The detection of primary biological material in submicron aerosol by means of thermal desorption/electron impact ionization aerosol mass spectrometry was investigated. Mass spectra of amino acids, carbohydrates, small peptides, and proteins, all of which are key building blocks of biological particles, were recorded in laboratory experiments. Several characteristic marker fragments were identified. The intensity of the marker signals relative to the total organic mass spectrum allows for an estimation of the content of primary biological material in ambient organic aerosol. The developed method was applied to mass spectra recorded during AMAZE-08, a field campaign conducted in the pristine rainforest of the central Amazon Basin, Brazil, during the wet season of February and March 2008. The low abundance of identified marker fragments places upper limits of 7.5% for amino acids and 5.6% for carbohydrates on the contribution of primary biological aerosol particles (PBAP) to the submicron organic aerosol mass concentration during this time period. Upper limits for the absolute submicron concentrations for both compound classes range from 0.01 to 0.1 mu g m(-3). Carbohydrates and proteins (composed of amino acids) make up for about two thirds of the dry mass of a biological cell. Thus, our findings suggest an upper limit for the PBAP mass fraction of about 20% to the submicron organic aerosol measured in Amazonia during AMAZE-08.
As part of the 2008 Campaign of Air Quality Research in Beijing and Surrounding Regions (CAREBeijing 2008), measurements of gaseous sulfuric acid (H2SO4) have been conducted at an urban site in Beijing, China from 7 July to 25 September 2008 using atmospheric pressure ion drift - chemical ionization mass spectrometry (AP-ID-CIMS). This represents the first gaseous H2SO4 measurements in China. Diurnal profile of sulfuric acid is strongly dependent on the actinic flux, reaching a daily maximum around noontime and with an hourly average concentration of 5 x 10(6) molecules cm(-3). Simulation of sulfuric acid on the basis of the measured sulfur dioxide concentration, photolysis rates of ozone and nitrogen dioxide, and aerosol surface areas captures the trend of the measured H2SO4 diurnal variation within the uncertainties, indicating that photochemical production and condensation onto preexisting particle surface dominate the observed diurnal H2SO4 profile. The frequency of the peak H2SO4 concentration exceeding 5 x 10(6) molecules cm(-3) increases by 16% during the period of the summer Olympic Games (8-24 August 2008), because of the implementation of air quality control regulations. Using a multivariate statistical method, the critical nucleus during nucleation events is inferred, containing two H2SO4 molecules (R-2 = 0.85). The calculated condensation rate of H2SO4 can only account for 10-25 % of PM1 sulfate formation, indicating that either much stronger sulfate production exists at the SO2 source region or other sulfate production mechanisms are responsible for the sulfate production.
Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large number of components. This paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. Furthermore, variable selection is effective in removing unnecessary covariates in the skewness, which means that there is little loss in allowing for skewness in the components when the data are actually symmetric. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables.