11. Thunersee / Brienzersee¶

Below: Map of survey locations March 2020 - May 2021. Marker diameter = the mean survey result in pieces of litter per 100 meters (p/100m).

11.1. Sample locations¶

# this is the data before the expanded foams and fragmented plastics are aggregated to Gfrags and Gfoams
before_agg = pd.read_csv("resources/checked_before_agg_sdata_eos_2020_21.csv")

# this is the aggregated survey data that is being used
# a_data is all the data in the survey period
a_data = pd.read_csv(F"resources/checked_sdata_eos_2020_21.csv")
a_data["date"] = pd.to_datetime(a_data.date)

a_data.rename(columns={"% to agg":"% ag", "% to recreation": "% recreation", "% to woods":"% woods", "% to buildings":"% buildings"}, inplace=True)
luse_exp = ["% buildings", "% recreation", "% ag", "% woods", "streets km", "intersects"]

fd = sut.feature_data(a_data, this_feature["level"], these_features=lakes_of_interest)

# 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)    

# daily survey totals
dt_all = fd.groupby(["loc_date","location",this_level, "date"], as_index=False).agg(agg_pcs_quantity )

# the materials table
fd_mat_totals = sut.the_ratio_object_to_total(code_totals)

# summary statistics, nsamples, nmunicipalities, names of citys, population
t = sut.make_table_values(fd, col_nunique=["location", "loc_date", "city"], col_sum=["quantity"], col_median=[])

# make a map to the population values for each survey location/city
fd_pop_map = dfBeaches.loc[fd.location.unique()][["city", "population"]].copy()
fd_pop_map.drop_duplicates(inplace=True)

# update t with the population data
t.update(sut.make_table_values(fd_pop_map, col_nunique=["city"], col_sum=["population"], col_median=[]))

# update t with the list of locations from fd
t.update({"locations":fd.location.unique()})

# join the strings into comma separated list
obj_string = "{:,}".format(t["quantity"])
surv_string = "{:,}".format(t["loc_date"])
pop_string = "{:,}".format(int(t["population"]))

# make strings
date_quantity_context = F"For the period between {start_date[:-3]} and {end_date[:-3]}, a total of {obj_string } objects were removed and identified over the course of {surv_string} surveys."
geo_context = F"The {this_feature['name']} results include {t['location']} different locations in {t['city']} different municipalities with a combined population of approximately {pop_string}."
munis_joined = ", ".join(sorted(fd_pop_map["city"]))

# put that all together:
lake_string = F"""
{date_quantity_context} {geo_context }

*{this_feature["name"]} municipalities:*\n\n>{munis_joined}
"""
md(lake_string)

For the period between 2020-03 and 2021-05, a total of 3,764 objects were removed and identified over the course of 36 surveys. The Thunersee / Brienzersee results include 10 different locations in 6 different municipalities with a combined population of approximately 69,120.

Thunersee / Brienzersee municipalities:

Beatenberg, Brienz (BE), Bönigen, Spiez, Thun, Unterseen

11.1.1. Cumulative totals by municipality¶

dims_parameters = dict(this_level=this_level, 
                       locations=fd.location.unique(), 
                       start_end=start_end, 
                       city_map=city_map, 
                       agg_dims=agg_dims)

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

# a map of total quantity for each component
q_map = fd.groupby(this_level).quantity.sum()

# assgin the quantity and sample numbers to the dims table
for name in dims_table.index:
    dims_table.loc[name, "samples"] = fd[fd[this_level] == name].loc_date.nunique()
    dims_table.loc[name, "quantity"] = q_map[name]

# make a total column for the top feature data, the sum of all the components
dims_table.loc[this_feature["name"]]= dims_table.sum(numeric_only=True, axis=0)

# change the column names to user friendly syle
dims_table.rename(columns=sut.update_dictionary(sut.dims_table_columns), inplace=True)

# format the numercial data
dims_table.sort_values(by=["items"], ascending=False, inplace=True)

# change to formatted 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))

# figure caption
agg_caption = F"""
*__Below:__ The cumulative weights and measures for {this_feature["name"]} and municipalities*
"""
md(agg_caption)

