Delta RMP TAC Meeting Agenda Package 2019-07-14, page 1
Technical Advisory Committee Meeting Agenda Friday, July 19, 2019 10:00 am – 3:30 pm
Sunset Maple Room, Regional San 10060 Goethe Road, Sacramento, CA 95827
Remote Access: Phone number: (415) 594-5500 Access Code: 238-626-034# Screen Sharing: https://join.me/sfei-conf-cw2
# Agenda Item and Desired Outcomes Attachments Start & Lead
1 Introductions and Review Agenda 10:00 Review and agree on agenda and desired outcomes. Gita Kapahi
2 Decision: Approve previous TAC meeting summaries and Draft TAC Meeting 10:05 confirm/set future TAC meeting dates Summary from Stephen May 9, 2019 McCord Upcoming Scheduled Meetings (Please bring your calendar) Draft TAC Teleconference භ SC Meeting, Aug 5, 2019, DWR West Sacramento Summary from භ Long-Range Planning Workshop, July 31, 2019, May 24, 2019 Regional San භ TAC Meeting, Sept 13, 2019, Regional San භ Schedule in-person meeting for the winter 2019/2020.
Desired outcome: Ɣ Approve TAC meeting summaries Ɣ Confirm future TAC/SC meeting dates
3 Information: Steering Committee Update Draft SC Meeting Summary 10:15 - 10:30 TAC co-Cchair will summarize the May 29, 2019 SC from May 29, 2019 Stephen meeting, including the decisions and action items relevant McCord to the TAC. Matt Heberger Desired Outcome: Ɣ Inform TAC regarding SC decisions and activities. Ɣ Explain the rationale and context for agenda items below. Delta RMP TAC Meeting Agenda Package 2019-07-14, page 2
# Agenda Item and Desired Outcomes Attachments Start & Lead
4 Technical Subcommittee and Monitoring Updates Dashboard Tables (PDF in 10:30 – 11:30 Updates on subcommittees, monitoring activities, and special agenda package, live link here): Mercury: Jay projects. Davis Desired outcome: 1. Status of Delta RMP භ Review running table of past and upcoming sampling Datasets Nutrients: Matt events. 2. Past & Planned Heberger භ Inform TAC of subcommittee activities and Monitoring Events recommendations. Pesticides: Jim Orlando
CECs: Matt Heberger
5 Update on the Pesticides Interpretive Report Deltares Deliverable 3.3 11:30– 12:00 Our consultant Deltares has produced three sets of Final memorandum on Matt Heberger deliverables, including the database and a technical memo analytical methods to be describing their planned approach, and held a meeting with used (link) Delta RMP stakeholders on July 15. Staff asked the contractor wait for a “notice to proceed” pending TAC review of the data and methods. Desired outcome: x Notice to proceed with the study x Feedback from the TAC to the contractor Lunch (provided) 12:00 – 1:00 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 3
# Agenda Item and Desired Outcomes Attachments Start & Lead 6 Recommendation: Mercury Monitoring in Tidal Wetlands Expanded mercury 1:00 – 2:00 Restoration Sites monitoring proposal Jay Davis At its May 2019 meeting, the SC requested more information (sent separately) about proposed mercury monitoring at tidal wetland restoration sites before committing funding. The SC asked for timelines for restoration projects, and where the proposed monitoring fit in to the project timeline, details on specific monitoring locations, and how the proposed restoration monitoring would benefit the Delta as a whole. Since then, staff scientists have revised and expanded the proposal for review by the TAC. Desired outcome: x TAC recommendation to the SC on whether the proposed study merits funding.
7 Discussion: New Assessment Question Related to Pesticides Memo on proposed new 2:00 – 2:30 and Human Health assessment question for Matt Heberger The Pesticides Subcommittee recommended that the pesticides program consider adding a management question related to human health and drinking water impacts.
Desired outcome: x TAC recommendation to the SC to add a new assessment question.
8 Discussion: Long-Range Planning Workshop Preview Table of Management 2:30 – 3:00 Drivers (link) On July 31, we will have an all-day long-range planning Matt Heberger workshop with TAC and SC members. The goals are to set priorities for monitoring and studies for the next 5 years, to SWOT worksheet make the most effective use of the program’s resources. (strengths, weaknesses, opportunities, threats)
Desired outcomes: x TAC familiarity with the goals of the workshop and tools that we will use. x Input into the goals and format of the workshop.
9 Information: Status of Deliverables and Action Items Delta RMP Stoplight 3:00 – 3:15 Desired outcomes: Reports භ Inform TAC about the status of RMP deliverables. Matt Heberger භ Review action items from today’s meeting. 10 Updates and Wrap-up 3:15 – 3:30 Desired outcomes: Stephen භ Plan agenda items for future meetings McCord
Adjourn 3:30 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 24
Materials for Agenda Item 4
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 25
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Materials for Agenda Item 5
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 30 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 31 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 32 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 33 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 34
Contents
1 Analysis of the final database 2 1.1 Toxic Unit (TU) approach 3 1.1.1 Introduction 3 1.1.2 Selection of TU approach 3 1.2 Bioavailibilty 5 1.3 Relating observed and calculated toxicity 6 1.4 msPAF approach 8 1.5 Handling Censored Data 11 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 35
1 Analysis of the final database
In recent years, a number of monitoring programs and projects regarding pesticides have been carried out in the Sacramento and San Joaquin Delta. In 2018, Deltares was appointed by SFEI to carry out an evaluation and interpretation of the available information. This Deliverable describes the methods proposed for this evaluation and interpretation. It is a follow-up of Deliverable 3.2, which contained a draft version of the analytical methods to be used in the interpretation and evaluation. In the underlying Deliverable, the comments received from the stakeholders on Deliverable 3.2 have been taken into account.
In Deliverables 2.1, 2.2 and 2.3, three databases were provided (1. Water, 2. Sediment, 3. Toxicity). These are a compilation of the data available from three compendium databases (SURF, CEDEN, USGS). In summary, the data consist of:
1. Concentrations of pesticides available in the water matrix, specifically in the water after filtering and in the suspended sediment. 2. Concentrations of pesticides in the bed sediment. 3. Toxicity data for the filtered water, overlying water and bed sediment samples.
Only sampling locations within the legal boundaries of the Sacramento-San Joaquin Delta were included, as well as a number of locations in the main upstream rivers. A further review in consultation with relevant stakeholders determined that some of the locations could be merged and some could be discarded.
The four main questions that are to be answered in the final report are: 1 To what extent do pesticides contribute to the observed toxicity in the Delta? 2 Which pesticides or metabolites have the highest potential to cause toxicity in the Delta and should therefore be the priority for monitoring and management? 3 What are the spatial and temporal extents of lethal and sub-lethal aquatic and sediment toxicity observed in the Delta? 4 What are the spatial/temporal distributions of concentrations of currently used pesticides identified as likely causes of observed toxicity?
To answer these questions, the concentrations measured have to be translated to risk figures. This is due to fact that concentrations of compounds as such do not quantify the potential risks or toxicities of a compound or the toxicities and risks of a mixture of compounds. Therefore, the concentrations in databases (1) Water and (2) Sediment of Deliverable 2.2 need to be related to a potential risk. A number of methods are applicable to characterize the risk. Two of the approaches will be used in this analysis, namely (1) the Toxic Unit (TU) Approach, and (2) the multi-stressor Potentially Affected Fraction (msPAF) approach. The TU approach will be used to link particular compounds with the toxicity observed at a site while the msPAF approach will be used to identify the toxic compounds which pose the highest risk and therefore should be monitored. Delta RMP TAC Meeting Agenda Package 2019-07-14, page 36
1.1 Toxic Unit (TU) approach
1.1.1 Introduction
Using the TU approach, every single concentration of a compound (a) is compared with a threshold value for this compound, according to formula (1).
௧௧ሺሻ ܷܶሺܽሻ ൌ (1) ௧௫௧௬ሺሻ
Within one sample, all these TU values for the single compounds present in the sample are added together using the concept of Concentration Addition (CA). This results in a TU for the sample, TUs, according to formula (2).
௧௧ሺሻ ܷܶݏ ൌ σ (2) ଵୀଵ ௧௫௧௬ሺሻ
It is known that for compounds with a similar mode of action, CA may generally predict the toxicity quite accurately. However, for compounds with different modes of action, CA tends to overestimate the toxicity of a mixture1. In addition, the toxicity threshold values may originate from different sources and can be based on different endpoints (e.g. LC50, NOEC), which, in principle should not be summed. Therefore, the outcome of the TU calculation must be seen as a coarse estimate of the potential risk of a mixture of chemicals.
Additionally, these thresholds can be calculated for a single trophic level (e.g. only for fish species) or for the most sensitive species or summed up for a number of species. The choices made for determining the threshold may influence the outcome of the TU results substantially. Therefore, the TU methodology must be used with care.
1.1.2 Selection of TU approach
It is proposed to use the TU values based on single trophic levels, i.e. calculating separate TU values for fish, invertebrates, and algae. There are two reasons to use this approach:
1. The first reason is a very pragmatic one, namely that hardly any harmonized threshold values based upon all the toxicity values from different organisms are available for these compounds. The only harmonized threshold values, to the author’s knowledge, are the National Recommended Aquatic Life Criteria, which can be found at https://www.epa.gov/wqc/national-recommended-water-quality-criteria- aquatic-life-criteria-table. While these are useful, they do not cover all the compounds of interest.
The second reason is more important. One of the goals of this project is to relate the concentrations of the chemicals to the observed toxicity from the toxicity tests performed on the
1 T. Backhaus R. Altenburger W. Boedeker M. Faust M. Scholze L.H. Grimme (2009) Predictability of the toxicity of a multiple mixture of dissimilarly acting chemicals to Vibrio fischeri. Environ. Toxicol, chem., Volume19, Issue9, pp 2348-2356 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 37
field samples. To achieve the study’s goals, the TU values per trophic level (fish, invertebrate, algae) were calculated for all of the compounds detected in the sample. These were compared to the results with the observed toxicity from the tests of the field samples (see paragraph 3.2 for details). With this method, it was possible to analyze if there was a relationship between the observed and calculated potential risk and which of these compounds also explain the observed toxicity.
At the moment, two databases are available with threshold values per trophic level. These databases are complementary to each other. The first database was obtained from the EPA Office of Pesticide Programs (OPP) Aquatic Life Benchmarks. This database contains aquatic ecotoxicity benchmark values from risk assessments developed by the EPA for individual pesticides during the re-registration program (https://www.epa.gov/pesticide-science-and- assessing-pesticide-risks/aquatic-life-benchmarks-and-ecological-risk). Threshold values for fish (acute and chronic), invertebrates (acute and chronic) and algae for over 615 compounds are provided, but these compounds do not fully cover all the compounds detected in the Delta. A second database was developed by Nowell et al. (2014)2. The study of Nowell et al. resulted in a database containing threshold values for fish and invertebrates for 472 compounds. The Nowell study determined two different thresholds, one based on the Median Toxicity Concentration (MTC) and one based on the Sensitive Toxicity Concentration (STC). This STC is based on the 5th percentile or the Minimum Toxicity Concentration, depending on the number of toxicity values available. A limitation of Nowell’s study is that it does not contain threshold values for algae. In the present study, the OPP Aquatic Life Benchmarks was used as a starting point. Data from the Nowell database were added where needed. Since the data in the OPP and the Nowell database have undergone unlike evaluation processes, values taken from each database were clearly identified and the consequences of the database selected on the performed calculations was evaluated. To extend the OPP benchmarks, STC values from the Nowell study were appended. Despite the fact that two different databases were used, for some compounds which are detected, no thresholds will be available in the databases. Therefore, it will not be possible to generate TU values for these compounds. The consequences of these missing values will be evaluated. In appendix B, the compounds are listed for which no threshold values were available in both databases. As can be seen from that list, most of these compounds are metabolites. Some threshold levels only indicate whether or not the threshold is less than (>) or greater than a certain value, without specifying an exact threshold level. In these cases, where the threshold level only indicates if it is above or below a certain value, the value itself was selected as an indicative threshold level to be used in the calculation of the TU values. This implied that if the threshold was less than a certain value, the TU value could be underestimated. Likewise, if the threshold was greater than a given number, the TU value might be overestimated. These cases are marked with red for the numbers that might give an underestimation and green when it might give an overestimation. The generation of new threshold values is outside the scope of this project.
TU values were calculated for all events (unique location, time) using the data from the (1) Water and (2) Sediment databases of Deliverable 2.1, 2.2 and 2.3. The results were evaluated to identify the compounds causing the highest potential toxicity and to identify hotspots with the highest calculated toxicity for different trophic levels.
2 Nowell, L.H., J. E. Norman, P. W. Moran, J.D. Martin, W.W. Stone (2014) “Pesticide Toxicity Index—A Tool for Assessing Potential Toxicity of Pesticide Mixtures to Freshwater Aquatic Organisms.” Science of the Total Environment 476–477: 144–57. https://doi.org/10.1016/j.scitotenv.2013.12.088. Delta RMP TAC Meeting Agenda Package 2019-07-14, page 38
Both the OPP and the Nowell databases with TU values are included together with this Deliverable: Excel files called Aquatic Life Benchmarks and Appendix B_Nowell et al. 2014, respectively.
1.2 Bioavailibilty
Data in the databases contain both dissolved fractions and total fractions of the various measured substances. When the dissolved concentration of a substance was measured, this concentration was assumed to present the bioavailable fraction. To determine the bioavailable fraction of a total concentration, corrections were made according to the following rules:
For metal (only copper):
Fraction Cu ions = 10^(-7.3295 + 1.7941 * log(Cdissolved) - 2.0364 * log(DOC) + 1.8044 * log(Ca) - 0.33617 * pH - 10-5 3 1.48* * Ca) / Cdissolved
with -6 4 Cdissolved =Ctotal* (1/(1+TSS*10 *Kd)
For organics -6 -6 3 Cdissolved = Ctotal / (1 + TSS * 10 x POC*10 x Koc)
The concentration in sediment was converted to a concentration in pore water by using specific soil adsorption coefficients (Kd values), assuming equilibrium between the sediment and pore water according to:
Kd=Csediment/Cporewater
Kd values were determined by:
Kd=Koc*foc
If DOC values and pH concentrations were measured at a certain location and certain time point, in addition to the pesticide data, these values were used in the calculations. If no specific DOC value was available for a sample taken at a specific time at a certain location, the average DOC concentration of 4.65 mg/L was used. This is the average of measured DOC values in the database. If no specific pH was available for a sample taken at a specific time at a specific location, an average pH of 7.69 was used. This value was calculated using measured pH values in the database. A default concentration of 100,000 μg/L Ca was chosen since this is the default value that has been used in the RIVM tool (described in the msPAF approach, see for further details section 1.3).
3 H.P. Bootsma, J.P.M. Vink (2016) Simple equations for the calculation of free metal ion activities in natural surface waters. Deltares report 1210758-000-ZWS-0004 4 L. Posthuma, D. De Zwart, L. Osté, R. van der Oost & J. Postma (2016) Ecologische sleutelfactor toxiciteit, deel 1. Deltares report 1210758, STOWA report 2016-15 A, ISBN 978.90.5773.727.5 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 39
Specific TSS concentrations were not available since the location and date of the collected TSS samples did not correspond to those of the pesticide samples. Therefore, a calculated average of 39.5 mg/L TSS was included, based on site-specific TSS concentrations. DOC and TSS data were collected from the CEDEN database.
Specific Koc (soil organic carbon-water partitioning coefficient) values were collected from the RIVM database (see paragraph 3.3), which includes Koc values from the EPIsuite database. If Koc values could not be retrieved from the RIVM database, the Koc values were retrieved from EPIsuite directly. The fraction of organic carbon was determined from the total organic carbon concentration. For these calculations, specific total organic carbon concentrations were included if measured at the same location and date as a specific pesticide sample. Total organic carbon data was collected from the CEDEN database and some records were retrieved from the USGS database. From this, a foc was calculated. If specific data regarding the total organic carbon concentration were not available, the foc was determined by the average of the calculated foc. As the calculated concentration in pore water already reflects the dissolved fraction, only a correction for the bioavailability of copper had to be done to correct for the fraction of free copper ions.
TU values are visualized in time per location and displayed in graphs, showing the TUs values and the compounds contributing to this value in pie charts. In the case of TU’s, the magnitude of the pie chart will display the magnitude of the TU, and the different parts of the pie will display the contribution of the different compounds to the TU. With these displays, questions 3 and 4 of the project will be addressed.
1.3 Relating observed and calculated toxicity
The first research question is: “To what extent do pesticides contribute to the observed toxicity in the Delta?”
To answer this question, a comparison between the observed toxicity in the field for the different trophic levels and the potential risk calculated for each trophic level using TU values will be done. A distinction between acute and chronic toxicity will be made, resulting in five comparisons, namely for fish (acute), fish (chronic), invertebrates (acute), invertebrates (chronic), and algae.
A stepwise approach was carried out to link compounds with observed toxicity. First, in selecting the observed toxicity data from the field observation, a distinction will be made between the tests that show an adverse effect according to the California Environmental Data Exchange Network (CEDEN) criteria and the tests not display an adverse effect. For the first category, data from database (3) Toxicity with the levels Not Significant Less Similarity, Significant Greater Similarity, and Significant Less Similarity selected. The results will be evaluated in these different categories, taking the Quality Criteria, mentioned per test in the database, into account to determine whether an observation is of sufficient quality to be included in the analysis. Next, the percentage effect of the observed toxicity tests in the field from database (3) Toxicity were used to compare them with the computed results. In other words, the percentage effect of the data originating from step 2 from one event (unique location, time) was compared with the ƩTU value (the sum of the TUs for all compounds together) from Delta RMP TAC Meeting Agenda Package 2019-07-14, page 40
the same event. For the second category, when tests showed no adverse effects, the data from database (3) Toxicity with the level Not Significant Greater Similarity will be selected. Since these data show no significant effect, the percentage effect s therefore defined as zero. These values were also related to the potential risk according to the calculated TU values. As an initial evaluation, the observed toxicity was regressed against the ƩTU value to generate the correlation coefficient for the paired samples, as well as the 95% confidence interval. Linear regression and log/linear regression were used depending upon which method generates stronger correlations. Finally, the observed toxicity will be regressed against the TU for the individual compounds. Note that the sample sizes of this second-tier analysis were very small. Displays of the correlation with the 95% confidence interval will be presented.
The third assessment will be a qualitative comparison of results from Dutch toxicity testing and the results found in the Delta. Work in the Netherlands has indicated that in vivo toxicity testing may be too coarse a measure of toxicity to quantify the toxicity of surface water samples. Rather, either concentrating the samples, or in vitro testing, has proven to be a more accurate measure of toxicity. Recommendations for monitoring toxicity in the Delta based on the Dutch studies are presented. Delta RMP TAC Meeting Agenda Package 2019-07-14, page 41
1.4 msPAF approach
A second important goal of the evaluation of the chemical results is an analysis of those pesticides or metabolites that have the highest potential of causing toxicity in the Delta (question 2 on page 2).
In paragraph 1.1.2, we introduced the TU approach as a method to evaluate chemical results. Although the TU approach is a robust method, it has two drawbacks: 1. The first drawback of the TU approach is that only a risk figure (TU) for a specific trophic level (algae, invertebrates, fish), and not for all the potential organisms present in the ecosystem, is calculated. This hampers the overall ecological risk assessment of a certain location or event. 2. The second one is that the CA approach, which is commonly used in the TU approach for computing a general risk assessment for an event (unique location/time), is likely to generate an overestimation of the effects. This is due to the fact that most of the field samples include compounds with a wide range of Modes of Actions.
Therefore, the so-called msPAF approach was applied, using Species Sensitivity Distributions (SSD) per compound. This provides an analysis of the pesticides or metabolites with the highest potential to cause toxicity in the Delta and that should therefore be the priority for monitoring and management.
