their 

trends over the specified cities located in different geographical regions.

2 Methodology and data 

2.1 MERRAero aerosol reanalysis

The NASA GEOS-5 model was used to extend the MERRA reanalysis with five 

atmospheric aerosol components (sulfates, organic carbon, black carbon, mineral dust and sea 

salt). GEOS-5 is the latest version of the NASA GMAO Earth system model. GEOS-5 contains 

components for atmospheric circulation and composition (including atmospheric data 

assimilation), ocean circulation and biogeochemistry, and land surface processes. GEOS-5

includes a module representing atmospheric aerosols [Colarco et al., 2010, and references 

therein]. This aerosol module is based on a version of the Goddard Chemistry, Aerosol, 

Radiation and Transport (GOCART) model [Chin et al., 2002]. GOCART treats the sources, 

sinks and chemistry of dust, sulfate, sea salt, and black and organic carbon aerosols. Aerosol 

species are assumed to be external mixtures. Except for biomass burning sources, aerosol 

emissions are based on the AeroCom version 2 hindcast inventories [Dr. Thomas Diehl, personal 

communication, and http://aerocom.met.no/emissions.html]; daily biomass burning aerosols are 

from the Quick Fire Emission Dataset (QFED, Darmenov and da Silva, 2013). Total mass of 

sulfate and carbonaceous aerosols are tracked, while for dust and sea salt, the particle size 

distribution is explicitly resolved across five non-interacting size bins for each. Both dust and 

sea salt have wind speed dependent emission functions, while sulfate and carbonaceous species 

have emissions principally from fossil fuel combustion, biomass burning and bio-fuel 

consumption, with additional biogenic sources of organic carbon. Sulfate has additional 

chemical production from oxidation of SO2 and dimethylsulfide (DMS), as well as a database of 

volcanic SO2 emissions and injection heights. GEOS-5 also includes assimilation of AOD 

observations from the MODIS sensor on both Terra and Aqua.

2.2 Method

In order to identify the cities of interest, Alpert et al. [2012] used data from City 

Population (http://www.citypopulation.de/world/Agglomerations.html), where all cities with a 

minimum population of 2 million inhabitants were selected. We use the same list of cities, even 

though the data from City Population is constantly being updated and its current list differs 

slightly from Alpert et al.'s [2012] list. A given city's population is only a mere criteria for 

selection, and therefore we have no reason to believe that neither Alpert et al.'s [2012] list, nor 

the current list from City Population, would produce significantly different results.

In order to retrieve data from MERRAero, a consistent resolution of 0.5o × 0.5o was used 

over every city, which is approximately MERRAero’s finest resolution. In order to define 

meridional and zonal boundaries, the coordinates (x, y) of a given city's center were subtracted 

3
and added by 0.25o, i.e. for longitudinal boundaries: x ± 0.25o; and similarly for latitudinal 

boundaries: y ± 0.25o. First, MERRAero data were extracted for a period of 10 years, from July 

2002 to June 2012. Then, the ten-year mean AOD was obtained for total and every one of the 

aerosol species. The AOD trend values in percentage form (for total and every one of the 

modeled aerosol species) correspond to the difference between the AOD averaged over the last 

5-year period (July 2007 – June 2012) and the AOD averaged over the first 5-year period (July 

2002 – June 2007), using as reference the AOD average over the first period.

3 Results 

We first analyzed the raw data of total AOD averaged over the whole 10-year period. 

Table 1 illustrates 20 cities with the highest total AOD. Interestingly, Chinese cities occupy the 

entire list except for two Pakistani cities. If the list went down further, we would notice that all 

of the 50 cities with highest AOD are in Asia, except for six African cities. By contrast, cities 

with the lowest AOD don't seem to exhibit any particular geographical resemblance, spread over 

all other regions of the world.

Table 1. Cities with the highest average total AOD values

Rank City Country AOD

1 Wuhan China 0.723

2 Changsha China 0.719

3 Zhengzhou China 0.706

4 Nanjing China 0.655

5 Nanchang China 0.654

6 Shantou China 0.642

7 Wuxi China 0.613

8 Jinan China 0.610

9 Tientsin China 0.601

10 Suzhou China 0.600

11 Zibo China 0.584

12 Shijiazhuang China 0.580

13 Shanghai China 0.561

14 Nanning China 0.557

15 Qingdao China 0.552

16 Beijing China 0.550

17 Lyallpur Pakistan 0.537

18 Guangzhou China 0.528

19 Lahore Pakistan 0.521

20 Xi'an China 0.511

Next, we analyzed the same data for every one of the aerosol species. These are illustrated 

in Tables 2 to 6 for the 10 cities with highest AOD respective to the species. Values of SU AOD 

(Table 2) are the highest among all species with Chinese cities occupying the first 19 cities with 

highest values and Asian cities occupying the first 52 cities, confirming high consumption of 

4
fossil fuels in this region. Cities with the highest OC AOD values (Table 3) are mostly in China 

and India, with a few African cities included in the list, indicative of fuel and/or biomass 

burning. This is somewhat confirmed by BC AOD values (Table 4). The top 10 cities for this 

particular species are all in China while the top 22 cities are either in China or India. Relatively, 

BC AOD values are much lower compared to other species. SS AOD values (Table 5) are also 

fairly low. Nonetheless, cities with highest SS AOD values tend to be located close to the ocean 

and/or on a small island. DU AOD values (Table 6) are highest in cities of northern Africa and 

the Middle East, areas characterized by arid deserts. This brief analysis highlights, as expected, 

among the cities with the highest AOD, the fact that anthropogenic aerosols, mostly SU, are 

responsible for these high values. Tables 1 to 6 also exhibit an obvious geographical dependence 

not only for total AOD, but also for AOD of all aerosol species.

Table 2. Cities with the highest average SU AOD values

Rank City Country AOD

1 Changsha China 0.496

2 Wuhan China 0.494

3 Zhengzhou China 0.449

4 Nanchang China 0.446

5 Nanjing China 0.437

6 Shantou China 0.416

7 Wuxi China 0.403

8 Suzhou China 0.394

9 Jinan China 0.385

10 Tianjin China 0.372

Table 3. Cities with the highest average OC AOD values

Rank City Country AOD

1 Kinshasa Congo 0.206

2 Luanda Angola 0.197

3 Nanning China 0.139

4 Changsha China 0.122

5 Asunción Paraguay 0.117

6 Nanchang China 0.112

7 Wuhan China 0.111

8 Guangzhou China 0.110

9 Ibadan Nigeria 0.110

10 Patna India 0.108

The geographical dependence is also illustrated by Fig. 1 which represents the proportion 

of aerosol species in the ten-year mean total AOD over the world’s megacities (i.e. the cities with 

population over 10 million). One can see that (a) megacities in the industrialized regions of 

North America, Europe and eastern Asia exhibit a strong presence of SU aerosols; (b) dust 

aerosol was dominant in megacities in northern Africa and the Middle East; and (c) South 

American megacities were characterized by a high proportion of organic carbon. In the 

following subsections, we concentrate on specific geographical regions.

5
Table 4. Cities with the highest average BC AOD values

Rank City Country AOD

1 Zhengzhou China 0.074

2 Shijiazhuang China 0.066

3 Tianjin China 0.064

4 Beijing China 0.064

5 Wuhan China 0.060

6 Jinan China 0.059

7 Shantou China 0.058

8 Changsha China 0.056

9 Nanjing China 0.055

10 Zibo China 0.055

Table 5. Cities with the highest average SS AOD values

Rank City Country AOD

1 Fortaleza Brazil 0.058

2 San Juan Puerto Rico 0.054

3 Taipei Taiwan 0.052

4 Karachi Pakistan 0.052

5 Santo Domingo Dominican Republic 0.052

6 Salvador Brazil 0.051

7 Dar es Salaam Tanzania 0.051

8 Belem Brazil 0.049

9 Manila Philippines 0.048

10 Mumbai India 0.048

Tables 6. Cities with the highest average DU AOD values

Rank City Country AOD

1 Kano Nigeria 0.378

2 Dakar Senegal 0.317

3 Khartoum Sudan 0.290

4 Baghdad Iraq 0.275

5 Faisalabad Pakistan 0.269

6 Lahore Pakistan 0.248

7 Hyderabad Pakistan 0.243

8 Gujranwala Pakistan 0.232

9 Jidda Saudi Arabia 0.218

10 Karachi Pakistan 0.216

6
Fig. 1. Proportion of aerosol species in the MERRAero 10-year mean total AOD for the world’s 

megacities.

7
3.1 Eastern and central United States and Canada

Total AOD values for cities in this part of the world are given in Table 7. The northeast is 

recognized as the most industrialized part of North America. According to Hand et al. [2012], 

the eastern states emit a substantially higher amount of SO2 compared to the western states. This 

can explain why the majority of total AOD comes from SU aerosols in most cities displayed in 

Fig. 2. Note that cities in Florida, because they are surrounded by the Atlantic Ocean and the 

Gulf of Mexico, experience some significant contribution from SS aerosols. OC is second in 

importance and contributes about a quarter to total AOD in all the cities except Miami (19.0%).

