Articles | Volume 13, issue 6
https://doi.org/10.5194/acp-13-3517-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/acp-13-3517-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Mar 2013
Research article |
| 27 Mar 2013
A robust calibration approach for PM10 prediction from MODIS aerosol optical depth
X. Q. Yap
Institute of Geospatial Science & Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia
M. Hashim
Institute of Geospatial Science & Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia
Viewed
Total article views: 6,799 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 06 Dec 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,498 | 4,147 | 154 | 6,799 | 118 | 91 |
- HTML: 2,498
- PDF: 4,147
- XML: 154
- Total: 6,799
- BibTeX: 118
- EndNote: 91
Total article views: 5,742 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Mar 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,963 | 3,658 | 121 | 5,742 | 95 | 86 |
- HTML: 1,963
- PDF: 3,658
- XML: 121
- Total: 5,742
- BibTeX: 95
- EndNote: 86
Total article views: 1,057 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 06 Dec 2012)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 535 | 489 | 33 | 1,057 | 23 | 5 |
- HTML: 535
- PDF: 489
- XML: 33
- Total: 1,057
- BibTeX: 23
- EndNote: 5
Cited
54 citations as recorded by crossref.
- Estimation of particulate matter pollution using WRF-Chem during dust storm event over India M. Soni et al. 10.1016/j.uclim.2022.101202
- Elemental analysis of PM10 in southwest Mexico City and source apportionment using positive matrix factorization L. Mejía-Ponce et al. 10.1007/s10874-022-09435-2
- Comparison of Four Ground-Level PM2.5 Estimation Models Using PARASOL Aerosol Optical Depth Data from China H. Guo et al. 10.3390/ijerph13020180
- Validation of the Atmospheric Boundary Layer Height Estimated from the MODIS Atmospheric Profile Data at an Equatorial Site S. Onyango et al. 10.3390/atmos11090908
- Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia M. Mazeli et al. 10.1016/j.scitotenv.2023.162130
- Daily Ambient NO2Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2Data and Land Use Regression H. Lee & P. Koutrakis 10.1021/es404845f
- Validation of the improved GOES-16 aerosol optical depth product over North America D. Fu et al. 10.1016/j.atmosenv.2023.119642
- A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
- Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model S. Ghotbi et al. 10.1016/j.atmosenv.2016.06.057
- Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters M. Soni et al. 10.1016/j.apr.2018.03.001
- Potential Approach for Single-Peak Extinction Fitting of Aerosol Profiles Based on In Situ Measurements for the Improvement of Surface PM2.5 Retrieval from Satellite AOD Product T. Lin et al. 10.3390/rs12132174
- Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count W. You et al. 10.1016/j.rse.2015.07.020
- A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data I. Kloog et al. 10.1016/j.atmosenv.2014.07.014
- Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia N. Kamarul Zaman et al. 10.1016/j.atmosres.2017.04.019
- COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas I. Mandal & S. Pal 10.1016/j.scitotenv.2020.139281
- Estimation of monthly bulk nitrate deposition in China based on satellite NO2 measurement by the Ozone Monitoring Instrument L. Liu et al. 10.1016/j.rse.2017.07.005
- Appraising city-scale pollution monitoring capabilities of multi-satellite datasets using portable pollutant monitors Y. Aliyu & J. Botai 10.1016/j.atmosenv.2018.02.034
- Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China W. Guo et al. 10.1016/j.apr.2021.101154
- Estimation of Particulate Matter (PM2.5) Over Kolkata J. Singh et al. 10.1007/s00024-023-03418-4
- Spatio-temporal variations in the estimation of PM10 from MODIS-derived aerosol optical depth for the urban areas in the Central Indo-Gangetic Plain S. Chitranshi et al. 10.1007/s00703-014-0347-z
- Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China X. Mao et al. 10.1016/j.apr.2017.04.002
- Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates Y. Aliyu & J. Botai 10.1016/j.atmosenv.2018.09.001
- Air Quality Assessment over Sudan using NASA Remote Sensing Satellites Data and MERRA-2 Model I. Muntasir et al. 10.30799/jespr.207.20060302
- Assessment of CALIOP and MODIS aerosol products over Iran to explore air quality S. Zahedi Asl et al. 10.1007/s00704-018-2555-9
- PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India P. Gogikar et al. 10.1007/s12652-020-02457-2
- Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India S. Chitranshi et al. 10.1007/s11869-014-0271-x
- Overview of atmospheric aerosol studies in Malaysia: Known and unknown K. Kanniah et al. 10.1016/j.atmosres.2016.08.002
- The relation between columnar and surface aerosol optical properties in a background environment D. Szczepanik & K. Markowicz 10.1016/j.apr.2017.10.001
- Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures Z. Chen et al. 10.1016/j.atmosenv.2017.10.055
- An effective approach to linear calibration estimation with its applications F. Muhammad et al. 10.1080/03610926.2019.1615092
- Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models S. Michaelides et al. 10.1155/2017/2954010
- Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters S. Sahu et al. 10.1016/j.chemosphere.2020.126969
- Validation of MERIS/AATSR synergy algorithm for aerosol retrieval against globally distributed AERONET observations and comparison with MODIS aerosol product N. Benas et al. 10.1016/j.atmosres.2013.05.011
- Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM2.5 T. Narayan et al. 10.1007/s40010-020-00718-5
- Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012 S. Loría-Salazar et al. 10.1016/j.atmosenv.2016.08.070
- Temporal and spatial variation of particulate matter and its correlation with other criteria of air pollutants in Lanzhou, China, in spring-summer periods M. Filonchyk et al. 10.1016/j.apr.2018.04.011
- A nonlinear model for estimating ground-level PM10 concentration in Xi'an using MODIS aerosol optical depth retrieval W. You et al. 10.1016/j.atmosres.2015.09.008
- Fall of oxidized while rise of reduced reactive nitrogen deposition in China L. Liu et al. 10.1016/j.jclepro.2020.122875
- A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China W. Song et al. 10.1016/j.rse.2014.08.008
- Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models W. You et al. 10.1016/j.scitotenv.2014.11.024
- COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity A. Gayen et al. 10.1016/j.uclim.2021.100786
- Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes C. Lin et al. 10.3390/ijerph13060553
- Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD W. You et al. 10.1007/s11356-015-6027-9
- Comparison of different models for assessing air quality in Krasnoyarsk using satellite data K. Krasnoshchekov et al. 10.1051/e3sconf/202022303022
- WITHDRAWN: Prevention and control of motor vehicle exhaust pollution based on internet of things system and cloud computing Y. Zhang et al. 10.1016/j.micpro.2020.103373
- Estimation of PM 2.5 using high-resolution satellite data and its mortality risk in an area of Iran G. Li et al. 10.1080/09603123.2024.2325629
- Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT‐PM10 regression model with synoptic meteorology classification K. Leelasakultum & N. Kim Oanh 10.1002/2017GH000059
- Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms S. Nabavi et al. 10.1016/j.apr.2018.12.017
- Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models J. Garcia et al. 10.1080/09593330.2016.1149228
- Development of a model for particulate matter pollution in Australia with implications for other satellite-based models G. Pereira et al. 10.1016/j.envres.2017.07.044
- Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations N. Benas et al. 10.1016/j.atmosenv.2013.07.012
- Establishment of PM2.5 Prediction Model Based on MAIAC AOD Data of High Resolution Remote Sensing Images X. Han et al. 10.1142/S0218001419540090
- A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. 10.1007/s13143-020-00215-0
- A sparse representation-based fusion model for improving daily MODIS C6.1 aerosol products on a 3 km grid Y. Zhao et al. 10.1080/01431161.2020.1823040
47 citations as recorded by crossref.
- Estimation of particulate matter pollution using WRF-Chem during dust storm event over India M. Soni et al. 10.1016/j.uclim.2022.101202
- Elemental analysis of PM10 in southwest Mexico City and source apportionment using positive matrix factorization L. Mejía-Ponce et al. 10.1007/s10874-022-09435-2
- Comparison of Four Ground-Level PM2.5 Estimation Models Using PARASOL Aerosol Optical Depth Data from China H. Guo et al. 10.3390/ijerph13020180
- Validation of the Atmospheric Boundary Layer Height Estimated from the MODIS Atmospheric Profile Data at an Equatorial Site S. Onyango et al. 10.3390/atmos11090908
- Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia M. Mazeli et al. 10.1016/j.scitotenv.2023.162130
- Daily Ambient NO2Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2Data and Land Use Regression H. Lee & P. Koutrakis 10.1021/es404845f
- Validation of the improved GOES-16 aerosol optical depth product over North America D. Fu et al. 10.1016/j.atmosenv.2023.119642
- A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli 10.1016/j.atmosres.2024.107514
- Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model S. Ghotbi et al. 10.1016/j.atmosenv.2016.06.057
- Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters M. Soni et al. 10.1016/j.apr.2018.03.001
- Potential Approach for Single-Peak Extinction Fitting of Aerosol Profiles Based on In Situ Measurements for the Improvement of Surface PM2.5 Retrieval from Satellite AOD Product T. Lin et al. 10.3390/rs12132174
- Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count W. You et al. 10.1016/j.rse.2015.07.020
- A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data I. Kloog et al. 10.1016/j.atmosenv.2014.07.014
- Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia N. Kamarul Zaman et al. 10.1016/j.atmosres.2017.04.019
- COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas I. Mandal & S. Pal 10.1016/j.scitotenv.2020.139281
