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

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.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  H. Lamb,et al.  Types and spells of weather around the year in the British Isles : Annual trends, seasonal structure of the year, singularities , 1950 .

[3]  Synoptic Climatology , 1958, Nature.

[4]  Harold S. Javitz,et al.  METHODOLOGY FOR EVALUATING HIGHWAY AIR POLLUTION DISPERSION MODELS , 1981 .

[5]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[6]  G. Gutman,et al.  Dust Intrusion Events into the Mediterranean Basin. , 1991 .

[7]  D. Tanré,et al.  Assessment of the African airborne dust mass over the western Mediterranean Sea using Meteosat data , 1992 .

[8]  Brent Yarnal,et al.  Synoptic Climatology in Environmental Analysis: A Primer , 1994 .

[9]  Michael B. Richman,et al.  On the Application of Cluster Analysis to Growing Season Precipitation Data in North America East of the Rockies , 1995 .

[10]  D. Tanré,et al.  Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances , 1997 .

[11]  François Dulac,et al.  Long‐term daily monitoring of Saharan dust load over ocean using Meteosat ISCCP‐B2 data: 1. Methodology and preliminary results for 1983–1994 in the Mediterranean , 1997 .

[12]  Adrianos Lachanas,et al.  Regional weather prediction on small network of workstations , 1998, Proceedings. 24th EUROMICRO Conference (Cat. No.98EX204).

[13]  Paraskevas Evripidou,et al.  Monitoring and predicting Saharan Desert dust events in the eastern Mediterranean , 1999 .

[14]  Adrianos Retalis,et al.  Assessment of the distribution of aerosols in the area of Athens with the use of Landsat Thematic Mapper data , 1999 .

[15]  Constantinos S. Pattichis,et al.  Classification of rainfall variability by using artificial neural networks , 2001 .

[16]  B. Hewitson,et al.  Self-organizing maps: applications to synoptic climatology , 2002 .

[17]  Yoav Benjamini,et al.  The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values , 2002 .

[18]  C. R. I. Clayton,et al.  The Assessment of Atmospheric Pollution using Satellite Remote Sensing Technology in Large Cities in the Vicinity of Airports , 2002 .

[19]  Alex J. Cannon,et al.  Synoptic Map-Pattern Classification Using Recursive Partitioning and Principal Component Analysis , 2002 .

[20]  Maria João Costa,et al.  Aerosol characterization and optical thickness retrievalsusing GOME and METEOSAT satellite data , 2002 .

[21]  Yoram J. Kaufman,et al.  Relationship between surface reflectance in the visible and mid‐IR used in MODIS aerosol algorithm ‐ theory , 2002 .

[22]  Gin-Rong Liu,et al.  Monitoring the atmospheric aerosol optical depth with SPOT data in complex terrain , 2002 .

[23]  C. Borrego,et al.  Quality Assurance of Air Pollution Models , 2003 .

[24]  B. Holben,et al.  Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) , 2003 .

[25]  Jun Wang,et al.  Intercomparison between satellite‐derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies , 2003 .

[26]  Archontoula Chaloulakou,et al.  Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment , 2003, Journal of the Air & Waste Management Association.

[27]  Yun Zeng,et al.  Progress in developing an ANN model for air pollution index forecast , 2004 .

[28]  E. Vermote,et al.  The MODIS Aerosol Algorithm, Products, and Validation , 2005 .

[29]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[30]  Georgios Grivas,et al.  Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece , 2006 .

[31]  P. Goyal,et al.  Statistical models for the prediction of respirable suspended particulate matter in urban cities , 2006 .

[32]  Nicolas Moussiopoulos,et al.  PM10 forecasting for Thessaloniki, Greece , 2006, Environ. Model. Softw..

[33]  Jorge Reyes,et al.  An integrated neural network model for PM10 forecasting , 2006 .

[34]  Jun Wang,et al.  Satellite remote sensing of particulate matter and air quality assessment over global cities , 2006 .

[35]  Christos N. Schizas,et al.  Synoptic Classification and Establishment of Analogues with Artificial Neural Networks , 2007 .

[36]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[37]  Naresh Kumar,et al.  An empirical relationship between PM(2.5) and aerosol optical depth in Delhi Metropolitan. , 2007, Atmospheric environment.

[38]  P. Alpert,et al.  Synoptic patterns associated with dusty and non-dusty seasons in the Sahara , 2008 .

[39]  Siegfried Hörmann,et al.  Quality and performance of a PM10 daily forecasting model , 2008 .

[40]  A. Namdeo,et al.  Investigating the relationship between air pollution, health and social deprivation in Leeds, UK. , 2008, Environment international.