Table 7. List of cities in eastern and central United States and Canada, together with the 10-year 

mean total AOD and its trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

State or province City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Québec Montréal 0.163 -5.0 -3.2 -12.9 -5.5 4.9 9.2

Ontario Toronto 0.170 -5.0 -5.9 -7.7 -4.1 7.6 6.9

Massachusetts Boston 0.172 -6.4 -6.0 -12.4 -5.6 -3.7 8.8

New York New York City 0.195 -4.0 -5.1 -6.1 -3.1 4.5 6.5

Pennsylvania Philadelphia 0.199 -5.3 -7.0 -6.7 -3.7 4.5 6.0

Pittsburgh 0.191 -7.1 -9.4 -6.5 -4.5 3.8 7.7

D.C. Washington 0.198 -9.2 -11.2 -10.4 -6.6 -0.2 5.5

Michigan Detroit 0.183 -4.0 -4.9 -6.2 -4.1 12.0 7.7

Ohio Cleveland 0.188 -5.9 -7.4 -7.5 -4.4 11.0 8.0

Cincinnati 0.198 -7.1 -9.1 -7.6 -3.8 3.0 8.0

Illinois Chicago 0.183 -2.2 -3.3 -4.0 -2.2 10.8 10.0

Minnesota Minneapolis 0.161 -1.3 -1.4 -4.7 -2.5 12.7 7.9

Missouri St. Louis 0.187 -4.9 -5.7 -8.5 -4.2 10.6 8.9

Kansas City 0.176 -1.1 -1.6 -5.1 -1.5 21.6 9.1

Colorado Denver 0.096 -5.9 -7.5 -10.4 -2.8 3.6 4.9

Texas

Dallas 0.185 -4.2 -8.2 -6.0 -1.2 28.6 5.2

Houston 0.197 -6.3 -9.5 -12.3 -4.0 13.6 4.5

San Antonio 0.183 -5.5 -12.2 -4.8 1.6 13.0 6.4

Georgia Atlanta 0.176 -9.1 -10.5 -11.0 -6.0 2.3 1.5

Florida

Orlando 0.164 -7.6 -7.7 -10.7 -5.8 -7.7 3.4

Tampa 0.158 -9.6 -8.5 -17.6 -9.3 -6.8 2.7

Miami 0.137 -7.5 -4.9 -15.5 -8.9 -6.2 -4.3

We found that, with respect to aerosol trends, every single city in this region noticed a 

decrease in total AOD as well as in AOD for every anthropogenic aerosol species (SU, OC and 

BC), except for the 1.6% BC AOD increase in San Antonio (Table 7). Our results on AOD 

trends are in line with previous studies [de Meij et al., 2012; Hsu et al., 2012] founded on 

ground-based and satellite data which indicated that AOD has decreased across the United States 

and in North America over the last decade. The declining AOD trends are most likely the result 

of effective air quality regulation in the United States known as the Clean Air Act, first adopted 

in 1970 and significantly amended in 1990. The latest air quality status report of the 

8
Environmental Protection Agency (EPA), which is responsible for enforcing the Act and 

monitoring its outcomes, acknowledged the success of this regulation at a national scale by 

reporting a 50% decrease in SO2 concentration, a precursor of SU aerosols, between 2001 and 

2010 [EPA, 2012]. Hand et al. [2012, 2013] analyzed ground-based sulfate concentration across 

the U.S. from the IMPROVE (rural) and CSN (urban/suburban) air quality networks between 

2000 and 2010. They reported a decrease at nearly all stations and in all regions of the U.S.; 

urban and rural concentrations decreased at a similar rate. Even more so, Hand et al. [2012] 

reported that sulfate concentration has decreased much more substantially over the eastern U.S. 

than over the western U.S., while Hand et al. [2013] and Xing et al. [2012] reported that the 

reduction in SO2 emissions is the highest over the eastern U.S. as well. They also compared 

these data with SO2 emissions obtained from the EPA's National Emissions Inventory and found 

a positive correlation between SO2 emissions and SU concentration. The IPCC reported a 24% 

decrease in SO2 emissions between 2000 and 2010 in the U.S. [de Meij et al., 2012]. Granier et 

al. [2011] conducted a review of different inventories of anthropogenic and biomass burning 

emissions on both global and regional scales between 1980 and 2010. For SO2 emissions in the 

U.S., the inventories agree well with decreasing trends since 2000. While analyzing air quality 

trends in New York City, Buckley and Mitchell [2011] found some significant decrease in both 

SO2 concentration and PM2.5 concentration between the years 2000 and 2007.

Fig. 2. Cities in eastern and central United States and Canada with proportion of aerosol species 

in the MERRAero 10-year mean total AOD.

9
Although southern Ontario has its own active industrial sector, it is documented that air 

advection from the United States to Canada leads to poor air quality when meteorological 

conditions are appropriate [Chung, 1978]. In this respect, it is possible to link decreased AOD 

values in north-eastern U.S. cities to decreased AOD values in Canadian cities in proximity, such 

as Toronto and Montréal. As well, reports from both Ontario's Ministry of the Environment 

[2013] and Québec's Ministère du Développement Durable, de l'Environnement, de la Faune et 

des Parcs [MDDEFP, 2011] indicated a substantial drop in SO2 emissions in their respective 

province since the year 2002.

3.2 Western United States and Canada

For cities in this part of the world, SU aerosols account for most of total AOD, as shown in 

Fig. 3. The second biggest contribution is from OC, which accounts from a quarter to a third of 

total AOD. DU aerosols account for a significant portion of total AOD, especially in Phoenix, 

located in a particularly arid area.

Fig. 3. Cities in western United States and Canada with proportion of aerosol species in the 

MERRAero 10-year mean total AOD.

10
Five of the eight cities listed in Table 8 have increasing total AOD trends. This region was 

also targeted by air quality regulations, and therefore arguments presented in Section 3.1 should 

apply here as well. In some cases, the total AOD increase is entirely attributed to an increase in 

OC AOD. The most probable cause could be increasing wildfire activity in this region over the 

last decade. Wildfire activity is measured by two variables: the number of wildfires and the area 

burned. The former isn't a reliable metric to measure carbon emissions because the relative size 

of wildfires is an important factor to consider. As noted by Miller and Safford [2012], wildfires 

400 ha or larger account for only 5% of all wildfires in the western U.S. since 1984, but are 

responsible for 95% of total area burned. The latter is the more reliable metric but still 

insufficient because the type and density of burning biomass affect the amount of carbon 

released. Spracklen et al. [2007], who modeled OC emissions from summer wildfires in the 

western U.S. between 1980 and 2004, found some correlation between the area burned and OC 

emissions. However, varying wildfires in different ecosystems is the reason why these two 

variables don't always correlate. They arrived at the conclusion that variability of OC 

concentration in the western U.S. was largely due to the variability in wildfire emissions. It is 

worth noting, however, that their study period barely overlaps with our own, so we cannot 

extrapolate their findings to our results. Nevertheless, they offer a likely explanation that 

justifies the positive OC AOD trends in Table 8. Since wildfires usually occur in forested 

(remote) areas, the extent to which they affect air quality in cities is dependent on regional 

meteorology. This could explain why some cities in Table 8, such as San Francisco and San 

Diego, were not affected by wildfire emissions.

Table 8. List of cities in western United States and Canada, together with the 10-year mean total 

AOD and its trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust.

State or province City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

British Columbia Vancouver 0.122 10.2 6.3 21.1 8.5 8.5 2.6

Washington Seattle 0.125 5.9 4.3 8.3 3.4 14.8 3.3

Oregon Portland 0.121 5.5 3.7 6.0 2.8 17.6 4.9

California

Sacramento 0.116 1.5 -4.0 9.2 3.8 -9.3 5.6

San Francisco 0.108 -2.1 -2.2 -4.7 -0.9 -6.1 5.2

Los Angeles 0.126 8.0 -5.6 31.9 9.4 -3.5 2.1

San Diego 0.114 -6.9 -5.8 -13.4 -8.9 -4.9 1.9

Arizona Phoenix 0.130 -0.8 -3.7 -6.3 -0.6 11.0 7.7

3.3 Mexico and Central America

Total AOD values over cities in this region are listed in Table 9. The three Mexican cities, 

as well as the sole continental Central American city, Guatemala City, have the most important 

contribution from SU aerosols. OC aerosols account for 27.4% to 33.8% of total AOD (Fig. 4). 

Cities in the Caribbean have a more heterogeneous distribution of aerosol species, with SS, DU 

and SU aerosols being the most important.

11
Table 9. List of cities in Mexico and Central America, with the 10-year mean total AOD and its 

trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust.

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Mexico

Monterrey 0.139 -3.6 -9.2 4.8 4.2 -1.5 -1.8

Guadalajara 0.137 -9.4 -11.6 -7.5 -5.0 -4.8 -8.2

Mexico City 0.143 -6.4 -9.5 -2.5 2.4 -4.3 -9.2

Guatemala Guatemala City 0.161 1.0 4.9 0.4 -5.1 -11.0 -23.0

Haiti Port-au-Prince 0.110 -6.7 -4.4 -12.4 -4.8 -2.7 -11.2

Dominican Rep. Santo Domingo 0.141 -6.2 -4.8 -16.6 -6.6 -2.3 -9.4

Puerto Rico San Juan 0.129 -2.4 -4.7 -11.7 -6.9 3.5 -6.5

Fig. 4. Cities in Mexico and Central America with proportion of aerosol species in the 

MERRAero 10-year average total AOD.

The trends in total AOD over all the cities are decreasing, except for the trend over 

Guatemala City which showed a slight increase of 1.0% (Table 9). In Mexican cities, the 

decrease in total AOD is attributed to the decrease in SU AOD. Much like the United States 

government, the Mexican government implemented management programs and incentives to 

improve air quality in the 1980's. This effort has proven to be fairly effective, as the 

concentration of SO2 and that of particulate matter have decreased in Mexico City since then 

[Molina and Molina, 2004a, b]. Parrish et al. [2011] showed that the trend in SO2 concentration 

keeps decreasing until 2010.

The decline in total AOD over the three Caribbean cities is due to a decrease of DU, OC 

and SU aerosols AOD. The dust load is the result of transport from the Sahara desert. The only 

12
locations where dust aerosols are monitored in the Caribbean are Barbados and Puerto Rico. 

Prospero and Mayol-Bracero [2013] published a portion of this record, for Barbados, between 

2004 and 2009. The seasonal variability of dust load is quite large, but overall, there seems to be 

a slight decrease over the period. Unfortunately, according to Prospero and Mayol-Bracero

[2013], the meteorological dynamics that drive dust transport are not well understood.

3.4 South America

For the distribution of aerosol species, Caracas is the city with the most heterogeneous 

distribution (Fig. 5). Since it is located close to the Caribbean Sea and quite far from all the 

other major cities in South America, SS aerosols account for 30.0% of its total AOD. SU and 

OC aerosols AOD account together for 50.8% of the total. Santiago is the only city west of the 

Andes and the only one where SU aerosols account for the majority of total AOD. The three 

north-most cities in Brazil (Salvador, Fortaleza and Belém), located on the eastern coast of the 

country, have some significant contribution from SS aerosols. All other cities between Buenos 

Aires and Brasilia have the significant contribution to total AOD from OC aerosols. In most of 

them, OC is the major contributor to total AOD while SU is second in importance (Fig. 5). 

