1. Lakes and rivers¶

Map of survey locations IQAASL March 2020 - May 2021.

Fig. 1.1 ¶

Figure 1.1: The survey locations were grouped by survey area for analysis. The size of the marker is set by the median survey total for that location.

1.1. Land use profile¶

The survey areas are grouped by river catchment systems, this report combines several catchment areas for analysis of regional trends:

Aare : Emme, Aare
Linth: Reuss, Linth, Limmat
Rhône: Rhône
Ticino: Ticino, Lago di Lugano, Laggo Maggiore

The land use profile is the percent of total area attributed to each land use category within a 1500m radius of each survey location.

% of land attributed to buildings
% of land attributed to agriculture
% of land attributed to forestry
% of land attributed to recreation

Streets are reported as the total number of kilometers of streets within the 1500m radius. Intersects are an ordinal ranking of the number of rivers/canals that intersect a lake within 1500m of the survey location.

The ratio of the number of samples completed at the different land use profiles is an indicator of the environmental and economic conditions that the samples were taken in.

For more information Land use profiles

Distribution of land use characteristics

# this is the data before the expanded fragmented plastics and foams are aggregated to Gfrags and Gfoams
before_agg = pd.read_csv("resources/checked_before_agg_sdata_eos_2020_21.csv")
before_agg["loc_date"] = list(zip(before_agg.location, before_agg["date"]))

# this is the aggregated survey data that is being used
fd = pd.read_csv(F"resources/checked_sdata_eos_2020_21.csv")
fd["loc_date"] = list(zip(fd.location, fd["date"]))

fd.rename(columns={"% to agg":"% ag", "% to recreation": "% recreation", "% to woods":"% woods", "% to buildings":"% buildings"}, inplace=True)

# explanatory variables
luse_exp = ["% buildings", "% recreation", "% ag", "% woods", "streets km", "intersects"]

# cumulative statistics for each code
code_totals = sut.the_aggregated_object_values(fd, agg=agg_pcs_median, description_map=code_description_map, material_map=code_material_map)    

dt_nw = fd.groupby(use_these_cols[:-2], as_index=False).agg({unit_label:"sum"})

sns.set_style("whitegrid")

fig, axs = plt.subplots(2, 3, figsize=(9,9), sharey="row")

for i, n in enumerate(luse_exp):
    # get the dist for each survey area here
    r = i%2
    c = i%3
    ax=axs[r,c]
    for element in comps:
        the_data = ECDF(dt_nw[dt_nw[this_level] == element][n].values)
        sns.lineplot(x=the_data.x, y=the_data.y, ax=ax, label=comp_labels[element], color=bassin_pallette[element])
    
    # get the dist for all here
    a_all_surveys =  ECDF(dt_nw[n].values)
    sns.lineplot(x=a_all_surveys.x, y=a_all_surveys.y, ax=ax, label="All surveys", color="magenta", linewidth=2.5)
 
    # get the median from the data
    the_median = np.median(dt_nw[n].values)
    
    # plot the median and drop horzontal and vertical lines
    ax.scatter([the_median], 0.5, color="red",s=50, linewidth=2, zorder=100, label="the median")
    ax.vlines(x=the_median, ymin=0, ymax=0.5, color="red", linewidth=2)
    ax.hlines(xmax=the_median, xmin=0, y=0.5, color="red", linewidth=2)
    
    # format the % of total on the xaxis:
    if i <= 3:
        if c == 0:            
            ax.set_ylabel("Ratio of samples", **ck.xlab_k)
        ax.xaxis.set_major_formatter(ticker.PercentFormatter(1.0, 0, "%"))        
    else:
        pass
    
    ax.set_xlabel(n, **ck.xlab_k)
    handles, labels = ax.get_legend_handles_labels()
    ax.get_legend().remove()
    ax.set_title(F"median: {(round(the_median, 2))}",fontsize=12, loc="left")

plt.tight_layout()
plt.subplots_adjust(top=.88, hspace=.35)
plt.suptitle("Land use within 1500m of the survey location", ha="center", y=1, fontsize=16)
fig.legend(handles, labels, bbox_to_anchor=(.53,.94), loc="center", ncol=6)

glue("eosluse", fig, display=False)
plt.close()

Fig. 1.2 ¶

Figure 1.2: The survey locations in the Rhône and Linth survey areas had the greatest median amount of land attributed to buildings at 47% and 40% and the least amount attributed to woods at 5% and 8% respectively. The Aare survey area had the lowest median amount of land attributed to buildings at 16% and the greatest amount of land attributed to agriculture at 30%. The land attributed to recreation represents sports fields, public beaches and other public gathering places.

1.2. Cumulative totals by survey area¶

# aggregate the dimensional data
dims_parameters = dict(this_level=this_level, 
                       locations=fd.location.unique(), 
                       start_end=start_end, 
                       agg_dims=agg_dims)

dims_table = sut.gather_dimensional_data(dfDims, **dims_parameters)

dt_all = fd.groupby(["loc_date","location",this_level, "city","date"], as_index=False).agg({unit_label:"sum", "quantity":"sum"})

for name in dims_table.index:
    dims_table.loc[name, "samples"] = fd[fd[this_level] == name].loc_date.nunique()
    dims_table.loc[name, "quantity"] =fd[fd[this_level] == name].quantity.sum()

# add proper names for display
dims_table["Survey area"] = dims_table.index.map(lambda x: comp_labels[x])
dims_table.set_index("Survey area", inplace=True)
   
# get the sum of all survey areas
dims_table.loc["All survey areas"]= dims_table.sum(numeric_only=True, axis=0)

# for display
dims_table.sort_values(by=["quantity"], ascending=False, inplace=True)
dims_table.rename(columns={"samples":"samples","quantity":"items", "total_w":"total kg", "mac_plast_w":"plastic kg", "area":"m²", "length":"meters"}, inplace=True)

# format kilos and text strings
dims_table["plastic kg"] = dims_table["plastic kg"]/1000
dims_table[["m²", "meters", "samples", "items"]] = dims_table[["m²", "meters", "samples", "items"]].applymap(lambda x: "{:,}".format(int(x)))
dims_table[["plastic kg", "total kg"]] = dims_table[["plastic kg", "total kg"]].applymap(lambda x: "{:.2f}".format(x))

# make table
data = dims_table.reset_index()

fig, axs = plt.subplots(figsize=(len(data.columns)*1.5,len(data)*.8))
sut.hide_spines_ticks_grids(axs)

table_one = sut.make_a_table(axs, data.values, colLabels=data.columns, colWidths=[.22, *[.13]*6],a_color=a_color)
table_one.get_celld()[(0,0)].get_text().set_text(" ")
table_one.set_fontsize(14)

plt.tight_layout()
glue("eos_summary_sarea", fig, display=False)
plt.close()

Fig. 1.3 ¶

Figure 1.3: Dimensional result totals for all survey areas. The Aare survey area had the greatest number of samples and the least number of total objects collected.

1.3. Survey results all objects¶

Distribution of survey results. Values are presented as the number of pieces identified per 100 meters (p/100 m).

