6. Ticino¶

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

sut.display_image_ipython(bassin_map, thumb=(800,450))

6.1. Sample locations and land use characteristics¶

# 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=[this_feature["slug"]])

# 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, "city","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()})

# the lake and river names in the survey area
lakes = dfBeaches.loc[(dfBeaches.index.isin(t["locations"]))&(dfBeaches.water == "l")]["water_name"].unique()
rivers = dfBeaches.loc[(dfBeaches.index.isin(t["locations"]))&(dfBeaches.water == "r")]["water_name"].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 {bassin_label} results include {t['location']} different locations, {t['city']} different municipalities with a combined population of approximately {pop_string}."
# admin_context = F"There are {t['city']} different municipalities represented in these results with a combined population of approximately {pop_string}."
munis_joined = ", ".join(sorted(fd_pop_map["city"]))
lakes_joined = ", ".join(sorted(lakes))
rivers_joined = ", ".join(sorted(rivers))

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

*{bassin_label} lakes:*\n\n>{lakes_joined}

*{bassin_label} rivers:*\n\n>{rivers_joined}

*{bassin_label} municipalities:*\n\n>{munis_joined}
"""
md(lake_string)

For the period between 2020-03 and 2021-05, a total of 3,031 objects were removed and identified over the course of 28 surveys. The Ticino survey area results include 20 different locations, 7 different municipalities with a combined population of approximately 141,943.

Ticino survey area lakes:

Lago Maggiore, Lago di Lugano

Ticino survey area rivers:

Cassarate, Maggia, Ticino

Ticino survey area municipalities:

Ascona, Bellinzona, Brissago, Gambarogno, Locarno, Lugano, Minusio

6.1.1. Land use profile of the surveys¶

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

Streets are reported as the total number of kilometers of streets within the 1500m radius. Intersects is also 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 under varying land use profiles gives an indication of the environmental and economic conditions of the survey sites.

For more information Land use profile

Below: Distribution of land use characteristics.

sns.set_style("whitegrid")
# the ratio of samples with respect to the different land use characteristics for each survey area
# the data to use is the unique combinations of loc_date and the land_use charcteristics of each location
project_profile = a_data[["loc_date", this_feature["level"], *luse_exp]].drop_duplicates()
dt_nw = fd[["loc_date", this_feature["level"], *luse_exp]].drop_duplicates()

# labels and levels
comps = [this_feature["slug"]]
comp_labels = {x:wname_wname.loc[x][0] for x in fd[this_level].unique()}

fig, axs = plt.subplots(2, 3, figsize=(9,8), sharey="row")

for i, n in enumerate(luse_exp):
    r = i%2
    c = i%3
    ax=axs[r,c]
    for element in[this_feature["slug"]]:
        data=dt_nw[dt_nw[this_feature["level"]] == element][n].values
        the_data = ECDF(data)
        
        # plot that
        sns.lineplot(x=the_data.x, y=the_data.y, ax=ax, label=bassin_label)
    
    # get the dist for all here
    a_all_surveys =  ECDF(project_profile[n].values)
    
    # plot that    
    sns.lineplot(x=a_all_surveys.x, y=a_all_surveys.y, ax=ax, label="All survey areas", color="magenta")
    
    # get the median from the data
    the_median = np.median(data)
    
    # 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)
    
    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
      
    
    handles, labels = ax.get_legend_handles_labels()
    ax.get_legend().remove()    
    ax.set_xlabel(n, **ck.xlab_k)
plt.tight_layout()
plt.subplots_adjust(top=.9, hspace=.3)
plt.suptitle("Land use within 1500m of the survey location", ha="center", y=1, fontsize=16)
fig.legend(handles, labels, bbox_to_anchor=(.5,.94), loc="center", ncol=3)    

plt.show()

6.1.2. Cumulative totals by water feature¶

# 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)

# map the qauntity to the dimensional data
q_map = fd.groupby(this_level).quantity.sum()

# collect the number of samples from the survey total data:
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]

# add proper names for display
dims_table["water_feature"] = dims_table.index.map(lambda x: comp_labels[x])
dims_table.set_index("water_feature", inplace=True)
   
# get the sum of all survey areas
dims_table.loc[this_feature["name"]]= 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))

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

Below: The cumulative weights and measures for the Ticino survey area and water bodies.