Deforestation and biomass burning for agricultural purposes in the Amazon rainforest could be 

responsible for such a substantial presence of OC aerosols in the air.

Fig. 5. Cities in South America with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

13
All the cities in the eastern part of South America experienced a declining trend in total 

AOD during the 10-year study period (Table 10). Moreover, with rare exceptions, all aerosol 

species exhibit declining (negative) AOD trends. Fortaleza is the only city with an increase 

(0.5%) in OC AOD, and Asunción is the only city with an increase (1.0%) in SU AOD (Table 

10).

Table 10. List of cities in South America, with the 10-year mean total AOD and its trend for 

various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Venezuela Caracas 0.134 -9.5 -2.3 -17.5 -12.5 -12.4 -6.0

Brazil

Belém 0.141 -10.6 -28.9 -15.4 -11.2 5.4 -9.5

Fortaleza 0.116 -8.9 -25.8 0.5 1.8 -7.2 -8.7

Salvador 0.106 -7.1 -16.1 -5.4 2.1 -4.6 1.3

Brasilia 0.107 -12.1 -13.4 -12.6 -4.8 -12.1 -12.1

Goiânia 0.117 -9.9 -8.6 -11.0 -6.2 -11.8 -13.4

Belo Horizonte 0.101 -13.8 -9.7 -18.4 -8.0 -11.7 -10.6

Rio de Janeiro 0.139 -9.0 -3.2 -17.0 -3.2 -7.1 -3.8

Campinas 0.140 -9.1 -3.9 -14.0 -6.0 -9.5 -8.9

São Paulo 0.143 -9.4 -4.3 -14.9 -5.2 -10.5 -6.5

Curitiba 0.127 -13.3 -4.3 -21.3 -12.4 -9.6 -1.0

Porto Alegre 0.164 -8.5 -5.9 -13.8 -7.5 2.1 7.1

Paraguay Asunción 0.200 -7.3 1.0 -11.7 -7.0 4.4 0.6

Argentina Buenos Aires 0.162 -0.8 -4.7 -6.1 0.7 13.8 27.4

Chile Santiago 0.095 4.3 4.4 -2.4 5.5 16.7 15.1

Previous publications support our findings on the declining AOD trends in South America. 

According to de Meij et al. [2012], between 2000 and 2009, ground-based observations from 

stations spread across South America showed a decline in total AOD over this region. Between 

1997 and 2009, van der Werf et al. [2010] modeled carbon mass emissions from wildfires in 

South America by using active fire data, area burned data and vegetation productivity indices 

from satellite. They detected some decrease in mass emissions from wildfires between 2002 and 

2009 (their Table 7 and Fig. 9). However, Cifuentes et al. [2005] discussed the fact that air 

quality data in Latin America is scarce, lacking, incomplete, outdated, unreliable or difficult to 

access. This complicates the analysis and explains why most papers on this topic use remote 

sensing instruments. Further research is needed to understand the probable reasons for the

obtained declining AOD trends.

3.5 Northern Africa

The standout feature of northern Africa is obviously the Sahara desert. Desert DU aerosols 

account for the most important contributor to total AOD in all the cities in this region (Fig. 6). In 

14
the cities located in immediate proximity to the desert (Cairo, Khartoum, Kano, Adis Abeba and 

Dakar), the contribution of DU to total AOD exceeds 70% in some of them. SU aerosols are the 

second most important contributor to total AOD in the four cities on the northern coast, while the 

four coastal cities near the equator (Abidjan, Accra, Lagos and Ibadan) experience a significant 

contribution from OC aerosols to total AOD.

Fig. 6. Cities in northern Africa with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

As shown in Table 11, in the cities located in immediate proximity to the desert (Cairo, 

Khartoum, Kano, Adis Abeba and Dakar), the increasing trend in total AOD is mostly driven by 

the increasing trend of DU AOD. The cities near the equator saw mainly a decrease in total, DU 

and OC AOD; the cities in the vicinity of the desert (Cairo, Khartoum, Kano, Adis Abeba) saw 

an increase in total and DU AOD; the northwestern coastal cities saw mainly a decrease in total,

DU and SU AOD.

It is important to mention that MERRAero doesn’t assimilate AOD data over bright 

surfaces such as deserts. Therefore, over the source region, the data is constrained primarily by 

parameterized emissions determined by wind speed. However, the darker surface of cities might 

have been sufficient to provide assimilated data in and around them.

15
Table 11. List of cities in northern Africa, together with the 10-year mean total AOD and its 

trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Morocco Casablanca 0.178 -1.9 0.3 -15.6 -4.2 1.0 -1.1

Algeria Algiers 0.190 -9.4 -0.8 -0.8 1.0 0.4 -17.9

Tunisia Tunis 0.200 -10.7 -5.6 -13.7 -4.1 -7.5 -14.7

Egypt Cairo 0.257 2.4 2.8 -2.3 3.0 -0.7 3.0

Senegal Dakar 0.411 0.5 -3.0 -16.3 -11.0 5.7 2.3

Côte d'Ivoire Abidjan 0.300 -9.3 -21.4 -5.2 -8.0 12.6 -11.8

Ghana Accra 0.353 -8.6 -21.5 -5.0 -7.1 15.4 -10.1

Nigeria

Lagos 0.394 -6.1 -19.7 -3.1 -5.3 12.4 -6.9

Ibadan 0.406 -3.2 -13.2 1.4 -4.0 24.1 -5.0

Kano 0.485 2.2 6.4 0.5 -3.7 13.9 2.2

Sudan Khartoum 0.396 7.0 11.4 8.5 4.6 8.5 6.0

Ethiopia Addis Abeba 0.164 15.5 5.7 15.5 9.1 17.4 23.6

3.6 Southern Africa

AOD values for cities in this part of the world are listed in Table 12. South Africa, being 

the most industrialized country in this region, is where SU aerosols account for the largest 

proportion of total AOD (Fig. 7). The two coastal cities of Cape Town and Durban are 

exceptions with a significant contribution from SS aerosols. Total AOD for the other cities 

further north is dominated by OC aerosols, except for Dar es Salaam located on the Indian Ocean 

coast.

Table 12. List of cities in southern Africa, together with the 10-year mean total AOD and its 

trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Congo Kinshasa 0.344 -9.0 -52.4 9.4 12.3 16.5 -24.2

Angola Luanda 0.350 -1.9 -38.2 6.6 7.8 26.8 -4.6

Kenya Nairobi 0.090 3.4 -21.2 14.3 6.8 17.7 29.4

Tanzania Dar es Salaam 0.129 -1.2 -27.4 10.9 9.8 1.3 30.0

Zimbabwe Harare 0.116 -2.9 -13.6 2.8 8.3 1.4 4.2

South Africa

Pretoria 0.163 2.4 -2.8 8.0 10.9 0.1 12.1

Johannesburg 0.163 1.8 -2.4 6.5 9.6 0.4 11.5

Durban 0.180 -1.4 -4.5 1.2 3.3 -1.2 6.0

Cape Town 0.085 -1.9 -6.5 -6.2 -1.1 7.1 2.1

Total AOD trends in this region are rather weak, except for the 9.0% decrease in Kinshasa 

(Table 12). Much like South America, the predominance of OC aerosols in the air could be 

16
explained by biomass burning in the African savannah and rainforests during the burning season 

(May to October). Although extensive fire data are available in six protected and confined areas 

in southern Africa [Archibald et al., 2010], forest cover and loss statistics are generally lacking 

[Mercier, 2012], as well as fire data [Ito et al., 2007]. This leaves only remote sensing and 

model output as means to study this issue broadly. Archibald et al. [2009] claimed that human 

activities are the main sources of ignition in this region, as opposed to natural causes, but also 

affirmed that increased human activities doesn't necessarily imply an increase in burned area, 

most likely because climatic factors determine to which extent ignited fires will burn [Archibald 

et al., 2010]. Although forest preservation policies have been implemented in Africa, Mercier 

[2012] calls them a failure and assures that deforestation is ongoing; from 2005 to 2010, the 

deforestation rate in Africa was the second highest among all continents, behind Central 

America. van der Werf et al.'s [2010, their Table 7 and Fig. 9] modeled carbon emission results 

suggested slight increasing trends in OC between 2002 and 2009. All of these findings support 

the OC AOD trends shown in Table 12.

Fig. 7. Cities in southern Africa with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

3.7 Western Europe

The situation in western Europe is similar to that in North America. As seen in Fig. 8, SU 

aerosols are the most important aerosol species there. Over cities located in southern Europe, 

17
DU aerosols are the second most important contributor to total AOD (Fig. 8). This is in line with 

Querol et al. [2009], who analyzed observations of Saharan dust transport over the 

Mediterranean.

Fig. 8. Cities in western Europe with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

Cities in this region saw mainly decreasing trends for total AOD and for every aerosol 

species (except for Lisbon and Munich's slight SU AOD increases of 0.6% and 1.7% 

respectively) (Table 13). This is in line with de Meij et al. [2012] and Hsu et al. [2012], who 

found that ground-based and satellite-based remote sensing observations showed decreasing 

AOD trends over Europe as a whole. The European Environmental Agency [EEA, 2013] 

reported that implementation of air quality policies and greener fuels and technology contributed 

to a 55% reduction of SO2 emissions between 2000 and 2011 among all members of the 

European Union. SO2 emissions data are shown in Fig. 9 for the six countries listed in Table 13. 

A near constant decrease is noticeable for all of them. Emissions of total suspended particulates 

also decreased by 14% during the same period. Numbers from the IPCC for the whole of Europe 

are more conservative (20% decrease between 2000 and 2010 for SO2 emissions [de Meij et al.,

2012]), but substantially decreasing nonetheless. Granier et al.'s [2011] emissions inventory 

revue claimed that most inventories agree with a decreasing trend since 2000 as well. Cusack et 

al. [2012] analyzed data from many monitoring stations across Europe also found decreasing 

PM2.5 concentration trends over the last decade.

