the map3s/raine precipitation chemistry network: statistical overview for the period 1976–1980
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Atmospheric Enwonmmt Vol. 16, No. 7, pp. 16031631, 1982 @.W4d981/S2/071603-29 SO3.00/0
Printed III Great Bntain a 1982 Pergamon Press Ltd.
THE MAP3S/RAINE PRECIPITATION CHEMISTRY NETWORK: STATISTICAL OVERVIEW FOR THE PERIOD
1976-1980
THE ~AP3S/RAINE RESEARCH COMMUNITY*
(First received 1 December 1980 and in,finalform 11 November 1981)
Abstract-This paper presents a basic statistical summary of the initial 3$ year operating period of the MAP3S/RAINE precipitation chemistry network. The overview considers statistical features of the pr~ipi~tion event data base, including temporal and variable-pair behavior and spatial (site-to-site) relationships. Seasonal variations in concentrations of the species total sulfur, SO,, free hydrogen ion and NH: are easily identifiable from both event and monthly average time trend analyses. Species-pair correlations are generally strong and positive among the major ionic species total S, nitrate, free hydrogen and ammonium; variations from this trend at individual sites can be related to geographical location. Though reasonable coherency is exhibited in site-to-site correlation analyses, the questions of proper averaging time and network density make interpretation of spatial statistics difficult. It is anticipated that this summary will be a directly useful resource for wet-deposition assessment in the northeastern United States, and should compose a convenient starting point for more detailed analysis by the extended research community.
INTRODUCTION
The MAP3S/RAINEt precipitation chemistry net- work was initiate in 1976 with the objective of creating a long-term, high-quality data base for the development of regional transport and deposition models. Numerous investigators have contributed to the growing data set, which is available on a continu- ous basis in both computer-formatted and printed form (MAP3S, 1977, 1979, 198Oa). Several authors (e.g. Pack and Pack, 1979: Pack, 1980; McNaughton, 1981; Henderson and Weingartner, 1980; Baker or al., 1981) have utilized the MAP3SjRAINE data base to examine specific aspects$ of chemical deposition and network design. Until the present time, however, there has been no complete or generally-available consoli- dation of these data in terms of their pertinent statistical features; and because of these results’ applicability to a variety of impact-assessment and scientific analyses, there has been a growing need for such a summary.
* Including participants from DOE, EPA, NOAA, USGS, Argonne National Laboratory, Battelle-Northwest Laboratory, Brookhaven National Laboratory, Lawrence Livermore Laboratory. Environmental Measurement Laboratory, Oak Ridge National Laboratory, Cornell University, University of Delaware, Illinois State Water Survey, Miami University, Pennsylvania State University, State University of New York (Albany) and University of Virginia. Correspondence of a technical nature should be addressed to Jeremy M. Hales. Battelle-Northwest, Richland. Washington; requests for machine-formatted data should be addressed to Carmen B. Benkovitz, Brookhaven National Laboratory. Upton. New York.
t Multistate Atmospheric Power Product Pollution Study/Regional Acidity of Inddstrial Emissions.
$ The presently-unpublished work of Henderson and Weingartner is the most generally-oriented analysis of those cited here.
This paper is addressed to the fulfillment of this need. It presents basic statistical features of the data set generated over the network’s initial 34 year operat- ing period, su~ividing these attributes into individual categories depending upon whether they involve tem- poral, combined-variable, or spatial relationships. Design, operation and special activities of the network are described as well.
The authors have selected the statistical techniques for this presentation to provide a concise and straight- forward overview, with the rationale that more elaborate statistical analyses, while potentially useful, should be subjects of individual publications. To preserve total objectivity we also have refrained from any conjecture regarding physical causes or mechanis- tic behavior. It is our intent that this article provide a starting point for a variety of interpretive studies by the extended research community; we have included some rather lengthy tabular material to facilitate detailed pursuit, and have attempted to identify key attributes which should lend tosignificant scientificadvancement upon detailed examination from a mechanistic viewpoint.
Additional features of precipitation chemistry in the United States will become resolved only after a more lengthy record of data is obtained. The present data base is sufficiently extensive, however, to allow a number of important features to be identified, and can be applied to considerable advantage on an interim basis as this expanded data set evolves.
NETWORK DESCRIPTION
Sampler locations on the MAP3S/RAINE network arp shown in Fig. 1 and Table 1. This array was chosen
1603
1604 THE MAP3SlRAINE RESEARCH COMMUNITY
i r I I
Fig. 1. Locations of MAP3S/RAINE network stations
Table 1. Locations and initiation dates of MAP3S/Raine network sites
Site Site
number Latitude/longitude Start date
Whiteface, NY (WF) Ithaca, NY (IT) Penn State, PA (PS) Charlottesville, VA (VA) Urbana, IL (IL) Brookhaven, NY (BK) Lewes, DE (LE) Oxford, OH (OH) Oak Ridge, TN
44” 23.5’173’ 51.5’ 42 23’116’43’
40’ 41.5~/71L 51.5’ 38’ 02.5’/78 32.5’
40” 03’/88’ 22’ 40” 52’112” 53’ 38” 46’175” 00 39’ 32’184 44 35 55’/84’ 20
11 Oct. 76 26 Oct. 76 22 Sept. 76 12 Dec. 76 20 Nov. 77
9 Feb. 78 1 March 78 1 Oct. 78 7 Jan. 81
so as to maximize information on regional precipi- tation chemistry in the northeastern quadrant of the United States, subject to the constraints of financial limitations on the total number of stations and the
geographical locations of skilled operating groups. Precipitation samples are obtained on an event basis, “event” being defined for present purposes as any 24-h period during which precipitation has occurred. More frequent sampling can be conducted at the discretion of the individual site operator, and sequential sampling on a network basis is possible, if desired for intensive
studies. Two basic sampler designs have been utilized during
the network’s initial period. The first of these is the specially-fabricated device shown in Fig. 2, which consists of a polyethylene funnel and a refrigerated. collection bottle, housed within a standard rain gauge container. Lid opening during precipitation periods is accomplished by an electric-motor drive mechanism, which removes the lid to a splash-free position to the side of the sampler. Precipitation sensing for this purpose is provided by a standard resistance grid sensor as indicated in the schematic.
The second sampler design which has been em- ployed on the MAP3S/RAINE network is the stan- dard HASL* unit. This sampler employs a lid-opening mechanism similar to that shown in Fig. 2, but utilizes a cylindrical bucket as a collector and does not refrigerate the sample.
The MAP3SJRAINE network began its operations routinely using the funnel-bottle samplers. Detailed intercomparisons at the Penn State site have shown this and the HASL unit to give comparable results (at least at this site); and based upon this finding the decision was made to standardize the network to the more simple HASL design. It should be noted, how- ever, that all samples included in the data set discussed in the present article were collected using samplers of the design shown in Fig. 2.
Bottles are removed and sealed during the collection
* U.S. Department of Energy Health and Safety Laboratory, now named Environmental Measurements Laboratory. Samplers of this design are available commer- cially from the Aerochem Metrics Corporation, Miami, Florida.
The MAP3S/RAINE precipitation chemistry network 1605
0 FIBERGLASS TOPSECTION
0 FIBERGLASS LOWERSECTION
0 ACCESS DOOR
@ALUMINUM ANGLE BASE
@ MOTOR DRIVESECTION
@HEATED POLYETHYLENEFUNNEL
0 POLY COLLECTION BOTTLE
@ JACKTOSUPPORT SAMPLE
@ RAINSENSOR UNIT
@REFRIGERATOR UNIT
@COOLINGCOILS
Fig. 2. Schematic of MAP3SiRAINE precipitation-chemistry sampler
procedure with the funnel-bottle sampler. This is followed by a deionized-water funnel rinse and inser- tion of a new bottle for the subsequent sample. The cylindrical HASL collectors are emptied into similar collection bottles which are subsequently shipped to the laboratory for analysis. Prior to sealing in the field,
separate sample aliquots are subjected to pH measure- ment and tetrachloromercurate addition for stabiliz- ation of dissolved SOZ. Time, temperature, and precipitation-amount data are recorded, and the sam- ples are shipped to the central laboratory for chemical analysis.
Rainfall amounts are recorded on standard rain gauges, which are located at the sampler s. :s. Although time-resolved rainfall data are obtained on the rain gauge records during each event, these values have not been reported routinely with the standard data set. Instead, these time-resolved measurements have been integrated throughout each sampling period, resulting in a single reported volume for each chemical sample. The desirability of possessing time- resolved rainfall data for interpretive analysis has led to their recent incorporation as part of the routinely- reported data.
Chemical analysis of the precipitation samples is performed at the Precipitation Chemistry Laboratory at Battelle-Northwest Laboratories.* The chemical species routinely measured are listed along with the associated analytical procedures, error estimates, and detection limits in Table 2. Typically, samples are analyzed and their results reported within three months of collection. They are stored under re-
* It should be noted that precipitation samples are not filtered prior to analysis in the MAP3S Precipitation Chemistry Laboratory.
frigerated conditions while awaiting analysis, and
archived in frozen storage afterward. Network quality control is described in detail else-
where (MAP3S 1977, 1979, 1980b). In addition to established siting criteria and sampling-methodology checks, a “blind” sample procedure is dmployed. This procedure provides for an independent groupt to introduce previously-analyzed dummy precipitation
samples to the laboratory in a manner such that the analysts are unaware that they are not routine. Subsequent comparison of the laboratory analysis with the prior analysis provides a periodic check on analytical accuracy.
Data from the network are reported in a number of
formats. Currently-evolving data are given in bimonthly reports, designed to minimize time lags between sampling and data dissemination. Annual data sum- maries are available in printed form in the MAP3S Precipitation Chemistry Network Periodic Summary Reports (MAP3S 1977, 1979, 1980a). Magnetic-tape records of the data are available from the MAP3S Data Bank at Brookhaven National Laboratory, and it is anticipated that they will become accessible as part of the EPA SAROD data base as well.
ADDITIONAL ASPECTS OF DATA BASE QUALITY: DAIA
RECOVERY AND EDITING
Successful interpretation of precipitation-chemistry behavior using the MAP3S/RAINE measurements will depend, in addition to numerous other factors, on data base quality. Several features of data quality were
.I Provided in initial years by the Environmental Measurements Laboratory of DOE. this function is currently fulfilled by the U.S. Geological Survey.
1606 THE MAP3S/RAINE RESEARCH COMMUNITY
Table 2. Chemical species and analysis details
Species Method* Instrument
Minimum detection
limit pm/-’
Estimated error$
H‘ (free) Electrode
Conductivity bridge
SO:-
so: -
NO,
NO,
Cl_
PO-_
NH,+
Na+
K+
Ca2+ Mg2+
AWC
IC
AWC
IC
IC
IC
IC
IC
IC
AA AA
Various pH meters Beckman RC- 16C Bridge, Yellow- Springs 3403 Cell Technicon Auto Analyzer Dionex System 10 (Anion) Technicon Auto Analyzer Dionex System 10 (Anion) Dionex System 10 (Anion) Dionex System IO (Anion) Dionex System IO (Cation) Dionex System 10 (Cation) Dionex System 10 (Cation) Perkin-Elmer 306 Perkin-Elmer 306
0.1
0.2
0.05
0.2
0.2t
0.2
0.6
0.4
0.25
0.25 0.4
+ 0.05 pH units 9:
+20”,,,
kGO(MDL or lo:,,)
+GO(MDL or lo:,,)
kGO(MDL or lo”,,)
+GO(MDL or 10:~“)
+GO(MDL or lo”,,)
+GO(MDL or lo”,,)
+GO(MDL or 104,)
+GO(MDL or lo;,)
+GO(MDL or lo’:,,)
iGO(MDL or IO”,) fGO(MDL or IOFJ
* AWC, Automated Wet Chemistry; IC, Ion Chromatography and AA, Atomic Absorption Spectrophotometry.
t Subject to fluctuation, arising from “solvent shift” in the IC analysis. $ MDL, Minimum detection limit and GO, “Greater Of”. 9 At pH = 4.
discussed in the previous section; two additional
aspects, however, influence interpretive procedures directly, and therefore demand explicit consideration at this point.