# the surveys to chart
dt_all["date"] = pd.to_datetime(dt_all["date"], format="%Y-%m-%d")
fd_n_samps = dt_all.loc_date.nunique()
fd_dindex = dt_all.set_index("date")

# monthly median survey total
monthly_plot = fd_dindex[unit_label].resample("M").median()

# scale the chart as needed to accomodate for extreme values
y_lim = 95
y_limit = np.percentile(dt_all[unit_label], y_lim)

# months locator, can be confusing
# https://matplotlib.org/stable/api/dates_api.html
months_loc = mdates.MonthLocator(interval=1)
months_fmt = mdates.DateFormatter("%b")
# days = mdates.DayLocator(interval=7)

fig, axs = plt.subplots(1,2, figsize=(10,5))

rate = "Monthly"

line_label = "monthly median"

ax = axs[0]
sns.scatterplot(data=fd_dindex, x="date", y=unit_label, hue=this_level, palette=bassin_pallette, alpha=0.8, ax=ax)

sns.lineplot(data=monthly_plot, x=monthly_plot.index, y=monthly_plot, color="magenta", label=line_label, ax=ax)

ax.set_ylim(0,y_limit)

ax.set_xlabel("")
ax.set_ylabel(unit_label, **ck.xlab_k14)

ax.xaxis.set_major_formatter(months_fmt)
ax.get_legend().remove()

axtwo = axs[1]
lake_dts = fd.groupby([this_level, "loc_date"], as_index=False)[unit_label].sum()

for i, a_name in enumerate(fd[this_level].unique()):
    label=comp_labels[a_name]
    
    a_ecdf = ECDF(lake_dts[lake_dts[this_level] == a_name][unit_label].values)
    
    sns.lineplot(x=a_ecdf.x, y=a_ecdf.y, color=bassin_pallette[a_name], ax=axtwo, label=label)

r_bassin = ECDF(dt_all[unit_label].values)

sns.lineplot(x=r_bassin.x, y=r_bassin.y, color="magenta", label=top_name[0], linewidth=2, ax=axtwo)

axtwo.set_xlabel(unit_label, **ck.xlab_k14)
axtwo.set_ylabel("Ratio of samples", **ck.xlab_k14)

plt.tight_layout()

glue("eosscatter", fig, display=False)
plt.close()

Fig. 1.4 ¶

Figure 1.4: Left; All survey totals from 2020-03 through 2021-05 grouped by survey area and aggregated to the monthly median. Values greater than 2324.3p/100m are not shown. Right: The empirical cumulative distribution of the survey totals.

Summary data and material types

# get the basic statistics from pd.describe
cs = dt_all[unit_label].describe().round(2)

# add project totals
cs["total objects"] = dt_all.quantity.sum()

# change the names
csx = sut.change_series_index_labels(cs, sut.create_summary_table_index(unit_label, lang="EN"))

combined_summary = sut.fmt_combined_summary(csx, nf=[])

fd_mat_totals = sut.the_ratio_object_to_total(code_totals)
fd_mat_totals = sut.fmt_pct_of_total(fd_mat_totals)
fd_mat_totals = sut.make_string_format(fd_mat_totals)

# applly new column names for printing
cols_to_use = {"material":"Material","quantity":"Quantity", "% of total":"% of total"}
fd_mat_t = fd_mat_totals[cols_to_use.keys()].values

# make tables
fig, axs = plt.subplots(1,2, figsize=(8,6))

# summary table
# names for the table columns
a_col = ["All survey areas", "total"]

axone = axs[0]
sut.hide_spines_ticks_grids(axone)

table_two = sut.make_a_table(axone, combined_summary,  colLabels=a_col, colWidths=[.5,.25,.25],  bbox=[0,0,1,1], **{"loc":"lower center"})
table_two.get_celld()[(0,0)].get_text().set_text(" ")
table_two.set_fontsize(14)

# material table
axtwo = axs[1]
axtwo.set_xlabel(" ")
sut.hide_spines_ticks_grids(axtwo)

table_three = sut.make_a_table(axtwo, fd_mat_t,  colLabels=list(cols_to_use.values()), colWidths=[.4, .3,.3],  bbox=[0,0,1,1], **{"loc":"lower center"})
table_three.get_celld()[(0,0)].get_text().set_text(" ")
table_three.set_fontsize(14)

plt.tight_layout()
glue("summarymaterial", fig, display=False)
plt.close()

Fig. 1.5 ¶

Figure 1.5: Left: Summary of survey totals for all survey areas. Right: Material type and percent of total for all survey areas.

1.4. The most common objects¶

The most common objects are the ten most abundant by quantity AND/OR objects identified in at least 50% of all surveys.

# the top ten by quantity
most_abundant = code_totals.sort_values(by="quantity", ascending=False)[:10]

# the most common
most_common = code_totals[code_totals["fail rate"] >= a_fail_rate ].sort_values(by="quantity", ascending=False)

# merge with most_common and drop duplicates
m_common = pd.concat([most_abundant, most_common]).drop_duplicates()

# get percent of total
m_common_percent_of_total = m_common.quantity.sum()/code_totals.quantity.sum()

# format values for table
m_common["item"] = m_common.index.map(lambda x: code_description_map.loc[x])
m_common["% of total"] = m_common["% of total"].map(lambda x: F"{x}%")
m_common["quantity"] = m_common.quantity.map(lambda x: "{:,}".format(x))
m_common["fail rate"] = m_common["fail rate"].map(lambda x: F"{x}%")
m_common[unit_label] = m_common[unit_label].map(lambda x: F"{np.ceil(x)}")

# final table data
cols_to_use = {"item":"Item","quantity":"quantity", "% of total":"% of total", "fail rate":"fail rate", unit_label:unit_label}
all_survey_areas = m_common[cols_to_use.keys()].values

fig, axs = plt.subplots(figsize=(9,len(m_common)*.6))

sut.hide_spines_ticks_grids(axs)

table_four = sut.make_a_table(axs, all_survey_areas,  colLabels=list(cols_to_use.values()), colWidths=[.52, .12,.12,.12, .12],  bbox=[0,0,1,1], **{"loc":"lower center"})
table_four.get_celld()[(0,0)].get_text().set_text(" ")
table_four.set_fontsize(14)

plt.tight_layout()
glue("mcommoneos", fig, display=False)
plt.close()

Fig. 1.6 ¶

Figure 1.6: The most common objects for all survey areas. The fail rate is the number of times an object was identified with respect to the number of surveys. Combined, the most common objects represent 68% of total objects found.

Most common objects median p/100m by survey area

# aggregated survey totals for the most common codes for all the survey areas
# aggregate the code totals by survey and then get the median value per survey
m_common_st = fd[fd.code.isin(m_common.index)].groupby([this_level, "loc_date","code"], as_index=False).agg(agg_pcs_quantity)
m_common_ft = m_common_st.groupby([this_level, "code"], as_index=False)[unit_label].median()

# proper name of water feature for display
m_common_ft["f_name"] = m_common_ft[this_level].map(lambda x: comp_labels[x])

# map the desctiption to the code
m_common_ft["item"] = m_common_ft.code.map(lambda x: code_description_map.loc[x])

# pivot that
m_c_p = m_common_ft[["item", unit_label, "f_name"]].pivot(columns="f_name", index="item")

# quash the hierarchal column index
m_c_p.columns = m_c_p.columns.get_level_values(1)

# the aggregated totals of all the data
a_s_a = fd[fd.code.isin(m_common.index)].groupby(["water_name_slug", "loc_date", "code"], as_index=False).agg(agg_pcs_quantity)
a_s_a_cols = sut.aggregate_to_code(a_s_a, code_description_map,name=top_name[0])

ad_t_ten = pd.concat([m_c_p, a_s_a_cols], axis=1).sort_values(by=top_name[0], ascending=False)

# chart that
fig, ax  = plt.subplots(figsize=(len(ad_t_ten.columns)*.9,len(ad_t_ten)*.9))
axone = ax

sns.heatmap(ad_t_ten, ax=axone, cmap=cmap2, annot=True, annot_kws={"fontsize":12}, fmt=".1f", square=True, cbar=False, linewidth=.1, linecolor="white")

axone.set_xlabel("")
axone.set_ylabel("")
axone.tick_params(labelsize=14, which="both", axis="both")

plt.setp(axone.get_xticklabels(), rotation=90)

glue("mcommonpcs", fig, display=False)
plt.close()

Fig. 1.7 ¶

Figure 1.7: The median p/100m of the most common objects for all survey areas. The rates at which the most common objects are identified are different for each survey area.

