# From parcels to people: development of a spatially explicit risk indicator
to monitor residential pesticide exposure in agricultural areas

Francesco Galimberti1, Stephanie Bopp1, Alessandro Carletti1, Rui Catarino1,
Martin Claverie1, Pietro Florio1, Alessio Ippolito 2, Arwyn Jones1, Flavio
Marchetto3, Michael Olvedy1, Alberto Pistocchi1, Astrid Verhegghen1, Marijn
Van Der Velde1, Diana Vieira1, Raphaël d’Andrimont 4 1 European Commission,
Joint Research Centre (JRC), Ispra , Italy
2 European Food Safety Authority (EFSA), Parma, Italy
3 European Chemicals Agency (ECHA), Helsinki, Finland
4 European Commission, Joint Research Centre (JRC), Brussels , Belgium

###### Abstract

The increase in global pesticide use has mirrored the rising demand for food
over the last decades, resulting in a boost in crop yields. However, concerns
about the impact of pesticides on biodiversity, ecosystems, and human health,
especially for populations residing close to cultivated areas, are growing.
This study investigates how exposure and possible risks to residents can be
estimated at high spatial granularity based on plant protection product data.
The complexities of such analysis were explored in France, where relevant data
with sufficient granularity are publicly available. Integrating a range of
spatial datasets and exposure assessment methodologies, we have developed an
indicator to monitor the levels of pesticide risk faced by residents. By
spatialising pesticide sales data according to their authorisation on specific
crops, we developed a detailed map depicting potential pesticide loads at
parcel level across France. This spatial distribution served as the basis for
an exposure and risk assessment, modelled following the European Food Safety
Authority’s guidelines. Combining the risk map with population distribution
data, we have developed an indicator that allows to monitor patterns in non-
dietary exposure to pesticides. Our results show that in France, on average,
13% of people might be exposed to pesticides due to living in the proximity to
treated crops: the exposure is in the lower range for 34%, moderate range for
40% and higher range for 25% of the exposed population. The risk evaluation is
based on worst case assumptions and values should not be taken as a regulatory
risk assessment but as indicator to use, for example, for monitoring time
trends. The purpose of this indicator is to demonstrate the usefulness of a
more granular pesticide data to monitor risk reduction strategies. harmonised
and high-resolution data can help in identifying regions where to focus more
efforts towards sustainable farming.

###### keywords:

Risk Assessment , Spatialisation , Plant Protection Products , European Union

††journal: Journal of Environmental Management

##  1 Introduction

Pesticides are substances intentionally released into the environment to
eliminate pests that have a negative impact on crop production such as weeds,
insects, fungi, and others. These substances have played a pivotal role in
achieving a four-fold increase in agricultural yields over the past 60 years
to meet the growing food demand of the global population Popp et al. [2013].
However, their extensive use can lead to adverse effects on biodiversity
[McLaughlin & Mineau, 1995, Mesnage et al., 2021, Khurana, Leena and
Chaturvedi, Payal and Sharma, Charu and Bhatnagar, Pradeep, 2023], with far-
reaching consequences for terrestrial, aquatic and marine ecosystems
[Pistocchi et al., 2023], as well as negative impacts on human health
[European Environmental Agency, 2023, Kim et al., 2017, Dereumeaux et al.,
2020]. Of particular concern is the health of residents living in close
proximity to these agricultural areas, since combined exposure to pesticides
remains largely unknown [Stehle & Schulz, 2015]. In response to these, the
European Commission has set targets to reduce pesticide use and associated
risks, including the Farm to Fork and Biodiversity Strategies [European
Commission, 2023b, 2022a] and Zero Pollution Ambition [Candel et al., 2023].
These targets are set against pesticide sales that have been stable in the EU
at around 350 thousand tonnes annually from 2011 to 2020 [EUROSTAT, 2023c, b,
a]. To improve the sustainable use of plant protection products (PPP),
evidence exists that integrated pest management and eliminating PPPs in
sensitive areas, e.g., by creating pesticide-free areas near urban locations,
can be effective in reducing exposure to residents [Martin et al., 2023].

Currently, harmonised Risk Indicators (HRI, A.1) are used to monitor risk from
pesticide use in the EU. These indicators rely on data from national sales and
reporting on emergency authorisations of PPPs [European Commission, 2022b,
Vekemans & Marchand, 2023]. HRI1, in particular, quantifies the amount of
pesticide active substances (AS) placed on the market, weighted according to
four groups that intend to represent increasing levels of hazard [GLOBAL 2000,
2022]. Nevertheless, there is no indicator able to assess appropriately the
use of pesticides at EU level and the associated risks to humans and
ecosystems [Vekemans & Marchand, 2023]. Particularly, an indicator that would
allow monitoring pesticide exposure of residents living near agricultural
fields is missing. Similarly, there is no detailed information on crops grown
near residential areas nor the percentage of the population affected.
Monitoring risk to residents living near treated crops would require a
comprehensive dataset containing at minimum the records of pesticide
application on fields, including the place and time of the application,
information on the agri-environmental landscape, and the distribution of
population residing in the proximity of agricultural fields. The development
of such a dataset is hampered by the lack of available data on pesticide use
on crops [GLOBAL 2000, 2022, PAN Europe, 2021]. While data collection by
public authorities does occur in some cases (e.g. for statistical purposes or
to control subsidy applications by farmers), the data is often published with
low spatial resolution and not harmonised across Member States (MS). At the EU
level, Eurostat serves as the main source of harmonised and reliable
information through the collection and publication of pesticide sales data
[EUROSTAT, 2023c]. However, data on pesticides are provided with granularity
of broad chemical groups at MS level. Similarly, reporting of statistical data
on pesticide use on crops is not properly harmonised among MSs, which renders
this data not suitable for risk monitoring. Efforts in developing a fine scale
spatialisation of pesticide use in various EU countries using official
surveys, sales data or remote sensing have been made before [Jørgensen et al.,
2019, Martin et al., 2023, Strassemeyer, J and Gutsche, V and Golla, B, 2010,
Ward et al., 2000, Galimberti et al., 2020]. Even though, in the future, this
data scarcity will be partially addressed by the Statistics on Agricultural
Input and Output Regulation (SAIO) [European Parliament, 2022] these changes
will still likely provide aggregated pesticide use data that will not allow
precise in-situ risk calculations, including proper assessments of risks from
pesticide mixtures. From 2026, professional users will have to record PPP use
electronically allowing national authorities to exploit this data for better
monitoring and risk reduction strategies.

To address these limitations, we propose to create an indicator monitoring the
pesticide risk for residents living near crops by modelling the spatial
application of pesticides across France, using parcel-scale land use data and
information on PPP sales and their authorised applications. France was chosen
for this case study due to the availability of PPP sales data at a higher
spatial granularity. This has allowed generation of pesticide use maps that
are closer to the reality and thus support the development of the resident
exposure indicator.

##  2 Materials and methods

In this section, we first depict the data used followed by the methodological
approach (section 2.1) combining various datasets to model pesticide
applications on crops and the level of residents’ exposure. Finally, the
evaluation of the spatialisation with independent data source is described.

###  2.1 Data

Data on pesticides are challenging to acquire, but some countries are more
proactive in making such data available. France is one of the countries that
releases datasets to the public. For this analysis, valuable datasets,
summarized in Table 1, were obtained in three main categories: Pesticides
(A.2.1), Agriculture (A.2.2) and Population (A.2.3). A detailed description of
the data can be found in the Supplementary material.

Table 1: List of used datasets with description and references are split into three categories: pesticide, agriculture and population. Dataset |  Description |  Reference(s)
---|---|---
Pesticides (section A.2.1) |  |
| Pesticide Sales - BNV-d |  Database containing pesticide sales data in France from 2013 to 2021, based on declarations by registered distributors. |  French Government [2023], LaPoste [2023]
|  Plant Protection Product (PPP) dataset |  Digitalized data on plant protection products, fertilizers, and more from the French government. |  [Ramalanjaona, 2020, Cherrier, Olivia, and Prima, Marie-Caroline and Rouveyrol, Paul, 2021]
|  Pesticide Properties DataBase (PPDB) |  Comprehensive database including pesticide chemical identity, physicochemical, human health, and eco-toxicological data. |  PPDB [2023]
| EUPDB |  EU database with details on approved and non-approved substances used in plant protection products. |  European Commission [2023a]
| Open EFSA |  Platform for accessing information related to the European Food Safety Authority’s (EFSA) scientific work. |  EFSA [2023]
| LUCAS-Soil Survey |  The ‘Land Use/Cover Area frame statistical Survey Soil’ (LUCAS Soil) is an extensive and regular topsoil survey that is carried out across the European Union to derive policy-relevant statistics on the effect of land management on soil characteristics. |  JRC [2023b]
|  Treatment Frequency Index (TFI) |  The Pesticide Treatment Frequency Index created by the Solagro Group. |  Chayre et al. [2023]
Agriculture (section A.2.2) |  |
|  Registre Parcellaire Graphique (RPG) |  Geo-dataset of agricultural parcels and crop type. |  IGN [2022]
Population (section A.2.3) |  |
| Gridded Population Data |  Data consisting of gridded information at a 200-meter resolution. Includes variables related to individuals’ age distribution, household characteristics, and income. Derived from tax files. |  [French Government, 2016, Insee, 2017]

####  2.1.1 Data interaction and schema

The data flow is presented in Table 1 and it is further schematized in Fig. 1.

Fig. 1: Overview of data utilisation in designing the Pesticide Risk Indicator
(PRI): the diagram illustrates the specific segments of various datasets
employed and their respective applications in the pesticide risk indicator’s
development.

###  2.2 Methods

Fig. 2 illustrates the full workflow of the methodology to calculate parcel
pesticide loads and pesticide exposure. This includes the development of the
Pesticide Risk Indicator (PRI) indicator as well as intermediate results and
the comparative assessment. The method was deployed for year 2018.

Fig. 2: Methodological diagram for the development of the final indicator,
including intermediate results and comparative assessment.

