Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

[1]  Xianfeng Zhang,et al.  Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations , 2019, Aerosol and Air Quality Research.

[2]  P. Colarco,et al.  The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies. , 2017, Journal of climate.

[3]  E. Cross,et al.  Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low- and middle-income countries , 2019 .

[4]  A. Just,et al.  Cardiovascular and Cerebrovascular Mortality Associated With Acute Exposure to PM2.5 in Mexico City , 2018, Stroke.

[5]  R. Martin,et al.  Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing , 2006 .

[6]  John Kaiser Calautit,et al.  A review of artificial neural network models for ambient air pollution prediction , 2019, Environ. Model. Softw..

[7]  J. L. García-Franco.  Air quality in Mexico city during the fuel shortage of January 2019 , 2020, Atmospheric Environment.

[8]  C. Flynn,et al.  The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation. , 2017, Journal of climate.

[9]  J. Rodríguez-Rodríguez,et al.  Diurnal and seasonal variations of carbonyls and their effect on ozone concentrations in the atmosphere of Monterrey, Mexico , 2015, Journal of the Air & Waste Management Association.

[10]  M. Fraser,et al.  Organic composition and source apportionment of fine aerosol at Monterrey, Mexico, based on organic markers , 2016 .

[11]  Tzu-Yi Pai,et al.  Comparisons of GM (1,1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan , 2015 .

[12]  Alberto Mendoza,et al.  A tunnel study to characterize PM2.5 emissions from gasoline-powered vehicles in Monterrey, Mexico , 2012 .

[13]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[14]  Bin Zhao,et al.  The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.

[15]  E. Seto,et al.  A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi'an, China. , 2015, Environmental pollution.

[16]  I. Mokhov,et al.  Exploring large-scale black‑carbon air pollution over Northern Eurasia in summer 2016 using MERRA-2 reanalysis data , 2020 .

[17]  Jingfeng Huang,et al.  A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China , 2014 .

[18]  A. Mendoza,et al.  Spatial differences in ambient coarse and fine particles in the Monterrey metropolitan area, Mexico: Implications for source contribution , 2019, Journal of the Air & Waste Management Association.

[19]  Andreas Kerschbaumer,et al.  A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas , 2015, Eng. Appl. Artif. Intell..

[20]  W. Malm,et al.  Uncertainties in PM2.5 Gravimetric and Speciation Measurements and What We Can Learn from Them , 2011, Journal of the Air & Waste Management Association.

[21]  M. Sánchez-Domínguez,et al.  Seasonal variation and chemical composition of particulate matter: A study by XPS, ICP-AES and sequential microanalysis using Raman with SEM/EDS. , 2018, Journal of environmental sciences.

[22]  Pinhas Alpert,et al.  AOD distributions and trends of major aerosol species over a selection of the world's most populated cities based on the 1st Version of NASA's MERRA Aerosol Reanalysis. , 2017, Urban climate.

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

[24]  I. Hernández-Paniagua,et al.  Use of Combined Observational- and Model-Derived Photochemical Indicators to Assess the O3-NOx-VOC System Sensitivity in Urban Areas , 2017 .

[25]  S. Grossman-Clarke,et al.  Urban Fluid Mechanics: Air Circulation and Contaminant Dispersion in Cities , 2001 .

[26]  A. Martínez-Arroyo,et al.  Atmospheric black carbon concentrations in Mexico , 2019 .

[27]  Zhongmin Zhu,et al.  A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth , 2016 .

[28]  Anthony J. Jakeman,et al.  Artificial Intelligence techniques: An introduction to their use for modelling environmental systems , 2008, Math. Comput. Simul..

[29]  Wei-Zhen Lu,et al.  Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm , 2006 .

[30]  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.

[31]  W. Malm,et al.  Spatial and seasonal trends in particle concentration and optical extinction in the United States , 1994 .

[32]  Xinbiao Guo,et al.  The association between PM2.5 exposure and neurological disorders: A systematic review and meta-analysis. , 2019, The Science of the total environment.

[33]  Qiuhong Tang,et al.  Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model , 2017 .

[34]  Kequan Zhang,et al.  The PM2.5 threshold for aerosol extinction in the Beijing megacity , 2017 .

[35]  Xiao Feng,et al.  Prediction of hourly ground-level PM2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China , 2017 .

[36]  M. Rogulski.  Using Low-Cost PM Monitors to Detect Local Changes of Air Quality , 2018 .

[37]  R. Zhu,et al.  Quantifying Influences of Nocturnal Mixing on Air Quality Using Atmospheric Radon Measurement-Case Study in Jinhua City, China , 2020 .

[38]  V. Sreekanth,et al.  Evaluation of MERRAero PM2.5 over Indian cities , 2019, Advances in Space Research.

[39]  Xiangao Xia,et al.  Diurnal and seasonal variability of PM2.5 and AOD in North China plain: Comparison of MERRA-2 products and ground measurements , 2018, Atmospheric Environment.

[40]  J. Chen,et al.  Variation, sources and historical trend of black carbon in Beijing, China based on ground observation and MERRA-2 reanalysis data. , 2019, Environmental pollution.

