scholarly journals Study of satellite retrieved aerosol optical depth spatial resolution effect on particulate matter concentration prediction

2014 ◽  
Vol 14 (18) ◽  
pp. 25869-25899 ◽  
Author(s):  
J. Strandgren ◽  
L. Mei ◽  
M. Vountas ◽  
J. P. Burrows ◽  
A. Lyapustin ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Linear Correlation ◽  
Continental Scale ◽  
Particulate Matter Concentration ◽  
The Us ◽  
Matter Concentration ◽  
Concentration Prediction

Abstract. The Aerosol Optical Depth (AOD) spatial resolution effect is investigated for the linear correlation between satellite retrieved AOD and ground level particulate matter concentrations (PM2.5). The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) for obtaining AOD with a high spatial resolution of 1 km and provides a good dataset for the study of the AOD spatial resolution effect on the particulate matter concentration prediction. 946 Environmental Protection Agency (EPA) ground monitoring stations across the contiguous US have been used to investigate the linear correlation between AOD and PM2.5 using AOD at different spatial resolutions (1, 3 and 10 km) and for different spatial scales (urban scale, meso-scale and continental scale). The main conclusions are: (1) for both urban, meso- and continental scale the correlation between PM2.5 and AOD increased significantly with increasing spatial resolution of the AOD, (2) the correlation between AOD and PM2.5 decreased significantly as the scale of study region increased for the eastern part of the US while vice versa for the western part of the US, (3) the correlation between PM2.5 and AOD is much more stable and better over the eastern part of the US compared to western part due to the surface characteristics and atmospheric conditions like the fine mode fraction.

Determination of aerosol optical depth and particulate matter concentrations using routine web cam measurements

2020 ◽  
Author(s):  
Philipp Weihs ◽  
Anita Frisch-Niggemeyer ◽  
Stefan Schreier
Keyword(s):  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Monitoring Network ◽  
Horizontal Line ◽  
Ground Truth Data ◽  
Visual Contrast ◽  
Particulate Matter Concentration ◽  
Matter Concentration

<p> </p><p>Visibility and visual contrast depend on several factors such as aerosol concentration, fog attenuation and humidity as well as gas characteristics. Usually, visibility is determined by observers or by visiometers. Routine web cam photographs of Vienna  have been performed for  2 years from the meteorological measurement platform situated on the roof of one of the buildings of University of Natural resources and Life Sciences overlooking the whole city of Vienna. Photographs are taken every 30 minutes in 6 different azimuthal directions. In the following study, we used routine web cam photographs digitalization to study the correlation between the ratio of some RGB channels as well as intensity fluctuations and the aerosol optical depth and on site particulate matter measurements. We first selected only photographs taken on clear sky days</p><p>For ground truth data, we used CIMEL sun photometer data of aerosol optical depth and liquid water content, relative humidity from routine measurements from our measurement platform as well as in situ measurements of particulate matter (PM10) performed by the air quality monitoring network of the city of Vienna.</p><p>First, the correlation between the contrast in a horizontal line and the aerosol amounts in the atmosphere and particulate matter concentration as a function of time of the day and azimuthal direction was investigated. We then examined the correlation between the blue to red ratio in a vertical and horizontal line with the aerosol amounts and particulate matter concentration in the atmosphere.</p><p>Results obtained showed at some azimuth angles and time of the day correlation coefficient R squared of up to 0.85 between horizontal line contrast and in situ PM 10 and between vertical line blue to red ratio and CIMEL aerosol optical depths measurements.</p><p> </p>

The NASA-TROPOMI Aerosol Algorithm: Evaluation of first results

2021 ◽  
Author(s):  
Omar Torres ◽  
Hiren Jethva ◽  
Changwoo Ahn ◽  
Glen Jaross ◽  
Diego Loyola
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Single Scattering ◽  
Initial Evaluation ◽  
Continental Scale ◽  
Related Information ◽  
Fine Spatial Resolution ◽  
Direct Measurements ◽  
First Results

<p>The NASA-TROPOMI aerosol algorithm (TropOMAER), is an adaptation of the currently operational OMI near-UV (OMAERUV & OMACA) inversion schemes, that take advantage of TROPOMI’s unprecedented fine spatial resolution at UV wavelengths, and the availability of ancillary aerosol-related information to derive aerosol loading in cloud-free and above-cloud aerosols scenes. In this presentation we will introduce the NASA TROPOMI aerosol algorithm and discuss initial evaluation results of retrieved aerosol optical depth (AOD) and single scattering albedo (SSA) by direct comparison to AERONET AOD direct measurements and SSA inversions. We will also demonstrate TropOMAER retrieval capabilities in the context of recent continental scale aerosol events.</p>

scholarly journals DAILY ESTIMATION OF FINE PARTICULATE MATTER MASS CONCENTRATION THROUGH SATELLITE BASED AEROSOL OPTICAL DEPTH

