Rainwater, composition

Willey JD, Bennett RI, Williams JM, Denne RK, Komegay CR, Perlotto MS, Moore BM. Effect of storm type on rainwater composition in southeastern North Carolina. Environ. Sci. Technol. 1988 22 41-46. 

Considerable Interest In the composition of rainwater has been expressed by members of the scientific community In the United States and elsewhere. "Add rain has been suggested as the culprit for observed degredatlon of terrestrial and aquatic ecosystems In the Northeastern United States, Canada, Germany, and Scandanavla. While some aspects of rainwater composition are understood, a large number of Important questions remain unresolved, particularly those relating to sources and controlling factors. 

Studies of rainwater composition typically Include the measurement of the concentrations of a number of chemical species, conductivity, and rain volume and sometimes Include supporting measurement of winds or other meteorological parameters. Much of the desired... 

The determination of which features the underlying factors are composed of provides a basis for attaching a physical Interpretation to the factors. Varlmax rotation of the PGA may be utilized to aid In the Interpretation of the factors. Hierarchical dendrograms Indicate feature clusters whose composition are analogous to PC factors. The physical Interpretation of the clusters and principal components Indicates the Influence of pollution emission sources or meteorological processes on the rainwater composition at an Individual monitoring site. 

Spatial Interrelationships In the chemical composition among two or more blocks (sites) can be calculated by partial least squares (PLS) (9 ). PLS calculates latent variables slmlllar to PG factors except that the PLS latent variables describe the correlated (variance common to both sites) variance of features between sites. Regional Influences on rainwater composition are thus Identified from the composition of latent variables extracted from the measurements made at several sites. Gomparlson of the results... 

SIMCA modeling was utilized to determine the separability of the samples collected at the three different sites. The results presented In Table IV Indicate the model cannot separate the samples from the West Seattle and Maple Leaf sites. Since both of these sites are located downwind of the major regional emission sources and experience similar meteorology their rainwater composition Is similar. The Tolt reservoir site Is separated from the Seattle sites with 79 percent of the samples collected there correctly classified by the SIMCA model. This site Is believed to be Influenced by the same emission sources as the other two sites but experiences different meteorological conditions (primarily longer transport times and more frequent and larger quantity of rainfall) due to Its location In the foothills of the Cascade Mountains (elevation 550 meters). Considering the uncertainty In the reported concentrations (see Table VII) and the similar air pollution emission sources the SIMCA results are reasonable. 

Any regional Influence on rainwater composition would be expected to affect all three sites reported here. A PLS two block model 9) was used to predict the variance In rainwater composition at one site from the variance In rainwater composition at an upwind site. 

The regression of the Tolt River rainwater composition on Maple Leaf data Indicated four components ... 

Three components are similar to the results for the West Seattle-Maple Leaf PLS model except that the acid aerosol component no longer has high a loading from nitrate. This specie Is ordinarily associated with automobile emissions. The Tolt site Is remote enough that auto emissions are not as Important an Influence on Che variability In rainwater composition as In Seattle. The fourth component for this PLS model might represent emissions from a cement plant which does not Influence Che West Seattle site. The soil factor is apparently local In nature since It appears In the PCA results but not the PLS results. 

With emission source chemical signatures and corresponding aerosol or rainwater sample measurements PLS can be used Co calculate a chemical element mass balance (CEB). Exact emission profiles for the copper smelter and for a power plant located further upwind were not available for calculation of source contributions to Western Washington rainwater composition. This type of calculation Is more difficult for rainwater Chan for aerosol samples due Co atmospheric gas to particle conversion of sulfur and nitrogen species and due Co variations In scavenging efficiencies among species. Gatz (14) has applied Che CEB to rainwater samples and discussed Che effect of variable solubility on the evaluation of Che soil or road dust factor. 

Although the measurement uncertainties limit the conclusions which can be drawn from these results, the data set proved useful for the determination of general Influences on rainwater composition In the Seattle area and for the demonstration of the application of these exploratory data analysis techniques. Current efforts to collect and analyze aerosol and rainwater samples over meteorologically appropriate time scales with precise analytical techniques are expected to provide better resolution of the factors controlling the composition of rainwater. 