18
Table 13. List of cities in western Europe, together with the 10-year mean total AOD and its 

trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Portugal Lisbon 0.131 -7.8 0.6 -24.0 -10.4 -1.2 -13.7

Spain Madrid 0.108 -9.5 -3.5 -15.7 -7.5 -8.4 -16.3

Barcelona 0.148 -9.4 -2.7 -13.0 -3.6 -3.2 -20.2

United Kingdom Birmingham 0.126 -10.2 -5.7 -24.2 -10.3 -6.9 -10.2

London 0.132 -9.1 -5.1 -20.5 -9.1 -6.0 -11.4

France Paris 0.143 -7.1 -3.3 -16.8 -5.5 -6.8 -8.4

Germany

Ruhr 0.149 -6.8 -1.4 -20.0 -6.9 -10.7 -6.3

Hamburg 0.145 -8.4 -4.5 -21.7 -9.9 -4.1 -7.7

Berlin 0.152 -7.2 -1.6 -19.6 -8.8 -10.1 -9.7

Munich 0.143 -2.5 1.7 -9.4 -3.1 -11.9 -6.8

Italy

Milan 0.223 -7.6 -6.1 -16.1 -6.1 -8.1 -6.7

Rome 0.165 -6.8 -3.5 -17.6 -6.4 -6.4 -7.8

Naples 0.177 -4.7 -2.1 -14.6 -5.4 -2.2 -5.2

Fig. 9. SO2 emissions in western European countries. Data is from the EEA: 2002-2004 data is 

from the 2012 report; 2005-2011 data is from the 2013 report.

Air quality regulations most likely helped decrease carbon aerosol loads as well, but the 

decrease of OC AOD observed in Table 13 clearly stands out in most cities. Reduced wildfire 

19
activity would explain such a decrease. Note that year-to-year variability is high and 

extraordinary wildfire activity in a given season can drag the trend considerably. This appears to 

have been the case in the exceptionally warm and dry summer of 2003, when raging wildfires in 

Portugal, southern France, Italy and the eastern coast of the Adriatic sea, combined with stagnant 

weather conditions, led to high aerosol load and high AOD values (> 0.3) in the Mediterranean 

basin during August [Hodzic et al., 2007]. AOD values were also relatively high in urban 

centers of northern Europe during August due to accumulated urban pollutants and smoke dust 

from the wildfires [Hodzic et al., 2007]. Advection of smoke from Portugal affected areas in 

Great Britain, France and Germany where AOD values above 0.8 were occasionally recorded 

from satellites [Vautard et al., 2007]. The evolution of our OC AOD results for selected cities 

during the period from 2003 to 2011 is shown in Fig. 10. The peak in OC AOD in the year 2003 

is clearly seen.

Fig. 10. Evolution of MERRAero yearly mean OC AOD for a selection of cities in western 

Europe.

By analyzing African dust outbreak trends at different locations in Spain, France, Italy and 

Greece (Greece is covered in Section 2.8) between 2001 and 2011, Pey et al. [2013] concluded 

that northeastern Spain, southeastern France and northern Italy experienced a decreasing trend 

from 2006 onward, explained by a negative shift of the North Atlantic Oscillation index during 

the summer since then. Their results support our findings on decreasing DU AOD trends based 

on MERRAero data (Table 13).

20
3.8 Eastern Europe, including Russia and Turkey

For the majority of cities in this region, SU aerosols are the main contributor to total AOD 

(Fig. 11). There are exceptions, however, such as Athens and the three Turkish cities which have 

a significant contribution from DU aerosols. This indicates that dust transport from the Sahara to 

eastern Europe could be more significant than that from the Sahara to western Europe, which is 

in line with Querol et al. [2009].

Fig. 11. Cities in eastern Europe with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

As shown in Table 14, in the majority of cities, total AOD trends were insignificant 

(between -3% and 3%). Exceptions are the increasing trend in Moscow (8.2%) and Katowice 

(5.6%), and the decreasing trend in Budapest (6.4%) and Vienna (4.9%). There was a strong 

decrease in OC AOD for all cities except Moscow. Athens and Budapest are the cities with 

decreasing SU AOD trends (Table 14), while in Katowice, Bucharest and Vienna, SU AOD 

trends were insignificant.

Although the wildfire season of 2003 wasn't as dramatic in Eastern Europe compared to its 

western counterpart (see Section 3.7), advection of smoke to this region and the severe weather 

conditions likely had an impact on the carbon aerosol load. Model simulations by Mues et al.

[2012] reproduced an abnormally high concentration of PM10 over most of Europe in the summer 

of 2003, compared to the average summer concentration for the years between 2003 and 2007. 

Mues et al. [2012] concluded that a combination of warm temperatures, low wind speed and 

little precipitation was sufficient to produce prolonged episodes of air pollution. Adding carbon 

emissions from the wildfires would only worsen this matter.

21
Table 14. List of cities in eastern Europe, together with the 10-year mean total AOD and its 

trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Sweden Stockholm 0.123 -1.2 4.3 -16.6 -6.8 -0.8 4.3

Poland Warsaw 0.173 -3.8 2.8 -18.8 -7.1 -13.9 -3.5

Katowice 0.175 -5.6 0.2 -18.2 -6.8 -14.3 -8.2

Austria Vienna 0.169 -4.9 0.5 -15.4 -6.7 -11.1 -9.5

Hungary Budapest 0.187 -6.4 -2.1 -17.5 -7.7 -8.9 -8.5

Russia St. Petersburg 0.139 2.7 7.3 -10.3 -2.2 -5.4 11.3

Moscow 0.174 8.2 6.8 7.7 3.8 -7.4 18.6

Ukraine Kiev 0.181 0.9 3.5 -9.2 -5.1 -5.3 7.3

Romania Bucharest 0.225 -1.2 0.1 -5.8 -2.0 -6.6 0.3

Greece Athens 0.189 -2.7 -2.5 -9.1 -3.0 -1.3 -1.2

Turkey

Istanbul 0.214 -1.0 -0.1 -10.6 -4.9 2.7 1.4

Ankara 0.145 0.9 4.4 -5.0 -0.4 0.7 -1.7

Izmir 0.181 -0.9 0.2 -10.6 -2.0 2.1 0.2

The eastern-most city in this region, Moscow, has an increasing OC AOD trend (Table 14). 

Western Russia was affected by its own severe wildfire season during the summer of 2010 

[Konovalov et al., 2011]. Indeed, average AERONET AOD observations in Moscow during 

July and August 2010 were above 1 for many days and reached a daily average peak of 3.6 on 

August 7, while observed concentrations of combined black and organic carbon were over 500 

   	
  
  
   Huijnen et al. 2012]. As shown in Fig. 12, the 

evolution of MERRAero data for Moscow during the period from 2003 to 2011 was able to 

reproduce the strong OC AOD peak in 2010. This peak, however, is probably underestimated. 

Occasionally, the smoke from the wildfires was so thick that MODIS incorrectly identified it as 

clouds [van Donkelaar et al., 2011].

Fig. 12. Evolution of MERRAero yearly mean OC AOD in Moscow.

22
3.9 Western Asia

This region includes all Asian cities west of India and China. Their AOD values are listed 

in Table 15. As shown in Fig. 13, DU aerosols account for most of total AOD due to dust 

transport from deserts in Saudi Arabia and the Middle East. SU aerosols are the second most 

important species. All the cities in Table 15 showed increasing trends in total AOD during the 

10-year study period, accompanied by increasing trends in DU AOD.

Table 15. List of cities in western Asia, together with the 10-year mean total AOD and its trend 

for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Yemen Sana'a 0.254 6.4 -4.8 2.3 2.1 -2.3 14.7

Saudi Arabia Jeddah 0.372 13.8 0.7 2.7 3.9 12.3 17.7

Jordan Amman 0.221 4.6 4.1 -2.0 2.0 -0.6 6.6

Israel Tel Aviv-Jaffa 0.223 3.3 3.6 -2.3 1.4 0.5 4.6

Syria Damascus 0.167 5.0 3.1 -4.6 2.0 -0.6 8.3

Aleppo 0.204 2.5 -1.5 -4.3 1.3 -2.4 7.9

Iraq Baghdad 0.396 10.4 1.0 -1.2 4.8 -2.0 14.8

Iran

Isfahan 0.202 9.0 3.4 1.3 1.4 10.7 13.3

Tehran 0.238 7.8 3.5 -1.4 2.7 10.6 13.6

Mashhad 0.218 12.6 8.5 -0.9 1.7 15.6 17.3

Uzbekistan Tashkent 0.244 9.2 5.2 -11.5 -6.5 7.7 17.3

Afghanistan Kabul 0.154 4.8 1.5 -13.5 -7.9 7.9 12.3

Pakistan

Rawalpindi 0.363 2.5 -4.4 -5.3 -0.3 0.1 9.8

Gujranwala 0.482 2.0 -2.2 -0.8 2.2 -3.5 5.6

Lahore 0.521 1.4 -1.6 0.5 2.8 -5.9 3.8

Faisalabad 0.537 0.7 -3.0 -1.7 2.2 -3.9 3.3

Hyderabad 0.449 2.9 2.3 4.9 4.6 -1.3 3.2

Karachi 0.414 3.4 1.0 6.0 6.7 0.5 4.5

23
Fig. 13. Cities in western Asia with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

3.10 India and Bangladesh

The cities in the northwestern part have the largest contribution from DU aerosols to total 

AOD, while SU aerosols are the second most important contributor (Fig. 14). By contrast, SU 

aerosols are the dominant species in southern and northeastern cities, while DU aerosols are the 

second in importance.

We obtained increasing trends in total AOD in every single city listed in Table 16. 

Similarly, increasing trends were obtained for SU, OC and BC AOD. This is the probable result 

of industrial development and population growth in India and Bangladesh. Indeed, Lu et al.