These additional features involve data loss, either
through absence of a reported entry for a given precipitation event, or through intentional deletion of reported samples. Predominant factors lending to these data losses in the context of the present analysis
are: (1) imperfect data capture, arising from sampler
malfunctions or from inadequate precipitation volumes for total chemical analysis;
(2) rejection of early data because of improvements in sampling or analytical methodology, which were adopted at intermediate times during the
network’s operational history and (3) rejection of data from fractional years to avoid
trend contamination arising from seasonal variations.
The above features give rise to a number of potential random and systematic errors in associated statistical estimates. Assuming that Type-l losses are totally random, they can for example add to the random uncertainties in attributes such as concentration
means, trends and correlation relationships. If such losses are not completely random but contain biases with regard to sample volume, season, or some other influential feature, then this systematic drift can
become reflected by corresponding systematic errors in statistical estimates. Even totally random data losses can result in pronounced systematic errors in de- position statistics, unless adequate care is taken to compensate these losses; this problem will be con- sidered in more detail, when deposition statistics are
presented in a later section. Table 3 provides an indication of sample-capture
performance for the MAP3S/RAINE network. In addition to these losses, we have in our present analysis culled reported samples according to the following indicator of sampler malperformance: if the precipi- tation water measured by the rain gauge exceeds that captured by the sampler beyond a set tolerance, then it is presumed that a biased collection was performed.*
* This situation is caused most frequently by Iight- precipitation events, where the sampler has difficulty sensing the occurrence of rainfall and remains closed for a portion of the event. It should be noted that this is a rather conservative criterion in the sense that it probably involves rejection of a number of valid samples.
The MAP3S/RAINE precipitation chemistry network 1607
Table 3. Data capture performance: fraction down- time for individual stations for periods indicated in
Table 4
Station Fraction down-time
Whiteface 0.002 Ithaca 0.048 Penn State 0.004 Virginia 0.023 Illinois 0.320 Brookhaven 0.032 Lewes 0.114 Ohio 0.068
The criterion employed was for sample rejection
whenever
Sample volume - Rain gauge volume
Rain gauge volume < -0.5
Standard tipping-bucket rain gauges are prone to underpredict at high rainfall rates, and this tendency was compensated in the present analysis by utilizing
sampler volumes for calculation purposes, except under circumstances where the above criterion was satisfied or when reported sample volumes were mis- sing from the data set. Rain gauge predictions were
used for all other calculation purposes. Roughly 10 y,, of the total data base was rejected on
the basis of the above volume-difference criterion. These losses were in general sufficiently small to preclude introduction of significant additional random error to the results; more specific statistical infor- mation for assessment of these effects is presented in tabular form in later sections.
Data rejection arising from Type-2 effects is con- fined to the species sodium, potassium and dissolved SO,. During late 1977 an improved SO,-preservation procedure was incorporated, with the result that measured SO, levels increased dramatically. Also in 1977 a switch to ion chromatography resulted in substantially improved analysis of sodium and pot-
assium. Both of these changes have resulted in virtual upward concentration trends of the associated species;
and this undesirable artifact has been removed from
the present analysis by deleting all sodium, potassium and SO, data obtained prior to January 1978.
Type-3 effects can seriously contaminate simple trend and correlation analyses if seasonal cycling is pronounced. We have chosen to eliminate this problem by considering, in most cases, integral years of record for each station, ending 1 January 1980. Analyses
specifically addressed to annual periodicity have not been constrained to this requirement. Data periods utilized by the present investigations are summarized
in Table 4.
SUMMARY OF PRECIPITATION FEATURES
Pollutant wet-deposition depends directly on the deposition of precipitation water; and it is thus important at this point to examine some of the pertinent features of precipitation delivery, as ob- served by the MAP3S/RAINE network. As noted previously the minimum reporting interval corres- ponds to an “event” or roughly 24 h, so resolution of actual precipitation rates is not practical in the present
context. There is some value, however, in observing features of the ensemble of individual samples; and it is of course absolutely necessary to compute average rainfall amounts for longer terms, if chemical de-
position is to be assessed over these periods. First, a comparison of precipitation catch between
the precipitation-chemistry samplers and the standard rain gauges is in order. This is summarized in Table 5,
which presents averages and extrema for each of the eight stations, over the periods of record given in Table
4. As can be noted from Table 5 there is a general tendency for the precipitation-chemistry samplers to collect somewhat less water then is recorded by the
rain gauges, an effect which probably is caused in most
Table 4. Data record start dates for general statistical analyses*t
Species
Statron
SO:-,NO,,Cl-,H+. NH:, Ca’+, Mg2’
Na+, K’,
SO,
Whiteface (WF) 1977 1978 Ithaca (IT) 1977 1978 Penn State (PS) 1977 1978 Virginia (VA) 1977 1978 lllmois (IL) 1978 1978 Brookhaven (BK) 1979 1979 Lewes (LE) 1979 1979 Ohro (OH) 1979 1979 Oak Ridge Not included in this analysis
* Data record starting I January of the year indicated, and ending 1 January, 1980.
+ Starting dates for periodic analysis not constrained to these years except for Ca’+. Mg” and SO,.
1608 THE MAP3S/RAINE RESEARCH COMMUNITY
Table 5. Comparison of collected precipitation vs rain gauge precipitation
Station
WF IT PS VA IL BK LE OH
P~ipi~tion, CM Average ratio
Sampler Rain gauge Sampler/ Min Max Avg Min Max Avg gauge
0.10 8.7 1.56 0.022 9.9 1.45 1.08 0.041 8.3 1.58 0.051 9.0 1.72 0.92 0.017 7.1 1.27 0.020 10.6 1.34 0.95 0.022 5.1 1.46 0.029 7.3 1.54 0.95 0.003 4.4 1.02 0.002 8.0 1.15 0.89 0.029 4.4 1.32 0.020 8.5 1.42 0.93 0.076 4.3 1.36 0.12 5.1 1.51 0.90 0.022 4.4 1.22 0.024 5.3 1.21 1.01
Table 6. Monthly precipitation amounts (cm)
Year
1976
Month
10 11 12
WH IT PS VA IL BK LE OH
3.5 3.0
1977 1 1.8 2 4.4 3 10 4 II 5 3.3 6 8.5 7 6.6 8 19 9 14
10 15 11 14 12 8.8
1978 1 2 3 4 5 6 7 8 9
IO 11 12
10
4.9 9.0 4.2 9.0 4.5 7.2 1.4
11 2.1 9.6
1979 1 7.1 2 5.4 3 6.0 4 7.4 5 9.7 6 5.6 7 6.3 8 14 9 14
10 13 11 8.2 12 9.9
Site
4.1
5.4 2.0 8.4 6.1
7.2 2.4
13 19 13
12
15 2.5 5.3 5.1 5.9 3.0
12
3.8
3.1 4.7
6.6 7.3 5.5 8.1 6.1 9.2
13 10 12 3.7
9.4 1.8 5.2
2.7 3.9
11 9.2 1.9
12 7.9 8.5
12 13 10 12
15
5.6 5.9
18 10 6.2 8.7 6.9 5.0 4.0 5.4
19 1.5 8.9 7.2
10 5.7
IO 17 14 12 12 8.1
1.1 5.4 5.2 6.5 3.0 2.0 4.3 3.8 7.2
21 9.3
17 0.4
10 5.6
7.1 12 11 5.6 2.2 7.2 8.3
18
9.3 8.9 9.2
22 3.0 7.1
30 7.8
2.2
8.1
7.5 8.6 8.6 1.3
14 2.9 10 11
6.8 3.9 10 5.6 5.7 3.1 13
22 1.7 1.6
6.2 11 3.5 9.0 1.6 4.8
12 1.9 15
9.2 7.2
19 3.7 5.8 7.3
14 6.6 4.2 7.6 8.9 9.6
15 16 14
20 6.8
14 I1 7.6
12 3.8
7.3 3.9 8.9
5.9 6.2 3.2 6.0 4.2 4.5
17 8.4
6.1 14 5.6
part by failure of the samplers to open immediately shown in Table 6. Infrequent data gaps in Table 6 arise after the onset of precipitation.* from periods where equipment down-time exceeded
Monthly averages of precipitation amount, as ob- ten per cent of the month; these periods are not served by the precipitation-chemistry network, are recommended for inclusion in subsequent analyses.
Further utilization of these results will occur in the
* For a more thorough exa~Mtion of pr~ipitation catch following section, when monthly-average concen- characteristics, see de Pena et al. (1980). trations are described.
The MAP3S/RAINE precipitation chemistry network 1609
STATISTICAL FEATURES: TEMPORAL BEHAVIOR
Frequency-distributions and linear-regression analyses of event data
Fundamental statistical attributes of the
MAP3S/RAINE precipitation chemistry data are
given in Table 7*. These include the means and
extrema, along with the standard deviations about the means, for all of the principal constituents. In addition,
linear-regression coefficients for concentration versus
time (slope B, intercept A) are presented. The strengths
and confidence levels associated with these regressions
are given in terms of the correlation coefficients (r) and the probabilities (P), respectively. Mathematical de- finitions of these statistics are provided in Appendix A.
Precipitation-chemistry data often tend to ap- proximate log-normal frequency-distribution be- havior, and because of this the log-normal parameters cg and eB have been included in Table 7 as well. Some visual appreciation for the frequency-distribution be-
* Table 7 and the following text give sulfate values in terms of “total sulfur” ( = SOi- + SO1), owing to the tendency for dissolved SO2 to oxidize in the samples while awaiting analysis. “Total sulfur” corresponds to “sulfate” reported in most other studies.