[41]  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 .

[42]  K. Clemitshaw,et al.  Observed trends in ground-level O 3 in Monterrey, Mexico, during 1993-2014: comparison with Mexico City and Guadalajara , 2017 .

[43]  A. Mendoza,et al.  Chemical characterization and factor analysis of PM2.5 in two sites of Monterrey, Mexico , 2012, Journal of the Air & Waste Management Association.

[44]  Armin Sorooshian,et al.  Air pollution prediction by using an artificial neural network model , 2019, Clean Technologies and Environmental Policy.

[45]  Temporal Analysis of PM10 in Metropolitan Monterrey, México , 2011, Journal of the Air & Waste Management Association.

[46]  Yang Liu,et al.  New Directions: Satellite driven PM2.5 exposure models to support targeted particle pollution health effects research , 2013 .

[47]  Hyuk-Jae Chang,et al.  PM2.5 concentration in the ambient air is a risk factor for the development of high-risk coronary plaques. , 2019, European heart journal cardiovascular Imaging.

[48]  K. Hubacek,et al.  The characteristics and drivers of fine particulate matter (PM2.5) distribution in China , 2017 .

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

[50]  Xiaofeng Xu,et al.  Characteristics of MERRA-2 black carbon variation in east China during 2000–2016 , 2020 .

[51]  Rita Durão,et al.  Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models , 2016 .

[52]  P. Saldiva,et al.  Short-term effects of fine particulate matter pollution on daily health events in Latin America: a systematic review and meta-analysis , 2017, International Journal of Public Health.

[53]  R. Levy,et al.  Impact of California Fires on Local and Regional Air Quality: The Role of a Low‐Cost Sensor Network and Satellite Observations , 2018, GeoHealth.

[54]  Judith C. Chow,et al.  Characterization and source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi'an, China , 2005 .

[55]  Cristina Becchio,et al.  Cross-Validation Approaches for Replicability in Psychology , 2018, Front. Psychol..

[56]  Li-Rong Zheng,et al.  Low cost air pollution monitoring systems: A review of protocols and enabling technologies , 2020, J. Ind. Inf. Integr..

[57]  J. Santos-Guzmán,et al.  Source apportionment of PM2.5 for supporting control strategies in the Monterrey Metropolitan Area, Mexico , 2016, Journal of the Air & Waste Management Association.

[58]  Jingjing Xie,et al.  Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions , 2016 .

[59]  Moustafa Elshafei,et al.  RBF neural network inferential sensor for process emission monitoring , 2013 .

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

[61]  Jing Li,et al.  An Intercomparison of AOD-converted PM 2.5 Concentrations Using Different Approaches for Estimating Aerosol Vertical Distribution , 2017 .

[62]  Kaiwen Zhong,et al.  Satellite-based high-resolution mapping of ground-level PM2.5 concentrations over East China using a spatiotemporal regression kriging model. , 2019, The Science of the total environment.

[63]  R. M. Mitchell,et al.  Assessment of atmospheric aerosols from two reanalysis products over Australia , 2019, Atmospheric Research.

[64]  J. Texcalac-Sangrador,et al.  Quantifying health impacts and economic costs of PM2.5 exposure in Mexican cities of the National Urban System , 2019, International Journal of Public Health.

[65]  A. da Silva,et al.  Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States , 2016 .

[66]  F. Yu,et al.  Seasonal variability of aerosol vertical profiles over east US and west Europe: GEOS-Chem/APM simulation and comparison with CALIPSO observations , 2014 .

[67]  Peipei He,et al.  Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence , 2019, Remote. Sens..

[68]  Allen L. Robinson,et al.  Estimating the Secondary Organic Aerosol Contribution to PM2.5 Using the EC Tracer Method Special Issue of Aerosol Science and Technology on Findings from the Fine Particulate Matter Supersites Program , 2004 .

[69]  D. Müller,et al.  Technical note: Absorption aerosol optical depth components from AERONET observations of mixed dust plumes , 2018, Atmospheric Measurement Techniques.

[70]  Amit Kumar Gorai,et al.  Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time , 2016 .

[71]  Yi Li,et al.  National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD , 2016, Remote. Sens..

[72]  Jiming Hao,et al.  Status and characteristics of ambient PM2.5 pollution in global megacities. , 2016, Environment international.

[73]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[74]  Lutz Prechelt,et al.  Automatic early stopping using cross validation: quantifying the criteria , 1998, Neural Networks.

[75]  Liang-pei Zhang,et al.  Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment , 2016, 1607.02976.

[76]  Perry Sadorsky,et al.  Modeling and forecasting petroleum futures volatility , 2006 .

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

[78]  Wei-Zhen Lu,et al.  Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong. , 2004, Environmental research.

[79]  Yang Shao,et al.  Comparison of Early Stopping Criteria for Neural-Network-Based Subpixel Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

[80]  M. Fraser,et al.  Secondary organic aerosol contributions to PM2.5 in Monterrey, Mexico: Temporal and seasonal variation , 2015 .