An SSD is derived by fitting a selected statistical model to compound-specific ecotoxicity data. An SSD reflects the observation that interspecies’ sensitivity differences to a compound resemble a bell-shaped distribution on a log scale, or an S shaped curve for the cumulative distribution. On the x-axis of this distribution, the concentration of the compound is displayed, and on the y-axis, the Potentially Affected Fraction (PAF). This PAF value always has a value between 0 and 1. Figure 1 displays a typical example of a (cumulative) SSD curve. The concentration at which 5% of the species is potentially affected is in general used in environmental policy as a maximum allowable concentration, also called the HC5 (HC= Hazardous Concentration). More information on the use of SSDs can be found on https://www.epa.gov/caddis-vol4/caddis-volume-4-data-analysis-advanced-analyses- controlling-for-natural-variability#tab-5. Delta RMP TAC Meeting Agenda Package 2019-07-14, page 42
Figure 1: typical example of an SSD curve
Depending on the substance, more or less toxicity values are available. For this specific study, acute ecotoxicity data regarding EC50 concentrations are included, which represents the concentration at which 50% of the tested organisms shows the observed effect. At least 6, but preferably over 10, unique species should be included to derive a reliable SSD. For some substances, toxicity data of only 3-5 unique species was included to determine the SSD. When insufficient acute EC50 data was available, chronic NOEC data were extrapolated to derive an SSD. .
From these individual PAF values, a multi-stressor PAF (msPAF) can be calculated for each concentration, assuming that each toxic Mode of Action (MOA) acts independently. First, Concentration Addition (CA) is applied for all compounds with a similar MOA. Next, Response Addition (RA) s applied for compounds with dissimilar MOA by applying formula (3).
ൌ ͳ െ ς୨൫ͳ െ ୨൯ (3)
In Equation 3, the msPAF is the overall risk value for the sample, PAF is the risk values for each individual compound, j is the number of compounds detected in the sample, and π indicates the product of the sequence.
More information on the use of msPAF values can be found in de Zwart and Posthuma (2005)5.
5 D De Zwart, L Posthuma (2005) Complex mixture toxicity for single and multiple species: proposed methodologies. Environmental toxicology and chemistry, vol. 24 (10), pp. 2665-2676 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 43
The Dutch National Institute of Public Health and the Environment (RIVM) has developed a database containing toxicity data for a large number of compounds. The primary MOA is mentioned in the RIVM database. Based on these data, SSDs of these compounds can be constructed using a tool developed by RIVM. One limitation of the curves from the RIVM tool is that the SSDs are constructed irrespective of the quality and the quantity of input data. This limitation can be overcome by adding a quality score to the individual SSD curves based on a number of quality criteria: (1) the quantity of the data; (2) the biodiversity range of the data set; (3) the origin and the accompanying quality of the data; and, (4) the extrapolation steps used in the dataset. An overview of the included number of toxicity results, species and taxa to derive an SSD for each specific substance is available and added included Appendix C. This overview also includes remarks on the reliability of the SSD based on the number of included species. The higher the number of the Quality score, the less reliable the results As can be seen from the table, for most of the compounds which have been detected, a Quality score of 1 or 2 is available. For 11 compounds a higher quality score is available.
The RIVM database and accompanying tool is publicly available. In this study, RIVM’s database and tool were used to calculate an msPAF value for each event. In addition to the msPAF value, the 5 compounds that are the determiners for the msPAF values were also calculated by the RIVM tool. Identifying the drivers for the msPAF value for each event determined which compounds have the highest potential to cause toxicity. The tool also accounts for the bioavailability of compounds, taking into account the Total Suspended Solids (TSS) content of a sample and the Dissolved Organic Carbon content (for copper). Comparable to the bioavailability correction of the TU calculations, for the water data, specific DOC concentrations were included for pesticide samples, if measured. Otherwise, an average of 4.66 mg/L of DOC was used. For TSS, an average was used, comparable to the TU calculations. For the calculation of msPAF values, sediment samples were converted to pore water concentrations as described under the TU approach. This is because the msPAF tool only includes toxicity data for aquatic species; therefore, the concentration in pore water is more relevant for the msPAF calculation. This means that no specific msPAF outcome s calculated for sediment dwelling species. The RIVM database and tool can be downloaded from: https://www.stowa.nl/sites/default/files/assets/PUBLICATIES/Publicaties%202016/STOWA%2 02016-15/STOWA%202016-15%20Tool%20Chemie%20Spoor.zip
In the accompanying Excel file called Compounds in database with Aquocode and CAS number for MS-PAF calculations_file 2019-03-29, the compounds that were included in the msPAF calculations according to the RIVM database are provided. For 27 of the 169 compounds selected, no SSD could be calculated at the moment (Table 1). Most of these were metabolites; some of these were detected in large fractions. For a number of compounds, no CAS number was available, hampering the coupling with the RIVM database. It may be possible, with input from stakeholders, to link these compounds with no CAS number to those in the RIVM database.
Table 1: Overview of availability of msPAF information for compounds in water samples.
Selection of compounds Number # compounds in water chemistry database, 429 database 1 # compounds detected 169 # compounds in RIVM msPAF database 142 # compounds not in RIVM msPAF database 27 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 44
An msPAF value was calculated for a total of 681 unique mixture samples taken from water and for a total of 55 unique mixture samples taken from sediment. Each mixture sample contained all detected substances for which an SSD could be calculated, all retrieved at the same location on the same date.
Based on the results obtained, chemicals contributing the most to the potential toxicity at a site are identified.
More information about the construction of the SSD curves for all the chemicals in the RIVM database can be found in a recent paper by Posthuma et al., (2019)6.
1.5 Handling Censored Data During this study, it became apparent that the handling of censored values in the water and sediment chemistry databases (databases (1) Water and (2) Sediment) s an important issue to address. As discussed during the steering committee meeting in December 2018, the methodology used in the Netherlands and the European Union, namely using half of the MDL as a value, was not regarded as suitable, since this methodology produces values that in principle do not exist. Instead, methods as developed by Helsel (2012)7 were recommended. However, these methods are only suitable in cases of presenting summary statistics per compound and do not calculate individual values. For the relationship between observed and calculated toxicity, single, unique values were used. In these cases, censored values or non- detects will not be an issue. Censored values were not used.
6L.Posthuma Van Gils, J., M. C. Zijp, D. Van de Meent, Dick De Zwart (2019) SPECIES SENSITIVITY DISTRIBUTIONS FOR USE IN ENVIRONMENTAL PROTECTION, ASSESSMENT AND MANAGEMENT OF AQUATIC ECOSYSTEMS FOR 12,386 CHEMICALS. Environmental toxicology and chemistry, vol.38, pp. 905-917. 7 Dennis R. Helsel (2012) Statistics for Censored Environmental Data Using Minitab® and R, Second Edition ISBN:9780470479889 |DOI:10.1002/9781118162729. Copyright © 2012 John Wiley & Sons, Inc. Delta RMP TAC Meeting Agenda Package 2019-07-14, page 45
A overview of compounds detected
Delta pesticide Not Dete Anal % # locations # locations detecte cted ysed Detect measured detected d ed Hexazinone 193 555 748 74 29 28 Diuron 257 407 664 61 35 27 Metolachlor 386 362 748 48 30 27 Azoxystrobin 266 381 647 59 27 26 Boscalid 225 211 436 48 26 25 Carbendazim 267 127 394 32 23 19 Clomazone 401 65 466 14 27 19 Copper 4 501 505 99 18 18 DCPMU 225 188 413 46 23 17 Simazine 543 289 832 35 32 17 linuron degradate 217 124 341 36 24 16 Fipronil Desulfinyl 652 40 692 6 28 14 Thiobencarb 704 141 845 17 32 13 Methoxyfenozide 107 287 394 73 23 11 Fluridone 163 80 243 33 26 11 Fluxapyroxad 261 77 338 23 23 11 Diazinon 973 151 1124 13 40 11 Imidacloprid 405 126 531 24 30 10 Chlorpyrifos 960 170 1130 15 41 10 Pendimethalin 623 76 699 11 29 10 Atrazine 739 62 801 8 30 10 Fipronil 642 50 692 7 28 9 Carbaryl 754 52 806 6 37 9 Pyrimethanil 418 18 436 4 26 9 Dichlorobenzenamine, 126 84 210 40 8 8 3,4- Permethrin, Total 633 102 735 14 31 8 Bifenthrin 606 91 697 13 31 8 Oxyfluorfen 763 44 807 5 31 8 Fipronil Sulfone 660 26 686 4 27 8 Chlorantraniliprole 130 55 185 30 22 7 Dithiopyr 250 88 338 26 23 7 Linuron 429 66 495 13 14 7 Oryzalin 431 50 481 10 28 7 Piperonyl Butoxide 565 52 617 8 26 7 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 46
Cyprodinil 425 11 436 3 26 7 Dieldrin 113 24 137 18 11 6 Cyhalothrin, Total 446 77 523 15 29 6 lambda- Propiconazole 600 69 669 10 26 6 EPTC 670 25 695 4 26 6 Imazalil 424 12 436 3 26 6 Tetraconazole 604 13 617 2 26 6 HCH, alpha- 32 22 54 41 9 5 Cyfluthrin, total 505 78 583 13 31 5 Tetramethrin 446 68 514 13 30 5 Dacthal 523 52 575 9 29 5 Malathion 819 77 896 9 35 5 Iprodione 417 25 442 6 26 5 DDD(p,p') 464 23 487 5 30 5 DDE(p,p') 464 23 487 5 30 5 Myclobutanil 659 24 683 4 26 5 Penoxsulam 206 7 213 3 23 5 Carbofuran 768 23 791 3 33 5 Chlorothalonil 449 11 460 2 34 5 Fenhexamid 429 7 436 2 26 5 Fipronil Sulfide 675 11 686 2 27 5 Napropamide 444 7 451 2 28 5 Deltamethrin/Tralomet 2 72 74 97 4 4 hrin Esfenvalerate/Fenvale 4 70 74 95 4 4 rate, Total Demeton, Total 76 68 144 47 6 4 Aldrin 26 21 47 45 9 4 Endosulfan Sulfate 90 21 111 19 9 4 Disulfoton 387 68 455 15 13 4 Dimethoate 296 52 348 15 10 4 T-Fluvalinate 446 68 514 13 30 4 Fenpropathrin 450 67 517 13 31 4 Cypermethrin, Total 504 73 577 13 30 4 Propoxur 222 28 250 11 9 4 Methomyl 261 30 291 10 10 4 Fluometuron 224 22 246 9 7 4 DCPU 385 28 413 7 23 4 Dichlorvos 387 16 403 4 8 4 Metalaxyl 626 18 644 3 23 4 Trifloxystrobin 611 6 617 1 26 4 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 47