Table 7. Temporal features of precipitation chemistry data: fundamental statistics
Concentrations
pm/-’
Standard deviations
pm/-’
Regression coefficients
Correlation coefficients, confidence estimates and numbers of data points
Log A B
Min Max Mean Mean Normal Log nrn!-’ pm/-' year-’ r P N
Species = total sulfur
20.5 2.3 30.1 33.0 2.2 35.3 37.1 2.4 43.0 37.6 2.2 43.0 38.6 1.8 77.7 18.9 2.3 - 2.4 26.5 2.1 21.5 22.3 1.8 48.6
Species = SO,
1.3 4.5 0.6 2.6 6.1 0.9 3.0 6.9 0.4 0.4 1.8 0.5 2.9 6.2 1.4 3.3 5.5 2.2 1.2 4.2 3.2 4.2 7.7 10.4
Species = nitrate
27.1 2.3 38.5 29.2 2.0 40.4 43.7 2.2 57.2 51.2 2.3 36.9 20.5 1.8 57.1 27.6 2.5 18.5
1.9 2.9 2.1 4.1 8.5 2.4 4.5 5.5
97.0 230.0 280.0 250.0 280.0
94.0 130.0 120.0
27.9 20.7 40.1 30.0
44.0 31.4 42.4 31.1 43.1 35.4 26.3 19.8 27.7 20.3 38.0 32.5
-0.125E 1 -0.053 0.511 172 0.228E 1 0.058 0.525 152 0.494E 0 0.011 0.139 239
- 0.322E 0 -0.007 0.063 135 -0.144E 2 -0.165 0.804 63
0.955E 1 0.147 0.664 45 0.203E 1 0.022 0.116 47
- 0.353E 1 -0.044 0.231 47
- 0.202E -1 -0.009 0.067 93 0.807E -1 0.018 0.138 101 0.498E 0 0.093 0.741 148
-0.153E 0 -0.218 0.896 57 - 0.397E -1 -0.006 0.036 59 - 0.335E 0 -0.029 0.170 57 - 0.9OOE 0 -0.209 0.845 48 - 0.277E 1 -0.192 0.843 56
- 0.408E 0.675E
- 0.259E 0.141E
- 0.903E 0.278E
-0.14lE - 0.236E
1 -0.131 0.915 -1 0.002 0.019
I -0.051 0.566 1 0.022 0.204 1 -0.195 0.871 1 0.029 0.152
173 153 239 135 62 45 47 47
- 0.205E 0.715E
- 0.227E -0.513E
0.322E 0.730E
-0.119E - 0.614E
2 -0.176 0.763 2 -0.245 0.904
0 -0.025 0.251 -I 0.008 0.074
0 -0.020 0.241 1 -0.195 0.972 I 0.144 0.734 2 0.252 0.906 2 -0.071 0.364 1 -0.199 0.810
171 153 235 127 62 45 47 45
1 0.033 0.325 161 I 0.040 0.349 132 I 0.029 0.308 186 2 0.118 0.793 116 2 -0.167 0.794 59
: -0.084 0.047 0.246 0.425 47 2 -0.311 0.967 47
WF IT
PS VA IL
2: OH
WF IT PS VA IL BK LE OH
WF IT PS VA IL BK
%I
WF IT PS VA IL BK
&
WF IT PS VA IL BK LE OH
0.1 0.1
7.4 0.6 0.1 20.0 1.1 0.2
17.0 1.6 0.3 3.0 0. I 0. I
15.0 1.3 0.2 19.0 1.2 0.2 6.1 0.5 0.1
25.0 2.1 0.4
0.1 0.1 0.1 0.1 0.1 0.1
1.8 190.0 31.2 22.7 2.5 210.0 40.6 32.4 2.0 280.0 52.1 38.6 3.2 530.0 39.7 27.8 7.2 120.0 35.3 29.9 1.8 140.0 26.9 17.8
3.4 130.0 24.6 17.3 6.2 140.0 35.6 29.0
22.8 2.3 26.7 1.9
Species =
7.3 5.9 7.9 4.1 9.7 4.1
20.6 2.9 9.9 2.5
84.4 2.8 48.0 2.5
8.1 2.0
67.5 106.4
chloride
6.2 7.1 9.2
22.5 - 0.4
- 173 84.2 27.3
0.1 48.0 0.1 53.0
5.8 2.4 7.2 4.1 8.8 4.9
12.1 7.2 7.4 5.1
46.3 24.8 48.1 31.2
8.8 6.6
0.1 84.0 0.1 210.0 0.1 79.0 2.5 540.0 4.9 170.0 2.0 38.0
Species = hydrogen (from lab pH)
37. I 2.0 56.6 0.141E 60.6 1.9 87.3 0.291E 67.6 2.1 90.6 0.236E 91.7 2.1 63.1 0.137E 36.7 3.9 89.8 -0.139E 50.8 3.4 30.9 0.8348 62.2 2.4 118.0 -0.182E
8.9 199.5 59.1 47.9 8.5 398.1 93.7 77.8 1.3 354.8 95.6 74.7 7.6 851.1 91.4 69.6 0.2 158.5 55.5 35.1 0.4 229. I 56.0 33.5
10.0 323.6 62.5 42.3 7.4 263.0 76.4 62.8 50.4 1.9 245.9 - 0.565E
1610 THE MAP3S/RAINE RESEARCH COMMUNITY
Table 7. (Contd.)
Standard Correlation coefhcients, Concentrations deviations Regression confidence estimates and
pm/-’ pm!-’ coefficients numbers of data points
LO8 A B Min Max Mean Mean Normal Log pm/-’ pm!-’ year-’ r P N
WF 0.4 85.0 16.6 9.9
IT 0.1 120.0 20.7 11.4 PS 0.1 170.0 22.1 13.6 VA 0.1 200.0 20.6 11.2 IL 2.3 140.0 31.4 23.5 BK 0.1 120.0 18.8 10.3 LE I .4 79.0 16.4 10.3 OH 1.3 83.0 25.4 18.9
WF 0.1 29.0 2.8 I.5 IT 0. I 22.0 2.8 1.5 PS 0. I 52.0 3.7 2.1 VA 0.1 80.0 6.0 2.6 IL 0.1 26.0 3.1 1.9 BK 0.7 450.0 40.9 18.7 LE 2.9 170.0 44. I 26.0 OH 0. I 19.0 4.0 2.2
WF 0.1 43.0 2.4 1.1 IT 0.1 25.0 1.5 0.8 PS 0.1 25.0 2. I I.0 VA 0.1 27.0 2.4 0.9 IL 0.1 11.0 2.0 1.2 BK 0. I 14.0 3.2 1.7 LE 0.4 12.0 2.5 1.8 OH 0.1 12.0 2.0 1.2
WF 0.1 16.0 2.7 1.4 IT 0.1 42.0 4.5 2.5 PS 0.1 98.0 6.5 3.3 VA 0.1 30.0 4. I 2.0 IL 1.2 59.0 10.6 7.2 BK 0.1 16.0 3.4 2.1 LE 0.1 10.0 2.9 2.1 OH 0.6 50.0 7.1 4.6
WF 0.1 5.6 0.8 0.3 IT 0.1 7.7 1.2 0.6 PS 0.1 16.0 1.5 0.7 VA 0.1 II.0 1.4 0.7 IL 0.1 9.0 2.2 1.4 BK 0.1 43.0 4.9 2.9 LE 0. I 18.0 5.1 3.2 OH 0.1 7.7 1.7 I.1
Species = ammonium 16.7 3.1 15.1 0.833E 21.5 3.9 13.0 0.365E 24.3 2.9 15.1 0.351E 25.9 3.6 18.0 0.131E 23.7 2.3 16.8 0.607E 21.4 3.7 25.4 - 0.218E 16.8 2.7 1.9 0.476E 19.4 2.3 48.4 - 0.766E
Species = sodium 4.4 3.1 2.1 0.279E 3.8 3.1 1.9 0.334E 5.6 2.8 -0.2 0.157E
10.8 3.6 9.3 -0.133E 3.9 2.8 -0.8 0.165E
77.0 3.3 - 182 0.743E 47.4 2.9 98.0 -0.177E
3.9 3.8 13.0 -0.299E
Species = potassium
5.0 3.7 3.3 -0.361E 2.6 3.5 -0.3 0.701E 3.3 3.9 - 1.2 0.133E 4.6 4.2 5.3 -0.116E 2.1 3.1 - 1.3 0.136E 3.4 3.7 2.2 0.315E 2.5 2.3 2.9 -0.lOlE 2.2 2.8 - 1.0 0.980E
Species = calcium 2.9 3.7 1.7 5.6 3.2 2.7
10.3 3.3 0.4 5.3 3.6 3.5
10.5 2.4 4.2 3.4 2.9 1.4 2.3 2.6 5.7 9.2 2.8 - 13.3
Species =magnesium
0.430E 123 0.771E 130 0.273E 186 0.248E III 0.267E 60 0.661E 45
-0.924E 47 0.699E 47
0 0.108 0 0.096 I 0.181 0 0.032 I 0.112 0 0.056 0 -0.114 I 0.212
0.767 0.724 0.987 0.259 0.607 0.285 0.556 0.847
I.1 4.2 0.9 -0.420E 1.3 3.6 0.8 0.145E 2.3 3.9 0.2 0.555E 1.8 3.8 1.3 0.475E 2.1 2.9 0.3 0.807E 7.0 2.9 -14.4 0.642E 4.8 2.9 I.5 3.2
113 123 177 102 60 45
8.0 - 0.954E 47 0.2 0.520E 47
-1 -0.027 0 0.073 0 0.159
-l- 0.016 0 0.169 1 0.267 0 -0.057 0 0.099
0.220 0.577 0.966 0.129 0.803 0.924 0.298 0.494
-
havior of typical pollutant species can be obtained from Fig. 3, which is a histogram of total sulfur concentrations observed at the Penn State site, includ- ing normal and log-normal curves computed from the parameters listed in Table 7. As can be noted from the fitted curves, total sulfur conforms rather well with log-normal behavior; this seems to be typical of the abundant nonvolatile species as well as ammonium ion. Sulfur dioxide tends to exhibit a bimodal character under some circumstances, although it is in general
0 0.042 0.410 163 I 0.143 0.922 152 1 0.121 0.933 228 I 0.041 0.361 133 I 0.113 0.618 62 I -0.030 0.153 45 1 0.081 0.412 47
I -0.110 0.537 47
0 0.037 0.278 93 0 0.050 0.392 107 I 0.159 0.951 153 1 -0.065 0.448 87 I 0.187 0.855 62 2 0.28 I 0.939 45 2 -0.107 0.526 47 I -0.21 I 0.846 47
0 -0.042 0.311 92 0 0.153 0.884 107 I 0.23 I 0.995 150 I -0.134 0.783 87 1 0.298 0.978 59 0 0.027 0.138 45 0 -0.011 0.060 47 0 0.123 0.591 47
characterized more satisfactorily by log-normal statis- tics as well. The Penn State station will be utilized frequently for illustration throughout the remainder of this article. This station was chosen for this purpose because it provides a centrally-located site, with a data run which is among the longest and most continuous in the network.
