### 2.3.1.2 | PBK modelling

A PBK model has been developed for acetamiprid and IM-2-1 for the OECD IATA Case Study no. 365 on acetamiprid (published in https://one.oecd.org/document/env/cbc/mono(2022)27/en/pdf and Annex I https://one.oecd.org/document/env/cbc/mono(2022)27/ann1/en/pdf) (OECD, 2022c). Furthermore, kinetic data were collected by the WG for possible rebuilding/adjustment of the PBK model.

## 2.3.2 | Methods

### 2.3.2.1 | Human biomonitoring data

Relevant studies were selected as described in the protocol (Appendix 1), which underwent a reliability assessment. The WG noted that the currently available appraisal tool for HOS was developed for studies addressing the relationship between compound exposure and a given (health) effect, whereas the studies to be evaluated for the exposure question should be assessed for reliability of the reported levels in biological fluids. Therefore, the WG developed a CAT tailored to assess the reliability of the reported levels of acetamiprid and/or IM-2-1 in the human biomonitoring studies. The CAT developed and applied can be found in Appendix 9. The studies were appraised by an expert of the WG and then reviewed by EFSA staff. The outcomes of the appraisals were discussed and agreed upon in a WG meeting.

### 2.3.2.2 | PBK modelling

A stepwise approach, as described in the protocol (Appendix 1) was followed to assess whether the available PBK model was adequate for the reverse dosimetry analysis (estimation of external (oral) acetamiprid dose related to reported concentrations of acetamiprid and IM-2-1 in biomonitoring studies) and forward dosimetry analysis (estimation of internal concentration of acetamiprid upon oral exposure to the HBGVs). Given that the question in step 3 of the stepwise approach ('Is the available PBK model adequate to answer the assessment question?') was answered as 'no' (see Section 2.3.3.2), it was evaluated whether the available PBK model can be rebuilt/adjusted. Details on the approach to be used for this assessment had not been predefined in the protocol. The WG considered it relevant to perform an 'ad hoc' data collection to assess the possibility of PBK model development. To that end, the steps as described below were followed.

a. Collection of kinetic data from studies reported in the revised RAR on acetamiprid (Netherlands, 2016)

b. Collection of kinetic data from the scientific peer-reviewed literature

* Set inclusion and exclusion criteria

* Literature search

* Data extraction

* Appraisal

c. Collection of kinetic data reported in the OECD IATA Case Study no. 365 on acetamiprid

d. Evaluation of whether the available kinetic data allows for PBK model development before use in reverse and forward dosimetry analyses

More details on the approach for kinetic data collection and appraisal can be found in Appendix 10.

## 2.3.3 | Assessment

### 2.3.3.1 | Results appraisal of human studies

Three HOS containing biomonitoring data were selected for appraisal (Figure 2). These include the study of Laubscher et al. (2022), as requested in the mandate and two other studies that were identified (Mahai et al., 2022; Oya et al., 2021). A short description of the three studies is provided in Table 17.

<table><caption>TABLE 17 Identified human biomonitoring studies for exposure estimation.</caption><thead><tr><td>Study</td><td>Subjects</td><td>Biological samples</td><td>Reported data on acetamiprid and/or metabolites</td></tr></thead><tbody><tr><td>Laubscher et al. (2022)</td><td>14 children (age range 3–18 years) treated for leukaemia and lymphomas and undergoing therapeutic lumbar punctures in the Lausanne University Hospital in Switzerland</td><td>Cerebrospinal fluid, plasma, urine</td><td>Acetamiprid was not detected (&lt; LOD) in any of the biological fluids analysed<br>Acetamiprid's metabolite IM-2-1 was detected above the LOQ in most of the analysed samples (13 cerebrospinal fluid samples [93%], 12 plasma samples [86%] and 13 urine samples [93%])</td></tr></tbody><tfoot><tr><td colspan="4">(Continues)</td></tr></tfoot></table>
<table><caption>TABLE 17 (Continued)</caption><thead><tr><th>Study</th><th>Subjects</th><th>Biological samples</th><th>Reported data on acetamiprid and/or metabolites</th></tr></thead><tbody><tr><td>Mahai et al. (2022)</td><td>408 pregnant women (average age of 29.5 years), residents of Wuhan undergoing prenatal cares and giving birth at Wuhan Women and Children Medical and Healthcare Center in Wuhan, Hubei Province, China</td><td>Urine samples collected in the three trimesters of pregnancy</td><td>For acetamiprid, detection frequencies amounted to 16.9%, 25.2% and 37.0% in the first, second and third trimester, respectively<br>For acetamiprid's metabolite IM-2-1, detection frequencies amounted to 99.8%, 99.3% and 100% in the first, second and third trimester, respectively</td></tr><tr><td>Oya et al. (2021)</td><td>1036 children (age range 16–23 months) living in urban and suburban locations in the Aichi prefecture, the central region of Japan</td><td>Extraction of disposable diapers, as surrogate for urine</td><td>The detection frequency for acetamiprid amounted to 37.3%<br>The detection frequency for acetamiprid's metabolite IM-2-1 amounted to 14.1%</td></tr></tbody></table>

The results of the reliability assessment performed for the selected human biomonitoring data sets is presented in **Table 18**. The studies of Laubscher et al. (2022) and Mahai et al. (2022) were scored as Tier 3, whereas the study of Oya et al. (2021) as Tier 2. A major limitation of the first two studies was the lack of quality assurance (QA)/quality control (QC), or lack of reporting on this. More details on the results of the appraisal can be found in Appendix 11.

<table><caption>TABLE 18 Heatmap results of the RoB for human biomonitoring studies retrieved from public literature.</caption><thead><tr><th>Human biomonitoring study</th><th>Q1</th><th>Q2</th><th>Q3</th><th>Q4</th><th>Q5</th><th>Q6</th><th>Q7</th><th>Q8</th><th>Tier</th></tr></thead><tbody><tr><td>Laubscher et al. (2022)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3</td></tr><tr><td>Mahai et al. (2022)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>3</td></tr><tr><td>Oya et al. (2021)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>2</td></tr></tbody></table>

Notes: The heatmap includes the endpoint-specific ratings for the RoB for each of the 8 questions in the CAT (i.e. number of participants, analyte measured and biological sample used, potential contamination or non-specific binding to the collection tube, sample storage, method's validation, QA/QC, analytical instrumentation, limited of detection/quantification reported). Each question is scored as either low RoB (green), medium RoB (orange), or high RoB (red). The RoB questions highlighted in yellow correspond to the key RoB criteria considered in the final Tier (1 [low RoB], 2 [medium RoB] or 3 [high RoB]) of the endpoint (Q2 analyte measured; Q5 method's validation; Q6 QA/QC; Q7 analytical instrumentation). For detailed rationale for each rating, see Appendix 11.

The WG considered the data reported by Laubscher et al. (2022) as not representative for the general children population, given that the subjects included were children with lymphohematopoietic tumours (selection bias), and tumours may affect the blood-brain barrier and choroidal plexus barrier, thus allowing chemicals to gain access to the brain. The results of the Mahai et al. (2022) and Oya et al. (2021) studies could be extrapolated to their respective contexts (geographical areas, customs, diet, ethnic peculiarities, etc.). The WG also noted that the Laubscher et al. (2022) study is the only one assessing subjects residing in Europe while the other studies with subjects residing in China and Japan (Table 17) may be of less relevance to estimate exposure in the European situation as they rather reflect exposure in these countries. In that respect, it is reiterated that it was not intended to perform a full exposure characterisation based on these biomonitoring studies, but it was rather aimed to assess whether the available human biomonitoring datasets would point to possible exceedance of HBGVs of acetamiprid for those specific studies.

### 2.3.3.2 | Results stepwise approach PBK modelling

The steps as described in the protocol (Appendix 1) were followed. The results of the assessment performed related to the predefined steps in the protocol are described below.

#### Step 1) Define required characteristics of PBK model to answer the assessment questions

The minimal requirements for the PBK model were defined by the WG.

Regarding the reverse dosimetry question, the WG noted that the human biomonitoring data referred to in the mandate (from the Laubscher et al. (2022) study) include only data on the metabolite IM-2-1, as acetamiprid was not detected. Therefore, the WG acknowledged the importance of adequate description of the kinetics of IM-2-1 in the PBK model. Robust modelling (informed mechanisms and compound-specific data) of the relation between oral exposure to acetamiprid and related internal concentrations (plasma and brain) and urinary levels of IM-2-1 was considered essential. For the other identified studies (Mahai et al., 2022; Oya et al., 2021), only data are included on reported levels of acetamiprid and IM-2-1 in urine. For exposure estimation of these data sets, the WG noted that a PBK model is required that provides a robust modelling of the relation between oral exposure to acetamiprid and related urinary levels of acetamiprid and IM-2-1. Regarding relevant life stages to be included in the PBK model, data from the Laubscher et al. (2022) study were obtained from children hospitalised in Europe with an age range between 3 and 18 years. The data of the Mahai et al. (2022) study
were obtained from pregnant Chinese women with an average age of 29.5 years, and the data of the Oya et al. (2021) study were obtained from Japanese toddlers with ages ranging from 16 to 23 months. For a low tier assessment of the reverse dosimetry question, the WG considered application of a PBK model of a human, adjusted with life-stage-specific physiological parameters (for children, pregnant women) as a minimum, acceptable.

Regarding the PBK modelling application for interpretation of the in vitro studies, it was noted that in vitro (DNT-related) toxicity studies were only available for the parent compound (acetamiprid) and not for the metabolite (IM-2-1). Therefore, for this application, the WG considered the minimal requirement of the PBK model to provide a robust modelling of internal acetamiprid dosimetry upon oral exposure. For a low tier assessment, the WG considered a relatively simple PBK model, e.g. describing internal (plasma) concentrations, sufficient. For a low tier assessment of the forward dosimetry question, the WG considered application of a PBK model of a human, adjusted with life stage-specific physiological parameters (for children, pregnant women), acceptable. For a higher tier assessment, a robust description of brain and/or fetal dosimetry was considered relevant.

## Step 2) Provide description of available PBK model(s)

One PBK model of acetamiprid is currently available. This PBK model has been developed for the OECD IATA Case Study no. 365 on acetamiprid within the framework of the EUTOXRisk project. Whole-body rat and human PBK models were developed using the rat and human Simcyp Simulator (V19) (https://www.simcyp.com). The aims of the PBK modelling work described in the OECD IATA Case study no. 365 on acetamiprid were to develop PBK models to predict comparative human exposure of acetamiprid using quantitative in vitro to in vivo extrapolation (qIVIVE) approaches, and to examine the inter-individual variability in the simulated exposure of acetamiprid in humans. The PBK models describe the internal dosimetry (plasma and brain concentrations) and urinary levels of acetamiprid and IM-2-1 in rats and humans. Although the OECD IATA Case Study no. 365 does not report PBK model simulations for children or pregnant women (only for human adults), physiological parameters of these specific groups are available in the literature and may be selected in the Simcyp software. As such, the human PBK model was considered to be relevant for both the reverse dosimetry question (external exposure estimation based on human biomonitoring data) and the forward dosimetry question (internal exposure estimation upon exposure to HBGV).

## Step 3) Determine whether available PBK model(s) is/are adequate to answer assessment questions: Yes or No

Although the Simcyp human PBK model was considered to be relevant, it could not be applied by the WG to answer assessment question 1b, since Simcyp Simulator software is a proprietary software that can only be used following a dedicated training. Therefore, given the timeframe of the mandate, the WG considered application of the available PBK model not feasible.

Given that the answer for step 3 is therefore 'no', the WG proceeded to step 5, according to the step-wise approach described in the protocol (Appendix 1).

## Step 5) Assess whether PBK model(s) can be rebuilt/adjusted to answer assessment questions: Yes or No

Given that the answer for step 3 is 'no', it was concluded that assessment question 1b cannot be answered with the available PBK model. Therefore, given the timeframe of the mandate, the WG considered as best approach to attempt to rebuild the PBK model in PK-Sim software (https://www.open-systems-pharmacology.org/), which is a freely available software, providing similar possibilities as the Simcyp Simulator as relevant for the PBK modelling questions in the mandate.

First, it was assessed whether the parameters applied in the Simcyp model (as described in the OECD IATA Case Study no. 365) could be applied in the PK-Sim model. To that end, the described approach in that case study was critically assessed. As also indicated in the Case Study no. 365, the available kinetic data were limited, hampering the development of the PBK model. The WG concluded that uncertainty related to the description of the absorption and distribution processes were limited. It should be noted that the distribution parameters (partition coefficients) were predicted solely using in silico methods (on the basis of the physico-chemical properties of acetamiprid and IM-2-1, and the composition of the tissues), and were not evaluated against in vivo data. Although evaluation with in vivo data would improve the accuracy of the applied partition coefficients in the PBK model, the WG considered the associated uncertainty as acceptable.

The uncertainties in model parameterisation of metabolism and excretion processes were considered to be high and various selected chemical-specific parameter values were not supported by data. The major uncertainties identified by the WG related to metabolism and excretion processes are summarised in Table 19 and Figure 11.
<table><caption>TABLE 19 Overview of identified major uncertainties related to metabolism and excretion processes of the available PBK model for acetamiprid.</caption><thead><tr><th>Uncertainty</th><th>Uncertainty number</th><th>Description</th></tr></thead><tbody><tr><td>Parameter value for hepatic clearance acetamiprid</td><td>U1</td><td>There is uncertainty in the parameter values for hepatic clearance in the rat and human PBK models. In the rat PBK model, the value for hepatic clearance was estimated based on fitting it in such a way that the rat PBK model simulations describe the reported data on total radioactivity in rat serum (using kinetic data of kinetic study reported in the revised RAR on acetamiprid [Netherlands, 2016]). This was considered the best approach in the absence of information on internal exposure to acetamiprid (parent compound) in the in vivo kinetic studies available and in the absence of in vitro data on rat hepatic clearance. The limitation of the approach was acknowledged by the PBK model developers in the OECD IATA Case Study no. 365, and they assessed the impact of different values for hepatic clearance on PBK model predicted internal acetamiprid concentrations (plasma), called a 'sensitivity analysis' in the Case Study. Starting with this uncertainty in the model parameter value for hepatic clearance in rat, the approach of scaling the obtained value to a human value for the human PBK model was considered to bring an extra uncertainty in the hepatic clearance value in the human PBK model according to the WG. Based on the in vitro clearance study performed with human hepatocytes referred to in the IATA Case Study no. 365, no decrease of acetamiprid was detected, suggesting no or limited hepatic clearance based on the in vitro study. That in vitro result is not in line with the hepatic clearance value applied in the human PBK model. Altogether, the WG considered the parameter value related to the hepatic clearance in the PBK model as uncertain</td></tr><tr><td>Parameter value for IM-2-1 formation</td><td>U2</td><td>The formation of IM-2-1 was set as amounting to 80% of the hepatic clearance of acetamiprid in rats and humans. As far as could be evaluated by the WG, the selection of this value was arbitrary and not supported by data. Therefore, the WG considered the parameter values related to the formation of IM-2-1 as uncertain</td></tr><tr><td>Parameter values for metabolic (hepatic) clearance of IM-2-1</td><td>U3</td><td>For the human PBK model, a further metabolic (hepatic) clearance of IM-2-1 is described. Further biotransformation may be expected when considering the in vivo rat toxicokinetic studies as reported in the revised RAR on acetamiprid (Netherlands, 2016), showing that downstream metabolites of IM-2-1 have been detected in excreta. However, no qualitative nor quantitative information on this reaction in humans is available (a comparative in vitro metabolism study is lacking in the revised RAR on acetamiprid (Netherlands, 2016)). As far as could be evaluated by the WG, the parameter value for this reaction chosen was arbitrary and not directly supported by data. Therefore, the WG considered the parameter value related to the biotransformation of IM-2-1 as uncertain</td></tr><tr><td>Parameter values for renal clearance of acetamiprid</td><td>U4</td><td>Renal clearance of acetamiprid was described in the PBK model. Renal clearance for acetamiprid was reported to be determined based on in vivo kinetic data as indicated in the OECD IATA Case Study no. 365. For rats, 5%-7% of acetamiprid excreted unchanged in urine are mentioned in the Case Study, but it is not clear to the WG which data from the revised RAR on acetamiprid (Netherlands, 2016) were used to come to these values, and to which time point after dosing they apply, making the estimation of kinetic parameter values related to renal clearance uncertain. For humans, a value of 2.6% excreted unchanged in urine is mentioned in the Case Study, which is in line with the value reported by Harada et al. (2016) showing cumulative acetamiprid levels in urine 96 h after exposure (2.6 ± 3.4%). Given the high variation reported and the relatively late time point (96 h), the WG considered the estimation of kinetic parameter values related to renal clearance in humans uncertain for acetamiprid</td></tr><tr><td>Parameter values for renal clearance of IM-2-1</td><td>U5</td><td>The PBK model for humans also describes renal clearance of IM-2-1. The value for IM-2-1 renal clearance (0.95 L/h) is more than 10 times higher than the renal clearance value for acetamiprid (0.082 L/h) applied in the PBK model developed in the OECD IATA Case Study no. 365. The kinetic dataset used for calculating this clearance value was based on data from Harada et al. (2016), reporting urinary levels of IM-2-1 at 4 time points after dosing (24, 48, 72, 96 h). A renal clearance value was obtained by applying an arbitrary split (not supported by data) between an (arbitrary) value of hepatic IM-2-1 clearance (see U3) and renal clearance. Therefore, the WG considered the estimation of the PBK model parameter values related to renal clearance also as uncertain</td></tr><tr><td>Description of internal concentrations of acetamiprid</td><td>U6</td><td>Given the uncertainty in the parameter values as described for U1, U2, and U4, the related resulting description of internal concentrations of acetamiprid were considered uncertain by the WG. Practically an unlimited number of combinations of parameter values related to the 3 kinetic processes described for U1, U2, U4 could result in an adequate description of the in vivo reported concentrations of acetamiprid in urine (the only in vivo kinetic data available for acetamiprid in rats and humans). However, different choices of these parameters are expected to result in a large range of predicted internal concentrations of acetamiprid. Given that no in vivo kinetic data on internal concentrations of acetamiprid were used for model development/evaluation (because not available), the WG considered the prediction of internal concentrations of acetamiprid as uncertain</td></tr><tr><td>Description of internal concentrations of IM-2-1</td><td>U7</td><td>Given the uncertainty in the parameter values as described for U1-U5, the related resulting description of internal concentrations of IM-2-1 were considered uncertain by the WG. Practically an unlimited number of combinations of parameter values related to the 5 kinetic processes described for U1-U5 could result in an adequate description of the in vivo reported concentrations of IM-2-1 in urine (the only in vivo kinetic data available for IM-2-1 in rats and humans). However, different choices of these parameters are expected to result in a large range of predicted internal concentrations of IM-2-1. Given that no in vivo kinetic data on internal concentrations of IM-2-1 were used for model development/evaluation, the WG considered the prediction of internal concentrations of IM-2-1 as uncertain</td></tr></tbody></table>
<figure><img src="image_13.png" /><figcaption>**FIGURE 11** Schematic overview of interrelation of identified uncertainties related to metabolism and excretion processes of acetamiprid and IM-2-1 in the available PBK model for acetamiprid. The PBK model parameter values chosen for the kinetic processes related to U1, U2 and U4 impact on predicted internal and urinary acetamiprid concentrations (U6). The PBK model parameter values chosen for the kinetic processes related to U1-U5 impact on predicted internal and urinary IM-2-1 concentrations (U7).</figcaption></figure>

The WG considered that uncertainty in certain model parameters can be accepted. However, as the combination of these different uncertain model parameters will affect the PBK model predictions for the internal dosimetry and urinary levels of both acetamiprid and IM-2-1, i.e. the critical PBK model outcomes that are required to answer the assessment question 1b, the WG considered that a translation of the available PBK model in Simcyp to a similar PBK model in PK-Sim would not result in a PBK model that is sufficiently robust to answer the assessment question 1b of the mandate. Also, given the differences in the two software packages, some model parameters may not be directly transferable from the Simcyp model to a PK-Sim model.

Therefore, the WG attempted to develop a new PBK model in PK-Sim software, taking also into account possible new available kinetic data that have become available after the OECD IATA Case Study no. 365 on acetamiprid. To that end, the WG first performed a kinetic data search, to include all possible relevant kinetic data for PBK model development/evaluation and subsequently appraised the obtained studies from the scientific literature. An overview of the resulting kinetic data collection and appraisal results are presented in Appendix 12. The extracted kinetic data can be found in Appendix 13 and the detailed results of the appraisals are presented in Appendix 14.

Some new kinetic studies were identified that were not used for the PBK model development as described in the OECD IATA Case Study no. 365. After the kinetic data collection and appraisal, the WG evaluated whether the uncertainties identified for the available PBK model (see Table 19) could be resolved by the newly available kinetic data. The WG noted that only new data were identified that could be of use to parameterise PBK models for IM-2-1 formation in the rat liver (Khidkhan et al., 2021; Kolanczyck et al., 2020) and human liver (Khidkhan et al., 2021), referring to U2 in Table 19. However, given the high RoB of the studies (Tier 3) and the large difference in reported values for IM-2-1 formation kinetics for rat liver microsomes in the Kolanczyck et al. (2020) and Khidkhan et al. (2021) studies, the WG was of the opinion that uncertainty in definition of parameter values for hepatic IM-2-1 formation in the PBK model would remain. The WG concluded that none of the newly identified studies provided data that could be used to decrease the uncertainty in the parameter values as described in Table 19. The WG considered that practically an infinite number of combinations of parameter values can be fitted for the metabolism and renal clearance processes of acetamiprid and IM-2-1 to obtain an adequate fit of the limited available in vivo kinetic data of acetamiprid and IM-2-1 in rat and human urine. The different choices in parameter values for the hepatic and renal clearance processes would, however, result in different internal dosimetry of acetamiprid and IM-2-1, and therefore the WG concluded that the available kinetic data set should be considered as being too limited to develop a sufficiently robust and reliable PBK model to answer the questions of the mandate.

