Expanded
Summary
Round 1 LT2ESWTR Cryptosporidium monitoring
results
Ale x a O b ole n s ky a nd M i c h ael Hotal ing
http://dx.doi.org/10.5942/jawwa.2013.105.0117
Cryptosporidium data from the first round of monitoring
(round 1) under the Long Term 2 Enhanced Surface Water
Treatment Rule (LT2ESWTR) were analyzed. Under the
LT2ESWTR the US Environmental Protection Agency
(USEPA) requires public water systems treating surface
water (SW) or groundwater under the direct influence of
surface water (GWUDI) to determine the average source
water Cryptosporidium concentration and corresponding
bin for each treatment plant. This article describes
publicly available round 1 results, including quality
assurance review of the data, binning outcomes, USEPA
method 1623 oocyst recovery, and relationships between
Cryptosporidium and Escherichia coli results. Overall,
7% of plants at large systems fell in bin 2 and must
implement additional treatment for Cryptosporidium (no
plants fell in bins 3 or 4). Plants with flowing stream (FS)
surface water supplies are more highly affected (15% in
bin 2) than those with GWUDI (2.5%) or reservoir/lake
(RL; 3.1%) surface water supplies. Data were insufficient
to provide reliable small system binning estimates.
Following the conclusion of LT2ESWTR round 1 monitoring in April 2012, results from the centralized Data
Collection and Tracking System (DCTS) were made publicly available (USEPA, 2012a, 2012b, 2006). LT2ESWTR
schedule 1–3 systems, which include all large systems
(population ≥ 10,000 people) and any small systems
interconnected with a large system, were required to
electronically submit data to the DCTS. However, schedule 4 systems (population < 10,000 people) only submitted results to DCTS voluntarily, and most of these only
reported E. coli data because their findings did not trigger
subsequent Cryptosporidium monitoring. Consequently
the DCTS data described here include round 1 monitoring results for most of the LT2-applicable schedule 1–3
systems (74%) but for very few of the corresponding
schedule 4 systems (3%). Grandfathered data compose
most of the missing schedule 1–3 systems.
Before analysis the raw DCTS data required extensive
processing and cleaning to resolve information conflicts
and other data-quality issues. Based on available information, monitoring locations (source waters) were classified
as either SW or GWUDI, and surface water locations
were divided into FS or RL subtypes. Almost a quarter of
the DCTS locations had missing or ambiguous source
classification information that precluded their classification as one of SW-FS, SW-RL, or GWUDI.
wise specified. As outlined in Table 1, Cryptosporidium
was observed at 62% of SW-FS sites, 34% of SW-RL
sites, and 23% of GWUDI sites. Because oocysts were
usually detected in just a small fraction of samples where
they were found, sample observation rates were much
lower than location observation rates; Cryptosporidium
was observed in 11, 3, and 4% of samples from SW-FS,
SW-RL, and GWUDI sources, respectively (Table 1).
No DCTS locations fell in filtered bins 3 or 4. Among
filtered schedule 1–3 locations, 15% of SW-FS, 3.1% of
SW-RL, and 2.5% of GWUDI locations fell in bin 2,
requiring the plants to install additional treatment from
the microbial toolbox (McTigue et al, 2013). Results suggest that the 100 cfu/100 mL annual average E. coli
monitoring trigger is less effective at indicating which
SW-RL–type sources are most likely to have average
Cryptosporidium concentrations at or above the
0.075-oocysts/L bin 2 threshold than it is for SW-FS– or
GWUDI–type sources.
CRYPTOSPORIDIUM RESULTS
The DCTS data comprise 2,277 source water monitoring locations at 1,885 public water systems, including 28
unfiltered plant sources. Cryptosporidium results are
available for 1,777 of these sites (41,598 samples), including 21 unfiltered supplies. The current discussion is limited to schedule 1–3 filtered plants results unless other-
Method 1623 matrix spike recovery
The cleaned DCTS data include results for 3,321 Cryptosporidium matrix spike (MS) samples from 1,678
monitored source waters analyzed by 48 laboratories
approved to conduct Cryptosporidium analyses. Overall
mean and median oocyst recoveries were both 40%, close
to the 43% average from the Supplemental Survey, which
provided a basis for method performance assumptions
underlying the LT2ESWTR framework. Although the
central tendency of recovery in the DCTS data matched
expectations for method 1623, results were highly variable. MS recovery ranged from –11% to 341%, with half
the results outside 23–57% (interquartile range) and 10%
of results outside 3–75% (5th–95th percentiles). This
O B O LENS K Y & H O TA LING | 105: 8 • JO U R NA L AWWA | A U G U S T 2013
2013 © American Water Works Association
49
required extensive processing
data repository be maintained for the second round of
LT2ESWTR monitoring. Data-quality issues encountered
with the DCTS extracts from round 1 can be largely
avoided by conventional database design improvements.