Most common objects monthly average

# collect the survey results of the most common objects
m_common_m = fd[(fd.code.isin(m_common.index))].groupby(["loc_date","date","code", "groupname"], as_index=False).agg(agg_pcs_quantity)
m_common_m["date"] = pd.to_datetime(m_common_m["date"], format="%Y-%m-%d")
m_common_m.set_index("date", inplace=True)


# set the order of the chart, group the codes by groupname columns
an_order = m_common_m.groupby(["code","groupname"], as_index=False).quantity.sum().sort_values(by="groupname")["code"].values

# a manager dict for the monthly results of each code
mgr = {}

# get the monhtly results for each code:
for a_group in an_order:
    # resample by month
    a_plot = m_common_m[(m_common_m.code==a_group)][unit_label].resample("M").mean().fillna(0)
    this_group = {a_group:a_plot}
    mgr.update(this_group)

months={
    0:"Jan",
    1:"Feb",
    2:"Mar",
    3:"Apr",
    4:"May",
    5:"Jun",
    6:"Jul",
    7:"Aug",
    8:"Sep",
    9:"Oct",
    10:"Nov",
    11:"Dec"
}

# convenience function to lable x axis
def new_month(x):
    if x <= 11:
        this_month = x
    else:
        this_month=x-12    
    return this_month

fig, ax = plt.subplots(figsize=(9,7))

# define a bottom
bottom = [0]*len(mgr["G27"])

# the monhtly survey average for all objects and locations
monthly_fd = dt_all.copy()
monthly_fd.set_index("date", inplace=True)
m_fd = monthly_fd[unit_label].resample("M").mean().fillna(0)

# define the xaxis
this_x = [i for i,x in  enumerate(m_fd.index)]

# plot the monthly total survey average
ax.bar(this_x, m_fd.to_numpy(), color=a_color, alpha=0.2, linewidth=1, edgecolor="teal", width=1, label="Monthly survey average") 

# plot the monthly survey average of the most common objects
for i, a_group in enumerate(an_order): 
    
    # define the axis
    this_x = [i for i,x in  enumerate(mgr[a_group].index)]
    
    # collect the month
    this_month = [x.month for i,x in enumerate(mgr[a_group].index)]
    
    # if i == 0 laydown the first bars
    if i == 0:
        ax.bar(this_x, mgr[a_group].to_numpy(), label=a_group, color=colors_palette[a_group], linewidth=1, alpha=0.6 ) 
    # else use the previous results to define the bottom
    else:
        bottom += mgr[an_order[i-1]].to_numpy()        
        ax.bar(this_x, mgr[a_group].to_numpy(), bottom=bottom, label=a_group, color=colors_palette[a_group], linewidth=1, alpha=0.8)
        
# collect the handles and labels from the legend
handles, labels = ax.get_legend_handles_labels()

# set the location of the x ticks
ax.xaxis.set_major_locator(ticker.FixedLocator([i for i in np.arange(len(this_x))]))
ax.set_ylabel(unit_label, **ck.xlab_k14)

# label the xticks by month
axisticks = ax.get_xticks()
labelsx = [months[new_month(x-1)] for x in  this_month]
plt.xticks(ticks=axisticks, labels=labelsx)

# make the legend
# swap out codes for descriptions
new_labels = [code_description_map.loc[x] for x in labels[1:]]
new_labels = new_labels[::-1]

# insert a label for the monthly average
new_labels.insert(0,"Monthly survey average")
handles = [handles[0], *handles[1:][::-1]]
    
ax.set_title("")    
plt.legend(handles=handles, labels=new_labels, bbox_to_anchor=(.5, -.05), loc="upper center",  ncol=2, fontsize=14)      
glue("monthlyeos", fig, display=False)
plt.close()

Fig. 1.8 ¶

Figure 1.8: Monthly average survey result as p/100m of the most common objects for all survey areas.

1.5. Survey results and land use¶

The survey results with respect to land use at the national level are discussed in detail in The land use profile and Shared responsibility.

1.6. Utility of the objects found¶

The utility type is based on the utilization of the object prior to it being discarded or item description if the original use is undetermined. Identified objects are classified into one of the predefined categories. The categories are grouped according to utilization or item description.

wastewater: items released from water treatment plants includes items likely toilet flushed
micro plastics (< 5mm): fragmented plastics and pre-production plastic resins
infrastructure: items related to construction and maintenance of buildings, roads and water/power supplies
food and drink: all materials related to consuming food and drink
agriculture: primarily industrial sheeting i.e., mulch and row covers, greenhouses, soil fumigation, bale wraps. Includes hard plastics for agricultural fencing, flowerpots etc.
tobacco: primarily cigarette filters, includes all smoking related material
recreation: objects related to sports and leisure i.e., fishing, hunting, hiking etc.
packaging non food and drink: packaging material not identified as food, drink nor tobacco related
plastic fragments: plastic pieces of undetermined origin or use
personal items: accessories, hygiene and clothing related

See the annex for the complete list of objects identified, includes descriptions and group classification. The section Code groups describes each code group in detail and provides a comprehensive list of all objects in a group.

All survey areas utility of objects found % of total by survey area. Fragmented objects with no clear identification remain classified by size:

# code groups aggregated by survey for each survey area
groups = ["loc_date","groupname"]
cg_t = fd.groupby([this_level,"loc_date", "groupname"], as_index=False).agg(agg_pcs_quantity)

# aggregate all that for each survey area
cg_t = cg_t.groupby([this_level, "groupname"], as_index=False).agg({unit_label:"median", "quantity":"sum", "loc_date":"nunique"})

# quantity per water survey area
cg_tq = cg_t.groupby(this_level).quantity.sum()

# assign survey area total to each record
for a_feature in cg_tq.index:
    cg_t.loc[cg_t[this_level] == a_feature, "f_total"] = cg_tq.loc[a_feature]

# get the percent of total for each group for each survey area
cg_t["pt"] = (cg_t.quantity/cg_t.f_total).round(2)

# pivot that
data_table = cg_t.pivot(columns=this_level, index="groupname", values="pt")

data_table[top_name[0]] = sut.aggregate_to_group_name(fd, unit_label=unit_label, column="groupname", name=top_name[0], val="pt")

data_table.rename(columns={x:comp_labels[x] for x in data_table.columns[:-1]}, inplace=True)

fig, ax = plt.subplots(figsize=(10,10))

axone = ax
sns.heatmap(data_table , ax=axone, cmap=cmap2, annot=True, annot_kws={"fontsize":12}, cbar=False, fmt=".0%", linewidth=.1, square=True, linecolor="white")

axone.set_xlabel("")
axone.set_ylabel("")
axone.tick_params(labelsize=14, which="both", axis="both", labeltop=False, labelbottom=True)

plt.setp(axone.get_xticklabels(), rotation=90, fontsize=14)
plt.setp(axone.get_yticklabels(), rotation=0, fontsize=14)
glue("utility", fig, display=False)

plt.close()

Fig. 1.9 ¶

Figure 1.9: The utility of objects as a % of total for all the survey areas.

