Monitoring Pollen Counts and Pollen Allergy Index Using Satellite Observations in East Coast of the United States

...................................................................................................................... xi CHAPTER 1: INTRODUCTION ........................................................................................1 1.1. Problem Statement and Description ......................................................................1 1.2. Thesis Statement and Research Objectives ...........................................................3 CHAPTER 2: LITERATURE REVIEW .............................................................................5 2.1. Pollen ........................................................................................................................5 2.2. Factors Affecting Pollen Dispersal .......................................................................6 2.2.1. Environment .................................................................................................... 6 2.2.2. Human-induced factors.................................................................................... 7 2.2.3. Vegetation ........................................................................................................ 8 2.2.4. Topography ....................................................................................................... 9 2.3. Pollen Allergy and Measurements ........................................................................9 2.3.1. Remote Sensing and Neural Network ........................................................... 12 2.3.1.1. Remote Sensing ............................................................................................ 12

[1]  C. Hervás-Martínez,et al.  The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: the southern part of the Iberian Peninsula , 2002, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[2]  M. Leroy,et al.  Evidence of surface reflectance bidirectional effects from a NOAA/ AVHRR multi-temporal data set , 1992 .

[3]  A. Huete,et al.  The Macroecology of Airborne Pollen in Australian and New Zealand Urban Areas , 2014, PloS one.

[4]  Ah-Hwee Tan,et al.  Predictive neural networks for gene expression data analysis , 2005, Neural Networks.

[5]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[6]  Y. Adebayo.  Day-time effects of urbanization on relative humidity and vapour pressure in a tropical city , 1991 .

[7]  B. Whelan,et al.  Early season detection and mapping of Pseudomonas syringae pv. actinidae infected kiwifruit (Actinidia sp.) orchards , 2014 .

[8]  L. Moseholm,et al.  Airborne Pollen Records in Denmark, 1977–1986 , 1988 .

[9]  Josep Peñuelas,et al.  Complex spatiotemporal phenological shifts as a response to rainfall changes. , 2004, The New phytologist.

[10]  N. Åberg,et al.  Asthma and allergic rhinitis in Swedish conscripts , 1989, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[11]  Dailiang Peng,et al.  Response of Spectral Reflectances and Vegetation Indices on Varying Juniper Cone Densities , 2013, Remote. Sens..

[12]  S. Rutherford,et al.  Atmospheric Poaceae pollen frequencies and associations with meteorological parameters in Brisbane, Australia: a 5-year record, 1994–1999 , 2004, International journal of biometeorology.

[13]  AIRBORNE POLLEN FORECASTING : EVALUATION OF ARIMA AND NEURAL NETWORK MODELS , .

[14]  Paolo Lauriola,et al.  Forecasting airborne pollen concentrations: Development of local models , 2003 .

[15]  J. Blair,et al.  Rainfall Variability, Carbon Cycling, and Plant Species Diversity in a Mesic Grassland , 2002, Science.

[16]  B. Tomassetti,et al.  Mapping of Alternaria and Pleospora concentrations in Central Italy using meteorological forecast and neural network estimator , 2013, Aerobiologia.

[17]  P. Bogawski,et al.  Flowering phenology and potential pollen emission of three Artemisia species in relation to airborne pollen data in Poznań (Western Poland) , 2015, Aerobiologia.

[18]  Rudolf de Groot,et al.  The influence of temperature and climate change on the timing of pollen release in the Netherlands , 2002 .

[19]  Pinki Mondal,et al.  Quantifying surface gradients with a 2-band Enhanced Vegetation Index (EVI2) , 2011 .

[20]  Xiangming Xiao,et al.  Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems , 2011 .

[21]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[22]  Jones,et al.  Regional variations in grass pollen seasons in the UK, long‐term trends and forecast models , 1999, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[23]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[24]  David John Lary,et al.  Using machine learning to estimate atmospheric Ambrosia pollen concentrations in Tulsa, OK , 2017, Environmental health insights.

[25]  B. Pinty,et al.  GEMI: a non-linear index to monitor global vegetation from satellites , 1992, Vegetatio.

[26]  C. Galán,et al.  Regional phenological models for forecasting the start and peak of the Quercus pollen season in Spain , 2008 .