It is important to re-emphasize the above-noted tendency toward log-normality throughout the re- mainder of this presentation, and one should note that
The MAP?IS/RAINE precipitation chemistry network 1611
0 25 50 75 100 125
CONCENTRATION, CI MOLES PERLITER
Fig. 3. Histogram of total sulfur data; Penn State site
many relationships, often taken for granted when normal statistics prevail, will not remain valid under present circumstances. One can observe from Fig. 3, for example, that while the arithmetic mean still characterizes the centroid of the distribution, it does not coincide with its median or its mode. Likewise, the normal variance (or standard deviation) continues to provide a useful measure of dispersion, but does not
adhere to the convenient generalities (e.g. 68 x of the data fall within one standard deviation of the mean) that are often applied when true normality exists. It is especially important to note that most tests for
statistical significance (including the Student c-test, whose results are shown in Table 7) are based on assumptions of normal-distribution behavior; their application to the present data base, therefore, will involve some degree of approximation which will depend upon just how closely the normal-distribution
equation is able to fit the actual data. The statistics in Table 7, consequently, are to be interpreted as valid features of the data set; but prudence should be
exercised in their practical application. Linear-regression coefficients A and Bare defined by
the equation c = A+&, (1)
where c is the predicted concentration (pm /- ’ ) and t corresponds to time in years since 1 July 1976. B can be interpreted as an indicator of trend in the data, with the probability P giving a measure of confidence in this trend. P values in Table 7 were derived from the Student-t distribution and can be interpreted as fol- lows: “The probability that the population from which the data set was obtained is characterized by a non zero trend of the same sign as B.” A P-value of 0.187 for
sulfur at Penn State, for example, would indicate that there is about a 20 % chance that precipitation at this
station experienced some sbrt of an increase in sulfur
concentration during this period of record. The P values in Table 7 should be viewed as only
approximate indicators for several reasons. This test assumes statistical independence of individual obser-
vations; and if any significant autocorrelation exists between sequential samples of the time series, a corresponding alteration in the predicted P value will
result.* In addition, the predicted probabilities are somewhat sensitive to choice of the minimum detec-
tion limit (set to O.OS~mf~’ for these ~I~uIations), which inevitably necessitates some degree of judgment on behalf of the investigator. Finally there is the above- noted impact of non-normality of the density func- tions. This latter feature was examined by conducting a
parallel set of computations, where near-normal be- havior was induced by expressing the dependent variables as logarithms of the concentrations. Not
presented here, the associated P values generally conformed with those given in Table 7; thus the
reported probabilities can be considered as reasonable qualitative indicators of statistical significance.
From Table 7 one notes that the indicated trends for the stations having more Iengthy data sets, at least, are not particularly pronounced. This is worthy of some special mention, owing to the rather extensive upward and downward excursions observed in other, longer- term data sets (cf. Likens, 1972; Hales, 1980) for periods of this length; and on this basis it would not
seem particularly surprising if the MAP3S/RAINE data were to deviate from this rather docile behavior during future years. In this regard it should be noted that the parameters Band r, as well as the probabilities
* We are indebted to Professor C. S. Hirtzel of RPI, for bringing this point to our attention.
1612 THE MAP3S/RAINE RESEARCH COMMUNITY
p are influenced by several factors, which include: (i) shifts in meteorological behavior (storm
dynamics, steering patterns, precipitation
features, .); (ii) changes in pollutant sources and
(iii) changes in sampling and analytical practices.
Because of the difficulty in isolating the first two of these individual factors (cf. Granat, 1978; Munn and Rodhe, 1971), extreme caution is advised in attempting to assess causeeffect relationships, or anticipate future behavior on the basis of this limited record of
data.
Periodic analysis of event data
As noted previously, visual examination of the MAP3S/RAINE data suggests strongly that pro- nounced annual cycling occurs in the concentrations of many of the chemical species. We have attempted to quantify this cyclic behavior by fitting the individual station data to a simple periodic expression of the form
c = a+/?t+ysin(2nt+4), (2)
where the predicted concentration c is expressed in terms of time t (again in years beyond 1 July 1976), and the fitted parameters a, 8, y and r#~*.
l The coefficient oft in (2) was constrained to the value 2n, after initial optimization tests established the predominance of annual periodicity in most cases.
TOTAL SULFUR .
. . .
. . . .
. . .- . . .
0.5 LO 15 2.0 25 3.0 3.5 .
NITRATE . .
” u.5 LO L5 2.0 2.5 3.0 3.5
YEARS SINCEJULY 1. ,976
HYDROGEN .
3Lm
I . . . . . . . . . . . : . . . .
203 L . .* . .
. .
:.. . .
. . . l : “.
0’ - . / I - I
I50 ,O Q5 LO 15 20 2.5 3.0 3.5
I AMhlONlA . . . IW 1 I l
.
Figure 4 provides examples of such an exercise. These are for sulfur, hydrogen, ammonium and nitrate ion concentrations at the Penn State site and show the summertime sulfur maximum, which is a well-known feature of north-eastern U.S. precipitation-chemistry data. The degree of periodicity can be assessed by noting the amplitudes of the fitted curves (given numerically by y in Table 8) and also the relative variances between the data points and the fitted curves given by Equations (1) and (2). It is also interesting to observe the relative lack of periodicity for nitrate concentration, as noted in Fig. 4. Although the causes for the dissimilar behavior in Fig. 4 are largely unknown at the present time, these differences undoubtedly reflect mechanistic dissimilarities in the source-transport-transformation--deposition se- quence of these two species. It should be noted that the data set employed for fitting (2) was not truncated to an integral number of years, as was the case for the linear-regression analysis. Plots of the linear regres-
sions are shown in Fig. 4 for comparison, but are terminated by dashed lines beyond their respective periods of record.
Parameter values for individual network stations are given in Table 8. As was the case for the linear analysis presented in Table 7, the parameters given here tend to be much more meaningful for stations having longer histories and therefore more robust data sets. Table 8 contains a variety of interesting features,
0 0.5 LO 15 2.0 2.5 3.0 3.5
YEARS SINCE JULY 1. 1976
Fig. 4. Event concentration measurements at the Penn State site; comparison of curve fits using Equations (1) and (2).
The MAP3S/RAINE precipitation chemistry network 1613
Table 8. Results of least-squares fit of concentration data to Equation (2)
Station a B Y 4,
qrn/-’ FrnB-’ pm!-’ radians
WF
g VA IL BK LE OH
28.6 35.5 43.5 43.0 62.9 39.4 41.0 49.5
WF 1.26 IT 1.06 PS 0.00 VA 0.48 IL 1.36 BK 0.03 LE .54 OH 1.30
WF IT PS VA IL BK
!!zI
36.1 38.3 53.8 36.1 so.5 41.1 58.0 60.4
WF 6.11 IT 6.13 PS 9.87 VA 21.55 iL 2.01 BK - 14.93 LE 138.58 OH 4.30
WF 53.69 IT 86.66 PS 97.17 VA 71.66 IL 125.19 BK 102.07 LE 109.27 OH 171.85
WF 14.47 IT 12.92 Ps 17.32 VA 17.09 IL 19.35 BK 21.13 LE 11.84 OH 55.65
WF 0.99 IT 2.29 PS 2.15 VA 4.18 IL -0.16 BK 17.71 LE 50.08 OH 0.98
Species = total sulfur - 0.893 13.3 1.48
1.952 24.9 1.71 - 0.266 23.7 1.31 - 1.586 15.8 1.09
- 10.78 15.7 1.36 - 4.45 11.9 0.82 - 4.00 14.3 1.75 - 4.00 6.3 1.43
Species = SO,
- 0.272 0.86 4.96 0.773 1.63 4.61 0.724 1.93 4.45
-0.161 0.16 4.92 0.338 2.81 4.65 0.420 1.60 4.41
-0.017 0.62 4.41 - 3.63 1.77 4.17
Species = nitrate
- 2.55 9.26 4.00 1.67 10.75 3.10
- 1.09 3.42 4.55 1.63 1.82 0.09
- 6.67 1.32 2.30 - 4.26 0 4.55
- 10.89 6.05 2.34 -9.01 0 0.00
Species = chloride - 0.29 2.86 1.15
0.40 1.13 3.66 -0.55 2.70 5.48 - 4.48 3.49 3.80
2.25 1.46 3.56 20.19 19.14 6.04
- 25.29 43.89 3.31 1.37 2.00 3.15
Species = hydrogen (from lab pH) 3.52 13.36 1.33 4.50 41.44 1.72 0.56 30.17 1.43
10.96 22.45 0.60 - 24.35 13.69 3.61
- 6.84 34.56 1.25 - 15.63 15.04 1.04 - 32.58 15.80 2.07
Species = ammonium
0.92 5.33 1.39 3.72 8.85 1.89 2.24 7.94 0.80 1.27 5.30 1.28 4.89 1.25 0.31
- 0.59 13.65 1.52 1.49 7.63 1.53
- 10.00 11.31 2.29
Species = sodium 0.73 1.25 3.81 0.25 1.73 4.16 0.02 0.09 2.44 0.78 2.68 4.43 1.45 0.80 3.89 7.20 35.04 0.07
- 1.30 13.76 4.13 0.97 2.02 4.01
Variance Variance (Equation 2) (Equation 1)
(urn/-‘)’ (pm (- I)’ ___I- _-
333 422 757 1084
1023 1305 1251 1425 1264 1433 537 634 603 731 406 454
1.50 1.82 5.53 7.06 7.10 9.23 0.14 0.16 5.36 8.70 6.69 8.26 1.00 1.26
16.17 18.55
669 727 771 849
1908 1942 2538 2614 485 515
1273 1341 893 951 593 636
48 53 60 62
107 112 396 41s 86 92
4468 4892 9818 11063
51 5-l
1336 1451 2931 3881 4091 4801 8462 8959 3714 3983 7021 7908 4412 4734 1765 2007
267 287 401 450 533 572 636 670 533 587 191 931 204 245 386 41s
13.6 19.7 12.7 14.8 30.3 31.5 94.6 120.6 12.8 15.3
5092.7 5851.6 2089.3 2373.5
11.6 15.7
1614 THE MAP3QRAINE RESEARCH COMMUNITY
Station
Table 8. (Contd.)
Variance Variance
a B Y # (Equation 2) (Equation 1 f
flrnP_’ pm/-’ year pm!-’ radians @me-‘)’ (vrn-‘)’
WF 2.75 IT -0.19 PS 0.18 VA 4.98 IL 0.83 BK 2.14 LE 2.81 OH 0.44
WF 1.91 IT 3.22 PS 0.77 VA 4.13 IL 7.51 BK 1.04 LE 7.80 OH - 7.96
WF 1.02 IT 0.87 PS 0.33 VA 1.42 IL 0.75 BK 1.36 LE 14.2 OH - 0.67
Species = potassium
-0.18 1.44 1.55 0.64 0.24 0.83 0.78 0.96 0.00
-.- 1.12 0.97 2.14 0.45 0.87 3.73 0.35 2.32 1.64
- 0.09 0.14 4.90 0.52 0.59 5.10
Species = calcium
0.40 1.04 2.17 0.78 2.89 3.02 2.57 1.05 1.76
- 0.06 1.25 2.06 1.13 2.07 2.03 0.78 0.59 1.34
- 1.42 2.15 3.00 5.19 1.04 6.03
Species = magnesium
- 0.08 0.35 1.67 0.16 0.59 2.64 0.48 0.39 0.46
-0.01 0.28 2.68 0.55 0.55 I .65 1.18 1.78 0.09
-2.51 4.74 3.12 0.78 0.00 4.73
24.00 26.07 6.61 6.89 9.99 10.62
19.70 21.10 1.55 4.05 8.94 12.67 6.30 6.88 4.54 5.11
7.65 8.44 26.57 31.73
100.92 103.67 26.87 28.63
101.14 109.62 II.97 12.77 14.53 16.91 66.70 72.16
1.16 1.26 1.53 1.78 5.01 5.20 3.25 3.42 3.75 4.11
35.72 39.19 111.34 125.16
1.82 I .96
which are potentially useful as indicators of mechanis- These as well as additional features of Table 8 are tic behavior; these include the following: suggested as potentially fruitful starting points for
0)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
The phase angles 4 for sulfate are closely further investigation of wet removal mechanisms and aligned between stations throughout the rates. network. Some additionaf discussion of the significance of the The amplitudes y for sulfate vary between values reported in Table 8 is in order. It was noted stations. Stations closer to large source areas previously that the degree of periodicity could be seem to possess larger seasonal variations. estimated by observation of the amplitudes and the Hydrogen-ion concentration is in phase with rektive variances. Insofar as the variances are con- suifate con~ntration, (as expected), but its cerned it is useful to reconsider the definition of the relative periodicity is attenuated. conventional coefficient of determination:
r2 _ variance of data about mean -variance of data about Equation (1)
~
variance of data about mean - _. .__
If by analogy we define a pseudo coefficient E such that
(3)
[variance of data about Equation (1) -variance of data about Equation (2)]
Li’--- variance 07 data about Equation (1) ~ (4)
SO, concentration is strongly periodic and we have an expression which can be utilized, qualita- out of phase with sulfate concentration. tively at least, as a convenient indicator of significance. Ammonium ion is decidedly periodic, and r”? for total sulfur at Penn State, for example, equals roughly in phase with total sulfur and hydro- 0.22, whiie that for nitrate is equal to 0.01. gen ion. One should be careful not to rely solely on the above Nitrate ion shows relatively little periodicity indicators, however. Fits to periodic functions such as throughout the network. (2) can be contaminated severely if nonperiodic pheno- Sodium and chloride concentrations exhibit mena introduce rapid excursions in the data. Such a a moderately-strong mutual dependence, but circumstance appears to have occurred in the case of differences exist. The phase angle for sodium sodium and chloride where, because of hurricanes leads that for chloride in most instances. Fredrick and David, abnormally high sea-salt concen-
The MAP3S/RAINE precipitation chemistry network 1615
trations were deposited throughout the network during the fall of 1979. This single excursion forced the
data-fitting algorithm to predict a significant level of periodicity, when in actuality comparatively little existed. Again, prudence is mandatory in the interpre- tation and application of these results.