####  2.2.1 Data Spatialisation

The rationale behind this analysis is straightforward. Pesticide sales data in
France were used as a proxy for actual pesticide use on crops in a given
agricultural year (2018). Pesticide sales data in France is available at the
postcode level, representing a territorial unit finer than NUTS3 (i.e.,
representing 6051 postcodes at country level). By combining this information
with the authorisations of pesticides on crops (PPP dataset) and the parcel-
level agricultural land use data (RPG), we estimated the potential quantity of
pesticides applied on each single parcel. The harmonised crop classification
from the EuroCrops dataset [Schneider et al., 2023] was employed to ensure
semantic legend consistency of the crop type classes. The pesticide sales
dataset was curated, excluding entries with missing or zero postal codes and
quantities. The result of this first data setup, resulted in 1.5 million
records after cleaning (i.e., -9% of the whole initial dataset). Authorisation
of crops for each Active Substance (AS) was integrated, yielding 43 million
records. This association was made through expert knowledge, linking the
information on labels all to the respective crop types or categories. It is
important to note that the crops reported on pesticide labels can vary in
specificity. Some labels may specify detailed crops like artichokes, while
others may use more general terms like cereals or arable crops. During the
matching process, all possible cases were considered (e.g., cereals = soft
wheat, durum wheat, maize, winter barley…). However, nearly 75% of hypotetical
pesticide applications did not align with actual crop coverage due to varied
crop specificity on pesticide labels and those applications were not
considered in the spatialisation of pesticide amounts on crops. Quantities of
ASs and products were split across agricultural parcels based on postcodes,
weighted in total area. The distribution did not take into account the
potential application doses reported in the PPP dataset [Ramalanjaona, 2020].
Instead, the fraction of each crop area was calculated relative to the total
agricultural area of the postal code for each PPP and AS content. This
fraction value was then multiplied by the quantities of ASs and PPPs to obtain
a relative pesticide load expressed in kilograms. The application dose was
subsequently calculated by dividing the resulting quantities by the area of
the potentially affected crops by the use of PPPs.

The Pesticide Load ($\text{PL}_{ijk}$) can be calculated with equation 1 where
$i$ is a given active substance, $j$ is the postal code and $k$ the crop
considered. The $PL$ is obtained by multiplying the quantity (kg) of a given
Active Substance, $AS_{i}$ in a given postcode ($j$) by the proportion of
authorised crop ($k$) for that active substance within the postcode:

${PL}_{ijk}=\text{AS}_{ij}\times\left(\frac{\text{authorised
Crop}_{ijk}}{\sum_{k=1}^{n}\text{authorised Crop}_{ijk}}\right)$ (1)

All procedures were automated using a combination of R, PostgreSQL, and QGIS
[Altmann et al., 2023, Dempsey et al., 2023, Eisentraut et al., 2023, Sutton
et al., 2023] on the Big Data Analytics Platform of the JRC [Soille et al.,
2018]. The R scripts are available as well as the spatial data at postcode
level. The link to have access to the data can be found in Supplementary
material A.3.

####  2.2.2 Pesticide Exposure and Risk Assessment

This spatialisation procedure served to provide the pesticide application
rates for the calculation of the modelled exposure and consequently the risk.
Exposure is defined as the concentration or amount of an agent that reaches a
target organism, system, or population within a specific frequency and
duration [World Health Organization, 2009, EFSA, 2022]. It is characterized
through exposure scenarios which describe the circumstances of exposure,
including sources, pathways, and amounts involved, and the organisms or
populations exposed [van Leeuwen & Vermeire, 2007]. These exposure scenarios
are used to build exposure models that employ algorithms and equations to
quantitatively estimate exposure doses via oral, dermal, or inhalation routes.
The modelled values are compared to a reference value known as the Acceptable
Operator Exposure Level (AOEL). The AOEL represents the maximum amount of AS
to which an operator (or resident) may be exposed without experiencing adverse
health effects, expressed in milligrams of AS per kilogram of body weight per
day. The ratio between the estimated value from the models and the AOEL
generates a Risk Quotient. A Risk Quotient below one indicates an acceptable
risk, while values above one require further refined assessment to determine
whether risk management measures need to be taken.

We relied on the ”Guidance on the assessment of exposure of operators,
workers, residents and bystanders in the risk assessment of plant protection
products” released by EFSA in 2022 [EFSA, 2022]. Specifically, our focus is on
residents, defined as individuals who reside, work, attend school, or are
present in an institution near an area that has been treated with pesticides.
Their presence is unrelated to pesticide-related work, but their location may
expose them to pesticides for up to 24 hours per day.

The exposure calculation was conducted for the following exposure routes:
spray drift, vapour, surface deposit, entry into treated fields. The $50^{th}$
percentile of exposure was chosen for the entire assessment as suggested by
the EFSA Guidelines. The model from EFSA was adapted to transform the
information from a tabular format to a spatially explicit one. For each AS
analysed in terms of pesticide load distribution based on their authorisations
for different crops, all necessary data for exposure calculation were
retrieved from different sources (PPDB, Open EFSA, EUPDB), and for each AS
analysed, an exposure assessment was performed. In 2022, EFSA introduced an
online tool called OPEX [EFSA, 2022] for assessing non-dietary pesticide
exposure, which includes the underlying equations used in the model. We used
the online tool to validate our results. Following the guidance, we summed
Hazard Quotients of individual AS to create a Hazard Index. This is a worst-
case approach, from exposure point of view, assuming simultaneous application
of all AS on a parcel, but also from a toxicological point of view, since no
consideration of the modes of action and related possible contribution to
combined effects was applied 5.1.2.

Finally, the risk mapping was performed using a parcel level map, with
calculations of the risk set at a 10-meter distance from the agricultural
fields. The 10-meter distance was determined to be the most suitable, in
accordance with the EFSA OPEX Guidance and in conjunction with the spatial
resolution of the agricultural and population layers employed for developing
the indicator. This assessment was carried out separately for both adults,
assuming a body weight of 60 kg, and children, with an estimated body weight
of 10 kg, representing the age range of 1 to 3 years old. The combined risk
map is the results of the sum of four exposure routes:

  1. 1.

\- Vapour exposure: it occurs when pesticides volatilize into the air and
individuals inhale the vapour. This can happen during and after the
application of pesticides.

  2. 2.

\- Spray Drift: it involves the movement of pesticide droplets away from the
target area during application. Drift can occur due to wind or improper
application techniques.

  3. 3.

\- Entry into Treated Fields: this route of exposure occurs when individuals
enter areas recently treated with pesticides. It involves direct contact with
treated surfaces, soil, or plants.

  4. 4.

\- Dermal Transfer: it involves the direct contact of pesticides with the
skin. This can occur during the application of pesticides or through contact
with surfaces, tools, or clothing that have been contaminated with pesticides.

####  2.2.3 Towards the indicator of risk from non-dietary exposure

In line with what has been shown previously, the combined risk map was
integrated with the population distribution map. To conduct this analysis, the
combined risk map was aggregated from a parcel-level resolution to match the
grid size of the population map, i.e., 200 meters. Only overlaying pixels from
both maps were considered to focus on residential areas close to agricultural
fields. To enhance the clarity of the ultimate map produced, both maps
underwent a reclassification process employing a scoring system that relied on
the quantile distributions of values within each of the two maps. A scoring
system ranging from 1 to 4 was applied to both the population distribution map
and the combined risk map Supplementary Fig. S1. The scores of the different
combinations were multiplied to result in an overall risk scoring and ranking
in five classes. The five classes represent the increasing potential risk from
1 to 5. The two maps were multiplied together, resulting in a discrete
indicator for mapping the risk to pesticides of residential population in
agricultural areas. The resulting map can be presented in two different ways:
either at the pixel level or by re-aggregating the indicator to the postcode
boundaries, by average value.

####  2.2.4 Population affected

We also analysed the percentage of population potentially affected within the
different indicator classes at postcode level. We performed this by dividing
the number of people potentially exposed in our analysis with the total
population living in the same area delimited by postcode boundaries. The
resulting ratios were depicted into a map. It is to be noted that there is a
discrepancy in the datasets utilized with the data on pesticide sales and crop
mapping being from the year 2018, while the population distribution map is
derived from the 2017 population census.

####  2.2.5 Data aggregation

AS aggregated distribution: to enhance the visualisation of areas and crops
with potentially widespread pesticide use, we created aggregated distribution
maps for ASs throughout France. We normalized the total spatialised ASs in
each crop at the postcode level, scaling the dimensionless values from 0 to 1
for a comprehensive analysis. We made the decision to visually compare the
Treatment Frequency Index (TFI, more can be found in A.2.1) [Pingault et al.,
2009, Chayre et al., 2023, Chayre & Poitereau, 2023], focusing on pesticide
use intensity based on application rates and recommended doses, with the
aggregated distribution map that normalizes total spatialised ASs in each crop
at the postcode level. This comparative analysis aims to provide a
comprehensive understanding of pesticide usage patterns by considering both
intensity and spatial distribution across different agricultural areas (as
described in section 2.3).

###  2.3 Validation of the spatialisation

To assess our results, efforts were made to utilize accessible data on
residues of ASs in the soil. For this purpose, we used a pesticide soil
residue database from the LUCAS in-situ survey [Orgiazzi et al., 2022, JRC,
2023b, Vieira et al., 2023]. LUCAS Soil surveys 2018 for France has 683
locations sampled with pesticide residues in Soil [mg AS/kg of soil] covering
118 AS and pesticide metabolites, see sampling point map in Supplementary Fig.
S2. The latitude and longitude coordinates of the selected soil French sites
were used to extract corresponding pesticide dataset information from LUCAS
Soil dataset. At the parcel level, our spatialised layer provided information
on application rates or quantities of ASs. To convert these application rates
into soil residues, the guidelines of the FOCUS (FOrum for the Co-ordination
of pesticide fate models and their Use) Work group on Soil Modelling was
followed, specifically using the first-tier equation that assumes worst-case
concentrations immediately after application (t0) [Boesten et al., 1997, EFSA,
2017].

