Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran

Sotoudeheian, Saeed; Arhami, Mohammad

doi:10.1186/s40201-014-0122-6

Cited by 45 publications

(25 citation statements)

References 32 publications

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“…An atempt to develop a single general model for an area found that the general model performed poorly compared with site-speciic models [32]. Some of these site-speciic issues are removed when a deterministic physiochemical modelling approach is used, but accuracy of such models is currently limited as many of the actual mechanisms involved in pollution generation, dispersal, dilution and removal are not fully understood.…”

Section: Discussionmentioning

confidence: 99%

“…One study using AOD from satellite imagery along with meteorological variables temperature, wind speed, wind direction, relative humidity and planetary boundary layer height to compare a non-linear regression model with a linear regression model found that the non-linear models outperformed their counterparts [32]. In the non-linear model, non-linear functions were used to relect the non-linear inluences of the explanatory variables.…”

Section: Regression Methodsmentioning

confidence: 99%

“…MODIS Moderate Resolution Imaging Spectroradiometer) and MISR Multi-angle Imaging Spectroradiometer) images are analysed using algorithms designed to calculate how much direct sunlight is prevented from reaching the ground by aerosol particles the aerosol optical depth AOD). Several studies have used AOD to estimate PM 2.5 and PM 10 concentrations [31] and have shown that there is a high linear correlation between particulate mater concentrations and AOD [32]. One study published in 2014 used AOD along with meteorological variables to predict ground-level PM 10 but did not evaluate the degree to which including AOD inluenced the outcome of PM 10 predictions [32].…”

Section: Explanatory Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Particulate Matter Sampling Techniques and Data Modelling Methods

Whalley¹,

Zandi²

2016

Air Quality - Measurement and Modeling

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Particulate mater with 10 μm or less in diameter PM 10 ) is known to have adverse efects on human health and the environment. For countries commited to reducing PM 10 emissions, it is essential to have models that accurately estimate and predict PM 10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM 10 is presented. This includes the instrumentation used to measure particulate mater, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM 10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM 10 trends in New Zealand a remote country of islands in the South Paciic Ocean are examined. In conclusion, the issues and challenges faced when modelling PM 10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM 10 prediction and estimation models are presented.

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Section: Discussionmentioning

confidence: 99%

Section: Regression Methodsmentioning

confidence: 99%

Section: Explanatory Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Particulate Matter Sampling Techniques and Data Modelling Methods

Whalley¹,

Zandi²

2016

Air Quality - Measurement and Modeling

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“…The Lands at ETM+ data should be used because they have high resolution, availability spatially and temporally, and easy to get (Techarat, 2013;Sotoudeheian and Arhami, 2014;Wang et al, 2017). The atmospheric correction, which is generally taken as preprocessing in remote sensing applicaions, has been effectively used for pollution distibution mapping (Marcello et al, 2016).…”

Section: Ijarsg-an Openmentioning

confidence: 99%

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

Roy

Jivani

Parekh

2017

IJARSG

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Remot e sensing imagery is a rich source of information with applications in varied fields. Monitoring of environment pollution is one of them. The work presented in this paper is foc used on estimation of the ambient concentration of pollut ant using remote sensing. Particulate Matter with particle sizes less than 10 mic rons (PM10) is estimated for the study area Vadodara. Landsat 7 E TM+ data of different wavelength has been processed and analyzed for the relationship with coincident ground station PM10 data. The radiance values observed by the satellit e and its difference with the radiance calculated after atmos pheric correction for the same pixel is considered as a measure to estimate PM10. This difference, called path radiance is calculated and correlated with the ground station PM10 values. Using regression analysis on the calculated data and the ground station PM10 data, the algorithm for PM10 estimation is generat ed and PM10 map is generated for the study area. The algorithm shows good results fo r the test data. P ollution estimation through remote sensing is an efficient technique as it can be carried out in less time. Estimation and analysis for larger area is possible using remote sensing approac h. The 30 meter resolution of Landsat satellite makes it more suitable for local and regional study.

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“…These dust particles emerge mostly from desert regions in neighboring countries, mainly Iraq, and increases PM levels in our target stations [29,30]. Using remote sensing data will provide us with a wealth of insights of dust activities.…”

Section: Introductionmentioning

confidence: 99%

Satellite and ground-based assessment of Middle East meteorological parameters impact on dust activities in western Iran

2016

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Abstract. In this article, the relation between meteorological parameters and dust activities in western Iran has been studied. Satellite-based data achieved from TOMS are used to investigate the dust activities within a time period of 30 years. In the rst part of this study, we examine the statistical trend of Aerosol Index (AI) and local meteorological parameters in 15 di erent stations. The same patterns of AI variations in all stations indicate that this region has always been subjected to dust storms which originate from similar sources in the neighboring countries that could be known as a sole dust transfer system. In the second part, we investigate the spatial correlation between the regional meteorological parameters in the Middle East and AI data to determine the contribution of meteorological parameters to dust levels. Broadly, results show that the precipitation in concurrent and antecedent months has a negative correlation with AI parameter of dusty months. Also, notably, we observed that the zonal wind speed in Iraq has a strong positive correlation with AI in our selected stations. This fact veri es that the zonal winds could be identi ed as the major cause of dust transfer system that was noted in the rst part of this study.

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Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran

Cited by 45 publications

References 32 publications

Particulate Matter Sampling Techniques and Data Modelling Methods

Particulate Matter Sampling Techniques and Data Modelling Methods

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

Satellite and ground-based assessment of Middle East meteorological parameters impact on dust activities in western Iran

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