2017 ◽  
Vol IV-4/W2 ◽  
pp. 175-181
Author(s):  
H. Karimian ◽  
Q. Li ◽  
C. C. Li ◽  
J. Fan ◽  
L. Jin ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Particulate Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Surface Concentration ◽  
Mean Value ◽  
Fine Particulate

Estimating exposure to fine Particulate Matter (PM<sub>2.5</sub>) requires surface with high spatial resolution. Aerosol optical depth (AOD) is one of MODIS products, being used to monitor PM<sub>2.5</sub> concentration on ground level indirectly. In this research, AOD was derived in fine spatial resolution of 1×1 Km by utilizing an algorithm developed in which local aerosol models and conditions were took into account. Afterwards, due to spatial varying the relation between AOD-PM<sub>2.5</sub>, a regional scale geographically weighted regression model (GWR) was developed to derive daily seamless surface concentration of PM<sub>2.5</sub> over Beijing, Tianjin and Hebei. For this purpose , various combinations of explanatory variables were investigated in the base of data availability, among which the best one includes AOD, PBL height, mean value of RH in boundary layer, mean value of temperature in boundary layer, wind speed and pressure was selected for the proposed GWR model over study area. The results show that, our model produces surface concentration of PM<sub>2.5</sub> with annual RMSE of 18.6μg/m<sup>3</sup>. Besides, the feasibility of our model in estimating air pollution level was also assessed and high compatibility between model and ground monitoring was observed, which demonstrates the capability of the MODIS AOD and proposed model to estimate ground level PM<sub>2.5</sub>.

scholarly journals An Urban Lagrangian Stochastic Dispersion Model for Simulating Traffic Particulate-Matter Concentration Fields

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 580
Author(s):  
Eyal Fattal ◽  
Hadas David-Saroussi ◽  
Ziv Klausner ◽  
Omri Buchman
Keyword(s):  
Surface Layer ◽  
Particulate Matter ◽  
Urban Area ◽  
Hybrid Approach ◽  
Urban Morphology ◽  
Dispersion Pattern ◽  
Vertical Column ◽  
Particulate Matter Concentration ◽  
Vertical Heterogeneity ◽  
Matter Concentration

The accumulated particulate matter concentration at a given vertical column due to traffic sources in urban area has many important consequences. This task, however, imposes a major challenge, since the problem of realistic pollutant dispersion in an urban environment is a very demanding task, both theoretically and computationally. This is mainly due to the highly inhomogeneous three dimensional turbulent flow regime in the urban canopy roughness sublayer, which is far from “local equilibrium” between shear production and dissipation. We present here a mass-consistent urban Lagrangian stochastic model for pollutants dispersion, where the flow field is modeled using a hybrid approach by which we model the surface layer based on the typical turbulent scales, both of the canopy and in the surface layer inertial sub-layer. In particular it relies on representing the canopy aerodynamically as a porous medium by spatial averaging the equations of motion, with the assumption that the canopy is laterally uniform on a scale much larger than the buildings but smaller than the urban block/neighbourhood, i.e., at the sub-urban-block scale. Choosing the spatial representative averaging volume allows the averaged variables to reflect the characteristic vertical heterogeneity of the canopy but to smooth out smaller scale spatial fluctuations caused as air flows in between the buildings. This modeling approach serves as the base for a realistic and efficient methodology for the calculation of the accumulated concentration from multiple traffic sources for any vertical column in the urban area. The existence of multiple traffic sources impose further difficulty since the computational effort required is very demanding for practical uses. Therefore, footprint analysis screening was introduced to identify the relevant part of the urban area which contributes to the chosen column. All the traffic sources in this footprint area where merged into several areal sources, further used for the evaluation of the concentration profile. This methodology was implemented for four cases in the Tel Aviv metropolitan area based on several selected summer climatological scenarios. We present different typical behaviors, demonstrating combination of source structure, urban morphology, flow characteristics, and the resultant dispersion pattern in each case.

scholarly journals Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity, India

2021 ◽  
Vol 67 (7) ◽  
pp. 2140-2150
Author(s):  
V. Sreekanth ◽  
Meenakshi Kushwaha ◽  
Padmavati Kulkarni ◽  
Adithi R. Upadhya ◽  
B. Spandana ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Particulate Matter Concentration ◽  
Fine Particulate ◽  
Matter Concentration ◽  
Concentration Levels
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