Altogether, the WG concluded that with the limitedly available amount of kinetic data, it was not possible to develop a PBK model that is sufficiently robust and reliable to answer the assessment question 1b of the mandate. The WG discussed what kinetic data would be required to develop a sufficiently robust PBK model. The outcome of these discussions is presented in Appendix 15.

# 3 | RESIDUES

## 3.1 | Metabolic pattern of acetamiprid and related metabolites identified in pesticide residue monitoring data for unprocessed and processed food of plant origin

### 3.1.1 | Data collection

In the framework of Article 31 of Regulation (EC) No 396/2005 on the yearly collection of monitoring data to draw the annual report on pesticide residues, EFSA has regularly received monitoring data on acetamiprid as the relevant marker of the residue definition for enforcement in commodities of plant origin. Data on acetamiprid main metabolite (N-desmethyl-acetamiprid (IM-2-1)) were also provided by some Member States on a voluntary basis, but since metabolites are not part of the residue definition established in Regulation (EC) No 396/2005, only limited information is available.
In line with the ToR (2a) of the present mandate, EFSA consulted EU Member States on the availability of additional monitoring data not yet submitted to EFSA, in particular monitoring data in plant commodities for the metabolite N-desmethyl-acetamiprid (IM-2-1) and other metabolites identified in the available metabolism studies performed with acetamiprid. The screening for the availability of such data was restricted to the period of 2018–2022. For IM-2-1, some Member States reported that such data are available; however, no information could be provided by Member States for other metabolites.

Following the consultation of Member States, EFSA launched an ad hoc data collection for monitoring data relevant for this mandate. Data could be submitted between 1 November and 30 November 2022 offering the possibility for Member States to provide monitoring data for N-desmethyl-acetamiprid (IM-2-1).

The LOQ reported by the national authorities for the analysis of acetamiprid in all reported samples ranged between 0.001 and 0.1 mg/kg. Most of the samples (99%) were analysed with an LOQ equal or less than 0.01 mg/kg. A small proportion of samples (0.4%) were analysed with LOQ of 0.05–0.1 mg/kg and mainly correspond to high oil content commodities (e.g. olives) or matrices difficult to analyse (e.g. tea, herbal infusion...). Therefore, these samples are all deemed relevant from the data analysis.

In 17,634 samples, both acetamiprid and metabolite IM-2-1 were analysed. These data have been reported only by Austria, Germany and Sweden (Table 20).

Most of the samples (94%) correspond to unprocessed commodities. The remaining 6% are from processed food. Considering that acetamiprid was found to be stable under conditions representative for food processing (EFSA, 2016a), EFSA decided to include the results for processed products in the data analysis.

The LOQ reported by the national authorities for the analysis of metabolite IM-2-1 in the reported samples ranged between 0.001 and 0.05 mg/kg. Most of the samples (99%) were analysed with an LOQ equal or less than 0.01 mg/kg. The samples analysed with LOQ of 0.05 mg/kg (0.2%) mainly correspond to matrices difficult to analyse (e.g. coffee, tea...). Therefore, these samples are all deemed relevant from the data analysis.

No data were excluded from the data analysis.

All the monitoring data received are available in the overview file (xls), which is published in Appendix 16 of the present statement. An overview is presented below.

<table><caption>TABLE 20 Number of samples analysed for both acetamiprid and metabolite IM-2-1 per reporting country and per year.</caption><thead><tr><th>Country</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th><th>TOTAL</th></tr></thead><tbody><tr><td>Austria</td><td>1053</td><td>975</td><td>899</td><td>943</td><td>97</td><td>3967</td></tr><tr><td>Germany</td><td>2701</td><td>2555</td><td>2264</td><td>2624</td><td>-</td><td>10,144</td></tr><tr><td>Sweden</td><td>-</td><td>1374</td><td>1408</td><td>741</td><td>-</td><td>3523</td></tr><tr><td>Total</td><td>3754</td><td>4904</td><td>4571</td><td>4308</td><td>97</td><td>17,634</td></tr></tbody></table>

### 3.1.2 | Overview of the results in samples analysed for parent and metabolite IM-2-1

Within the **17,634** samples of plant origin analysed for both acetamiprid and metabolite IM-2-1, there are commodities belonging to the groups of leafy crops, fruit crops, pulses/oilseeds, cereals, root crops, stem vegetables, fungi and algae.

Of these samples, acetamiprid was quantified above the LOQ in **1700** samples and the metabolite IM-2-1 was quantified above the LOQ in only **805** samples. The metabolite IM-2-1 was quantified above the LOQ mainly in fruit crops (645 quantifications) and leafy crops (148 quantifications) but a few positive findings were also retrieved in pulses/oilseeds and in stem vegetables (Tables 21 and 22).

<table><caption>TABLE 21 Overview of the available monitoring data.</caption><thead><tr><th>Crop category</th><th>N samples</th><th>Number of results above LOQ (VAL) for acetamiprid</th><th>Number of results above LOQ (VAL) for metabolite IM-2-1</th></tr></thead><tbody><tr><td>Leafy</td><td>2966</td><td>246</td><td>148</td></tr><tr><td>Fruit</td><td>10,779</td><td>1433</td><td>645</td></tr><tr><td>Pulses/oilseeds</td><td>914</td><td>15</td><td>11</td></tr><tr><td>Cereals</td><td>773</td><td>2</td><td>0</td></tr><tr><td>Root</td><td>1513</td><td>2</td><td>0</td></tr><tr><td>Stem</td><td>387</td><td>1</td><td>1</td></tr><tr><td>Fungi</td><td>294</td><td>1</td><td>0</td></tr><tr><td>Algae</td><td>4</td><td>0</td><td>0</td></tr><tr><td>Total</td><td>17,634</td><td>1700</td><td>805</td></tr></tbody></table>
A short list of raw commodities in which the metabolite IM-2-1 was quantified in more than 10 samples is reported in Table 22. These commodities all belong to the leafy and fruit crop groups. The max concentrations of IM-2-1 are above 0.01 mg/kg in all commodities of the short list. However, the overall average amounts of metabolite IM-2-1 is above 0.01 mg/kg in only 3 commodities: spinaches (0.05 mg/kg), Roman/rocket (0.013 mg/kg) and chilli peppers (0.011 mg/kg). These commodities are also those where the highest maximum levels of IM-2-1 were found: 4.8 mg/kg, 0.52 mg/kg and 0.23 mg/kg, respectively.

<table><caption>TABLE 22 Short-list of unprocessed commodities where metabolite IM-2-1 was quantified in more than 10 samples.</caption><thead><tr><th>Commodity</th><th>Number of results above LOQ for metabolite IM-2-1</th><th>Number of raw samples analysed for metabolite IM-2-1</th><th>Average value for metabolite IM-2-1<sup>a</sup> (mg/kg)</th><th>Max value for metabolite IM-2-1 (mg/kg)</th></tr></thead><tbody><tr><td>Sweet peppers/bell peppers</td><td>64</td><td>648</td><td>0.006</td><td>0.160</td></tr><tr><td>Courgettes</td><td>60</td><td>262</td><td>0.005</td><td>0.070</td></tr><tr><td>Cherries (sweet)</td><td>59</td><td>216</td><td>0.007</td><td>0.017</td></tr><tr><td>Apples</td><td>54</td><td>872</td><td>0.008</td><td>0.049</td></tr><tr><td>Spinaches</td><td>51</td><td>378</td><td>0.050</td><td>4.800</td></tr><tr><td>Table grapes</td><td>40</td><td>651</td><td>0.007</td><td>0.110</td></tr><tr><td>Grapefruits</td><td>37</td><td>174</td><td>0.007</td><td>0.100</td></tr><tr><td>Peaches</td><td>32</td><td>461</td><td>0.007</td><td>0.022</td></tr><tr><td>Tomatoes</td><td>31</td><td>727</td><td>0.007</td><td>0.110</td></tr><tr><td>Pears</td><td>30</td><td>504</td><td>0.007</td><td>0.017</td></tr><tr><td>Aubergines/eggplants</td><td>29</td><td>197</td><td>0.005</td><td>0.029</td></tr><tr><td>Cucumbers</td><td>26</td><td>550</td><td>0.007</td><td>0.013</td></tr><tr><td>Roman rocket/rucola</td><td>26</td><td>112</td><td>0.013</td><td>0.516</td></tr><tr><td>Lettuces</td><td>25</td><td>653</td><td>0.004</td><td>0.047</td></tr><tr><td>Blueberries</td><td>22</td><td>179</td><td>0.004</td><td>0.077</td></tr><tr><td>Granate apples/ pomegranates</td><td>22</td><td>232</td><td>0.005</td><td>0.030</td></tr><tr><td>Mandarins</td><td>22</td><td>338</td><td>0.007</td><td>0.023</td></tr><tr><td>Currants (black, red and white)</td><td>18</td><td>103</td><td>0.004</td><td>0.019</td></tr><tr><td>Plums</td><td>17</td><td>234</td><td>0.002</td><td>0.019</td></tr><tr><td>Chilli peppers</td><td>14</td><td>181</td><td>0.011</td><td>0.230</td></tr><tr><td>Melons</td><td>11</td><td>276</td><td>0.007</td><td>0.011</td></tr><tr><td>Oranges</td><td>10</td><td>384</td><td>0.007</td><td>0.015</td></tr></tbody></table>

In addition to the above results, it is noted that in commodities belonging to the pulses/oilseeds group, the metabolite IM-2-1 was found above the value of 0.01 mg/kg in only one sample of beans with pods (0.012 mg/kg), its average in each commodity not exceeding the value of 0.005 mg/kg.

## Processed commodities:

The occurrence of metabolite IM-2-1 in processed commodities is slightly lower compared to raw commodities and remain overall very small (Table 23). The metabolite IM-2-1 was found in a few processed items of leafy (3) and fruit (15) commodities, mainly corresponding to drying processes (e.g. dried apricots, dried herbs, dried peppers, goji dried berries, curry leaves, paprika powder, raisins, jasmine flowers...). Goji dried berry is the commodity where the max quantified values were found (0.12 mg/kg; 3 samples > LOQ), followed by curry leaves (0.058 mg/kg; 1 sample > LOQ) and Jasmine flowers (0.033 mg/kg; 1 sample > LOQ).

<footnote><sup>a</sup>Average value was calculated using the upper bound approach; therefore, sample results below LOQs were considered at the value of the LOQ for the calculations.</footnote>
<table><caption>TABLE 23 Occurrence of metabolite IM-2-1 in unprocessed vs processed commodities.</caption><thead><tr><th rowspan="2"></th><th colspan="2">N samples</th><th colspan="2">Number of results above LOQ (VAL) for metabolite IM-2-1</th><th colspan="2">% of occurrence above LOQ for metabolite IM-2-1</th></tr><tr><th>Unprocessed</th><th>Processed</th><th>Unprocessed</th><th>Processed</th><th>Unprocessed</th><th>Processed</th></tr></thead><tbody><tr><td>Leafy crops</td><td>2863</td><td>103</td><td>145</td><td>3</td><td>5.1</td><td>2.9</td></tr><tr><td>Fruit crops</td><td>10,156</td><td>623</td><td>630</td><td>15</td><td>6.2</td><td>2.4</td></tr><tr><td>Pulses/oilseeds</td><td>788</td><td>126</td><td>11</td><td>0</td><td>1.4</td><td>0</td></tr><tr><td>Cereals</td><td>553</td><td>220</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Root crops</td><td>1501</td><td>12</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Stem vegetable</td><td>382</td><td>5</td><td>1</td><td>0</td><td>0.3</td><td>0</td></tr><tr><td>Fungi</td><td>268</td><td>26</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Algae</td><td>0</td><td>4</td><td>0</td><td>0</td><td>n.r.</td><td>0</td></tr><tr><td>Total</td><td>16,511</td><td>1119</td><td>787</td><td>18</td><td>4.8</td><td>1.6</td></tr></tbody></table>

Abbreviation: n.r., not relevant.

Conclusion:

The available monitoring data indicate that metabolite IM-2-1 may be a relevant component of the residues in some commodities belonging to the fruit and leafy crop groups and, very unlikely in pulses and oilseeds crops. Therefore, further investigations on the relative amount of metabolite IM-2-1 compared to the parent compound was done in commodities belonging to these crop categories. No major differences were found between unprocessed and processed commodities.

### 3.1.3 Relative amount of metabolite IM-2-1 compared to parent compound

For the crop groups of concern based on the above screening (fruit crops, leafy crops and pulses and oilseeds crops), the relative amount of metabolite IM-2-1 compared to acetamiprid was further investigated using the available monitoring data.

In the samples of leafy crops, fruit crops and pulses/oilseeds analysed for acetamiprid and metabolite IM-2-1, most of the results (n = 12,921) simply indicate both acetamiprid and IM-2-1 to remain below the LOQ; these samples are called '[LOQ-LOQ]' samples in the text and tables below. These results are not deemed useful to investigate the ratio between the two compounds.

However, the subset of results where acetamiprid and/or IM-2-1 were quantified are of interest to investigate further the ratio between the two compounds. A total of 1738 samples were deemed relevant. The following abbreviations were applied to define the different cases:

- '[VAL-LOQ]': Samples where acetamiprid was quantified (VAL) but metabolite IM-2-1 was below the LOQ.

- '[LOQ-VAL]': Samples where acetamiprid was below the LOQ but metabolite IM-2-1 was quantified (VAL).

- '[VAL-VAL]': Samples where acetamiprid and metabolite IM-2-1 were quantified (VAL).

Taking the [LOQ-LOQ] samples apart and only focusing on the [VAL-LOQ], [LOQ-VAL] and [VAL-VAL] samples, it appears that the parent compound was quantified in most of the samples (97%); the metabolite IM-2-1 was quantified in approximately half of them (46%). There are only 44 samples (22 in fruit crops and 22 in leafy crops) where only the metabolite IM-2-1 was quantified, while acetamiprid was below the LOQ (Table 24).

<table><caption>TABLE 24 Acetamiprid and IM-2-1 quantification in different crop groups.</caption><thead><tr><th></th><th>[LOQ-LOQ] acetamiprid<br>LOQ-IM-2-1 LOQ</th><th>[VAL-LOQ] acetamiprid<br>VAL-IM-2-1 LOQ</th><th>[LOQ-VAL] acetamiprid<br>LOQ-IM-2-1 VAL</th><th>[VAL-VAL] acetamiprid<br>VAL-IM-2-1 VAL</th></tr></thead><tbody><tr><td>Leafy crops</td><td>2698</td><td>120</td><td>22</td><td>126</td></tr><tr><td>Fruit crops</td><td>9324</td><td>810</td><td>22</td><td>623</td></tr><tr><td>Pulses and Oilseeds</td><td>899</td><td>4</td><td>0</td><td>11</td></tr><tr><td>Total</td><td>12,921</td><td>934</td><td>44</td><td>760</td></tr></tbody></table>

To quantify the relative amount of metabolite IM-2-1 compared to acetamiprid, EFSA defined the ratio 'IM-2-1/acetamiprid' (concentration of IM-2-1 as reported in the monitoring data divided by concentration of acetamiprid as reported in the monitoring data) as a relevant indicator. This ratio could be calculated for each sample where data on acetamiprid and
metabolite IM-2-1 were available. However, the samples considered relevant to calculate this ratio were only those where acetamiprid and/or its metabolite IM-2-1 were quantified above the LOQ. The following considerations were made for each case:

- [VAL-LOQ]: These samples are relevant because they reflect cases of crops probably treated with acetamiprid (because of the demonstrated presence of acetamiprid) for which the parent compound was still the main component of the residues. For each sample, a ratio was calculated considering the LOQ for IM-2-1 divided by the parent concentration reported in the same sample.

- [LOQ-VAL]: These samples are relevant because they reflect cases of crops probably treated with acetamiprid (because of the demonstrated presence of IM-2-1) for which the metabolite was the main component of the residues. For each sample, a ratio was calculated considering the IM-2-1 concentration reported in the same sample divided by the LOQ for acetamiprid.

- [VAL-VAL]: These samples are relevant because they reflect cases of crops probably treated with acetamiprid (because of the presence of acetamiprid and its desmethyl metabolite). For each sample, a ratio 'metabolite IM-2-1/acetamiprid' could directly be calculated without further assumption.

- [LOQ-LOQ]: These samples were not considered to calculate ratios.

- As no major differences were found between unprocessed and processed commodities, all commodities were used together to derive ratios.

The summary statistics of the ratio calculations for each crop group are reported in Table 25. For the calculation of the average concentration for acetamiprid and metabolite IM-2-1, the upper bound approach was used. Therefore, sample results below LOQs were considered at the value of the LOQ.

<table><caption>TABLE 25 Summary statistics and ratios 'concentration of IM-2-1/ concentration of acetamiprid' in different crop groups.</caption><thead><tr><th></th><th>Average parent (± SD)<br>[Max Val] (mg/kg)<sup>a</sup></th><th>Average metabolite IM-2-1<sup>a</sup><br>(± SD) [Max VAL] (mg/kg)</th><th>Number of<br>relevant ratio<sup>b</sup></th><th>Average ratio<sup>b</sup> (± SD)<br>[Min-Max]</th><th>N ratio > 1</th></tr></thead><tbody><tr><td>Leafy crops</td><td>0.020 (±0.169) [4.6]</td><td>0.011 (±0.100) [4.8]</td><td>268</td><td>1.61 (±4.33) [0.01–35]</td><td>55</td></tr><tr><td>Fruit crops</td><td>0.011 (±0.051) [3.7]</td><td>0.006 (±0.006) [0.23]</td><td>1455</td><td>0.43 (±0.50) [0.01–10]</td><td>53</td></tr><tr><td>Pulses and<br>Oilseeds</td><td>0.007 (±0.005) [0.072]</td><td>0.007 (±0.004) [0.03*]<sup>c</sup></td><td>15</td><td>0.56 (±0.32) [0.08–1]</td><td>0</td></tr></tbody></table>

## Leafy crops:

Metabolite IM-2-1 is below the LOQ in 2818 samples out of 2966 samples available on leafy crops. Using the upper bound approach, the overall average concentration for the metabolite IM-2-1 in leafy crops is 0.011 mg/kg. It is two times lower than the average concentration for the parent compound in leafy crops (0.02 mg/kg) (the calculated average for acetamiprid is derived from the samples where acetamiprid and IM-2-1 were analysed).

Looking at the 268 relevant ratios 'IM-2-1/acetamiprid' calculated at sample level, the average is 1.61 with a wide distribution. This means that metabolite IM-2-1 does represent a significant part of the residues in some samples of leafy crops.

In 55 out of the 262 samples where a ratio could be calculated (21% of the cases), the concentration of IM-2-1 was higher than the concentration of acetamiprid (ratio above 1). The corresponding commodities are reported in Table 26, which also provides an overview of the occurrence of IM-2-1 and its relative amount compared to acetamiprid.

<table><caption>TABLE 26 IM-2-1 occurrence and relative amount compared to acetamiprid in leafy commodities where at least one ratio > 1 was observed.</caption><thead><tr><th>Commodity</th><th>Average IM-2-1<br>(mg/kg)</th><th>Max IM-2-1<br>(mg/kg)</th><th>Average ratio<br>(IM-2-1/acetamiprid)</th><th>N ratio > 1/N<br>relevant ratio</th></tr></thead><tbody><tr><td>Brussels sprouts</td><td>&lt; 0.01</td><td>0.01</td><td>1.02</td><td>1/4</td></tr><tr><td>Chives</td><td>&lt; 0.01</td><td>0.15</td><td><strong>3.62</strong></td><td><strong>3/3</strong></td></tr><tr><td>Kales</td><td>&lt; 0.01</td><td>0.18</td><td><strong>1.86</strong></td><td><strong>3/7</strong></td></tr><tr><td>Lettuces</td><td>&lt; 0.01</td><td>0.05</td><td>0.31</td><td>1/108</td></tr><tr><td>Parsley</td><td>&lt; 0.01</td><td>0.04</td><td>0.77</td><td>1/13</td></tr><tr><td>Spinaches</td><td>0.05</td><td>4.8</td><td><strong>6.57</strong></td><td><strong>46/51</strong></td></tr></tbody></table>

<footnote><sup>a</sup>The calculated average concentrations for acetamiprid and metabolite IM-2-1 are derived from the samples where acetamiprid and/or IM-2-1 were analysed. The upper bound approach was used (sample results below LOQs were considered at the value of the LOQ).</footnote>

<footnote><sup>b</sup>Average and median ratio derived from the individual ratios calculated for each sample, using the upper bound approach (sample results below LOQs were considered at the value of the LOQ).</footnote>

<footnote><sup>c</sup>It is noted that a higher value of 0.03* mg/kg reported for P/O corresponded to the enforcement LOQ used to analyse the sample. The highest quantified value is 0.012 mg/kg.</footnote>
The main commodities where ratios are above 1 are spinach (90% of the cases), and in minor proportions in chives and kale. The highest mean ratios calculated at commodities level were also found in spinaches, chives and kale (in bold in the table). In spinaches, the overall average for IM-2-1 is 0.05 mg/kg, which is also the highest average calculated at commodity level.

