Novel Decomposition Scheme for Characterizing Urban Air Quality with MODIS

Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have sufficient infrastructure to monitor air quality. Thus, satellite data is increasingly being employed to solve this limitation. In this paper, we use 16 years (2001–2016) of aerosol optical depth (AOD) and Angstrom exponent ( α ) datasets, retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on the National Aeronautics and Space Administration’s (NASA) Terra satellite to study air quality over 60 locations globally. We propose a novel technique, called AirRGB decomposition, to characterize urban air quality by decomposing AOD and α retrievals into ‘components’ of three distinct scenarios. In the AirRGB decomposition method, using AOD and α dataset three scenarios were investigated: ‘R’—high α and high AOD, ‘G’—high α and low AOD, and ‘B’—low α and low AOD values. These scenarios were mapped and quantified over a triangular red, green and blue color scale. This visualization easily segregates regions having a high concentration of industrial aerosol from only natural aerosols. Our analysis indicates that a sharp divide exists between North American and European cities and Asian cities in terms of baseline pollution and slopes of R and G trends. We found that while pollution in cities in China has started to decrease (e.g., since 2011 for Beijing), it continues to increase in South Asia and Southeast Asia. e.g., R offset of Beijing and New Delhi was 54.98 and 50.43 respectively but R slope was −0.04 and 0.08 respectively. High offset (≥45) and slope (≥0.025) of B for New York, Tokyo, Sydney and Sao Paolo shows that they have clean air, which is still getting better.

[1]  T. Eck,et al.  Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols , 1999 .

[2]  J. Schauer,et al.  Source apportionment of PM2.5 carbonaceous aerosol in Baghdad, Iraq , 2015 .

[3]  Mark Lawrence,et al.  Evaluation of emissions and air quality in megacities , 2008 .

[4]  Philip K. Hopke,et al.  Key issues in controlling air pollutants in Dhaka, Bangladesh , 2011 .

[5]  Anders Ångström,et al.  On the Atmospheric Transmission of Sun Radiation and on Dust in the Air , 1929 .

[6]  Andrew M. Sayer,et al.  Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data , 2013 .

[7]  Philip K. Hopke,et al.  Critical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe , 2013 .

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

[9]  Philip Demokritou,et al.  Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece , 2003 .

[10]  J M Baldasano,et al.  Air quality data from large cities. , 2003, The Science of the total environment.

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

[12]  A. Srivastava,et al.  SOURCE APPORTIONMENT OF TOTAL SUSPENDED PARTICULATE MATTER IN COARSE AND FINE SIZE RANGES OVER DELHI , 2008 .

[13]  Gene E. Likens,et al.  Trends in stream nitrogen concentrations for forested reference catchments across the USA , 2013 .

[14]  Thomas Blaschke,et al.  Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data , 2015 .

[15]  R. Negri,et al.  Study of atmospheric particulate matter in Buenos Aires city , 2003 .

[16]  Prakhar Misra,et al.  Analysis of air quality and nighttime light for Indian urban regions , 2016 .

[17]  Bernhard Vogel,et al.  Relationship of visibility, aerosol optical thickness and aerosol size distribution in an ageing air mass over South-West Germany , 2008 .

[18]  Steffen Beirle,et al.  A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances , 2015 .

[19]  Xiangao Xia,et al.  Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two Aerosol Robotic Network stations in China , 2007 .

[20]  Zbigniew Klimont,et al.  The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions , 2013 .

[21]  J. J. Zhang,et al.  The chain response of the magnetospheric and ground magnetic field to interplanetary shocks , 2014 .

[22]  Robert C. Levy,et al.  MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations , 2014 .

[23]  John P. Allen Urban Air Pollution in Megacities of the World , 1993 .

[24]  Roger Magnusson,et al.  Characterization of the size-distribution of aerosols and particle-bound content of oxygenated PAHs, PAHs, and n-alkanes in urban environments in Afghanistan , 2011 .

[25]  Renjian Zhang,et al.  Chemical characterization and source apportionment of PM 2 . 5 in Beijing : seasonal perspective , 2013 .