[2011] estimated that SO2 emissions increased by 51% between 2000 and 2010 in India, black 

carbon by 35%, and organic carbon by 29%. In Bangladesh, the government deployed actions in 

order to reduce air pollution. However, these incentives seem to be failing since measurements 

of fine particulate matter mass concentration in Dhaka indicated an increase of 69% between the 

2001-2002 period and the 2007-2009 period [Begum et al., 2013].

24
Fig. 14. Cities in India and Bangladesh with proportion of aerosol species in the MERRAero 10-

year mean total AOD.

Table 16 List of cities in India and Bangladesh, together with the 10-year mean total AOD 

values and its trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total

AOD

AOD Trend (%)

Total SU OC BC SS DU

India

Delhi 0.505 1.6 2.4 6.4 6.7 -8.3 -1.3

Jaipur 0.355 4.4 5.4 10.0 8.6 -4.4 1.9

Ahmadabad 0.360 6.9 7.3 12.4 10.4 -3.5 6.7

Surat 0.343 8.9 11.0 15.7 14.1 -4.0 7.9

Mumbai 0.336 11.4 12.2 18.4 16.5 -0.3 12.2

Pune 0.276 12.5 11.1 19.4 16.6 0.2 13.9

Lucknow 0.502 5.2 10.3 10.5 5.9 -11.5 -2.7

Kanpur 0.502 6.1 10.3 11.6 7.5 -9.2 -1.1

Bhilai 0.335 12.1 8.7 16.4 15.5 5.3 15.2

Nagpur 0.323 14.2 12.2 19.0 18.5 2.0 15.2

Hyderabad 0.305 11.8 7.6 16.4 17.2 3.8 17.3

Bangalore 0.199 8.9 5.0 11.4 10.9 -3.9 19.8

Chennai 0.272 8.2 5.6 10.1 12.8 -3.9 18.7

Patna 0.503 9.3 14.0 13.6 7.4 -4.3 0.1

Calcutta 0.431 11.8 11.4 14.4 13.1 7.4 10.4

Bangladesh Dhaka 0.390 10.5 10.6 13.2 11.1 14.7 5.5

Chittagong 0.292 9.0 8.0 11.7 11.8 13.2 5.1

25
3.11 China

China is the most populated country in the world and therefore has the highest number of 

cities in our list: a total of 33. As mentioned earlier, total AOD values for Chinese cities are 

among the highest in the world (Table 1). Unsurprisingly, SU aerosols account for the majority 

of total AOD: over 60% in most cities (Fig. 15).

Table 17. List of cities in China, together with the 10-year mean total AOD and its trend for 

various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Province City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Heilongjiang Haerbin 0.322 -0.6 3.8 -13.6 2.6 9.1 2.5

Jilin Changchun 0.360 0.3 3.5 -11.1 4.2 5.5 2.5

Liaoning

Shenyang 0.360 1.6 5.7 -11.7 4.6 6.9 2.0

Anshan 0.464 0.3 2.6 -11.1 3.1 -1.2 3.5

Dalian 0.508 1.4 3.0 -9.7 5.0 2.6 4.6

Beijing Beijing 0.550 4.8 8.5 -9.1 8.5 7.4 1.4

Tianjin Tianjin 0.601 4.9 7.8 -8.3 8.0 6.5 3.5

Hebei Shijiazhuang 0.580 4.9 6.3 -3.8 10.0 13.0 2.9

Shanxi Taiyuan 0.393 4.7 6.2 -4.0 11.5 11.7 2.3

Shandong

Jinan 0.610 3.9 4.6 -3.5 10.2 -0.2 4.4

Zibo 0.584 5.3 6.3 -2.0 10.8 -0.8 4.8

Qingdao 0.552 3.4 3.7 -2.0 9.3 -4.3 5.3

Gansu Lanzhou 0.304 0.8 0.6 -5.9 4,6 4.8 2.3

Shaanxi Xi'an 0.511 2.3 0.7 3.2 9.2 25.7 3.1

Henan Zhengzhou 0.706 1.9 1.1 -2.2 10.3 8.9 4.2

Jiangsu

Nanjing 0.655 6.8 6.5 6.5 12.9 -8.3 6.6

Wuxi 0.613 7.5 7.4 7.7 12.8 -6.6 6.9

Suzhou 0.600 7.4 7.3 8.1 12.6 -7.3 7.0

Shanghai Shanghai 0.561 7.5 7.6 8.2 13.2 -6.6 7.7

Hubei Wuhan 0.723 5.4 3.8 9.0 13.2 -4.4 5.0

Hunan Changsha 0.719 3.3 1.4 8.1 10.1 -0.7 4.2

Jiangxi Nanchang 0.654 3.6 2.2 7.1 9.4 -9.5 4.2

Zhejiang Ningbo 0.490 5.1 5.2 6.5 10.8 -8.3 5.8

Wenzhou 0.425 1.3 0.7 2.7 6.0 -6.7 5.5

Fujian Fuzhou 0.436 -0.8 -1.3 -1.5 2.2 -0.1 5.0

Quanzhou 0.410 -4.6 -4.7 -8.1 -1.1 -3.5 4.9

Guangdong

Shantou 0.642 5.5 4.9 4.3 13.4 -7.8 6.3

Guangzhou 0.528 -8.8 -8.8 -12.6 -2.4 -9.7 5.1

Shenzhen 0.452 -9.3 -8.3 -16.5 -4.8 -6.1 5.0

Hong Kong Hong Kong 0.443 -9.0 -7.8 -16.7 -4.9 -5.0 4.9

Guangxi Nanning 0.557 -2.9 -3.7 -4.4 3.6 -0.4 6.8

Yunnan Kunming 0.203 -1.1 -2.4 -2.7 3.0 9.9 8.2

Xinjiang Urumqi 0.227 6.3 -3.5 -16.1 1.8 18.3 21.3

26
Fig. 15. Cities in China with proportion of aerosol species in the MERRAero 10-year mean total 

AOD.

Total AOD trends are increasing in most cities which are usually accompanied by an 

increase in SU AOD (Table 17). The opposite is true for the cities with decreasing trends, all of 

which are located in the southeastern border and coast between Myanmar and the Taiwan Strait, 

except for Urumqi located farter to the northwest. OC AOD is decreasing in many cities.

Like India, China is emerging economically and is consuming consistently more coal since 

2000 [Lu et al., 2011]. However, trends in total and SU AOD are generally weaker over cities in 

China than over cities in India. The reason for this is most likely due to emission patterns 

observed in China. Lu et al.'s [2010, 2011] estimates of Chinese SO2 emissions have clearly 

risen between 2002 and 2010, overall by 28%. However, this is the result of a strong 38% 

increase from 2002 to 2006 (peak year) and a slow decrease since then to 2004 levels by 2010 

(7% decrease between 2006 and 2010). Other estimates vary by numbers but all agree with peak 

emissions in 2006 and a slow decrease since then [Wang and Hao, 2012; Zhang et al., 2012; 

Klimont et al., 2013; Zhao et al., 2013a; Zhao et al., 2013b]. Implementation of national 

comprehensive policies by the Chinese government in 2005 has been successful in this respect.

The Government also took advantage of international events, such as the 2008 Beijing Olympics, 

to temporarily and regionally push stricter air quality guidelines [Lin et al., 2013].

27
The trend of SO2 emissions is quite disproportionate throughout China. In order to 

evaluate our results for SU AOD trends, we compared them with Zhao et al.'s [2012a] and Zhao 

et al.'s [2013b] SO2 emission estimate trends in Chinese provinces between 2005 and 2010. 

There is good agreement between these two publications and our results in the cities of Haerbin, 

Wuhan, Guangzhou, Shenzhen and Hong Kong. Given China's disproportionate SO2 emissions 

in time as well, some cities with an overall increasing trend actually observed a decreasing trend 

between 2005 and 2010, which is consistent with Zhao et al. [2012a] and Zhao et al. [2013b]. 

This is indeed the case for the cities of Xi'an, Zhengzhou, Changsha and Wenzhou. The 

evolution of MERRAero SU AOD for these four cities is shown in Fig. 16.

Fig. 16. MERRAero yearly mean SU AOD for Chengsha, Zhengzhou, Xi'an and Wenzhou. 

Trend lines are included for the years 2005-2010.

3.12 Eastern Asia

This region includes all Asian cities east of China. SU aerosols are the major contributor 

to total AOD in all these cities (Fig. 17). The four cities in Japan all have decreasing total AOD 

trends caused by a decrease in SU AOD (Table 18). In Japan, air quality policies have been 

implemented since the 1960's. As a result, SO2 emissions and concentrations decreased 

substantially since the 1970's [Kanada et al., 2013]. Moreover, national air quality statistics data 

from the Ministry of the Environment (http://www.env.go.jp/en/statistics/contents/ 

index_e.html#iousankabutu) show that anthropogenic SO2 emissions have decreased by 15% 

during the period from 2002 to 2008, while SO2 concentration decreased from 0.005 ppm in 

2002 to 0.003 ppm in 2010. It is important to mention that volcanoes release some significant 

28
amount of SO2 in Japan's atmosphere. Between 2000 and 2007, both natural and anthropogenic 

SO2 emissions decreased, and this resulted in decreasing SO2 concentrations observed at most 

monitoring sites in Japan [Lu et al., 2010]. Lu et al. [2010] also noticed that most sites with 

increasing SO2 concentration between 2000 and 2007 (period corresponding with China's 

industrialization boom) were located in the southwestern tip, closest to China. Environmental 

policies in Japan had a positive impact on Tokyo's air quality as well. Tokyo has a welldeveloped air quality network of monitoring stations. Data from this record has been published 

by Hara et al. [2013] for suspended particulate matter. The average concentration based on

measurements from 45 ambient monitoring stations showed a decrease of 36% between 2002 

and 2010, while the average concentration based on measurements for 33 roadside monitoring 

stations indicate a decrease of 42% between the same two years. These observed decreasing 

tendencies support the obtained decreasing MERRAero AOD trend in Tokyo shown in Table 18.

Fig. 17. Cities in eastern Asia with proportion of aerosol species in the MERRAero 10-year 

mean total AOD.

