Overview of atmospheric aerosol studies in Malaysia: Known and unknown

Kanniah, Kasturi Devi; Kaskaoutis, Dimitris G.; Lim, H. S.; Latif, Mohd Talib; Zaman, Nurul Amalin Fatihah Kamarul; Liew, Ju Neng

doi:10.1016/j.atmosres.2016.08.002

Cited by 34 publications

(16 citation statements)

References 150 publications

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“…Aerosol optical depth (AOD) is a measure of the attenuation of solar radiation due to light absorption and scattering by the atmospheric aerosols. Over SEA, aerosols are mainly from urban and industrial emissions (organics, sulfate, nitrate, ammonium), black carbon from fossilfuel combustion and biofuel burning, volcanic ash, sea salts and dust transported by long distances, while during pre-monsoon extensive forest and vegetation fires occur (Chuersuwan et al, 2008;Hai and Kim Oanh, 2013;Tsai et al, 2013;Kanniah, 2014;Kanniah et al, 2016;Khan et al, 2016;Pani et al, 2018;Dahari et al, 2019). Therefore, aerosols are mixtures of various types creating hybrid particles and rendering their radiative effects highly uncertain (Pani et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

COVID-19's impact on the atmospheric environment in the Southeast Asia region

Kanniah

Zaman

Kaskaoutis

et al. 2020

Science of The Total Environment

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Section: Introductionmentioning

confidence: 99%

COVID-19's impact on the atmospheric environment in the Southeast Asia region

Kanniah

Zaman

Kaskaoutis

et al. 2020

Science of The Total Environment

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284

255

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“…MODIS 3 km product should also be included for analyses and compared by means of spatial variability in AOD levels. Furthermore, additional research is required to improve the accuracy of the AOD estimates above desert regions such as the Middle East (Sayers et al, 2014 andDe Meij et al, 2011) and in other regions such as South and Southeast Asia (Kanniah et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

An example of aerosol pattern variability over bright surface using high resolution MODIS MAIAC: The eastern and western areas of the Dead Sea and environs

Sever

Alpert

Lyapustin

et al. 2017

Atmospheric Environment

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The extreme rate of evaporation of the Dead Sea (DS) has serious implicatios for the surrounding area, including atmospheric conditions. This study analyzes the aerosol properties over the western and eastern parts of the DS during the year 2013, using MAIAC (Multi-Angle Implementation of Atmospheric Correction) for MODIS, which retrieves aerosol optical depth (AOD) data at a resolution of 1km. The main goal of the study is to evaluate MAIAC over the study area and determine, for the first time, the prevailing aerosol spatial patterns. First, the MAIAC-derived AOD data was compared with data from three nearby AERONET sites (Nes Ziona - an urban site, and Sede Boker and Masada - two arid sites), and with the conventional Dark Target (DT) and Deep Blue (DB) retrievals for the same days and locations, on a monthly basis throughout 2013. For the urban site, the correlation coefficient (r) for DT/DB products showed better performance than MAIAC (r=0.80, 0.75, and 0.64 respectively) year-round. However, in the arid zones, MAIAC showed better correspondence to AERONET sites than the conventional retrievals (r=0.58-0.60 and 0.48-0.50 respectively). We investigated the difference in AOD levels, and its variability, between the Dead Sea coasts on a seasonal basis and calculated monthly/seasonal AOD averages for presenting AOD patterns over arid zones. Thus, we demonstrated that aerosol concentrations show a strong preference for the western coast, particularly during the summer season. This preference, is most likely a result of local anthropogenic emissions combined with the typical seasonal synoptic conditions, the Mediterranean Sea breeze, and the region complex topography. Our results also indicate that a large industrial zone showed higher AOD levels compared to an adjacent reference-site, i.e., 13% during the winter season.

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“…Several studies have used only the satellite AOD via simple linear regressions for PM estimations from space resulting in correlation coefficients around 0.52 to 0.77 between measured and estimated PM 10 concentrations (Gupta and Christopher, 2008;Schaap et al, 2009;Kanniah et al, 2016). According to Gupta and Christopher (2009a, b), AOD alone is not a good predictor of PM, since AOD corresponds to the total columnar aerosol, whereas PM refers to particle concentration at the surface; therefore, the correlation between PM and AOD strongly depends on the vertical distribution of aerosols and several meteorological factors, i.e., profiles of potential temperature and wind regime (Chitranshi et al, 2014;Sinha et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

Zaman

Kanniah

Kaskaoutis

2017

Atmospheric Research

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The insufficient number of ground-based stations for measuring Particulate Matter < 10 μm (PM 10) in the developing countries hinders PM 10 monitoring at a regional scale. The present study aims to develop empirical models for PM 10 estimation from space over Malaysia using aerosol optical depth (AOD 550) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007-2011. The MODIS retrievals are found to be satisfactorily correlated with ground-based measurements at Malaysia. Multiple linear regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models for PM 10 estimation. The model development and training are performed via comparison with measured PM 10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R 2 = 0.71 and RMSE = 11.61 μg m − 3 compared to the MLR method (R 2 = 0.66 and RMSE = 12.39 μg m − 3). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD 550 is the most important parameter for PM 10 estimations (R 2 = 0.59 and RMSE = 13.61 μg m − 3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R 2 = 0.66, RMSE = 12.39 μg m − 3). The estimated PM 10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R 2 = 0.58, RMSE = 10.16 μg m − 3) and MLR technique (R 2 = 0.56, RMSE = 10.58 μg m − 3). The significant accuracy that has been attained in PM 10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM 10 distribution at large spatial and temporal scales.

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Overview of atmospheric aerosol studies in Malaysia: Known and unknown

Cited by 34 publications

References 150 publications

COVID-19's impact on the atmospheric environment in the Southeast Asia region

COVID-19's impact on the atmospheric environment in the Southeast Asia region

An example of aerosol pattern variability over bright surface using high resolution MODIS MAIAC: The eastern and western areas of the Dead Sea and environs

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

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