Methyl (3,4- 9 9 100 3 3 dichlorophenyl)carba mate Parathion, Ethyl 6 67 73 92 5 3 Chlordane 2 20 22 91 4 3 Pentachlorophenol 5 16 21 76 6 3 Toxaphene 9 17 26 65 8 3 Glyphosate 97 152 249 61 8 3 Ethion 72 67 139 48 6 3 Endrin Aldehyde 23 20 43 47 7 3 Endrin Ketone 23 20 43 47 7 3 Endosulfan I 27 20 47 43 9 3 Endosulfan II 27 20 47 43 9 3 Heptachlor 27 20 47 43 9 3 Heptachlor Epoxide 27 20 47 43 9 3 HCH, gamma- 32 20 52 38 8 3 HCH, beta- 34 20 54 37 9 3 HCH, delta- 34 20 54 37 9 3 Endrin 37 20 57 35 9 3 Methoxychlor 39 20 59 34 10 3 Barban 41 21 62 34 6 3 Monuron 41 21 62 34 6 3 Fenuron 42 21 63 33 7 3 Neburon 42 21 63 33 7 3 Propham 42 21 63 33 7 3 Chlorpropham 45 21 66 32 8 3 Chlordane, cis- 25 10 35 29 8 3 Chlordane, trans- 25 10 35 29 8 3 Methiocarb 87 21 108 19 10 3 Bentazon 184 30 214 14 5 3 Allethrin 450 66 516 13 30 3 Parathion, Methyl 629 67 696 10 35 3 Azinphos Methyl 697 67 764 9 27 3 Siduron 225 21 246 9 7 3 Triclopyr 229 20 249 8 8 3 Oxamyl 270 21 291 7 10 3 DDT(p,p') 467 20 487 4 30 3 Trifluralin 598 20 618 3 30 3 Thiamethoxam 336 6 342 2 27 3 Pheophytin a 4 4 100 2 2 AMPA 2 154 156 99 2 2 Trichlorfon 2 4 6 67 2 2 atrazine degradate 68 63 131 48 2 2 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 48
Demethyl hexazinone 95 88 183 48 2 2 B Metolachlor SA 104 79 183 43 2 2 desthio- 205 101 306 33 21 2 prothioconazole degradate lambda cyhalothrin 93 32 125 26 2 2 degradate metolachlor degradate 300 99 399 25 2 2 trans-Propiconazole 50 16 66 24 2 2 cis-Propiconazole 51 15 66 23 2 2 Paraquat 46 13 59 22 5 2 flubendiamide 210 56 266 21 2 2 alachlor degradate 213 53 266 20 2 2 oxamyl degradate 109 24 133 18 2 2 molinate degradate 222 44 266 17 2 2 Tebuthiuron 253 50 303 17 5 2 hydroxysimazine 152 29 181 16 2 2 Tetrachlorvinphos 76 13 89 15 6 2 Mevinphos 79 13 92 14 8 2 Hydroxymetolachlor 161 22 183 12 2 2 2,4-d 167 22 189 12 5 2 sulfometuron methyl 163 21 184 11 2 2 norflurazon 167 20 187 11 4 2 Bromacil 215 25 240 10 7 2 Prometryn 632 54 686 8 28 2 tebuthiuron degradate 489 41 530 8 2 2 Butylate 584 48 632 8 25 2 hexazinone degradate 871 60 931 6 2 2 Naled 205 14 219 6 9 2 metolachlor oxa 125 8 133 6 2 2 (oxanilic acid) sulfosulfuron 125 8 133 6 2 2 degradate Thiabendazole 201 12 213 6 23 2 pendimethalin 124 7 131 5 2 2 degradate Fluopyram 90 5 95 5 14 2 metribuzin degradate 249 12 261 5 2 2 Ethoprop 325 13 338 4 7 2 chlorothalonil 128 5 133 4 2 2 degradate Propanil 690 23 713 3 27 2 Dinotefuran 284 8 292 3 25 2 Oxadiazon 330 8 338 2 23 2 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 49
Clothianidin 334 8 342 2 27 2 nicarbazin 130 3 133 2 2 2 nicosulfuron 181 3 184 2 2 2 fipronil degradate 131 2 133 2 2 2 prometryn degradate 131 2 133 2 2 2 Flonicamid 210 3 213 1 23 2 Pyraclostrobin 609 8 617 1 26 2 terbuthylazine 246 3 249 1 2 2 Quinoxyfen 334 4 338 1 23 2 4- 181 2 183 1 2 2 Hydroxychlorothalonil sulfosulfuron 181 2 183 1 2 2 Fipronil Amide 182 2 184 1 3 2 Prometon 676 7 683 1 26 2 Fipronil Desulfinyl 238 2 240 1 5 2 Amide Acibenzolar-S-methyl 336 2 338 1 23 2 Flusilazole 428 2 430 0 25 2 desulfinyl fipronil 430 2 432 0 23 2 amide Difenoconazole 434 2 436 0 26 2 triallate 517 2 519 0 23 2 Benzoic Acid 12 12 100 1 1 Dibromo-3- 12 12 100 1 1 Chloropropane, 1,2- Diquat 12 12 100 1 1 EPN 12 12 100 1 1 Iodomethane 2 2 100 1 1 Merphos 4 12 16 75 3 1 Sulfotep 6 12 18 67 3 1 metolachlor esa 83 50 133 38 2 1 (ethane sulfonic acid) Demethyl norflurazon 135 48 183 26 2 1 Aminocarb 41 12 53 23 4 1 Chloroxuron 41 12 53 23 4 1 Mexacarbate 41 12 53 23 4 1 Propachlor 41 12 53 23 4 1 Benomyl 42 12 54 22 5 1 Dechlorometolachlor 146 37 183 20 2 1 Bolstar 77 12 89 13 6 1 Fenchlorphos 83 12 95 13 6 1 Fensulfothion 83 12 95 13 6 1 Tokuthion 83 12 95 13 6 1 s-cypermethrin 76 5 81 6 6 1 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 50
Aldicarb 270 12 282 4 8 1 Metolachlor OA 176 7 183 4 2 1 Coumaphos 344 12 356 3 25 1 ethoprop degradate 257 8 265 3 2 1 Fenthion 421 12 433 3 27 1 Dimethenamid 179 4 183 2 2 1 metribuzin 246 3 249 1 2 1 Prodiamine 334 4 338 1 23 1 4-Hydroxyhexazinone 181 2 183 1 2 1 A acephate 181 2 183 1 2 1 Desamino metribuzin 181 2 183 1 2 1 imazethapyr 182 2 184 1 2 1 Cyanazine 297 3 300 1 5 1 tebufenozide 198 2 200 1 10 1 Famoxadone 536 4 540 1 26 1 Cyhalothrin 427 3 430 1 25 1 glufosinate 155 1 156 1 2 1 Methomyl oxime 160 1 161 1 2 1 Acetochlor 2nd amide 182 1 183 1 2 1 Ametryn 182 1 183 1 2 1 Hexazinone TP C 182 1 183 1 2 1 Hexazinone TP G 182 1 183 1 2 1 imazamox, ammonium 182 1 183 1 2 1 salt Tebuthiuron TP 109 182 1 183 1 2 1 (OH) Aldicarb sulfone 183 1 184 1 2 1 Cyantraniliprole 212 1 213 0 23 1 Diflubenzuron 226 1 227 0 5 1 MCPA 231 1 232 0 7 1 disulfoton sulfone 248 1 249 0 2 1 methyl paraoxon 248 1 249 0 2 1 Fonofos 254 1 255 0 4 1 Metconazole 612 2 614 0 26 1 Tebuconazole 615 2 617 0 26 1 azinphos-methyl-oa 390 1 391 0 15 1 Benfluralin 406 1 407 0 25 1 Indoxacarb 518 1 519 0 23 1 Propargite 588 1 589 0 25 1 Propyzamide 682 1 683 0 26 1 Molinate 685 1 686 0 28 1 Methidathion 769 1 770 0 33 1 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 51
B Compounds with lacking threshold values
Acibenzolar-S-methyl MCPA Aldrin_Total Methomyl oxime atrazine degradate methyl paraoxon 4-Hydroxyhexazinone A metolachlor degradate Acetochlor 2nd amide Metolachlor SA alachlor degradate metribuzin degradate azinphos-methyl-oa Mevinphos chlorothalonil degradate molinate degradate DDE(p,p') nicarbazin Cyhalothrin oxamyl degradate DCPA Paraquat_Quinoxyfen DCPMU pendimethalin degradate DCPU Prodiamine DDD(p,p') prometryn degradate DDT(p,p') sulfosulfuron degradate Dechlorometolachlor_ tebuthiuron degradate_Dissolved Demethyl hexazinone B Tebuthiuron TP 109 (OH)_Dissolved Demethyl norflurazon Demeton, Total_Total Desamino metribuzin_ desthio-prothioconazole degradate desulfinyl fipronil amide Dieldrin ethoprop degradate Dichlorobenzenamine, 3,4- Famoxadone Fipronil Desulfinyl Fipronil Sulfide Fipronil Amide fipronil degradate Fipronil Desulfinyl Amide Fipronil Sulfone flubendiamide Fluopyram Fluxapyroxad hexazinone degradate Hydroxymetolachlor Hydroxysimazine Flusilazole_Dissolved Fonofos_Dissolved HCH, alpha-_Total Imazethapyr Imazalil Metconazole metolachlor esa (ethane sulfonic acid) Iprodione lambda cyhalothrin degradate linuron degradate Delta RMP TAC Meeting Agenda Package 2019-07-14, page 52
C SSD Metadata available per compound
MS- #ToxDat #Speci #TaxCla QualitySco Substance CAS PAF a es ss re Remarks 2,4-d 94757 TRUE 98 51 9 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs acephate 30560191 TRUE 111 38 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 13515854 Acibenzolar-S-methyl 2 TRUE 10 8 5 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Aldicarb sulfone 1646884 TRUE 9 4 2 43 Lumped data - marginally enough species (3-5) for ERA with SSDs Aldrin 309002 TRUE 86 61 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Allethrin 584792 TRUE 28 23 3 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Ametryn 834128 TRUE 52 32 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs AMPA 1066519 TRUE 7 5 5 43 Lumped data - marginally enough species (3-5) for ERA with SSDs Atrazine 1912249 TRUE 328 134 20 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 13186033 Azoxystrobin 8 TRUE 28 19 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Benfluralin 1861401 TRUE 29 14 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Bentazon 25057890 TRUE 14 11 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Bifenthrin 82657043 TRUE 50 24 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 18842585 Boscalid 6 TRUE 11 11 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Bromacil 314409 TRUE 27 13 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Butylate 2008415 TRUE 36 8 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Carbaryl 63252 TRUE 373 161 14 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Carbendazim 10605217 TRUE 64 29 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Carbofuran 1563662 TRUE 135 61 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 50000845 Chlorantraniliprole 7 TRUE 23 16 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Delta RMP TAC Meeting Agenda Package 2019-07-14, page 53
Chlorothalonil 1897456 TRUE 178 66 15 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 99999999 chlorothalonil degradate 5 FALSE 5 4 3 43 Lumped data - marginally enough species (3-5) for ERA with SSDs Chlorpyrifos 2921882 TRUE 572 195 17 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs cis-Propiconazole 60207901 TRUE 96 38 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Clomazone 81777891 TRUE 13 9 5 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 21088092 Clothianidin 5 TRUE 15 11 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Copper 7440508 TRUE 116 71 14 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Cyfluthrin, total 68359375 TRUE 54 21 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Cyhalothrin 68085858 TRUE 14 8 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Cyhalothrin, Total lambda- 91465086 TRUE 52 22 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Cypermethrin, Total 52315078 TRUE 186 84 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 12155261 Cyprodinil 2 TRUE 22 18 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Dacthal 1861321 TRUE 29 11 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs DDD(p,p') 72548 TRUE 31 25 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs DDE(p,p') 72559 TRUE 14 14 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs DDT(p,p') 50293 TRUE 322 182 13 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Deltamethrin/Tralomethrin 52918635 TRUE 177 62 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 99999903 Demeton, Total 95 FALSE #N/A #N/A #N/A #N/A #N/A Diazinon 333415 TRUE 237 95 11 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Dichlorobenzenamine, 3,4- 95761 TRUE 108 32 13 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Dichlorvos 62737 TRUE 186 97 11 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Dieldrin 60571 TRUE 154 108 10 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 11944668 Difenoconazole 3 TRUE 21 12 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Diflubenzuron 35367385 TRUE 89 40 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Extrapolated from Chronic NOEC - Officially enough species (>10) for ERA Dimethenamid 87674688 TRUE 24 13 7 31 with SSDs Dimethoate 60515 TRUE 150 82 11 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Delta RMP TAC Meeting Agenda Package 2019-07-14, page 54