The three cities in South Korea have rather insignificant trends in both total and SU AOD 

(Table 18). SO2 emissions have slightly decreased between 2000 and 2007 in that country [Kim 

et al., 2011]. Although, Lu et al. [2010] estimated that a 1% increase in Chinese SO2 emissions 

leads to 0.71% and 1.15% increase in "background" concentration in Japan and South Korea 

respectively. SO2 emissions in Seoul between 2001 and 2008 were quite sporadic and did not 

show any significant trend [Jeong et al., 2013]. The aforementioned facts could explain the 

obtained insignificant MERRAero AOD trends in South Korea's cities as shown in Table 18.

29
Table 18. List of cities in eastern Asia, together with the 10-year mean total AOD and its trend 

for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Japan

Tokyo 0.272 -7.6 -11.7 -6.9 -0.2 -1.7 2.2

Nagoya 0.263 -7.2 -12.1 -2.2 -1.0 0.0 2.3

Osaka 0.260 -5.5 -9.5 -2.7 -0.5 -2.5 3.4

Fukuoka 0.305 -1.7 -3.0 0.9 2.7 -9.6 3.9

North Korea Pyongyang 0.453 1.1 1.5 -5.3 3.6 5.4 4.6

South Korea

Seoul 0.427 0.8 0.2 -2.7 4.2 5.1 4.8

Daegu 0.337 0.5 -0.8 1.1 3.9 0.5 3.4

Busan 0.340 0.0 -1.3 1.5 3.7 -3.9 3.3

Taiwan

Taipei 0.352 -3.8 -4.9 -11.7 -4.0 6.6 6.0

Taichung 0.305 -7.1 -6.1 -18.4 -6.9 3.7 5.3

Kaohsiung 0.247 -10.3 -9.5 -22.5 -9.9 1.1 2.4

Taiwan faces the southeastern region of China, where all the Chinese cities with a 

decreasing AOD trend are located (Section 3.11). This could explain the obtained decreasing 

MERRAero AOD trend in Taiwanese cities shown in Table 18.

3.13 Southeastern Asia and Oceania

This subsection includes cities east of India and south of China, as well as cities in 

Oceania. Cities outside of Australia have the highest contribution from SU aerosols to their total 

AOD (Fig. 18), although the contribution of OC aerosols to total AOD is also significant. SS 

aerosols are important in Filipino cities given their insular location. Cities in Australia have a 

heterogeneous distribution of aerosol species; SU, OC and SS aerosols being the most important. 

Total AOD showed decreasing trends in every city listed in Table 19. This is mainly due to 

decreasing trends in SU and OC AOD. Wildfire activity attenuation could be a likely cause but 

we don't have data in this part of the world to support this assertion. Although, according to van 

der Werf et al. [2010, their Table 7 and Fig. 9], modeled carbon emissions from wildfires in 

Equatorial Asia (including Indonesia) and Oceania suggest some decreasing tendencies between 

2002 and 2009.

30
Fig. 18. Cities in southeastern Asia and Oceania with proportion of aerosol species in the 

MERRAero 10-year mean total AOD.

Table 19. List of cities in southeastern Asia and Oceania, together with the 10-year mean total 

AOD and its trend for various aerosol species.

Legend – SU: sulfate; OC: organic carbon; BC: black carbon; SS: sea salt; DU: dust

Country City Total 

AOD

AOD Trend (%)

Total SU OC BC SS DU

Myanmar Yangon 0.237 -1.1 -4.8 3.0 -0.4 -2.4 4.8

Thailand Bangkok 0.284 -1.9 -0.3 -2.9 1.8 -18.4 12.1

Vietnam Saigon 0.163 -10.6 -9.1 -13.8 -9.7 -11.6 4.2

Philippines Manila 0.138 -9.9 -8.9 -26.6 -19.1 -0.8 -14.1

Cebu 0.101 -17.5 -19.9 -33.6 -25.6 -5.0 -19.5

Indonesia

Jakarta 0.260 -11.5 -2.8 -32.4 -16.2 -15.0 9.4

Bandung 0.193 -12.9 -5.4 -31.0 -17.2 -18.9 2.9

Surabaya 0.191 -10.7 -3.6 -27.1 -14.9 -12.7 1.9

Australia

Brisbane 0.096 -11.8 -8.1 -27.2 -18.0 -5.1 0.9

Sydney 0.114 -16.0 -0.9 -43.0 -22.5 -6.8 -2.6

Melbourne 0.093 -6.7 -1.0 -20.9 -8.0 1.6 1.5

31
4 Discussion 

The recently developed ten-year (July 2002 – June 2012) MERRAero aerosol reanalysis 

was applied to the study of AOD of various aerosol species and their trends over the world’s 

major cities. The model reproduced high SU AOD values over cities in industrialized regions 

such as North America, Europe, India, China and eastern Asia, caused by fossil fuel 

consumption. The model also reproduced high DU AOD values over cities in proximity to the 

major deserts (northern Africa and western Asia), and high OC AOD values over cities in 

regions where biomass burning is common practice (South America and Africa south of the 

Sahara).

MERRAero also reproduced declining trends in SU AOD over North American, European 

and Japanese cities, where effective air quality regulation was implemented. The situation in 

China is more delicate since air quality regulation in that country has only been implemented 

recently. MERRAero was able to show increasing trends of SU AOD over Indian cities which is 

consistent with increasing anthropogenic emissions.

Since OC and BC are also by-products of fossil fuel combustion, in many cities, 

MERRAero showed correspondence between AOD trends in carbonaceous aerosols and those in 

sulfate aerosols. In some cities, however, AOD trend in carbonaceous aerosols was 

disproportionate or opposite to SU AOD trend. This is probably due to varying wildfire and 

biomass burning activities. We observed such trends in cities on the western coast of the U.S. 

and Canada, Europe, western Russia, Indonesia and Australia.

5 References 

Alpert, P., O. Shvainshtein and P. Kishcha (2012), AOD trends over megacities based on space 

monitoring using MODIS and MISR, Am. J. Climate Change, 1, 117-131, 

doi:10.4236/ajcc.2012.13010.

Archibald, S., D.P. Roy, B.W. Van Wilgen and R.J. Scholes (2009), What limits fire? An 

examination of drivers of burnt area in Southern Africa, Global Change Biol., 15, 613-630,

doi:10.1111/j.1365-2486.2008.01754.x.

Archibald, S., A. Nickless, N. Govender, R.J. Scholes and V. Lehsten (2010), Climate and the interannual variability of fire in Southern Africa: a meta-analysis using long-term field data and 

satellite-derived burnt area data, Global Ecol. Biogeogr., 19, 794-809, doi:10.1111/j.1466-

8238.2010.00568.x.

Begum, B.A., P.K. Hopke and A. Markwitz (2013), Air pollution by fine particulate matter in 

Bangladesh, Atmos. Pollut. Res., 4, 75-86, doi:10.5094/APR.2013.008.

Buckley, S.M. and M.J. Mitchell (2011), Improvements in urban air quality: case studies from New 

York State, USA, Water Air Soil Pollut., 214, 93-106, doi:10.1007/s11270-010-0407-z.

Charlson, R.J. (1969), Atmospheric visibility related to aerosol mass concentration, Environ. Sci. 

Technol., 3, 913-918, doi:10.1021/es60033a002.

32
Cheng, M.T. and Y.I. Tsai (2000), Characterization of visibility and atmospheric aerosols in urban, 

suburban, and remote areas, Sci. Total Environ., 263, 101-114, doi:10.1016/S0048-

96970000670-7.

Chin, M., P. Ginoux, S. Kinne, O. Torres, B. Holben, B.N. Duncan, R.V. Martin, J. Logan, A. 

Higurashi and T. Nakajima (2002), Tropospheric aerosol optical thickness from the 

GOCART model and comparisons with satellite and sun photometer measurements, J. 

Atmos. Sci., 59, 461–483, doi:10.1175/1520-0469(2002)059.

Chung, Y.S. (1978), The distribution of atmospheric sulfates in Canada and its relationship to longrange transport of air pollutants, Atmos. Environ., 12, 1471-1480, doi:10.1016/ 0004-

69817890090-2.

Cifuentes, L.A., A.J. Krupnick, R. O'ryan and M.A. Toman (2005), Urban air quality and human 

health in Latin America and the Caribbean, IDB Publications 25378, 73 p.

Colarco, P., A. da Silva, M. Chin, and T. Diehl (2010), Online simulations of global aerosol 

distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based 

aerosol optical depth. J. Geophys. Res., 115, D14207, doi:10.1029/2009JD012820.

Cusack,M., A. Alastuey, N. Pérez, J. Pey, and X. Querol (2012), Trends of particulate matter 

(PM2.5) and chemical composition at a regional background site in the Western 

Mediterranean over the last nine years (2002–2010), Atmos. Chem. Phys., 12, 8341-8357,

doi:10.5194/acp-12-8341-2012.

de Meij, A., A. Pozzer and J. Lelieveld (2012), Trend analysis in aerosol optical depths and 

pollutant emission estimates between 2000 and 2009, Atmos. Environ., 51, 75-85,

doi:10.1016/j.atmosenv.2012.01.059.

Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. Hauglustaine, 

C. Heinze, E. Holland, D. Jacob, U. Lohmann, S. Ramachandran, P.L. Da Silva Dias, S.C. 

Wofsy and X. Zhang (2007), Couplings between changes in the climate system and 

biogeochemistry, in Climate change 2007 – The physical science basis, edited by S. 

Solomon et al., Cambridge University Press, Cambridge, UK and New York, USA, pp. 499-

587.

EEA (2012), European Union emission inventory report 1990-2010 under the UNECE convention 

on long-range transboundary air pollution (LRTAP), available on-line: 

http://www.eea.europa.eu/publications/eu-emission-inventory-report-1990-2010

EEA (2013), European Union emission inventory report 1990-2011 under the UNECE convention 

on long-range transboundary air pollution (LRTAP), available on-line: 

http://www.eea.europa.eu/publications/eu-emission-inventory-report-lrtap

EPA (2012), Our nation's air – Status and trends through 2010, available on-line:

http://www.epa.gov/airtrends/2011/

Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, 

D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland (2007), 

33
Changes in atmospheric constituents and in radiative forcing, in Climate change 2007 – The 

physical science basis, edited by S. Solomon et al., Cambridge University Press, Cambridge, 

UK and New York, USA, pp. 129-234.