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

fig, ax = plt.subplots(figsize=(len(colLabels)*1.8,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=a_color)
table_one.get_celld()[(0,0)].get_text().set_text(" ")
table_one.set_fontsize(14)

plt.tight_layout()
plt.show()

6.1.3. Distribution of survey results¶

# the 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)
dts_date.head()

# the survey totals from all other survey areas
dts_date = dts_date.groupby(["loc_date","date"], as_index=False)[unit_label].sum()
dts_date.set_index("date", inplace=True)

# 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 = 95
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)} 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: Ticino survey area, 2020-03 through 2021-05, n=28. Values greater than 1551.4 not shown. Right: Ticino survey area 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)

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()

6.1.4. Summary data and material types¶

Left: Ticino survey area summary of survey totals. Right: Ticino survey area 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(" ")
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(" ")

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

6.2. 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: Ticino survey area most common objects: fail rate >/= 50% and/or top ten by quantity. Combined, the most abundant objects represent 74% 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)}")

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=(10,len(m_common)*.7))

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()
plt.show()

6.2.1. Most common objects by water feature¶

Below: Ticino survey area 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()

# 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 for the survey area

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 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()

6.2.2. 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}, with detail of the most common objects.*
"""
md(monthly_mc)

Below: Ticino survey area, monthly average survey result p/100m, with detail of the most common objects.

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=(10,7))

# 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))]))
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,"Quartlerly 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.show()

6.3. Survey results and land use¶

The land use mix is a unique representation of the type and amplitude of the economic activity and the environmental conditions around the survey location. The key indicators from the survey results are compared against the land use rates for a radius of 1500m from the survey location.

An association is a relationship between the survey results and the land use profile that is unlikely due to chance. The magnitude of the relationship is neither defined nor linear.

Ranked correlation is a non-parametric test to determine if there is a statistically significant relationship between land use and the objects identified in a litter survey.

The method used is the Spearman’s rho or Spearmans ranked correlation coefficient. The test results are evaluated at p<0.05 for all valid lake samples in the survey area.

Red/rose is a positive association
Yellow is a negative association
White means that p>0.05, there is no statistical basis to assume an association

corr_data = fd[(fd.code.isin(m_common.index))&(fd.water_name_slug.isin(lakes_of_interest))].copy()

alert_less_than_100 = len(corr_data.loc_date.unique()) <= 100

if alert_less_than_100:
    warning = F"""**There are less than 100 samples, proceed with caution. Beach litter surveys have alot of variance**"""
else:
    warning = ""

association = F"""*__Below:__ {this_feature["name"]} ranked correlation of the most common objects with respect to land use profile.
For all valid lake samples n={len(corr_data.loc_date.unique())}.*

{warning}
"""
md(association)

Below: Ticino survey area ranked correlation of the most common objects with respect to land use profile. For all valid lake samples n=22.

There are less than 100 samples, proceed with caution. Beach litter surveys have alot of variance

# chart the results of test for association
fig, axs = plt.subplots(len(m_common.index),len(luse_exp), figsize=(len(luse_exp)+7,len(m_common.index)+1), sharey="row")

# the test is conducted on the survey results for each code
for i,code in enumerate(m_common.index):
    # slice the data
    data = corr_data[corr_data.code == code]
    
    # run the test on for each land use feature
    for j, n in enumerate(luse_exp):       
        # assign ax and set some parameters
        ax=axs[i, j]
        ax.grid(False)
        ax.tick_params(axis="both", which="both",bottom=False,top=False,labelbottom=False, labelleft=False, left=False)
        
        # check the axis and set titles and labels       
        if i == 0:
            ax.set_title(F"{n}")
        else:
            pass
        
        if j == 0:
            ax.set_ylabel(F"{code_description_map[code]}", rotation=0, ha="right", **ck.xlab_k14)
            ax.set_xlabel(" ")
        else:
            ax.set_xlabel(" ")
            ax.set_ylabel(" ")
        # run test
        _, corr, a_p = sut.make_plot_with_spearmans(data, ax, n)
        