The average ratios 'IM-2-1/acetamiprid' calculated in leafy commodities other than spinach, chives and kales are significantly lower, ranging between 0.02 (curry leaves) and 1 (Brussels sprouts).

Overall, it is concluded that the metabolite IM-2-1 is a relevant component of the residues in leafy crops. However, this conclusion is mainly driven by observations made in the monitoring of a few specific commodities. In particular, very high proportions of IM-2-1 compared to acetamiprid were found in spinaches, kales and chives. Spinach was also present in the short-listed commodities where metabolite IM-2-1 was quantified in more than 10 samples (see Section 3.1.2).

## Fruit crops:

Metabolite IM-2-1 is below the LOQ in 10,134 samples out of 10,779 samples available on fruit crops. Furthermore, the overall average for the metabolite IM-2-1 in fruit crops is below 0.01 mg/kg and thus remains in lower proportions compared to the parent compound. At commodity level, the average concentrations of IM-2-1 also remain low, not exceeding 0.01 mg/kg in any commodity. However, as identified in Table 22, the metabolite IM-2-1 was found in high concentrations in single samples of several commodities, with max values up to 0.23 mg/kg (chilli peppers).

Looking at the 1358 ratios calculated at sample level, the average is 0.43, with a standard deviation of 0.52. This means that metabolite IM-2-1 levels are generally lower than those of parent in fruit crops samples, however not negligible.

In 53 out of the 1455 samples where a ratio could be calculated (4% of the cases), the concentration of IM-2-1 was higher than the concentration of acetamiprid (ratio above 1). The corresponding commodities are reported in Table 27, which also provides an overview of the occurrence of IM-2-1 and its relative amount compared to acetamiprid.

<table><caption>TABLE 27 IM-2-1 occurrence and relative amount compared to acetamiprid in fruit commodities where at least one ratio >1 was observed.</caption><thead><tr><th>Commodity</th><th>Average IM-2-1 (mg/kg)</th><th>Max IM-2-1 (mg/kg)</th><th>Average ratio (IM-2-1 /acetamiprid)</th><th>N ratio > 1/N relevant ratio</th></tr></thead><tbody><tr><td>Apples</td><td>&lt;0.01</td><td>0.05</td><td>0.45</td><td>7/170</td></tr><tr><td>Avocados</td><td>&lt;0.01</td><td>0.02</td><td>1.25</td><td>1/2</td></tr><tr><td>Courgettes</td><td>&lt;0.01</td><td>0.07</td><td>1.01</td><td>21/64</td></tr><tr><td>Grapefruits</td><td>&lt;0.01</td><td>0.10</td><td>0.49</td><td>1/54</td></tr><tr><td>Peaches</td><td>&lt;0.01</td><td>0.02</td><td>0.51</td><td>3/89</td></tr><tr><td>Plums</td><td>&lt;0.01</td><td>0.02</td><td>0.64</td><td>4/45</td></tr><tr><td>Prickly pears/cactus fruits</td><td>&lt;0.01</td><td>0.01</td><td>5.0</td><td>1/1</td></tr><tr><td>Sweet peppers/bell peppers</td><td>&lt;0.01</td><td>0.16</td><td>0.52</td><td>8/105</td></tr><tr><td>Tomatoes</td><td>&lt;0.01</td><td>0.11</td><td>0.61</td><td>7/47</td></tr></tbody></table>

The main commodities where ratios are above 1 are courgettes (33% of the cases), tomatoes (15%), plums (9%), sweet peppers (8%), apples (4%) and peaches (3%). The highest mean ratios calculated at commodities level were found in prickly pears/cactus fruits, avocadoes and courgettes.

In prickly pears/cactus fruits, the ratio of 5 was based on one sample only where the concentration of IM-2-1 was quantified at the level of 0.005 mg/kg while acetamiprid was not quantified (LOQ = 0.001 mg/kg). A similar situation is identified in avocadoes with the value for IM-2-1 of 0.003 mg/kg. The max values for IM-2-1 observed in these commodities are 0.01 mg/kg and 0.02 mg/kg, respectively. Therefore, the calculated ratios are not reflecting a significant occurrence of metabolite IM-2-1 in these two commodities.

In courgettes, the average concentration of IM-2-1 is low (< 0.01 mg/kg) and ranges between 0.001 and 0.07 mg/kg. The highest ratio values (above 1) are observed in samples where quantified concentrations of parent and IM-2-1 are between 0.014 and 0.017 mg/kg, respectively.

In the other commodities listed in Table 27, the average ratios are between 0.49 and 0.64. This means that in those commodities (apples, grapefruits, peaches, plums, sweet peppers/bell peppers and tomatoes), the metabolite IM-2-1 accounts for 50% of the parent compound concentration, on average.

Overall, it can be concluded that the metabolite IM-2-1 is a relevant component of the residues in fruit crops. This conclusion is driven by observations made in the monitoring of major fruit crops (courgettes, apples, grapefruits, peaches, plums, sweet peppers/bell peppers and tomatoes). In these commodities, the proportions of IM-2-1 are generally not higher than those of acetamiprid, but significant. It should also be noted that these commodities were all present in the short-listed commodities where metabolite IM-2-1 was quantified in more than 10 samples (see Table 22).
## Pulses/oilseeds:

The overall average for the metabolite IM-2-1 in pulses and oilseeds is below 0.01 mg/kg. Metabolite IM-2-1 is below the LOQ in 903 out of the 914 available samples available on pulses and oilseeds. The 11 samples in which metabolite IM-2-1 was quantified correspond to beans with pods (7), peas with pods (2) and peas without pods (2). Therefore, there are no pulses and oilseeds commodities in the short-listed commodities where metabolite IM-2-1 was quantified in more than 10 samples.

The quantified values ranged between 0.001 and 0.012 mg/kg.

In all samples, the calculated ratio was below the value of 1, meaning that the concentration of IM-2-1 was always lower than the one of acetamiprid.

Overall, it is concluded that the metabolite IM-2-1 is not a relevant component of the residues in pulses and oilseeds, based to the available monitoring samples.

## 3.2 | Metabolic pattern identified in metabolism studies relevant for food products of plant origin

### 3.2.1 | Primary crop metabolism overview

In the framework of the renewal of the approval for the active substance acetamiprid under Commission Implementing Regulation 844/2012, the applicant submitted metabolism studies in primary crops representative for fruit and fruiting vegetables, root crops, leafy vegetables and pulses/oilseeds. These studies were performed to elucidate the metabolic pattern expected in food derived from crops treated with acetamiprid. The key parameters on the design of these metabolism studies are summarised in Table 28.

These studies were previously assessed at EU level and therefore the assessment of the validity of these metabolism studies was not reopened by EFSA under the present mandate. Furthermore, no new metabolism studies have been collected under the present mandate.

<table><caption>TABLE 28 Available primary crop plant metabolism studies performed with radiolabelled acetamiprid.</caption><thead><tr><th>Crop group</th><th>Crops</th><th>Application</th><th>Sampling (day, DAT)</th><th>Comments</th></tr></thead><tbody><tr><td rowspan="3">Fruit</td><td>Aubergine</td><td>Foliar (dotting on leave and fruit surface) (1x 9.5 g/100 L)</td><td>7, 14 DAT</td><td>[pyridine-2,6-<sup>14</sup>C]-labelled acetamiprid</td></tr><tr><td rowspan="2">Apple</td><td>Foliar (dotting on leave: 1x 208 g/ha)</td><td>0, 7, 14, 28, 62, 90 DAT</td><td>[pyridine-2,6-<sup>14</sup>C]-labelled acetamiprid</td></tr><tr><td>Foliar (dotting on fruit: 1x 104 g/ha)</td><td>0, 14, 28, 62 DAT</td><td></td></tr><tr><td>Root</td><td>Carrot</td><td>Foliar (2x 100 g/ha)</td><td>14 DAT</td><td>[pyridine-2,6-<sup>14</sup>C]-labelled acetamiprid</td></tr><tr><td rowspan="3">Leafy</td><td rowspan="3">Cabbage</td><td>Foliar (1x 301.5 g/ha) (study 1)</td><td>0, 7, 14, 21, 28 DAT</td><td>[pyridine-2,6-<sup>14</sup>C]-labelled acetamiprid</td></tr><tr><td>Soil treatment (1x 5940 g/ha)</td><td>7, 14, 28 DAT</td><td></td></tr><tr><td>Foliar (298.5 g/ha) (study 2)</td><td>0, 7, 14, 28, 63 DAT</td><td>Cyano-<sup>14</sup>C labelled acetamiprid</td></tr><tr><td>Pulses and Oilseeds</td><td>Cotton</td><td>Foliar (4x 123 g/ha)</td><td>14, 28 DAT</td><td>[pyridine-2,6-<sup>14</sup>C]-labelled acetamiprid</td></tr></tbody></table>

Abbreviation: DAT, days after treatment.

**Acetamiprid** was identified as a major component of the total radioactive residue (TRR) in all crop parts except cabbage head and cotton seed. Acetamiprid accounted for 79%–97% TRR in aubergine and apple fruits, for 61%–99% TRR in cabbage aerial parts, and for 27%–34% TRR in carrot mature samples (root and tops).

In cabbage head and cotton seeds, acetamiprid accounted for no more than 0.3% TRR and 5% TRR, respectively. The main components identified in these crop parts was the **6-chloronicotinic acid metabolite (IC-0)** (up to 46% TRR cabbage head and cotton seeds) and its methyl-ester (up to 13% TRR in cotton seed). IC-0 was also detected in carrot roots up to 26% TRR (0.02 mg/kg).

The **metabolite IM-2-1 (N-desmethyl-acetamiprid)** was below 10% of the TRR in all crop parts (≤ 4% TRR in aubergine and apple fruits, ≤ 7% TRR in cabbage samples, ≤ 6% TRR in carrot mature samples, ≤ 8% TRR in cotton seeds) except in apple leaf samples. In the latter, the metabolite IM-2-1 was found up to 16% TRR (3.64 mg eq./kg) at longer pre-harvest intervals (PHI).

The metabolite **IM-1-4** was also found in significant proportions, but only in carrot leaves: 15% TRR in mature samples (43% TRR in immature samples). In carrot tops samples taken at maturity, another compound (conjugate form of **IM-0**) was found up to 33% TRR. The IM-0 was also found in immature carrot flesh samples (14% TRR) (Table 29).
<table><caption>TABLE 29 Compounds identified above 10% TRR in different plant matrices.</caption><thead><tr><th>Main compounds</th><th>Apple, aubergines (fruits) [%TRR]</th><th>Apple leaf [%TRR]</th><th>Cabbage (aerial parts) [%TRR]</th><th>Cabbage (head) [%TRR]</th><th>Carrot root/flesh (mature samples) [%TRR]</th><th>Carrot tops (mature samples) [%TRR]</th><th>Cotton seed [%TRR]</th></tr></thead><tbody><tr><td>Acetamiprid</td><td>79-97</td><td>49-96</td><td>61-99</td><td>≤ 0.3</td><td>33/34</td><td>27</td><td>≤ 5</td></tr><tr><td>IM-2-1</td><td>2-4</td><td>1-16</td><td>≤ 7</td><td>-</td><td>4/4</td><td>6</td><td>6-8</td></tr><tr><td>IM-1-4</td><td>≤ 1</td><td>≤ 2</td><td>≤ 1</td><td>-</td><td>5/6</td><td>15</td><td>-</td></tr><tr><td>IC-0</td><td>≤ 1</td><td>≤ 2</td><td>≤ 1</td><td>46</td><td>26/31</td><td>1</td><td>24-46</td></tr><tr><td>IC-0-methyl ester</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>7-13</td></tr><tr><td>IM-0 (and conjugate)</td><td>-</td><td>≤ 1</td><td>≤ 1</td><td>-</td><td>7/7</td><td>33<sup>a</sup></td><td>0</td></tr></tbody></table>

### 3.2.2 | Metabolite IM-2-1 in fruit and leafy crops according to metabolism studies

Further detailed results of metabolism studies are presented below for the metabolite IM-2-1 in fruit and leafy commodities since, based on the monitoring results this compound was significantly found in samples of crops belonging to these crop groups (see Section 3.1). Additionally, data on the leafy parts of other crops (e.g. carrots), where available, were considered regarding the occurrence of metabolite IM-2-1.

In fruit crops (apples and aubergines, see Table 30), the metabolite IM-2-1 did not exceed the absolute level of 0.03 mg/kg (maximum value identified in apples at PHI 14 days). At longer PHI, the parent compound seems to be degraded (to 80.8% TRR at PHI 62 days), and therefore, a slight increase of IM-2-1 is observed (from 0.6 mg eq./kg at PHI 0 days to 3.7 mg eq./kg at PHI 62 days). However, metabolite IM-2-1 always remains in negligible proportions (maximum 2%) compared to the parent compound. It should be noted that the method of applications used in the metabolism assay to generate data on fruits (dotting on fruit surface only) is not fully reflecting a broadcast foliar application on fruits and leafy parts of the crops at the same time.

<table><caption>TABLE 30 Detailed results for metabolite IM-2-1 in fruit commodities.</caption><thead><tr><th rowspan="2">Commodity/study</th><th>PHI</th><th colspan="2">Acetamiprid</th><th colspan="2">IM-2-1</th><th rowspan="2">Ratio (IM-2-1/acetamiprid) [based on mg/kg]</th></tr><tr><th>Days</th><th>mg/kg [%TRR]</th><th>(as parent)</th><th>mg/kg [%TRR]</th><th>(as parent)</th></tr></thead><tbody><tr><td rowspan="2">Aubergines/fruit treatment</td><td>7</td><td>95.9</td><td>0.38</td><td>0.4</td><td>0.0</td><td>n.r.</td></tr><tr><td>14</td><td>93.9</td><td>1.10</td><td>n.d.</td><td>n.d.</td><td>n.r.</td></tr><tr><td rowspan="4">Apples/fruit treatment</td><td>0</td><td>97.1</td><td>0.47</td><td>0.6</td><td>0</td><td>n.r.</td></tr><tr><td>14</td><td>89.9</td><td>1.3</td><td>2.3</td><td>0.03</td><td><strong>0.02</strong></td></tr><tr><td>28</td><td>79.2</td><td>0.42</td><td>2.1</td><td>0.01</td><td>&lt; 0.01</td></tr><tr><td>62</td><td>80.8</td><td>0.24</td><td>3.7</td><td>0.01</td><td>&lt; 0.01</td></tr></tbody></table>

Abbreviations: n.d., not detected; n.r., not relevant.

In leafy crop (cabbage), the metabolite IM-2-1 was found at levels up to 1.25 mg eq./kg at PHI 14 days after soil treatment, and at significant levels after foliar treatments (0.13; 0.20; 0.23 mg eq./kg at PHIs of 28-63 days), generally occurring at higher levels after longer PHI. The proportion of IM-2-1 compared to the parent compound remains low but reaches percentage around 6% and 11% after foliar treatments at PHI of 28 days and 63 days (study 1), respectively. It should be noted that cabbage was the only leafy crop used in the metabolism studies and may not be representative of other leafy crops (and crops cycles) on which different findings have been observed in the monitoring data. However, according to applicable guidelines, the study was considered sufficient to depict the metabolism of acetamiprid in leafy crops following foliar treatment.

Furthermore, data on leafy parts of apples and aubergines were also collected after target applications on leaves (dotting). In apples leaves, very high levels of IM-2-1 were observed at long PHI (16% TRR; 3.64 mg eq./kg at PHI 90 days), also representing high proportions compared to the parent compound (32%). In aubergine leaves, metabolite IM-2-1 was present at lower proportions compared to the parent compound (maximum 2%).

In the metabolism study performed with carrots, data of residue composition in leafy parts of the crop (tops) were provided. Following foliar treatment, at the PHI of 14 days, metabolite IM-2-1 in carrot tops was identified at 5.92% TRR, accounting for up to 22% of the parent compound.

<footnote>ªIM-0 glc conjugate.</footnote>
<table><caption>TABLE 31 Detailed results for metabolite IM-2-1 in leafy commodities and leafy parts.</caption><thead><tr><th rowspan="2">Commodity/study</th><th>PHI</th><th colspan="2">Acetamiprid</th><th colspan="2">IM-2-1</th><th rowspan="2">Ratio (IM-2-1/ acetamiprid) [based on mg/kg]</th></tr><tr><th>Days</th><th>[%TRR]</th><th>mg/kg (as parent)</th><th>[%TRR]</th><th>mg/kg (as parent)</th></tr></thead><tbody><tr><td rowspan="2">Aubergine leaves/foliar treatment</td><td>7</td><td>89.2</td><td>20.02</td><td>1.0</td><td>0.22</td><td>0.01</td></tr><tr><td>14</td><td>85.2</td><td>17.02</td><td>1.8</td><td>0.35</td><td>0.02</td></tr><tr><td rowspan="6">Apples leaves/foliar treatment</td><td>0</td><td>97.4</td><td>34.85</td><td>0.6</td><td>0.21</td><td>0.01</td></tr><tr><td>7</td><td>94.4</td><td>35.71</td><td>1</td><td>0.39</td><td>0.01</td></tr><tr><td>14</td><td>89.7</td><td>28.03</td><td>2.1</td><td>0.67</td><td>0.02</td></tr><tr><td>28</td><td>80.2</td><td>19.98</td><td>4.8</td><td>1.19</td><td>0.06</td></tr><tr><td>62</td><td>61.1</td><td>15.69</td><td>10.5</td><td>2.7</td><td>0.17</td></tr><tr><td>90</td><td>49</td><td>11.5</td><td>15.6</td><td>3.64</td><td><strong>0.32</strong></td></tr><tr><td rowspan="6">Cabbage/Study 1 - foliar treatment</td><td>0</td><td>84.6</td><td>6.69</td><td>0.1</td><td>0.01</td><td>0.00</td></tr><tr><td>7</td><td>90.8</td><td>4.54</td><td>1.6</td><td>0.08</td><td>0.02</td></tr><tr><td>14</td><td>83.9</td><td>2.97</td><td>2</td><td>0.07</td><td>0.02</td></tr><tr><td>21</td><td>76.5</td><td>2.32</td><td>3.4</td><td>0.1</td><td>0.04</td></tr><tr><td>28</td><td>78.8</td><td>1.89</td><td>4.7</td><td>0.11</td><td>0.06</td></tr><tr><td>63</td><td>66.7</td><td>1.84</td><td>7.2</td><td>0.2</td><td>0.11</td></tr><tr><td rowspan="3">Cabbage/Study 1 - soil treatment</td><td>7</td><td>90.2</td><td>93.11</td><td>0.8</td><td>0.84</td><td>0.01</td></tr><tr><td>14</td><td>86.5</td><td>58.01</td><td>1.9</td><td>1.25</td><td>0.02</td></tr><tr><td>28</td><td>60.5</td><td>17.2</td><td>2</td><td>0.56</td><td>0.03</td></tr><tr><td rowspan="6">Cabbage/Study 2 - foliar treatment</td><td>0</td><td>99.3</td><td>5.12</td><td>n.d.</td><td>n.d.</td><td>n.r.</td></tr><tr><td>7</td><td>95.58</td><td>4.8</td><td>0.47</td><td>0.02</td><td>&lt; 0.01</td></tr><tr><td>14</td><td>88.65</td><td>3.56</td><td>2.05</td><td>0.08</td><td>0.02</td></tr><tr><td>28</td><td>78.43</td><td>4.04</td><td>4.39</td><td>0.23</td><td>0.06</td></tr><tr><td>63</td><td>63.84</td><td>2.09</td><td>4.09</td><td>0.13</td><td>0.06</td></tr><tr><td>14</td><td>26.85</td><td>0.12</td><td>5.92</td><td>0.03</td><td>0.22</td></tr></tbody></table>

Abbreviations: n.d., not detected; n.r., not relevant.

### 3.2.3 | Other available metabolism studies (rotational crops and hydrolysis studies)

Studies investigating the uptake of residues from soil resulting from the use of acetamiprid in primary crops (rotational crop field studies) and rotational crop metabolism studies in root crops (turnips), leafy crops (spinaches), cereals and oil-seed crops (wheat) have been provided in the dossier submitted in the framework of the renewal of the approval of acetamiprid. In the framework of an application to modify the existing MRLs for acetamiprid in honey and various oilseed crops, an additional rotational crops study was assessed by Austria (EMS) (EFSA, 2022).

Since acetamiprid has a low persistence in soil (highest field $DT_{90}$ 43 days), the metabolism studies in rotational crops were not conducted with acetamiprid but using the more persistent soil metabolite IM-1-5 ($DT_{50}$ ranging from 319 to 663 days). The metabolism of metabolite IM-1-5 has been investigated after bare soil treatment at application rates of 266 g/ha and 160 g/ha (EFSA, 2016a, 2022). In both studies, crops were planted/sown on the day of the treatment (0 day plant back interval [PBI]).