The equation used is as follows:

$\text{{Initial PECsoil}}=\frac{{\text{{Application
Rate}}\times(1-\text{{Foliar Interception}})}}{{100\times\text{{soil
depth}}\times\text{{Bulk density}}}}$

For this analysis, the foliar interception value was set to 0 as a worst-case
assumption, the soil depth was set to 20 cm as sampled in the soil dataset,
and the bulk density values reported in soil for each site were utilized. The
calculated values, expressed in mg AS/kg of soil, were compared with those
measured residues from the pesticide soil dataset. It is important to note
that the overlap of the two datasets resulted in 50 pesticides present in both
datasets, which is a lower number compared to the original 118 substances in
the soil database and 388 substances from our spatialisation. This is because
the substances in the soil database include pesticide metabolites and ASs that
are no longer approved, while our layer is based on sales data of substances
that are approved or recently withdrawn. Additionally, the number of sites
included in the analysis decreased from 683 to 519 due to the exclusion of
sites not included in the RPG layer. A dataset was created matching the
pesticide residues analysed in the same postal code and crops for both the
datasets. For all the analysis, we are only considering AS residues belonging
to the same crop and postal code according to the LUCAS site positions.

##  3 Results

This section presents the results including detailed mapping of ASs,
evaluation of resident exposure risks, and quantification of population
affected.

###  3.1 Spatial indicator for residential pesticide exposure and risks

####  3.1.1 Mapping the active substances potential use at parcel level.

By consolidating and standardizing the quantities of all ASs, we created 388
maps (one for each AS), depicting potential pesticide loads at the parcel
level in France, which comprises 9.5 million parcels. As an example, Fig. 3
displays a detailed map illustrating the potential application of the
herbicide Fluroxypyr near the city of Orléans. Fluroxypyr is authorised for
post-emergence use throughout the EU, specifically targeting broadleaf weeds
and woody brush. It finds application in diverse settings, including cereal
fields (e.g., wheat and maize), orchards, and vineyards. All these maps can
represent the potential amount (in kg) of ASs applied on a single parcel, or
the potential application rate, considering also the parcel extent (kg/ha). A
list of all the AS analysed is provided in the Supplementary material
LABEL:tab:ASlist.

Fig. 3: A highlight of the north-west Orléans agricultural area, where a
potential application of the AS Fluroxypyr is estimated based on sales. The
colour scale represents the potential applied amount in kg distributed among
an area which is the areas belonging from per sum of the same crop type and
areas within the same postcode. Globally, 388 AS and 9.5 M parcels were mapped

####  3.1.2 Fine scale resident pesticide exposure risk.

After mapping out potential pesticide use, we produced a risk map measuring
the combined exposure experienced by generic individuals living near
agricultural fields. Here we are exploring the possible risk in these areas
not yet considering if there are people living in that area. This potential
combined risk of all the ASs to Adults within the 10-m buffer strips is shown
in Fig. 4. As shown in the legend, the map depicts the sum of the ratio
between EFSA modelled exposure and the toxicological endpoint AOEL. In
Supplementary Fig. S3 is presented the combined risk map for children.

Fig. 4: Potential combined risk of all the ASs, for adults living within 10 m
from agricultural areas. The colour scale depicts these worst-case risk levels
calculated as sum of risk quotients of the modelled exposure divided by the
reference values AOEL. The risk calculation serves as an indicator of
potential concern levels rather than providing exact risk values, as its
primary purpose is to facilitate comparison of risks between different crops
and regions rather than conducting an exact risk evaluation.

####  3.1.3 Risk indicator applied to population.

We then identified regions potentially affected by risk to the actual
population by incorporating population maps. The results can be presented in
two formats: a raster format with a pixel size of 200 x 200 meters and a
vector layer delineated at the postcode level on the map (averaged values over
postal code areas) as presented in Fig. 5. We classified the indicator into
five distinct classes ranging from low risk (class 1) to moderate and
potentially elevated risk (class 5). In Supplementary Fig. S4 shows the
indicator calculated for children.

Fig. 5: Pesticide risk indicator: integration of combined pesticide risk with
French population distribution map at postal code level. The results,
initially detailed at a 200 square meter resolution, were aggregated by
averaging pixel values and then displayed at the postal code level, providing
a more comprehensive overview than the finer administrative division of
municipalities in France. For the combined pesticide exposure assessment map,
we performed a reclassification based on its quantile value distribution in
conjunction with the Population distribution map. e.g. the higher the modelled
exposure and risk near the field in combination with a higher number of people
living in the relevant area, the higher the indicator value.

The Supplementary Fig. S5 and Supplementary Fig. S6 presented the four
different exposure routes for adults and children respectively, showing that
entry into the fields is a dominant route followed by vapour, while systemic
and spray drift exposure are contributing less.

####  3.1.4 Potentially affected population

To ascertain the proportion of France’s population potentially exposed to
pesticides, we conducted an analysis at the postal code level. The map in Fig.
6 shows the proportion of adult population exposed to pesticides per postcode.
The proportion of exposed children is depicted in Supplementary Fig. S7.

Fig. 6: Exposure of the adult population to pesticides versus the total
population at the postal code level.

##  4 Validation of the spatialisation

###  4.1 Soil concentration comparison

An initial analysis focused on comparing the crop types reported in both
datasets. In the soil dataset, the crop type is determined in-situ, while in
our spatialisation, the main crop is extracted from RPG records. Out of the
total sites (n=520), in 93% (n=482) of the cases, the reported crop type
matched. For 7% (n=37) of the cases, although the crop types did not match,
they were likely rotational crops associated with the main crop of our
spatialisation. In only 0.2% (n=1) of cases, the reported crop type does not
match, and it is unlikely to be a rotational crop (e.g., vineyards vs.
grassland). Next, our focus shifted towards analysing the values of residues
in greater detail. Supplementary Fig. S8 displays a scatter plot showcasing
the pesticide residue values in soil for our modelled data compared to the
soil dataset on average. It can be observed that the majority of points are
distributed within a two-order-of-magnitude window around the diagonal, with
the exception of Chlorpyrifos, Carbendazim and Prosulfocarb. The marginal
distributions of the two data series show that the distribution curve of our
data is much less pronounced and elongated along its horizontal axis compared
to the distribution of pesticide residue values in the LUCAS soil survey,
which shows a less homogeneous distribution and a more pronounced clustering
around the centre. To compare the two datasets we calculated some model
performance parameters. The coefficient of determination (R2) for this point
distribution has a value of 0.14, indicating a weak relationship between the
variables. The value of root mean square error (RMSE) is 0.02, representing
good predictions. The Pearson correlation coefficient is equal to 0.37,
suggesting a low positive correlation between the variables. More on model
performance evaluation can be found in the Supplementary material (A.4).

The data assessment continued from two perspectives: active substances and
crops. In the first part, the focus was on individual active substances
detected in various crops. Consequently, a procedure was developed to generate
scatter plots of both residue data for a single active substance in relation
to one or more utilized crops and conversely a scatter plot procedure for a
single crop with several active substances. Supplementary Fig. S13 shows bar
graphs with the three correlation indices calculated for all active substances
considered (fig. Supplementary Fig. S13) and all crops (fig. Supplementary
Fig. S13). Also, this in-depth analysis does not improve the resulting
correlations between the datasets. The sporadically high correlations reflect
on low statistical power due to limited amount of population involved in
calculating the indexes.

###  4.2 Treatment Frequency Index comparison

We conducted an additional effort to compare and validate our data using the
pesticide Treatment Frequency Index (TFI) from the ADONIS map provided by the
Solagro. Essentially, the municipal pesticide TFI serves as an estimation of
pesticide utilization levels within each municipality in France. This
estimation is derived from factors such as the municipality’s crop rotation,
the farming practices employed (whether conventional or organic), and regional
reference TFIs, which are established through statistical or data locally
surveyed. The resulting value serves as both a reference point for local
farmers to gauge their own farm TFI and as an indicator of the potential risks
associated with water, air, and food contamination stemming from the use of
PPPs in agriculture. Through visual comparisons and analyses conducted at
postal code level, we devised a strategy to crosscheck the TFI obtained from
the [Pingault et al., 2009, Chayre et al., 2023, Chayre & Poitereau, 2023]
with the Aggregated Distribution map of AS. This approach enabled us to
evaluate the coherence and congruence between the TFI data and our results and
could additionally offer a more nuanced understanding of pesticide use
Supplementary Fig. S14.

##  5 Discussion

In this work we developed an indicator to monitor the pesticide exposure of
people living near agricultural fields.

###  5.1 Interpretation of Results

####  5.1.1 Spatialisation of active substances sales data

The spatialisation of AS quantities and PPP usage on crops was a critical
initial step in understanding how farmers applied the products they purchased
in their respective regions. However, the sales data inherently came with a
significant level of uncertainty. Some purchased products might be stockpiled
for future use. Postcode information was also anonymized when it could
potentially identify a small number of farmers, a privacy measure.
Additionally, a product authorized for multiple uses might be used exclusively
on one crop, contrary to our assumptions. All reported pesticide sales within
an administrative postal code are entirely utilized within the boundaries of
that specific postal code, as we did in our work. This assumption, however,
has a weakness. For instance, if a farm possesses fields spanning across three
different administrative postal codes, and its main office is located in a
fourth postal code, all pesticides acquired by that farm will be attributed to
the fourth postal code. In reality, this postal code might not have received
any pesticide treatment, yielding possible anomalies in the results. Martin et
al. [2023] partially solved this issue with non-anonymised RPG data. These
data cannot be shared due to privacy constrains. A shortcoming of the RPG
layer is that it does not cover all the agricultural parcels in France. We
considered the option of enhancing the RPG layer by integrating missing
information through the supplementary RPG complété dataset provided by the
French government [French Government, 2022]. This integration had the
potential to significantly expand the coverage of parcels throughout France.
However, unlike the conventional RPG dataset, this additional layer lacked a
harmonised classification system of crop type. Aligning the two datasets would
have been a time-intensive process, posing challenges in ensuring consistency
and uniformity between the datasets. As a result, despite the potential
increase in coverage, the absence of a harmonised classification made this
approach less feasible for our analysis. We assess the percentage of crop
missing by not considering RPG complété as a complemental map to the RPG and
we focused on the Département La Haute-Vienne (NUTS code FRI23). By comparing
all RPG complété parcels with the RPG ones, included in this administrative
area, the land loss is 10%. But if looking at only intensive agricultural
crops and permanent crops, the percentage loss is 0.5%. We conclude from this
analysis that the enhancing from the RPG could be a marginal improvement.