Monthly averages: concentration and deposition
Monthly averages of concentration for each net-
work station are given in Appendix B. These are precipitation-volume weighted averages, computed from the formula
N N
c^ = 1 CiVi/ c vi, i=l i=,
where ci and Vi correspond to the concentrations and volumes of the N individual samples obtained during
the month. Assuming no sample deterioration and total capture, c^ corresponds to the concentration that would be observed in the sample obtained in a collector left in continuous operation throughout the total month.
Monthly values of deposition appear in Appendix B as well. These were obtained by direct multiplication
of the precipitation amounts (Table 6) with the deposition-weighted averages. One should note once again that any loss of rainfall data will bias the chemical-deposition estimates downward; and to ensure against significant systematic errors of this type as well as potentially unrepresentative concentration values, we have subjected the data set to additional editing, which has led to the appearance of occasional null entries in Appendix B.
The additional editing criteria for monthly statistics are as follows: monthly deposition-weighted concen- trations and the corresponding deposition amounts are not reported in Appendix B if:
(i) the deposition of precipitation, corres- ponding to the sum of concentration- averaged precipitation samples for a station in a given month, is less than 80 “/,
of the respective value given in Table 6;
(ii) the precipitation-chemistry sampler was down for more than 107, of the month;
(iii) there were fewer than two events during the period and
(iv) there was less than 0.5 cm of precipitation during the period.
Deposition and concentration data are plotted in Fig. 5, again using the Penn State site as an example. From these curves one can note that the periodic relationships, discussed earlier for the case of event samples, tend to become more visually obvious when monthly averaging is performed. Cycling of the de- position curves tends to resemble that for the concen- tration data, although these are distorted by the rather erratic fluctuations in monthly precipitation amounts. One can note also the extent of the previously- discussed hurricane-induced excursions, particularly in sodium and chloride, which occurred during the fall of 1979.
Variability and averaging rime
Visual comparison of Figs 4 and 5 indicates the
effect of the time-averaging process in smoothing
precipitation-chemistry results. These figures dem- onstrate qualitatively the types of information loss that would be incurred if monthly samples were collected
and analyzed in preference to higher-frequency data; and in the context of practical network operations
(which always involve a trade-off between sampling frequency and economics), it is pertinent to seek a more quantitative means for assessing this effect.
A straightforward procedure to utilize for this
purpose is to determine the number of samples which would be consolidated in the averaging process, and to
assess the change in data variability that would result from averaging over periods of different length. The results of such an exercise, as applied to the MAP3S/RAINE data, are shown in Table 9. These are based on concentration for the total network, for the periods of record given in Table 4; they indicate the
effects of time averaging over periods of l/52 and l/l2 year, using sequential applications of Equation (3).
Addressing first the numbers of samples involved,
one notes that in the case of total sulfur. 902 event
precipitation-chemistry samples would have been con- solidated into 609 if weekly sampling had been con- ducted, and into 194 if monthly sampling had occur- red. Focusing next on sulfur concentrations, it is seen that data variability (as indicated by the sample variance) is reduced from 1019 (p moles / ’ )’ to 723 in
the case of weekly sampling and to 343 for the simulated monthly collections. This is typical of be- havior for the variances of other species as well. One must view the concentration-averaging process with
some caution, however, because the volume-weighting
procedure allows the possibility of a non-monotonic decrease in variance with increasing averaging time. In
the case ofchloride for example, the 589 (p moles C I)’ event variance increases to 746 for weekly averaging. This behavior is obviously an artifact of the weighted- averaging procedure, and gives an indication of the limits of this type of analysis for assessment of
information loss. It also flags the important cautionary point that one must be extremely careful, in analyzing
variability of rain-chemistry, to be cognizant of the fact
that such effects can arise. The above discussion has centered around the
problem of information loss in precipitation-chemistry network data and has resulted in a semiquantitative description of these losses as a function of sample averaging time. Just how tolerable these losses are, of course, depends upon the intended use of the data, and in the final analysis must be decided on the basis of the user’s requirements.
STATISTICAL FEATURES: VARIABLE-PAIR
RELATIONSHIPS
Correlation analyses
Variable-pair correlation coefficients have been computed for all network stations and all possible
1616 THE MAP3S/RAINE RESEARCH COMMUNITY
IOTM SULFUR
60
40
m
0
M
20
10
0 0 1.0 2.0 3.0
POTASSIUM I
I CAICIUM 1 1
t MAGNESIUM
6
4
2
0 L_&dk&i 0 1.0 2.0 3.0
MARS SINCE I JULY1916
Fig. 5. Monthly-average concentrations (lines) and deposition amounts (bars) at the Penn State site.
The MAP3S/RAINE precipitation chemistry network 1617
Table 9. Effects of sample averaging time on concentration variances (~molesf-‘)Z and sample numbers: constructed from MAP3S/RAINE net-
work event data
Sample averaging time species
“Event” variance N
Weekly variance N
Monthly variance N
Total sulfur so, Nitrate Chloride Hydrogen Ammonium Sodium Potassium Calcium Magnesium
1019 7.04 1308 589
4488 482 601 12.2 51.9 7.27
902 619 903 888 676 878 641 634 749 714
concentration pairs, but are not treated here because of the extensive tabulations required.
These computations demonstrate that concen- trations of non-volatile species. marked p~ominantly by significant positive correlations, are in general conformance with expectations from aerosol scaveng- ing theory; they can be characterized by the following simple observation: “dirty rain generally correlates nicely with dirty rain.” Peculiar relationships at indi- vidual stations can in general be explained on the basis of geographical location. The coastal stations at Lewes and Brookhaven, for example, show relatively strong chloride-sodium correlations, while the more western stations at Illinois and Ohio exhibit stronger de- pendencies between soil constituents. One aspect of variable-pair analysis of particular interest pertains to the comparison between field-measured and laboratory-measured hydrogen-ion concentrations. Field/laboratory measurements were in generally good agreement throughout the data set. Averages and standard deviations agreed to within 0.1 pH units for all but one of the stations, although there was some tendency for extreme values of the laboratory samples to shift in the direction of increased pH. Field pH data from the Appalachian stations demonstrated a pecu- liar tendency to show stronger temporal trends than did their laboratory counterparts, although the direc- tions of these trends did not deviate from those indicated in Table 7.
Multiple regression analyses
As a consequence of current emphasis to apply regional air quality models as predicators of hydrogen ion in precipitation, we have subjected the MAP3S/ RAINE data set to a limited multiple regression analysis, setting hydrogen ion as the dependent variable. This analysis is centered around the equation
cHt = a + hc,,,l s + k.,Oc + bNH, + + b,c,z+
+ b,q,-, (6)
where hydrogen-ion concentration cH+ is predict& on the basis of the regression coeficients a, bi and the
723 5.05 1106 146
3784 377 704 12.9 27.1 7.41
609 343 194
420 3.26 141 610 221 194 602 335 194 474 1239 157 602 106 194 445 170 147 441 3.2 146 517 8.6 167 496 2.01 163
concentrations of the indicated species. Ordering of the species in the regression analysis was chosen on the basis of descending significances of the variables and from the current emphasis on specific species in present regional models.
The results of the multiple regression analysis are presented in Table 10, which provides values of a, b,, correlation coefficients between predicted and ob- served values, and associated standard deviations for increasing numbers of predictor species. Individual sets are given for each network station, and overall network values are included in the final portion of the table.
STATISTICAL FEATURES: SPATIAL RELATIONSHIP
There are numerous aspects of spatial variability of precipitation-chemistry data; and at the outset several features, which are at least intuitively obvious, should be noted. These are itemized as follows:
(1)
(2)
(3)
(4)
Spatial variability of precipitation chemistry is
intrinsi~lly related to its temporal variability. Correlations between behavior at separate sam-
pling stations can be expected to vary in a complex manner as averaging time increases. Our capability to provide valid map contours of concentration from station data depends upon the degree of covariability of adjacent stations. Owing to the filtering of random effects, the contouring of spatial distributions should become much more practicable as averaging times become longer. The stochastic and episodic nature of precipi- tation makes the interpretation of spatial vari- ability exceedingly complex in situations involv- ing regional networks. The design of the MAP3S/RAINE network allows temporal variability to be observed in much greater detail than spatial variability.
