AJCCAmerican Journal of Climate Change2167-9495Scientific Research Publishing10.4236/ajcc.2012.13010AJCC-22472ArticlesEarth&Environmental Sciences AOD Trends over Megacities Based on Space Monitoring Using MODIS and MISR inhasAlpert1OlgaShvainshtein1PavelKishcha1*Department of Geophysical, Atmospheric and Planetary Sciences, Tel Aviv University, Tel-Aviv, Israel* E-mail:pavelk@post.tau.ac.il(PK);190920120103117131June 21, 2012July 20, 2012 July 30, 2012© Copyright 2014 by authors and Scientific Research Publishing Inc. 2014This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

Space monitoring of aerosol optical depth (AOD) trends over megacities can serve as a potential space indicator of global anthropogenic air-pollution changes. Three space aerosol sensors, MODIS-Terra, MODIS-Aqua and MISR, were used in order to study recent decadal trends of AOD over megacities around the world. Space monitoring of AOD trends has the advantage of global coverage and applies the same approach to detecting AOD trends over different sites. In spite of instrumental and time differences among the three sensors investigated, their global pictures of AOD trends over the 189 largest cities in the world are quite similar. The increasing AOD trends over the largest cities in the Indian subcontinent, the Middle East, and North China can be clearly seen. By contrast, megacities in Europe, the north-east of US, and South-East Asia show mainly declining AOD trends. In the cases where all three sensors show similar signs of AOD trends, the results can be considered as reliable. This is supported by the observed trends in surface solar radiation, obtained by using network pyranometer measurements in North and South China, India, and Europe. In the cases where the three sensors show differing signs of AOD trends (e.g. South America), additional research is required in order to verify the obtained AOD trends.

Megacities; Aerosols; Aerosol Optical Depth; Space Monitoring1. Introduction

In megacities, which are defined as metropolitan areas with population exceeding 10 million inhabitants, air quality is worsening as the population, traffic, industrialization and energy use are increasing [1,2]. Evaluating air pollution over megacities is crucial, because of pollution transport between different parts of the world. Aircraft and satellite data reveal that, within a week, emissions can be transported half way around the world into trans-oceanic and trans-continental plumes, no matter whether they are from Asia, North America, or Africa [3]. Therefore, emissions and ambient concentrations of pollutants in megacities can have widespread effects. Anthropogenic emissions can impact health; visibility; regional ecosystems; regional climate change; and global pollutant transport, as discussed in many studies, e.g. [1, 4-7]. The London smog of 1952 is one of history’s most important air pollution episodes in terms of its impact on public perception of air pollution and subsequent government regulation [4]. Decker et al. [5] claimed that rapid population growth in megacities in developing countries is accompanied by significant contamination of urban territories, as well as air and water pollution.

Because of increasing anthropogenic pollution, changes in atmospheric aerosol concentration over megacities can cause radiative forcing of the climate (known as the aerosol direct effect) and modify cloud properties (known as the aerosol indirect effect) [8-10]. Solar dimming is a widespread decrease in surface solar radiation by several percents [11,12] and is considered to be a consequence of increasing anthropogenic pollution. Using the Global Energy Balance Archive (GEBA) of pyranometer network data, Alpert et al. [13] showed that, during the period 1964-1989, solar dimming was stronger over large urban sites than over sparsely-populated sites. Alpert and Kishcha [14] found that, in general, the average surface solar radiation flux, based on worldwide pyranometer measurements, decreases with population density as a monotonic function. Furthermore, Kishcha et al. [15] showed that, over extensive areas with differing population densities in the Indian subcontinent, the higher the averaged population density—the larger the averaged AOD. In addition, the larger the population growth is, the stronger the increasing AOD trends are observed.

Unlike ground-based measurements, satellite remote sensing of aerosols has the advantage of providing global coverage on a regular basis [10]. This provides us with an opportunity to compare aerosol tendencies in different megacities using satellite data of the same sensors. The current study was aimed at estimating aerosol optical depth (AOD) trends over the largest cities in the world in relation with the aerosol emission changes during the period 2002-2010. In the current study, global distribution of AOD tendencies over the largest cities in the world was verified by comparing the following three sensors: MODIS-Terra, MODIS-Aqua, and MISR. MODISAqua and MODIS-Terra have a wide viewing swath and their cameras are focused straight down on the Earth’s surface. MISR is a multi-angle imaging spectro-radiometer; its cameras acquire images with several angles relative to the Earth’s surface [16]. The multi-angle views ensure that MISR can provide aerosol optical thickness retrievals in areas where the Sun’s glint precludes MODIS from doing so. MISR and MODIS aerosol retrievals successfully complement each other [17]. Therefore, comparisons between aerosol optical depth and its tendencies based on both MODIS and MISR data can help us expand our knowledge about aerosol tendencies over the largest cities in the world.

2. Data

Our approach to estimating the effect of urbanization on AOD over the largest cities in the world was based on analyzing long-term variations of AOD. To attain the goal we used AOD data from the three aforementioned aerosol sensors on board the NASA Terra satellite (launched in December 1999) and the NASA Aqua satellite (launched in May 2002). The effect of urbanization on AOD was estimated for the eight-year period from July 2002 to June 2010, when data from the all three sensors were available. Note that, for MODIS-Terra, a comparison between the ten-year AOD trends and the eightyear AOD trends have shown very similar results; therefore, we preferred to study the results for the three sensors during the aforementioned eight-year period.