In the different rotational crops investigated (wheat, turnip, spinaches), the metabolite IM-1-5, major soil metabolite of acetamiprid (which was also the test material of the studies) was the main component of the radioactive residues accounting in mature plant at harvest for 77%-94% TRR. No other metabolites or unidentified residues were observed in significant proportions in any crop commodity. A limited metabolism of IM-1-5 was observed in the rotational crops and no metabolic pathway was proposed for IM-1-5.

Field rotational crop studies evaluated under the EU pesticides peer review with acetamiprid applied onto the bare soil at ca. 300 g/ha, confirmed that acetamiprid, IM-1-4 and IM-1-5 residues are not expected to be present in rotational crops (EFSA, 2016a).

The effect of processing on the nature of acetamiprid was also investigated in the framework of the EU pesticides peer review (EFSA, 2016a). Studies were performed with the standard processing conditions representative of pasteurisation (20 min, 90°C, pH 4), baking/brewing/boiling (60 min, 100°C, pH 5) and sterilisation (20 min, 120°C, pH 6).

The standard hydrolysis studies showed that acetamiprid is hydrolytically stable under standard processing conditions representative of pasteurisation, baking/brewing/boiling and sterilisation.
## 3.3 Residue definitions in plant commodities and conversions factors

### 3.3.1 Residue definitions

The existing risk assessment and enforcement residue definition in plant commodities has been set by the EU pesticides peer review and is limited to parent acetamiprid (EFSA, 2016a).

The re-assessment of the residue definition for risk assessment in plant commodities under the ToR 2b of present mandate was performed following the principles defined in OECD guidance (OECD, 2009). Available studies investigating the metabolism acetamiprid in plant matrices as well as the monitoring data collected in this mandate, including occurrence data for the metabolite IM-2-1 were considered for this assessment.

With respect to the metabolic pathway of acetamiprid in plants, particular consideration was given to the following acetamiprid metabolites, which have been identified in the available metabolism studies: metabolite IC-0 and its conjugate, metabolite IM-1-4 and metabolite IM-2-1.

Metabolite **6-chloronicotinic acid (IC-0)** and its methyl-ester were the main metabolites identified in cabbage head and cotton seeds (> 10% TRR). Metabolite IC-0 was also found in carrot roots. The metabolite 6-chloronicotinic acid (IC-0) was observed in the rat metabolism, and therefore, its toxicity was concluded to be covered by the toxicity of the parent acetamiprid (EFSA, 2016a). Furthermore, this metabolite was not deemed relevant for the risk assessment residue definition in the previous EFSA assessments due to its lower acute toxicity compared to the parent compound and its absence of genotoxic potential (EFSA, 2011). For this compound, no monitoring data were reported to EFSA and, in the framework of the present mandate, there are no new data concerning its toxicity. Therefore, there are no new elements to rediscuss the inclusion of metabolite 6-chloronicotinic acid (IC-0) in the risk assessment residue definition.

Regarding metabolite **IM-0** (and its conjugate) and metabolite **IM-1-4** which were not found in edible parts of crops, but only in immature samples of carrots, there are also no new element (monitoring data or toxicological data) to rediscuss their inclusion in the risk assessment residue definition.

Consequently, metabolite 6-chloronicotinic acid (IC-0) (and its methyl-ester), metabolite IM-0 (and its conjugate) and metabolite IM-1-4 are not proposed to be included in the updated risk assessment residue definitions for plant commodities.

The desmethyl-metabolite of acetamiprid (**IM-2-1**) was observed in the rat metabolism and its toxicity is covered by the parent compound. In the framework of the present mandate, this conclusion was confirmed.

According to the available metabolism studies in primary crops, metabolite IM-2-1 was below 10% of the TRR (between 2% and 8%) in all edible crop parts (aubergine, apple, cabbage samples, carrot mature samples, cotton seeds) and therefore not considered relevant for the risk assessment by the EU pesticides peer review. However, samples of apple leaf gave an indication that levels of metabolite IM-2-1 could reach higher proportions at longer pre-harvest intervals of 62 and 90 days (PHI), up to 17% and 32% of the parent compound (ca. 11% and 16% TRR, respectively). Also, in carrot leaves metabolite IM-2-1 was present at 5.92% TRR, accounting for up to 22% of the parent acetamiprid (see Section 3.1). Since this metabolite was not considered relevant for the risk assessment by the EU pesticides peer review, residue trial data on the magnitude of IM-2-1 are not available and have not been required to authorise acetamiprid uses.

In the framework of the present mandate, monitoring data on the metabolite IM-2-1 were collected which confirm the occurrence of this metabolite in several commodities belonging to the groups of leafy and fruit commodities (see Section 3.1). Furthermore, in 6 commodities belonging to the leafy crops group and in 9 commodities belonging to the fruit crops group, the concentration of metabolite IM-2-1 was found to be above the concentration of acetamiprid in at least one sample (see Section 3.1.3). In each of these commodities, the average proportion of metabolite IM-2-1 compared to parent compound (mean ratio 'IM-2-1/acetamiprid') has been found to be above 30% (see Section 3.1.3). This information was considered sufficiently relevant to reconsider the conclusions of the EU pesticide peer review from the renewal on the relevance of metabolite IM-2-1 residues in plant commodities.

Based on these findings, it is therefore proposed to include metabolite IM-2-1 in the residue definition for risk assessment in leafy and fruit crops (Table 32), which is currently limited to acetamiprid.

Regarding pulses/oilseeds, root crops and cereals, the collected monitoring data do not reveal a significant occurrence of IM-2-1 in commodities belonging to these categories. Therefore, it is not proposed to modify the residue definition for risk assessment in pulses/oilseeds, root crops and cereals, which therefore remains acetamiprid.

Regarding the residue definition for enforcement, the available data do not indicate a need to modify the existing definition because acetamiprid is still a sufficient marker of the residues in all crop groups.

The conclusions of EFSA derived in the present assessment on the risk assessment and enforcement residue definitions for acetamiprid residues in plant commodities are compiled in a table below:

<table><caption>TABLE 32 Updated residue definitions in plant commodities.</caption><tr><td>Plant residue definition for monitoring (RD-Mo)</td><td>- Acetamiprid (all metabolism groups)</td></tr><tr><td>Plant residue definition for risk assessment (RD-RA)</td><td>- Fruit crops: sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid<br/>- Leafy crops: sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid<br/>- Pulses/oilseeds: acetamiprid<br/>- Root crops: acetamiprid<br/>- Cereals: acetamiprid</td></tr></table>
### 3.3.2 | Conversion factors

For leafy and fruit crops, conversion factors (CFs) from monitoring to risk assessment should be derived to consider the newly derived residue definition in the consumer risk assessment. Conversions factors are intended to recalculate the residue concentration expressed according to the residue definition for monitoring (acetamiprid) to the residue concentration expressed according to the residue definition for risk assessment (sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid). Therefore, CFs are generally calculated according to the following formula:

$$CF = [RD - RA]mg/kg / [RD - Mo]mg/kg.$$

In the absence of supervised residues trials analysing for acetamiprid and metabolite IM-2-1, tentative CFs can be derived on the basis of monitoring data and metabolism studies.

An estimate of the CFs can be derived at crop group level, using the **median ratio** of residues in each crop group as calculated from the submitted monitoring data. To minimise the potential overestimation of the derived conversion factor due to the LOQ values, EFSA did not consider for the calculation those samples where the metabolite N-desmethyl-acetamiprid (IM-2-1) was not quantified above the LOQ. Indeed, when these samples are considered using the upper bound approach, it artificially results in high ratio 'IM-2-1/parent' in the samples where the parent compound is quantified in low amounts. The median ratios 'IM-2-1/acetamiprid' were calculated for leafy crops and fruit crops separately. The median ratios 'IM-2-1/acetamiprid' were then recalculated expressing the metabolite IM-2-1 as parent compound,<fnref n="14" /> resulting in ratios of 0.44 and 0.21 for leafy crops and fruit crops, respectively. Consequently, the corresponding CFs from enforcement to risk assessment could be derived for leafy crops and fruit crops, as follows (Table 33):

<table><caption>TABLE 33 Proposed conversion factors for leafy and fruit crops based on median ratios [IM-2-1]/[acetamiprid].</caption><thead><tr><th></th><th>Median ratio<sup>a</sup>: [IM-2-1]<sub>as reported</sub>/[acetamiprid]</th><th>Median ratio recalculated<sup>b</sup>: [IM-2-1]<sub>as parent</sub>/[acetamiprid]</th><th>Median CF: [RD-RA]/[RD-Mo]</th></tr></thead><tbody><tr><td>Leafy crops</td><td>0.42</td><td>0.44</td><td>1.44</td></tr><tr><td>Fruit crops</td><td>0.20</td><td>0.21</td><td>1.21</td></tr></tbody></table>

Furthermore, the proposed conversion factors can be discussed in the light of the metabolism studies available on leafy and fruit crops.

For leafy crops, the highest ratio 'IM-2-1/acetamiprid' observed in the samples taken in the metabolism studies is 0.32 and was found in apple leaf samples taken at PHI 90 days (see Table 31). This is in the same order of magnitude as the median derived from the monitoring data (0.4). Therefore, the CF of 1.4 (derived from median ratio of 0.4 in monitoring data) is considered appropriate to perform a conservative risk assessment for leafy crops.

Regarding fruit crops, the levels of metabolite IM-2-1 found in the metabolism studies are clearly lower than those found in leafy crops. The highest concentration of IM-2-1 was found in apple samples taken at PHI 14 days: 0.03 mg eq./kg (see Table 30). Considering the concentration of parent compound (2.3 mg/kg) found in the same sample, a ratio 'IM-2-1/acetamiprid' of 0.02 is calculated. This is 10 times lower than the median ratio derived from the monitoring data (0.2). The CF of 1.2 (derived from median ratio of 0.2 in monitoring data) is considered appropriate to perform a conservative risk assessment for fruit crops.

It should be noted that the proposed CFs for leafy and fruit crops derived from monitoring data should be considered tentative only. However, in the absence of supervised field trials analysing for parent and metabolite IM-2-1 simultaneously, these are the best estimates possible based on the available evidence.

### 3.4 | Consumer risk assessment and identification of MRLs posing risk to consumers

As defined in the ToR 3 of the mandate, EFSA performed the assessment of the chronic and acute consumer risk related to the existing EU MRLs for acetamiprid in all plant and animal products, using revision 3.1 of the EFSA PRIMo (EFSA, 2018a, 2019). The assessment was performed considering the Good Agricultural Practices (GAPs) and supporting residue trials already available to EFSA before this mandate. It includes GAPs and residue trials assessed in the framework of the initial MRL review (EFSA, 2011) and in the framework of the focussed assessment of certain MRLs of concern for acetamiprid (EFSA, 2018c) as well as in the framework of several MRL applications submitted in accordance with Article 6 of Regulation

<footnote><sup>a</sup>Median ratio derived from the individual ratios calculated for each sample. Only samples where metabolite IM-2-1 was quantified were considered. The ratios are derived considering metabolite IM-2-1 expressed as IM-2-1.</footnote>

<footnote><sup>b</sup>Median ratios recalculated considering metabolite IM-2-1 expressed as parent compound.</footnote>

<footnote n="14">¹⁴Considering the molecular mass of 222.67 for acetamiprid and of 208.67 for metabolite IM-2-1.</footnote>
(EC) 396/2005 (EFSA, 2012, 2013, 2014, 2015, 2016c, 2021, 2022). For certain plant commodities and animal commodities the EU MRL has been set on the basis of the Codex MRLs and therefore the JMPR evaluations were also considered.

The existing toxicological reference values (ADI = 0.025 mg/kg bw per day; ARfD = 0.025 mg/kg bw) for acetamiprid were derived in the framework of the EU pesticides peer review (European Commission, 2018). However, in the framework of the present mandate, EFSA derived new toxicological reference values for acetamiprid. The newly derived toxicological reference values are lower than the existing ones: ADI = 0.005 mg/kg bw per day; ARfD = 0.005 mg/kg bw. Consequently, in line with ToR 3, the newly proposed revised values were used to perform the risk assessment.

In the framework of the present mandate, EFSA also derived a new residue definition for risk assessment in leafy and fruit crops. Consequently, in line with ToR 3, EFSA performed two separate risk assessment scenarios: for the existing residue definitions for risk assessment (**scenario 1**) and for the newly proposed residue definitions for risk assessment (**scenario 2**).

### 3.4.1 | Consumer risk-assessment using the newly derived HBGVs and existing residue definition for risk assessment (scenario 1)

EFSA updated the assessment performed in the framework of the most recent MRL application, modifying the existing MRLs for acetamiprid in honey and various oilseed crops (EFSA, 2022) with the newly derived toxicological reference values. It is noted that the MRL proposals derived under the most recent MRL assessment (EFSA, 2022) have not been implemented yet but are reported in the draft Regulation SANTE/11278/2021. Therefore, these MRLs and risk assessment values were considered under the present scenario. In addition, the CXL for pistachios, assessed in 2021 (FAO, 2021) and recommended by EFSA for the implementation in the EU legislation and its corresponding risk assessment values were also considered for a complete updated picture of the potential consumer exposure.

The input values were therefore the risk assessment values as derived in previous EFSA assessments or in the evaluations by the JMPR, or, in few cases, the existing EU MRL. Where available, the peeling factors were applied to refine the exposure assessment. The reliable processing factors assessed in previous EFSA opinions were also considered to assess the exposure calculated for processed commodities. Therefore, the PF of 0.35 previously derived for gherkins pickles was considered. For orange juice, it was acknowledged that the PF of 0.13 previously derived was not fully supported by data. However, considering the significant effect of peeling in orange (peeling factor of 0.03), it was considered appropriate to refine the exposure calculation for orange juice with the available PF.

The input values used in the exposure calculations are reported in Appendix 17 and the PRIMo file is published in Appendix 20.

Based on this calculation, EFSA identified an exceedance of the ARfD for **36 commodities**, for which acute exposure ranged between **102%** (Roman rocket/rucola) and **582%** (pears) of the ARfD. An overview of the commodities for which exceedance of the ARfD was identified is presented in Table 34. Exceedances of the ARfD were also identified for several processed commodities. For most of them, exceedance of the ARfD was also identified for the corresponding raw agricultural commodities (RAC) listed in Table 34. However, exceedances of ARfD were also identified for elderberries juice (204% of the ARfD) and pumpkins boiled (195% of the ARfD) while for the RAC of these commodities, no exceedances of the ARfD were identified. For the remaining commodities the short-term exposure did not exceed the ARfD.

The highest estimated long-term dietary intake was **81%** of the **ADI** (NL toddler).

Overall, considering the updated hazard assessment for acetamiprid and its metabolites and considering the existing residue definition for risk assessment, the existing MRLs (or the MRL proposed in SANTE/11278/2021) for 38 commodities (36 plant commodities, 2 commodities of animal origin) were found to lead to acute intake concerns when assessed with the newest revision 3.1 of PRIMo. Regarding the long-term dietary intake, no exceedance of the ADI was identified.

### 3.4.2 | Consumer risk-assessment using the new endpoints derived in the present statement (scenario 2)

The new proposed residue definition for risk assessment in leafy and fruit crops is the 'sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid'. The metabolite IM-2-1 included in the risk assessment residue definition is still concluded to be of similar toxicity as the parent compound. Tentative conversion factors from monitoring to risk assessment were also derived for leafy and fruit commodities based on monitoring data (see Section 3.3.2).

The second scenario for the consumer risk assessment was performed using the new HBGVs, and the new residue definitions and conversion factors derived in the present statement. For fruit crops and leafy crops, the tentative conversion factors (1.21 and 1.44, respectively) were applied. The input values used in the exposure calculations are reported in Appendix 17 and the PRIMo file is published in Appendix 21.

Based on this calculation, EFSA identified an exceedance of the ARfD for **38 commodities**, for which acute exposure ranged between **106%** (table olives) and **822%** (lettuces) of the ARfD. An overview of the commodities for which exceedance of the ARfD was identified is presented in Table 34. For strawberries, it is noted that the margin of safety is very low (99% of the ARfD).

Exceedances of the ARfD were also identified for several processed commodities. For most of them, exceedance of the ARfD was also identified for the corresponding raw agricultural commodities (RAC) listed Table 34. However, exceedances
of ARfD were also identified for elderberries juice (247% of the ARfD) and pumpkins boiled (236% of the ARfD) while for the RAC of these commodities, no exceedances of the ARfD were identified.

Regarding **elderberries juice**, it is noted that the acute exposure result is obtained for a large portion of 257.59 g reported for the DE children. It is likely that this large portion does not correspond to pure juice (i.e. 100% fruits). Therefore, the calculated exposure may not consider the dilution factor possibly applicable for these consumption data. In the absence of processing trials for this commodity, EFSA was not able to perform further refinement. The available results are therefore likely overestimating the exposure resulting from the existing critical authorisations identified for elderberries.

Regarding **pumpkins boiled**, in the absence of processing trials for this commodity, EFSA was not able to proceed with further refinement.

The existing authorised GAPs of acetamiprid for which the acute consumer intake concerns were identified have been assessed and reported in the previous EFSA assessments. These data are therefore not reiterated in the present assessment.

The highest estimated long-term dietary intake was **93%** of the **ADI** (NL toddler).

Overall, considering the updated hazard assessment for acetamiprid and the updated residue definition for risk assessment, the existing MRLs (or the MRL proposed in SANTE/11278/2021) for 40 commodities (38 plant commodities and 2 commodities of animal origin) were found to lead to acute intake concerns when assessed with the newest revision 3.1 of PRIMo. Regarding the long-term dietary intake, no exceedance of the ADI was identified but the margin of safety is very small.

It is noted that compared to scenario 1, where the newly proposed residue definition for risk assessment was not considered, exceedances of the ARfD are identified for the same list of commodities, with the only addition of cranberries and table olives to the list (see Table 34).

<table><caption>TABLE 34 Raw Agricultural Commodities for which exceedance of the ARfD is identified in scenario 1 and/or in scenario 2 for the existing MRLs and associated risk assessment values.</caption><thead><tr><th rowspan="2">Commodity</th><th rowspan="2">Existing MRL<sup>a</sup></th><th colspan="2">Scenario 1</th><th colspan="2">Scenario 2</th></tr><tr><th>IASTI in % ARfD</th><th>Input value for RA (mg/kg)</th><th>IASTI in % ArfD</th><th>Input value for RA (mg/kg)</th></tr></thead><tbody><tr><td>Lettuces</td><td>1.5</td><td>571% (children)</td><td>0.75</td><td>822% (children)</td><td>1.08</td></tr><tr><td>Pears</td><td>0.4</td><td>582% (children)</td><td>0.21</td><td>704% (children)</td><td>0.25</td></tr><tr><td>Apples</td><td>0.4</td><td>453% (children)</td><td>0.21</td><td>548% (children)</td><td>0.25</td></tr><tr><td>Apricots</td><td>0.8</td><td>399% (children)</td><td>0.57</td><td>482% (children)</td><td>0.69</td></tr><tr><td>Table grapes</td><td>0.5</td><td>365% (children)</td><td>0.25</td><td>441% (children)</td><td>0.3</td></tr><tr><td>Melons</td><td>0.2</td><td>334% (children)</td><td>0.11</td><td>404% (children)</td><td>0.13</td></tr><tr><td>Tomatoes</td><td>0.5</td><td>326% (children)</td><td>0.28</td><td>394% (children)</td><td>0.34</td></tr><tr><td>Quinces</td><td>0.8</td><td>315% (children)</td><td>0.64</td><td>381% (children)</td><td>0.77</td></tr><tr><td>Cauliflowers</td><td>0.4</td><td>255% (children)</td><td>0.22</td><td>367% (children)</td><td>0.32</td></tr><tr><td>Sweet peppers/bell peppers</td><td>0.4</td><td>274% (children)</td><td>0.23</td><td>331% (children)</td><td>0.28</td></tr><tr><td>Watermelons</td><td>0.2</td><td>269% (children)</td><td>0.11</td><td>325% (children)</td><td>0.13</td></tr><tr><td>Cucumbers</td><td>0.4</td><td>262% (children)</td><td>0.2</td><td>317% (children)</td><td>0.36</td></tr><tr><td>Head cabbages</td><td>0.4</td><td>221% (children)</td><td>0.25</td><td>319% (children)</td><td>0.24</td></tr><tr><td>Broccoli</td><td>0.4</td><td>208% (children)</td><td>0.25</td><td>300% (children)</td><td>0.36</td></tr><tr><td>Red mustards</td><td>3</td><td>202% (adults)</td><td>1.9</td><td>209% (adults)</td><td>2.74</td></tr><tr><td>Escaroles/broad-leaved endives</td><td>0.4</td><td>201% (children)</td><td>0.25</td><td>289% (children)</td><td>0.36</td></tr><tr><td>Cherries (sweet)</td><td>1.5</td><td>215% (children)</td><td>0.88</td><td>260% (children)</td><td>1.06</td></tr><tr><td>Blackberries</td><td>2</td><td>214% (children)</td><td>1</td><td>259% (children)</td><td>1.21</td></tr><tr><td>Bananas</td><td>0.4</td><td>209% (children)</td><td>0.11</td><td>253% (children)</td><td>0.13</td></tr><tr><td>Peaches</td><td>0.2</td><td>190% (children)</td><td>0.1</td><td>230% (children)</td><td>0.12</td></tr><tr><td>Courgettes</td><td>0.4</td><td>186% (children)</td><td>0.2</td><td>225% (children)</td><td>0.24</td></tr><tr><td>Raspberries (red and yellow)</td><td>2</td><td>185% (children)</td><td>1</td><td>224% (children)</td><td>1.21</td></tr><tr><td>Blueberries</td><td>2</td><td>182% (adults)</td><td>1</td><td>221% (adults)</td><td>1.21</td></tr><tr><td>Medlar</td><td>0.8</td><td>177% (children)</td><td>0.64</td><td>214% (children)</td><td>0.77</td></tr><tr><td>Granate apples/pomegranates</td><td>0.3</td><td>176% (children)</td><td>0.16</td><td>213% (children)</td><td>0.19</td></tr><tr><td>Spinaches</td><td>0.6</td><td>140% (children)</td><td>0.31</td><td>202% (children)</td><td>0.45</td></tr><tr><td>Currants (red, black and white)</td><td>2</td><td>158% (children)</td><td>1</td><td>191% (children)</td><td>1.21</td></tr><tr><td>Asparagus</td><td>0.8</td><td>166% (children)</td><td>0.43</td><td>166% (children)</td><td>0.43</td></tr><tr><td>Chards/beet leaves</td><td>0.6</td><td>117% (adults)</td><td>0.31</td><td>169% (adults)</td><td>0.45</td></tr></tbody></table>

