These findings underline the importance of WSIS as one of the major factors affecting the albedo coefficient and, thus, lead to a comparatively strong control of regional climate.Īrtificial Neural Networks (ANNs) (i.e., recursive neural network, feedforward neural network (FNN), and multiple linear regression analysis (MLRA)) and nonlinear autoregressive models are extremely useful algorithms for the precise forecasting of respiratory mortality and mobility associated with exposure to NO 2, SO 2, O 3, PM 10, and PM 2.5.
Field observations of the aerosol optical properties and WSIS contents in aerosols at a resolution of 1 h from April to May 2020 in urban Shanghai showed a positive linear relationship between the mass concentrations of SO 4 2−, NO 3 −, Cl −, and NH 4 + and the absorption coefficient at 532 nm (α a,532). The degree of scattering by both submicron and super-micron particles is predominantly governed by the type of WSIS in maritime aerosols.
The impacts of non-sea salt sulphate, sea salt, and the total aerosol on backscattering albedo efficiency have been thoroughly examined during the first aerosol characterisation experiment (ACE1) from November to December 1995 in the Southern Ocean region south of Australia. Apart from rainfall and WD, numerous meteorological parameters can also influence the variation in WSIS and carbonaceous particles. The alteration of the chemical constituents and physicochemical properties of the clouds related to these various classifications of particulate matters can influence cloud formation and processes.
The WD can greatly enhance the atmospheric contents of some WSIS, such as SO 4 2−, Cl −, Mg 2+, K +, and Ca 2+, in clouds from a tropical montane cloud forest in Puerto Rico, particularly when air masses originate from Northwest Africa. A deliberate investigation of the scavenging coefficients measured in this study indicated that the wet deposition of particulate WSIS was dramatically affected by the precipitation intensity and was aerosol size-dependent. Additionally, Mg 2+ and Ca 2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.Įarlier findings have also shown that wet deposition plays a major role in the removal of NH 4 +, Cl −, SO 4 2−, and NO 3 − in the tropical atmosphere of Southeast Asia. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM 2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. A total of 191 sets of PM 2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand.
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