Below: The cumulative weights and measures for Thunersee / Brienzersee and municipalities

# make table
data = dims_table.reset_index()
colLabels = data.columns

fig, ax = plt.subplots(figsize=(len(colLabels)*2,len(data)*.7))

sut.hide_spines_ticks_grids(ax)
table_one = sut.make_a_table(ax, data.values, colLabels=colLabels, colWidths=[.28, *[.12]*6], a_color="saddlebrown", )
table_one.get_celld()[(0,0)].get_text().set_text(" ")

plt.show()
plt.tight_layout()
plt.close()

11.1.2. Distribution of Survey results¶

# the feature surveys to chart
fd_dindex = dt_all.set_index("date")

# all the other surveys
ots = dict(level_to_exclude=this_feature["level"], components_to_exclude=fd[this_feature["level"]].unique())
dts_date = sut.the_other_surveys(a_data, **ots)

# group the outher surveys by date and total pcs_m
ots_params = dict(agg_this = {unit_label:"sum"}, these_columns = ["loc_date","date"])
dts_date = sut.group_these_columns(dts_date, **ots_params)

# get the monthly or quarterly results for the feature
resample_plot, rate = sut.quarterly_or_monthly_values(fd_dindex , this_feature["name"], vals=unit_label, quarterly=["ticino"])    

# scale the chart as needed to accomodate for extreme values
y_lim = 98
y_limit = np.percentile(dts_date[unit_label], y_lim)

# label for the chart that alerts to the scale
not_included = F"Values greater than {round(y_limit, 1)}{unit_label} not shown."

# figure caption
chart_notes = F"""
*__Left:__ {this_feature['name']}, {start_date[:7]} through {end_date[:7]}, n={t["loc_date"]}. {not_included} __Right:__ {this_feature['name']} empirical cumulative distribution of survey results.*
"""
md(chart_notes )

Left: Thunersee / Brienzersee, 2020-03 through 2021-05, n=36. Values greater than 2375.0p/100m not shown. Right: Thunersee / Brienzersee empirical cumulative distribution of survey results.

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

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

# the survey totals by day
ax = axs[0]

# feature surveys
sns.scatterplot(data=dts_date, x=dts_date.index, y=unit_label, label=top, color="black", alpha=0.4,  ax=ax)
# all other surveys
sns.scatterplot(data=fd_dindex, x=fd_dindex.index, y=unit_label, label=this_feature["name"], color="red", s=34, ec="white", ax=ax)

# monthly or quaterly plot
sns.lineplot(data=resample_plot, x=resample_plot.index, y=resample_plot, label=F"{this_feature['name']}: {rate} median", color="magenta", ax=ax)

ax.set_ylim(0,y_limit )
ax.set_ylabel(unit_label, **ck.xlab_k14)

ax.set_xlabel("")
ax.xaxis.set_minor_locator(days)
ax.xaxis.set_major_formatter(months_fmt)
ax.legend()

# the cumlative distributions:
axtwo = axs[1]

# the feature of interest
feature_ecd = ECDF(dt_all[unit_label].values)    
sns.lineplot(x=feature_ecd.x, y=feature_ecd.y, color="darkblue", ax=axtwo, label=this_feature["name"])

# the other features
other_features = ECDF(dts_date[unit_label].values)
sns.lineplot(x=other_features.x, y=other_features.y, color="magenta", label=top, linewidth=1, ax=axtwo)

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

plt.tight_layout()
plt.show()

11.1.3. Summary data and material types¶

Left: Thunersee / Brienzersee summary of survey totals. Right: Thunersee / Brienzersee material type and percent of total

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

# 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.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 = [this_feature["name"], "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(" ")

# 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(" ")

plt.tight_layout()
plt.subplots_adjust(wspace=0.2)
plt.show()

11.2. 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()

# figure caption
rb_string = F"""
*__Below:__ {this_feature['name']} most common objects: fail rate >/= {a_fail_rate}%  and/or top ten by quantity. Combined, the most abundant objects represent {int(m_common_percent_of_total*100)}% of all objects found. 
Note : {unit_label} = median survey value.*
"""
md(rb_string)

Below: Thunersee / Brienzersee most common objects: fail rate >/= 50% and/or top ten by quantity. Combined, the most abundant objects represent 72% of all objects found. Note : p/100m = median survey value.

