12. Walensee¶

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

12.1. 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 3,694 objects were removed and identified over the course of 31 surveys. The Walensee results include 9 different locations in 4 different municipalities with a combined population of approximately 28,823.

Walensee municipalities:

Glarus Nord, Quarten, Walenstadt, Weesen

12.1.1. 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 Walensee 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()

12.1.2. 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: Walensee, 2020-03 through 2021-05, n=31. Values greater than 2368.3p/100m not shown. Right: Walensee 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()

12.1.3. Summary data and material types¶

Left: Walensee summary of survey totals. Right: Walensee 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()

12.2. 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: Walensee most common objects: fail rate >/= 50% and/or top ten by quantity. Combined, the most abundant objects represent 75% 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()

12.2.1. Most common objects results by municipality¶

Below: Walensee 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()

12.2.2. 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: Walensee, 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()

12.3. 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: Walensee 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: Walensee 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()

12.4. Annex¶

12.4.1. 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: Walensee 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()

12.4.2. 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
gasi-strand	47.128442	9.110831	Weesen
walensee_walenstadt_wysse	47.121828	9.299552	Walenstadt
untertenzen	47.115260	9.254780	Quarten
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
murg-bad	47.115307	9.215691	Quarten
flibach-river-right-bank	47.133742	9.105461	Weesen