Some aspects of spatial relationships of the MAP3SJRAlNE data have been examined by previous authors. Baker et al. (1981) have criticized the
1618 THE MAP3SiRAINE RESEARCH COMMUNITY
Table 10. Results of multiple regression analysis (Equation 6)*
(C$ (to:1 S) b2 h, h,
(NO;) NH: 1 (Ca* +)
Station = Whiteface
u
16.5 1.618 10.6 1.207 8.2 1.481 7.4 1.746 1.5 1.747
28.X 1.669 21.9 1.324 14.8 1.907 15.5 1.811 13.6 1.808
31.5 1.628 25.3 1.215 24.0 1.488 16.4 1.803 9.0 1.936
3.0 2.324 13.2 1.069 9.3 1.423
10.9 1.524 12.8 1.494
2.3 1.573 2.1 1.554
15.9 1.836 18.5 1.594 20.1 1.611
- 2.1 2.115 - 1.9 0.994
1.1 1.211 3.4 1.703 3.5 1.704
1.8 2.177 - 4.4 1.226 - 3.3 1.847
2.0 2.001 2.9 2.040
7.5 5.0 9.0 9.9 7.8
14.1 10.3 9.4 9.3 9.9
1.808 1.324 1.565 1.701 1.631
1.864 1.184 1.627 1.744 1.745
s r
18.41 0.869 15.34 0.912 14.48 0.922 12.15 0.947 12.15 0.947
0.600 0.734 -0.509 0.857 -0.423 -3.957 0.857 - 0.424 - 3.948
Station = Ithaca
- 0.022
28.40 0.873 26.24 0.894 21.68 0.929 20.31 0.939 19.93 0.942
0.508 0.762 - 1.203 0.884 -0.844 - 2.057 0.818 - 0.766 - 2.405
Station = Penn State
0.680
33.29 0.863 30.83 0.886 29.58 0.896 22.72 0.940 21.51 0.947
0.500 0.589 -0.651 0.974 - 0.942 - 2.584 0.785 - 0.963 -2.714
Station = Virginia
1.671
40.62 0.903 24.16 0.967 23.45 0.969 19.95 0.978 19.83 0.379
1.007 1.086 -0.660 1.119 -0.314 - 3.549 1.152 - 0.290 - 3.367
Station = Illinois
-0.312
37.05 37.37 24.36 16.67 16.42
0.717 0.717 0.027
0.528 - 1.365 0.995 - 0.987 - 2.039 0.982 -0.997 - 1.982
0.892 0.952 0.954 -0.351
Station = Brookhaven
29.83 0.805 21.03 0.911 17.73 0.939 12.41 0.97 1 12.41 0.971
1.084 1.192 -0.614 1.181 - 0.592 - 4.503 1.174 - 0.599 - 4.309 -0.010
Station = Lewes
22.08 0.934 0.975 0.982 0.988 0.988
1.331 1.208 - 0.939 1.304 - 1.018 - 3.635 1.260 - 1.080 - 3.096
Station = Ohio
13.97 12.10 10.04 10.04 -0.032
29.86 0.805 27.86 0.837 23.11 0.894 14.00 0.963 13.53 0.967
0.589 1.073 - 1.195 1.111 - 0.806 - 2.280 1.069 - 0.765 - 2.228
Combined stations
0.552
33.46 28.14 24.71 18.87 18.82
0.856 0.901 0.925 0.957 0.957
0.767 0.917 -0.973 1.061 - 0.783 - 2.609 1 SK? -0.787 - 2.589 -0.052
* Based on units of pmoles f-r, s denotes the “scatter”, defined as the square root of the variance of data points about the predicted value of cH+ ; r denotes the corresponding correlation coefficient.
The MAP3S/RAINE precipitation chemistry network 1619
network’s layout for having an inadequate sampler density to resolve event data, noting that the variability of precipitation rate is pronounced over much finer
scales than the distances between MAP3S/RAINE stations. Pack and Pack (1979) have investigated spatial covariability of H+, NH:, NO; and SOi- concentrations using the complete data set existing at the time.* This analysis was hindered somewhat by the short data records available for some of the stations; but significant spatial covariability was apparent, at least for the original four Appalation stations. This observation should be tempered, however, in view of
the fact that the noted seasonal cycling in H+, SOi- and NH: will tend to induce a natural degree of covariability between stations if longer-term data records are employed. Pack (1980) has utilized ap- proximately one-year averages of data and combined the MAP3S/RAINE and EPRIjSURE networks to produce regional isopleths of hydrogen, sulfate, nit- rate and ammonium ions. This work again indicates a
general coherency between stations on an annually- averaged basis, a situation which can be noted also from a general observation of the results given in
Appendix B. Coherency as a function of sample averaging time
can be.examined in somewhat more detail by observing
station-station correlations for deposition-weighted averages of event samples. An example of such an application is shown in Table 11, which provides stationstation correlation coefficients for total sulfur for weekly and monthly averaging times. The data have been stratified to include winter months only (November-March), to reduce the undesirable in- fluence of seasonal cycling.
* These averaging times ranged from 27 months for the Penn State site to 1 month for the Lewes. Delaware station.
Several aspects of Table 11 are worthy of mention. Proximate stations do seem to exhibit coherency*,
although some peculiar features exist. The
Virginia-Ithaca weekly coefficient, for example, is only
0.054, whereas the mutual dependence of surrounding stations is much higher. Increasing averaging time
from one week to one month seems to affect the correlation coefficients in a rather complex fashion,
which undoubtedly involves the counteractive effects of decreased variation about the average and grouping
of data over effectively larger spatial domains. Similar tables for other species and seasons tend
to show similar features. Correlations for summer periods are substantially smaller than those in Table
11, while total annual values fall in between. All of this
seems to be in concordance with the different spatial scales of storms in the warm and colder months; although it is interesting to note that the annual
cycling, noted in the previous text, seems to have only a secondary effect on the station-station correlation
coefficients. Because of item (4) above, one rapidly approaches a
point of diminishing returns in applying the current
data set to examine spatial relationships. It should be noted in this context that combined-network analysis (MAP3S/RAINE-NADP-EPRI) is presently being
contemplated for this purpose, and that Shannon
(1980) has recently applied an objective-analysis pro- cedure to locate the sites of (virtual) additional stations in a manner such that information retrieval is maxi-
mized. Much more substantial information regarding
* Similar station-station correlations for weekly averages have been noted with the EPRIjSURE network data. We are indebted to Dr. Paul Switzer of Stanford University and to Drs Ralph Perhac, Charles Hakkarinen and Mr. John Jansen of EPRI for informative discussions on this point.
Table Il. Station-station correlation coefficients for weekly-and monthly-averaged precipitation samples: species = total sulfur*
___~~ ~~~~__~~~
WF IT PS VA IL BK LE .__
Weekly averages IT 0.825 PS 0.601 0.619 VA 0.820 0.054 0.865 IL 0.275 0.539 0.241 - 0.435 BK 0.33 1 0.023 0.332 -0.050 * LE 0.703 0.367 -0.018 * 0.542 OH 0.197 0.270 0.528 -0:377 * 0.294 0.028
Monthly averages
IT 0.612 PS 0.879 0.641 VA 0.579 0.402 0.815
IL BK 0.8fll
*
0.222 0.3;0 -0:417 * LE 0.89X 0.289 * 0.861 OH 0.862 0.404 0.9;6 0.;39 * 0.385 0.639
* Values are not reported when less than five averages are available for computation. November-March data for period of record given in Table 4.
spatial-distribution behavior is expected as the result
of high-density network operations, which have been conducted recently in eastern North America as a part of a multi-agency intensive field experiment.
Munn R. E. and Rodhe H. (1971) On the meteorological interpretation of the chemical composition of monthly precipitation samples. Tells 23, 1 -i2.
Pack D. H. (1980) Precipitation chemistrv oatterns: a two
CONCLUSIONS
A statistical overview of the initial MAP3S/RAINE precipitationshemistry network data, obtained be- tween 1976 and 1980, has been presented. This sum- mary is intended to provide a directly usable resource for wet-deposition assessment, and to compose also a convenient starting point for more detailed exami- nation by the extended research community.
A number of significant features of the data set have been identified. These include seasonal cycling of some species, variability of the data, and interrelationships between pollutants. An especially notewo~hy aspect is the indication of very little trend to the data, over the approximately three-year period of record. Based upon experience elsewhere, pronounced excursions may be expected to emerge as lbnger records become available; whether or not this feature does indeed emerge must remain unresolved until several ad- ditional years operating experience with the network are accumulated.
network d&a s&. Science 208, 1143-1 IiS.. Pack D. H. and Pack D. W. (1979) Seasonal and annual
behavior of different ions in acidic pr~ipi~tion. Proc. WMO Tech. Conf. on Regionul and G&&al ~bse~at~o~ of Atmospheric Pollution Relative to Climate, Boulder, CO 20-24 August.
Pena R. G., Pena J. A. and Bowersox V. C. (1980) Precipi- tation collectors intercomparison study. Final Report Contract No. 1416309, Dept. of Meteorology, Penn State University, University Park, PA. -_
Scott B. C. (1978) Pa~eteri~tion of sulfate removal bv precipitatibn. i uppl. Met. 17, 1375-1389.
Shannon J. (1980) Ongoing research at Argonne National Laboratory, Argonne, Illinois.
APPENDIX A: DEFINITIONS AND COMPUTATIONAL FORMULAE APPLIED IN TEXT
Means
arithmetic: c, = !Eci “.
logarithmic: In cg = ! 2 In ci “.
volume-weighted: c^ = 1 ci~,/c oj. n 11
Standard deviations
REFERENCES
Baker M. B., Caniparoli D. and Harrison H. (1981) An analysis of the first year of MAP3S rain chemistry measure- ments. Atmospheric Environment 15, 43-56.
Granat L. (1978) Sulfate in precipitation as observed by the European air chemistry network. Atmospheric Enoiron- ment 12, 413-424.
Hales J. M. (1980) Precipitation chemtstry: its behavior and its calculation. Proc. Int. Con/. on Air Poilui~ts and Their E@ecrs on the Terrestrial Eeoqstem. Banff, to-17 May (in press).
Henderson R. G. and Weingartner K. (1980) An analysis of MAP3S precipitation chemistry data-implications for modeling. Report to the MITRE Corporation, McLean, VA.
arithmetic, about mean: uz = &I (ci -cm)* n
(sample estimate of population a)
logarithmic, about ce: ln’a, = ti C(Inci - lnc,)’ n
(sample estimate of population a)
about some function, h: s2 = -F, Elfi-hlZ n
(sample estimate of population 6)
df = degrees of freedom.
Probability-density functions
logarithmic: g(lnc) = __ - (In c - In cm.)*
Znlna, exp -- 2,n2cr,
Likens G. E. (1972) The chemistry of precipitation in the central finger lakes region. Technical Report 50, Cornell University Water Resources and Marine Sciences Center.
MAP3S (1977) The MAP3S Precipitation Chemistry Network: first periodic summary report (September 1976- June 1977) U.S. DOE Report, PNL-2402.*
MAP3S (1979) The MAP3S Precipitation Chemistry Network: second periodic summary report (July 1977-
Correlation coeficient
June 1978) U.S. DOE Report, PNL-2829.’ MAP3S (198Oa) The MAP3S Precipitation Chemistry
Network: third periodic summary report (July 1978- December 1979) U.S. EPA/DOE Report, PNL-3&O.*
MAP3S (1980b) The MAP3SIRAINE Precipitation Chem- istry Network: quality control. U.S. EPA/DOE Report PNL-3612*
c(~,-~mY [nEx’-(CXJ”] [,~,.~-(&.y] n n
where x is the independent variable and hi = A + Bx, is the predicted value of c,.
McNaughton D. 1. (1981) Relationships between sulfate and nitrate ion concentrations and rainfall pH for use in Regression coe&cients
modeling appli~tions. Atmospheric Environment 1% 1075-1080.
linear: c = A -+ Bx; x = independent variable
B = c (x-i - %A w; - c,,,/c (Xi -x,)2 II n
* Available from NTIS under these numbers ~~... _.-__.- ___. A=c,-Bx,
1620 THE MAP3SjRAfNE RESEARCH COMMUNITY
1
The MAP3S/RAINE precipitation chemistry network 1621
multiple: c = a +6,x, + b,x, + b,x, + . . periodic: c = a + j!Jx + y sin(2nx t (b) a, b, . computed using least-squares from a a, fl, y and r$ obtained from an optimization standard computer-library multiple-regression algorithm designed to minimize the variance routine between predicted and observed c values.