# median p/50m solve cg_t for unit_label
data_table = cg_t.pivot(columns=this_level, index="groupname", values=unit_label)

# column for all the data
a = fd.groupby(["groupname", "loc_date"], as_index=False)[unit_label].sum()
a[top_name[0]] = a[unit_label]
a = a.groupby("groupname")[top_name[0]].median()

# stick that together
data = pd.concat([data_table, a], axis=1)

# format for display
data.rename(columns={x:comp_labels[x] for x in data.columns[:-1]}, inplace=True)

fig, ax = plt.subplots(figsize=(10,10))

axone = ax
sns.heatmap(data , ax=axone, cmap=cmap2, annot=True, annot_kws={"fontsize":12}, fmt="g", cbar=False, linewidth=.1, square=True, linecolor="white")

axone.set_xlabel("")
axone.set_ylabel("")
axone.tick_params(labelsize=14, which="both", axis="both", labeltop=False, labelbottom=True)

plt.setp(axone.get_xticklabels(), rotation=90, fontsize=14)
plt.setp(axone.get_yticklabels(), rotation=0, fontsize=14)

glue("utility2", fig, display=False)

plt.close()

Fig. 1.10 ¶

Figure 1.10: The Rhône survey area has the highest median values for the most common objects. However, as a percentage of total, abundant items related to tobacco, food and drink are less than infrastructure related objects.

1.7. Rivers¶

rivers = fd[fd.w_t == "r"].copy()
r_smps = rivers.groupby(["loc_date", "date", "location", "water_name_slug"], as_index=False).agg(agg_pcs_quantity)
l_smps = fd[fd.w_t == "l"].groupby(["loc_date","date","location", "water_name_slug"], as_index=False).agg(agg_pcs_quantity)

cs = r_smps[unit_label].describe().round(2)

# add project totals
cs["total objects"] = r_smps.quantity.sum()

# change the names
csx = sut.change_series_index_labels(cs, sut.create_summary_table_index(unit_label, lang="EN"))

combined_summary = sut.fmt_combined_summary(csx, nf=[])

# make the charts
fig = plt.figure(figsize=(11,6))

aspec = fig.add_gridspec(ncols=11, nrows=3)

ax = fig.add_subplot(aspec[:, :6])

line_label = F"{rate} median:{top_name[0]}"

sns.scatterplot(data=l_smps, x="date", y=unit_label, color="black", alpha=0.4, label="Lake surveys", ax=ax)
sns.scatterplot(data=r_smps, x="date", y=unit_label, color="red", s=34, ec="white",label="River surveys", ax=ax)

ax.set_ylim(-10,y_limit)

ax.set_xlabel("")
ax.set_ylabel(unit_label, **ck.xlab_k14)

ax.xaxis.set_major_locator(months_loc)
ax.xaxis.set_major_formatter(months_fmt)

a_col = [top_name[0], "total"]

axone = fig.add_subplot(aspec[:, 7:])
sut.hide_spines_ticks_grids(axone)

table_five = sut.make_a_table(axone, combined_summary,  colLabels=a_col, colWidths=[.5,.25,.25],  bbox=[0,0,1,1], **{"loc":"lower center"})
table_five.get_celld()[(0,0)].get_text().set_text(" ")
table_five.set_fontsize(14)

glue("rivers", fig, display=False)

plt.close()

Fig. 1.11 ¶

Figure 1.11: Left: All survey areas river survey totals, 2020-03 through 2021-05, n=55. Values greater than 2324.3p/100m 98% not shown. Right: Summary data.

Most common objects

# the most common items rivers
r_codes = rivers.groupby("code").agg({"quantity":"sum", "fail":"sum", unit_label:"median"})
r_codes["fail rate"] = (r_codes.fail/r_smps.loc_date.nunique()*100).round(2)

# top ten
r_byq = r_codes.sort_values(by="quantity", ascending=False)[:10]

# most common
r_byfail = r_codes[r_codes["fail rate"] >= a_fail_rate]
r_common = list(set(r_byq) | set(r_byfail))

r_mc = pd.concat([r_byfail, r_byq]).drop_duplicates()
# format for display
r_mc["item"] = r_mc.index.map(lambda x: code_description_map.loc[x])
r_mc.sort_values(by="quantity", ascending=False, inplace=True)

r_mc["% of total"]=((r_mc.quantity/r_codes.quantity.sum())*100).round(2)
r_mc["% of total"] = r_mc["% of total"].map(lambda x: F"{x}%")
r_mc["quantity"] = r_mc.quantity.map(lambda x: "{:,}".format(x))
r_mc["fail rate"] = r_mc["fail rate"].map(lambda x: F"{x}%")
r_mc[unit_label] = r_mc[unit_label].map(lambda x: F"{np.ceil(x)}")

cols_to_use = {"item":"Item","quantity":"Quantity"}
r_mc.rename(columns=cols_to_use, inplace=True)

data=r_mc[["Item","Quantity", "% of total", "fail rate", unit_label]]

fig, axs = plt.subplots(figsize=(11,len(data)*.6))

sut.hide_spines_ticks_grids(axs)

table_six = sut.make_a_table(axs, data.values,  colLabels=data.columns, colWidths=[.52, .12,.12,.12, .12],  bbox=[0,0,1,1], **{"loc":"lower center"})
table_six.get_celld()[(0,0)].get_text().set_text(" ")
table_six.set_fontsize(14)


plt.tight_layout()
glue("rivers2", fig, display=False)
plt.close()

Fig. 1.12 ¶

Figure 1.12: The most common objects from river surveys. Diapers - wipes and plastic bags are not present in the most common objects when lakes are considered.

1.8. Annex¶

1.8.1. Fragmented foams and plastics by size group¶

These are the components of Gfoam and Gfrags, objects labled “Expanded foams” are grouped with Gfoam the rest are grouped under Gfrags.

All survey areas fragmented plastics and foamed plastics by size, median p/100m, number of pieces, and percent of total.

# collect the data before aggregating foams for all locations in the survye area
# group by loc_date and code
# Combine the different sizes of fragmented plastics and styrofoam
# the codes for the foams
some_foams = ["G81", "G82", "G83", "G74"]

# the codes for the fragmented plastics
some_frag_plas = list(before_agg[before_agg.groupname == "plastic pieces"].code.unique())

fd_frags_foams = before_agg[(before_agg.code.isin([*some_frag_plas, *some_foams]))&(before_agg.location.isin(fd.location.unique()))].groupby(["loc_date","code"], as_index=False).agg(agg_pcs_quantity)
fd_frags_foams = fd_frags_foams.groupby("code").agg({unit_label:"median", "quantity":"sum"})

# add code description and format for printing
fd_frags_foams["item"] = fd_frags_foams.index.map(lambda x: code_description_map.loc[x])
fd_frags_foams["% of total"] = (fd_frags_foams.quantity/fd.quantity.sum()*100).round(2)
fd_frags_foams["% of total"] = fd_frags_foams["% of total"].map(lambda x: F"{x}%")
fd_frags_foams["quantity"] = fd_frags_foams["quantity"].map(lambda x: F"{x:,}")

