Retrieval of PM10 Concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) derived AOD in Peninsular Malaysia Xen Quan Yap, *Mazlan Hashim and Maged Marghany Institute of Geospatial Science & Technology (INSTeG) Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia emails:
[email protected],
[email protected], maged@ utm.my .
ABSTRACT In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) derived aerosol optical depth (AOD) was used to retrieve air pollution (PM10) in Peninsular Malaysia. Located in an equatorial and maritime region, the strength of the correlation between MODIS derived AOD and in situ data were given less attention due to data availability, and monsoon wind effect. To make used of MODIS derived data, long term (2001 to 2006) retrieval and investigation on its reliability was performed. The cloud cover and wind effect were taken care by using a modified 5x5 moving mean filter to smoothen and extrapolate gaps in MODIS AOD dataset. Besides, relationship between daily and monthly average MODIS AOD and PM10 were established. Monthly average shows higher correlation of 0.6 with 12.90 μg/m3 RMSE for six years. These suggest that, MODIS estimated PM10 has the potential to be utilized in air quality monitoring over Peninsular Malaysia. Index Terms— PM10, air quality, AOD, MODIS 1. INTRODUCTION Air quality is an issue that concerns the general public the most as it can cause a country’s economy to stagnant besides its well known threat to the environment and human health. In many developed and developing nation, air pollution has caused an estimated side effect of approximately two million premature deaths worldwide per year. As such, many international organizations such as European Environment Agency (EEA), World Meteorology Organization (WMO), and etc. were using satellite remote sensing technique that combines with in situ measurement to deliver state-of-the-art information on air quality [4]. Some of this air quality models are MOCAGE (Meteo-France), EURAD (RIU), and CHIMERE (INERIS) [3]. In a country of maritime climate such as Malaysia, severe cases of air pollution are generally affected by our neighboring country as a result of forest fire and monsoon wind. This event usually occurred during Southwest
Monsoon season (between May and September), which brings haze from Sumatra region to the western side of Peninsular Malaysia. Other local sources of air pollutions include vehicle emission, power generation, industrial emission, open burning and forest fires [9]. In Malaysia, the health damage resulted from air pollution had a record of approximate US$43 million and others related productivity losses approximate US$1.5 million in year 1997 [9]. In 2006, air pollution has caused Singapore US$50 million of estimated economic loss at since the start of the haze [10]. In order to make use of the MODIS AOD in monitoring air quality parameter such as PM10, a conversion factor between them is needed [5]. In other words, the relationship between AOD and PM10 concentration needs to be determined. Many attempts were made to assess this relationship regionally throughout the world. However, these relationships established are mostly of linear relationship under continental region and not in maritime region such as Peninsular Malaysia. For example, [2] acquired a good correlation coefficient, R that is 0.82 in Italy, [3] acquired R=0.63 in Europe, [1] acquired R=0.92 in Beijing, [8] acquired correlation coefficient, R of 0.42, 0.31, 0.58, 0.5, 0.53, 0.55, 0.51, and 0.66 for eastern China region (i.e. Panyu, Benxi, Zhengzhou, Gullin, Xi’an, Nanning, Gucheng, Lushan, Lin’an, Longfengshan and Shangdianzi respectively). Countries situated under maritime climate are more dynamic in its atmospheric turbulence compare to continental region due to the influence of seasonal monsoon wind. Thus, long-term (six years) quantitative evaluation of relationship and effectiveness of satellite derived AOD in monitoring PM10 is necessary to identify its uncertainty and implication of using such data. To be more precise, the objective of this paper is to establish a relationship between satellite-retrieved AOD and surface measured PM10 in a country under maritime climate that is Peninsular Malaysia and to investigate its reliability in monitoring PM10 concentration. Besides, accuracy assessment was also performed to quantitatively evaluate the final results. Such effort also provide a platform to further understand the performance of MODIS AOD product in the global air .
