High resolution aerosol data from MODIS satellite for urban air quality studies

Chudnovsky, Alexandra; Lyapustin, A.; Wang, Y.; Tang, C.; Schwartz, Joel; Koutrakis, Petros

doi:10.2478/s13533-012-0145-4

Cited by 27 publications

(29 citation statements)

References 22 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our overall RMSPE of 21.67 µg/m 3 from model cross validation is higher than those in studies on areas in the United States (<9.0 µg/m 3 ) [9,[13][14][15][16][17][35][36][37], but much lower than other results over the whole of China (32.98 µg/m 3 ), which might be mainly due to the much denser ground monitoring sites in our study area. Our method has a slightly higher overall RMSPE than do those studies in the same study area (Beijing) using the coarser 3 km spatial resolution for PM 2.5 estimation by Li (16.04 µg/m 3 ) [24] and Xie (17.85 µg/m 3 ) [25], but with higher R 2 .…”

Section: Discussioncontrasting

confidence: 91%

Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

et al. 2018

View full text Add to dashboard Cite

High-spatiotemporal-resolution PM2.5 data are critical to assessing the impacts of prolonged exposure to PM2.5 on human health, especially for urban areas. Satellite-derived aerosol optical thickness (AOT) is highly correlated to ground-level PM2.5, providing an effective way to reveal spatiotemporal variations of PM2.5 across urban landscapes. In this paper, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOT and ground-based PM2.5 measurements were fused to estimate daily ground-level PM2.5 concentrations in Beijing for 2013–2017 at 1 km resolution through a linear mixed effect model (LMEM). The results showed a good agreement between the estimated and measured PM2.5 and effectively revealed the characteristics of its spatiotemporal variations across Beijing: (1) the PM2.5 level is higher in the central and southern areas, while it is lower in the northern and northwestern areas; (2) the PM2.5 level is higher in autumn and winter, while it is lower in spring and summer. Moreover, annual PM2.5 concentrations decreased by 24.03% for the whole of Beijing and 31.46% for the downtown area from 2013 to 2017. The PM2.5 data products we generated can be used to assess the long-term impacts of PM2.5 on human health and support relevant government policy decision-making, and the methodology can be applied to other heavily polluted urban areas.

show abstract

Section: Discussioncontrasting

confidence: 91%

Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The role of RH has been controversially discussed in recent studies. While some do not find a significant influence of RH on the correlation of AOD and PM2.5 [28], others report that AOD is more representative of PM2.5 mass concentration under dry conditions, i.e., RH < 50% [12]. Results of a field campaign conducted in northeastern U.S. suggest that water uptake by particles can indeed play a major role in the relationship between AOD and PM2.5 [32].…”

Section: Introductionmentioning

confidence: 95%

“…They conclude that when a low BLH is present, the satellite observes a very similar amount of particles as detected by ground-based measurements. In another study, [28] argue that in winter conditions in the Boston area, U.S.A., a shallow BLH would rather lead to low correlations between AOD and PM2.5. In these situations, particle concentrations are low in the upper parts of the atmosphere as particles are mostly confined within the shallow boundary layer.…”

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10

2018

View full text Add to dashboard Cite

Air pollution can endanger human health, especially in urban areas. Assessment of air quality primarily relies on ground-based measurements, but these provide only limited information on the spatial distribution of pollutants. In recent years, satellite derived Aerosol Optical Depth (AOD) has been used to approximate particulate matter (PM) with varying success. In this study, the relationship between hourly mean concentrations of particulate matter with a diameter of 10 micrometers or less (PM10) and instantaneous AOD measurements is investigated for Berlin, Germany, for 2001–2015. It is found that the relationship between AOD and PM10 is rarely linear and strongly influenced by ambient relative humidity (RH), boundary layer height (BLH), wind direction and wind speed. Generally, when a moderately dry atmosphere (30% < RH ≤ 50%) coincides with a medium BLH (600–1200 m), AOD and PM10 are in the same range on a semi-quantitative scale. AOD increases with ambient RH, leading to an overestimation of the dry particle concentration near ground. However, this effect can be compensated if a low boundary layer (<600 m) is present, which in turn significantly increases PM10, eventually leading to satellite AOD and PM10 measurements of similar magnitude. Insights of this study potentially influence future efforts to estimate near-ground PM concentrations based on satellite AOD.

show abstract

“…There are extensive studies investigating the PM2.5-AOD relationship by the use of either an empirical statistical method (Engel-Cox et al, 2004;Liu et al, 2005Liu et al, , 2009Gupta et al, 2006;Koelemeijer et al, 2006;Gupta and Christopher, 2008;Paciorek et al, 2008;Di Nicolantonio et al, 2009;Schaap et al, 2009;Lee et al, 2012;Sorek-Hamer et al, 2013;Strawa et al, 2013;Chudnovsky et al, 2014;Ma et al, 2014) or a chemical transportation model (Liu et al, 2004;Van Donkelaar et al, 2006Kessner et al, 2013;Xu et al, 2015). In these studies, aerosol vertical distributions are estimated based on model simulation or under an assumption that aerosols are well mixed within the boundary layer and then decrease exponentially with height.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

Joseph

Min

et al. 2017

Atmos. Meas. Tech.

View full text Add to dashboard Cite

Abstract. Monitoring PM2.5 (particulate matter with aerodynamic diameter d ≤ 2.5 µm) mass concentration has become of more importance recently because of the negative impacts of fine particles on human health. However, monitoring PM2.5 during cloudy and nighttime periods is difficult since nearly all the passive instruments used for aerosol remote sensing are not able to measure aerosol optical depth (AOD) under either cloudy or nighttime conditions. In this study, an empirical model based on the regression between PM2.5 and the near-surface backscatter measured by ceilometers was developed and tested using 6 years of data (2006 to 2011) from the Howard University Beltsville Campus (HUBC) site. The empirical model can explain ∼ 56, ∼ 34 and ∼ 42 % of the variability in the hourly average PM2.5 during daytime clear, daytime cloudy and nighttime periods, respectively. Meteorological conditions and seasons were found to influence the relationship between PM2.5 mass concentration and the surface backscatter. Overall the model can explain ∼ 48 % of the variability in the hourly average PM2.5 at the HUBC site when considering the seasonal variation. The model also was tested using 4 years of data (2012 to 2015) from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, which was geographically and climatologically different from the HUBC site. The results show that the empirical model can explain ∼ 66 and ∼ 82 % of the variability in the daily average PM2.5 at the ARM SGP site and HUBC site, respectively. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring under cloudy and nighttime conditions. Since ceilometers are used broadly over the world, they may provide an important supplemental source of information of aerosols to determine surface PM2.5 concentrations.

show abstract

High resolution aerosol data from MODIS satellite for urban air quality studies

Cited by 27 publications

References 22 publications

Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

Contact Info

Product

Resources

About