Literature DB >> 15587550

Recommendations on the use of satellite remote-sensing data for urban air quality.

Jill A Engel-Cox1, Raymond M Hoff, A D J Haymet.   

Abstract

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.

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Year:  2004        PMID: 15587550     DOI: 10.1080/10473289.2004.10471005

Source DB:  PubMed          Journal:  J Air Waste Manag Assoc        ISSN: 1096-2247            Impact factor:   2.235


  15 in total

1.  Analysis of spatial and seasonal distributions of MODIS aerosol optical properties and ground-based measurements of mass concentrations in the Yellow Sea region in 2009.

Authors:  Hak-Sung Kim; Yong-Seung Chung; Sun-Gu Lee
Journal:  Environ Monit Assess       Date:  2012-02-24       Impact factor: 2.513

2.  Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD.

Authors:  Wei You; Zengliang Zang; Lifeng Zhang; Yi Li; Weiqi Wang
Journal:  Environ Sci Pollut Res Int       Date:  2016-01-16       Impact factor: 4.223

Review 3.  Wildfire and prescribed burning impacts on air quality in the United States.

Authors:  Daniel A Jaffe; Susan M O'Neill; Narasimhan K Larkin; Amara L Holder; David L Peterson; Jessica E Halofsky; Ana G Rappold
Journal:  J Air Waste Manag Assoc       Date:  2020-06       Impact factor: 2.235

4.  Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application.

Authors:  Aaron van Donkelaar; Randall V Martin; Michael Brauer; Ralph Kahn; Robert Levy; Carolyn Verduzco; Paul J Villeneuve
Journal:  Environ Health Perspect       Date:  2010-06       Impact factor: 9.031

5.  Health impact assessment of exposure to fine particulate matter based on satellite and meteorological information.

Authors:  Hak-Kan Lai; Hilda Tsang; Thuan-Quoc Thach; Chit-Ming Wong
Journal:  Environ Sci Process Impacts       Date:  2014-02       Impact factor: 4.238

6.  Investigation of aerosols pollution across the eastern basin of Urmia lake using satellite remote sensing data and HYSPLIT model.

Authors:  Shokufeh Delfi; Mohammad Mosaferi; Mohammad Sadegh Hassanvand; Shahram Maleki
Journal:  J Environ Health Sci Eng       Date:  2019-12-10

7.  Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies.

Authors:  D J Lary; T Lary; B Sattler
Journal:  Environ Health Insights       Date:  2015-05-12

8.  Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model.

Authors:  Tianhao Zhang; Gang Liu; Zhongmin Zhu; Wei Gong; Yuxi Ji; Yusi Huang
Journal:  Int J Environ Res Public Health       Date:  2016-09-30       Impact factor: 3.390

9.  Insights Into the Morphology of the East Asia PM2.5 Annual Cycle Provided by Machine Learning.

Authors:  Daji Wu; Gebreab K Zewdie; Xun Liu; Melanie Anne Kneen; David John Lary
Journal:  Environ Health Insights       Date:  2017-03-29

10.  Analysis of airborne particulate matter (PM2.5) over Hong Kong using remote sensing and GIS.

Authors:  Wenzhong Shi; Man Sing Wong; Jingzhi Wang; Yuanling Zhao
Journal:  Sensors (Basel)       Date:  2012-05-25       Impact factor: 3.576

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