Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image

Abstract

The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city-Iraq.The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations.The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS).The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10.The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and rootmean-square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration to an acceptable level of accuracy.

Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models

“…Levels of AOD at 0.55 μm were considered for this study. Landsat‐based data were used in many other studies (Saleh & Hasan, 2014) to estimate PM. It was understood that Landsat‐based datasets could estimate the above‐mentioned concentrations of PM.…”

Relationship between Remotely Sensed Ambient PM10 and PM2.5 and Urban Forest in Seoul, South Korea

“…The acquisition time of Landsat 8 images over the study area was approximately 02:00 UTC (11:00 a.m. in local time). Multi-spectral band data, from band 2 to band 7, extracted from the Landsat 8 images, were pre-processed with simple dark-object subtraction and radiometric and atmospheric correction to estimate AOD values based on the algorithms used in earlier aerosol studies [18,19,[29][30][31]. From pre-testing the entire multi-spectral band data collected for the Landsat 8 images, band 7 for PM10 models and band 4 for PM2.5 models were excluded due to lack of sensitivity of their AOD values to the changes in PM concentration.…”

“…To improve the PM model accuracy compared to previous MODIS-based models, single-day models derived from Landsat 8 imagery were examined. The modeling approach suggested by earlier studies was applied to the process of this study [18,19,22]. In a single day, Landsat 8 scenes contained 25 monitoring sites as sample sites of PM measurements.…”

Spatial model of Particulate Matter PM10 in Bekasi City, West Java Province using Landsat-8

“…Combustion of vehicle engines can produce PM10 [7]. PM10 is particulate matter with a diameter of fewer than 10 micrometers [8]. PM10 can enter the human respiratory system and cause acute respiratory disease [9].…”

“…Regarding those limitations, air quality monitoring did by emission inventory [17] and remote sensing using satellite data [18]. Geographic Information System (GIS) is a branch of geography that can help process pollutant data such as PM10 [8] by creating a spatial model [19]. The spatial model of air quality has several benefits [13] [20].…”