Dinotefuran #N/A #N/A #N/A #N/A #N/A #N/A #N/A Disulfoton 298044 TRUE 73 27 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs disulfoton sulfone 2497065 TRUE 5 4 2 43 Lumped data - marginally enough species (3-5) for ERA with SSDs Dithiopyr 97886458 TRUE 11 7 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Diuron 330541 TRUE 141 82 14 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Endosulfan Sulfate 1031078 TRUE #N/A #N/A #N/A #N/A #N/A EPTC 759944 TRUE 79 25 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Esfenvalerate/Fenvalerate, Total 66230044 TRUE 50 17 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Ethoprop 13194484 TRUE 67 21 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 13180757 Famoxadone 3 TRUE 10 7 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 12683317 Fenhexamid 8 TRUE 11 7 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 12006837 Fipronil 3 TRUE 59 30 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Fipronil Desulfinyl #N/A #N/A #N/A #N/A #N/A #N/A #N/A Fipronil Sulfide #N/A #N/A #N/A #N/A #N/A #N/A #N/A Fipronil Sulfone #N/A #N/A #N/A #N/A #N/A #N/A #N/A 15806267 Flonicamid 0 TRUE 10 7 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 27245165 flubendiamide 7 TRUE 23 15 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Fluometuron 2164172 TRUE 61 27 9 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 65806635 Fluopyram 4 TRUE 17 13 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Fluridone 59756604 TRUE 61 20 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Flusilazole 85509199 TRUE #N/A #N/A #N/A #N/A #N/A 90720431 Fluxapyroxad 3 TRUE 6 3 3 43 Lumped data - marginally enough species (3-5) for ERA with SSDs Fonofos 944229 TRUE 39 16 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs glufosinate 51276472 TRUE 5 3 1 43 Lumped data - marginally enough species (3-5) for ERA with SSDs Delta RMP TAC Meeting Agenda Package 2019-07-14, page 55
Glyphosate 1071836 TRUE 75 36 10 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs HCH, alpha- 319846 TRUE 12 9 5 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Hexazinone 51235042 TRUE 44 27 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Hexazinone TP C #N/A #N/A #N/A #N/A #N/A #N/A #N/A Hexazinone TP G #N/A #N/A #N/A #N/A #N/A #N/A #N/A Extrapolated from Chronic NOEC - Enough species (6-10) for ERA with Imazalil 35554440 TRUE 6 6 2 32 SSDs 11431132 imazamox, ammonium salt 9 TRUE 20 10 6 42 Lumped data - Enough species (6-10) for ERA with SSDs imazethapyr 81335775 TRUE 17 10 5 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 13826141 Imidacloprid 3 TRUE 59 35 9 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 17358444 Indoxacarb 6 TRUE 17 8 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Iprodione 36734197 TRUE 30 10 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Linuron 330552 TRUE 42 20 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs linuron degradate #N/A #N/A #N/A #N/A #N/A #N/A #N/A Malathion 121755 TRUE 475 208 11 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs MCPA 94746 TRUE 40 22 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Metalaxyl 57837191 TRUE 48 21 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 12511623 Metconazole 6 TRUE #N/A #N/A #N/A #N/A #N/A Methidathion 950378 TRUE 49 26 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Methomyl 16752775 TRUE 129 49 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 16105058 Methoxyfenozide 4 TRUE 6 6 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs methyl paraoxon 950356 TRUE #N/A #N/A #N/A #N/A #N/A Metolachlor 51218452 TRUE 66 33 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs metribuzin 21087649 TRUE 41 16 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Mevinphos 7786347 TRUE 85 33 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Molinate 2212671 TRUE 93 40 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Myclobutanil 88671890 TRUE 13 10 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Delta RMP TAC Meeting Agenda Package 2019-07-14, page 56
Naled 300765 TRUE 63 34 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Napropamide 15299997 TRUE 26 12 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs nicarbazin #N/A #N/A #N/A #N/A #N/A #N/A #N/A 11199109 nicosulfuron 4 TRUE 23 6 3 42 Lumped data - Enough species (6-10) for ERA with SSDs norflurazon 27314132 TRUE 11 7 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Oryzalin 19044883 TRUE 13 9 4 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Oxadiazon 19666309 TRUE 35 14 3 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Oxyfluorfen 42874033 TRUE 22 13 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Paraquat 4685147 TRUE 45 29 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Pendimethalin 40487421 TRUE 62 31 9 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Penoxsulam #N/A #N/A #N/A #N/A #N/A #N/A #N/A Permethrin, Total 52645531 TRUE 314 104 10 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Piperonyl Butoxide 51036 TRUE 64 29 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Prodiamine #N/A #N/A #N/A #N/A #N/A #N/A #N/A Prometon 1610180 TRUE 27 13 3 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Prometryn 7287196 TRUE 38 23 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Propanil 709988 TRUE 51 34 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Propargite 2312358 TRUE 30 18 5 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Propiconazole 60207901 TRUE 96 38 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Propoxur 114261 TRUE 140 53 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Propyzamide 23950585 TRUE 20 11 3 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 17501318 Pyraclostrobin 0 TRUE 14 8 5 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Pyrimethanil 53112280 TRUE 15 13 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 12449518 Extrapolated from Chronic NOEC - Officially enough species (>10) for ERA Quinoxyfen 7 TRUE 20 11 7 31 with SSDs s-cypermethrin 52315078 TRUE 186 84 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Simazine 122349 TRUE 101 56 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Delta RMP TAC Meeting Agenda Package 2019-07-14, page 57
sulfometuron methyl #N/A #N/A #N/A #N/A #N/A #N/A #N/A 14177632 sulfosulfuron 1 TRUE 6 6 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 10753496 Tebuconazole 3 TRUE 30 13 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 11241023 tebufenozide 8 TRUE 19 14 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Tebuthiuron 34014181 TRUE 13 9 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs terbuthylazine 5915413 TRUE 31 17 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Tetrachlorvinphos 22248799 TRUE 10 8 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 11228177 Tetraconazole 3 TRUE 14 8 5 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Tetramethrin 7696120 TRUE 13 6 2 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 10285106 T-Fluvalinate 9 TRUE 13 7 2 2 Not extrapolated - Enough species (6-10) for ERA with SSDs Thiabendazole 148798 TRUE 31 10 3 2 Not extrapolated - Enough species (6-10) for ERA with SSDs 15371923 Thiamethoxam 4 TRUE 16 15 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Thiobencarb 28249776 TRUE 112 49 10 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs trans-Propiconazole 60207901 TRUE 96 38 7 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs triallate 2303175 TRUE 35 17 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Trichlorfon 52686 TRUE 168 84 12 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Triclopyr 55335063 TRUE 28 14 4 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs 14151721 Trifloxystrobin 7 TRUE 16 12 6 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs Trifluralin 1582098 TRUE 95 48 8 1 Not extrapolated - Officially enough species (>10) for ERA with SSDs
not included in msPAF #N/A tool TRUE/FALSE included in MS-PAF tool Delta RMP TAC Meeting Agenda Package 2019-07-14, page 58
Materials for Agenda Item 7
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 59
MEMO
To: Technical Advisory Committee
From: Matthew Heberger, program manager
Date: July 10, 2019
Subject: Proposed new assessment question for pesticides
The management and assessment questions were developed in 2014 and published in the initial Monitoring Design Summary published in 2015. (See attachment 2.) The TAC has recommended that each subcommittee review these questions as a part of the multi-year planning process.
This spring, the Pesticides Subcommittee discussed this subject (meeting summary here), and recommended that the program consider adding a management question related to human health and drinking water impacts. There is broad public concern about this issue. In the past, it was assumed that the threshold concentrations for human health impacts of most pesticides was much much higher than the ecotoxicological thresholds. However, there is mounting evidence that this is no longer true. A recent study concluded that ¼ to ⅓ of pesticides pose risks to human health from chronic exposure, often due to endocrine disruption or carcinogenicity.1
The subcommittee directed staff draft a new assessment question related to drinking water impacts for consideration by the TAC and SC. The current Delta RMP Management and Assessment Questions for Pesticides and Aquatic Toxicity are shown below, with the proposed new question inserted in blue text.