# table data
data = fd_frags_foams[["item",unit_label, "quantity", "% of total"]]

fig, axs = plt.subplots(figsize=(len(data.columns)*2.1,len(data)*.6))
sut.hide_spines_ticks_grids(axs)

table_seven = sut.make_a_table(axs, data.values,  colLabels=data.columns, colWidths=[.6, .13, .13, .13],a_color=a_color)
table_seven.get_celld()[(0,0)].get_text().set_text(" ")
table_seven.set_fontsize(14)

plt.tight_layout()
plt.show()

1.8.2. Organizations:¶

Precious Plastic Léman
Association pour la Sauvegarde du Léman
Geneva international School
Solid waste engineering: École polytechnique fédéral Lausanne
Summit foundation
University of Raperswill
Hackuarium

1.8.3. Municipalities, lakes and rivers with surveys¶

lakes = dfBeaches.loc[(dfBeaches.index.isin(fd.location.unique()))&(dfBeaches.water == "l")]["water_name"].unique()
rivers = dfBeaches.loc[(dfBeaches.index.isin(fd.location.unique()))&(dfBeaches.water == "r")]["water_name"].unique()

# gather the municpalities and the population:
fd_pop_map = dfBeaches.loc[fd.location.unique()][["city","population"]].copy()
fd_pop_map.drop_duplicates(inplace=True)
fd_pop_map.set_index("city", drop=True, inplace=True)
munis_joined = ", ".join(sorted(fd_pop_map.index))

muni_string = F"""**The municipalities in this report:**\n\n >{munis_joined}
"""
md(muni_string)

The municipalities in this report:

Aarau, Allaman, Ascona, Beatenberg, Bellinzona, Bern, Biel/Bienne, Boudry, Bourg-en-Lavaux, Brienz (BE), Brissago, Brugg, Brügg, Burgdorf, Bönigen, Cheyres-Châbles, Cudrefin, Dietikon, Erlach, Estavayer, Freienbach, Gals, Gambarogno, Gebenstorf, Genève, Gland, Glarus Nord, Grandson, Hauterive (NE), Hünenberg, Kallnach, Köniz, Küsnacht (ZH), La Tour-de-Peilz, Lausanne, Lavey-Morcles, Le Landeron, Leuk, Ligerz, Locarno, Lugano, Luterbach, Lüscherz, Merenschwand, Minusio, Montreux, Neuchâtel, Nidau, Port, Préverenges, Quarten, Rapperswil-Jona, Richterswil, Riddes, Rubigen, Saint-Gingolph, Saint-Sulpice (VD), Salgesch, Schmerikon, Sion, Solothurn, Spiez, Stäfa, Thun, Tolochenaz, Unterengstringen, Unterseen, Versoix, Vevey, Vinelz, Walenstadt, Walperswil, Weesen, Weggis, Yverdon-les-Bains, Zug, Zürich

lakes_joined = ", ".join(sorted(lakes))

lake_string = F"""**The lakes in this report:**\n\n >{lakes_joined}
"""
md(lake_string)

The lakes in this report:

Bielersee, Brienzersee, Lac Léman, Lago Maggiore, Lago di Lugano, Neuenburgersee, Quatre Cantons, Thunersee, Walensee, Zugersee, Zurichsee

The rivers in this report:

Aare, Aare|Nidau-Büren-Kanal, Cassarate, Dorfbach, Emme, Escherkanal, Jona, La Thièle, Limmat, Linthkanal, Maggia, Reuss, Rhône, Schüss, Seez, Sihl, Ticino

# summary statistics:
# number of samples
fd_n_samps = len(dt_all)
# quantity
fd_n_obj = fd.quantity.sum()
# number of locations
fd_n_locs = fd.location.nunique()
# number of cities
fd_n_munis = len(fd_pop_map.index)
# populations
fd_effected_population = fd_pop_map.sum()
# list of locations
fd_locs = fd.location.unique()
# list of survey keys
fd_samps = fd.loc_date.unique()

obj_string = "{:,}".format(fd_n_obj)
surv_string = "{:,}".format(fd_n_samps)
pop_string = "{:,}".format(int(fd_effected_population[0]))

date_quantity_context = F"For the period between {start_date[:-3]} and {end_date[:-3]}, {obj_string } objects were removed and identified in the course of {surv_string} surveys."
geo_context = F"Those surveys were conducted at {fd_n_locs} different locations."
admin_context = F"There are {fd_n_munis} different municipalities represented in these results with a combined population of approximately {pop_string}."
md(F"{date_quantity_context} {geo_context } {admin_context}")

For the period between 2020-03 and 2021-05, 54,744 objects were removed and identified in the course of 386 surveys. Those surveys were conducted at 143 different locations. There are 77 different municipalities represented in these results with a combined population of approximately 1,735,991.

The survey locations

# display the survey locations
pd.set_option("display.max_rows", None)

disp_columns = ["latitude", "longitude", "city"]
disp_beaches = dfBeaches.loc[fd_locs][disp_columns]
disp_beaches.reset_index(inplace=True)
disp_beaches.rename(columns={"slug":"location"}, inplace=True)
disp_beaches.set_index("location", inplace=True, drop=True)