####  5.1.2 Assessing possible pesticide exposure and risk for residents near
agricultural fields

Our approach to estimating exposure and risks to people living close to
agricultural fields represents a worst-case scenario, both in terms of
exposure estimation and toxicological considerations by assuming simultaneous
application of all ASs on a parcel without considering the modes of action and
potential combined effects. It is important to note that the EFSA OPEX
guidance also acknowledges weaknesses in constructing scenarios for residents
due to a lack of available data. EFSA aims to initiate data collection efforts
to address these gaps in the near future. In establishing the model, we
considered dilutions for dermal absorption in the case of spray drift in a
generic manner, following an expert judgment framework rather than considering
the actual label-specific dilutions, which would have involved nearly 2000
different PPPs. Granules formulations were not taken into account.
Furthermore, the timing of application was not factored in, and in the
combined exposure assessment, it was assumed that all products were used
simultaneously on a hypothetical treatment day for the agricultural parcel. It
is important to recognize that this represents a simplification and a worst-
case scenario. The risk values of the map (Fig. 4) ranges from 0 to greater
than 100. We need to be cautious in interpretation of these results and
possible true concerns considering the worst-case assumptions detailed above.
Also important to keep in mind is that this part of our work is looking into
the theoretical risk to people in the vicinity of agricultural fields while
this does not yet factor in whether there are people residing in these areas
which follows in the next step. Values shown here should be only seen as a
basis for comparing different regions and crops regarding risk ranges to
residents. Upon analysing the map, it becomes evident that in the northern
part of France, including Amiens, Rouen, Orléans, Troyes, and Reims, as well
as in Bretagne and key wine-producing areas such as Alsace, Champagne,
Burgundy, the Loire Valley, Bordeaux, and Provence, the potential risk could
be elevated. Meanwhile, the risk falls into intermediate ranges almost
everywhere else in France, except for the lower central part where the
modelled risk falls within low risk levels for residents. The higher risk
situations are likely related to outliers or peaks in sales data in specific
postcodes. For example, the modelled exposure and the population affected
indicate low pesticide risk in the city of Marseille, yet the risk map
classified this area in the upper category of risk. Such situation might have
been caused by higher concentration of agricultural holdings in this city and
low numbers of crop parcels. If concentrating on single exposure routes
(Supplementary Fig. S5 and Supplementary Fig. S6), it emerges that dermal
transfer and spray drift are exposure routes of lower concern for resident,
according to our potential pesticide applications. Entry into treated fields
and vapour seem to be the ones driving the risk.

####  5.1.3 Pesticide risk indicator map

The next step then is to include the actual population distribution. The
resulting map (Fig. 5) appears to be similar to the combined pesticide risk
map. Some differences between the two maps, derive from the population
distribution input, but also from the applied transformations to the pesticide
risk indicator map: vector to grid data, discretisation and quantile
classification, and along with data averaging at postcode level. The map
values range from 0 to 16, as a result of the multiplied scores, and values
are grouped into 5 risk classes. It is worth noting that in the lower risk
ranges [class 1 and 2], there may be instances where the estimated exposure
and resulting combined risk is elevated, but the population residing in those
areas is very small. Conversely, the more elevated estimated risk classes show
regions where factors such as landscape configuration, population density, and
pesticide exposure assessment converge, which could lead to higher levels of
pesticide exposure for the residents. The overall pattern is similar to the
combined pesticide risk map identifying mostly the same areas to be
investigated further as before. To gain deeper insights, we integrated the
crop layer with the indicator map to perform an analysis of the crops involved
in our study and assess which crops are most commonly cultivated near
residential areas within agricultural sites. We accomplished this by creating
a radius buffer of 100 meters from the centroids of each indicator pixel and
examining the composition of the buffer zone in terms of crop coverage versus
the average value of the PRI indicator Supplementary Fig. S15.

In this figure, the extents of 17 different crops can be observed, which
together represent over 98% of the total analysed area. Additionally, the
figure presents boxplots illustrating the distribution of the indicator values
for each crop throughout France. Crops with an average PRI value greater than
8 (i.e. PRI risk class 4) like vineyards, spring barley, potatoes and beet
roots can be considered as crops impacting the pesticide exposure the most.
More data are reported in Table 2.

| Million Hectares | % total analyzed buffer area | % in whole France
---|---|---|---
Beet roots | $0.01$ | $1$ | $3$
Vineyards | $0.05$ | $3$ | $8$
Spring barley | $0.01$ | $1$ | $3$
Potatoes | $0.01$ | $0.6$ | $6$
Table 2: crop composition of the selected buffer and extent vs crop extent in
France

These findings provide valuable insights into the crops contributing
significantly to pesticide exposure in proximity to residential areas within
agricultural sites. The predominant route of exposure can vary depending on
the pesticide, application method, and environmental conditions. In many
cases, dermal exposure is considered a significant route, especially for those
directly handling pesticides, which, in this study, is not the case.
Operators, and workers are supposed to use personal protective equipment
(PPE), whereas residents are unlikely to wear these kinds of protections. This
is also why, entry into treated fields can be a significant route, especially
for individuals living in close proximity to agricultural areas. Vapour
exposure and spray drift are more likely to affect individuals in the
immediate vicinity of application sites and they are routes of exposure
directly dependent from the application methods. In addition, vapour exposure
is influenced by climatic conditions and pesticide volatility. Spray drift is
mainly influenced by wind speed and direction, droplet size, spray equipment
and height of application Supplementary Fig. S14

####  5.1.4 Exposed population

On average, the proportion of people potentially exposed due to living in the
proximity of agricultural fields compared to the entire French population is
13%. The map (Fig. 6) shows that the percentage of population potentially
exposed to pesticides is quite low when looking at larger towns like Paris,
Rennes, Lyon, Nantes, Toulouse and so on. This depends on the density of the
population but also on the proximity to agricultural fields. Then, some
natural parks are depicted too with low exposed population as well as the
Mediterranean coastal area of France. In Table 3 the exposed population is
presented according to the indicator risk classes.

| % average population exposed | N°postcode involved
---|---|---
Risk class 1 | $9$ | $65$
Risk class 2 | $16$ | $1734$
Risk class 3 | $15$ | $2108$
Risk class 4 | $13$ | $1311$
Risk class 5 | $9$ | $86$
Table 3: subdivision of exposed population by indicator risk classes

###  5.2 Validation of the approach

####  5.2.1 Validation with soil residue estimates

. The same timing considerations seen before, can be applied to our attempt to
compare the data with the soil residue database. In our work, pesticide
residues in soil were calculated using the application rate resulting from the
model we developed, applying the formula for calculating the PEC soil at time
0. This approach does not reflect the actual presence of pesticide residues in
the soil. Additionally, the LUCAS soil residue data sampling does not account
for potential pesticide applications in selected agricultural fields; it
focuses mainly on assessing the qualitative state of European soils. While it
is a unique survey, it is more focused on the history of pesticide
applications on specific agricultural plots. This results in a comparison of
residue data obtained in different ways (modelled vs analysed in the soil
dataset) and for different investigative purposes. Low correlation could also
be a consequence to the result of deposit from spray drift from adjacent
parcels. However, this does not diminish the importance of conducting an
analytical comparison. Observed soil residues tend to be higher than our
conservative estimate (initial soil concentration). This is also what emerges
from the comparison with water samples in Pistocchi et al. [2023], and hints
at a general underestimation of actual pesticide use/presence in the
environment. One possible reason is the persistence of pesticides in the
environment, reflecting past uses.

####  5.2.2 AS aggregated distribution and TFI

The TFI map provides insights into the intensity of pesticide application
practices. On the other hand, the aggregated distribution map provides a
broader view of the spatial distribution of pesticides, encompassing multiple
factors that contribute to overall pesticide presence. By comparing these
metrics, researchers and policymakers can gain a more comprehensive
understanding of pesticide usage trends, potentially identifying areas with
high intensity of use as well as areas where specific types of pesticides are
more prevalent. This comparative visual analysis could be valuable for
informed decision-making in agricultural and environmental management. The two
maps exhibit predominantly similar class distributions; however, the key
disparity in visualisation arises from the differing scales: TFI is
represented at the municipality level, whereas the Aggregated Distribution of
AS is depicted at the crop level. Consequently, the colours denoting crops
appear less dense compared to the municipalities represented in TFI,
influencing the overall visual contrast between the two maps.

By concluding, the term ”indicator” in its name signifies its role as a signal
identifying agricultural areas in France where the combined use of various ASs
could potentially expose the resident population to pesticide risks, sometimes
at elevated levels. Profitable agricultural zones, like vineyards, motivate
farmers to maximize the use of available land for farming, giving rise to
distinct features. Ongoing research aims to characterize a small buffer zone
around urbanized areas throughout Europe, assessing the extent of these areas,
the types of crops involved, and the PPPs used. Combining this analysis with
the results of our study will provide a model for upscaling findings from a
single member state to encompass the entire European continent.

###  5.3 Comparison with Previous Studies

Upon reviewing recent scientific publications, it is clear that various
methodologies have been used to spatialise pesticides on agricultural land.
These methodologies can be categorized as follows:

\- Extrapolation from official surveys: researchers in different countries
have used data from official surveys of agricultural practices to estimate
pesticide application rates. Examples include studies in Germany Röpke et al.
[2004], England Brown et al. [2007], and Belgium Habran et al. [2022].