1 Moran, Kelly D, and Bonny Starr. “Is Protecting Aquatic Life from Pesticides Sufficient to Ensure Human Health Protection in Sources of Drinking Water?” 16 pages. Sacramento River Source Water Protection Program, 2018. See attached.
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 60
Excerpts from the 2015 Monitoring Design:
Management Questions (for all monitoring elements)
Status & Trends
Is there a problem or are there signs of a problem? a. Is water quality currently, or trending towards, adversely affecting beneficial uses of the Delta? b. Which constituents may be impairing beneficial uses in subregions of the Delta? c. Are trends similar or different across different subregions of the Delta?
Assessment Questions for Pesticides & Toxicity
1. To what extent do current use pesticides contribute to observed toxicity in the Delta? 1.1. Which pesticides have the highest potential to be causing toxicity in the Delta and therefore should be the priority for monitoring or management? 1.1.1. If samples are toxic, do detected pesticides explain the toxicity? 1.1.2. If samples are not toxic, do detected pesticide concentrations exceed other thresholds of concern (e.g., water quality objectives or Office of Pesticide Programs aquatic toxicity benchmarks)? 1.2. What are the spatial and temporal extents of lethal and sublethal water column and sediment toxicity observed in the Delta? 1.3. Do water column or sediment toxicity tests at targeted sites indicate a toxic response? 1.4. B. If answer to 1.3 is yes, which other toxicity indicator(s) should guide monitoring and management of pesticides in Years 2+? 2. What are the spatial/temporal distributions of concentrations of current use pesticides identified as likely causes of observed toxicity? 2.1. Which pesticides have the highest risk potential (based on DPR’s risk prioritization model ) and should be included in chemical analyses? 2.1.1. Is the list of pesticides included in USGS pesticide scan sufficient for Delta RMP monitoring design? 2.1.2. Are methods available to monitor pesticides with high-risk potential not included in USGS pesticide scan? 2.2. How do concentrations of the pesticides with the highest risk potential vary seasonally and spatially?
Proposed New Question: 3. To what extent to current use pesticides contribute to human health risk in the Delta? 3.1. Do pesticides occur at concentrations that exceed water quality regulatory values and benchmarks (“reference values”) for human health? Delta RMP TAC Meeting Agenda Package 2019-07-14, page 61
Is Protecting Aquatic Life from Pesticides Sufficient to Ensure Human Health Protection in Sources of Drinking Water? Kelly D. Moran, Ph.D., TDC Environmental, LLC Bonny Starr, P.E., Starr Consulting October 1, 2018 Abstract California water and pesticides regulators have long operated under the informal assumption that programs to protect aquatic life from currently used pesticides will also ensure the safety of surface water drinking water sources. This paper examines the scientific validity of this assumption for the agricultural pesticides in California’s Central Valley by comparing water quality regulatory values and benchmarks (“reference values”) for human health with those for aquatic life. Because numeric water quality criteria and other numeric regulatory values established for water quality protection exist for only a handful of currently used pesticides, the comparison relies heavily on US EPA pesticides human health and aquatic life benchmarks. For acute endpoints, both human health and aquatic life reference values typically use a one-day exposure time frame, but chronic endpoint exposure periods differ, with aquatic life exposure periods (4 to 60 days) usually shorter than human health exposure periods (annual). The evaluation looked in detail at 301 agricultural pesticides with human health reference values. Of these 301 pesticides, only 46% had aquatic life reference values that were equal to or lower than the human health reference value. For 54% of these pesticides, either no aquatic life reference value existed or the aquatic life reference value was higher than the human health reference value. In these cases, aquatic life protection actions would not suffice to protect human health. INTRODUCTION Drinking water quality protection is among California’s highest priorities for its water quality programs. This is evident in the State Water Quality Control Board’s (State Water Board’s) mission, which is “to preserve, enhance, and restore the quality of California’s water resources and drinking water for the protection of the environment, public health, and all beneficial uses, and to ensure proper water resource allocation and efficient use, for the benefit of present and future generations.” California draws its drinking water from both surface waters and groundwater. Protecting these waters from pesticides pollution poses special challenges due the large number of pesticide chemicals, their inherent toxicity, and continual changes in the pesticides used. Three major Federal laws regulate pesticides in sources of drinking water: the Clean Water Act (CWA), the Safe Drinking Water Act (SDWA), and the Federal Insecticide, Fungicide and Rodenticide Act (FIFRA). The US Environmental Protection Agency (EPA), which implements all three laws, has never integrated their implementation. Similarly, California EPA, which implements these Federal laws and additional state laws, does not have an integrated implementation framework. In response to pesticides groundwater pollution, the California legislature passed the Pesticide Contamination Prevention Act, which establishes a special interagency framework for monitoring and management of pesticides in groundwater drinking water sources. Although no special framework exists for management of pesticides in surface water sources of drinking water, California pesticides and water quality regulators have authorities and obligations under both California and Federal law to prevent pesticides pollution of drinking water sources.
Prepared for the Sacramento River Source Water Protection Program Page 1 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 62 Final Draft
According to California Department of Pesticide Regulation (DPR) data, of the >1,000 currently California registered pesticide chemicals,1 927 were reported sold in 2015,2 and about 300 were reported used in volumes >5,000 pounds statewide.3 Pesticides flow into sources of drinking water from drift, with seepage and discharges from flooded pesticide-treated fields, with runoff from agricultural and urban areas, and in wastewater treatment plant effluent. The human health hazard posed by a pesticide in drinking water depends on two pesticide-specific factors: the pesticide’s inherent toxicity and the exposure level. California water and pesticides regulators have long operated under the informal assumption that programs to protect aquatic life from pesticides will also ensure human health protection in California’s surface water sources of drinking water. This paper examines the scientific validity of this assumption for currently used pesticides. REFERENCE VALUES Water quality managers generally use available numeric reference values to facilitate determination if a currently used pesticide detected in surface water may indicate a potential human health or aquatic life risk. Comparing the reference values for human health to those for aquatic life provides insights as to whether aquatic-life protection decisions based on available reference values will also suffice to protect human health. Available reference values include regulatory standards and values and US EPA pesticides benchmarks. Standards for Currently Used Pesticides in Sources of Drinking Water The Clean Water Act established the national policy prohibiting the discharge of toxic pollutants in toxic amounts. (33 United States Code 1251). To implement this policy, California water quality protection programs have adopted both narrative and numeric standards for pesticides in surface waters. Narrative objectives typically drive implementation of drinking water source protection from pesticides, as numeric water quality criteria and other numeric regulatory values established for human drinking water quality protection exist for only a handful of currently used pesticides. Ideally, monitoring and management programs would flow directly from the narrative objective, i.e., be based on toxicity measurements. This is impossible in the case of human toxicity. No indicator organisms are available to test the toxicity of drinking water sources to humans. While bioanalytical methods may soon be available to examine one or two modes of toxicity, no method to examine the plethora of human toxicity endpoints is currently reasonably foreseeable. This forces managers to use pesticide-specific values to implement the narrative human toxicity objective. Tables 1 and 2 summarize the fresh water numeric water quality regulatory values for pesticides currently registered for use in California. These include values adopted under the CWA, the SDWA, and related state laws, but exclude location-specific values that may have been adopted by California Regional Water Quality Control Boards (e.g., water quality objectives, TMDL targets). FIFRA and California pesticides law do not involve the establishment of numeric regulatory values for pesticides in surface water. Under the CWA, US EPA establishes both human health and aquatic life protection regulatory values for current pesticides in surface water (see Table 1). These include enforceable standards
1 DPR 2017. List of “Actively Registered AI's by Common Name.” Downloaded August 31, 2017. 2 DPR 2017. Pounds Sold Report. Year 2016. Generated July 11, 2017. 3 DPR 2017. Pesticide Use Reporting System. Report generated August 31, 2017. Prepared for the Sacramento River Source Water Protection Program Page 2 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 63 Final Draft
(in bold) and national recommended criteria. CWA human health regulatory values are designed for protection of humans consuming both surface water and organisms that live within surface waters (Human Health columns in Table 1). A separate set of CWA human health regulatory values (not included here) exists for consumption of organisms only from waters that are not sources of drinking water. CWA aquatic life protection values are designed to protect aquatic ecosystems (Aquatic Life columns in Table 1). The science-based values in Table 1 only have regulatory force when adopted as enforceable standards in state or region-specific regulatory documents. For various legal reasons, US EPA Region 9 established California’s CWA pesticides water quality standards in the year 2000 (known as the California Toxics Rule [CTR]4) (bold values in Table 1). US EPA CWA standards for pesticides chemicals that are not in this regulation and science-based CWA water quality criteria updates since the regulation’s adoption are not currently part of California’s CWA regulatory program. Table 1. Clean Water Act Water Quality Criteria for Current Use Pesticides (µg/L) Human Health Aquatic Life (for consumption of water (fresh water) and organisms) Criterion Current Current Criterion Maximum Continuous Pesticide Enforceable US EPA Concentration Concentration (CTR) Recommended (CMC) (CCC) 1,3-dichloropropene 10 0.27 -- -- Acrolein 320 3 3 3 Carbaryl -- -- 2.1 2.1 Chlorpyrifos -- -- 0.083 0.041 2,4-D -- 1,300 -- -- Diazinon -- -- 0.17 0.17 Endosulfan 110 20 0.22 0.056 Malathion ------0.1 Methyl bromide 48 100 -- -- Pentachlorophenol 0.28 0.03 19 15 Phenol 21,000 4,000 -- -- Bold Values = Adopted California Regulatory Values from US EPA California Toxics Rule (40 CFR Part 131) Sources: US EPA National Recommended Water Quality Criteria (https://www.epa.gov/wqc/national-recommended-water- quality-criteria accessed August 2017) and 40 CFR Part 131.