Considering the large variation in Cryptosporidium MS
recovery with method 1623 and the poor understanding
of what drives such variability and what it means for the
monitoring results, some thought should be given to collecting additional supporting water quality information
(e.g., ultraviolet light absorbance at 254 nm, alkalinity)
that might help explain recovery differences. Finally,
capturing small system and grandfathered data in a centralized data system would allow for a fuller picture of
national occurrence and better estimates of the
LT2ESWTR’s regulatory effects.
and cleaning to resolve information
REFERENCES
variability could not be explained with the available data,
although matrix effects on recovery were evident in a few
cases in which multiple MS samples were reported for the
same source and analyzed by the same laboratory.
ConclusionS
Estimates of national Cryptosporidium occurrence and
associated LT2ESWTR regulatory effects are likely to be
highly sensitive to the composition of source water types
as well as the particular sites represented in a monitoring
Before analysis the raw DCTS data
conflicts and other data-quality issues.
McTigue, N.E.; Cornwell, D.; & Via, S., 2013. Utilization of the Microbial
Toolbox for LT2ESWTR Compliance by Utilities. Journal AWWA,
105:8:E395. http.dx.doi.org/10.5942/jawwa.2013.105.0123.
USEPA (United States Environmental Protection Agency), 2006. 40CFR
Parts 141 and 142. National Primary Drinking Water Regulations:
Long-Term 2 Enhanced Surface Water Treatment Rule, Final Rule.
Federal Register, 71:3:653.
program or set of data under evaluation. Although the
DCTS data provide a fairly complete picture of monitoring results for large systems, the occurrence picture and
regulatory effects for small systems cannot be inferred
from these data.
Lessons learned from round 1 are of value in preparing
for implementation of the next monitoring round under
the LT2ESWTR, which is expected to begin in 2015. Use
of the DCTS in round 1 enabled fairly rapid analysis of
the monitoring data on a national level. This would be
impractical and perhaps infeasible if data are dispersed
in different information systems with different formats.
Therefore it is highly recommended that a centralized
TABLE 1
USEPA, 2012a. Cryptosporidium Raw Data From Monitoring Under the
LT2 Rule (CSV). http://water.epa.gov/lawsregs/rulesregs/sdwa/lt2/
upload/cryptodatareported.csv (accessed June 20, 2012).
USEPA, 2012b. E. coli Raw Data From Monitoring Under the LT2 Rule
(CSV). http://water.epa.gov/lawsregs/rulesregs/sdwa/lt2/upload/
ecolidatareported.csv (accessed June 20, 2012).
Corresponding author: Alexa Obolensky is the owner
of Obolensky Consulting, 153 E. Coulter St.,
Philadelphia, PA 19144; [email protected].
Summary of LT2ESWTR round 1 DCTS Cryptosporidium results by source water classification for schedule 1–3
filtered plants
Number of
Samples
Samples With
Observed
Oocysts
%
Number of
Locations
Locations With
Observed
Oocysts
%
Number of Binned
Locations
Bin 2
%
SW-FS
10,140
SW-RL
13,821
11.0
437
62.0
325
15.1
3.2
578
33.9
448
3.1
543
8.1
23
60.9
15
6.7
5,925
6.6
234
46.6
186
5.4
941
8.8
51
45.1
26
7.7
GWUDI
2,553
3.8
117
23.1
81
2.5
Variable‡
2,568
6.4
99
57.6
85
5.9
Total
36,492
6.4
1,539
45.3
1,166
7.1
Source
SW-Mixed
SW-Variable*
SW-Unknown†
DCTS—Data Collection and Tracking System, GWUDI—groundwater under direct influence of surface water, LT2SWTR—Long Term 2 Enhanced Surface Water
Treatment Rule, SW-FS—surface water-flowing stream, SW-RL—surface water-reservoir/lake
*SW-VARIABLE locations are surface water sources with inconsistent subtype descriptions in the DCTS data.
†SW-UNKNOWN locations are surface water sources with no subtype information available in the DCTS data.
‡VARIABLE locations are characterized in the DCTS data as SW sources in some sampling events and as GWUDI sources in other sampling events.
50
AUGUST 2 0 1 3 | JO U R N A L AW WA • 1 0 5 :8 | O B O L E N S K Y & H O TA LING
2013 © American Water Works Association