(Continues)
<table><caption>**TABLE 34** (Continued)</caption><thead><tr><th rowspan="2">Commodity</th><th rowspan="2">Existing MRL<sup>a</sup></th><th colspan="2">Scenario 1</th><th colspan="2">Scenario 2</th></tr><tr><th>LESTI in % ARfD</th><th>Input value for RA (mg/kg)</th><th>LESTI in % ARfD</th><th>Input value for RA (mg/kg)</th></tr></thead><tbody><tr><td>Lamb's lettuce/corn salads</td><td>3</td><td>107% (children)</td><td>1.9</td><td>154% (children)</td><td>2.74</td></tr><tr><td>Roman rocket/rucola</td><td>3</td><td>102% (children)</td><td>1.9</td><td>147% (children)</td><td>2.74</td></tr><tr><td>Bovine: Liver</td><td>1</td><td>144% (children)</td><td>0.89</td><td>144% (children)</td><td>0.89</td></tr><tr><td>Wine grapes</td><td>0.5</td><td>119% (adults)</td><td>0.25</td><td>143% (adults)</td><td>0.3</td></tr><tr><td>Gooseberries (green, red and yellow)</td><td>2</td><td>118% (children)</td><td>1</td><td>142% (children)</td><td>1.21</td></tr><tr><td>Bovine: Edible offals (other than liver and kidney)</td><td>1</td><td>130% (children)</td><td>0.89</td><td>130% (children)</td><td>0.89</td></tr><tr><td>Aubergines/egg plants</td><td>0.4</td><td>103% (adults)</td><td>0.19</td><td>125% (adults)</td><td>0.23</td></tr><tr><td>Cranberries</td><td>2</td><td>90% (children)</td><td>1</td><td>109% (children)</td><td>1.21</td></tr><tr><td>Table olives</td><td>3</td><td>88% (children)</td><td>1.3</td><td>106% (children)</td><td>1.57</td></tr></tbody></table>

## 3.5 | Identification of MRLs that do not pose risk to consumers

In line with Terms of Reference 3, EFSA performed further assessments to identify lower MRLs for acetamiprid that do not pose unacceptable risk to consumers for all the commodities listed in Table 34. In addition, as the margin of safety was found to be very low for strawberries, further attempts to identify lower MRL options were also performed for this commodity. In addition, further attention was also given to elderberries and pumpkins for which exceedance of the ARfD was only identified for processed commodities (juice and boiled, respectively).

### 3.5.1 | Data call

In the framework of the extended mandate, EFSA organised a call for data to identify fall-back GAPs and supporting residue trials that would lead to safe scenarios for all plant commodities identified in Table 34 and for strawberries.

The data call was issued for EU Member States. Consequently, data directly sent by applicants were not in the scope of this mandate. In addition, only authorised GAPs for acetamiprid and supporting valid residue trials previously assessed by Member State Authorities were in the scope of this data call.

EFSA launched the data call on **3 October 2023**. Member States were invited to submit their authorised Good Agricultural Practices (GAPs) and supporting residue data, which might support the setting of fall-back safe MRLs for acetamiprid. Member States were invited to upload their GAP forms (Excel format) and Evaluation Reports (word document) within the deadline of **30 November 2023**. All documents received are available as background documents to the present statement.

A total of 13 Member States replied to the call for data. Regarding the plant commodities of concern, a total of 509 GAPs were reported to EFSA. It is noted that 179 GAPs for crops not listed in Table 34 were also reported, partly due to possible overreporting when using crop grouping. These GAPs were therefore not considered by EFSA in the framework of the present mandate.

Based on the validation criteria available in the GAP form, a GAP was considered clear if the following key parameters were available: Formulation type; Application methods; Number of application(s) and interval between applications; Application rate; PHI (or growth stage during application). All the GAPs received within this data call are available in the GAP overview file (xls), which is published in Appendix 18 of the present statement. An overview of the collected data which are relevant in the scope of the present mandate is reported in Table 35 below.

<table><caption>**TABLE 35** Overview of the data received during the call for data October-November 2023 for fall-back GAPs and supporting residue trials.</caption><thead><tr><td>Member State</td><td>Number of relevant GAPs</td><td>Evaluation report</td></tr></thead><tbody><tr><td>Austria</td><td>27</td><td>Austria (2023) (30 pages)</td></tr><tr><td>Belgium</td><td>36</td><td>Belgium (2023) (55 pages)</td></tr><tr><td>Czech Republic</td><td>13</td><td>Czech Republic (2023) (16 pages)</td></tr><tr><td>Finland</td><td>10</td><td>Finland (2023) (22 pages)</td></tr><tr><td>France</td><td>0</td><td>No report</td></tr><tr><td>Germany</td><td>58</td><td>Germany (2023) (34 pages)</td></tr></tbody></table>

<footnote><sup>a</sup>Existing MRL or MRL proposed in SANTE/11278/2021.</footnote>
<table><caption>**TABLE 35** (Continued)</caption><thead><tr><td>Member State</td><td>Number of relevant GAPs</td><td>Evaluation report</td></tr></thead><tbody><tr><td>Greece</td><td>123</td><td>Greece (2023) (450 pages)</td></tr><tr><td>Hungary</td><td>19</td><td>No report</td></tr><tr><td>Italy</td><td>79</td><td>Italy (2023) (412 pages)</td></tr><tr><td>Portugal</td><td>84</td><td>Portugal (2023) (195 pages)</td></tr><tr><td>Spain</td><td>35</td><td>Spain (2023) (183 pages)</td></tr><tr><td>Sweden</td><td>16</td><td>Sweden (2023) (47 pages)</td></tr><tr><td>The Netherlands</td><td>9</td><td>The Netherlands (2023) (76 pages)</td></tr></tbody></table>

### 3.5.2 | Methodology

EFSA screened the 509 relevant GAPs received during the data call using the following stepwise approach for each GAP:

- Check if GAP compliant trials are available in the evaluation report of the Member State which submitted the GAP or in the evaluation reports of other Member States.

- If no GAP compliant trials are available, the GAP was discarded.

- If more than one GAP compliant trials are available, check the highest residue value (HR) reported by the Member States.

- If the HR does not pose acute intake concerns for raw agricultural commodities using PRIMo rev. 3.1, the revised HBGVs and the revised residue definition for risk assessment (RD-RA) and CFs, the GAP was retained as a candidate to derive fall-back MRL; further assessment was then performed.

- If the HR shows acute concerns for raw agricultural commodities using PRIMo rev. 3.1, the revised HBGVs and revised RD-RA and CFs, the GAP was discarded.

- If a safe fall-back GAP (and MRL) was identified for a given commodity (i.e. no acute consumer intake concerns), no further detailed assessment was performed for the less critical GAPs leading to lower MRLs and RA values.

The result of this screening is available in the GAP overview file (xls), which is published in **Appendix 18** the present statement.

For those GAPs retained for further attention, EFSA performed validity assessment of residue trials in accordance with the Technical guidelines on data requirements for setting maximum residue levels, comparability of residue trials and extrapolation on residue data on products from plant and animal origin (European Commission, 2020). According to the cited Guidance, one parameter of the residue trials (e.g. application rate) may deviate by ± 25% compared to the GAP under assessment, if other parameters remain unchanged. In some cases, Member State Authorities proposed to use residue data from trials conducted with different application rates to support the reported GAPs, using the proportionality concept and applying a scaling of residue data to the nominal application rate. EFSA assessed the Member State proposals in accordance with the EFSA Technical Report on the recommendations on the use of the proportionality approach in the framework of risk assessment for pesticide residues (EFSA, 2018b). This concept was applied to data from field trials conducted with application rates falling within a range of 0.3x and 4x the GAP rate, all other parameters being the same.

It is noted that detailed assessment of the validity of the residue trial studies (e.g. validation of analytical methods) was not undertaken by EFSA, relying on the Member States that only valid residue trials data were reported.

### 3.5.3 | Plant commodities with identified fall-back options

Following the approach defined in Section 3.5.2, fall-back MRL options could be identified for 30 plant commodities (including strawberries). The assessment of the fall-back GAPs, the individual results of the supporting valid residue trials and the derived fall-back MRLs and associated risk assessment values are reported in this section. The full description of the fall-back GAPs identified in this section is reported in **Appendix 19**.

Samples from all residue trials have been analysed for parent acetamiprid and no data on the magnitude of metabolite IM-2-1 were available. Residue trials data submitted for the fall-back GAPs are summarised in **Table 36**.

**Apples and pears:**

Identified fall-back GAP: 2 × 51 g a.s./ha; PHI: 14 days (BE, NEU, Outdoor, SG formulation)

The GAP reported by Belgium is supported by 7 GAP compliant residue trials performed on apples (Belgium, 2023). Residue data can be extrapolated to pears. One additional residue trial supporting this GAP should be required to derive a robust MRL proposal. In the meantime, tentative MRL and risk assessment values can be proposed for these commodities.
## Quinces

Identified fall-back GAP: 2 × 75 g a.s./ha; PHI: 14 days (AT, NEU, Outdoor, SL formulation)

The GAP reported by Austria is supported by seven residue trials performed on apples (Austria, 2023). The available trials were performed with SP formulation and deemed acceptable to support the GAP defined for SL formulation. Residue data can be extrapolated to quinces. It is noted that three trials are GAP compliant (with 25% tolerance on the application rate) and that four trials are slightly overdosed (up to 109 g a.s./ha per application). Considering that four overdosed trials are not compliant with the GAP, five additional GAP compliant residue trials supporting this GAP should be required to derive a robust MRL proposal. In the meantime, tentative MRL and risk assessment values can be proposed for this commodity.

## Medlar

Identified fall-back GAP: 2 × 100 g a.s./ha; PHI: 14 days (EL/PT, SEU, Outdoor, SL formulation)

The GAP reported by Greece and Portugal is supported by eight GAP compliant residue trials performed on apples (Portugal, 2023). Residue data can be extrapolated to medlar. It is noted that the available trials were performed with SP formulation and deemed acceptable to support the GAP defined for SL formulation. MRL and risk assessment values can be proposed for this commodity.

## Apricots

Identified fall-back GAP: 2 × 51 g a.s./ha; PHI: 14 days (BE, NEU, Outdoor, SG formulation)

The GAP reported by Belgium is supported by four GAP compliant trials performed on peaches (Belgium, 2023). It is noted that the available trials were performed with SP formulation and deemed acceptable to support the GAP defined for SG formulation. Furthermore, apricots being a minor crop in northern EU, four residue trials are sufficient. However, according to the current extrapolation rules, a minimum of 50% of trials performed on apricots is required to support the use and to derive an MRL on apricots. Therefore, two additional GAP compliant residue trials performed on apricots should be required to derive a robust MRL proposal. In the meantime, tentative MRL and risk assessment values can be proposed for this commodity.

## Peaches:

Identified fall-back GAP: 2 × 51 g a.s./ha; PHI: 14 days (BE, NEU, Outdoor, SG formulation)

The GAP reported by Belgium is supported by four GAP compliant residue trials performed on peaches (Belgium, 2023). It is noted that the available trials were performed with SP formulation and deemed acceptable to support the GAP defined for SG formulation. Peaches being a minor crop in northern EU, 4 residue trials are sufficient. MRL and risk assessment values can be proposed for this commodity.

## Cherries

Identified fall-back GAP 1: 2 × 70 g a.s./ha; PHI: 3 days (EL, SEU, Outdoor, SL formulation)

The GAP reported by Greece is supported by four GAP compliant residue trials on cherries (Greece, 2023), all performed with SL formulation. Residues in the whole fruit were calculated as (weight of flesh × residue level)/weight of fruit. Cherries being a minor crop in southern EU, four residue trials are sufficient. MRL and risk assessment values can be proposed for this commodity.

Identified fall-back GAP 2: 2 × 100 g a.s./ha; PHI: 14 days (EL, SEU, Outdoor, SL formulation)

The GAP reported by Greece is supported by six GAP compliant residue trials, all performed with SL formulation (Greece, 2023). Cherries being minor crop in southern EU, the available residue trials are sufficient. MRL and risk assessment values can be proposed for this commodity.

## Table and wine grapes:

Identified fall-back GAP: 1 × 75 g a.s./ha; PHI: 14 days (PT, SEU, Outdoor, SG/SP formulations)

The GAP reported by Portugal is supported by eight GAP compliant residue trials on grapes, all performed with SG formulation (Portugal, 2023). MRL and risk assessment values can be proposed for these commodities.
Strawberries:

Identified fall-back GAP: 1 × 50 g a.s./ha; PHI: 3 days (SE, EU, Indoor, SG formulation)

EFSA notes that acute consumer intake concerns have not been identified for strawberries for the existing use. However, as the margin of safety for consumer exposure was small, a fall-back GAP was assessed to provide supporting information should risk managers decide to revise the existing EU MRL in strawberries. The GAP reported by Sweden is supported by nine residue trials on strawberries, all performed with two applications instead of one, all other trial parameters being compliant with GAP (Sweden, 2023). The nine trials were performed with SG formulation. Considering the over-estimation of residues in the crop due to the higher number of applications, tentative MRL and risk assessment values can be proposed for this commodity. Eight GAP compliant residue trials should still be required to derive a robust MRL proposal.

Blackberries, Raspberries

Identified fall-back GAP: 2 × 50 g a.s./ha; PHI: 7 days (AT/BE/DE, NEU, Outdoor, SG formulation)

The GAP reported by Austria, Belgium and Germany is fully supported by four GAP compliant residue trials performed on raspberries, all performed with SG formulation (Austria, 2023; Belgium, 2023; Germany, 2023). In three of the trials, the sample storage period exceeded the demonstrated storage stability period of 12 months (i.e. 15 months); however, this was not considered a major deviation. Blackberries and raspberries being minor crops in EU, the available residue trials are sufficient. MRL and risk assessment values can be proposed for these commodities.

Blueberries, Cranberries, Currants, Gooseberries

Identified fall-back GAP: 2 × 60 g a.s./ha; PHI: 7 days (BE, NEU, Outdoor, SG formulation)

The GAP reported by Belgium is fully supported by 9 residue trials performed on currants, all performed with SG formulation (Belgium, 2023). Residue data can be extrapolated from currants to blueberries, cranberries and gooseberries. The trials were performed with an application rate of 50 g a.s/ha and are therefore underdosed but within the acceptable deviation of 25%. Nevertheless, Belgium (2023) proposed to apply a scaling factor of 1.2 to the residue data set. This approach is acceptable in accordance with EFSA technical report (EFSA, 2018b). MRL and risk assessment values can be proposed for these commodities.

Elderberries

Identified fall-back GAP: 2 × 50 g a.s./ha; PHI: 7 days (AT, NEU, Outdoor, SG formulation)

EFSA notes that acute consumer intake concerns have not been identified for the raw agricultural commodity elderberry for the existing use. However, as an exceedance of the ARfD for elderberries juice was identified, a fall-back GAP was assessed to provide supporting information should risk managers decide to revise the existing EU MRL in elderberries. The GAP authorised in Austria is fully supported by nine residue trials performed on currants, all performed with SG formulation (Belgium, 2023). Residue data can be extrapolated from currants to elderberries. The trials were performed with an application rate of 50 g a.s/ha and are therefore compliant with GAP. MRL and risk assessment values can be proposed for this commodity.

Table olives

Identified fall-back GAP: 2 × 70 g a.s./ha; PHI: 28 days (PT, SEU, Outdoor, SG formulation)

The GAP reported by Portugal is fully supported by 8 GAP compliant residue trials performed on table olives, all performed with SG formulation (Portugal, 2023). MRL and risk assessment values can be proposed for this commodity.

Tomatoes

Identified fall-back GAP: 2 × 100 g a.s./ha; PHI: 7 days (EL, SEU, Outdoor, SL formulation)

The GAP reported by Greece is supported by eight residue trials on tomatoes. The trials were performed with an application rate ranging between 90 and 94 g a.s./ha and are therefore deemed compliant with the GAP considering that the difference compared to the authorised GAP is less than 25% (Portugal, 2023). It is noted that the available trials were performed with SP formulation and deemed acceptable to support the GAP defined for SL formulation. MRL and risk assessment values can be proposed for this commodity.
## Sweet peppers

Identified fall-back GAP: 2 × 80 g a.s./ha; PHI: 7 days (ES, SEU, Outdoor, SG formulation)

The GAP reported by Spain is supported by eight residue trials on peppers (Spain, 2023). It is noted that five trials are GAP compliant (within 25% tolerance on the application rate) and three trials are slightly overdosed (up to 112 g a.s./ha) and thus not fully GAP compliant. It is also noted that four trials were performed with SG formulation while the four others were performed with SP formulation, all deemed acceptable to support the GAP defined for SG formulation. However, three additional GAP compliant residue trials supporting this GAP should be required to derive a robust MRL proposal. In the meantime, tentative MRL and risk assessment values can be proposed for this commodity.

## Aubergines

Identified fall-back GAP: 2 × 50 g a.s./ha; PHI: 3 days (EL/ES, EU, Indoor, SG formulation)

The GAP reported by Greece and Spain is not supported by GAP compliant trials. However, eight overdosed trials on to-matoes performed with an application rate ranging between 94 and 109 g a.s./ha are available. Spain proposed to apply the proportionality concept and scaled the residue results accordingly. The proportionality approach is applicable. All trials were performed with SG formulation in line with the reported GAP. Consequently, MRL and risk assessment can be proposed for this commodity.

## Cucumber, Courgettes

Identified fall-back GAP: 2 × 50 g a.s./ha; PHI: 3 days (ES, SEU, Outdoor, SP formulation)

The GAP reported by Spain is supported by eight residue trials performed on courgettes (Spain, 2023). Residue data can be extrapolated from courgettes to cucumbers. It is noted that only one trial is GAP compliant and that seven trials are slightly overdosed (up to 69 g a.s./ha) and thus not fully GAP compliant. It is also noted that the available trials were performed with SG formulation and deemed acceptable to support the GAP defined for SP formulation. However, seven additional GAP compliant residue trials supporting this GAP should be required to derive a robust MRL proposal. In the meantime, tentative MRL and risk assessment values can be proposed for these commodities.

## Melons, Pumpkins, Watermelons

Identified fall-back GAP: 2 × 100 g a.s./ha; PHI: 14 days (EL, SEU, Outdoor, SL formulation)

The GAP reported by Greece is supported by 10 GAP compliant residue trials performed on melons (Greece, 2023). Residue data can be extrapolated from melons to other cucurbits with inedible peel. Residues in pulp samples were also collected for a refined exposure assessment. All trials were performed with SL formulation, therefore fully supporting MRL and risk assessment values for melons, pumpkins and watermelons.

## Broccoli, Cauliflower

Identified fall-back GAP: 1 × 70 g a.s./ha; PHI: 14 days (EL/ES/IT, SEU, Outdoor, SL formulation)

The GAP reported by Greece, Italy and Spain is fully supported by eight GAP compliant residue trials performed on broc-choli (4) and cauliflower (4), all performed with SL formulation (Greece, 2023; Italy, 2023; Spain, 2023). An additional GAP-compliant trial on broccoli was available but was considered a statistical outlier explained by agronomical conditions by all Member States who assessed this trial (Greece, Italy, Portugal, Spain). Therefore, EFSA considered appropriate to disregard this trial. MRL and risk assessment values can be proposed for these commodities.

## Head cabbages

Identified fall-back GAP: 1 × 70 g a.s./ha; PHI: 14 days (EL/ES, SEU, Outdoor, SL formulation)

The GAP reported by Greece and Spain is supported by eight GAP-compliant residue trials (within 25% tolerance on the application rate) performed on head cabbages, all performed with SL formulation (Greece, 2023). MRL and risk assessment values can be proposed for this commodity.
## Lamb's lettuce, Roman rocket

*Identified fall-back GAP: 2 × 50 g a.s./ha; PHI: 3 days (DE, NEU, Outdoor, SG formulation)*

The GAP reported by Germany is supported by eight GAP compliant trials performed on lettuces already assessed in the framework of previous MRL review (EFSA, 2011) and focussed MRL assessment of acetamiprid (EFSA, 2018c). Residue data can be extrapolated from lettuce to lamb's lettuce and rocket. MRL and risk assessment values can be proposed for these commodities.