V. A. Chekushin, V.A. Pavlov, Rainwater composition in eight arctic catchments in northern Europe (Finland, Norway and Russia), Atmos. Environ. 31 (1997) 159-170. H.V. Churchill, Occurrence of fluorides in some water of the United States, Ind. Eng. Chem. 23(1931)996-998. 

The approach taken to observe the impact of the copper smelter on mesoscale variations rainwater composition was to determine the spatial, temporal, and experimental components of the variability of a number of appropriate chemical species in the rainwater. This paper presents results for 1985, during smelter operation, and includes (1) estimates of the experimental variability in chemical composition, (2) an approach for a two step chemical and statistical screening of the data set, (3) the spatial variation in rainwater composition for a storm collected on February 14-15, and (4) a principal component analysis of the rainwater concentrations to help identify source factors influencing our samples. 

Examination of the spatia-J. variation in the concentrations of excess S04, As, and H+ reveals an area of influence of some source of sulfur and arsenic on rainwater composition immediately to the northeast (downwind) of Tacoma,WA. Since the Tacoma smelter is the major emission... 

Measurement of the experimental uncertainties in our rain sampling procedures and the application of these uncertainties in a screening procedure to eliminate questionable samples from the data set increases the confidence in the interpretation of spatial variations in rainwater composition and of the principal component analysis. Results presented here for a storm collected during smelter operation suggest that it was the major source of the downwind excess SO. elevation above background and the pH depress ion oelow the background value of 5. 

Vong,R.J., Frank,I.E., CharIson,R.J., and Kowalski, B.R., Exploratory Data Analysis of Rainwater Composition, in Environmental Applications of Chemometrics, ACS Symposium Series 292, (ed. J.J. 

Rainwater composition is determined by the atmospheric composition of the precipitation zone and by that of the zone of origin of the clouds and the winds that carry them to the precipitation site. It varies geographically and depends on the natural and anthropogenic input of gases and particles into the atmosphere, as well as on the atmospheric reactions that may transform them. Activity of the local biomes (see definition in Section 7.3) also affects rainwater composition as does the local weather, which in turn defines the amount of precipitation and therefore the concentration of components. 

Table 3.3 Rainwater composition at Lake Calado, West of Manaus.

Information available in the literature is restricted to isolated attempts to describe and quantify specific processes like litterfall and decomposition (Peres et al. 1983), rainwater composition (Schiavini 1983, Delitti 1984), soil fertility gradients (Lopes and Cox 1977), leaf nutrient concentrations (Haridasan 1987, 1992, Araujo and Haridasan 1988), primary productivity of the ground layer (Batmanian and Haridasan 1985, Meirelles and Henrique 1992), effects of burning (Coutinho 1990,... 

In addition to solute from CCN, clouds contain dissolved gases (e.g. SO2, NH3, HCHO, H2O2, HNO3, and many more). In turn, some of these may react in the cloud droplets to form other substances which subsequently can appear in rainwater. Finally, falling raindrops can collect other materials (e.g. large dust particles) on their way to the Earth s surface. Thus, rainwater composition does not uniquely reflect the chemistry of the CCN. 

This standard deviation includes analytical errors as well as the natural variation in the rainwater composition of the samples collected, but it does not include variation between samplers at the same site, systematic errors due to improper sample collection and preservation(p, or spatial variability(8). The coefficient of variation (s /[X]) is seldom less than 10% or more than 30% for major ions in precipitation collected by month, by storm and by increments within storms in the United States(5). Other averaging procedures can give variabilities of slightly less than 10% to more than 30% (9). In the absence of data on the standard deviation of the mean, 10% and 30% will be assumed to be lower and upper bounds, respectively, for the uncertainty in mean values. 

By excluding water from the mass balance, the same model has been applied to rainwater composition where the sources are typically sea salt, soil dust, ammonia, nitric acid and sulfuric acid. In addition to mass, acidity/alkalinity from each source is conserved. 

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