Remote sensing of ground-level PM2.5 combining AOD and backscattering profile

Li, Siwei; Joseph, Everette; Min, Qilong

doi:10.1016/j.rse.2016.05.025

Cited by 39 publications

(18 citation statements)

References 64 publications

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“…Figure 1b displays that the aerosol mostly occurs under 5 km m.s.l., and its extinction coefficient is exponentially weakened with the increase in altitude in South Asia. This result is consistent with previous work [42,43]. Moreover, the vertical aerosol extinction coefficient increases distinctly during the monsoon season in South Asia from 2006 to 2015, especially below 2 km.…”

Section: Of 17supporting

confidence: 93%

Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season

et al. 2018

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South Asia is experiencing a levelling-off trend in solar radiation and even a transition from dimming to brightening. Any change in incident solar radiation, which is the only significant energy source of the global ecosystem, profoundly affects our habitats. Here, we use multiple observations of the A-Train constellation to evaluate the impacts of three-dimensional (3D) aerosol, cloud, and water vapor variations on the changes in surface solar radiation during the monsoon season (June-September) in South Asia from 2006 to 2015. Results show that surface shortwave radiation (SSR) has possibly increased by 16.2 W m −2 during this period. However, an increase in aerosol loading is inconsistent with the SSR variations. Instead, clouds are generally reduced and thinned by approximately 8.8% and 280 m, respectively, with a decrease in both cloud water path (by 34.7 g m −2 ) and particle number concentration under cloudy conditions. Consequently, the shortwave cloud radiative effect decreases by approximately 45.5 W m −2 at the surface. Moreover, precipitable water in clear-sky conditions decreases by 2.8 mm (mainly below 2 km), and related solar brightening increases by 2.5 W m −2 . Overall, the decreases in 3D water vapor and clouds distinctly result in increased absorption of SSR and subsequent surface brightening.

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Section: Of 17supporting

confidence: 93%

Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season

et al. 2018

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“…The relationship between surface PM2.5 and column-integrated AOD is associated with vertical and size distribution of aerosols [38,39]. The particulate matter vertical distribution has been considered from a physics perspective, which could improve the correlation between PM2.5 and AOD [40,41].…”

Section: Discussionmentioning

confidence: 99%

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Wang

Mao

et al. 2017

Remote Sensing

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Abstract:Monitoring fine particulate matter with diameters of less than 2.5 µm (PM2.5) is a critical endeavor in the Beijing-Tianjin-Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R 2 (0.86) and low RMSE (24.5 µg/m 3 ). The average estimated PM2.5 in the BTH region during the study time range was about 55 µg/m 3 . The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 µg/m 3 (1600 local time) to 65.5 ± 54.6 µg/m 3 (1100 local time) at different hours.

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“…Gaps of in situ measurement networks can be filled by data from remote sensing techniques (e.g., Gupta et al, 2006;Martin, 2008) or numerical models. To better understand -or supplementdirect observations, air quality may be linked to integral parameters such as the aerosol optical depth (e.g., Koelemeijer et al, 2006;Schäfer et al, 2008;Li et al, 2016) or to meteorological parameters such as the height of the mixing layer (henceforward referred to as mixing layer height (MLH) or H ml ). The MLH can be considered as a measure for the vertical mixing within the atmospheric boundary layer and determines the dilution of pollutants which are emitted near the ground.…”

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confidence: 99%

Mixing layer height as an indicator for urban air quality?

Geiß

Wiegner²,

Bonn

et al. 2017

Atmos. Meas. Tech.

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Abstract. The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near-surface pollutant concentrations has been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted from June to August 2014. To derive the MLH, three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 min and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM 10 , O 3 and NO x ) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM 10 are quite different for different sites without showing a clear pattern, whereas the correlation with NO x seems to depend on the vicinity of emission sources in main roads. In the case of ozone as a secondary pollutant, a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near-surface pollutant concentrations representative for a city like Berlin (flat terrain), in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, for example as input to dispersion models and for the validation of chemical transport models.

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Remote sensing of ground-level PM2.5 combining AOD and backscattering profile

Cited by 39 publications

References 64 publications

Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season

Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Mixing layer height as an indicator for urban air quality?

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