A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables

ABSTRACTFine particulate matter (PM2.5) has recently gained attention worldwide as being responsible for severe respiratory and cardiovascular diseases, but point based ground monitoring stations are inadequate for understanding the spatial distribution of PM2.5 over complex urban surfaces. In this study, a new approach is introduced for prediction of PM2.5 which uses satellite aerosol optical depth (AOD) and binning of meteorological variables. AOD from the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) aerosol products, MOD04_3k Dark-Target (DT) at 3 km, MOD04 DT at 10 km, and MOD04 Deep-Blue (DB) at 10 km spatial resolution, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution were obtained for Hong Kong and the industrialized Pearl River Delta (PRD) region. The SARA AOD at 500 m alone achieved a higher correlation (R = 0.72) with PM2.5 concentrations than the MODIS C6 DT AOD at 3 km (R = 0.60), the DT AOD at 10 km (R = 0.61), and the DB AOD at 10 km (R = 0.51). The SARA binning model ([PM2.5] = 110.5 [AOD] + 12.56) was developed using SARA AOD and binning of surface pressure (996–1010 hPa). This model exhibits good correlation, accurate slope, low intercept, low errors, and accurately represents the spatial distribution of PM2.5 at 500 m resolution over urban areas. Overall, the prediction power of the SARA binning model is much better than for previous models reported for Hong Kong and East Asia, and indicates the potential value of applying meteorologically-specific empirical models and incorporating boundary layer height in operational PM2.5 forecasting from satellite AOD retrievals.

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

[2]  J. Sunyer,et al.  Long-Term Effects of Ambient Air Pollution on Lung Function: A Review , 2008, Epidemiology.

[3]  Yang Liu,et al.  Estimating ground-level PM2.5 in China using satellite remote sensing. , 2014, Environmental science & technology.

[4]  Michael J Kleeman,et al.  Source apportionment of secondary airborne particulate matter in a polluted atmosphere. , 2002, Environmental science & technology.

[5]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[6]  S. Christopher,et al.  Multi year satellite remote sensing of particulate matter air quality over Sydney, Australia , 2007 .

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

[8]  R. Koelemeijer,et al.  Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe , 2006 .

[9]  Xin Fang,et al.  Spatial modeling of PM2.5 concentrations with a multifactoral radial basis function neural network , 2015, Environmental Science and Pollution Research.

[10]  T. Eck,et al.  Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations , 2002 .

[11]  Lorraine A. Remer,et al.  MODIS 3 km aerosol product: algorithm and global perspective , 2013 .

[12]  N. Englert.  Fine particles and human health--a review of epidemiological studies. , 2004, Toxicology letters.

[13]  J. Ayres,et al.  Particulate air pollution and panel studies in children: a systematic review , 2004, Occupational and Environmental Medicine.

[14]  Albert Ansmann,et al.  High aerosol load over the Pearl River Delta, China, observed with Raman lidar and Sun photometer , 2005 .

[15]  Yang Liu,et al.  Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS , 2007 .

[16]  Muhammad Bilal,et al.  Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms , 2014 .

[17]  Bin Zou,et al.  High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression , 2016, IEEE Geoscience and Remote Sensing Letters.

[18]  Derek Griffith,et al.  SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications , 2014 .

[19]  Alexis K.H. Lau,et al.  Analysis of aerosol vertical distribution and variability in Hong Kong , 2008 .

[20]  Keith D. Hutchison,et al.  Correlating MODIS aerosol optical thickness data with ground-based PM2.5 observations across Texas for use in a real-time air quality prediction system , 2005 .

[21]  Kebin He,et al.  Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model , 2015 .

[22]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[23]  David G Streets,et al.  Modeling study of air pollution due to the manufacture of export goods in China's Pearl River Delta. , 2006, Environmental science & technology.

[24]  J. Stockman,et al.  Fine-Particulate Air Pollution and Life Expectancy in the United States , 2010 .

[25]  Alexis K.H. Lau,et al.  Numerical study on seasonal variations of gaseous pollutants and particulate matters in Hong Kong and Pearl River Delta Region , 2010 .