Appendix B: Monthly deposition-weighted concentrations (pm P - ’ ) and deposition (,um m-‘)
Year Month WH ---
1976 II
12
1917 1
2
3
4
5
6
7
8
9
10
11
12
1978 I
2
3
4
5
6
7
8
1978 9
10
II
12
1979 I
2
3
IT PS VA IL BR LE OH
Spectes = total sulfur
22 22 22 760 900 400
16 13 13 480 290 680
4.4 77 16
690 17
1720 21
2210 46
1490 35
2980 45
2920 29
5510 25
3530 17
2550 10
1510 8.6
7.50
24 470
14 1200
43 2610
II 300
22 860
21 2420
28 2580
67 1250
51 6170
150 3510
47 6120
37 4370
24 3120
16 1650
20 2400
9.2 1420
7.2 750
14 680
33 2960
21 880
38 3470
41 2420
77 2310
44
64 4560
38 2810
20 2160
29 610
58 2200
21 840
26 1440
36 2120
23 4210
62 6200
45 2770
44 3830
41 2830
35 1750
41 1640
13 700
S.0 570
14 170
20 1220
21 1390
26 1900
12 2220
23 1730
24 2140
15 170 23
1240 22
1140 61
3970 54
1620 110
2180 43
1850 54
2050
22 2050
24 2500
32 1780
39 4520
31 3500
40 2240
41 900
37
12 1120
24 4510
45 1670
22 41 1680 2380
35 460
29 830
29 24 20 2900 2710 1320
57 38 3880 1600
37 13 21 41 1420 1310 1600 2990
30 17 25 27 1680 970 2230 1050
24 16 17 22 760 2020 1630 1960
8.2 12 1760 1840
22 39 370 2420
32 27 510 860
1622 THE MAP3S/RAINE RESEARCH COMMUNITY
Appendix B (Contd.)
Year Month WI-I IT PS VA IL BR LE OH
Species = total sulfur (Contd.)
4
5
6
1978 1 2
1979 1
2
8
9
10
11
12
19 1420
22 2180
35 1980
32 2010
24 3450
13 1920
26 3460
33 1820
30 2430
39 2380
58 5340
34 33 26 2450 2940 1610
31 20 3260 1840
28 32 32 1600 7070 520
16 1630
13 1640
34 3430
13 1610
18 660
36 58 37 3600 1710 4140
49 3770
18 2600
22 2600
36 4430
16 16 1300 350
Species = SO,
0.43 21
0.20 18 0 0 0.69
62
0 0 0 0.45
33 0.49
52 0.07 1.5
0.24 13
1.7 87
0 0 0.06 1.8 0 14
2.9 450
0.15 8.3 0.67
40 0.27
49 0.36
36 0.22
0 0
0.32 28
0.24 16 2.2
110 0.79 2.1
24 81 7.7
420
1.3 92
0 0 0.74
44 0 0 0.02 2.0 0 0 0 0 0 0 0 0 0.19
25
5.4 1000
0.59 4.3 39 320
6.8 1.1 490 98
1.5 4.2 83 300
0 0.03 0 3.2 0 0 0 0 0 0 0 0
0 0 0 0 0.97 5.2
98 610
1.1 2.5 110 200
0.10 0.51 10 96 0 0 0 0
1.8 0 140 0
0 0 0.13
3.8 0 0
0 0 0.15
10 0.15 5.8 5.9
330 7.2
220
2.1 120
4.9 620
0.41 88
0.20 3.4 5.1
82
0 0 0 0 0.47
36 1.3
120 0.93
89
0.30 19
0 0 0 0
0 0
0
0 0
0 0
0.36 33
0.43 66
0 0
0 0 0 0
1.3
28 2520
12 1130
16 1150
37 2520
10 1090
29 2200
II 410
0 49
41 2460
27 1130
39 1760
44 7700
46 3860
21 2940
27 1510
0.92 67
5.8 230
12 1070
2.1 120
4.5 280
2.4 77
7.1 430
2.3 97
0 0 0 0 0.56
47 1.4
63
2.6 360
3.4 190
The MAP3S/RAINE precipitation chemistry network
Appendix B (Contd.)
1623
Year Month WH IT PS VA IL BR LE OH
1976 11
12
197? 1
2
3
4
5
6
7
8
9
10
11
12
1978 I
3
4
5
6
7
8
9
10
I1
12
1979 1
2
3
4
5
6
7
8
21 720
18 540
36 630
55 2410
23 2320
19 2050
34 1110
25 2130
26 1700
18 3420
21 2940
22 3340
14 1930
17 1490
14 1460
27 1330
32 2870
17 710
29 2610
40 2840
27 1980
20 2140
37 790
20 1430
16 860
32 1920
21 15.50
24 2330
19 1060
22 1390
14
30 1230
110 2170
21 1760
35 2130
51 1220
29 3800
18 2160
25 1320
43 2190
39 2300
51 1550
30 3450
44 1690
39 1220
36 2380
31 2260
33 1820
27 2190
32 1950
31 2850
Species = Nitrate
59 1060
30 1560
43 1150
46 1780
27 3100
29 2670
59 1100
30 3630
32 3780
27 3450
17 1750
30 3600
20 3080
29 1610
44 2600
23 4210
41 4100
27 1670
28 2440
30 2070
42 2100
68 2690
21 1130
19 3520
35 2630
23 2050
39 2810
31 3260
16 910
30 3000
14 150 20
1090 22
1140 44
2860 41
1230 43
840 32
1390 41
1560
22 2050
27 2810
29 1610
17 1280
33 3830
16 1810
20 1120
33 720
51 3690
12 1000
12 2150
13 1210
29 2580
21 1940
19 4200
39 1150
31 2390
19 1900
32 1230
27 1510
33 1030
18 3380
31 1150
21 1220
31 400
20 580
24 19 2710 1250
46 26 3130 1100
11 21 1110 1600
12 44 680 3920
14 15 1760 1440
4.5 11 980 1680
15 260
43 690
21 1300
24 2160
29 480
27 3020
31 2110
31 2260
23 900
11 980
42 2600
27 860
28 1680
20 840
28 1260
28 4900
25 2100
1624 THE MAP3S/RAINE RESEARCH COMMUNITY
Appendix B (Contd.)
Year Month WH
Species = Nitrate (Contd.)
1976 11
12
1977 1
2
3
4
5
6
7
8
9 10
11
12
1978 1
3
4
5
6
7
8
9
10
11
12
1979 1
2
3
7.0 1000
18 2360
33 3270
9.0 13 1130 1880
27 25 2730 2950
11 37 1400 4550
40 23 15 1460 1850 330
Species = chloride
2.4 72
0.72 13 2.7
120 1.4
140 0.17
18 1.8
59 8.4
710 17
1110 11
2090
13 230
3.5 180 4.7
130 6.7 7.1
130 270 3.4 1.6
290 180 4.7 1.6
290 150 4.4
84
14 340
7.1 930
3.2 490
4.2 580
1.9 170
4.7 490
5.0 250
4.3 390
6.1 260
14 1260
4.8 350
3.2 240
2.7 290
7.0 150
3.2 420
2.8 290
3.2 6.3 380 760
5.5 850
4.1 3.6 220 200
8.2 5.5 420 320
7.7 1.0 450 180
13 8.6 390 860
3.1 4.4 360 270
3.7 320
6.0 5.8 230 400
4.8 240
4.0 7.2 130 290
4.5 240
3.9 4.4 280 180
2.4 10 4.7 130 660 350
7.0 5.9 15 420 430 1340
IT PS VA IL BR LE OH
14 150
7.7 230 23
450 7.1
310 19
720
12 1070
8.9 930
24 1340
2.0 150
9.6 1110
19 4.1 2150 410
2.9 160
6.3 5.2 140 200
9.3 5.4 670 300
2.0 5.0 170 160
6.8 1220
9.7
6.5 680
27 2050
11 17 1030 2380
13 9.4 21 940 360 1180
160 29100
160 5940
300 17400
25 330
19 550
11 13 1240 860
19 15 1290 630
29 3.9 2200 280
69 44 6.6 3930 3920 260
28 69 3.6 3530 6620 320
17 51 3660 7800
29 8.1 490 500
23 5.9 900 370 190
The MAP3S/RAINE precipitation chemistry network
Appendix B (Contd.)
Year Month WH IT PS VA IL BR LE OH
1625
Species = chloride (Contd.)
4
5
6
7
8
9
10
Ii
12
1976 11
12
1977 2
3
4
5
6
I
9
IO
11
12
1978 1
3
4
S
6
7
8
9
10
II
12
1.9 140
2.7 260
4.3 240
10 650
2.8 400
1.6 1090
6.6 860
4.9 490
32 960
51 2240
43 4340
31 3350
63 2080
74 6290
83 5440
55 7700
40 6080
30 4140
30 2630
26 2700
65 5850
37 1550
68 6120
130 9360
59 4370
50 5350
74 1580
4.5 6.9 5.8 5.5 250 500 520 340
3.4 4.2 3.4 280 440 310
1.8 1.7 3.0 4.9 110 97 660 80
9.4 5.7 6.8 27 860 570 200 3020
6.9 530
1.8 3.3 230 480
14 5.5 1410 650
5.1 28 630 3440
14 5.1 23 510 410 510
Species = free hydrogen (from PH)
83 1490
49 2550
100 62 2100 2420
29 50 2420 5750
95 70 5800 6440
140 2660
110 12700
60 7800
41 4220
36 56 4320 6720
36 5440
64 80 3390 4440
84 92 4280 5430
100 51 6020 9330
150 130 4540 13300
110 110 13200 6830
96 8350
120 88 4640 6070
84 4200
88 130 2130 5020
48 2590
20 220
35 1890
53 2760
120 7870
88 2640
190 3710
58 5390
63 6550
70 3920
38 2850
80 9280
61 58 6890 5800
70 3920
120 64 2640 2460
110 60 8140 3360
42 76 3490 2360
12 750
17 2.7 1530 110
4.9 220
19 7.4 1290 1300
20 1680
79 8300
24 1820
35 4.3 3220 600
21 31 6.2 1510 1180 350
34 6390
51 1890
52 3020
93 1210
39 1130
62 46 7010 3040
130 90 8650 3780
26 50 83 2630 3800 6060
45 100 66 2550 9070 2570
42 39 46 5290 3740 4090
1626 THE MAP3S/RAINE RESEARCH COMMUNITY
Appendix B (Contd.)
Year Month WH IT PS VA IL BR LE OH
Species = free hydrogen (from PH) (Contd.)
1979 1
2
3
4
5
6
7
8
9
10
11
12
1976 11
12
1977 1
2
3
4
5
6
7
8
9
10
11
12
1978 1
3
4
5
6
I
32 2270
48 2590
67 4020
51 3770
47 4560
60 3360
83 5230
59 8380
25 3580
60 7860
43 3530
52 5150
18 630
4.1 120
3.0 53
15 1620
27 890
15 1280
17 1120
11 2090
14 1960
8.2 1250
5.8 800
3.1 270
2.9 300
10 490
15 1350
11 460
23 2070
72 5830
84 5120
130 12200
85 6380
63 5610
82 5900
74 7770
58 3310
85 8500
34 34 4280 4910
76 49 7680 5780
28 32 3460 3940
58 34 2120 2750
47 8410
33 3070
72 41 6410 2540
65 5980
85 33 18800 540
130 67 3780 7500
100 7850
40 880
Species = ammonium
7.6 310
17 340
8.4 710
11 670
31 740
21 2750
3.3 400
9.7 510
20 1020
15 890
43 1290
17 1960
21 490
6.9 360
6.0 160
13 510
12 1320
13 1200
36 680
21 2540
20 2360
14 1820
7.9 810
8.2 980
3.2 490
13 720
20 1180
12 2200
28 2800
9.8 610
9.0 99 13
700 13
680 30
1950 29
870 22
430 16
690
8.2 760
9.1 940
11 620
15 1130
19 2200
17 27 45 3650 4130 2660
48 84 820 5210
84 60 1340 1920
76 4560
38 49 3420 2060
78 3510
91 94 6190 16450
99 99 15000 8320
13 1370
50 46 34.50 3500
25 37 2300 5180
39 27 48 2810 1030 2690
13 2440
15 870
15 200
75 2180
The MAP3S/RAINE precipitation chemistry network 1621
Appendix B (Contd.)
