Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

Grgurić, Sanja; Križan, Josip; Gašparac, Goran; Antonić, Oleg; Špirić, Zdravko; Mamouri, Rodanthi-Elisavet; Christodoulou, A.; Nisantzi, Argyro; Αγαπίου, Άθως; Themistocleous, Kyriacos; Fedra, K.; Panayiotou, Charalambos; Hadjimitsis, Diofantos G.

doi:10.2478/s13533-012-0135-6

Cited by 20 publications

(14 citation statements)

References 27 publications

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“…the simulations of AQMs under statically stable boundary conditions (e.g. Gašparac et al, 2016;Grisogono and Belušić, 2008) focusing on dynamic model aspects during episodes of elevated (PM 10 ) d concentrations over central and eastern Europe. Here, two different regional AQMs, namely, EMEP and WRF-Chem, were applied to evaluate their individual state-of-the-art performance and to investigate the processes that contributed to a high (PM 10 ) d concentration during pollution episodes that occurred in Europe.…”

Section: Discussionmentioning

confidence: 99%

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

Gašparac

Jeričević²,

Kumar

et al. 2020

Atmos. Chem. Phys.

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Abstract. The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on eastern and central Europe. The temporal and spatial variability of observed PM was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled European Monitoring and Evaluation Programme, EMEP, and online coupled Weather Research and Forecasting with Chemistry) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high-pollution episodes. Background PM measurements from rural background stations, wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of the accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements.

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Section: Discussionmentioning

confidence: 99%

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

Gašparac

Jeričević²,

Kumar

et al. 2020

Atmos. Chem. Phys.

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“…Both classes of particulate matter are highly dependent on climatic factors such as precipitation, relative humidity, wind speed, and air temperature, and it is positively correlated with the aerosol optical depth (AOD) [2,3]. In particular, AOD, which can be retrieved from remote sensing products, is considered a good proxy for PM2.5 [4].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Seasonal Climate Variability on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach based on Artificial Neural Network and Spatial Analysis

Yu¹,

Mohebbi²,

Cai³

et al. 2020

Preprint

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This study aims to develop a hybrid approach based on backpropagation Artificial Neural Network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California based on Aerosol Optical Depth (AOD) and the climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data was generated using Motor Vehicle Emission Simulator (MOVES). The measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA's Moderate Resolution Spectroradiometer (MODIS), respectively. The data was resampled to a singular seasonal format and was scaled down over grids of 10 by 10 to 150 by 150. Coefficient of determination (R2), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with R2 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the least performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model developed is capable of accurately predicting the PM2.5 concentration and can be used to forecast future trends.

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“…Several studies have focused on the use of satellite retrieved AOD as the main input parameter in models (simple linear regressive models, nonlinear and multilinear models) to predict the ambient particulate matter concentrations [3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models

Michaelides

Paronis

Retalis

et al. 2017

Advances in Meteorology

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This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM10 (particulate matter with aerodynamic diameter less than 10 μm) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed.

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Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

Cited by 20 publications

References 27 publications

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

The Influence of Seasonal Climate Variability on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach based on Artificial Neural Network and Spatial Analysis

Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models

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