Under the SDWA and under California law, US EPA and the state of California establish human health protection regulatory values for current pesticides in drinking water sources (Table 2). Two types of Federal (SDWA) regulatory values exist: Maximum Contaminant Levels (MCLs) and Health Advisories (HAs).5 HAs serve as the technical guidance for unregulated drinking water contaminants to assist Federal, State and local officials, and managers of public or community water systems in protecting public health as needed. They are not to be construed as
4 40 CFR Part 131 5 US EPA 2018. 2018 Edition of the Drinking Water Standards and Health Advisories Tables (EPA 822-F-18-001) https://www.epa.gov/dwstandardsregulations/2018-drinking-water-standards-and-advisory-tables Prepared for the Sacramento River Source Water Protection Program Page 3 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 64 Final Draft
legally enforceable Federal standards.5 However, they are included in this paper as regulatory levels, because if they are exceeded actions need to be considered to protect public health. In addition to adopting Federal MCLs, California has established additional and more stringent drinking water source regulatory values under state law to address California-specific situations. These values include state-specific MCLs,6 Notification Levels (NLs),7 and Archived Advisory Levels (AALs).8,9 The relatively small number of SDWA and CWA human health regulatory standards for current use pesticides in surface waters is a direct consequence of differing data requirements for the implementation of the SDWA, CWA, and FIFRA. Pesticide registration data requirements established under FIFRA, though many and expensive for registrants, do not provide sufficient data to meet adopted SDWA and CWA requirements for establishing regulatory standards under those laws. Resource limitations also play a role in the inability of these regulatory programs to keep pace with the changing pesticides market. Filling the Management Gap – US EPA Human Health Benchmarks for Pesticides Recognizing the nation’s need to identify the potential for current use pesticides to cause human health hazards in drinking water supplies, in the 2010s, US EPA developed Human Health Benchmarks for Pesticides (HHBPs).10 This represented the first US EPA step toward integration of its SDWA, CWA, and FIFRA implementation programs. These values are designed to fill the gaps in available Safe Drinking Water Act regulatory values. To provide smooth integration with the SDWA, the HHBPs are calculated using the same methods that US EPA uses to calculate SDWA HAs. US EPA updates HHBPs periodically. The most recent update, reflecting 394 HHBPs for pesticides and pesticide degradates was published in January 2017. Together, the SDWA MCLs, HAs, California regulatory values, and the US EPA HHBPs provide a means to identify potential human health risks in sources of drinking water. Thanks to the development of the HHBPs, most common currently used pesticides now have a science- based drinking water reference value. US EPA has also developed similar benchmarks for pesticides hazards to aquatic life, the “Aquatic Life Benchmarks for Pesticide Registration” (ALBs).11 The most recent update, reflecting 584 ALBs for pesticides and pesticide degradates was published in late 2017. Benchmarks do not exist for all pesticides due to lack of relevant toxicity data (e.g., a data gap for aquatic invertebrates), because US EPA has not yet invested in completing the data reviews necessary for development of a benchmark, or because US EPA’s evaluation of toxicity data concluded that the chemical is relatively non-toxic.
6 California State Water Resources Control Board Division of Drinking Water 2018. Maximum Contaminant Levels and Regulatory Dates for Drinking Water, U.S. EPA vs. California https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/Chemicalcontaminants.html 7 California State Water Resources Control Board Division of Drinking Water 2018. Drinking Water Notification Levels and Response Levels: An Overview https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/NotificationLevels.html 8 California Department of Public Health (CDPH) 2010. CDPH’s Archived Advisory Levels for Drinking Water https://www.waterboards.ca.gov/water_issues/programs/tmdl/records/state_board/2010/ref3729.pdf 9 While some information sources describe NLs and AALs as non-regulatory, they have regulatory function as exceeding these values requires actions by drinking water suppliers. 10 US EPA Human Health Benchmarks for Pesticides (web resource) (updated January 2017) https://iaspub.epa.gov/apex/pesticides/f?p=HHBP:home:1871379433268262 11 US EPA Office of Pesticide Programs Aquatic Life Benchmarks (web resource) (August 2017) https://iaspub.epa.gov/apex/pesticides/f?p=HHBP:home:1871379433268262 Prepared for the Sacramento River Source Water Protection Program Page 4 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 65 Final Draft
Despite the general tendency to prioritize aquatic life protection in management approaches, some California agencies do use both sets of benchmarks. For example, California Department of Pesticide Regulation’s (DPR’s) Surface Water Monitoring Prioritization Model can be run with either set of benchmarks.12 Table 2. Safe Drinking Water Act and California State Drinking Water Source Regulatory Standards for Current Pesticides (µg/L) Primary Secondary Health Advisory Notification Archived Pesticide MCLa MCLa (Type)c Level Advisory Level 300 (10-day) 2,4-D 70 ------1,000 (1-day) Atrazine 1 (CA) ------Captan ------15 1,000 (1-day) Carbaryl ------700 40 (Cancer)b Chloropicrin ------50 Chlorpropham (CIPC) ------1,200 20 (1-day) Diazinon -- -- 1.2 -- 1 (Lifetime) 11,000 (1-day) p-Dichlorobenzene 5 (CA) ------75 (Lifetime) 30 (1-day) 1,3-Dichloropropene 0.5 (CA) ------0.4 (Cancer) Dimethoate ------1 Diquat 20 ------800 (1-day) Endothall 100 ------50 (Lifetime) 5,000 (10-day) Formaldehyde -- -- 10,000 (1-day) 100 -- 1,000 (Lifetime) Glyphosate 700 -- 20,000 (1-day) -- -- 200 (1-day) Malathion ------160 500 (Lifetime) Methylisothiocyanate ------190 (degradate)
N-methyl dithiocarbamate ------0.19
Oxamyl 50 (CA) -- 10 (1-day) -- -- Pentachloronitrobenzene ------20 (PCNB) 300 (10-day) 1,000 (1-day) Pentachlorophenol 1 ------40 (Lifetime) 0.09 (Cancer) 6,000 (1-day) Phenol ------4,200 2,000 (Lifetime) 40 (1-day) Propoxur (Baygon) ------30 3 (Lifetime) Simazine 4 ------Thiobencarb 70 (CA) 1 (CA) -- --
12 DPR 2015. SWPP Monitoring Prioritization Model User Manual (Version 3.0) Prepared for the Sacramento River Source Water Protection Program Page 5 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 66 Final Draft
aIf both California and Federal MCLs exist and are not identical, the lower value is provided. California MCLs are identified with (CA). bFor cancer used 10-6 risk for consistency with California laws and regulations (e.g., Proposition 65). c10-day HAs that are identical to 1-day HAs are not noted in this table. Sources: US EPA 2018. 2018 Edition of the Drinking Water Standards and Health Advisories Tables. EPA 822-F-18-001.; California State Water Board Division of Drinking Water 2018. Drinking Water Notification Levels and Response Levels: An Overview.; CDPH 2010. Archived Advisory Levels for Drinking Water; California State Water Board Division of Drinking Water 2018. Maximum Contaminant Levels and Regulatory Dates for Drinking Water, U.S. EPA vs. California Time Frames Associated with Human Health and Aquatic Life Reference Values All water quality standards and benchmarks are derived from toxicity data associated with specific exposure durations. Most standards and benchmarks include an exposure time frame, but some do not. Table 3 summarizes time frames that are specified in association with pesticide water quality standards and benchmarks and (where applicable) time frames used for compliance determination. Table 3. Time Frames Associated with Pesticide Water Quality Standards and Benchmarks (Where no specified value, common practice provided in italics) Acute Chronic/Cancer Category (Short-Term) (Long-Term) Clean Water Act Water Quality Criteria – Aquatic Life 1 hour 4 days Not specified Clean Water Act Water Quality Criteria – Human Health (30 days) Any confirmed Maximum Contaminant Level Annual Average exceedance* 1 day or 10 days Not specified Health Advisory (specified in listing) (Annual Average) Any confirmed Notification Level/Archived Advisory Level Annual Average exceedance* Not specified Human Health Benchmarks for Pesticides 1 day (Annual Average) Not specified Not specified Aquatic Life Benchmarks for Pesticide Registration (1 day) (varies) *A confirmed exceedance requires a response action but may not require use of the water source to be discontinued. Sources: US EPA National Recommended Water Quality Criteria; California Code of Regulations Title 22 Sections 64444 and 64449; US EPA 2018. 2018 Edition of the Drinking Water Standards and Health Advisories Tables. EPA 822-F-18-001.; Drinking Water Notification Levels and Response Levels: An Overview.; CDPH 2010. Archived Advisory Levels for Drinking Water; US EPA Human Health Benchmarks for Pesticides (web resource) (updated January 2017); US EPA Office of Pesticide Programs Aquatic Life Benchmarks (web resource) (August 2017).