[41]  Petros Koutrakis,et al.  A 10-year time-series analysis of respiratory and cardiovascular morbidity in Nicosia, Cyprus: the effect of short-term changes in air pollution and dust storms , 2008, Environmental health : a global access science source.

[42]  Andrew D. Foster,et al.  Remote sensing of ambient particles in Delhi and its environs: estimation and validation , 2008, International journal of remote sensing.

[43]  Ferhat Karaca,et al.  An online air pollution forecasting system using neural networks. , 2008, Environment international.

[44]  G. Leeuw,et al.  Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands , 2008 .

[45]  Sundar A. Christopher,et al.  Seven year particulate matter air quality assessment from surface and satellite measurements , 2008 .

[46]  S. Christopher,et al.  Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? , 2009, Journal of the Air & Waste Management Association.

[47]  Adrianos Retalis,et al.  Synergetic use of TERRA/MODIS imagery and meteorological data for studying aerosol dust events in Cyprus , 2009 .

[48]  Judit Bartholy,et al.  Cost733cat - A database of weather and circulation type classifications , 2010 .

[49]  M. Brauer,et al.  Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application , 2010, Environmental health perspectives.

[50]  Silas Michaelides,et al.  Association of geopotential height patterns with heavy rainfall events in Cyprus , 2010 .

[51]  Diofantos G. Hadjimitsis,et al.  Comparison of aerosol optical thickness with in situ visibility data over Cyprus , 2010 .

[52]  Andrew D. Foster,et al.  Satellite Remote Sensing for Developing Time and Space Resolved Estimates of Ambient Particulate in Cleveland, OH , 2011, Aerosol science and technology : the journal of the American Association for Aerosol Research.

[53]  Anastasia K Paschalidou,et al.  Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management , 2011, Environmental science and pollution research international.

[54]  Ari Karppinen,et al.  Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. , 2011, The Science of the total environment.

[55]  J. Schwartz,et al.  A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations , 2011 .

[56]  José Luis Sánchez,et al.  Atmospheric patterns associated with hailstorm days in the Ebro Valley, Spain , 2011 .

[57]  Hazrul Abdul Hamid,et al.  Comparison Between Multiple Linear Regression And Feed forward Back propagation Neural Network Models For Predicting PM10 Concentration Level Based On Gaseous And Meteorological Parameters , 2011 .

[58]  E. Fattahi,et al.  Study of dust storm synoptical patterns in southwest of Iran. , 2012 .

[59]  W. Marsden.  I and J , 2012 .

[60]  M. Hashim,et al.  A robust calibration approach for PM 10 prediction from MODIS aerosol optical depth , 2012 .

[61]  Study of Air Pollution with the Use of MODIS Data, LIDAR and Sun Photometers in Cyprus , 2013 .

[62]  José Luis Sánchez,et al.  Synoptic environment, mesoscale configurations and forecast parameters for hailstorms in Southwestern Europe , 2013 .

[63]  Athos Agapiou,et al.  Connection of Heat Events in Cyprus with Synoptic Upper Air Patterns , 2013 .

[64]  M. Kavianpour,et al.  Synoptic analysis of dust storms in the Middle East , 2013, Asia-Pacific Journal of Atmospheric Sciences.

[65]  J. Lelieveld,et al.  Air Pollution from Space , 2013 .

[66]  kais J. Al-Jumaily,et al.  Analysis of synoptic situation for dust storms in Iraq , 2013 .

[67]  Silas Michaelides,et al.  Trends of Dust Transport Episodes in Cyprus Using a Classification of Synoptic Types Established with Artificial Neural Networks , 2013 .

[68]  A. Awad,et al.  The Synoptic Patterns Associated with Spring Widespread Dusty Days in Central and Eastern Saudi Arabia , 2014 .

[69]  K. Fedra,et al.  Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia , 2014 .

[70]  S. Morman,et al.  Dust and Human Health , 2014 .

[71]  J. Schwartz,et al.  PM10 concentration levels at an urban and background site in Cyprus: The impact of urban sources and dust storms , 2014, Journal of the Air & Waste Management Association.

[72]  Jan-Berend W Stuut,et al.  Mineral Dust: A Key Player in the Earth System , 2014 .

[73]  Brent N. Holben,et al.  Estimation of PM 10 concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign , 2015 .

[74]  Itai Kloog,et al.  Assessment of PM2.5 concentrations over bright surfaces using MODIS satellite observations , 2015 .

[75]  S. Vardoulakis,et al.  Human mortality in Cyprus: the role of temperature and particulate air pollution , 2016, Regional Environmental Change.

[76]  Jinyuan Xin,et al.  The empirical correlations between PM2.5, PM10 and AOD in the Beijing metropolitan region and the PM2.5, PM10 distributions retrieved by MODIS. , 2016, Environmental pollution.

[77]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.