[27]  Mikhail Sofiev,et al.  Allergenic Pollen: A Review of the Production, Release, Distribution and Health Impacts , 2013 .

[28]  J. Mejuto,et al.  Airborne castanea pollen forecasting model for ecological and allergological implementation. , 2016, The Science of the total environment.

[29]  Soo Cheng,et al.  Pollen counts in relation to the prevalence of allergic rhinoconjunctivitis, asthma and atopic eczema in the International Study of Asthma and Allergies in Childhood (ISAAC) , 2003, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[30]  Ramesh Sharda,et al.  Neural Networks for the MS/OR Analyst: An Application Bibliography , 1994 .

[31]  Michael E. Schaepman,et al.  Impact of elevation and aspect on the spatial distribution of vegetation in the Qilian mountain area with remote sensing data , 2008 .

[32]  Toshiro Takai,et al.  Allergens in modern society: Updated catalogs and future prospects. , 2015, Allergology international : official journal of the Japanese Society of Allergology.

[33]  J. Silverberg,et al.  Association between climate factors, pollen counts, and childhood hay fever prevalence in the United States. , 2015, The Journal of allergy and clinical immunology.

[34]  Stein Rune Karlsen,et al.  A satellite-based map of onset of birch (Betula) flowering in Norway , 2009 .

[35]  Ketan K. Sheth,et al.  Burden of allergic rhinitis: results from the Pediatric Allergies in America survey. , 2009, The Journal of allergy and clinical immunology.

[36]  S. Bonini,et al.  Allergenic pollen and pollen allergy in Europe , 2007, Allergy.

[37]  Mitchel Klein,et al.  Ambient pollen concentrations and emergency department visits for asthma and wheeze. , 2012, The Journal of allergy and clinical immunology.

[38]  Ragip Ince,et al.  Prediction of fracture parameters of concrete by Artificial Neural Networks , 2004 .

[39]  H. Helenius,et al.  Farm environment in childhood prevents the development of allergies , 2000, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[40]  O. Hertel,et al.  Identifying urban sources as cause of elevated grass pollen concentrations using GIS and remote sensing , 2012 .

[41]  A. Knutsson,et al.  Atopic sensitization and respiratory symptoms among Polish and Swedish school children , 1994, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[42]  Jianguo Wu,et al.  Effects of urbanization on plant flowering phenology: A review , 2006, Urban Ecosystems.

[43]  Dan Zhang,et al.  Temporal and geographical variation in the onset of climatological spring in Northeast China , 2013, Theoretical and Applied Climatology.

[44]  A. Huete,et al.  Amazon rainforests green‐up with sunlight in dry season , 2006 .

[45]  M. Gassmann,et al.  An evaluation of the airborne pollen–precipitation relationship with the superposed epoch method , 2009 .

[46]  I. Jalbert,et al.  Environmental aeroallergens and allergic rhino-conjunctivitis , 2015, Current opinion in allergy and clinical immunology.

[47]  I. Vukušič,et al.  Atmospheric pollen season in Zagreb (Croatia) and its relationship with temperature and precipitation , 2004, International journal of biometeorology.

[48]  F. Fernández‐González,et al.  Characterisation of the airborne pollen spectrum in Guadalajara (central Spain) and estimation of the potential allergy risk , 2016, Environmental Monitoring and Assessment.

[49]  Ehud Reiter,et al.  Generating Spatio-Temporal Descriptions in Pollen Forecasts , 2006, EACL.

[50]  C. Arizmendi,et al.  Detection of chaos: New approach to atmospheric pollen time-series analysis , 1992 .

[51]  F. Cristofolini,et al.  Spring airborne pollen data in two sites in Trentino (Northern Italy): a comparison with meteorological data , 1997 .

[52]  Adrian V. Rocha,et al.  Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes , 2009 .

[53]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[54]  Kazuhiko Ito,et al.  The associations between daily spring pollen counts, over-the-counter allergy medication sales, and asthma syndrome emergency department visits in New York City, 2002-2012 , 2015, Environmental Health.

[55]  Adnan Sözen,et al.  Forecasting based on neural network approach of solar potential in Turkey , 2005 .

[56]  Use of neural networks to short-term forecast of airborne pollen data , 2004 .

[57]  H. Steinkellner,et al.  Pollen dispersal inferred from paternity analysis in a mixed oak stand of Quercus robur L. and Q. petraea (Matt.) Liebl. , 1999 .