# 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"{round(x,1)}")

# 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

colWidths=[.52, .12, .12, .12, .12]
bbox=[0,0,1,1],
kwargs = {"loc":"lower center"}
colLabels=list(cols_to_use.values())
figsize=(len(colLabels)*2,len(all_survey_areas)*.7)

fig, axs = plt.subplots(figsize=figsize)

sut.hide_spines_ticks_grids(axs)

table_four = sut.make_a_table(axs, all_survey_areas, colLabels=colLabels, colWidths=colWidths, a_color="saddlebrown")
table_four.get_celld()[(0,0)].get_text().set_text(" ")
plt.tight_layout()
plt.show()

11.2.1. Most common objects results by municipality¶

Below: Thunersee / Brienzersee most common objects: median p/100m

# aggregated survey totals for the most common codes for all the water features
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()

# 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", this_level, unit_label]].pivot(columns=this_level, index="item")

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

## the aggregated totals for the feature data
c = sut.aggregate_to_group_name(fd[fd.code.isin(m_common.index)], column="code", name=this_feature["name"], val="med")
m_c_p[this_feature["name"]]= sut.change_series_index_labels(c, {x:code_description_map.loc[x] for x in c.index})

# the aggregated totals of the survey area
c = sut.aggregate_to_group_name(a_data[(a_data.river_bassin == this_bassin)&(a_data.code.isin(m_common.index))], column="code", name=top, val="med")
m_c_p[bassin_label] = sut.change_series_index_labels(c, {x:code_description_map.loc[x] for x in c.index})

# the aggregated totals of all the data
c = sut.aggregate_to_group_name(a_data[(a_data.code.isin(m_common.index))], column="code", name=top, val="med")
m_c_p[top] = sut.change_series_index_labels(c, {x:code_description_map.loc[x] for x in c.index})

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

sns.heatmap(m_c_p, 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="x")
axone.tick_params(labelsize=12, which="both", axis="y")

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

plt.show()
plt.close()

11.2.2. 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.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)

monthly_mc = F"""
*__Below:__ {this_feature['name']}, monthly average survey result {unit_label}. Detail of the most common objects*
"""
md(monthly_mc)

Below: Thunersee / Brienzersee, monthly average survey result p/100m. Detail of the most common objects

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

fig, ax = plt.subplots(figsize=(9,8))

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

# the monhtly survey average for all objects and locations
monthly_fd = fd.groupby(["loc_date", "date"], as_index=False).agg(agg_pcs_quantity)
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))]))

#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)

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

# 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 average")
handles = [handles[0], *handles[1:][::-1]]
    
plt.legend(handles=handles, labels=new_labels, bbox_to_anchor=(.5, -.05), loc="upper center",  ncol=2, fontsize=14)       
plt.tight_layout()
plt.show()

11.3. Utility of the objects found¶

The utility type is based on the utilization of the object prior to it being discarded or object description if the original use is undetermined. Identified objects are classified into one of 260 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.

Below: Thunersee / Brienzersee utility of objects found % of total by municipality. Fragmented objects with no clear identification remain classified by size:

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

# the total per water feature
cg_tq = cg_t.groupby(this_level).quantity.sum()

# get the fail rates for each group per survey
cg_t["fail"]=False
cg_t["fail"] = cg_t.quantity.where(lambda x: x == 0, True)

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

# 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")

# aggregated values for the lake
data_table[this_feature["name"]]= sut.aggregate_to_group_name(fd, unit_label=unit_label, column="groupname", name=bassin_label, val="pt")

# repeat for the survey area
data_table[bassin_label] = sut.aggregate_to_group_name(a_data[a_data.river_bassin == this_bassin], unit_label=unit_label, column="groupname", name=bassin_label, val="pt")