disp_beaches

	latitude	longitude	city
location
maladaire	46.446296	6.876960	La Tour-de-Peilz
preverenges	46.512690	6.527657	Préverenges
caprino	45.987963	8.986241	Lugano
foce-del-cassarate	46.002411	8.961477	Lugano
lido	46.002004	8.962156	Lugano
lugano-centro	46.002627	8.950724	Lugano
spiaggia-parco-ciani	46.002510	8.960820	Lugano
via-brunari-spiaggia	46.202350	9.016910	Bellinzona
golene-gudo	46.170655	8.946657	Bellinzona
isole-di-brissago-porto	46.132760	8.735030	Brissago
magadino-lido	46.149349	8.855663	Gambarogno
rivapiana	46.172079	8.813255	Minusio
via-mirasole-spiaggia	46.199530	9.014930	Bellinzona
aare-port	47.116170	7.269550	Port
limmat_dietikon_keiserp	47.407989	8.409540	Dietikon
schutzenmatte	47.057666	7.634001	Burgdorf
zugerseecholler_cham_blarerm	47.178216	8.480013	Zug
la-petite-plage	46.785054	6.656877	Yverdon-les-Bains
vidy-ruines	46.516221	6.596279	Lausanne
baby-plage-geneva	46.208558	6.162923	Genève
grand-clos	46.387746	6.843686	Saint-Gingolph
weissenau-neuhaus	46.676583	7.817528	Unterseen
evole-plage	46.989477	6.923920	Neuchâtel
oberi-chlihochstetten	46.896025	7.532114	Rubigen
plage-de-serriere	46.984850	6.913450	Neuchâtel
vierwaldstattersee_weggis_schoberls_1	47.044532	8.418569	Weggis
vierwaldstattersee_weggis_schoberls_2	47.043953	8.417308	Weggis
vierwaldstattersee_weggis_schoberls_3	47.029957	8.410801	Weggis
mullermatte	47.133339	7.227907	Biel/Bienne
limmat_unterengstringen_oggierbuhrer	47.409400	8.446933	Unterengstringen
limmat_zuerich_wagnerc	47.403496	8.477770	Zürich
limmat_zurich_mortensena_meiera	47.400252	8.485411	Zürich
quai-maria-belgia	46.460156	6.836718	Vevey
sihl_zuerich_eggerskoehlingera	47.381898	8.538328	Zürich
bielersee_vinelz_fankhausers	47.038398	7.108311	Vinelz
erlach-camping-strand	47.047159	7.097854	Erlach
anarchy-beach	46.447216	6.859612	La Tour-de-Peilz
gasi-strand	47.128442	9.110831	Weesen
rastplatz-stampf	47.215177	8.844286	Rapperswil-Jona
zuerichsee_richterswil_benkoem_2	47.217646	8.698713	Richterswil
sentiero-giro-del-golf-spiaggia	46.145770	8.790430	Ascona
vira-gambarogno	46.143343	8.838759	Gambarogno
canale-saleggi	46.200625	9.015853	Bellinzona
gummligrabbe	46.989290	7.250730	Kallnach
mannewil	46.996382	7.239024	Kallnach
lavey-les-bains-2	46.207726	7.011685	Lavey-Morcles
schusspark-strand	47.146500	7.268620	Biel/Bienne
walensee_walenstadt_wysse	47.121828	9.299552	Walenstadt
pfafikon-bad	47.206766	8.774182	Freienbach
zurichsee_kusnachterhorn_thirkell-whitej	47.317685	8.576860	Küsnacht (ZH)
plage-de-cheyres	46.818689	6.782256	Cheyres-Châbles
leuk-mattenstrasse	46.314754	7.622521	Leuk
thun-strandbad	46.739939	7.633520	Thun
zurichsee_wollishofen_langendorfm	47.345890	8.536155	Zürich
pont-sous-terre	46.202960	6.131577	Genève
oben-am-see	46.744451	8.049921	Brienz (BE)
thunersee_spiez_meierd_1	46.704437	7.657882	Spiez
cully-plage	46.488887	6.741396	Bourg-en-Lavaux
preverenges-le-sout	46.508905	6.534526	Préverenges
la-pecherie	46.463919	6.385732	Allaman
villa-barton	46.222350	6.152500	Genève
untertenzen	47.115260	9.254780	Quarten
zurcher-strand	47.364200	8.537420	Zürich
oyonne	46.456682	6.852262	La Tour-de-Peilz
lavey-la-source	46.200804	7.021866	Lavey-Morcles
lavey-les-bains	46.205159	7.012722	Lavey-Morcles
spackmatt	47.223749	7.576711	Luterbach
luscherz-plage	47.047955	7.151242	Lüscherz
strandboden-biel	47.132510	7.233142	Biel/Bienne
jona-river-rastplatz	47.216100	8.844430	Rapperswil-Jona
reuss_hunenberg_eberhardy	47.232976	8.401012	Hünenberg
reuss_ottenbach_schoenenbergerl	47.278354	8.394224	Merenschwand
zuerichsee_staefa_hennm	47.234643	8.769881	Stäfa
signalpain	46.786200	6.647360	Yverdon-les-Bains
baby-plage-ii-geneve	46.208940	6.164330	Genève
pointe-dareuse	46.946190	6.870970	Boudry
les-glariers	46.176736	7.228925	Riddes
nidau-strand	47.127196	7.232613	Nidau
tiger-duck-beach	46.518256	6.582546	Saint-Sulpice (VD)
linthdammweg	47.131790	9.091910	Weesen
mols-rocks	47.114343	9.288174	Quarten
seeflechsen	47.130223	9.103400	Glarus Nord
seemuhlestrasse-strand	47.128640	9.295100	Walenstadt
muhlehorn-dorf	47.118448	9.172124	Glarus Nord
zuerichsee_waedenswil_colomboc_1	47.219547	8.691961	Richterswil
camp-des-peches	47.052812	7.074053	Le Landeron
delta-park	46.720078	7.635304	Spiez
le-pierrier	46.439727	6.888968	Montreux
pecos-plage	46.803590	6.636650	Grandson
beich	47.048495	7.200528	Walperswil
aare_suhrespitz_badert	47.405669	8.066018	Aarau
tschilljus	46.304399	7.580262	Salgesch
sundbach-strand	46.684386	7.794768	Beatenberg
wycheley	46.740370	8.048640	Brienz (BE)
aare_koniz_hoppej	46.934588	7.458170	Köniz
camping-gwatt-strand	46.727140	7.629620	Thun
bern-tiergarten	46.933157	7.452004	Bern
boiron	46.491030	6.480162	Tolochenaz
rocky-plage	46.209737	6.164952	Genève
lacleman_gland_lecoanets	46.402811	6.281959	Gland
route-13	46.162521	8.782579	Locarno
murg-bad	47.115307	9.215691	Quarten
schmerikon-bahnhof-strand	47.224780	8.944180	Schmerikon
aabach	47.220989	8.940365	Schmerikon
la-thiele_le-mujon_confluence	46.775340	6.630900	Yverdon-les-Bains
nouvelle-plage	46.856646	6.848428	Estavayer
baye-de-montreux-g	46.430834	6.908778	Montreux
les-vieux-ronquoz	46.222049	7.361664	Sion
sihl_zuerich_eichenbergerd	47.339588	8.516697	Zürich
versoix	46.289194	6.170569	Versoix
augustmutzenbergstrandweg	46.686640	7.689760	Spiez
parc-des-pierrettes	46.515215	6.575531	Saint-Sulpice (VD)
plage-de-st-sulpice	46.513265	6.570977	Saint-Sulpice (VD)
aare_bern_gerberm	46.989363	7.452098	Bern
brugg-be-buren-bsg-standort	47.122220	7.281410	Brügg
kusnachter-dorfbach-tobelweg-1-4	47.317770	8.588258	Küsnacht (ZH)
ruisseau-de-la-croix-plage	46.813920	6.774700	Cheyres-Châbles
ligerz-strand	47.083979	7.135894	Ligerz
hafeli	46.690283	7.898592	Bönigen
aare-solothurn-lido-strand	47.196949	7.521643	Solothurn
bern-fahrstrasse	46.975866	7.444210	Bern
gals-reserve	47.046272	7.085007	Gals
ascona-traghetto-spiaggia	46.153882	8.768480	Ascona
pacha-mama-gerra	46.123473	8.783508	Gambarogno
gerra-gambarogno	46.123366	8.785786	Gambarogno
san-nazzaro-gambarogno	46.130443	8.798657	Gambarogno
hauterive-petite-plage	47.010797	6.980304	Hauterive (NE)
flibach-river-right-bank	47.133742	9.105461	Weesen
aare-limmatspitz	47.501060	8.237371	Gebenstorf
aare_brugg_buchie	47.494855	8.236558	Brugg
linth_route9brucke	47.125730	9.117340	Glarus Nord
seez_spennwiesenbrucke	47.114948	9.310123	Walenstadt
bain-des-dames	46.450507	6.858092	La Tour-de-Peilz
luscherz-two	47.047519	7.152829	Lüscherz
impromptu_cudrefin	46.964496	7.027936	Cudrefin
vira-spiaggia-left-of-river	46.142900	8.838407	Gambarogno
linth_mollis	47.131370	9.094768	Glarus Nord
seez	47.119830	9.300251	Walenstadt
tolochenaz	46.497509	6.482875	Tolochenaz
limmat_zuerich_suterdglauserp	47.394539	8.513443	Zürich
aare_post	47.116665	7.271953	Port
aare_bern_scheurerk	46.970967	7.452586	Bern
strandbeiz	47.215862	8.843098	Rapperswil-Jona