\- Global gridded maps: a global approach involved creating comprehensive
gridded maps displaying crop-specific application rates for various crop
classes worldwide [Maggi et al., 2019].

\- Use of sales data: researchers utilized disaggregated country-based data on
pesticide use and finer spatial resolution sales data, providing valuable
insights into regional pesticide application patterns [Schriever & Liess,
2007, Richards et al., 2012].

\- Fine-scale data: certain regions have benefited from mandatory reporting by
farmers at very fine scales, allowing for detailed analysis of pesticide use
in these areas Martin et al. [2023].

\- Remote sensing and satellite imagery: advanced techniques were used to
estimate pesticide use by observing changes in crop conditions over time,
providing real-time data for understanding spatial variations in pesticide
application [Ward et al., 2000].

\- Crowd-sourced data: crowd sourcing platforms and citizen science
initiatives have played a role in collecting localized data from farmers and
local communities about pesticide use, providing nuanced insights into
regional pesticide application practices [Haklay et al., 2014].

Our approach is based on the meticulous analysis of fine-scale pesticide sales
data combined with crop authorisation information. This method was chosen due
to the availability of detailed parcel-level datasets, providing valuable
insights into pesticide usage patterns with precision. Additionally, the
integration of a harmonised classification system ensures uniformity and
consistency across diverse datasets and regions, enhancing the accuracy of our
analysis and facilitating meaningful comparisons.

###  5.4 Recommendations for future research

In the upcoming endeavors, we have outlined several key areas of focus:

Update and enhance spatialisation analysis: by updating the spatialisation
analysis by incorporating pesticide application rates and the number of
applications. While these data are digitally available, the lack of
harmonisation poses a challenge. Harmonizing this information is crucial for
creating a solid dataset. Integrating application rates and the number of
applications will significantly enhance the accuracy of our spatialisation.
Modelling approaches will be applied to allocate data to specific crops,
ensuring precision in our analysis. In addition, the Commission Implementing
Regulation (EU) 2023/564, will be applicable from January 2026 and will make
electronic record keeping obligatory. The real use data instead of proxy data
will considerably increase the accuracy of our analysis.

Organic vs. conventional parcels: a dataset on organic and conventional
parcels exists [French-Government, 2023]. Although it is essential to
recognize that organic farming does not imply the absence of pesticides, by
incorporating it into our analysis it would increase the accuracy of the
results in terms of better define treated vs less treated fields, considering
also which AS are approved for use in organic farming.

An in-depth analysis on PPPs and their potential applications at parcel level
could improve our knowledge of most hazardous pesticides on particular crops
as well as it could increase the ability to look for pesticide substitutes to
smooth and mitigate the pesticide risk in highlighted areas.

Trend analysis of exposure: utilizing nearly a decade’s worth of crop
distribution data at the parcel level, along with corresponding pesticide
sales data, the trends in exposure assessment from the early 2010s to the
present can be explored. This historical analysis will provide valuable
insights into how exposures have evolved over time. Population maps, although
not complete, will complement this analysis.

EU-wide upscaling: upscaling this analysis to cover the entire European Union
is within future researchers’ reach. To achieve this, there is the need to a
transition to a more harmonised layer, such as the EUCROPMAP available for
2018 and 2022 [d’Andrimont et al., 2021, JRC, 2021, Ghassemi et al., 2023].
EUCROPMAP offers high-resolution data at 10x10m pixel width, encompassing a
wide range of crops (19 crop types). Replacing the farmers’ declaration parcel
dataset with EUCROPMAP would allow this case study in France to be extended to
other MS where data are available, like Denmark and Northern Italy, ensuring a
consistent approach across regions.

Population data enhancement: Currently relying on French population
distribution data, future EU-wide analysis will leverage the Global Human
Settlement Population Grid layer (GHS-POP) from the JRC [Schiavina, M et al.,
2023]. GHSL provides global, comprehensive and high-resolution dataset [JRC,
2023a], enabling us to pinpoint resident populations in agricultural areas
with accuracy. An experimental disaggregation of population on a grid with
enhanced resolution for Europe, compared to the global product at 100 m
resolution, will be attempted to improve the exposure accuracy.

Recent studies such as from Galimberti et al. [2020] and Udias et al. [2023]
helped to perform our analysis as precursors that tried to implement a
spatialisation of pesticide uses at EU scale, on which this study is clearly
an improvement enabled by previously inaccessible data. Other ongoing European
projects, including the Horizon 2020 SPRINT
project111https://sprint-h2020.eu/, have the potential to enhance our
analysis. This is possible because these projects share similar objectives,
allowing for valuable data sharing and collaboration.

These steps represent a comprehensive strategy to enhance the depth and scope
of our analysis, ensuring a robust and insightful assessment of pesticide
usage patterns in agricultural landscapes at different levels.

##  6 Conclusions

This study introduces a robust indicator designed to assess the potential
pesticide exposure risk for residents living near agricultural fields in
France. By meticulously analysing and spatialising a diverse range of
datasets, we have developed a detailed model to estimate potential pesticide
loads at a parcel level. The detailed spatial results allow to derive a
picture of how relevant non-dietary exposure might be in certain areas and to
identify areas where a more in-depth analysis might be required. Considering
that we use a combination of several worst-case assumptions with several
associated uncertainties, the results for the potential risks should not be
considered an accurate risk assessment but a first indication of levels of
concern that can only be used to tailor follow up actions and to compare among
crops and regions. While our comparative assessment with existing datasets
such as the LUCAS soil survey and TFI revealed areas for improvement, it
represents a significant step towards a more nuanced understanding of
pesticide risk. This effort not only underscores the need for high-resolution,
harmonised data in pesticide risk assessment but also provides a strong
foundation for extending this methodology across the European Union. With
ambitious pesticide reduction targets on the horizon, our indicator serves as
a valuable tool to assist stakeholders and policymakers in steering towards a
safer and more sustainable agricultural landscape.

## Acknowledgements

We gratefully acknowledge the support of this research by the JRC Exploratory
Research program through the PESTIRISK project. We thank Solagro for providing
the ADONIS IFT dataset.

##  7 Declaration of generative AI and AI-assisted technologies in the writing
process

During the preparation of this work, the author(s) used GPT@JRC Prototype to
increase the manuscript readability. After using this tool/service, the
author(s) reviewed and edited the content as needed and take(s) full
responsibility for the content of the publication.

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###### Contents

  1. 1 Introduction
  2. 2 Materials and methods
    1. 2.1 Data
      1. 2.1.1 Data interaction and schema
    2. 2.2 Methods
      1. 2.2.1 Data Spatialisation
      2. 2.2.2 Pesticide Exposure and Risk Assessment
      3. 2.2.3 Towards the indicator of risk from non-dietary exposure
      4. 2.2.4 Population affected
      5. 2.2.5 Data aggregation
    3. 2.3 Validation of the spatialisation
  3. 3 Results
    1. 3.1 Spatial indicator for residential pesticide exposure and risks
      1. 3.1.1 Mapping the active substances potential use at parcel level.
      2. 3.1.2 Fine scale resident pesticide exposure risk.
      3. 3.1.3 Risk indicator applied to population.
      4. 3.1.4 Potentially affected population
  4. 4 Validation of the spatialisation
    1. 4.1 Soil concentration comparison
    2. 4.2 Treatment Frequency Index comparison
  5. 5 Discussion
    1. 5.1 Interpretation of Results
      1. 5.1.1 Spatialisation of active substances sales data
      2. 5.1.2 Assessing possible pesticide exposure and risk for residents near agricultural fields
      3. 5.1.3 Pesticide risk indicator map
      4. 5.1.4 Exposed population
    2. 5.2 Validation of the approach
      1. 5.2.1 Validation with soil residue estimates
      2. 5.2.2 AS aggregated distribution and TFI
    3. 5.3 Comparison with Previous Studies
    4. 5.4 Recommendations for future research
  6. 6 Conclusions
  7. 7 Declaration of generative AI and AI-assisted technologies in the writing process
  8. A
    1. A.1 harmonised Risk Indicator 1
    2. A.2 Dataset detailed description
      1. A.2.1 Pesticides
      2. A.2.2 Agriculture
      3. A.2.3 Population
    3. A.3 Data access FTP
    4. A.4 Model performance evaluation
    5. A.5 Figures and Tables
    6. A.6 List of Active Substances