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Clean Water Act water quality criteria for “Although the human health ambient water quality criteria (AWQC) are based on chronic health effects data (both cancer and non-cancer effects), the aquatic life protection criteria are intended to also be protective against adverse effects that may specify associated time reasonably be expected to occur as a result of elevated acute or short-term frames: Criterion exposures. That is, through the use of conservative assumptions with respect Maximum Concentration to both toxicity and exposure parameters, the resulting AWQC should provide – 1 hour; Criterion adequate protection not only for the general population over a lifetime of exposure, but also for special subpopulations who, because of high water or Continuous fish intake rates, or because of biological sensitivities, have an increased risk 13 Concentration – 4 days. of receiving a dose that would elicit adverse effects. The Agency recognizes Although Clean Water that there may be some cases where the AWQC based on chronic toxicity may Act human health not provide adequate protection for a subpopulation at special risk from criteria do not specify shorter-term exposures. The Agency encourages States, Tribes, and others employing the 2000 Human Health Methodology to give consideration to such implementation time circumstances in deriving criteria to ensure that adequate protection is frames, based on EPA afforded to all identifiable subpopulations.” guidance, they are US EPA (2013) “Human Health Ambient Water Quality Criteria and implemented based on Fish Consumption Rates: Frequently Asked Questions” 30-day averages.14 These time frames do not directly correlate with the human and aquatic toxicity testing that underlies these values. Anecdotal information suggests that these time frames were set to be deliberately conservative to be protective and recognizing that most monitoring regimes are not (due to cost) conducted with a frequency and sample duration that reflects actual environmental exposures. While Federal MCLs and California MCLs, NLs, and AALs are generally implemented on the basis of annual average concentrations, any single confirmed exceedance has consequences. Upon any confirmed exceedance of one of these levels, actions are required, such as increased monitoring or public notification. Significant exceedances and trends suggesting the likelihood of continued exceedances may trigger additional evaluation of source water protection options, treatment options, and potential alternative supplies. Short-term US EPA Safe Drinking Water Act HAs specify their time frames (1 day or 10 days).15 Similarly, acute HHBPs have a specified time frame of 1 day. Lifetime and cancer HAs and chronic HHBPs do not have specified time frames. The applicable time frame for these chronic human toxicity values depends on the sensitive life stage that is the basis of the level and may be as short as one year (e.g., infants). Typically, annual average concentrations are compared to these chronic human health reference values. For example, in pesticides risk assessments, US EPA Office of Pesticide Programs (OPP) uses annual mean water concentrations (highest single-year annual mean in 10 years of modeled exposure) to evaluate chronic human health risks from pesticides in drinking water supplies. For cancer risks, longer time frames may be used, such as US EPA OPP’s use of the estimated 30-year mean concentration to evaluate drinking water cancer risks. Aquatic Life Benchmarks for Pesticide Registration do not list specific timeframes.16 Acute values are usually based on data from short-term tests (i.e., 2-4 days for fish and aquatic invertebrates, up to 10 days for plants). Chronic values are usually based on life-cycle tests with
13 US EPA National Recommended Water Quality Criteria (web resource, August 2017) https://www.epa.gov/wqc/national-recommended-water-quality-criteria 14 US EPA Office of Water 2010. NPDES Permit Writers’ Manual. EPA-833-K-10-001. 15 US EPA 2018. 2018 Edition of the Drinking Water Standards and Health Advisories Tables. EPA 822-F-18-001. 16 See footnotes in US EPA Office of Pesticide Programs Aquatic Life Benchmarks https://www.epa.gov/pesticide- science-and-assessing-pesticide-risks/aquatic-life-benchmarks-and-ecological-risk Prepared for the Sacramento River Source Water Protection Program Page 7 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 68 Final Draft
species-specific durations (typically in the range of 10-30 days). Typically, to be protective, time frames used with these values should be no longer than the toxicity test duration, but there are exceptions. For example, US EPA OPP ecological risk assessments use estimated one-day concentrations to evaluate potential acute aquatic toxicity risks – a time frame shorter than most underlying toxicity data – but use 60-day mean concentrations to examine potential chronic risks, which is longer than the exposure period in many chronic toxicity tests. METHODS This analysis used as its starting point the Central Valley Water Board list of agricultural pesticides that may be used in California’s Central Valley, called the “Irrigated Lands Regulatory Program Pesticides Evaluation Protocol Executive Officer List of Pesticides.”17 This list of 373 pesticides includes pesticides registered in California as of October 31, 2016 that were identified as having potential to be used in agriculture.18 The list excludes substances considered to be low toxicity, like oils, clays, polymers, sulfur, solvents, soaps, petroleum, biopesticides, most mineral salts, adjuvants, and pheromones. To avoid potentially inaccurate comparisons due to chemical form changes in the environment, 20 metals and inorganic salts were excluded from the analysis. For completeness, the Water Board list was supplemented to add seven pesticides used only on rice (which were omitted from the Water Board list) and six pesticide degradates from the Central Valley Water Board Executive Officer List of Degradates19 that have human health reference values. This process created a list of 366 agricultural pesticides and degradates. For purposes of this analysis, “reference value” was defined to include CWA, SDWA, and California regulatory values (Tables 1 and 2) supplemented by US EPA pesticides benchmarks.20 Where multiple values exist for the same pesticide (e.g., the pesticide had values for different types of aquatic organisms or the pesticide had both a California MCL and a US EPA HHBP), the lowest human health value and the lowest aquatic life value were selected. To address nomenclature inconsistency among reference lists, when no reference value was initially identified, the search was expanded to include synonyms. Two searches for synonyms were done, using DPR’s chemical list search tool21 and the “synonyms” link from the US EPA pesticide search web page.22 Available reference values were obtained from these data sources and compiled into a single table (see Table 4, attached). RESULTS AND DISCUSSION Human health reference values were identified for 301 of the 366 agricultural pesticides. The remaining 65 pesticides did not have human health reference values and consequently were excluded from further analysis since no comparison would be possible. (Excluded pesticides have brown shading in Table 4).
17 Central Valley Water Board 2016. Irrigated Land Regulatory Program. Prioritizing and Selecting Pesticides for Surface Water Monitoring. (ILRP Pesticides Evaluation Protocol.) 18 Pesticides in the California DPR product/label database with uses in the “agricultural crops” site group category. 19 See Attachment 1 of Central Valley Water Board 2016. Irrigated Land Regulatory Program. Prioritizing and Selecting Pesticides for Surface Water Monitoring. 20 US EPA 2017 Human Health Benchmarks for Pesticides (web resource) (updated January 2017) https://iaspub.epa.gov/apex/pesticides/f?p=HHBP:home:1871379433268262 ; US EPA 2017 Office of Pesticide Programs Aquatic Life Benchmarks (web resource accessed August 2017) https://www.epa.gov/pesticide-science- and-assessing-pesticide-risks/aquatic-life-benchmarks-and-ecological-risk 21 http://www.cdpr.ca.gov/docs/label/chemcode.htm 22 http://iaspub.epa.gov/apex/pesticides/f?p=chemicalsearch:1:4374205614359044 Prepared for the Sacramento River Source Water Protection Program Page 8 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 69 Final Draft
Table 5 summarizes the outcome of the reference value comparison. Both aquatic life and human health reference values were identified for 235 pesticides; for the remaining 66, no aquatic life reference value was identified. Table 5. Summary Comparison of Human Health and Aquatic Life Reference Values for Current Use Agricultural Pesticides in California’s Central Valley Fraction of Category Number Evaluated Pesticides Human Health Reference Value, but no Aquatic Life Reference 66 22% Value Lowest Human Health Reference Value < Lowest Aquatic Life 97 32% Reference Value Lowest Human Health Reference Value > Lowest Aquatic Life 136 45% Reference Value Lowest Human Health Reference Value = Lowest Aquatic Life 2 1% Reference Value Pesticides Evaluated 301 Pesticides excluded due to lack of human health reference value 65
For 97 of the 235 pesticides, the lowest human health reference value was less than (a lower concentration than) the lowest aquatic life reference value. For 136 pesticides, the lowest aquatic life reference value was less than (a lower concentration than) the lowest human health reference value. For two pesticides (Acrolein, Thiobencarb), the lowest human health reference value and the lowest aquatic life reference value were equal. Only 64% of the pesticides examined had both human health and aquatic life reference values. About 36% of the evaluated pesticides did not have both human health and aquatic life reference values. Agencies that develop these reference values do not automatically develop reference values for every pesticide. Due to resource constraints and data gaps, agencies prioritize their resources toward those pesticides that based on available data appear to pose the greatest hazards to human health or aquatic life. The lack of a reference value might indicate data gaps or it might signal that the pesticide has relatively low toxicity to aquatic life or to humans. CONCLUSIONS For the group of agricultural pesticides evaluated, where human health reference values exist (301 pesticides), only 46% had aquatic life reference values that were equal to or lower than the human health reference value. For 54% of these pesticides, either no aquatic life reference value existed or the aquatic life reference value was higher. In these cases, aquatic life protection actions would not suffice to protect human health. No aquatic life reference value existed for 22% of the pesticides. Pesticides without aquatic life reference values are usually excluded from aquatic life protection management systems due to the lack of a value. For acute endpoints, both human health and aquatic life reference values typically use a one-day exposure time frame, but chronic endpoint exposure periods differ, with aquatic life exposure periods (4 to 60 days) usually shorter than human health exposure periods (annual). In general, evaluation based on shorter exposure time frames is more protective, suggesting that where aquatic reference values are lower than human health reference values, surface water
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management for aquatic life protection purposes would suffice to protect drinking water supplies. For human health standards and benchmarks with unspecified time frames, the common practice of using one day for acute and annual average for chronic appears appropriate for screening-level evaluations. Detailed evaluation of a specific pesticide in comparison to a standard or benchmark without a specified time frame should consider the time frame associated with the toxicity data underlying the pesticide-specific standard or benchmark, particularly in cases potentially leading to management actions (e.g., incidents of measured or projected exceedances).
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elta RMP TAC Meeting Agenda Package 2019-07-14, page 72
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elta RMP TAC Meeting Agenda Package 2019-07-14, page 73
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elta RMP TAC Meeting Agenda Package 2019-07-14, page 74
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elta RMP TAC Meeting Agenda Package 2019-07-14, page 75
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�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elta RMP TAC Meeting Agenda Package 2019-07-14, page 76
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
) $,#+!'# ) ) % -#, * /!*+2+ #&. *+) ) % -#, .** # + &) + &)0 %#0*!* /#, *$ +#*% *#+* % )6,* >=6C)!*"+& &%*!*+ %+.!+ #! &)%!#.% ) ,#+!&%*7 4 42)&'&*!+!&%CB8 9669$ %*%&) ) % -#, .*! %+! !
! $" %&) ,$% #+ % $)"* &) *+!! *G (,+! ! % $)"* &) *+!! *G )!%"!% + )+% ) *% #+ -!*&)! * /!$,$&%+$!%%+ - #G #+ -!*&)0G #! &)%!)!%"!% + ) ,#+&)0 - #* /!$,$&%+$!%%+ - #G G) !- -!*&)0 - # G&+! !+!&% - # 6*+#!* #! &)%!+ ),#!+0+% ) * #! &)%!&/!*,# G +!&%# &$$ % + ),#!+0)!+ )!G )!+ )!&%&%+!%,&,*&% %+)+!&%G )!+ )!&%$/!$,$&% %+)+!&%G ) * + )G #++ )G ,#! #+ &#*) %&+!%#, !%+ !*%#0*!*4 Delta RMP TAC Meeting Agenda Package 2019-07-14, page 77
Materials for Agenda Item 8
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 78
SWOT Analysis Worksheet
Strengths Weaknesses What do we do well? What could we improve? What unique resources can we draw on? Where do we have fewer resources than others? What do others see as our strengths? What are others likely to see as our weaknesses?
Opportunities Threats What opportunities are open to us? What threats could challenge our effectiveness? What trends could we take advantage of? What are other Delta monitoring efforts doing? How can we turn our strengths into opportunities? What threats do our weaknesses expose us to?
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 79
Materials for Agenda Item 9
Delta RMP TAC Meeting Agenda Package 2019-07-14, page 80 'HOWD 503 'HOLYHUDEOHV
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