The inventory

complete_inventory = code_totals[code_totals.quantity>0][["item", "groupname", "quantity", "% of total","fail rate"]]
complete_inventory.sort_values(by="quantity", ascending=False)

	item	groupname	quantity	% of total	fail rate
code
G27	Cigarette filters	tobacco	8485	15.50	87
Gfrags	Fragmented plastics	plastic pieces	7400	13.52	86
Gfoam	Expanded polystyrene	infrastructure	5563	10.16	68
G30	Food wrappers; candy, snacks	food and drink	3325	6.07	85
G67	Industrial sheeting	agriculture	2534	4.63	69
G200	Glass drink bottles, pieces	food and drink	2136	3.90	65
G112	Industrial pellets (nurdles)	micro plastics (< 5mm)	1968	3.59	30
G74	Insulation foams	infrastructure	1702	3.11	53
G95	Cotton bud/swab sticks	waste water	1406	2.57	50
G117	Expanded foams < 5mm	micro plastics (< 5mm)	1209	2.21	25
G89	Plastic construction waste	infrastructure	992	1.81	52
G941	Packaging films nonfood or unknown	packaging non food	894	1.63	38
G178	Metal bottle caps and lids	food and drink	700	1.28	52
G25	Tobacco; plastic packaging, containers	tobacco	649	1.19	46
G21	Drink lids	food and drink	623	1.14	37
G106	Plastic fragments angular <5mm	micro plastics (< 5mm)	592	1.08	20
G98	Diapers - wipes	waste water	588	1.07	26
G24	Plastic lid rings	food and drink	541	0.99	42
G177	Foil wrappers, aluminum foil	food and drink	521	0.95	47
G10	Food containers single use foamed or plastic	food and drink	448	0.82	31
G23	Lids unidentified	packaging non food	423	0.77	35
G156	Paper fragments	packaging non food	420	0.77	32
G35	Straws and stirrers	food and drink	419	0.77	42
G70	Shotgun cartridges	recreation	391	0.71	21
G31	Lollypop sticks	food and drink	381	0.70	37
G73	Foamed items & pieces (non packaging/insulatio...	recreation	335	0.61	23
G33	Lids for togo drinks plastic	food and drink	328	0.60	34
G32	Toys and party favors	recreation	320	0.58	38
G904	Plastic fireworks	recreation	301	0.55	28
G3	Plastic bags, carier bags	packaging non food	285	0.52	18
G22	Lids for chemicals, detergents (non-food)	infrastructure	257	0.47	19
G211	Swabs, bandaging, medical	personal items	256	0.47	31
G922	Labels, bar codes	packaging non food	252	0.46	27
G66	Straps/bands; hard, plastic package fastener	infrastructure	248	0.45	32
G100	Medical; containers/tubes/ packaging	waste water	244	0.45	28
G923	Tissue, toilet paper, napkins, paper towels	personal items	241	0.44	25
G103	Plastic fragments rounded <5mm	micro plastics (< 5mm)	238	0.43	3
G921	Ceramic tile and pieces	infrastructure	231	0.42	13
G96	Sanitary-pads/tampons, applicators	waste water	215	0.39	18
G50	String < 1cm	recreation	205	0.37	29
G153	Cups, food containers, wrappers (paper)	food and drink	201	0.37	19
G125	Balloons and balloon sticks	recreation	170	0.31	20
G91	Biomass holder	waste water	162	0.30	19
G159	Corks	food and drink	162	0.30	21
G38	Coverings; plastic packaging, sheeting for pro...	unclassified	160	0.29	4
G940	Foamed EVA for crafts and sports	recreation	158	0.29	7
G208	Glass or ceramic fragments > 2.5 cm	unclassified	145	0.26	10
G186	Industrial scrap	infrastructure	141	0.26	15
G213	Paraffin wax	recreation	138	0.25	13
G165	Ice cream sticks, toothpicks, chopsticks	food and drink	126	0.23	15
G204	Bricks, pipes not plastic	infrastructure	125	0.23	9
G155	Fireworks paper tubes and fragments	recreation	124	0.23	6
G93	Cable ties; steggel, zip, zap straps	infrastructure	121	0.22	16
G149	Paper packaging	packaging non food	121	0.22	8
G152	Cigarette boxes, tobacco related paper/cardboard	tobacco	120	0.22	13
G90	Plastic flower pots	agriculture	119	0.22	14
G137	Clothing, towels & rags	personal items	118	0.22	13
G908	Tape; electrical, insulating	infrastructure	117	0.21	15
G203	Tableware ceramic or glass, cups, plates, pieces	food and drink	115	0.21	11
G936	Sheeting ag. greenhouse film	agriculture	110	0.20	12
G34	Cutlery, plates and trays	food and drink	109	0.20	16
G905	Hair clip, hair ties, personal accessories pl...	personal items	105	0.19	18
G198	Other metal pieces < 50cm	infrastructure	104	0.19	16
G914	Paperclips, clothespins, plastic utility items	personal items	99	0.18	12
G131	Rubber bands	personal items	94	0.17	16
G191	Wire and mesh	agriculture	90	0.16	12
G28	Pens, lids, mechanical pencils etc.	personal items	88	0.16	13
G115	Foamed plastic <5mm	micro plastics (< 5mm)	88	0.16	4
G124	Other plastic or foam products	unclassified	87	0.16	8
G87	Tape, masking/duct/packing	infrastructure	87	0.16	14
G148	Cardboard (boxes and fragments)	packaging non food	85	0.16	8
G928	Ribbons and bows	personal items	84	0.15	8
G26	Cigarette lighters	tobacco	83	0.15	14
G146	Paper, cardboard	packaging non food	82	0.15	6
G175	Cans, beverage	food and drink	76	0.14	11
G157	Paper	packaging non food	66	0.12	6
G7	Drink bottles < = 0.5L	food and drink	65	0.12	9
G4	Small plastic bags; freezer, zip-lock etc.	packaging non food	64	0.12	8
G142	Rope , string or nets	recreation	62	0.11	10
G135	Clothes, footware, headware, gloves	personal items	61	0.11	9
G105	Plastic fragments subangular <5mm	micro plastics (< 5mm)	59	0.11	2
G927	String trimmer line, used to cut grass, weeds,...	infrastructure	57	0.10	8
G201	Jars, includes pieces	food and drink	55	0.10	7
G942	Plastic shavings from lathes, CNC machining	unclassified	53	0.10	8
G194	Cables, metal wire(s) often inside rubber or p...	infrastructure	52	0.09	10
G939	Flowers, plants plastic	personal items	49	0.09	6
G20	Caps and lids	packaging non food	48	0.09	6
G65	Buckets	agriculture	48	0.09	6
G943	Fencing agriculture, plastic	agriculture	47	0.09	5
G144	Tampons	waste water	46	0.08	3
G48	Rope, synthetic	recreation	46	0.08	8
G134	Other rubber	unclassified	45	0.08	8
G210	Other glass/ceramic	unclassified	43	0.08	3
G170	Wood (processed)	agriculture	43	0.08	6
G937	Pheromone baits for vineyards	agriculture	38	0.07	4
G101	Dog feces bag	personal items	37	0.07	8
G917	Terracotta balls	unclassified	36	0.07	4
G59	Fishing line monofilament (angling)	recreation	35	0.06	6