Year Month WH IT PS VA IL BR LE OH
Species = ammonium (Contd.)
8
9
10
11
12
31 2230
43 3180
7.2 780
30 640
14 1220
20
9.1 1030
18 1010
26 570
21 1510
3.5 290
2.6 470
11 1100
9.3 1050
27 1840
5.3
10 660
18 760
14 1060
11 980
6.0 580
3.1 470
1979 1
2
3
4
5
6
7
8
9
10
11
12
1976 11
12
1977 1
2
9
10
11
12
1978 1
3
4
5
6
7
8
3.4 240
9.4 510
12 720
13 960
16 1550
6.5 360
17 1070
14 1990
6.4 920 11
1440
12 1190
0.20 6.9
0.62 87
1.6 240
1.2 160
0.89 78
2.0 210
2.0 98
3 270
2.5 110
1.8 160
1.2 86
31 1180 1380
17 850
34 1310
25 1400
17 530
27 1970
14 550
23 2050
540 4.9 17 15
530 600 280 5.6
710
1.7 370
7.0 120
11 180
5.2 280
5.4 1000
7.4 560
11 980
24 1730
16 1680
14 800
18 1800
3.6 240
13 950
19 1050
14
21 1300
19 610
29 1740
31 1300
26 1170
16 2800
17 1430
5.4 500
20 1780
9.7 890
12 2650
22 650
24 1850
22 1360
41 3690 1130
23 40 640 1400
36 3310
22 1500
4.7 490
5.9 12 740 1730
24 15 2420 1770
6.1 25 750 3080
18 7.0 660 570
21 1600
4.1 380
7.1 510
13 1820
18 1010
10 220
7.5 290
Species = sodium
0.17 7.0
17 380
4.4 88
1.1 130
2.2 120
2.2 110
4.4 260
1.7 50
1.1 130
0.61 11
12 320
1.9 220
0.83 110
2.2 230
2.2 260
1.7 260
3.2 180
3.0 180
0.90 170
1.2 120
0.63 39 0.81
70
4.4 410
6.9 720
2.5 140
140 26300
320 12000
2.5 220 190 12700
19 250
11 320
4.4 6.2 7.7 440 700 510
3.2 370
0.95 110
1628 THE MAP3S/RAINE RESEARCH COMMUNITY
Appendix B (Contd.)
Year Month WH IT PS VA IL BR LE OH
Species = sodium (Contd.)
1979 1
2
3
4
5
6
7
8
9
10
11
12
1976 11
12
1977 1
2
9
10
11
12
1978 1
‘3
4
5
6
7
8
1.2 88
1.1 120
4.3 91
0.71 51 2.0
110 5.0
300 1.4
100 1.4
140 1.3
73 7.9
500 0.90
130 1.4
200 1.4
180
4.7 470
0.30 10
0.47 66
0.31 47
0.25 35 0.52
0.6Y 67
0.83 41
1.3 120
0.74 31 8.5
760
1.2 9.7 1.3 46 660 73
1.5 3.2 75 70
1.8 2.7 7.2 56 110 520
1.9 1.1 100 91
2.0 4.5 370 810
4.8 2.0 320 150
2.9 12 11 210 1070 1020
2.6 5.7 5.7 140 410 510
0.58 0.80 1.7 46 84 160
0.99 1.1 1.8 60 63 400
1.2 2.1 2.1 110 210 63
1.6 120
1.2 4.0 150 580
2.3 1.1 230 130
4.7 20 580 2460
3.5 2.1 18 130 170 400
Species = potassium
2.2 0.15 90 2.7
1.5 33
1.3 35
0.87 17
1.0 120
0.50 65 0.34
35 0.12 0.59 1.1
14 0.9:’
100
1.8 69
9k’ 3.0
93
5.1 320
3.4 56 4.5
500
140 0.52 0.63 1.2
28 35 120 1.0 1.4 1.1
51 83 62 0.70 1.1 1.7
41 200 130 3.5 0.97
110 97 0.37 0.36 0.48
15 8.0 1020 340
43 25 4340 1900
66 39 3760 3470
28 68 3530 6530
14 51 3010 7800
28 480
6.1 98
15 1350
15 1060
60 6300
76 5780
27 2480
19 13 1370 490
3.5 660
7.9 290
6.7 390
1.7 22 13
43 22 56 380 0.42 0.46 0.27 0.63 1.6
37 52 27 71 110
2.3 170
4.5 180
0.82 73
9.4 580
4.1 130
3.4 200
0.52 22 2.8
130 3.1
540 3.8
320
3.8 530
3.2 180
The MAP3S/RAINE precipitation chemistry network 1629
Appendix B (Contd.)
Year Month WH IT PS VA IL BR LE OH
Species = potassium (Contd.)
9
10
11
12
1979 1
2
3
4
5
6
7
8
9
10
11
12
1978 1
3
4
5
6
7
8
9
10
11
1.8 130
1.1 120
2.1 45
0.70 50
0.66 36
2.1 130
1.4 100
1.6 160
1.5 84
6.5 410
0.86 120
0.96 140
7.2 940
0.58 57
1.7 320
1.1 150
1.1 170
0.21 29
0.62 55
2 210
5.1 460
2.6 110
4.4 400
3.0 220
3.2 240
2.9 310
3.6 76
1.5 2.3 0.49 57 130 33
7.0 2.5 1.1 0.95 350 55 42 96
1.5 1.7 0.92 1.4 2.4 47 65 66 78 140
0.79 0.13 1.0 1.1
43 11 31 140
0.97 180
0.80 0.10 53 7.5
0.04 0.67 2.9 60 0.89 1.6
49 120 0.56 1.1
45 120 0.88 0.66
54 38 3.5 1.2
320 120
0.29 52
0.82 76
0.97 86
1.5 140
0.65 140
1.3 38
1.2 92
0.53 110
2.9 49
1.1 18
2.6 160
4.6 410
5.6 92
1.3 3.5 160 510
2 1.4 200 170
0.48 7.8 59 960
1.4 0.69 49 56
0.74 16
0.87 80
1.1 79
Species = calcium
1.5 180
0.62 80
1.1 110
0.84 2.1 100 250
1.4 220
2.7 4.0 140 220
7.1 5.1 360 300
3.3 1.2 190 220
7.1 5.4 210 540
2.6 3.0 300 190
2.0 170
5.8 4.6 220 320
4.0 200
1.8 4.2 56 170
6.4 280
1.7 65
1.1 100
2.9 6.0 300 1130
3.1 12 170 440
1.9 6.7 210 390
2.8 320
2.5 280
3.6 200
3.2 70
2.2 160
2.2 30
3.7 110
4.8 1.1 480 120
2.6 180
7.2 2.0 280 200
3.7 1.7 210 97
1.8 120
1.3 55
1.7 5.3 130 390
2.4 1.4 210 55
0.78 33
1.0 0.94 76 69
0.84 0.60 75 23
1.9 0.07 180 6.2
1.1 170
4.2 260
0.91 29
1.2 72
2.4 100
0.94 42
1.5 1.0 100 180
4.5 380
1.8 190
4.9 370
2.6 360
0.97 2.2 37 120
1630 THE MAP3S/RAINE RESEARCH COMMUNITY
Appendix B (Contd.)
Year Month WH IT PS VA IL BR LE OH
Species = calcium (Contd.)
12
1979 1
2
4
5
6
7
8
9
10
11
12
1977 10
11
12
1978 1
8
9
10
11
12
1979 1
2
3
4
0.40 28 0.72
39 1.5
90 2.1
160 2.6
250 2
110 1.7
110 1.6
230 1.6
230 6.2
810
0.94 170
4.1 2.1 270 160
2.3 2.4 170 210
3.1 6.2 170 450
2.1 3.7 170 390
4.3 3.3 260 190
3.4 2.7 310 270
2.3 230
0.94 1.5 120 220
2.3 2.7 230 320
1.1 6.4 140 790
5.4 5.6 200 450
Species = magnesium
1.1 0.30 170 39
0.18 0.46 25 47 0.26 0.46 0.67
23 55 80
0.10 10
1.1 99 0.80
34 1.4
120
0.69 50 0.82
60 2.0
210 0.66
14
0.13 20
0.75 0.80 40 44
1.4 1.0 71 59
1.3 0.35 74 64 2.4 1.2
73 120 0.53 0.61
61 38 0.27
23 1.7 0.97
65 67 0.61
31 0.43 0.59
13 24 0.40
22
0.10 0.38 7.1 70 0.27 1.1 0.40
15 74 30 0.73 0.83 1.6
44 61 140 0.57 0.85 1.4
42 47 100
1.7 92
0.55 4.0 46 120
0.38 68
1.4 130
2.6 8.0 230 500
1.6 150
0.96 15 210 240
2.6 7.1 77 800
3.8 290
1.5 33
0.63 59
1.3 140
0.98 55
0.90 100
0.50 1.1 56 110 0.91 1.6
51 37 0.86 1.4
0.94 2.3 120 220
0.46 1.7 99 260
3.8 65
5.4 86
2.5 230
2.2 150
2.7 280
4.2 320
1.4 120
1.3 0.87 94 33
14 2640
35 1300
26 1510
2.7 35 4.5
130 1.9 1.6
210 110 2.7 1.4
180 59 4.4 2.8
19 0.95
68 0.13
11
0.61 110
1.2 110
1.1 98
55 440 210 0.65 6.1 4.5
36 350 400 0.50 2.9 6.2
16 370 600
1.7 5.1 370 780
3.4 58 2.6
42 1.7
110
1.2 110
4.1 250
6.2 200
7.4 440
2.9 120
11 500
4.5 790
1.9 160
2.2 310
2.8 160
1.0 73 0.81
32 0.16
14
1.6 99
1.6 51
1.5 90
The MAP3S/RAINE precipitation chemistry network
Appendix B (Contd.)
1631
Year Month WH IT PS VA IL BR LE OH
Species = magnesium (Contd.)
5 0.75 0.38 0.62 73 31 65
6 0.25 1.1 0.22 14 67 13
7 0.37 0.98 0.68 23 90 68
8 0.15 21
9 0.38 0.14 0.33 54 is 48
10 3.0 0.46 0.54 390 46 64
11 0.17 4.3 21 530
12 0.36 0.64 2.5 36 23 200
0.40 2.4 37 220
0.18 4.3 40 71 0.97 1.7
29 190 0.66
51
3.5 320
1.9 2.3 42 170
2.3 160
7.9 830
8.2 620
1.4 53
i.1 46
2.3 100
1.4 250
0.40 34
0.26 36 1
56