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

Abstract

<jats:p>The vertical distribution of aerosols is important for accurate surface PM2.5 retrieval and initial modeling forecasts of air pollution, but the observation of aerosol profiles on the regional scale is usually limited. Therefore, in this study, an approach to aerosol extinction profile fitting is proposed to improve surface PM2.5 retrieval from satellite observations. Owing to the high similarity of the single-peak extinction profile in the distribution pattern, the log-normal distribution is explored for the fitting model based on a decadal dataset (3248 in total) from Micro Pulse LiDAR (MPL) measurements. The logarithmic mean, standard deviation, and the height of peak extinction near-surface (Mode) are manually derived as the references for model construction. Considering the seasonal impacts on the planetary boundary layer height (PBLH), Mode, and the height of the surface layer, the extinction profile is then constructed in terms of the planetary boundary layer height (PBLH) and the total column aerosol optical depth (AOD). A comparison between fitted profiles and in situ measurements showed a high level of consistency in terms of the correlation coefficient (0.8973) and root-mean-square error (0.0415). The satellite AOD is subsequently applied for three-dimensional aerosol extinction, and the good agreement of the extinction coefficient with the PM2.5 within the surface layer indicates the good performance of the proposed fitting approach and the potential of satellite observations for providing accurate PM2.5 data on a regional scale.</jats:p>

Particle Size Distributions and Extinction Coefficients of Aerosol Particles in Land Battlefield Environments

“…Table 2 lists the probability density function of each PSD. Here, Γ(•) is the Gamma function, x > 0 is the diameter of the aerosol particle, α > 0 and β > 0 are the shape parameters, θ > 0 and γ > 0 are the scale parameters, and µ σ are the mean and standard deviation of ln x, respectively [25]. As shown in Table 2, the generalized gamma distribution can be converted into a gamma distribution (β = 1), Rosin-Rammler distribution (α = 1), exponential distribution (α = 1, β = 1), or Rayleigh distribution…”