###### List of Figures

  1. Fig. 1 Overview of data utilisation in designing the Pesticide Risk Indicator (PRI): the diagram illustrates the specific segments of various datasets employed and their respective applications in the pesticide risk indicator’s development.
  2. Fig. 2 Methodological diagram for the development of the final indicator, including intermediate results and comparative assessment.
  3. Fig. 3 A highlight of the north-west Orléans agricultural area, where a potential application of the AS Fluroxypyr is estimated based on sales. The colour scale represents the potential applied amount in kg distributed among an area which is the areas belonging from per sum of the same crop type and areas within the same postcode. Globally, 388 AS and 9.5 M parcels were mapped
  4. Fig. 4 Potential combined risk of all the ASs, for adults living within 10 m from agricultural areas. The colour scale depicts these worst-case risk levels calculated as sum of risk quotients of the modelled exposure divided by the reference values AOEL. The risk calculation serves as an indicator of potential concern levels rather than providing exact risk values, as its primary purpose is to facilitate comparison of risks between different crops and regions rather than conducting an exact risk evaluation.
  5. Fig. 5 Pesticide risk indicator: integration of combined pesticide risk with French population distribution map at postal code level. The results, initially detailed at a 200 square meter resolution, were aggregated by averaging pixel values and then displayed at the postal code level, providing a more comprehensive overview than the finer administrative division of municipalities in France. For the combined pesticide exposure assessment map, we performed a reclassification based on its quantile value distribution in conjunction with the Population distribution map. e.g. the higher the modelled exposure and risk near the field in combination with a higher number of people living in the relevant area, the higher the indicator value.
  6. Fig. 6 Exposure of the adult population to pesticides versus the total population at the postal code level.
  7. Supplementary Fig. S1 This figure represents the data flow from individual maps of population distribution and combined pesticide Risk to discretisation using a quantile classification.
  8. Supplementary Fig. S2 LUCAS Soil surveys (2018) provides 683 samples of pesticide residues in Soil [mgas/kgsoil] with 118 Active Substances and pesticide metabolites
  9. Supplementary Fig. S3 In this figure, it is presented the combined Risk assessment for Children - 10 meters from agricultural fields
  10. Supplementary Fig. S4 In this figure, it is presented the risk indicator map for Children, living in agricultural areas, exposed to pesticides
  11. Supplementary Fig. S5 These are the four routes utilized by the model to assess the pesticide risk exposure for resident (adults). A) Vapour, B) Spray Drift, C) Entry into treated fields, D) Dermal transfer
  12. Supplementary Fig. S6 These are the four routes utilized by the model to assess the pesticide risk exposure for resident (children). A) Vapour, B) Spray Drift, C) Entry into treated fields, D) Systemic exposure
  13. Supplementary Fig. S7 In this figure, it is presented the children exposed to pesticides i.e. the sum of children selected in the risk assessment per postcode, divided by all children in the same postcode
  14. Supplementary Fig. S8 Scatter plot of the average modelled pesticide residue values in soils compared to the LUCAS soil database. The plot is in a logarithmic scale and includes standard deviation bars, marginal distribution, and a range that extends one order higher and lower than the diagonal (highlighted in red).
  15. Supplementary Fig. S9 HRI1 by postcode: HRI1-2018 vs HRI1-2013
  16. Supplementary Fig. S10 HRI1 by postcode: HRI1-2018 vs HRI1-2013 by group of pesticide
  17. Supplementary Fig. S11 Aggregated Distribution of AS at parcel
  18. Supplementary Fig. S12 Aggregated Distribution of AS at parcel levels by group of pesticide: A) the aggregated distribution by ASs of the pesticide types considered together within the same range from 0 to 1. This visualisation allows to see differences in magnitude within the 4 selected pesticide types. B) Each pesticide type is considered singularly within the range 0 to 1. This visualisation shows the differences within each pesticide type.
  19. Supplementary Fig. S13 Statistical analysis within scatter plots was considered with three different model performance parameters: number of records, R2, Pearson Correlation and RMSE for individual Active Substance (AS) across all crops (A) and for all AS together within each crop (B). (A) the AS were fixed and the scatterplots were based on crops were AS were found. (B) the Crops were fixed and the scatterplots were based on Active Substance distribution.
  20. Supplementary Fig. S14 Comparison between Treatment Frequency Index (TFI, panel a) and Aggregated Distribution of Active Substance (AS) at parcel level (panel b). On the reader left hand side, the Adonis map is presented at the Municipality level scale (Source: Pesticide Adonis map, Solagro, 2022). On the right hand side, the Aggregated Distribution of AS at parcel level is outlined. Both maps have the same color schema from green to red. For TFI it goes from 0 to 21.9 whereas for the AS Aggregated distribution it goes from 0 to 1.
  21. Supplementary Fig. S15 In the provided boxplots on the left, we present the distribution of indicator values within the 17 selected crops. On the right side of the figure, the bars, depict the distribution of crop coverage in France, measured in hectares. This representation allows us to visualize both the variation in indicator values across these 17 crops and their respective extents within the agricultural landscape of France. The two additional percentages at the bottom of the bars represent the proportion of crops extents in the selected areas in respect to the whole crop extent in France and the proportion of the composition all crop areas included in the selected areas (sum of all 100 meter buffer areas divided by crop type).
  22. Supplementary Fig. S16 averaged pesticide exposure risk for selected crops coming from the buffer analysis and categorized by the four exposure routes

###### List of Tables

  1. Table 1 List of used datasets with description and references are split into three categories: pesticide, agriculture and population.
  2. Table 2 crop composition of the selected buffer and extent vs crop extent in France
  3. Table 3 subdivision of exposed population by indicator risk classes
  4. A.6 List of Active Substances

## Supplementary material

## Appendix A

###  A.1 harmonised Risk Indicator 1

The harmonised Risk Indicator 1 (HRI1), is computed nationally using pesticide
sales data, categorized by pesticide types and assigned specific weights. In
France, pesticide sales data are available at the postcode level from 2013 to
2022. Therefore, we applied the same methodology used to spatialise the data
for the year 2018, and calculate pesticide quantities for the year 2013. This
approach enabled us to calculate HRI1 not only for 2013 over France but also
at the postcode level. The data were weighted and categorized following the
guidelines outlined in the Methodology for calculating the harmonised Risk
Indicators annexes [Eurostat, 2021b, a]. Using the generated map and computing
HRI1 for the available years, we were able to monitor the trend of HRI1 at a
remarkably detailed scale over the past decade. To elaborate further, we
utilized fine spatialisation techniques to calculate the HRI1 at a more
precise scale. Additionally, we assessed the overall distribution of different
AS in percentage terms. The HRI map, showcasing the calculated values for 2018
and 2013 (Supplementary Fig. S9, Supplementary Fig. S10), along with the
corresponding map illustrating the percentage distribution of AS
(Supplementary Fig. S11, Supplementary Fig. S12).
Supplementary Fig. S9) was developed as an example of increasing granularity
that can be reached with such data disaggregation. It appears that Bretagne
and the area around Lille, as well as the southern areas of France are the
most impacted by a change in the sales of pesticides. Our analysis uses just
two points in time (2013 and 2018). It illustrates the value for looking at
trends at a finer spatial scale, but to investigate trends more in depth would
require some additional analysis over the years.

###  A.2 Dataset detailed description

####  A.2.1 Pesticides

We utilized several datasets to gather information on pesticides:

\- The pesticide sales dataset, obtained from the BNV-d (Banque Nationale des
Ventes de produits phytopharmaceutiques par les Distributeurs agréés)
database, provided data on pesticide sales (Plant Protection Product – PPP and
Active Substance - AS) in France from 2013 to 2021 [French Government, 2023].
This dataset was based on declarations made by registered distributors and
aggregated by postal code [LaPoste, 2023]. We used this dataset for taking
into account the amount of PPP and Active Substances (AS) by postal code.

\- The PPP dataset from the French government website offered digitalized data
on plant protection products, including authorised and withdrawn products,
product uses and doses, risk phrases, active substances, crop information, and
parallel trade permits [Ramalanjaona, 2020, Cherrier, Olivia, and Prima,
Marie-Caroline and Rouveyrol, Paul, 2021]. We mainly used this dataset to
draft the relation between PPP and authorisation on crops.

\- The Pesticide Properties Database (PPDB) provided pesticide chemical
identity, physicochemical, human health, and eco-toxicological data. We used
this database to translate active substance names from French to English and
collect toxicological endpoints for exposure assessment analysis [PPDB, 2023].

\- The EU Pesticide Database (EUPDB) provided information on approved and non-
approved active substances, including low-risk substitutes within the EU. It
also included Maximum Residue Levels in food products and data on emergency
authorisations of plant protection products granted by Member States [European
Commission, 2023a]. We used this database for collecting AS authorisations and
toxicological endpoints.

\- The Open EFSA platform served as a resource for accessing information
related to the European Food Safety Authority’s (EFSA) scientific work. It
provided details on ongoing assessments, non-confidential versions of dossiers
and studies, meeting agendas and minutes, and information about experts
involved in the process [EFSA, 2023]. We used the Open EFSA platform for
consistency checks within different datasets.

\- The LUCAS Soil Survey 2018, conducted by the European Commission, is a
module of the Land Use/Cover Area frame Survey (LUCAS) providing systematic
sampling and analysis of pesticide residues in soil across European
territories [d’Andrimont et al., 2020, JRC, 2023b]. This survey provided
insights into the environmental impact of agricultural practices and
contributed to evidence-based decision-making and the formulation of
agricultural policies [Orgiazzi et al., 2022, Vieira et al., 2023].

\- The pesticide  Treatment Frequency Index (TFI) from the ADONIS map222The
ADONIS map was provided by SOLAGRO and is available on a viewer on
https://solagro.org/nos-domaines-d-intervention/agroecologie/carte-pesticides-
adonis. estimated pesticide utilisation levels within each municipality or
greater areas in France. It considered factors such as crop rotation, farming
practices, and regional reference acTFIs to gauge potential risks associated
with water, air, and food contamination [Pingault et al., 2009, Chayre et al.,
2023, Chayre & Poitereau, 2023].

####  A.2.2 Agriculture

We utilized the  Registre Parcellaire Graphique (RPG) is a geo-referenced
dataset of agricultural parcels in France [IGN, 2022, French Government,
2022]. Each parcel is associated with declared cultivated crop types. The crop
types were semantically harmonised into English EuroCrops classification
[Schneider et al., 2023].

####  A.2.3 Population

We utilized a gridded population dataset at a 200-meter resolution, which
included variables related to age distribution, household characteristics, and
income. The dataset was derived from tax files and adress data privacy by
grouping cells with fewer households into larger aggregates (Filosofi -
Income, poverty and standard of living in 2017 - gridded data system localized
social and fiscal file) [French Government, 2016, Insee, 2017].

###  A.3 Data access FTP

The data and R scripts related to the manuscript can be downloaded from the
Joint Research Centre’s Open Data Portal. The files include
ExposedPOP_AD_postcode.gpkg, ExposedPOP_CH_postcode.gpkg,
PRI_AD_postcode.gpkg, and PR_CH_postcode.gpkg, which contain detailed
information on populations exposed to pesticides and Pesticide Risk Indicators
(PRI) at the postcode level for specific regions. Additionally, a
copyright.txt file provides the necessary copyright information. These
resources are essential for researchers and policymakers interested in
assessing pesticide exposure risks and formulating appropriate management
strategies. For more details and to download the files, visit
https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/DRLL/PESTIRISK/.