Granier, C., B. Bessagnet, T. Bond, A. D'Angiola, H.D. van der Gon, G.J. Frost, A. Heil, J.W. 

Kaiser, S. Kinne, Z. Klimont, S. Kloster, J.F. Lamarque, C. Liousse, T. Masui, F. Meleux, A. 

Mieville, T. Ohara, J.C. Raut, K. Riahi, M.G. Schultz, S.J. Smith, A. Thompson, J. van 

Aardenne, G.R. van der Werf and D.P. van Vuuren (2011), Evolution of anthropogenic and 

biomass burning emissions of air pollutants at global and regional scales during the 1980-

2010 period, Climatic Change, 109, 163-190, doi:10.1007/s10584-011-0154-1.

Hand, J.L., B.A. Schichtel, W.C. Malm and M.L. Pitchford (2012), Particulate sulfate ion 

concentration and SO2 emission trends in the United States from the early 1990s through 

2010, Atmos. Chem. Phys., 12, 10353-10365, doi:10.5194/acp-12-10353-2012.

Hand, J.L., B.A. Schichtel, W.C. Malm and M. Pitchford (2013), Widespread reductions in sulfate 

across the United States since the early 1990s, AIP Conf. Proc., 1527, 495-498,

doi:10.1063/1.4803313.

Haywood, J. and O. Boucher (2000), Estimates of the direct and indirect radiative forcing due to 

tropospheric aerosols: a review, Rev. Geophys., 38, 513-543, doi:10.1029/1999RG000078.

Hodzic, A., S. Madronich, B. Bohn, S. Massie and C. Wiedinmyer (2007), Wildfire particulate 

matter in Europe during summer 2003: meso-scale modeling of smoke emissions, transport 

and radiative effects, Atmos. Chem. Phys., 7, 4043-4064, doi:10.5194/acp-7-4043-2007.

Hara, K., J. Homma, K. Tamura, M. Inoue, K. Karita and E. Yano (2013), Decreasing trends of 

suspended particulate matter and PM2.5 concentrations in Tokyo, 1990-2010, J. Air Waste 

Manage., 63, 737-748, doi:10.1080/10962247.2013.782372.

Hsu, N.C., R. Gautam, A.M. Sayer, C. Bettenhausen, C. Li, M.J. Jeong, S.C. Tsay and B.N. Holbeu 

(2012), Global and regional trends of aerosol optical depth over land and ocean using 

SeaWiFS measurements from 1997 to 2010, Atmos. Chem. Phys., 12, 8037-8053, 

doi:10.5194/acp-12-8037-2012.

Huijnen, V., J. Flemming, J.W. Kaiser, A. Inness, J. Leitao, A. Heil, H.J. Eskes, M.G. Schultz, A. 

Benedetti, J. Hadji-Lazaro, G. Dufour and M. Eremenko (2012), Hindcast experiments of 

tropospheric composition during the summer 2010 fires over Western Russia, Atmos. Chem.

Phys., 12, 4341-4364, doi:10.5194/acp-12-4341-2012.

Ito, A., A. Ito and H. Akimoto (2007), Seasonal and interannual variations in CO and BC emissions 

from open biomass burning in Southern Africa during 1998-2005, Global Biogeochem. Cy.,

21, GB2011, doi:10.1029/2006GB002848.

Jeong, U., H. Lee, J. Kim, W. Kim, H. Hong and C.K. Song (2013), Determination of the interannual and spatial characteristics of the contribution of long-range transport to SO2 levels in 

Seoul between 2001 and 2010 based on conditional potential source contribution function 

(CPSCF), Atmos. Environ., 70, 307-317, doi:10.1016/j.atmosenv.2013.01.009.

34
Kanada, M., T. Fujita, M. Fujii and S. Ohnishi (2013), The long-term impacts of air pollution 

control policy: historical links between municipal actions and industrial energy efficiency in 

Kawasaki City, Japan, J. Clean. Prod., 58, 92-101, doi:10.1016/j.jclepro.2013.04.015

Kim, N.K., Y.P. Kim and C.H. Kang (2011), Long-term trend of aerosol composition and direct 

radiative forcing due to aerosols over Gosan: TSP, PM10, and PM2.5 data between 1992-

2008, Atmos. Environ., 45, 6107-6115, doi:10.1016/j.atmosenv.2011.08.051.

Kishcha, P., B. Starobinets, O. Kalashnikova and P. Alpert (2011), Aerosol optical thickness trends 

and population growth in the Indian subcontinent, Int. J. Remote Sens., 32, 9137-9149,

doi:10.1080/01431161.2010.550333.

Kishcha, P., da Silva, A., Starobinets, B., and Alpert, P. (2014), Air pollution over the Ganges basin 

and north-west Bay of Bengal in the early post-monsoon season based on NASA 

MERRAero data. J. Geophys. Res. Atmos., 119, doi:10.1002/2013JD020328,

Klimont, Z., S.J. Smith and J. Cofala (2013), The last decade of global anthropogenic sulfur dioxide: 

2000-2011 emissions, Environ. Res. Lett., 8, 014003, doi:10.1088/1748-9326/8/1/014003.

Konovalov, I.B., M. Beekmann, I.N. Kuznetsova, A. Yurova and A.M. Zvyagintsev (2011), 

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and 

measurements of an extreme air pollution episode in the Moscow region, Atmos. Chem. 

Phys., 11, 10031-10056, doi:10.5194/acpd-11-12141-2011.

Lin, J.T., D. Pan and R.X. Zhang (2013), Trend and interannual variability of Chinese air pollution 

since 2000 in association with socioeconomic development: a brief overview, Atmos. 

Oceanic Sci. Lett., 6, 84-89.

Lohmann, U. and J. Feichter (2005), Global indirect aerosol effects: a review, Atmos. Chem. Phys.,

5, 715-737, doi:10.5194/acp-5-715-2005.

Lu, Z., D.G. Streets, Q. Zhang, S. Wang, G.R. Carmichael, Y.F. Cheng, C. Wei, M. Chin, T. Diehl 

and Q. Tau (2010), Sulfur dioxide emissions in China and sulfur trends in East Asia since 

2000, Atmos. Chem. Phys., 10, 6311-6331, doi:10.5194/acp-10-6311-2010.

Lu, Z., Q. Zhang and D.G. Streets (2011), Sulfur dioxide and primary carbonaceous aerosol 

emissions in China and India, 1996-2010, Atmos. Chem. Phys., 11, 9839-9864,

doi:10.5194/acp-11-9839-2011.

MDDEFP (2011), Inventaire des émissions des principaux contaminants atmosphériques au Québec 

en 2008 et évolution depuis 1990, available on-line (in French): 

http://www.mddefp.gouv.qc.ca/air/inventaire/index.htm

Mercier, J.R. (2012), Revisiting deforestation in Africa (1990-2010): one more lost generation, 

Madagascar Conservation & Development, 7, 5-8, doi:10.4314/mcd.v7i1.2

Miller, J.D. and H. Safford (2012), Trends in wildfire severity: 1984 to 2010 in the Sierra Nevada, 

Modoc Plateau, and Southern Cascades, California, USA, Fire Ecology, 8, 41-57, 

doi:10.4996/fireecology.0803041.

35
Molina, L.T. and M.J. Molina (2004a), Improving air quality in megacities: Mexico City case study, 

Ann. NY Acad. Sci., 1023, 142-158, doi:10.1196/annals.1319.006.

Molina, M.J. and L.T. Molina (2004b), Megacities and atmospheric pollution, J. Air Waste 

Manage., 54, 644-680, doi:10.1080/10473289.2004.10470936.

Moore, M., P. Gould and B.S. Keary (2003), Global urbanization and impact on health, Int. J. Hyg. 

Environ. Health, 206, 269-278, doi:10.1078/1438-4639-00223.

Mues, A., A. Manders, M. Schaap, A. Kerschbaumer, R. Stern and P. Builtjes (2012), Impact of the 

extreme meteorological conditions during the summer 2003 in Europe on particulate matter 

concentrations, Atmos. Environ., 55, 377-391, doi:10.1016/j.atmosenv.2012.03.002.

Parrish, D.D., H.B. Singh, L. Molina and S. Madronich (2011), Air quality progress in North 

American megacities: a review, Atmos. Environ., 45, 7015-7025,

doi:10.1016/j.atmosenv.2011.09.039.

Pey, J., X. Querol, A. Alastuey, F. Forastiere and M. Stafoggia (2013), African dust outbreaks over 

the Mediterranean basin during 2001-2011: PM10 concentrations, phenomenology and 

trends, and its relation with synoptic and mesoscale meteorology, Atmos. Chem. Phys., 13, 

1395-1410, doi:10.5194/acp-13-1395-2013.

Poschl, U. (2005), Atmospheric aerosols: composition, transformation, climate and health effects, 

Angew. Chem. Int. Edit., 44, 7520-7540, doi:10.1002/anie.200501122.

Prospero, J.M. and O.L. Mayol-Bracero (2013), Understanding the transport and impact of African 

dust on the Caribbean basin, B. Am. Meteorol. Soc., 94, 1329-1337, doi:10.1175/bams-d-12-

00142.1.

Ontario Ministry of the Environment (2013), Air quality in Ontario – Report for 2011, available online: 

http://www.ene.gov.on.ca/environment/en/resources/STDPROD_104487.html

Querol, X., J. Pey, M. Pandolfi, A. Alastuey, M. Cusack, N. Pérez, T. Monero, M. Viana, N. 

Mihalopoulos, G. Kallos and S. Kleanthous (2009), African dust contributions to mean 

ambient PM10 mass-level across the Mediterranean basin, Atmos. Environ., 43, 4266-4277, 

doi:10.1016/j.atmosenv.2009.06.013.