G944	Pellet mass from injection molding	unclassified	34	0.06	0
G2	Bags	packaging non food	32	0.06	4
G12	Cosmetics, non-beach use personal care containers	personal items	31	0.06	5
G207	Octopus pots	unclassified	30	0.05	0
G126	Balls	recreation	30	0.05	5
G901	Mask medical, synthetic	personal items	29	0.05	6
G918	Safety pins, paper clips, small metal utility ...	personal items	28	0.05	5
G931	Tape-caution for barrier, police, construction...	infrastructure	28	0.05	3
G99	Syringes - needles	personal items	28	0.05	6
G919	Nails, screws, bolts etc.	infrastructure	27	0.05	3
G158	Other paper items	packaging non food	27	0.05	2
G167	Matches or fireworks	recreation	26	0.05	2
G933	Bags, cases for accessories; glasses, electron...	personal items	26	0.05	4
G6	Bottles and containers, plastic non food/drink	packaging non food	25	0.05	2
G154	Newspapers or magazines	personal items	25	0.05	2
G161	Processed timber	agriculture	24	0.04	4
G182	Fishing; hooks, weights, lures, sinkers etc.	recreation	23	0.04	3
G8	Drink bottles > 0.5L	food and drink	23	0.04	3
G145	Other textiles	personal items	22	0.04	4
G114	Films <5mm	micro plastics (< 5mm)	22	0.04	1
G43	Tags fishing or industry (security tags, seals)	recreation	21	0.04	2
G930	Foam earplugs	personal items	20	0.04	4
G133	Condoms incl. packaging	waste water	20	0.04	4
G68	Fiberglass fragments	infrastructure	20	0.04	4
G929	Electronics and pieces; sensors, headsets etc.	personal items	20	0.04	4
G176	Cans, food	food and drink	19	0.03	3
G925	Packets: desiccant/ moisture absorbers, plasti...	packaging non food	19	0.03	4
G938	Toothpicks, dental floss plastic	food and drink	18	0.03	4
G926	Chewing gum, often contains plastics	food and drink	18	0.03	3
G36	Bags/sacks heavy duty plastic for 25 Kg or mor...	agriculture	18	0.03	2
G118	Small industrial spheres <5mm	micro plastics (< 5mm)	17	0.03	2
G123	Polyurethane granules < 5mm	micro plastics (< 5mm)	17	0.03	0
G11	Cosmetics for the beach, e.g. sunblock	recreation	17	0.03	3
G64	Fenders	unclassified	16	0.03	1
G119	Sheetlike user plastic (>1mm)	micro plastics (< 5mm)	16	0.03	1
G49	Rope > 1cm	recreation	14	0.03	2
G113	Filaments <5mm	micro plastics (< 5mm)	14	0.03	1
G197	Other metal	infrastructure	13	0.02	3
G199	Other metal pieces > 50cm	infrastructure	13	0.02	1
G29	Combs, brushes and sunglasses	personal items	13	0.02	3
G195	Batteries - household	personal items	12	0.02	2
G128	Tires and belts	unclassified	12	0.02	2
G916	Pencils and pieces	personal items	12	0.02	2
G136	Shoes	personal items	11	0.02	2
G37	Mesh bags	personal items	11	0.02	2
G906	coffee capsules aluminum	food and drink	10	0.02	1
G900	Gloves latex personal protective equipment	personal items	10	0.02	2
G61	Other fishing related	recreation	9	0.02	2
G913	Pacifier	personal items	8	0.01	2
G104	Plastic fragments subrounded <5mm	micro plastics (< 5mm)	7	0.01	1
G40	Gloves household/gardening	personal items	7	0.01	1
G122	Plastic fragments ( >1mm)	micro plastics (< 5mm)	7	0.01	0
G19	Car parts	unclassified	7	0.01	1
G129	Inner tubes and rubber sheets	unclassified	7	0.01	1
G17	Injection gun cartridge	infrastructure	7	0.01	1
G141	Carpet	unclassified	6	0.01	1
G5	Generic plastic bags	packaging non food	6	0.01	1
G181	Tableware metal; cups, cutlery etc.	food and drink	6	0.01	1
G171	Other wood < 50cm	agriculture	6	0.01	1
G97	Toilet fresheners	waste water	6	0.01	1
G915	Reflectors, plastic mobility items	personal items	6	0.01	1
G151	Cartons, Tetrapacks	food and drink	6	0.01	1
G147	Paper bags	packaging non food	6	0.01	1
G107	Cylindrical pellets < 5mm	micro plastics (< 5mm)	6	0.01	1
G903	Hand sanitizer containers & packets	personal items	5	0.01	1
G92	Bait containers	recreation	5	0.01	1
G71	Shoes sandals	personal items	5	0.01	1
G138	Shoes and sandals	personal items	5	0.01	1
G108	disk pellets <5mm	micro plastics (< 5mm)	5	0.01	1
G172	Other wood > 50cm	agriculture	4	0.01	1
G945	Razor blades	personal items	4	0.01	1
G53	Nets and pieces < 50cm	recreation	4	0.01	0
G52	Nets and pieces	recreation	4	0.01	1
G907	coffee capsules plastic	food and drink	4	0.01	1
G39	Gloves	personal items	4	0.01	0
G111	Spheruloid pellets < 5mm	micro plastics (< 5mm)	4	0.01	1
G62	Floats for nets	unclassified	4	0.01	0
G13	Bottles, containers, drums to transport, store...	agriculture	4	0.01	0
G202	Light bulbs	unclassified	3	0.01	0
G174	Aerosol spray cans	infrastructure	3	0.01	0
G41	Glove industrial/professional	agriculture	3	0.01	0
G934	Sandbag, plastic for flood, erosion control etc..	agriculture	3	0.01	0
G143	Sails and canvas	recreation	3	0.01	0
G63	Buoys	recreation	3	0.01	0
G140	Bags, burlap, hessian, jute or hemp	agriculture	3	0.01	0
G179	Disposable BBQs	food and drink	3	0.01	0
G116	Granules <5mm	micro plastics (< 5mm)	3	0.01	0
G109	Flat pellets <5mm	micro plastics (< 5mm)	3	0.01	0
G14	Engine oil bottles	unclassified	3	0.01	0
G139	Backpacks	personal items	3	0.01	0
G902	Mask medical, cloth	personal items	3	0.01	0
G102	Flip-flops	personal items	3	0.01	0
G150	Milk cartons, tetrapack	food and drink	3	0.01	0
G999	No objects found on this survey	unclassified	2	0.00	0
G55	Fishing line (entangled)	recreation	2	0.00	0
G935	Walking stick pads and pieces, often elastomer...	personal items	2	0.00	0
G932	Bio-beads, micro plastic for wastewater treatm...	waste water	2	0.00	0
G173	Other	unclassified	2	0.00	0
G185	Middle size containers	unclassified	2	0.00	0
G188	Other cans < 4 L	infrastructure	2	0.00	0
G190	Paint cans	infrastructure	2	0.00	0
G214	Oil/tar	infrastructure	2	0.00	0
G132	Bobbers (fishing)	recreation	2	0.00	0
G166	Paint brushes	infrastructure	1	0.00	0
G94	Table cloth	recreation	1	0.00	0
G84	CD or CD box	personal items	1	0.00	0
G196	Large metallic objects	unclassified	1	0.00	0
G9	Cleaner, chemical bottles & containers	infrastructure	1	0.00	0
G183	Fish hook remains	recreation	1	0.00	0
G60	Light sticks	recreation	1	0.00	0
G51	Fishing net	recreation	1	0.00	0
G193	car parts and batteries	unclassified	1	0.00	0

Lakes and rivers

Contents