## Red mustards

*Identified fall-back GAP: 1 × 70 g a.s./ha; PHI: 7 days (IT, SEU, Outdoor, SL formulation)*

The GAP reported by Italy is supported by eight GAP compliant residue trials performed on lettuces (Italy, 2023). Residue data can be extrapolated from lettuce to red mustard. All trials were performed on open leaf varieties with SL formulation, therefore fully supporting MRL and risk assessment values proposed for red mustards.
<table><caption>TABLE 36 Summary of residues data from the supervised residue trials supporting fall back GAPs received in the framework of the present mandate.</caption><thead><tr><th>Commodity</th><th>Region (MS)<sup>a</sup></th><th>Residue levels observed in the supervised residue trials<sup>b</sup> (mg/kg)</th><th>Comments/source</th><th>Calculated MRL (mg/kg)</th><th>HR<sup>c</sup> (mg/kg)</th><th>STMR<sup>d</sup> (mg/kg)</th><th>CF<sup>b</sup></th></tr></thead><tbody><tr><td colspan="8"><strong>Enforcement residue definition:</strong> acetamiprid</td></tr><tr><td colspan="8"><strong>New proposed risk assessment residue definition:</strong> sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid</td></tr><tr><td>Apples<br>Pears</td><td>NEU - Outdoor (BE)</td><td>0.016; 0.017; 0.019; 0.022; 0.026; 0.026; 0.029</td><td>Tentative MRL can derived based on 7 GAP compliant trials performed on apples (Belgium, 2023)</td><td>0.07 (tentative)</td><td>0.029</td><td>0.022</td><td>1.21</td></tr><tr><td>Quinces</td><td>NEU - Outdoor (AT)</td><td><u>GAP compliant trials:</u><br>2×0.03; 0.071<br><u>Overdosed trials (96-109 g a.s./ha):</u><br>2×0.03; 0.05; 0.07</td><td>Tentative MRL can derived based on a combined data set of trials performed on apples: 3 GAP compliant (with 25% tolerance on the application rate) and 4 overdosed (Austria, 2023)</td><td>0.15 (tentative)</td><td>0.071</td><td>0.03</td><td>1.21</td></tr><tr><td>Medlar</td><td>SEU - Outdoor (EL/PT)</td><td>2×0.02; 0.05; 0.06; 0.07; 0.08; 0.09; 0.20</td><td>MRL fully supported by GAP compliant trials performed on apples (Portugal, 2023)</td><td>0.3</td><td>0.20</td><td>0.065</td><td>1.21</td></tr><tr><td>Apricots</td><td>NEU - Outdoor (BE)</td><td>&lt;0.01; 0.02; 0.029; 0.042</td><td>Tentative MRL can derived based on 4 GAP compliant trials performed on peaches (Belgium, 2023)</td><td>0.08 (tentative)</td><td>0.042</td><td>0.025</td><td>1.21</td></tr><tr><td>Cherries</td><td>SEU - Outdoor (EL GAP 1)</td><td>0.20; 0.21; 0.22; 0.33</td><td>MRL fully supported by GAP compliant trials performed on cherries (Greece, 2023)</td><td>0.8</td><td>0.33</td><td>0.215</td><td>1.21</td></tr><tr><td>Cherries</td><td>SEU - Outdoor (EL GAP 2)</td><td>0.10; 0.11; 0.17; 0.18; 0.21; 0.23</td><td>MRL fully supported by GAP compliant trials performed on cherries (Greece, 2023)</td><td>0.5</td><td>0.23</td><td>0.18</td><td>1.21</td></tr><tr><td>Peaches</td><td>NEU - Outdoor (BE)</td><td>&lt;0.01; 0.02; 0.029; 0.042</td><td>MRL fully supported by GAP compliant trials performed on peaches (Belgium, 2023)</td><td>0.08</td><td>0.042</td><td>0.025</td><td>1.21</td></tr><tr><td>Table grapes<br>Wine grapes</td><td>SEU - Outdoor (PT)</td><td>2×0.01; 3×0.02; 0.03; 2×0.04</td><td>MRL fully supported by GAP compliant trials on grapes (Portugal, 2023)</td><td>0.08</td><td>0.04</td><td>0.02</td><td>1.21</td></tr><tr><td>Strawberries</td><td>NEU - Outdoor (BE)</td><td>0.03; 0.04; 0.05; 2×0.10; 0.11; 2×0.13; 0.18</td><td>Tentative MRL can derived based on 9 trials performed with 2 applications instead of 1 (Sweden, 2023). Residue trial on strawberries</td><td>0.3 (tentative)</td><td>0.18</td><td>0.10</td><td>1.21</td></tr><tr><td>Blackberries<br>Raspberries</td><td>NEU - Outdoor (AT/BE/DE)</td><td>0.11; 0.14; 0.21; 0.29</td><td>MRL fully supported by GAP compliant trials performed on raspberries (Austria, 2023; Belgium, 2023; Germany, 2023)</td><td>0.6</td><td>0.29</td><td>0.175</td><td>1.21</td></tr><tr><td>Blueberries<br>Cranberries<br>Currants<br>Gooseberries</td><td>NEU - Outdoor (BE)</td><td><u>Scaled results:</u><br>0.072; 3×0.144; 2×0.204; 0.28; 0.30; 0.34<br><u>Results from underdosed trials (50 g a.s./ha):</u><br>0.06; 3×0.12; 2×0.17; 0.23; 0.25; 0.28]</td><td>MRL fully supported by underdosed trials (within 25% tolerance on the application rate) performed on currants (Belgium, 2023). The scaling-up of residue data by a factor of 1.2 was proposed by Belgium (2023). This approach is acceptable in accordance with EFSA (2018b)</td><td>0.7</td><td>0.34</td><td>0.204</td><td>1.21</td></tr><tr><td>Elderberries</td><td>NEU - Outdoor (AT)</td><td>0.06; 3×0.12; 2×0.17; 0.23; 0.25; 0.28</td><td>MRL fully supported by GAP compliant trials performed on currants (Belgium, 2023)</td><td>0.5</td><td>0.28</td><td>0.17</td><td>1.21</td></tr><tr><td>Table olives</td><td>SEU - Outdoor (PT)</td><td>0.012; 0.17; 0.22; 0.23; 0.25; 0.37; 0.42; 0.48</td><td>MRL fully supported by GAP compliant trials performed on table olives (Portugal, 2023)</td><td>0.9</td><td>0.48</td><td>0.24</td><td>1.21</td></tr></tbody></table>
<table><caption>TABLE 36 (Continued)</caption><thead><tr><th>Commodity</th><th>Region (MS)<sup>a</sup></th><th>Residue levels observed in the supervised residue trials<sup>b</sup> (mg/kg)</th><th>Comments/source</th><th>Calculated MRL (mg/kg)</th><th>HR<sup>c</sup> (mg/kg)</th><th>STMR<sup>d</sup> (mg/kg)</th><th>CF<sup>b</sup></th></tr></thead><tbody><tr><td>Tomatoes</td><td>SEU - Outdoor (EL)</td><td>4 &times; &lt; 0.01; 0.012; 0.015; 0.026; 0.04</td><td>MRL fully supported by GAP compliant trials (within 25% tolerance on the application rate) on tomatoes (Portugal, 2023)</td><td>0.06</td><td>0.04</td><td>0.011</td><td>1.21</td></tr><tr><td>Sweet peppers</td><td>SEU - Outdoor (ES)</td><td>GAP compliant trials:<br>0.032; 0.036; 0.038; 0.04; 0.051<br>Overdosed trials (103-112 g a.s./ha):<br>&lt; 0.01; 2 &times; 0.02</td><td>Tentative MRL can derived based on a combined data set of trials performed on sweet peppers: 5 GAP compliant (within 25% tolerance on the application rate) and 3 overdosed (Spain, 2023)</td><td>0.09 (tentative)</td><td>0.051</td><td>0.034</td><td>1.21</td></tr><tr><td>Aubergines</td><td>EU - Indoor (EL/ES)</td><td>Scaled results:<br>2 &times; 0.03; 0.04; 0.06; 2 &times; 0.07; 0.08; 0.09<br>[Results from overdosed trials<br>(94-109 g a.s./ha):<br>2 &times; 0.06; 0.07; 0.11; 2 &times; 0.13; 0.15; 0.19]</td><td>MRL fully supported by overdosed residue trials performed on tomatoes (down-scaling factor of 0.5). Spain applied the proportionality principle accordingly (Spain, 2023)</td><td>0.2</td><td>0.09</td><td>0.065</td><td>1.21</td></tr><tr><td>Cucumbers<br>Courgettes</td><td>SEU - Outdoor (ES)</td><td>GAP compliant trials:<br>0.03<br>Overdosed trials (63-69 g a.s./ha):<br>2 &times; &lt; 0.01; 0.01; 4 &times; 0.02</td><td>Tentative MRL can be derived based on trials performed on courgettes: 1 GAP compliant and 7 overdosed (Spain, 2023)</td><td>0.05 (tentative)</td><td>0.03</td><td>0.02</td><td>1.21</td></tr><tr><td>Melons<br>Pumpkins<br>Watermelons</td><td>SEU - Outdoor (EL)</td><td>2 &times; 0.011; 3 &times; 0.012; 0.013; 0.016; 0.017; 0.024; 0.06<br>(Pulp: 9 &times; &lt; 0.01; 0.011)</td><td>MRL fully supported by GAP compliant trials performed on melons (Greece, 2023). One value (0.06 mg/kg) was obtained at a PHI longer than 14 days</td><td>0.08</td><td>0.06 (pulp: 0.011)</td><td>0.013 (pulp: 0.01)</td><td>1.21</td></tr><tr><td>Broccoli<br>Cauliflowers</td><td>SEU - Outdoor (EL/ES/IT)</td><td>Broccoli: &lt; 0.01, 3 &times; 0.02<br>Cauliflower: 3 &times; &lt; 0.01; 0.04</td><td>MRL fully supported by GAP compliant trials performed on broccoli and cauliflowers (Greece, 2023; Italy, 2023; Spain, 2023)</td><td>0.06</td><td>0.04</td><td>0.015</td><td>1.44</td></tr><tr><td>Head cabbages</td><td>SEU - Outdoor (EL/ES)</td><td>5 &times; &lt; 0.01; 0.01; 0.015; 0.021</td><td>MRL fully supported by GAP compliant trials (within 25% tolerance on the application rate) performed on head cabbages (Greece, 2023)</td><td>0.03</td><td>0.021</td><td>0.010</td><td>1.44</td></tr><tr><td>Lamb's lettuce<br>Roman rocket</td><td>NEU - Outdoor (DE)</td><td>0.15; 0.19; 0.32; 0.39; 0.58; 0.63; 0.66; 0.75</td><td>MRL fully supported by GAP compliant trials performed on lettuce (EFSA, 2018c)</td><td>1.5</td><td>0.75</td><td>0.49</td><td>1.44</td></tr><tr><td>Red mustards</td><td>SEU - Outdoor (IT)</td><td>0.02; 0.03; 0.06; 0.06; 0.24; 0.29; 0.35; 0.49</td><td>MRL fully supported by GAP compliant trials performed on lettuce (Italy, 2023)</td><td>0.9</td><td>0.49</td><td>0.15</td><td>1.44</td></tr></tbody></table>

<footnote><sup>a</sup>NEU: northern EU; SEU: southern EU. The Member State who reported the GAP is reported between brackets.</footnote>

<footnote><sup>b</sup>Samples from all residue trials have been analysed for parent acetamiprid only. No data on the magnitude of metabolite IM-2-1 are available. Tentative conversion factor from enforcement to risk assessment derived in the present statement are based on monitoring data (see Section 3.3.2).</footnote>

<footnote><sup>c</sup>Highest residue level from the available dataset derived according to the residue definition for monitoring.</footnote>

<footnote><sup>d</sup>Median residue level from the available dataset derived according to the residue definition for monitoring.</footnote>
### 3.5.4 | Plant commodities with LOQ as the only fall-back MRL option

For two commodities (granate apples and asparagus), the less critical GAPs reported by Member states (see Appendix 19), although supported by data or by acceptable waiver, did not allow to derive a fall-back MRL above the enforcement LOQ.

#### Granate apples/pomegranates

*Identified fall-back GAP: 1 × 50 g a.s./ha at BBCH 59; no PHI (PT, SEU, Outdoor, SG formulation)*

The GAP reported by Portugal is supported by 4 GAP compliant residue trials (Spain, 2023). All trials were performed with SG formulation, in line with the reported GAP. All samples taken at maturity show residues below LOQ. Therefore, although MRL and risk assessment can be derived for this crop, the MRL proposal is at the LOQ.

#### Asparagus

*Identified fall-back GAP: 2 × 65 g a.s./ha; post-harvest application on the field (DE, NEU, Outdoor, SG formulation)*

The GAP reported by Germany consists of applications performed on the field after harvest of asparagus. No residues are expected in the crops harvested in the succeeding year. Therefore, based on the assessment of Germany, no residue trials are necessary. Although this rationale might be acceptable, it would only allow to derive MRL proposal at the LOQ.

### 3.5.5 | Plant commodities with no fall-back option

For the remaining commodities (bananas, lettuces, escaroles, spinaches, chards/beet leaves), several GAPs supported by GAP compliant residue trials were reported by Member States. However, none of these GAPs allowed to derive safe fall-back MRLs. It was found that even the least critical GAPs collected in the framework of this mandate (i.e. leading to the lowest HR value and MRLs) would still result in an exceedance of the newly derived ARfD, when using the available HR for the consumer risk assessment.

A summary of the acute exposure estimates resulting from the least critical GAPs identified for bananas, lettuces, escaroles, spinaches, chards/beet leaves is reported in Table 37. A complete of overview of all the GAPs received in the framework of this mandate is available in the GAP overview file (xls), which is published in Appendix 18 of the present statement.

It should be noted that for bananas, the GAP was not reported by Portugal in the GAP overview file. However, Portugal reported the GAP in its evaluation report (Portugal, 2023). Furthermore, this GAP and its supporting residue trials were assessed in a previous EFSA opinion (EFSA, 2014).

<table><caption>**TABLE 37** Summary assessment for the plant commodities where no safe MRL proposal could be derived in the framework of the present mandate.</caption><thead><tr><th>Commodity</th><th>Least critical GAP identified in the data call<sup>a</sup></th><th>HR<sup>b</sup> (mg/kg)</th><th>CF<sup>c</sup></th><th>%ARfD (with CF)<sup>d</sup></th><th>%ARfD (w/o CF)<sup>e</sup></th></tr></thead><tbody><tr><td>Bananas</td><td>1 × 100 g a.s./ha; PHI: 21 days (PT - outdoor)<sup>f</sup></td><td>0.049 (pulp)<sup>f</sup></td><td>1.21</td><td>115% (children)</td><td>95% (children)</td></tr><tr><td>Lettuces</td><td>2 × 50 g a.s./ha; PHI of 7 days (EL/IT/PT - outdoor)</td><td>0.17</td><td>1.44</td><td>186% (children)</td><td>129% (children)</td></tr><tr><td>Escaroles/broad-leaved endives</td><td>2 × 30 g a.s./ha; PHI: 14 days (BE - outdoor)</td><td>0.16</td><td>1.44</td><td>185% (children)</td><td>129% (children)</td></tr><tr><td>Spinach</td><td>1 × 60 g a.s./ha; PHI of 10 days (PT - outdoor)</td><td>0.29</td><td>1.44</td><td>189% (children)</td><td>131% (children)</td></tr><tr><td>Chards/beet leaves</td><td>2 × 50 g a.s./ha; PHI of 7 days (BE - outdoor)</td><td>0.31</td><td>1.44</td><td>169% (adults)</td><td>117% (adults)</td></tr></tbody></table>

For bananas, an exceedance of the ARfD is identified when using the conversion factor of 1.21 tentatively derived for fruit crops. This approach takes into consideration the possible occurrence of metabolite IM-2-1 (for up to 21%) in fruit

<footnote><sup>a</sup>See also GAP overview file (xls), which is published as Appendix 18 of the present statement.</footnote>

<footnote><sup>b</sup>HRs reported in this table are based on MS assessment according to the Evaluation Reports of the respective Member States. The HR is reported for acetamiprid only because no data are available for the metabolite IM-2-1.</footnote>

<footnote><sup>c</sup>Tentative conversion factor from enforcement to risk assessment derived from monitoring data submitted in the present statement (see Section 3.3.2).</footnote>

<footnote><sup>d</sup>Using the newly proposed ARfD of 0.005 mg/kg bw and the newly derived residue definition for risk assessment, therefore using the conversion factor derived in the present statement.</footnote>

<footnote><sup>e</sup>Using the newly proposed ARfD of 0.005 mg/kg bw derived in the present statement but without considering the conversion factor (i.e. considering the existing residue definition for risk assessment).</footnote>

<footnote><sup>f</sup>The GAP on bananas was not reported in the GAP overview file. However, Portugal reported the GAP in its evaluation report (Portugal, 2023). The residue trials supporting the outdoor GAP on bananas were assessed in a previous EFSA opinion (EFSA, 2014). The value of the HR in pulp (0.49 mg/kg) is retrieved from this previous EFSA opinion.</footnote>
crops, in accordance with the residue definition for risk assessment proposed in the present statement. The application of the tentative CF of 1.21, based on monitoring data, is adding non-standard uncertainty to the exposure assessment. Considering the order of magnitude of the calculated acute exposure (of 115% ARfD), it is expected that additional GAP compliant residue trials on banana analysing acetamiprid and metabolite IM-2-1 may allow further refinements for this crop, however, only in case a lower CF than 1.21 would be derived.

For lettuce, escaroles/broad-leaved endive, spinaches, chards/beet leaves, an exceedance of the ARfD is identified when using the conversion factor of 1.44. This approach takes into consideration the possible occurrence of metabolite IM-2-1 (for up to 44%) in leafy crops, in accordance with the residue definition for risk assessment proposed in the present statement. The application of the tentative CF of 1.44, based on monitoring data, is adding non-standard uncertainty to the exposure assessment. However, it should be noted that even without using these CFs, exceedance of the ARfD would still be identified (117%–131% ARfD). Although additional GAP compliant residue trials analysing acetamiprid and metabolite IM-2-1 would allow further refinement, it is not expected that the reported GAPs can support safe fall-back MRLs for these crops even in case where metabolite IM-2-1 would not be quantified at levels above LOQ.

**Conclusion:**

For bananas, lettuces, escaroles, spinaches, chards/beet leaves, despite the data call issued in the framework of the present mandate, it was not possible to demonstrate that the alternative lower MRL options would not pose acute intake risk to consumers.

### 3.5.6 | Fall-back options for commodities of animal origin

The existing MRLs for commodities of bovine and swine origin correspond to Codex MRLs derived by the JMPR (FAO, 2015), which were implemented into Regulation (EU) 2017/626. These Codex MRLs were considered safe for consumers based on a risk assessment performed with the ARfD of 0.025 mg/kg bw (EFSA, 2016b).

In the framework of the present mandate, using the newly derived ARfD of 0.005 mg/kg bw (see scenario 2), a risk for consumers has been identified for the existing MRLs on bovine liver (1 mg/kg) and bovine other edible offals (1 mg/kg). The consumer risk assessment was performed with the HR of 0.89 mg/kg derived in the Codex assessment (FAO, 2015). It is noted that this HR was based on the highest residues identified in kidney for the Australian beef cattle dietary burden (maximum dietary burden of 18 mg/kg dry matter (DM)). For liver, further refinement of this assessment is in principle possible, using the HR directly derived for liver (0.67 mg/kg; FAO, 2015). Nevertheless, the calculated acute exposure would still exceed the ARfD (108% ARfD).

Consequently, risk managers may consider withdrawing the existing Codex MRLs for bovine liver and bovine other edible offals from the EU Regulation.

In order to assess the residue concentrations expected in bovine liver and bovine other edible offals based on an EU livestock diet, EFSA updated the existing EU livestock dietary burden calculations. The last assessment of the EU dietary burden was performed in the framework of the focussed MRL assessment (EFSA, 2018c). EFSA now updated these previous calculations considering that fall-back MRLs and risk assessment values were derived in the present statement for plant commodities that can be fed to livestock (apples and head cabbage) and considering the possible impact of the new risk assessment residue definition derived for fruit and leafy crops (using indicative CFs).

The livestock dietary burden was therefore recalculated, for cattle only, according to OECD guidance (OECD, 2013) considering livestock intake of all feed products containing acetamiprid residues resulting from all authorised EU and authorised import tolerance uses, except for apples and head cabbages where fall-back GAPs were considered. The input values for all relevant commodities are summarised in Appendix 17 while the livestock dietary burden calculator (xls) file is published in Appendix 23.

The calculated dietary burden for cattle and the updated EU MRLs for bovine tissues are reported in Tables 38 and 39 below.

It is noted that the MRLs for bovine tissues calculated in this section are expressed for the residue definition currently implemented in the EU legislation (sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid). During the peer review for the renewal of the active substance acetamiprid (EFSA, 2016a), it was proposed to limit the residue definition for enforcement in animal commodities to metabolite IM-2-1 only, but this proposal was not further implemented in the MRL legislation. However, it is not the scope of the present mandate to recalculate the MRLs for commodities of animal origin for the residue definition proposed during the renewal of the active substance acetamiprid.