###  A.4 Model performance evaluation

Model performance was evaluated using three commonly used statistical
indicators: Pearson’s correlation coefficient and coefficient of determination
(R2) describe the degree of collinearity between simulated and measured data.
The correlation coefficient is an index that is used to investigate the degree
of linear relationship between observed and simulated data [Moriasi et al.,
2015].

The root mean square error (RMSE) is the square root of mean square error
(MSE). The MSE is also known as standard error of the estimate in regression
analysis. The RMSE is measured in the same units as the model output response
of interest and is representative of the size of a typical error [Moriasi et
al., 2015].

###  A.5 Figures and Tables

Supplementary Fig. S1: This figure represents the data flow from individual
maps of population distribution and combined pesticide Risk to discretisation
using a quantile classification.  Supplementary Fig. S2: LUCAS Soil surveys
(2018) provides 683 samples of pesticide residues in Soil [mgas/kgsoil] with
118 Active Substances and pesticide metabolites Supplementary Fig. S3: In this
figure, it is presented the combined Risk assessment for Children - 10 meters
from agricultural fields Supplementary Fig. S4: In this figure, it is
presented the risk indicator map for Children, living in agricultural areas,
exposed to pesticides Supplementary Fig. S5: These are the four routes
utilized by the model to assess the pesticide risk exposure for resident
(adults). A) Vapour, B) Spray Drift, C) Entry into treated fields, D) Dermal
transfer Supplementary Fig. S6: These are the four routes utilized by the
model to assess the pesticide risk exposure for resident (children). A)
Vapour, B) Spray Drift, C) Entry into treated fields, D) Systemic exposure
Supplementary Fig. S7: In this figure, it is presented the children exposed to
pesticides i.e. the sum of children selected in the risk assessment per
postcode, divided by all children in the same postcode Supplementary Fig. S8:
Scatter plot of the average modelled pesticide residue values in soils
compared to the LUCAS soil database. The plot is in a logarithmic scale and
includes standard deviation bars, marginal distribution, and a range that
extends one order higher and lower than the diagonal (highlighted in red).
Supplementary Fig. S9: HRI1 by postcode: HRI1-2018 vs HRI1-2013 Supplementary
Fig. S10: HRI1 by postcode: HRI1-2018 vs HRI1-2013 by group of pesticide
Supplementary Fig. S11: Aggregated Distribution of AS at parcel  Supplementary
Fig. S12: Aggregated Distribution of AS at parcel levels by group of
pesticide: A) the aggregated distribution by ASs of the pesticide types
considered together within the same range from 0 to 1. This visualisation
allows to see differences in magnitude within the 4 selected pesticide types.
B) Each pesticide type is considered singularly within the range 0 to 1. This
visualisation shows the differences within each pesticide type.

Supplementary Fig. S13: Statistical analysis within scatter plots was
considered with three different model performance parameters: number of
records, R2, Pearson Correlation and RMSE for individual Active Substance (AS)
across all crops (A) and for all AS together within each crop (B). (A) the AS
were fixed and the scatterplots were based on crops were AS were found. (B)
the Crops were fixed and the scatterplots were based on Active Substance
distribution. Supplementary Fig. S14: Comparison between Treatment Frequency
Index (TFI, panel a) and Aggregated Distribution of Active Substance (AS) at
parcel level (panel b). On the reader left hand side, the Adonis map is
presented at the Municipality level scale (Source: Pesticide Adonis map,
Solagro, 2022). On the right hand side, the Aggregated Distribution of AS at
parcel level is outlined. Both maps have the same color schema from green to
red. For TFI it goes from 0 to 21.9 whereas for the AS Aggregated distribution
it goes from 0 to 1. Supplementary Fig. S15: In the provided boxplots on the
left, we present the distribution of indicator values within the 17 selected
crops. On the right side of the figure, the bars, depict the distribution of
crop coverage in France, measured in hectares. This representation allows us
to visualize both the variation in indicator values across these 17 crops and
their respective extents within the agricultural landscape of France. The two
additional percentages at the bottom of the bars represent the proportion of
crops extents in the selected areas in respect to the whole crop extent in
France and the proportion of the composition all crop areas included in the
selected areas (sum of all 100 meter buffer areas divided by crop type).
Supplementary Fig. S16: averaged pesticide exposure risk for selected crops
coming from the buffer analysis and categorized by the four exposure routes