[26]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

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

[28]  M. Nalls,et al.  Genome-Wide Association Study of Retinopathy in Individuals without Diabetes , 2013, PloS one.

[29]  T. Eck,et al.  Global evaluation of the Collection 5 MODIS dark-target aerosol products over land , 2010 .

[30]  P. Artaxo,et al.  Wintertime and summertime São Paulo aerosol source apportionment study , 2001 .

[31]  Ming Zhao,et al.  Global‐scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products , 2012 .

[32]  Chad W. Higgins,et al.  Evapotranspiration: A process driving mass transport and energy exchange in the soil‐plant‐atmosphere‐climate system , 2012 .

[33]  Liangfu Chen,et al.  Comparison and evaluation of the MODIS Collection 6 aerosol data in China , 2015 .

[34]  George Christakos,et al.  Estimation of Citywide Air Pollution in Beijing , 2013, PloS one.

[35]  Debabrata Das,et al.  The 2030 Agenda for Sustainable Development: Where Does India Stand? , 2019, Journal of Rural Development.

[36]  David P. Roy,et al.  The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring , 2006, IEEE Geoscience and Remote Sensing Letters.

[37]  T. Nakajima,et al.  A use of two‐channel radiances for an aerosol characterization from space , 1998 .

[38]  María Encarnación Rodríguez,et al.  Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain). , 2014, The Science of the total environment.

[39]  Anikender Kumar,et al.  Intercomparison of Aerosol Optical Thickness Derived from MODIS and in Situ Ground Datasets over Jaipur, a Semi-arid Zone in India. , 2015, Environmental science & technology.

[40]  H. Mayer Air pollution in cities , 1999 .

[41]  T. Nakajima,et al.  Modeling study of long‐range transport of Asian dust and anthropogenic aerosols from East Asia , 2002 .

[42]  Lorraine Remer,et al.  A Critical Look at Deriving Monthly Aerosol Optical Depth From Satellite Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Sonoyo Mukai,et al.  Suspended particulate matter sampling at an urban AERONET site in Japan, part 1: clustering analysis of aerosols , 2007 .

[44]  P. Hopke,et al.  Assessment of trends and present ambient concentrations of PM2.2 and PM10 in Dhaka, Bangladesh , 2008 .

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

[46]  Yang Zhang,et al.  Online coupled regional meteorology chemistry models in Europe: current status and prospects , 2013 .

[47]  Shuxiao Wang,et al.  Particulate Matter Distributions in China during a Winter Period with Frequent Pollution Episodes (January 2013) , 2015 .

[48]  R. Martin,et al.  Space-based detection of missing sulfur dioxide sources of global air pollution , 2016 .

[49]  Annmarie Eldering,et al.  An air monitoring network using continuous particle size distribution monitors: Connecting pollutant properties to visibility via Mie scattering calculations , 1994 .

[50]  G. Meister,et al.  Effect of MODIS Terra radiometric calibration improvements on Collection 6 Deep Blue aerosol products: Validation and Terra/Aqua consistency , 2015 .

[51]  Sonoyo Mukai,et al.  Suspended particulate matter sampling at an urban AERONET site in Japan, part 2: relationship between column aerosol optical thickness and PM 2.5 concentration , 2010 .

[52]  Jiming Hao,et al.  Quantifying the air pollutants emission reduction during the 2008 Olympic games in Beijing. , 2010, Environmental science & technology.

[53]  Judith C. Chow,et al.  Spatial and seasonal distributions of carbonaceous aerosols over China , 2007 .

[54]  H. Wingfors,et al.  Broad exposure screening of air pollutants in the occupational environment of Swedish soldiers deployed in Afghanistan. , 2012, Military medicine.

[55]  Josino Costa Moreira,et al.  Evaluation of levels, sources and distribution of toxic elements in PM10 in a suburban industrial region, Rio de Janeiro, Brazil , 2008, Environmental monitoring and assessment.

[56]  William M. Putman,et al.  A global perspective of atmospheric carbon dioxide concentrations , 2016, Parallel Comput..