Regarding the analytical method for enforcement, it should be noted that the QuEChERS multiresidue method with HPLC-MS/MS was considered sufficiently validated to enforce both acetamiprid and metabolite IM-2-1 at the LOQ of 0.01 mg/kg for each compound (EFSA, 2016a). Therefore, it is concluded that the LOQ for enforcement for the existing residue definition (sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid) is 0.02 mg/kg.
<table><caption>TABLE 38 Updated dietary burden calculations for EU cattle.</caption><thead><tr><th rowspan="3">Relevant groups</th><th colspan="4">Dietary burden expressed in</th><th rowspan="3">Most critical diet</th><th rowspan="3">Most critical commodity</th></tr><tr><th colspan="2">mg/kg bw per day</th><th colspan="2">mg/kg DM</th></tr><tr><th>Median</th><th>Maximum</th><th>Median</th><th>Maximum</th></tr></thead><tbody><tr><td>Cattle (all diets)</td><td>0.026</td><td>0.046</td><td>0.68</td><td>1.32</td><td>Dairy cattle</td><td>Citrus dried pulp</td></tr></tbody></table>

<table><caption>TABLE 39 MRL and risk assessment values calculations estimated for the updated dietary burden for EU cattle.</caption><thead><tr><th rowspan="2">Animal commodity</th><th colspan="2">Residues at the closet feeding level (mg/kg)</th><th colspan="3">Estimated value at 1N level</th><th rowspan="2">CF</th></tr><tr><th>Mean</th><th>Highest</th><th>STMR (mg/kg)</th><th>HR (mg/kg)</th><th>MRL proposal (mg/kg)</th></tr></thead><tbody><tr><td>Cattle (all diets)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Closest feeding level:</td><td>0.21</td><td>mg/kg bw</td><td>4.5</td><td>N Dairy cattle (highest diet)</td><td></td><td></td></tr><tr><td>Muscle</td><td>0.04</td><td>0.05</td><td>&lt;0.01</td><td>0.01</td><td>0.02</td><td>1</td></tr><tr><td>Fat</td><td>0.03</td><td>0.06</td><td>&lt;0.01</td><td>0.01</td><td>0.02</td><td>1</td></tr><tr><td>Liver</td><td>0.15</td><td>0.15</td><td>0.02</td><td>0.03</td><td><strong>0.03</strong></td><td>1</td></tr><tr><td>Kidney</td><td>0.24</td><td>0.25</td><td>0.02</td><td>0.04</td><td><strong>0.05</strong></td><td>1</td></tr></tbody></table>

Considering the above calculations, a fall-back MRL of 0.03 mg/kg is proposed for bovine liver and a fall-back MRL of 0.05 mg/kg is proposed for bovine other edible offals; this latter being extrapolated from the MRL derived on kidney.

### 3.5.7 | Consumer risk assessment using the new endpoints derived in the present statement and considering the identified fall-back MRL options (scenario 3)

A third scenario for the consumer risk assessment was performed, using the new HBGVs (ADI = 0.005 mg/kg bw per day; ARfD = 0.005 mg/kg bw), the residue definitions for plant commodities and conversion factors derived in the present statement, and considering risk mitigations measures (i.e. fall-back MRLs) for those commodities where a risk was identified in scenario 2 (see section 3.4.2).

For the plant commodities for which an exceedance of the new ARfD was identified in scenario 2 and for which safe fall-back MRLs and risk assessment values could be identified in the present mandate (i.e. apples, pears, quinces, medlars, apricots, peaches, cherries, table and wine grapes, blackberries, raspberries, blueberries, cranberries, currants, gooseberries, elderberries, table olives, tomatoes, sweet peppers, aubergines, cucumbers, courgettes, melons, pumpkins, watermelons, broccoli, cauliflowers, head cabbages, lamb's lettuce, roman rocket and red mustards), the risk assessment values derived from the residue trials submitted in support of the identified fall-back GAPs were included in the consumer risk assessment calculations (see Section 3.5.3).

For granate apples and asparagus, MRLs and risk assessment values at the LOQ were included in the consumer risk assessment calculations (see Section 3.5.4).

For commodities of animal origin, the input values as derived in EFSA (2018a, 2018b, 2018c) were used for all commodities, except for bovine liver and bovine edible offals (other than liver and kidney) for which fall-back MRLs and risk assessment values derived in the present mandate were considered (see Section 3.5.6). It is noted that MRL values are expressed according to the residue definition for animal commodities currently implemented in the EU legislation (sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid). Under scenario 3, it is therefore assumed that the existing Codex MRLs for bovine liver and bovine other edible offals (other than liver and kidney) are withdrawn from the EU Regulation.

The plant commodities for which no safe fall-back MRL options could be identified (i.e. bananas, lettuces, escaroles, spinaches, chards/beet leaves) were excluded from the consumer risk assessment. For these crops, even the least critical GAPs collected in the framework of this mandate would still result in an exceedance of the newly derived ARfD (see Section 3.5.5). Consequently, EFSA recommends that MRLs for bananas, lettuces, escaroles, spinaches, chards/beet leaves are lowered to the enforcement LOQ. In this scenario 3, it is therefore assumed that the existing uses of acetamiprid on bananas, lettuces, escaroles, spinaches and chards/beet leaves are withdrawn.

The input values used in the exposure calculations are reported in Appendix 17 and the PRIMo file is published in Appendix 22.

The highest estimated long-term dietary intake is 48% of the ADI (NL toddler).

Regarding the short-term dietary intake, no exceedances of the ARfD were identified for raw agricultural commodity and for commodities of animal origin.

However, an exceedance of the ARfD was still identified for the processed commodity currants juice (141% of the ARfD, children). It is noted that this result is obtained for a large portion of 525.8 g reported for the NL children. It is likely that this
large portion does not correspond to pure juice (i.e. 100% fruits). Therefore, the calculated exposure may not consider the dilution factor possibly applicable for these consumption data. In the absence of processing trials for this commodity, EFSA was not able to perform further refinement. The available results are therefore likely overestimating the exposure resulting from the fall-back GAP identified for currants.

Furthermore, it is noted that the margin of safety is very low for strawberries (99% ARfD), sweet cherries (98% ARfD), pears (97% ARfD) and peaches (97% ARfD). Furthermore, these results are affected by an additional non-standard uncertainty related to the use of a tentative conversion factor, derived from monitoring data, for all fruit crops. Therefore, EFSA made further attempts to identify lower MRL options for these four commodities. For strawberries and sweet cherries, the less critical GAPs assessed in this mandate would allow to derive lower MRL values of 0.3 mg/kg (tentative) and 0.5 mg/kg, respectively. These MRLs would allow to decrease the acute exposure to 71% ARfD for strawberries and to 68% ARfD for sweet cherries. For pears and peaches, no other alternative than the enforcement LOQ could be identified in the framework of the present mandate.

# 4 | CONCLUSIONS

## 4.1 | Human health

### 4.1.1 | Hazard assessment acetamiprid

The ToR1 concerning the toxicological properties of acetamiprid and its metabolites, including toxicological endpoints, as requested in the mandate, was addressed in the present statement by an EFSA Working Group by applying the current methodologies, including experts of the PPR Panel a transparent and trackable evidence-based AOP-informed IATA approach.

The ADI, the ARfD, the AOEL and the AAOEL of acetamiprid were set in 2016 at 0.025 mg/kg bw (per day) on the basis of the rat DNT study (uncertainty factor (UF) 100; EFSA, 2016a). In the peer review meeting conducted in the framework of the renewal process, the experts agreed that there was a treatment related reduction of auditory startle responses in offspring from 10 mg/kg bw per day onward, resulting in a NOAEL of 2.5 mg/kg bw per day for this endpoint. In addition, the experts noted that the data do not allow for any firm conclusion, since important endpoints such as motor activity, learning and memory evaluation could not be properly assessed (EFSA, 2016a).

In the present assessment, an AOP-informed IATA approach was used, relying on a combination of multiple layers of evidence (i.e. systematic literature review of human epidemiological studies, existing in vivo, in vitro, zebrafish data; and integration of the DNT IVB data). The evidence was organised according to the AOP framework, as in case of previous OECD case study projects from EFSA.

In an AOP-informed IATA framework, integration of mechanistic understanding derived from in vitro methods is crucial throughout the iterative process of implementing the postulated AOP and its use in the weight of evidence (WoE). With the currently available relevant and reliable evidence on acetamiprid, it was concluded that acetamiprid causes nAChR activation and rapid desensitisation of the receptors at concentrations starting from 1 µM. This is considered per se as a molecular and cellular effect that could lead to an adverse outcome at organism level, and therefore representing a DNT concern.

However, the results of the WoE in the AOP-informed IATA for acetamiprid indicated that there are major uncertainties in the body of evidence (BoE) for the DNT properties of acetamiprid. Further data are therefore needed to clarify the current uncertainties, to come to a more robust mechanistic understanding, to identify all DNT effects of acetamiprid and obtain concordant dose-response relationships for them to enable hazard and risk assessment.

In addition, the WG noted that the data gaps of the in vivo BoE (including the lack of an acceptable measurement of learning and memory, motor activity and morphometrics evaluation in the available non-guideline DNT study) would warrant a re-evaluation of the current HBGVs of acetamiprid and this lack of knowledge and regulatory data gap should be filled.

The WG considered that the conduction of a study following the OECD TG 426, with adequate measurement of exposure, would currently represent the most appropriate solution to minimise the identified uncertainties. Despite this, the WG acknowledged that the current in vivo DNT studies may not be sensitive enough to detect subtle effects, such as changes in cognition, or brain morphometry, which could result in false negatives, and for which lack of mechanistic understanding is an aggravating uncertainty.

It was concluded, by answering the problem formulation, that based on the EFSA systematic literature review and data collection for DNT effects of acetamiprid and its assessment, and using an AOP-informed IATA framework, the current HBGVs for acetamiprid may not be sufficiently protective.

It is also noted that the PPR Panel recommended an assessment of endocrine-disrupting properties for acetamiprid in line with the EFSA/ECHA (2018) guidance document for the identification of endocrine disruptors (EFSA PPR Panel, 2022); however, this is outside of the scope of this mandate.

To immediately address the impact of the identified uncertainties/limitations on the consumer risk assessment, the WG proposed to apply an additional UF of 5 to the current HBGVs to cover the uncertainties in the DNT assessment, as follows:

* The ADI is currently set at 0.025 mg/kg bw per day and the WG proposed to lower it to 0.005 mg/kg bw per day

* The ARfD is currently set at 0.025 mg/kg bw and the WG proposed to lower it to 0.005 mg/kg bw
The WG noted that this is in line with Regulation (EU) No 283/2013,<fnref n="15" /> which lays down: 'When indicated by observations in other studies or the mode of action of the test substance, supplementary studies or information may be required to provide information on the postnatal manifestation of effects such as developmental neurotoxicity' and with Annex II of the Regulation No 1107/2009, that stipulates 'When the critical effect is judged of particular significance, such as developmental neurotoxic or immunotoxic effects, an increased margin of safety shall be considered, and applied if necessary'.

The WG recognised that although the additional UF of 5 may appear as an arbitrary decision, it is based on the standard approach of the regulatory peer review process to apply an UF of 10 when there is no regulatory DNT study, but a DNT concern exists. In the case of acetamiprid, there is a NOAEL and a LOAEL for a decreased auditory startle response, but given the uncertainties around this study, the WG considered that an additional UF of 5 would be sufficiently conservative considering that a DNT concern still remains.

The WG also noted that the current assessment of the available regulatory in vivo DNT study is more conservative than the one conducted by the PPR Panel in 2013. This is because (a) the approach used herein follows the OECD Principles and Key Elements for Establishing a Weight of Evidence for Chemical Assessment (use of AOP-informed IATA methodology); and (b) current regulatory toxicology gives more weight to mechanistic considerations in support of biological plausibility. In the case of acetamiprid, the nAChR activation and further desensitisation along with the current conceptual understanding of neuronal network/brain development raise a DNT concern. Moreover, the available in vivo data were independently re-analysed with high scrutiny leveraging on the experience gained in the last years in the assessment of DNT studies.

The WG considered that the new studies notified by PAN Europe as part of the extension of the mandate did not provide relevant and reliable new data for the assessment of DNT potential of acetamiprid or its metabolites, nor on non-DNT endpoints.

Finally, EFSA notes that the same additional UF would be applied for setting the (acute) acceptable operator exposure level ((A)AOEL).

### 4.1.2 | Hazard assessment of acetamiprid metabolites

New data were available only for metabolite IM-2-1, no data was available for any other metabolite, as provided by the sole applicant. Based on the available genotoxicity studies (Ames test, in vitro micronucleus test, mouse lymphoma assay), it was concluded that metabolite IM-2-1 is unlikely to be genotoxic. The available evidence on IM-2-1 (i.e. it is a major rat metabolite; with large structural similarities with the parent; and the available 28-day rat study) does not allow to conclude on a different qualitative or quantitative toxicological profile of this metabolite compared to the parent. Therefore, it was agreed that the toxicological profile of IM-2-1 is considered as covered by that of acetamiprid and the same HBGVs (ADI of 0.005 mg/kg bw per day and ARfD of 0.005 mg/kg bw) proposed for the parent should also apply to the metabolite.

### 4.1.3 | Human exposure estimation based on human biomonitoring data

The study of Laubscher et al. (2022), as mentioned in the mandate was appraised as High RoB (tier 3). Two other identified human studies containing biomonitoring data for exposure estimation were appraised as high RoB (Mahai et al., 2022; tier 3) or medium RoB (Oya et al., 2021; tier 2). As the WG considered the available kinetic data as being too limited to develop a sufficiently robust physiologically based kinetic (PBK) model, no external exposure estimation could be made related to the human biomonitoring data reported by Laubscher et al. (2022), nor for the other human biomonitoring data sets identified. Correspondingly, no estimation of internal acetamiprid concentrations upon exposure to acetamiprid's HBGVs was feasible.

The WG noted that, even if a robust PBK model for acetamiprid would be available, exposure estimation of acetamiprid based on reported IM-2-1 concentrations in human biomonitoring studies is cumbersome, given that human exposure to both acetamiprid and IM-2-1 can be expected (e.g. reported by Ospina et al. (2019) and based on residue assessment as presented in Section 3), which may both result in internal IM-2-1 exposure and detection of IM-2-1 concentrations in different body fluids, as reported by Laubscher et al. (2022). Hence, it is possible that a large fraction of the IM-2-1 found in the cerebrospinal fluid of 13 out of 14 children assessed in that study, in which the parent compound (acetamiprid) was not detected, may be due to direct exposure to IM-2-1 as this metabolite can be formed in the environment as a result of environmental degradation of acetamiprid (Ospina et al., 2019). This represents an additional uncertainty that hampers to carry out an exposure estimation for acetamiprid based on the biomonitoring data reported by Laubscher et al. (2022).

## 4.2 | Residues

The ToR 2a concerning a data call for monitoring data for plant products was addressed by EFSA with a call for data, inviting Member States to submit to EFSA results of pesticide residue analysis (monitoring data) for acetamiprid and its

<footnote n="15">¹⁵Commission Regulation (EU) No 283/2013 of 1 March 2013 setting out the data requirements for active substances, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market. OJ L 93, 3.4.2013, p. 1–84.</footnote>
metabolites in food of plant origin derived by national competent authorities. Data were provided in the format developed for submitting pesticide residue data under Article 31 of Regulation (EC) No 396/2005. Samples with residue analysis for parent acetamiprid and metabolite N-desmethyl-acetamiprid (IM-2-1) were reported to EFSA. The new monitoring data were combined with the monitoring data previously submitted to EFSA.

The **ToR 2b** related to the consideration of the residue definitions for risk assessment and enforcement for plant products was addressed by EFSA with a detailed analysis of the data received under ToR 2a and further analysis of acetamiprid metabolism studies previously submitted and evaluated in the framework of the renewal of the approval of acetamiprid under Regulation (EU) No 844/2012, or in other relevant applications. The available studies investigating the metabolism of acetamiprid in plants gave an indication that the metabolite IM-2-1 is formed at relatively low levels in edible parts of fruit crops and leafy crops (between 2% and 8% of the TRR; up to 0.3 mg/kg in fruits; up to 1.25 mg/kg in leafy). In inedible leafy matrices, however, this metabolite occurs at higher proportions related to the parent compound, IM-2-1 representing up to 32% of the parent compound (16% of the TRR) in apple leaf at longer preharvest intervals. The monitoring data on the metabolite IM-2-1 confirmed its occurrence in several commodities belonging to the groups of leafy and fruit commodities. In these crop groups, the median proportion of metabolite IM-2-1 compared to the parent compound was found to be significant in fruit and leafy crops (median ratio IM-2-1/acetamiprid accounting for 21%-44%, respectively). It was therefore proposed to include metabolite IM-2-1 in the residue definition of risk assessment for leafy and fruit crops, which is currently limited to the parent acetamiprid. It was confirmed that the toxicological profile of IM-2-1 is considered as covered by that of acetamiprid. A revised residue definition for risk assessment (RD-RA) was proposed for leafy and fruit crops as follows:

* **Revised RD-RA (leafy and fruit crops):** sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid.

Regarding pulses/oilseeds, root crops and cereals, the new data received under ToR 2a did not indicate a need to modify the existing residue definition for risk assessment, which therefore remains as parent acetamiprid.

Regarding the residue definition for enforcement, the available data did not indicate a need to modify the existing definition because acetamiprid is still a sufficient marker of the residues in all crop groups.

The **ToR 3** concerning consumer risk assessment related to the existing EU MRLs for acetamiprid in all plant and animal products was addressed by EFSA by means of three consumer risk assessment scenarios performed with the latest PRIMo rev 3.1:

* **Scenario 1** (using existing HBGVs for acetamiprid and IM-2-1 and the existing residue definitions for risk assessment). These calculations indicated exceedances of the ARfD for 38 commodities. Regarding the long-term dietary intake, the exposure remained below the ADI (81% ADI).

* **Scenario 2** (using the newly derived HBGVs for acetamiprid and IM-2-1 and the newly derived residue definitions for risk assessment proposed in ToR 2b). These calculations indicated exceedances of the ARfD for 40 commodities. Regarding the long-term dietary intake, the margin of safety was small, but the exposure remained below the ADI (93% ADI).

* **In order to identify safe fall-back MRL options** for the crops for which acute intake concerns were identified in scenario 2, EFSA launched a call for data to Member States. From the information provided, EFSA was able to derive fall-back MRLs based on fall-back GAPs for several plant commodities and for 2 commodities of animal origin. For the 5 remaining commodities (bananas, lettuces, escarole/broad-leaved endives, spinach, chard/beet leaves), it was not possible to identify GAPs leading to safe MRL options.

* **Scenario 3** (using the newly derived HBGVs for acetamiprid and IM-2-1, the newly derived residue definitions for risk assessment proposed in ToR 2b and the fall-back MRL options, where possible). These calculations did not indicate exceedances of the ARfD, except for currant juice (141% ARfD). Regarding the long-term dietary intake, the exposure was below the ADI (max. 48% ADI).

Consequently, as reply to ToR3, EFSA was able to make recommendation for safe MRL options and to provide risk managers with advice on the different options (see Recommendations).

# 5 | RECOMMENDATIONS

## 5.1 | Human health

### For the assessment of DNT in vivo studies

* The reliability of the DNT studies submitted for chemical safety assessment is a relevant uncertainty. The availability of the appropriate historical control data (HCD) and positive control data (PCD) from the performing laboratory should be always considered as part of the DNT data package. The assessing authority should be sufficiently confident that the reliability of the HCD and PCD is adequate (e.g. by discussing it during the pre-submission advice). It is highly recommended that all available evidence should be submitted, to allow regulators to conduct a thorough assessment and to reduce uncertainties in general.
## For the AOP-informed IATA methodology

* The EFSA AOP-informed IATA framework is fit for purpose and recommended for DNT hazard identification and characterisation and is the current recommended approach for integrating the results of the DNT IVB.

* The IATA case study developed for DNT hazard characterisation of acetamiprid should be submitted to the OECD IATA Case Study Project.

* It is recommended to discuss with the OECD the IATA framework for DNT hazard characterisation used by EFSA and the lessons learnt from the 3 IATA case studies on pesticides published by EFSA (deltamethrin, flufenacet, acetamiprid; OECD, 2022a, 2022b, 2022c) in order to develop a tiered approach for DNT hazard characterisation.

* Further development of the AOP with nAChR activation (as MIE) leading to behavioural and/or other DNT AOs and its submission to the OECD AOP development programme for endorsement is recommended. This is considered of high benefit for developing an AOP-informed IATA methodology to identify and characterise the DNT potential of neonicotinoids and its metabolites. It is acknowledged that, within AOPs, the relevance of early/upstream functional KEs, such as receptor desensitisation, could be further substantiated as a basis for in vitro assay implementation within the DNT IVB and derivation of reference points (aka points of departure).

## For the implementation of the DNT IVB in the risk assessment

* The test systems of the DNT IVB should be further improved as regards molecular and functional characterisation according to the purpose of the assay. Molecular and functional characterisation of the cell system remains a critical step in understanding the relevance of the test system application (e.g. receptor expression and functionality). This exercise represents a priority for pesticides with a neurotoxic mode of action (OECD, 2023). In addition, it is recommended to further characterise different cell systems, applied in the neural network formation (NNF) assay using positive controls.

* To resolve the uncertainties in the AOP-informed IATA, nicotine and other neonicotinoids should be tested in an appropriate (i.e. human cell-based system) and properly characterised test system.