12.4.3. 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
Gfoam	Expanded polystyrene	infrastructure	718	19.44	93
G27	Cigarette filters	tobacco	657	17.79	96
Gfrags	Fragmented plastics	plastic pieces	445	12.05	87
G67	Industrial sheeting	agriculture	310	8.39	87
G30	Food wrappers; candy, snacks	food and drink	160	4.33	80
G74	Insulation foams	infrastructure	156	4.22	61
G117	Expanded foams < 5mm	micro plastics (< 5mm)	107	2.90	45
G89	Plastic construction waste	infrastructure	71	1.92	64
G200	Glass drink bottles, pieces	food and drink	59	1.60	61
G106	Plastic fragments angular <5mm	micro plastics (< 5mm)	41	1.11	22
G156	Paper fragments	packaging non food	39	1.06	45
G941	Packaging films nonfood or unknown	packaging non food	37	1.00	29
G95	Cotton bud/swab sticks	waste water	36	0.97	48
G25	Tobacco; plastic packaging, containers	tobacco	36	0.97	41
G177	Foil wrappers, aluminum foil	food and drink	35	0.95	38
G115	Foamed plastic <5mm	micro plastics (< 5mm)	33	0.89	12
G24	Plastic lid rings	food and drink	32	0.87	61
G178	Metal bottle caps and lids	food and drink	31	0.84	54
G21	Drink lids	food and drink	30	0.81	45
G936	Sheeting ag. greenhouse film	agriculture	29	0.79	29
G940	Foamed EVA for crafts and sports	recreation	27	0.73	12
G10	Food containers single use foamed or plastic	food and drink	25	0.68	38
G904	Plastic fireworks	recreation	22	0.60	35
G32	Toys and party favors	recreation	22	0.60	45
G917	Terracotta balls	unclassified	21	0.57	19
G66	Straps/bands; hard, plastic package fastener	infrastructure	20	0.54	38
G112	Industrial pellets (nurdles)	micro plastics (< 5mm)	20	0.54	22
G125	Balloons and balloon sticks	recreation	20	0.54	32
G922	Labels, bar codes	packaging non food	19	0.51	29
G35	Straws and stirrers	food and drink	19	0.51	38
G23	Lids unidentified	packaging non food	18	0.49	38
G923	Tissue, toilet paper, napkins, paper towels	personal items	18	0.49	32
G31	Lollypop sticks	food and drink	16	0.43	29
G33	Lids for togo drinks plastic	food and drink	16	0.43	25
G211	Swabs, bandaging, medical	personal items	12	0.32	25
G50	String < 1cm	recreation	11	0.30	32
G152	Cigarette boxes, tobacco related paper/cardboard	tobacco	11	0.30	12
G131	Rubber bands	personal items	10	0.27	19
G34	Cutlery, plates and trays	food and drink	10	0.27	16
G213	Paraffin wax	recreation	10	0.27	12
G153	Cups, food containers, wrappers (paper)	food and drink	10	0.27	16
G208	Glass or ceramic fragments > 2.5 cm	unclassified	9	0.24	19
G90	Plastic flower pots	agriculture	9	0.24	22
G943	Fencing agriculture, plastic	agriculture	8	0.22	12
G22	Lids for chemicals, detergents (non-food)	infrastructure	8	0.22	16
G165	Ice cream sticks, toothpicks, chopsticks	food and drink	8	0.22	16
G98	Diapers - wipes	waste water	7	0.19	16
G149	Paper packaging	packaging non food	7	0.19	6
G3	Plastic bags, carier bags	packaging non food	7	0.19	19
G927	String trimmer line, used to cut grass, weeds,...	infrastructure	7	0.19	16
G96	Sanitary-pads/tampons, applicators	waste water	7	0.19	19
G942	Plastic shavings from lathes, CNC machining	unclassified	7	0.19	9
G175	Cans, beverage	food and drink	7	0.19	19
G126	Balls	recreation	6	0.16	16
G146	Paper, cardboard	packaging non food	6	0.16	9
G170	Wood (processed)	agriculture	6	0.16	9
G73	Foamed items & pieces (non packaging/insulatio...	recreation	6	0.16	9
G137	Clothing, towels & rags	personal items	6	0.16	16
G155	Fireworks paper tubes and fragments	recreation	6	0.16	6
G65	Buckets	agriculture	6	0.16	9
G914	Paperclips, clothespins, plastic utility items	personal items	6	0.16	12
G91	Biomass holder	waste water	6	0.16	9
G4	Small plastic bags; freezer, zip-lock etc.	packaging non food	5	0.14	12
G100	Medical; containers/tubes/ packaging	waste water	4	0.11	9
G20	Caps and lids	packaging non food	4	0.11	6
G135	Clothes, footware, headware, gloves	personal items	4	0.11	9
G26	Cigarette lighters	tobacco	4	0.11	9
G59	Fishing line monofilament (angling)	recreation	4	0.11	6
G198	Other metal pieces < 50cm	infrastructure	4	0.11	12
G148	Cardboard (boxes and fragments)	packaging non food	3	0.08	6
G101	Dog feces bag	personal items	3	0.08	9
G159	Corks	food and drink	3	0.08	9
G919	Nails, screws, bolts etc.	infrastructure	3	0.08	6
G142	Rope , string or nets	recreation	3	0.08	9
G186	Industrial scrap	infrastructure	3	0.08	9
G103	Plastic fragments rounded <5mm	micro plastics (< 5mm)	3	0.08	3
G931	Tape-caution for barrier, police, construction...	infrastructure	3	0.08	6
G204	Bricks, pipes not plastic	infrastructure	3	0.08	6
G7	Drink bottles < = 0.5L	food and drink	3	0.08	6
G921	Ceramic tile and pieces	infrastructure	2	0.05	3
G93	Cable ties; steggel, zip, zap straps	infrastructure	2	0.05	6
G908	Tape; electrical, insulating	infrastructure	2	0.05	6
G925	Packets: desiccant/ moisture absorbers, plasti...	packaging non food	2	0.05	6
G87	Tape, masking/duct/packing	infrastructure	2	0.05	6
G928	Ribbons and bows	personal items	2	0.05	6
G8	Drink bottles > 0.5L	food and drink	2	0.05	6
G12	Cosmetics, non-beach use personal care containers	personal items	2	0.05	6
G49	Rope > 1cm	recreation	2	0.05	6
G201	Jars, includes pieces	food and drink	2	0.05	3
G68	Fiberglass fragments	infrastructure	2	0.05	6
G124	Other plastic or foam products	unclassified	2	0.05	6
G203	Tableware ceramic or glass, cups, plates, pieces	food and drink	2	0.05	6
G6	Bottles and containers, plastic non food/drink	packaging non food	2	0.05	3
G161	Processed timber	agriculture	2	0.05	6
G29	Combs, brushes and sunglasses	personal items	2	0.05	6
G191	Wire and mesh	agriculture	2	0.05	6
G134	Other rubber	unclassified	2	0.05	6
G43	Tags fishing or industry (security tags, seals)	recreation	2	0.05	3
G194	Cables, metal wire(s) often inside rubber or p...	infrastructure	2	0.05	6
G36	Bags/sacks heavy duty plastic for 25 Kg or mor...	agriculture	2	0.05	6
G114	Films <5mm	micro plastics (< 5mm)	1	0.03	3
G930	Foam earplugs	personal items	1	0.03	3
G197	Other metal	infrastructure	1	0.03	3
G933	Bags, cases for accessories; glasses, electron...	personal items	1	0.03	3
G71	Shoes sandals	personal items	1	0.03	3
G939	Flowers, plants plastic	personal items	1	0.03	3
G179	Disposable BBQs	food and drink	1	0.03	3
G176	Cans, food	food and drink	1	0.03	3
G17	Injection gun cartridge	infrastructure	1	0.03	3
G99	Syringes - needles	personal items	1	0.03	3
G154	Newspapers or magazines	personal items	1	0.03	3
G11	Cosmetics for the beach, e.g. sunblock	recreation	1	0.03	3
G133	Condoms incl. packaging	waste water	1	0.03	3
G929	Electronics and pieces; sensors, headsets etc.	personal items	1	0.03	3
G926	Chewing gum, often contains plastics	food and drink	1	0.03	3
G70	Shotgun cartridges	recreation	1	0.03	3
G132	Bobbers (fishing)	recreation	1	0.03	3
G92	Bait containers	recreation	1	0.03	3
G38	Coverings; plastic packaging, sheeting for pro...	unclassified	1	0.03	3
G41	Glove industrial/professional	agriculture	1	0.03	3
G48	Rope, synthetic	recreation	1	0.03	3
G5	Generic plastic bags	packaging non food	1	0.03	3
G907	coffee capsules plastic	food and drink	1	0.03	3
G905	Hair clip, hair ties, personal accessories pl...	personal items	1	0.03	3
G53	Nets and pieces < 50cm	recreation	1	0.03	3
G901	Mask medical, synthetic	personal items	1	0.03	3
G119	Sheetlike user plastic (>1mm)	micro plastics (< 5mm)	1	0.03	3
G61	Other fishing related	recreation	1	0.03	3
G144	Tampons	waste water	1	0.03	3

Walensee

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