- Estimation of monthly bulk nitrate deposition in China based on satellite NO2 measurement by the Ozone Monitoring Instrument L. Liu et al. 10.1016/j.rse.2017.07.005
- Appraising city-scale pollution monitoring capabilities of multi-satellite datasets using portable pollutant monitors Y. Aliyu & J. Botai 10.1016/j.atmosenv.2018.02.034
- Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China W. Guo et al. 10.1016/j.apr.2021.101154
- Estimation of Particulate Matter (PM2.5) Over Kolkata J. Singh et al. 10.1007/s00024-023-03418-4
- Spatio-temporal variations in the estimation of PM10 from MODIS-derived aerosol optical depth for the urban areas in the Central Indo-Gangetic Plain S. Chitranshi et al. 10.1007/s00703-014-0347-z
- Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China X. Mao et al. 10.1016/j.apr.2017.04.002
- Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates Y. Aliyu & J. Botai 10.1016/j.atmosenv.2018.09.001
- Air Quality Assessment over Sudan using NASA Remote Sensing Satellites Data and MERRA-2 Model I. Muntasir et al. 10.30799/jespr.207.20060302
- Assessment of CALIOP and MODIS aerosol products over Iran to explore air quality S. Zahedi Asl et al. 10.1007/s00704-018-2555-9
- PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India P. Gogikar et al. 10.1007/s12652-020-02457-2
- Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India S. Chitranshi et al. 10.1007/s11869-014-0271-x
- Overview of atmospheric aerosol studies in Malaysia: Known and unknown K. Kanniah et al. 10.1016/j.atmosres.2016.08.002
- The relation between columnar and surface aerosol optical properties in a background environment D. Szczepanik & K. Markowicz 10.1016/j.apr.2017.10.001
- Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures Z. Chen et al. 10.1016/j.atmosenv.2017.10.055
- An effective approach to linear calibration estimation with its applications F. Muhammad et al. 10.1080/03610926.2019.1615092
- Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models S. Michaelides et al. 10.1155/2017/2954010
- Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters S. Sahu et al. 10.1016/j.chemosphere.2020.126969
- Validation of MERIS/AATSR synergy algorithm for aerosol retrieval against globally distributed AERONET observations and comparison with MODIS aerosol product N. Benas et al. 10.1016/j.atmosres.2013.05.011
- Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM2.5 T. Narayan et al. 10.1007/s40010-020-00718-5
- Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012 S. Loría-Salazar et al. 10.1016/j.atmosenv.2016.08.070
- Temporal and spatial variation of particulate matter and its correlation with other criteria of air pollutants in Lanzhou, China, in spring-summer periods M. Filonchyk et al. 10.1016/j.apr.2018.04.011
- A nonlinear model for estimating ground-level PM10 concentration in Xi'an using MODIS aerosol optical depth retrieval W. You et al. 10.1016/j.atmosres.2015.09.008
- Fall of oxidized while rise of reduced reactive nitrogen deposition in China L. Liu et al. 10.1016/j.jclepro.2020.122875
- A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China W. Song et al. 10.1016/j.rse.2014.08.008
- Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models W. You et al. 10.1016/j.scitotenv.2014.11.024
- COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity A. Gayen et al. 10.1016/j.uclim.2021.100786
- Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes C. Lin et al. 10.3390/ijerph13060553
- Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD W. You et al. 10.1007/s11356-015-6027-9
- Comparison of different models for assessing air quality in Krasnoyarsk using satellite data K. Krasnoshchekov et al. 10.1051/e3sconf/202022303022
- WITHDRAWN: Prevention and control of motor vehicle exhaust pollution based on internet of things system and cloud computing Y. Zhang et al. 10.1016/j.micpro.2020.103373
- Estimation of PM 2.5 using high-resolution satellite data and its mortality risk in an area of Iran G. Li et al. 10.1080/09603123.2024.2325629
- Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT‐PM10 regression model with synoptic meteorology classification K. Leelasakultum & N. Kim Oanh 10.1002/2017GH000059
7 citations as recorded by crossref.
- Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms S. Nabavi et al. 10.1016/j.apr.2018.12.017
- Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models J. Garcia et al. 10.1080/09593330.2016.1149228
- Development of a model for particulate matter pollution in Australia with implications for other satellite-based models G. Pereira et al. 10.1016/j.envres.2017.07.044
- Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations N. Benas et al. 10.1016/j.atmosenv.2013.07.012
- Establishment of PM2.5 Prediction Model Based on MAIAC AOD Data of High Resolution Remote Sensing Images X. Han et al. 10.1142/S0218001419540090
- A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. 10.1007/s13143-020-00215-0
- A sparse representation-based fusion model for improving daily MODIS C6.1 aerosol products on a 3 km grid Y. Zhao et al. 10.1080/01431161.2020.1823040
Saved (final revised paper)
Latest update: 13 Dec 2024
Altmetrics
Final-revised paper
Preprint