Abstract
In this paper, the feasibility of retrieving the aerosol fine-mode fraction (FMF) from ground-based sky light measurements is investigated. An inversion algorithm, based on the optimal estimation (OE) theory, is presented to retrieve FMF from single-viewing multi-spectral radiance measurements and to evaluate the impact of utilization of near-infrared (NIR) measurements at a wavelength of 1610 nm in aerosol remote sensing. Self-consistency tests based on synthetic data produced a mean relative retrieval error of 4.5%, which represented the good performance of the OE inversion algorithm. The proposed algorithm was also performed on real data taken from field experiments in Beijing during a haze pollution event. The correlation coefficients (R) for the retrieved aerosol volume fine-mode fraction (FMFv) and optical fine-mode fraction (FMFo) against AErosol RObotic NETwork (AERONET) products were 0.94 and 0.95 respectively, and the mean residual error was 4.95%. Consequently, the inversion of FMFv and FMFo could be well constrained by single-viewing multi-spectral radiance measurement. In addition, by introducing measurements of 1610 nm wavelength into the retrieval, the validation results showed a significant improvement in the R value for FMFo (from 0.89–0.94). These results confirm the high value of NIR measurements for the retrieval of coarse mode aerosols.
Highlights
Exposure to ambient air pollution has serious impacts on human health, such as respiratory diseases and cardiovascular diseases [1,2,3,4]
As a parameter to describe the proportion of fine particles in aerosols, fine-mode fraction (FMF) is a key factor in the physical model, which can increase people’s understanding of anthropogenic aerosols and help to analyze the impacts of human activities on environmental changes and human health [10,11]
With the support of relatively accurate a priori knowledge from AErosol RObotic NETwork (AERONET) products, we mainly made a test on the inversion algorithm by introducing various observation errors
Summary
Exposure to ambient air pollution has serious impacts on human health, such as respiratory diseases and cardiovascular diseases [1,2,3,4]. The estimation of the PM2.5 mass concentration near the surface has been extensively studied using ground-based and satellite platforms, such as the pure physical PM2.5 remote sensing (PMRS) method [7,8,9]. As a parameter to describe the proportion of fine particles in aerosols, fine-mode fraction (FMF) is a key factor in the physical model, which can increase people’s understanding of anthropogenic aerosols and help to analyze the impacts of human activities on environmental changes and human health [10,11]. Previous studies attempted to retrieve FMF over the dark target region from observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) [12,13,14]. The accuracy of retrieved FMF was not sufficient for estimating PM2.5 mass concentration near the ground. There are some related algorithms that try to retrieve FMF from measurements of the multi-viewing polarized satellite sensor, such as the Polarization and Directionality of the Earth’s
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