Shao, M., X. Tang, Y. Zhang and W. Li (2006), City clusters in China: air and surface water 

pollution, Front. Ecol. Environ., 4, 353-361, doi:10.1890/1540-9295(2006)004.

Subbotina, T.P. (2004), Beyond economic growth: an introduction to sustainable development, 2nd 

edition, The International Bank for Reconstruction and Development, Washington, USA,

205 p.

Spracklen, D.V., J.A. Logan, L.J. Mickley, R.J. Park, R. Yevich, A.L. Westerling and D.A. Jaffe 

(2007), Wildfires drive interannual variability of organic carbon aerosol in the Western U.S. 

in summer, Geophys. Res. Lett., 34, L16816, doi:10.1029/2007GL030037.

36
Tager, I.B. (2013), Health effects of aerosols: mechanisms and epidemiology, in Aerosols 

handbook: measurement, dosimetry, and health effects, 2nd edition, edited by L.S. Ruzer and 

N.H. Harley, CRC Press, Boca Raton, USA, pp. 565-636.

van der Werf, G.R., J.T. Randerson, L. Giglio, G.J. Collatz, M. Mu, P.S. Kasibhatla, D.C. Morton, 

R.S. Defries, Y. Jin and T.T. Van Leeuwen (2010), Global fire emissions and the 

contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009), 

Atmos. Chem. Phys., 10, 11707-11735, doi:10.5194/acp-10-11707-2010.

van Donkelaar, A., R.V. Martin, R.C. Levy, A.M. da Silva, M. Krzyzanowski, N.E. Chubarova, E. 

Semutnikova and A.J. Cohen (2011), Satellite-based estimates of ground-level fine 

particulate matter during extreme events: a case study of the Moscow fires in 2010, Atmos. 

Environ., 45, 6225-6232, doi:10.1016/j.atmosenv.2011.07.068

Vautard, R., M. Beekmann, J. Desplat, A. Hodzic and S. Morel (2007), Air quality in Europe during 

the summer of 2003 as a prototype of air quality in a warmer climate, C. R. Geosci., 339, 

747-763, doi:10.1016/j.crte.2007.08.003.

Vestreng, V., G. Myhre, H. Fagerli, S. Reis and L. Tarrason (2007), Twenty-five years of 

continuous sulfur dioxide emission reduction in Europe, Atmos. Chem. Phys., 7, 3663-3681, 

doi:10.5194/acp-7-3663-2007.

Wang, S. and J. Hao (2012), Air quality management in China: issues, challenges, and options, J. 

Environ. Sci., 21, 2-13, doi:10.1016/S1001-0742(11)60724-9.

Xing, J., J. Pleim, R. Mathur, G. Pouliot, C. Hogrefe, C.M. Gan and C. Wei (2013), Historical 

gaseous and primary aerosol emissions in the United States from 1990-2010, Atmos. Chem. 

Phys., 13, 7531-7549, doi:10.5194/acpd-12-30327-2012.

Zhang, Q., K. He and H. Huo (2012), Cleaning China's air, Nature, 484, 161-162,

doi:10.1038/484161a.

Zhao, B., S. Wang, X. Dong, J. Wang, L. Duan, X. Fu, J. Hao and J. Fu (2013a), Environmental 

effects of the recent emission changes in China: implications for particulate matter pollution 

and soil acidification, Environ. Res. Lett., 8, 024031, doi:10.1088/1748-9326/8/2/024031.

Zhao, Y., J. Zhang and C.P. Nielsen (2013b), The effects of recent control policies on trends in 

emissions of anthropogenic atmospheric pollutants and CO2 in China, Atmos. Chem. Phys.,

13, 487-508, doi:10.5194/acp-13-487-2013.

6 Appendix: Acknowledgements 

This study was made with support from und cooperation in the international Virtual Institute 

DESERVE (Dead Sea Research Venue), funded by the German Helmholtz Association.

37
Previous Volumes in This Series

Volume 1

September 1994

Documentation of the Goddard Earth Observing System (GEOS) general 

circulation model - Version 1 

L.L. Takacs, A. Molod, and T. Wang

Volume 2

October 1994

Direct solution of the implicit formulation of fourth order horizontal 

diffusion for gridpoint models on the sphere 

Y. Li, S. Moorthi, and J.R. Bates

Volume 3

December 1994

An efficient thermal infrared radiation parameterization for use in general 

circulation models 

M.-D. Chou and M.J. Suarez 

Volume 4

January 1995

Documentation of the Goddard Earth Observing System (GEOS) Data 

Assimilation System - Version 1 

James Pfaendtner, Stephen Bloom, David Lamich, Michael Seablom, 

Meta Sienkiewicz, James Stobie, and Arlindo da Silva

Volume 5

April 1995

Documentation of the Aries-GEOS dynamical core: Version 2 

Max J. Suarez and Lawrence L. Takacs

Volume 6

April 1995

A Multiyear Assimilation with the GEOS-1 System: Overview and Results 

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu, Wayne Higgins, 

Yelena Kondratyeva, Andrea Molod, Lawrence Takacs, Michael 

Seablom, and Richard Rood

Volume 7

September 1995

Proceedings of the Workshop on the GEOS-1 Five-Year Assimilation 

Siegfried D. Schubert and Richard B. Rood

Volume 8

March 1996

Documentation of the Tangent Linear Model and Its Adjoint of the 

Adiabatic Version of the NASA GEOS-1 C-Grid GCM: Version 5.2 

Weiyu Yang and I. Michael Navon

Volume 9

March 1996

Energy and Water Balance Calculations in the Mosaic LSM 

Randal D. Koster and Max J. Suarez

Volume 10

April 1996

Dynamical Aspects of Climate Simulations Using the GEOS General 

Circulation Model 

Lawrence L. Takacs and Max J. Suarez

Volume 11

May 1997

Documentation of the Tangent Linear and its Adjoint Models of the 

Relaxed Arakawa-Schubert Moisture Parameterization Package of the 

NASA GEOS-1 GCM (Version 5.2) 

Weiyu Yang, I. Michael Navon, and Ricardo Todling

Volume 12

August 1997

Comparison of Satellite Global Rainfall Algorithms 

Alfred T.C. Chang and Long S. Chiu

Volume 13

December 1997

Interannual Variability and Potential Predictability in Reanalysis Products 

Wie Ming and Siegfried D. Schubert

38
Volume 14

August 1998

A Comparison of GEOS Assimilated Data with FIFE Observations 

Michael G. Bosilovich and Siegfried D. Schubert

Volume 15

June 1999

A Solar Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou and Max J. Suarez

Volume 16

November 1999

Filtering Techniques on a Stretched Grid General Circulation Model 

Lawrence Takacs, William Sawyer, Max J. Suarez, and Michael S. 

Fox-Rabinowitz

Volume 17

July 2000

Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric GCM 

Julio T. Bacmeister, Philip J. Pegion, Siegfried D. Schubert, and Max J. 

Suarez

Volume 18

December 2000

An Assessment of the Predictability of Northern Winter Seasonal Means 

with the NSIPP1 AGCM 

Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez

Volume 19

July 2001

A Thermal Infrared Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou, Max J. Suarez, Xin-Zhong, and Michael M.-H. Yan

Volume 20

August 2001

The Climate of the FVCCM-3 Model 

Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin, Sharon Nebuda, 

and Bo-Wen Shen

Volume 21

September 2001

Design and Implementation of a Parallel Multivariate Ensemble Kalman 

Filter for the Poseidon Ocean General Circulation Model 

Christian L. Keppenne and Michele M. Rienecker

Volume 22

August 2002

Coupled Ocean-Atmosphere Radiative Model for Global Ocean 

Biogeochemical Models 

Watson W. Gregg

Volume 23

November 2002

Prospects for Improved Forecasts of Weather and Short-term Climate 

Variability on Subseasonal (2-Week to 2-Month) Time Scales 

Siegfried D. Schubert, Randall Dole, Huang van den Dool, Max J. 

Suarez, and Duane Waliser

Volume 24

July 2003

Temperature Data Assimilation with Salinity Corrections: Validation for 

the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 

1993–1998 

Alberto Troccoli, Michele M. Rienecker, Christian L. Keppenne, and 

Gregory C. Johnson

Volume 25

December 2003

Modeling, Simulation, and Forecasting of Subseasonal Variability 

Duane Waliser, Siegfried D. Schubert, Arun Kumar, Klaus 

Weickmann, and Randall Dole

Volume 26

April 2005

Documentation and Validation of the Goddard Earth Observing System 

(GEOS) Data Assimilation System – Version 4

Senior Authors: S. Bloom, A. da Silva and D. Dee

Contributing Authors: M. Bosilovich, J-D. Chern, S. Pawson, S. 

Schubert, M. Sienkiewicz, I. Stajner, W-W. Tan, and M-L. Wu

39
Volume 27

December 2008

The GEOS-5 Data Assimilation System - Documentation of Versions 5.0.1, 

5.1.0, and 5.2.0.

M.M. Rienecker, M.J. Suarez, R. Todling, J. Bacmeister, L. Takacs, 

H.-C. Liu, W. Gu, M. Sienkiewicz, R.D. Koster, R. Gelaro, I. Stajner, 

and J.E. Nielsen

Volume 28

April 2012

The GEOS-5 Atmospheric General Circulation Model: Mean Climate and

Development from MERRA to Fortuna

Andrea Molod, Lawrence Takacs, Max Suarez, Julio Bacmeister, InSun Song, and Andrew Eichmann

Volume 29

May 2012

Atmospheric Reanalyses – Recent Progress and Prospects for the Future.

A Report from a Technical Workshop, April 2010

Michele M. Rienecker, Dick Dee, Jack Woollen, Gilbert P. Compo, 

Kazutoshi Onogi, Ron Gelaro, Michael G. Bosilovich, Arlindo da Silva, 

Steven Pawson, Siegfried Schubert, Max Suarez, Dale Barker, 

Hirotaka Kamahori, Robert Kistler, and Suranjana Saha

Volume 30

September 2012

The GEOS-ODAS, description and evaluation

Guillaume Vernieres, Michele M. Rienecker, Robin Kovach and 

Christian L. Keppenne

40