Long-Term and Seasonal Variations in CO2: Linkages to Catchment Alkalinity Generation

As atmospheric emissions of S have declined in the Northern Hemisphere, there has been an expectation of increased pH and alkalinity in streams believed to have been acidified by excess S and N. Many streams and lakes have not recovered. Evidence from East Bear Brook in Maine, USA and modelling with the groundwater acid-base model MAGIC (Cosby et al. 1985a,b) indicate that seasonal and yearly variations in soil PCO2 are adequate to enhance or even reverse acid-base (alkalinity) changes anticipated from modest decreases of SO4 in surface waters. Alkalinity is generated in the soil by exchange of H+ from dissociation of H2CO3, which in turn is derived from the dissolving of soil CO2. The variation in soil PCO2 produces an alkalinity variation of up to 15 mu eq L-1 in stream water. Detecting and relating increases in alkalinity to decreases in stream SO4 are significantly more difficult in the short term because of this effect. For example, modelled alkalinity recovery at Bear Brook due to a decline of 20 mu eq SO4 L-1 in soil solution is compensated by a decline from 0.4 to 0.2% for soil air PCO2. This compensation ability decays over time as base saturation declines. Variable PCO2 has less effect in more acidic soils. Short-term decreases of PCO2 below the long-term average value produce short-term decreases in alkalinity, whereas short-term increases in PCO2 produce shortterm alkalization. Trend analysis for detecting recovery of streams and lakes from acidification after reduced atmospheric emissions will require a longer monitoring period for statistical significance than previously appreciated.

A High-Resolution Record of Atmospheric Dust Composition and Variability Since Ad 1650 from a Mount Everest Ice Core

A Mount Everest ice core analyzed at high resolution for major and trace elements (Sr, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Tb, Dy, Ho, Er, Tm, Yb, Lu, Bi, U, Tl, Al, S, Ca, Ti, V, Cr, Mn, Fe, Co) and spanning the period A. D. 1650- 2002 is used to investigate the sources of and variations in atmospheric dust through time. The chemical composition of dust varies seasonally, and peak dust concentrations occur during the winter-spring months. Significant correlations between the Everest dust record and dust observations at stations suggest that the Everest record is representative of regional variations in atmospheric dust loading. Back-trajectory analysis in addition to a significant correlation of Everest dust concentrations and the Total Ozone Mapping Spectrometer (TOMS) aerosol index indicates that the dominant winter sources of dust are the Arabian Peninsula, Thar Desert, and northern Sahara. Factors that contribute to dust generation at the surface include soil moisture and temperature, and the long-range transport of dust aerosols appears to be sensitive to the strength of 500-mb zonal winds. There are periods of high dust concentration throughout the 350-yr Mount Everest dust record; however, there is an increase in these periods since the early 1800s. The record was examined for recent increases in dust emissions associated with anthropogenic activities, but no recent dust variations can be conclusively attributed to anthropogenic inputs of dust.

Twentieth Century Increase of Atmospheric Ammonia Recorded in Mount Everest Ice Core

An NH4+ record covering the period A.D. 1845-1997 was reconstructed using an 80.4 m ice core from East Rongbuk Glacier at an elevation of 6450 m on the northern slope of Mount Everest. Variations in NH4+ are characterized by a dramatic increase since the 1950s. The highest NH4+ concentrations occur in the 1980s. They are about twofold more than those in the first half of twentieth century. Empirical orthogonal function (EOF) analysis on the eight major ion (Na+,K+,Mg2+,NH4+,Ca2+,NO3-,SO42- and Cl-) series from this core indicates that NH4+ is loaded mainly on EOF3 (60% of NH4+ variance), suggesting that NH4+ has a unique signature. Instrumental sea level pressure (SLP) and regional temperatures are used to explore the relationship between NH4+ variations and both atmospheric circulation and natural source strength over Asia. Higher NH4+ concentrations are associated with an enhanced winter Mongolian High and a deepened summer Mongolian Low. A positive relationship also exists between NH4+ concentrations and regional temperature changes of the GIS Box 36 (Indian subcontinent), indicating that an increase in temperature may contribute to the strengthening of natural ammonia emissions (e. g., from plants and soils). A close positive correlation between NH4+ and acidic species (SO42- plus NO3-) concentrations suggests that a portion of the increase in NH4+ concentrations could be contributed by enhanced atmospheric acidification. Anthropogenic ammonia emissions from enhanced agricultural activities and energy consumption over Asia in concert with population increase since the 1950s appear also to be a significant factor in the dramatic increase of NH4+ concentrations during the last few decades.

Impact of Preindustrial Biomass-Burning Emissions on the Oxidation Pathways of Tropospheric Sulfur and Nitrogen

Ice core measurements (H2O2 and CH4/HCHO) and modeling studies indicate a change in the oxidation capacity of the atmosphere since the onset of the Industrial Revolution due to increases in fossil fuel burning emissions [e. g., Lelieveld et al., 2002; Hauglustaine and Brasseur, 2001; Wang and Jacob, 1998; Staffelbach et al., 1991]. The mass-independent fractionation (MIF) in the oxygen isotopes of sulfate and nitrate from a Greenland ice core reveal that biomass-burning events in North America just prior to the Industrial Revolution significantly impacted the oxidation pathways of sulfur and nitrogen species deposited in Greenland ice. This finding highlights the importance of biomass-burning emissions for atmospheric chemistry in preindustrial North America and warrants the inclusion of this impact in modeling studies estimating changes in atmospheric oxidant chemistry since the Industrial Revolution, particularly when using paleo-oxidant data as a reference for model evaluation.