2.1. MODIS Data

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a sensor with the ability to characterize the spatial and temporal characteristics of the global aerosol field. MODIS has 36 channels spanning the spectral range from 0.41 to 15 µm. MODIS with its 2330 km viewing swath provides almost daily global coverage. The MODIS AOD uncertainty over the land is ΔAOD = ±(0.05 + 15%) [18,19]. Collection 5 (MOD08_M3.050) of MODIS-Terra and collection 5.1 of MODIS-Aqua (MYD08_ M3.051) level-3 monthly aerosol data with global 1˚ × 1˚ grid were used in the current study.

2.2. MISR Data

The Multi-angle Imaging SpectroRadiometer (MISR) [16] employs nine discrete cameras pointed at fixed angles, one viewing the nadir (vertically downward, 0˚) direction and four each viewing the forward and afterward directions (26.1, 45.6, 60.0, and 70.5 degrees). Each camera measures in four different wavelengths: 443 nm (blue band), 555 nm (green band), 670 nm (red band) and 865 nm (near-infrared). MISR provides global coverage data every 9 days. According to Liu et al. [20] the overall retrieval accuracy of MISR AOD fall within ΔAOD = ±0.04 ± 0.18 AOD. It should be mentioned that Liu et al. [20] used older version of the MISR AOD product than we used in the current study. In our study, the MISR monthly level-3 data aerosol product with global grid of 0.5˚ × 0.5˚ was used.

Recently, Oo et al. [21] compared MODIS AOD Level 2 data of 10-km standard resolution with AERONET AOD measurements in New York City. They showed that, for pixels in immediate proximity to the AERONET site, MODIS AOD overestimated AERONET AOD, while MODIS AOD, averaged over a 80 km × 80 km area centered at the AERONET site and included both urban and vegetation surface types, much better corresponded to AERONET AOD [21]. In the current study, we used 1˚ × 1˚ MODIS and 0.5˚ × 0.5˚ MISR gridded monthly data of AOD. This could minimize some existing problems of the underestimation of surface reflection over urban areas by MODIS and MISR.

2.3. Cloudiness Effects

MODIS and MISR have quite a limited opportunity to view aerosols if cloud cover is higher than 0.8 [22-26]. It means that satellite aerosol retrievals obtained under such overcast conditions are less accurate than AOD obtained when cloud presence is rather low. Moreover, in accordance with Remer et al. [26] and Zhang et al. [23], it is possible that, when cloud fraction exceeds 0.8, satellite aerosol retrievals are overestimated because of cloud contamination: The aerosol retrievals interpret, in error, cloud droplets as coarse mode particles. Therefore, months with high cloud coverage over megacities are unfavorable for studying relationships between urbanization and satellite-based AOD. In order to minimize the AOD retrieval uncertainty, AOD data were used only for months with cloud fraction less than 0.7. In order to estimate cloud fraction over megacities, Collection 5 MODISTerra 1˚ × 1˚ and Collection 5.1 MODIS-Aqua 1˚ × 1˚ monthly data of cloud fraction were used.

2.4. Population Data

The population of cities, including suburbs, was taken for the year 2010 from Brinkhoff [27] (www.citypopulation.de). In addition, the gridded global population density of World Version 3 (GPWv3) data set of the year 2000, from Socioeconomic Data and Applications Center (SEDAC) of Columbia University, was used (http://sedac. ciesin.columbia.edu/gpw). The full list of the largest cities examined (including the 26 megacities of over 10 million inhabitants), with information about their population; latitude-longitude coordinates; and countries, is given in Table A1 in the Appendix.

3. Results3.1. Capability of Satellite Aerosol Sensors in Detecting the Impact of Megacities on AOD

In order to ensure that satellite aerosol sensors could differentiate between AOD over megacities and over surrounding rural areas, 8-year mean AOD distributions over areas neighboring megacities were analyzed. In particular, we have investigated latitudinal distribution of 8-year mean AOD over 26 megacities with population exceeding 10 million people. Each latitudinal distribution has an east-west direction and is centered over the megacity center. In order to compare AOD distributions over differing megacities, for each latitudinal distribution, 8-year mean AOD values were normalized on the 8-year mean AOD over the megacity.

As an example, Figure 1 shows latitudinal distributions of 8-year normalized mean AOD over 13 megacities based on MODIS-Terra (Figure 1(a)) and MODISAqua (Figure 1(b)) data sets. All these distributions show maximum AOD over their megacity which decreases with distance from the megacity. The steepest decreasing slope over some cities, such as Buenos Aires, can be explained by the fact that the city under consideration is surrounded by rural areas. On the other hand, megacities, such as Paris, show a much gentler slope, which can be explained by the presence of other cities and/or industrial centers on the periphery affecting AOD. This makes it more difficult to distinguish the megacity aerosol signature from space. Two independent sensors,

MODIS-Terra and MODIS-Aqua, show similar latitudinal distributions of normalized mean AOD over the same megacities.

Figure 2 shows the averaged east-west latitudinal distribution of normalized AOD for all top 26 megacities. The error bars show the standard error of the mean AOD. One can see a clear bell-shaped form, with a maximum over the city center and a decrease away from the city. This indicates that the two MODIS aerosol sensors are able to distinguish between urban and rural areas.

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