A review of biomarker compounds as source indicators and tracers for air pollution

An overview of the application of organic geochemistry to the analysis of organic matter on aerosol particles is presented here. This organic matter is analyzed as solvent extractable bitumen/ lipids by gas chromatography-mass spectrometry. The organic geochemical approach assesses the origin, the environmental history and the nature of secondary products of organic matter by using the data derived from specific molecular analyses. Evaluations of production and fluxes, with cross-correlations can thus be made by the application of the same separation and analytical procedures to samples from point source emissions and the ambient atmosphere. This will be illustrated here with typical examples from the ambient atmosphere (aerosol particles) and from emissions of biomass burning (smoke). Organic matter in aerosols is derived from two major sources and is admixed depending on the geographic relief of the air shed. These sources are biogenic detritus (e.g., plant wax, microbes, etc.) and anthropogenic particle emissions (e.g., oils, soot, synthetics, etc.). Both biogenic detritus and some of the anthropogenic particle emissions contain organic materials which have unique and distinguishable compound distribution patterns (C₁₄-C₄₀). Microbial and vascular plant lipids are the dominant biogenic residues and petroleum hydrocarbons, with lesser amounts of the pyrogenic polynuclear aromatic hydrocarbons (PAH) and synthetics (e.g., chlorinated compounds), are the major anthropogenic residues. Biomass combustion is another important primary source of particles injected into the global atmosphere. It contributes many trace substances which are reactants in atmospheric chemistry and soot paniculate matter with adsorbed biomarker compounds, most of which are unknown chemical structures. The injection of natural product organic compounds into smoke occurs primarily by direct volatilization/steam stripping and by thermal alteration based on combustion temperature. Although the molecular composition of organic matter in smoke particles is highly variable, the molecular tracers are generally still source specific. Retene has been utilized as a tracer for conifer smoke in urban aerosols, but is not always detectable. Dehydroabietic acid is generally more concentrated in the atmosphere from the same emission sources. Degradation products from biopolymers (e.g., levoglucosan from cellulose) are also excellent tracers. An overview of the biomarker compositions of biomass smoke types is presented here. Defining additional tracers of thermally-altered and directly-emitted natural products in smoke aids the assessment of the organic matter type and input from biomass combustion to aerosols. The precursor to product approach of compound characterization by organic geochemistry can be applied successfully to provide tracers for studying the chemistry and dispersion of ambient aerosols and smoke plumes. Presented at the 6th FECS Conference on Chemistry and the Environment, Atmospheric Chemistry and Air Pollution, August 26–28, 1998, Copenhagen.     

Metals in biomass

Background, Aim and Scope

Metal ions generally share the ability/tendency of interacting with biological material by forming complexes, except possibly for the heavy alkali metals K, Rb and Cs. This is unrelated to the metals being either essential for sustaining life and its reproduction, apparently insignificant for biology, although perhaps undergoing bioconcentration or even being outright toxic, even at low admission levels. Yet, those different kinds of metal-biomass interactions should in some way depend on properties describing coordination chemistries of these very metals. Nevertheless, both ubiquitously essential metals and others sometimes used in biology should share these properties in numeric terms, since it can be anticipated that they will be distinguished from nonessential and/or toxic ones. These features noted above include bioconcentration, the involvement of metal ions such as Zn, Mg, Cu, Fe, etc. in biocatalysis as crucial components of metalloenzymes and the introduction of a certain set of essential metals common to (almost) all living beings (K, Mg, Mo, Mn, Fe, Cu and Zn), which occurred probably very early in biological evolution by ‘natural selection of the chemical elements’ (more exactly speaking, of the metallomes).

Materials and Methods

The approach is semiempirical and consists of three consecutive steps: 1) derivation of a regression equation which links complex stability data of different complexes containing the same metal ion to electrochemical data pertinent to the (replaced) ligands, thus describing properties of metal ions in complexes, 2) a graphical representation of the properties-two typical numbers c and x for each metal ion-in some map across the c/x-space, which additionally contains information about biological functions of these metal ions, i.e. whether they are essential in general (e.g. Mg, Mn, Zn) or, for a few organisms of various kinds (e.g. Cd, V), not essential (e.g. rare earth element ions) or even generally highly toxic (Hg, U). It is hypothesized that, if coordination properties of metals control their biological ‘feasibility’ in some way, this should show up in the mappings (one each for mono and bidentate-bonding ligands). 3) eventually, the regression equation produced in step 1) is inverted to calculate complex stabilities pertinent to biological systems: 3a) complex stabilities are mapped for ligands delivered to soil (-water) by green plants (e.g. citrate, malate) and fungi and, compared to their unlike selectivities and demands of metal use (photosynthesis taking place or not), 3b) the evolution of the metallome during late chemical evolution is reconstructed.

Results

These maps show some ‘window of essentiality’, a small, contrived range/area of c and x parameters in which essential metal ions gather almost exclusively. c and x thus control the possibility of a metal ion becoming essential by their influencing details of metal-substrate or (in cases of catalytic activities) metal-product interactions. Exceptions are not known to be involved in biocatalysis anyhow.

Discussion

Effects of ligands secreted, e.g. from tree roots or agaric mycelia to the soil on the respective modes (selectivities) of metal bioconcentration can be calculated by the equation giving complex stability constants, with obvious ramifications for a thorough, systematic interpretation of biomonitoring data. Eventually, alterations of C, N and P-compounds during chemical evolution are investigated — which converted CH₄ or CO₂, N₂ and other non-ligands to amino acids, etc., for example, with the latter behaving as efficient chelating ligands: Did they cause metal ions to accumulate in what was going to become biological matter and was there a selectivity, a positive bias in favour of nowessential metals (see above) in this process? Though there was no complete selectivity of this kind, neither a RNA world in which early ribozymes effected most of biocatalysis, nor a paleoatmosphere containing substantial amounts of CO could have paved the way to the present biochemistry and metallomes.

Conclusions

This way of reasoning provides a causal account for abundance distributions described earlier in the Biological System of Elements (BSE; Markert 1994, Fränzle &; Markert 2000, 2002). There is a pronounced change from chemical evolution, where but few transformations depended on metal ion catalysis to biology.

Recommendations and Perspectives

The application of this numerical approach can be used for modified, weighted evaluation of biomonitoring analytical data, likewise for the prediction of bioconcentration hazards due to a manifold of metal ions, including organometallic ones. This is relevant in ecotoxicology and biomonitoring. In combining apoproteins or peptides synthesized from scratch for purposes of catalysing certain transformations, the map and numerical approaches might prove useful for the selection of central ions which are even more efficient than the ‘natural’ ones, like for Co²⁺ in many Zn enzymes.