# repeat for all the data
data_table[top] = sut.aggregate_to_group_name(a_data, unit_label=unit_label, column="groupname", name=top, val="pt")

data = data_table

fig, ax = plt.subplots(figsize=(11,10))

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

axone.set_ylabel("")
axone.set_xlabel("")
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)

plt.show()

cg_medpcm = F"""
<br></br>
*__Below:__ {this_feature['name']} utility of objects found median {unit_label}. Fragmented objects with no clear identification remain classified by size:*
"""
md(cg_medpcm)

Below: Thunersee / Brienzersee utility of objects found median p/100m. Fragmented objects with no clear identification remain classified by size:

# median p/50m of all the water features
data_table = cg_t.pivot(columns=this_level, index="groupname", values=unit_label)

# aggregated values for the lake
data_table[this_feature["name"]]= sut.aggregate_to_group_name(fd, unit_label=unit_label, column="groupname", name=bassin_label, val="med")

# the survey area columns
data_table[bassin_label] = sut.aggregate_to_group_name(a_data[a_data.river_bassin == this_bassin], unit_label=unit_label, column="groupname", name=bassin_label, val="med")

# column for all the surveys
data_table[top] = sut.aggregate_to_group_name(a_data, unit_label=unit_label, column="groupname", name=top, val="med")

# merge with data_table
data = data_table

fig, ax = plt.subplots(figsize=(11,10))

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)

plt.show()

11.4. Annex¶

11.4.1. Fragmented foams and plastics by size¶

The table below contains the “Gfoam” and “Gfrags” components grouped for analysis. Objects labeled expanded foams are grouped as Gfoam and includes all expanded polystyrene foamed plastics > 0.5 cm. Plastic pieces and objects made of combined plastic and foamed plastic materials > 0.5 cm. are grouped for analysis as Gfrags.

Below: Thunersee / Brienzersee fragmented foams and plastics by size group.

# collect the data before aggregating foams for all locations in the survey area
# 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())

# aggregate all the codes by loc_date and get the total quantity and the median pcs/m
fd_frags_foams = before_agg[(before_agg.code.isin([*some_frag_plas, *some_foams]))&(before_agg.location.isin(t["locations"]))].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=(11,len(data)*.7))
sut.hide_spines_ticks_grids(axs)

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


plt.show()
plt.tight_layout()
plt.close()

11.4.2. The survey locations¶

# display the survey locations

disp_columns = ["latitude", "longitude", "city"]
disp_beaches = dfBeaches.loc[t["locations"]][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
weissenau-neuhaus	46.676583	7.817528	Unterseen
thun-strandbad	46.739939	7.633520	Thun
oben-am-see	46.744451	8.049921	Brienz (BE)
thunersee_spiez_meierd_1	46.704437	7.657882	Spiez
delta-park	46.720078	7.635304	Spiez
sundbach-strand	46.684386	7.794768	Beatenberg
wycheley	46.740370	8.048640	Brienz (BE)
camping-gwatt-strand	46.727140	7.629620	Thun
augustmutzenbergstrandweg	46.686640	7.689760	Spiez
hafeli	46.690283	7.898592	Bönigen

11.4.3. Inventory of items¶

pd.set_option("display.max_rows", None)