[26]  J. Schauer,et al.  Trimethylsilyl derivatives of organic compounds in source samples and in atmospheric fine particulate matter. , 2002, Environmental science & technology.

[27]  Jinyuan Xin,et al.  The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011 , 2014 .

[28]  R. Carey.  Atmospheric Science: An Introductory Survey , 1978 .

[29]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[30]  W. G. Collins,et al.  Dust and pollution transport on global scales: Aerosol measurements and model predictions , 2001 .

[31]  Kwon-Ho Lee,et al.  Monitoring 2.5 μm particulate matter within urbanized regions using satellite-derived aerosol optical thickness, a study in Hong Kong , 2011 .

[32]  Basil W. Coutant,et al.  Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality , 2004 .

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

[34]  P. Fu,et al.  Ambient suspended particulate matters and related chemical species study in central Taiwan, Taichung during 1998–2001 , 2002 .

[35]  C. Chan,et al.  Air pollution in mega cities in China , 2008 .

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

[37]  Michelle L. Bell,et al.  Ambient Air Pollution and Low Birth Weight in Connecticut and Massachusetts , 2007, Environmental health perspectives.

[38]  M. Keuken,et al.  Source contributions to PM2.5 and PM10 at an urban background and a street location , 2013 .

[39]  Wenzhong Shi,et al.  Analysis of Airborne Particulate Matter (PM2.5) over Hong Kong Using Remote Sensing and GIS , 2012, Sensors.

[40]  J. Reid,et al.  Impact of data quality and surface-to-column representativeness on the PM 2.5 / satellite AOD relationship for the contiguous United States , 2013 .

[41]  Song-You Hong,et al.  A new stable boundary‐layer mixing scheme and its impact on the simulated East Asian summer monsoon , 2010 .

[42]  Peter Höppe,et al.  Health effects of particles in ambient air. , 2004, International journal of hygiene and environmental health.

[43]  Jonathan D. W. Kahl,et al.  Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth , 2014 .

[44]  Mutasem El-Fadel,et al.  Vehicular Emissions in Roadway Tunnels: A Critical Review , 2001 .

[45]  Muhammad Bilal,et al.  A simplified high resolution MODIS aerosol retrieval algorithm (SARA) for use over mixed surfaces , 2013 .

[46]  Matthew P. Fraser,et al.  Source apportionment of fine particulate matter in Houston, TX, using organic molecular markers , 2003 .

[47]  P. Gupta,et al.  Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach , 2009 .

[48]  Yang Liu,et al.  Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013 , 2015, Environmental health perspectives.

[49]  Muhammad Bilal,et al.  Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events , 2015 .

[50]  Jen-Ping Chen,et al.  Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008 , 2011 .

[51]  J. Chow,et al.  Spatial and seasonal variations of atmospheric organic carbon and elemental carbon in Pearl River Delta Region, China , 2004 .

[52]  Petros Koutrakis,et al.  Symptoms and Medication Use in Children with Asthma and Traffic-Related Sources of Fine Particle Pollution , 2009, Environmental health perspectives.

[53]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[54]  M. Brauer,et al.  Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. , 2016, Environmental science & technology.

[55]  Bin Zou,et al.  Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective , 2015, Scientific Reports.

[56]  Judith C. Chow,et al.  Characteristics of carbonaceous aerosol in Pearl River Delta Region, China during 2001 winter period , 2003 .

[57]  D. Chu,et al.  Improving National Air Quality Forecasts with Satellite Aerosol Observations , 2005 .

[58]  F. Dominici,et al.  Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. , 2006, JAMA.

[59]  L. Guttman,et al.  Statistical Adjustment of Data , 1944 .

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

[61]  T. Eck,et al.  An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET , 2001 .

[62]  A. Miguel,et al.  Pedestrian Exposure to Ultrafine Particles in Hong kong under Heavy Traffic Conditions , 2008 .

[63]  Jie Tian,et al.  A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements , 2010 .

[64]  G. S. W. Haglera,et al.  Source areas and chemical composition of fine particulate matter in the Pearl River Delta region of China , 2006 .

[65]  C. Chan,et al.  Source apportionment of PM2.5 in urban area of Hong Kong. , 2006, Journal of hazardous materials.