* To expand the DNT IVB, further relevant in vitro test methods should be included, filling the current gaps, using test systems characterised accordingly (preferably based on human cells). Disruption of the NNF represents a downstream KE of high impact in the overall WoE for the DNT assessment. In particular, loss of neuronal electrical functionality, as measured in the NNF assay (MEA), could result from disruption of several early KEs or MIEs. The relevance and reliability of the assay is supported by reproducibility of the results using different test systems. It is therefore possible to apply the same NNF assay (MEA) to test systems representing different stages of human brain development (i.e. representative of receptor ontogeny).

## For the human exposure estimation based on human biomonitoring data

To contribute to the harmonisation of the development and use of PBK models in pesticide risk assessment (and in other regulatory areas), it is recommended:

* To define a set of minimal data requirements (from in vitro and in vivo studies) for PBK model development regarding parameterisation of chemical-specific model parameters for:

* ✓ Exposure estimation based on human biomonitoring data

* ✓ Quantitative interpretation (qIVIVE) of in vitro DNT data

* To develop guidance on the performance of reliable in vitro kinetic studies for providing chemical-specific input parameter values for PBK models (such as for parameterisation of intestinal uptake, (hepatic) clearance, (hepatic) metabolite formation and fraction unbound to plasma).

* To develop guidance on the application of in silico tools (e.g. determination of applicability domain) for derivation of PBK model parameter values (such as fraction unbound to plasma proteins and tissue:plasma partition coefficients).

* To update the OECD TG 417 for in vivo toxicokinetic studies, by including the measurement of internal concentrations (at least in blood or plasma) of the parent chemical and the major metabolites in the time course studies, in addition to total radioactivity.

## For the non-dietary risk assessment

It is recommended to re-evaluate the non-dietary risk assessment of acetamiprid by applying the new reference values.

## 5.2 | Residues

### For the consumer risk assessment

It is recommended:
* To modify the residue definition for risk assessment in leafy and fruit crops as follows: 'sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid'.

* To require additional supervised residue trials analysing simultaneously for acetamiprid and N-desmethyl-acetamiprid (IM-2-1), supporting existing and any new intended uses on leafy and fruit crops.

* To derive robust conversion factors (CF) from enforcement to risk assessment. Robust CFs for each plant commodity should be derived based on supervised residue trials compliant with authorised or intended GAPs and analysing simultaneously for acetamiprid and N-desmethyl-acetamiprid (IM-2-1).

## For the MRLs of acetamiprid in plant and animal commodities

A risk for consumer has been identified for 38 MRLs currently in place in the EU Regulation. Furthermore, for granate apples and aubergines, a risk for consumers is unlikely for the existing MRLs but a risk for consumer has been identified for the higher MRLs proposed in the draft MRL Regulation SANTE/11278/2021. Consequently, it is recommended to lower the existing MRLs for 38 commodities. For granate apples and aubergines, it is recommended to maintain the existing MRLs of 0.01* mg/kg (granate apples) and 0.2 mg/kg (aubergines) and not to implement the MRLs proposed in the draft MRL Regulation SANTE/11278/2021.

The lower MRL proposals derived by EFSA are reported in Table 40. Further considerations and recommendations are reported below:

* For apples, pears, quinces, apricots, sweet peppers, cucumbers and courgettes, the fall-back MRLs proposed in Table 40 are tentative, because not fully supported by residue data. Therefore, should these MRLs be implemented in the regulation, additional residue trials data should be requested.

* For pears, sweet cherries, peaches and strawberries, the MRL reported in the table is proposed for further risk management considerations, because of the low margin of safety and the non-standard uncertainty identified. For sweet cherries and strawberries, a lower MRL option is proposed for risk managers to increase the margin of safety. For pears and peaches, the only lower alternative is the LOQ.

* For elderberries, the existing MRL led to an exceedance of the ARfD for the processed commodity elderberries juice. Therefore, a lower MRL option, for which no exceedance of the ARfD is identified, was reported by EFSA. Noting the uncertainty resulting in a possible overestimation of the acute exposure calculated for elderberries juice, further risk management considerations are required. In the case where the higher MRL option would be implemented in the regulation, it is recommended to request additional trials investigating the effect of processing on the residue concentration in elderberry juice.

* For currants, the lowest fall-back MRL identified in this statement led to an exceedance of the ARfD for the processed commodity currant juice. The only lower MRL option for which no exceedance of the ARfD would be identified is the enforcement LOQ. Noting the uncertainty resulting in a possible overestimation of the acute exposure calculated for currants juice, further risk management considerations are required. In the case where the higher MRL option would be implemented in the Regulation, it is recommended to request additional trials investigating the effect of processing on the residue concentration in currant juice.

* For bananas, lettuces, escaroles/broad-leaved endives, spinaches, chards/beet leaves, it is recommended to lower the existing MRLs to the enforcement LOQ because no safe fall-back MRL options could be identified. Should the MRLs for these crops be lowered to the enforcement LOQ, the authorised uses on bananas, lettuces, escaroles/broad-leaved endives, spinaches, chards/beet leaves leading to acetamiprid residues above the LOQ may need to be reconsidered by national authorities.

* For bovine liver and bovine (other edible offals), it is recommended to withdraw the existing Codex MRLs from the EU Regulation. Lower alternative MRL options were derived by EFSA based on an updated EU livestock dietary burden calculation.

Furthermore, for plums (0.04 mg/kg), poppy seeds (0.3 mg/kg), mustard seed (0.15 mg/kg) and honey (0.3 mg/kg), it was concluded that risk for consumers was still unlikely for the new MRLs proposed in SANTE/11278/2021. For these crops, risk managers can therefore implement the MRLs proposed in SANTE/11278/2021.
<table><caption>TABLE 40 Recommended MRLs.</caption><thead><tr><th>Code<sup>a</sup></th><th>Commodity</th><th>Existing EU MRL (mg/kg)</th><th>SANTE/11278/ 2021 (not yet applicable) (mg/kg)</th><th>Proposed EU MRL (mg/kg)</th><th>Comment/justification</th></tr></thead><tbody><tr><td colspan="6"><strong>Enforcement residue definition: acetamiprid</strong></td></tr><tr><td>130010</td><td>Apples</td><td>0.4</td><td>0.4</td><td>0.07 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A tentative fall-back MRL can be proposed based on a less critical GAP (1 GAP compliant trial missing)<br>Risk for consumers unlikely with the fall-back MRL<br>Further risk management decision required</td></tr><tr><td>130020</td><td>Pears</td><td>0.4</td><td>0.4</td><td>0.07 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A tentative fall-back MRL can be proposed based on a less critical GAP (1 GAP compliant trial missing)<br>Risk for consumers unlikely with the fall-back MRL, but the margin of safety is low (96% ARfD). The only lower MRL option is the LOQ<br>Further risk management decision required</td></tr><tr><td>130030</td><td>Quinces</td><td>0.8</td><td>0.8</td><td>0.15 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A tentative fall-back MRL can be proposed based on a less critical GAP (5 GAP compliant trials missing)<br>Risk for consumers unlikely with the fall-back MRL<br>Further risk management decision required</td></tr><tr><td>130040</td><td>Medlars</td><td>0.8</td><td>0.8</td><td>0.3</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>140010</td><td>Apricots</td><td>0.8</td><td>0.8</td><td>0.08 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A tentative fall-back MRL can be proposed based on a less critical GAP (2 GAP compliant trials missing)<br>Risk for consumers unlikely with the fall-back MRL<br>Further risk management decision required</td></tr><tr><td>140020</td><td>Cherries (sweet)</td><td>1.5</td><td>1.5</td><td>0.8 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL but the margin of safety is low (98% ARfD)<br>This can be mitigated by a lower MRL option of 0.5 mg/kg, fully supported by data and resulting in 68% ARfD<br>Further risk management decision required</td></tr><tr><td>140030</td><td>Peaches</td><td>0.2</td><td>0.2</td><td>0.08 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL but the margin of safety is low (96% ARfD). The only lower MRL option is the LOQ<br>Further risk management decision required</td></tr><tr><td>151010</td><td>Table grapes</td><td>0.5</td><td>0.5</td><td>0.08</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>151020</td><td>Wine grapes</td><td>0.5</td><td>0.5</td><td>0.08</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>152000</td><td>Strawberries</td><td>0.5</td><td>0.5</td><td>0.5 (Further risk management considerations required)</td><td>Risk for consumers unlikely with the existing MRL but the margin of safety is low (99% ARfD)<br>This can be mitigated by a lower MRL option of 0.3 mg/kg, tentative because based on overdosed trials, resulting in 71% ARfD<br>Further risk management decision required</td></tr><tr><td>153010</td><td>Blackberries</td><td>2</td><td>2</td><td>0.6</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>153030</td><td>Raspberries (red and yellow)</td><td>2</td><td>2</td><td>0.6</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>154010</td><td>Blueberries</td><td>2</td><td>2</td><td>0.7</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>154020</td><td>Cranberries</td><td>2</td><td>2</td><td>0.7</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr></tbody></table>
<table><caption>TABLE 40 (Continued)</caption><thead><tr><th>Code<sup>a</sup></th><th>Commodity</th><th>Existing EU MRL (mg/kg)</th><th>SANTE/11278/ 2021 (not yet applicable) (mg/kg)</th><th>Proposed EU MRL (mg/kg)</th><th>Comment/justification</th></tr></thead><tbody><tr><td>154030</td><td>Currants (black, red and white)</td><td>2</td><td>2</td><td>0.7 or 0.01*<br>(Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL<br>A fall-back MRL can be derived based on a less critical GAP fully supported by data but an exceedance of the ARfD is still identified for the processed commodity currant juice (141% ARfD). No other fall-back MRLs were identified<br>Noting the uncertainty associated to acute exposure calculated for currants juice, further risk management considerations are required</td></tr><tr><td>154040</td><td>Gooseberries (green, red and yellow)</td><td>2</td><td>2</td><td>0.7</td><td>A risk for consumer is identified for the existing MRL.<br>A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>154080</td><td>Elderberries</td><td>2</td><td>2</td><td>2 or 0.5 (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL<br>because of exceedance of ARfD for elderberries juice (247%)<br>A fall-back MRL can be proposed based on a less critical GAP fully supported by data and for which risk for consumers is unlikely<br>Noting the uncertainty associated to acute exposure calculated for elderberries juice, further risk management considerations are required</td></tr><tr><td>161030</td><td>Table olives</td><td>3</td><td>3</td><td>0.9</td><td>A risk for consumer is identified for the existing MRL.<br>A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>163020</td><td>Bananas</td><td>0.4</td><td>0.4</td><td>0.01* (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL.<br>A risk for consumer has been identified for the lowest MRL options assessed in the framework of this mandate<br>No fall-back MRLs were identified. It is therefore proposed to lower the existing MRL at the LOQ<br>Further risk management decision required</td></tr><tr><td>163050</td><td>Granate apples/ pomegranates</td><td>0.01*</td><td>0.3</td><td>0.01*</td><td>Risk for consumers unlikely with the existing MRL at the LOQ. However, a risk for consumer is identified for the MRL proposed in SANTE/11278/2021<br>The existing MRL of 0.01* mg/kg, which covers the less critical GAPs assessed in the framework of this mandate, should be maintained</td></tr><tr><td>231010</td><td>Tomatoes</td><td>0.5</td><td>0.5</td><td>0.06</td><td>A risk for consumer is identified for the existing MRL.<br>A fall-back MRL can be proposed based on a less critical GAP fully supported by data<br>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>231020</td><td>Sweet peppers/ bell peppers</td><td>0.3</td><td>0.4</td><td>0.09 (Further risk management considerations required)</td><td>Risk for consumer is identified for the existing MRL<br>and for the MRL proposed in SANTE/11278/2021<br>A tentative fall-back MRL can be proposed based on a less critical GAP (3 GAP compliant trials missing)<br>Risk for consumers unlikely with the fall-back MRL<br>Further risk management decision required</td></tr><tr><td>231030</td><td>Aubergines/ eggplants</td><td>0.2</td><td>0.4</td><td>0.2</td><td>Risk for consumers unlikely with the existing MRL.<br>However, a risk for consumer is identified for the MRL proposed in SANTE/11278/2021<br>The existing MRL of 0.2 mg/kg, which covers the less critical GAPs assessed in the framework of this mandate, should be maintained</td></tr><tr><td>232010</td><td>Cucumbers</td><td>0.3</td><td>0.4</td><td>0.05 (Further risk management considerations required)</td><td>Risk for consumer is identified for the existing MRL<br>and for the MRL proposed in SANTE/11278/2021<br>A tentative fall-back MRL (mainly based on overdosed trials) can be proposed based on a less critical GAP</td></tr><tr><td>232030</td><td>Courgettes</td><td>0.3</td><td>0.4</td><td>0.05 (Further risk management considerations required)</td><td>Risk for consumers unlikely with the fall-back MRL<br>Further risk management decision required</td></tr></tbody></table>
<table><caption>TABLE 40 (Continued)</caption><thead><tr><th>Code<sup>a</sup></th><th>Commodity</th><th>Existing EU MRL (mg/kg)</th><th>SANTE/11278/ 2021 (not yet applicable) (mg/kg)</th><th>Proposed EU MRL (mg/kg)</th><th>Comment/justification</th></tr></thead><tbody><tr><td>233010</td><td>Melons</td><td>0.2</td><td>0.2</td><td>0.08</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data</td></tr><tr><td>233020</td><td>Pumpkins</td><td>0.2</td><td>0.2</td><td>0.08</td><td>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>233030</td><td>Watermelons</td><td>0.2</td><td>0.2</td><td>0.08</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data</td></tr><tr><td>241010</td><td>Broccoli</td><td>0.4</td><td>0.4</td><td>0.06</td><td>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>241020</td><td>Cauliflowers</td><td>0.4</td><td>0.4</td><td>0.06</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data</td></tr><tr><td>242020</td><td>Head cabbages</td><td>0.4</td><td>0.4</td><td>0.03</td><td>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>251010</td><td>Lamb's lettuces/ corn salads</td><td>3</td><td>3</td><td>1.5</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data</td></tr><tr><td>251020</td><td>Lettuces</td><td>1.5</td><td>1.5</td><td>0.01* (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A risk for consumer has been identified for the lowest MRL options assessed in the framework of this mandate</td></tr><tr><td>251030</td><td>Escaroles/broad-leaved endives</td><td>0.4</td><td>0.4</td><td>0.01* (Further risk management considerations required)</td><td>No fall-back MRLs were identified. It is therefore proposed to lower the existing MRL at the LOQ</td></tr><tr><td>251060</td><td>Roman rocket/ rucola</td><td>3</td><td>3</td><td>1.5</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL can be proposed based on a less critical GAP fully supported by data</td></tr><tr><td>251070</td><td>Red mustards</td><td>3</td><td>3</td><td>0.9</td><td>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td>252010</td><td>Spinaches</td><td>0.6</td><td>0.6</td><td>0.01* (Further risk management considerations required)</td><td>A risk for consumer is identified for the existing MRL. A risk for consumer has been identified for the lowest MRL options assessed in the framework of this mandate</td></tr><tr><td>252030</td><td>Chards/beet leaves</td><td>0.6</td><td>0.6</td><td>0.01* (Further risk management considerations required)</td><td>No fall-back MRLs were identified. It is therefore proposed to lower the existing MRL at the LOQ</td></tr><tr><td>270010</td><td>Asparagus</td><td>0.8</td><td>0.8</td><td>0.01*</td><td>A risk for consumer is identified for the existing MRL. A fall-back MRL at the LOQ can be proposed based on a less critical GAP fully supported by data</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Risk for consumers unlikely with the fall-back MRL</td></tr><tr><td colspan="6"><strong>Enforcement residue definition: Sum of acetamiprid and N-desmethyl-acetamiprid (IM-2-1), expressed as acetamiprid</strong></td></tr><tr><td>1012030</td><td>Bovine liver</td><td>1</td><td>1</td><td>0.03</td><td>A risk for consumer is identified for the existing MRL, which was derived from Codex MRL (AUT dietary burden)</td></tr><tr><td>1012050</td><td>Bovine other edible offals</td><td>1</td><td>1</td><td>0.05</td><td>Fall-back MRLs can be proposed for bovine liver and bovine other edible offals, based on an updated EU livestock dietary burden calculated in the framework of this mandate</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Risk for consumers unlikely with the fall-back MRLs</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Risk managers may consider withdrawing the existing Codex MRLs from the EU Regulation</td></tr></tbody></table>

Abbreviations: GAP, good agricultural practice; MRL, maximum residue level; NEU, northern Europe; SEU, southern Europe.

*Indicates that the MRL is set at the limit of analytical quantification (LOQ).

<sup>a</sup>Commodity code number according to Annex I of Regulation (EC) No 396/2005.
## ABBREVIATIONS

<table><tr><td>a.s.</td><td>active substance</td></tr><tr><td>ADI</td><td>acceptable daily intake</td></tr><tr><td>ADME</td><td>absorption, distribution, metabolism, and excretion</td></tr><tr><td>AIR</td><td>Annex I Renewal</td></tr><tr><td>AOEL</td><td>acceptable operator exposure level</td></tr><tr><td>ARfD</td><td>acute reference dose</td></tr><tr><td>BCF</td><td>bioconcentration factor</td></tr><tr><td>CAT</td><td>Critical Appraisal Tool</td></tr><tr><td>CF</td><td>conversion factors</td></tr><tr><td>CRA</td><td>consumer risk assessment</td></tr><tr><td>CSF</td><td>cerebro-spinal fluid</td></tr><tr><td>CTA</td><td>Critical Appraisal Tools</td></tr><tr><td>DNT</td><td>developmental neurotoxicity</td></tr><tr><td>ECHA</td><td>European Chemicals Agency</td></tr><tr><td>ED</td><td>endocrine disruption</td></tr><tr><td>EKE</td><td>Expert Knowledge Elicitation</td></tr><tr><td>EOGRTS</td><td>Extended One-Generation Reproductive Toxicity Study</td></tr><tr><td>EU</td><td>European Union</td></tr><tr><td>FOB</td><td>Functional Observational Battery</td></tr><tr><td>GAP</td><td>Good Agricultural Practice</td></tr><tr><td>GD</td><td>gestation day</td></tr><tr><td>GPER</td><td>G protein-coupled oestrogen receptor</td></tr><tr><td>HBGV</td><td>Health-based guidance value</td></tr><tr><td>HCD</td><td>historical control data</td></tr><tr><td>IVB</td><td>in vitro battery</td></tr><tr><td>KE</td><td>Key Event</td></tr><tr><td>KER</td><td>Key Events Relationships</td></tr><tr><td>LD</td><td>lethal dose</td></tr><tr><td>LOQ</td><td>limit of quantification</td></tr><tr><td>MEA</td><td>Micro Electrode Array</td></tr><tr><td>MIE</td><td>Molecular Initiating Event</td></tr><tr><td>MoA</td><td>mode of action</td></tr><tr><td>MRL</td><td>maximum residues level</td></tr><tr><td>NAM</td><td>new approach methodologies</td></tr><tr><td>nAChRs</td><td>Nicotinic acetylcholine receptors</td></tr><tr><td>NCE</td><td>Normo- chromatic erythrocytes</td></tr><tr><td>NNF</td><td>Neural Network Formation</td></tr><tr><td>OHAT/NTP</td><td>The Office of Health Assessment and Translation/National Toxicology Programme</td></tr><tr><td>PCE</td><td>Polychromatic erythrocyte</td></tr><tr><td>PFAS</td><td>Perfluoroalkyl substances</td></tr><tr><td>PFOS</td><td>Perfluorooctane sulfonate, perfluorooctane sulfonic acid</td></tr><tr><td>PHI</td><td>pre-harvest intervals</td></tr><tr><td>PKB</td><td>physiologically based kinetic</td></tr><tr><td>PRIMo</td><td>Pesticide Residue Intake Model</td></tr><tr><td>QA</td><td>quality assessment</td></tr><tr><td>QC</td><td>quality control</td></tr><tr><td>RAR</td><td>Renewal Assessment Report</td></tr><tr><td>RoB</td><td>Risk of Bias</td></tr><tr><td>SCoPAFF</td><td>Standing Committee on Plants, Animals, Food and Feed</td></tr><tr><td>SL</td><td>Soluble (liquid) concentrate</td></tr><tr><td>SG</td><td>water-soluble granule</td></tr><tr><td>SP</td><td>water-soluble powder</td></tr><tr><td>ToR</td><td>term of reference</td></tr><tr><td>TRR</td><td>total radioactive residue</td></tr><tr><td>UA</td><td>uncertainty analysis</td></tr><tr><td>US EPA</td><td>United States Environmental Protection Agency</td></tr></table>

## ACKNOWLEDGEMENTS

EFSA wishes to acknowledge the following hearing experts for the support provided to the scientific output: Mary Gilbert, Kevin Crofton, Marcel Leist. EFSA wishes to acknowledge all European competent institutions, Member State bodies and other organisations that provided data for this scientific output.
# CONFLICT OF INTEREST

If you wish to access the declaration of interests of any expert contributing to an EFSA scientific assessment, please contact interestmanagement@efsa.europa.eu.

# REQUESTOR

European Commission

# QUESTION NUMBER

EFSA-Q-2022-00589

# COPYRIGHT FOR NON-EFSA CONTENT

EFSA may include images or other content for which it does not hold copyright. In such cases, EFSA indicates the copyright holder and users should seek permission to reproduce the content from the original source.

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