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	748	19.87	88
Gfrags	Fragmented plastics	plastic pieces	515	13.68	91
G67	Industrial sheeting	agriculture	352	9.35	83
Gfoam	Expanded polystyrene	infrastructure	281	7.47	83
G30	Food wrappers; candy, snacks	food and drink	228	6.06	77
G74	Insulation foams	infrastructure	158	4.20	66
G200	Glass drink bottles, pieces	food and drink	141	3.75	72
G112	Industrial pellets (nurdles)	micro plastics (< 5mm)	115	3.06	11
G941	Packaging films nonfood or unknown	packaging non food	83	2.21	44
G89	Plastic construction waste	infrastructure	65	1.73	50
G904	Plastic fireworks	recreation	39	1.04	38
G21	Drink lids	food and drink	38	1.01	38
G117	Expanded foams < 5mm	micro plastics (< 5mm)	36	0.96	33
G156	Paper fragments	packaging non food	35	0.93	22
G95	Cotton bud/swab sticks	waste water	35	0.93	52
G106	Plastic fragments angular <5mm	micro plastics (< 5mm)	34	0.90	25
G944	Pellet mass from injection molding	unclassified	34	0.90	8
G23	Lids unidentified	packaging non food	30	0.80	30
G24	Plastic lid rings	food and drink	28	0.74	36
G213	Paraffin wax	recreation	27	0.72	33
G177	Foil wrappers, aluminum foil	food and drink	26	0.69	36
G936	Sheeting ag. greenhouse film	agriculture	26	0.69	22
G50	String < 1cm	recreation	25	0.66	33
G922	Labels, bar codes	packaging non food	24	0.64	33
G153	Cups, food containers, wrappers (paper)	food and drink	21	0.56	11
G186	Industrial scrap	infrastructure	20	0.53	25
G25	Tobacco; plastic packaging, containers	tobacco	19	0.50	27
G201	Jars, includes pieces	food and drink	18	0.48	16
G10	Food containers single use foamed or plastic	food and drink	18	0.48	27
G211	Swabs, bandaging, medical	personal items	17	0.45	27
G35	Straws and stirrers	food and drink	17	0.45	30
G32	Toys and party favors	recreation	16	0.43	25
G87	Tape, masking/duct/packing	infrastructure	16	0.43	19
G908	Tape; electrical, insulating	infrastructure	16	0.43	22
G91	Biomass holder	waste water	16	0.43	27
G31	Lollypop sticks	food and drink	16	0.43	33
G943	Fencing agriculture, plastic	agriculture	14	0.37	8
G208	Glass or ceramic fragments > 2.5 cm	unclassified	14	0.37	11
G204	Bricks, pipes not plastic	infrastructure	14	0.37	16
G178	Metal bottle caps and lids	food and drink	14	0.37	25
G66	Straps/bands; hard, plastic package fastener	infrastructure	14	0.37	33
G927	String trimmer line, used to cut grass, weeds,...	infrastructure	13	0.35	16
G70	Shotgun cartridges	recreation	13	0.35	22
G210	Other glass/ceramic	unclassified	12	0.32	5
G90	Plastic flower pots	agriculture	12	0.32	19
G942	Plastic shavings from lathes, CNC machining	unclassified	11	0.29	11
G3	Plastic bags, carier bags	packaging non food	11	0.29	19
G131	Rubber bands	personal items	10	0.27	19
G923	Tissue, toilet paper, napkins, paper towels	personal items	10	0.27	19
G149	Paper packaging	packaging non food	10	0.27	11
G203	Tableware ceramic or glass, cups, plates, pieces	food and drink	10	0.27	11
G98	Diapers - wipes	waste water	10	0.27	19
G152	Cigarette boxes, tobacco related paper/cardboard	tobacco	10	0.27	16
G905	Hair clip, hair ties, personal accessories pl...	personal items	9	0.24	16
G65	Buckets	agriculture	8	0.21	8
G100	Medical; containers/tubes/ packaging	waste water	8	0.21	16
G22	Lids for chemicals, detergents (non-food)	infrastructure	8	0.21	8
G48	Rope, synthetic	recreation	8	0.21	13
G33	Lids for togo drinks plastic	food and drink	8	0.21	16
G928	Ribbons and bows	personal items	8	0.21	8
G159	Corks	food and drink	7	0.19	16
G175	Cans, beverage	food and drink	7	0.19	13
G73	Foamed items & pieces (non packaging/insulatio...	recreation	6	0.16	13
G198	Other metal pieces < 50cm	infrastructure	6	0.16	16
G931	Tape-caution for barrier, police, construction...	infrastructure	5	0.13	5
G125	Balloons and balloon sticks	recreation	5	0.13	11
G170	Wood (processed)	agriculture	5	0.13	13
G34	Cutlery, plates and trays	food and drink	5	0.13	13
G191	Wire and mesh	agriculture	5	0.13	11
G940	Foamed EVA for crafts and sports	recreation	5	0.13	5
G155	Fireworks paper tubes and fragments	recreation	5	0.13	13
G4	Small plastic bags; freezer, zip-lock etc.	packaging non food	5	0.13	11
G148	Cardboard (boxes and fragments)	packaging non food	5	0.13	8
G917	Terracotta balls	unclassified	5	0.13	11
G925	Packets: desiccant/ moisture absorbers, plasti...	packaging non food	4	0.11	5
G59	Fishing line monofilament (angling)	recreation	4	0.11	8
G194	Cables, metal wire(s) often inside rubber or p...	infrastructure	4	0.11	8
G96	Sanitary-pads/tampons, applicators	waste water	4	0.11	11
G176	Cans, food	food and drink	3	0.08	5
G913	Pacifier	personal items	3	0.08	8
G933	Bags, cases for accessories; glasses, electron...	personal items	3	0.08	8
G37	Mesh bags	personal items	3	0.08	8
G6	Bottles and containers, plastic non food/drink	packaging non food	3	0.08	2
G914	Paperclips, clothespins, plastic utility items	personal items	3	0.08	8
G921	Ceramic tile and pieces	infrastructure	3	0.08	8
G134	Other rubber	unclassified	3	0.08	5
G158	Other paper items	packaging non food	3	0.08	8
G146	Paper, cardboard	packaging non food	3	0.08	2
G165	Ice cream sticks, toothpicks, chopsticks	food and drink	3	0.08	8
G103	Plastic fragments rounded <5mm	micro plastics (< 5mm)	2	0.05	5
G918	Safety pins, paper clips, small metal utility ...	personal items	2	0.05	5
G101	Dog feces bag	personal items	2	0.05	5
G124	Other plastic or foam products	unclassified	2	0.05	5
G133	Condoms incl. packaging	waste water	2	0.05	5
G2	Bags	packaging non food	2	0.05	2
G174	Aerosol spray cans	infrastructure	2	0.05	5
G49	Rope > 1cm	recreation	2	0.05	5
G26	Cigarette lighters	tobacco	2	0.05	5
G901	Mask medical, synthetic	personal items	1	0.03	2
G938	Toothpicks, dental floss plastic	food and drink	1	0.03	2
G939	Flowers, plants plastic	personal items	1	0.03	2
G20	Caps and lids	packaging non food	1	0.03	2
G111	Spheruloid pellets < 5mm	micro plastics (< 5mm)	1	0.03	2
G11	Cosmetics for the beach, e.g. sunblock	recreation	1	0.03	2
G197	Other metal	infrastructure	1	0.03	2
G202	Light bulbs	unclassified	1	0.03	2
G190	Paint cans	infrastructure	1	0.03	2
G182	Fishing; hooks, weights, lures, sinkers etc.	recreation	1	0.03	2
G97	Toilet fresheners	waste water	1	0.03	2
G161	Processed timber	agriculture	1	0.03	2
G114	Films <5mm	micro plastics (< 5mm)	1	0.03	2
G930	Foam earplugs	personal items	1	0.03	2
G214	Oil/tar	infrastructure	1	0.03	2
G129	Inner tubes and rubber sheets	unclassified	1	0.03	2
G93	Cable ties; steggel, zip, zap straps	infrastructure	1	0.03	2
G138	Shoes and sandals	personal items	1	0.03	2
G926	Chewing gum, often contains plastics	food and drink	1	0.03	2
G135	Clothes, footware, headware, gloves	personal items	1	0.03	2
G29	Combs, brushes and sunglasses	personal items	1	0.03	2
G12	Cosmetics, non-beach use personal care containers	personal items	1	0.03	2
G36	Bags/sacks heavy duty plastic for 25 Kg or mor...	agriculture	1	0.03	2
G919	Nails, screws, bolts etc.	infrastructure	1	0.03	2
G43	Tags fishing or industry (security tags, seals)	recreation	1	0.03	2
G167	Matches or fireworks	recreation	1	0.03	2
G64	Fenders	unclassified	1	0.03	2
G71	Shoes sandals	personal items	1	0.03	2
G28	Pens, lids, mechanical pencils etc.	personal items	1	0.03	2

Thunersee / Brienzersee

Contents