###  A.6 List of Active Substances

Active | CAS-Number
---|---
(E)-dodec-8-enyl acetate | 38363-29-0
(E,Z)-octadeca-3,13-dienyl acetate | 53120-26-6
(Z)-11-tetradecenyl acetate | 20711-10-8
(Z)-dodec-8-enyl acetate | 28079-04-1
(Z)-dodec-9-enyl acetate | 16974-11-1
1,4-dimethylnaphthalene | 571-58-4
1,5-Diaminopentane | 462-94-2
1-decanol | 112-30-1
1-dodecanol | 112-53-8
1-naphthylacetamide | 86-86-2
1-naphthylacetic acid | 86-87-3
2,13-octadecadienyl acetate | 86252-65-5
2,4-D | 94-75-7
2,4-DB | 94-82-6
5-decenol | 56578-18-8
5-decenyl acetate | 38421-90-8
6-benzyladenine | 1214-39-7
7,9-dodecadienyl acetate | 54364-62-4
8-Dodecenyl acetate | 37338-40-2
8-dodecenol | 40642-40-8
8-hydroxyquinoline | 148-24-3
9-tetradecenyl acetate | 16725-53-4
Aureobasidium Pullulans | 67891-88-7
Bacillus thuringiensis Berliner subsp. kurstaki | 68038-71-1
Bordeaux mixture | 8011-63-0
Decanoic acid | 334-48-5
Diallyl trisulfide | 8008-99-9
Dicamba-Dimethylammonium | 2300-66-5
Dipotassium Phosphite | 13492-26-7
Epoxiconazole | 133855-98-8
FENOXYCARB | 72490-01-8
Ferrous sulfate heptahydrate | 7782-63-0
Geraniol | 106-24-1
Gibberellin A4 mixture with Gibberellin A7 | 8030-53-3
Hexadecyl Acetate | 629-70-9
Lecanicillium lecanii | 67892-35-7
MCPA | 94-74-6
MCPB | 94-81-5
Mineral oil, petroleum distillates, hydrotreated light paraffinic | 64742-55-8
Octanoic Acid | 124-07-2
Polyalkyleneoxide modified heptamethyltrisiloxane | 27306-78-1
Potassium Laurate | 10124-65-9
Propineb | 9016-72-2
Rapeseed oil | 8002-13-9
S-metolachlor | 87392-12-9
White mineral oil | 9206-23-56
abamectin | 71751-41-2
acequinocyl | 57960-19-7
acetamiprid | 135410-20-7
acetic acid | 64-19-7
acibenzolar-S-methyl | 135158-54-2
aclonifen | 74070-46-5
acrinathrin | 101007-06-1
adoxophyes orana granulovirus souche bv-0001 |
alpha-cypermethrin | 67375-30-8
aluminium phosphide | 20859-73-8
ametoctradin | 865318-97-4
amidosulfuron | 120923-37-7
aminopyralid | 150114-71-9
amisulbrom | 348635-87-0
amitrole | 61-82-5
ammonium acetate | 631-61-8
ammonium carbonate | 506-87-6
ammonium thiocyanate | 1762-95-4
ampelomyces quisqualis souche aq10 |
anthraquinone | 84-65-1
asulam sodium | 2302-17-2
azadirachtin A | 11141-17-6
azimsulfuron | 120162-55-2
azoxystrobin | 131860-33-8
bacillus firmus i-1582 |
bacillus pumilus souche qst 2808 |
bacillus subtilis | 68038-70-0
beflubutamid | 113614-08-7
benalaxyl | 71626-11-4
benalaxyl-M | 98243-83-5
benfluralin | 1861-40-1
benoxacor | 98730-04-2
bensulfuron-methyl | 83055-99-6
bentazone | 25057-89-0
benthiavalicarb isopropyl | 177406-68-7
benzovindiflupyr | 1072957-71-1
bifenazate | 149877-41-8
bifenox | 42576-02-3
bifenthrin | 82657-04-3
bitertanol | 55179-31-2
bixafen | 581809-46-3
boscalid | 188425-85-6
bromadiolone | 28772-56-7
bromoxynil | 1689-84-5
bromoxynil octanoate | 1689-99-2
bromuconazole | 116255-48-2
bupirimate | 41483-43-6
calcium cyanamide | 156-62-7
captan | 133-06-2
carbendazim | 10605-21-7
carbetamide | 16118-49-3
carbofuran | 1563-66-2
carboxin | 5234-68-4
carfentrazone-ethyl | 128639-02-1
cerevisane |
chlorantraniliprole | 500008-45-7
chloridazon | 1698-60-8
chlormequat chloride | 999-81-5
chlorothalonil | 1897-45-6
chlorotoluron | 15545-48-9
chlorpropham | 101-21-3
chlorpyrifos | 2921-88-2
chlorpyrifos-methyl | 5598-13-0
choline chloride | 67-48-1
clethodim | 99129-21-2
clodinafop-propargyl | 105512-06-9
clomazone | 81777-89-1
clonostachys rosea j1446 (anciennement gliocladium catenulatum) |
clopyralid | 1702-17-6
clopyralid monoethanolamine salt | 57754-85-5
cloquintocet-mexyl | 99607-70-2
clothianidin | 210880-92-5
codlemone | 33956-49-9
coniothyrium minitans souche con/m/91-08 |
copper (1) oxide | 1317-39-1
copper II hydroxide | 20427-59-2
copper oxychloride | 1332-40-7
copper sulphate | 12527-76-3
cos-oga |
cuivre du tallate de cuivre | 71789-22-8
cyantraniliprole | 736994-63-1
cyazofamid | 120116-88-3
cycloxydim | 101205-02-1
cydia pomonella granulovirus |
cyflufenamid | 180409-60-3
cyfluthrin | 68359-37-5
cyhalofop-butyl | 122008-85-9
cymoxanil | 57966-95-7
cypermethrin | 52315-07-8
cyproconazole | 94361-06-5
cyprodinil | 121552-61-2
cyprosulfamide | 221667-31-8
cyromazine | 66215-27-8
daminozide | 1596-84-5
dazomet | 533-74-4
deltamethrin | 52918-63-5
desmedipham | 13684-56-5
diazinon | 333-41-5
dicamba | 1918-00-9
dichlobenil | 1194-65-6
dichlorprop-P | 15165-67-0
diclofop-methyl | 51338-27-3
dicofol | 115-32-2
difenoconazole | 119446-68-3
diflubenzuron | 35367-38-5
diflufenican | 83164-33-4
dimethachlor | 50563-36-5
dimethenamid-P | 163515-14-8
dimethomorph | 110488-70-5
dimoxystrobin | 149961-52-4
diquat dibromide | 85-00-7
disodium phosphonate | 13708-85-5
dithianon | 3347-22-6
diuron | 330-54-1
dodine | 03-10-2439
e8,e10-dodecadiene-1-ol + tetradecylacetate |
emamectin benzoate | 155569-91-8
esfenvalerate | 66230-04-4
essence de girofle |
ethephon | 16672-87-0
ethofumesate | 26225-79-6
ethoprophos | 13194-48-4
etofenprox | 80844-07-1
etoxazole | 153233-91-1
eugenol | 97-53-0
famoxadone | 131807-57-3
farine de sang |
fenamidone | 161326-34-7
fenazaquin | 120928-09-8
fenbuconazole | 114369-43-6
fenhexamid | 126833-17-8
fenitrothion | 122-14-5
fenoxaprop-P-ethyl | 71283-80-2
fenpropidin | 67306-00-7
fenpropimorph | 67564-91-4
fenpyrazamine | 473798-59-3
ferric phosphate | 10045-86-0
flazasulfuron | 104040-78-0
flonicamid | 158062-67-0
florasulam | 145701-23-1
fluazifop-P-butyl | 79241-46-6
fluazinam | 79622-59-6
fludioxonil | 131341-86-1
flufenacet | 142459-58-3
flumioxazin | 103361-09-7
fluopicolide | 239110-15-7
fluopyram | 658066-35-4
fluoxastrobin | 361377-29-9
flupyrsulfuron-methyl-sodium | 144740-54-5
flurochloridone | 61213-25-0
fluroxypyr | 69377-81-7
flurtamone | 96525-23-4
flusilazole | 85509-19-9
flutolanil | 66332-96-5
flutriafol | 76674-21-0
fluxapyroxad | 907204-31-3
folpet | 133-07-3
foramsulfuron | 173159-57-4
forchlorfenuron | 68157-60-8
fosetyl | 15845-66-6
fosetyl-aluminium | 39148-24-8
fosthiazate | 98886-44-3
gamma-cyhalothrin | 76703-62-3
gibberellic acid | 77-06-5
glufosinate-ammonium | 77182-82-2
glyphosate | 1071-83-6
graisse de mouton |
halauxifen | 943832-60-8
halosulfuron-methyl | 100784-20-1
heptamaloxyloglucan | 870721-81-6
hexythiazox | 78587-05-0
hydrolysat de proteines |
hymexazol | 10004-44-1
imazalil | 35554-44-0
imazamox | 114311-32-9
imazaquin | 81335-37-7
imidacloprid | 138261-41-3
indolylbutyric acid | 133-32-4
indoxacarb | 173584-44-6
iodosulfuron-methyl-sodium | 144550-36-7
ioxynil | 1689-83-4
ioxynil octanoate | 3861-47-0
ipconazole | 125225-28-7
iprodione | 36734-19-7
iprovalicarb | 140923-17-7
iron sulphate | 7720-78-7
isofetamid | 875915-78-9
isoproturon | 34123-59-6
isoxaben | 82558-50-7
isoxadifen ethyl | 163520-33-0
isoxaflutole | 141112-29-0
kaolin | 1332-58-7
kieselguhr |
kresoxim-methyl | 143390-89-0
lambda-cyhalothrin | 91465-08-6
laminarin | 9008-22-4
lenacil | 2164-08-01
lime sulphur | 1344-81-6
linuron | 330-55-2
magnesium phosphide | 12057-74-8
malathion | 121-75-5
maleic hydrazide | 123-33-1
maltodextrin | 9050-36-6
mancozeb | 8018-01-7
mandipropamid | 374726-62-2
maneb | 12427-38-2
mecoprop | 7085-19-0
mecoprop-P | 16484-77-8
mefenpyr diethyl | 135590-91-9
mepanipyrim | 110235-47-7
mepiquat chloride | 24307-26-4
meptyldinocap | 131-72-6
mesosulfuron-methyl | 208465-21-8
mesotrione | 104206-82-8
metalaxyl | 57837-19-1
metalaxyl-M | 70630-17-0
metaldehyde | 108-62-3
metam-sodium | 137-42-8
metamitron | 41394-05-2
metazachlor | 67129-08-2
metconazole | 125116-23-6
methiocarb | 2032-65-7
methoxyfenozide | 161050-58-4
metiram | 9006-42-2
metobromuron | 3060-89-7
metrafenone | 220899-03-6
metribuzin | 21087-64-9
metsulfuron-methyl | 74223-64-6
milbemectin | 51596-10-2
myclobutanil | 88671-89-0
myristyl alcohol | 112-72-1
napropamide | 15299-99-7
nicosulfuron | 111991-09-4
nucleopolyhedrovirus de spodoptera littoralis |
orange oil | 8028-48-6
oryzalin | 19044-88-3
oxadiazon | 19666-30-9
oxamyl | 23135-22-0
oxathiapiprolin | 1003318-67-9
oxyfluorfen | 42874-03-3
paclobutrazol | 76738-62-0
paecilomyces fumosoroseus apopka souche 97 |
paraffin oil (C11-C30) (4c) | 97862-82-3
paraffin oil (C18-C30) (1, not ASU) | 8042-47-5
pelargonic acid | 112-05-0
penconazole | 66246-88-6
pencycuron | 66063-05-6
pendimethalin | 40487-42-1
penoxsulam | 219714-96-2
penthiopyrad | 183675-82-3
pethoxamid | 106700-29-2
phenmedipham | 13684-63-4
phlebiopsis gigantea |
phosalone | 2310-17-0
phosmet | 732-11-6
picloram | 1918-02-1
picolinafen | 137641-05-5
picoxystrobin | 117428-22-5
pinoxaden | 243973-20-8
piperonyl butoxide | 51-03-6
pirimicarb | 23103-98-2
pirimiphos-methyl | 29232-93-7
poivre |
potassium bicarbonate | 298-14-6
prochloraz | 67747-09-5
prohexadione-calcium | 127277-53-6
propamocarb | 24579-73-5
propaquizafop | 111479-05-1
propiconazole | 60207-90-1
propoxycarbazone-sodium | 181274-15-7
propyzamide | 23950-58-5
proquinazid | 189278-12-4
prosulfocarb | 52888-80-9
prosulfuron | 94125-34-5
prothioconazole | 178928-70-6
pseudomonas chlororaphis souche ma 342 |
pymetrozine | 123312-89-0
pyraclostrobin | 175013-18-0
pyraflufen-ethyl | 129630-19-9
pyrethrins (mix) | 8003-34-7
pyridate | 55512-33-9
pyrimethanil | 53112-28-0
pyriofenone | 688046-61-9
pyriproxyfen | 95737-68-1
pyroxsulam | 422556-08-9
quinmerac | 90717-03-6
quinoclamine | 2797-51-5
quinoxyfen | 124495-18-7
quizalofop-P-ethyl | 100646-51-3
rimsulfuron | 122931-48-0
rotenone | 83-79-4
sedaxane | 874967-67-6
silthiofam | 175217-20-6
sintofen | 130561-48-7
sodium hypochlorite | 7681-52-9
spearmint oil | 8008-79-5
spinetoram | 935545-74-7
spinosad | 168316-95-8
spirodiclofen | 148477-71-8
spiromesifen | 283594-90-1
spirotetramat | 203313-25-1
spiroxamine | 118134-30-8
sulcotrione | 99105-77-8
sulfosulfuron | 141776-32-1
sulfuryl fluoride | 2699-79-8
sulphur | 7704-34-9
tau-fluvalinate | 102851-06-9
tebuconazole | 107534-96-3
tebufenozide | 112410-23-8
tebufenpyrad | 119168-77-3
tefluthrin | 79538-32-2
tembotrione | 335104-84-2
terbuthylazine | 5915-41-3
tetraconazole | 112281-77-3
thiabendazole | 148-79-8
thiacloprid | 111988-49-9
thiamethoxam | 153719-23-4
thiencarbazone-methyl | 317815-83-1
thifensulfuron-methyl | 79277-27-3
thiodicarb | 59669-26-0
thiophanate-methyl | 23564-05-8
thiram | 137-26-8
thymol | 89-83-8
tri-allate | 2303-17-5
triadimenol | 55219-65-3
tribenuron-methyl | 101200-48-0
trichoderma asperellum icc012 |
trichoderma asperellum tv1 |
trichoderma harzianum souche t22 |
triclopyr | 55335-06-3
trifloxystrobin | 141517-21-7
trifluralin | 1582-09-8
triflusulfuron-methyl | 126535-15-7
trimethylamine hydrochloride | 593-81-7
trinexapac-ethyl | 95266-40-3
triticonazole | 131983-72-7
tritosulfuron | 142469-14-5
valifenalate | 283159-90-0
virus de la granulose |
zinc phosphide | 1314-84-7
ziram | 137-30-4
zoxamide | 156052-68-5
  *[PPP]: Plant Protection Product
  *[PPDB]: Pesticide Properties DataBase
  *[TFI]: Treatment Frequency Index
  *[PRI]: Pesticide Risk Indicator
  *[AS]: Active Substances
  *[RPG]: Registre Parcellaire Graphique