978-1-4577-1005-6/11/$26.00 ©2011 IEEE
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* Corresponding author:
[email protected]
IGARSS 2011
due to the present of aerosol at different altitude that is
quality monitoring effort especially in a country of maritime climate. 2. MATERIAL AND METHODOLOGY The in situ data used in this study was obtained from Malaysian Department of Environment (ASMA) and AOD datasets were of MODIS terra (MOD04) at 550 nm. Here, a total of 34 sampling sites providing daily PM10 value at ground level for a period of six years (2001 to 2006) distributed across Peninsular Malaysia as shown in figure 1 were used in this study. According to [6], the AOD can be written as: (1) 2 -1 Where, S is the specific extinction efficiency (m g ) of the aerosol at ambient relative humidity (RH) and H is a wellmixed boundary layer of height assuming cloud-free skies, with no overlying aerosols, and similar optical properties. This correlation was then identified to enable AOD to be incorporated into air quality model to further improve air quality forecast and monitoring of that particular region. In this study, a linear regression was used to retrieve PM10 concentration in Peninsular Malaysia from MODIS AOD. The cloud cover and wind effect on pixels were taken care by using a modified 5x5 moving mean filter to smoothen and extrapolate invalid data of the MODIS AOD dataset. MODIS level 2 AOD dataset retrievals are at 10- by 10-km spatial resolution and PM10 ground measurements are point measurements with a daily temporal resolution. Typically the matchup in spatial location was restricted to less than 50-km offset [5]. At a 5-km hr-1 wind speed, it takes the aerosol 2 hour to cross a pixel. However, MODIS AOD has been compared with both hourly and 24-hr PM10 measurements.
Figure 1: Distribution of air stations site and type used in this study across Peninsular Malaysia.
3. RESULT AND DISCUSSION 3.1. Spatial variation of correlation coefficient To visualize the effectiveness of MODIS AOD product to monitor PM10, annual and a single annual average regression coefficient (R) map of year 2001 to 2006 was produced. This was done by interpolating regression coefficient (R) of each station and across Peninsular Malaysia by using inverse distance weighing (IDW) technique. The result of the interpolation is shown in figure 2. Interestingly, the result of the interpolation (figure 2) shows a large portion of Peninsular Malaysia has a good linear relationship starting from 50 North (Perak state) to the southern part (Johor state). In this region, the strength of the linear relationship produced has a value of 0.50 to 0.67. On the contrary, the northern Peninsular shows weak correlation especially in Perlis and Kedah state. These could be due to the presents of different size suspended PM in these regions as MODIS AOD was used to validate for different PM size. Besides, other possible reason for the poor results could be
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Figure 2: Variation of correlation of MODIS AOD in PM10 monitoring across Peninsular Malaysia undetectable by the ground measurement. 3.2. Temporal variation of PM10 concentration and MODIS AOD The temporal variations of monthly mean PM10 concentration observed at 34 stations across Peninsular Malaysia and MODIS AOD for year 2001 to 2006 are illustrated as the mean and standard deviation plot in figure 3. As a whole, the seasonal variation of PM10 concentration is consistent with MODIS derived AOD. The occurrence of peak seasons (where high PM10 concentration occurred) are observed both by MODIS sensor and validated by in situ data. Form figure 3, the monthly average PM10 and MODIS derived AOD exhibit similar pattern indicating the ability and possible improvement of the monthly average data in
computing long term exposure of PM10 to human health for future studies.
with 95% confident-level). The significant test shows that MODIS-generated PM10 is as good as in-situ observation. The regression slope obtained from long term regression Table 1. Long term regression analysis of PM10-AOD and RMSE of annual MODIS estimated PM10 concentration Year
Figure 3: Temporal variation of monthly average PM10 concentration and MODIS AOD for year 2001 to 2006.
Linear regression
R
y = 95.92x + 19.30 0.60 2001 y = 80.84x + 27.71 0.60 2002 y = 89.86x + 23.95 0.63 2003 y = 74.69x + 28.36 0.62 2004 y = 81.31x + 26.87 0.60 2005 y = 42.87x + 34.81 0.66 2006 2001y = 62.54x + 31.26 0.60 2006 Axis- y: PM10; Axis-x: MODIS AOD
n 315 325 322 367 366 381
RMSE (μg/m3) + 13.01 + 15.36 + 14.30 + 13.38 + 12.27 + 12.52
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+ 12.90
p
p