[58]  P. Gergen,et al.  The prevalence of allergic skin test reactivity to eight common aeroallergens in the U.S. population: results from the second National Health and Nutrition Examination Survey. , 1987, The Journal of allergy and clinical immunology.

[59]  E. Mutius,et al.  Reduced risk of hay fever and asthma among children of farmers , 2000, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[60]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[61]  Abbas Heiat,et al.  Comparison of artificial neural network and regression models for estimating software development effort , 2002, Inf. Softw. Technol..

[62]  D. J. Davis.  NIAID initiatives in allergy research. , 1972, The Journal of allergy and clinical immunology.

[63]  Bin Tan,et al.  Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982–2010 , 2014, International Journal of Biometeorology.

[64]  Kishan G. Mehrotra,et al.  Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.

[65]  W. Eder,et al.  Austrian children living on a farm have less hay fever, asthma and allergic sensitization , 2000, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[66]  James T. Smith,et al.  Neural Network Verification , 2006 .

[67]  R. Houborg,et al.  Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data , 2007 .

[68]  L. Ziska,et al.  Climate change, aerobiology, and public health in the Northeast United States , 2008 .

[69]  Xiaoyang Zhang,et al.  Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data , 2015 .

[70]  H. Arathi,et al.  Image Analysis Protocol for Detecting and Counting Viable and Inviable Pollen Grains , 2012 .

[71]  M. Puc.  Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland) , 2011, International Journal of Biometeorology.

[72]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

[73]  M. Castellano-Méndez,et al.  Artificial neural networks as a useful tool to predict the risk level of Betula pollen in the air , 2005, International journal of biometeorology.

[74]  A. Lapedes,et al.  Nonlinear Signal Processing Using Neural Networks , 1987 .

[75]  A. Geevarghese,et al.  The association between asthma-related emergency department visits and pollen and mold spore concentrations in the Bronx, 2001–2008 , 2014, The Journal of asthma : official journal of the Association for the Care of Asthma.

[76]  C. Arizmendi,et al.  Time series predictions with neural nets: Application to airborne pollen forecasting , 1993 .

[77]  W. Cookson,et al.  The alliance of genes and environment in asthma and allergy , 1999, Nature.

[78]  Roberto Baratti,et al.  River flow forecast for reservoir management through neural networks , 2003, Neurocomputing.

[79]  Z. Huang,et al.  Factors Affecting Pollinators and Pollination , 2012 .

[80]  P. Jonsson.  Vegetation as an urban climate control in the subtropical city of Gaborone, Botswana , 2004 .

[81]  M. Sopelete,et al.  Pollen allergic disease: pollens and its major allergens , 2006, Brazilian journal of otorhinolaryngology.

[82]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[83]  Nihat Tosun,et al.  A study of tool life in hot machining using artificial neural networks and regression analysis method , 2002 .

[84]  J. Bartková-Ščevková.  The influence of temperature, relative humidity and rainfall on the occurrence of pollen allergens (Betula, Poaceae, Ambrosia artemisiifolia) in the atmosphere of Bratislava (Slovakia) , 2003, International journal of biometeorology.

[85]  Mariko S. Marks,et al.  Pollen aeroallergens in the Washington, DC, metropolitan area: a 10-year volumetric survey (1998-2007). , 2010, Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology.

[86]  Habibollah Haron,et al.  Regression and ANN models for estimating minimum value of machining performance , 2012 .

[87]  César Hervás,et al.  The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate , 2005, International journal of biometeorology.

[88]  B. Wüthrich,et al.  Prevalence of hay fever and allergic sensitization in farmer's children and their peers living in the same rural community , 1999, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[89]  A. Huete,et al.  Differences in grass pollen allergen exposure across Australia , 2015, Australian and New Zealand journal of public health.

[90]  L. R. Dupuis,et al.  Relative humidity predictor equations based on environmental factors , 1991 .

[91]  D. Fernández-González,et al.  Biogeography and bioclimatology in pollen forecasting , 2001 .

[92]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[93]  E. Karabulut,et al.  Pollen counts and their relationship to meteorological factors in Ankara, Turkey during 2005–2008 , 2011, International journal of biometeorology.

[94]  Robert E. Wolfe,et al.  An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.