        # if siginficant set adjust color to direction
        if a_p < 0.05:
            if corr > 0:
                ax.patch.set_facecolor("salmon")
                ax.patch.set_alpha(0.5)
            else:
                ax.patch.set_facecolor("palegoldenrod")
                ax.patch.set_alpha(0.5)

plt.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()

Key: if p>0.05 = white, if p < 0.05 and \(\rho\) > 0 = red, if p < 0.05 and \(\rho\) < 0 = yellow

6.4. 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: Ticino survey area utility of objects found % of total by water feature. 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")

# repeat for the survey area
data_table[bassin_label] = sut.aggregate_to_group_name(fd, 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
data.rename(columns={x:wname_wname.loc[x][0] for x in data.columns[:-2]}, inplace=True)

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

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: Ticino survey area 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="water_name_slug", index="groupname", values=unit_label)

# the survey area columns
data_table[bassin_label] = sut.aggregate_to_group_name(fd, 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
data.rename(columns={x:wname_wname.loc[x][0] for x in data.columns[:-2]}, 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)

plt.show()

6.5. 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)

chart_notes = F"""
*__Left:__ {this_feature["name"]} rivers, {start_date[:7]} through {end_date[:7]}, n={len(r_smps.loc_date.unique())}. {not_included} __Right:__ Summary data.*
"""
md(chart_notes )

Left: Ticino survey area rivers, 2020-03 through 2021-05, n=6. Values greater than 1551.4 not shown. Right: Summary data.

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}"

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_minor_locator(days)
ax.xaxis.set_major_formatter(months_fmt)

a_col = [this_feature["name"], "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(" ")


plt.show()

6.5.1. Rivers most common objects¶

riv_mcommon = F"""
*__Below:__ {this_feature["name"]} rivers, most common objects {unit_label}: median survey value*
"""
md(riv_mcommon)

Below: Ticino survey area rivers, most common objects p/100m: median survey value

# 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).astype("int")

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

# most common
r_byfail = r_codes[r_codes["Fail rate"] > 49.99].index
r_most_common = list(set(r_byq) | set(r_byfail))

# format for display
r_mc= r_codes.loc[r_most_common].copy()
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).astype("int")
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["p/50m"] = r_mc[unit_label].map(lambda x: F"{np.ceil(x)}")
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=(13,len(data)*.7))

sut.hide_spines_ticks_grids(axs)

table_six = sut.make_a_table(axs, data.values,  colLabels=list(data.columns), colWidths=[.52, *[.12]*4], **{"loc":"lower center"})
table_six.get_celld()[(0,0)].get_text().set_text(" ")


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

6.6. Annex¶

6.6.1. 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: Ticino survey area fragmented foams and plastics by size group.

# 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(t["locations"]))].groupby(["loc_date","code"], as_index=False).agg(agg_pcs_quantity)
fd_frags_foams = fd_frags_foams.groupby("code").agg(agg_pcs_median)

# 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)*2.4,len(data)*.7))

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

6.6.2. 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
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
sentiero-giro-del-golf-spiaggia	46.145770	8.790430	Ascona
vira-gambarogno	46.143343	8.838759	Gambarogno
canale-saleggi	46.200625	9.015853	Bellinzona
route-13	46.162521	8.782579	Locarno
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
vira-spiaggia-left-of-river	46.142900	8.838407	Gambarogno

6.6.3. 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	743	24.51	96
G200	Glass drink bottles, pieces	food and drink	274	9.04	85
Gfrags	Fragmented plastics	plastic pieces	259	8.55	85
G112	Industrial pellets (nurdles)	micro plastics (< 5mm)	247	8.15	17
Gfoam	Expanded polystyrene	infrastructure	237	7.82	57
G30	Food wrappers; candy, snacks	food and drink	130	4.29	71
G67	Industrial sheeting	agriculture	80	2.64	82
G941	Packaging films nonfood or unknown	packaging non food	72	2.38	57
G117	Expanded foams < 5mm	micro plastics (< 5mm)	65	2.14	32
G74	Insulation foams	infrastructure	64	2.11	67
G178	Metal bottle caps and lids	food and drink	54	1.78	50
G89	Plastic construction waste	infrastructure	42	1.39	57
G149	Paper packaging	packaging non food	36	1.19	14
G921	Ceramic tile and pieces	infrastructure	34	1.12	35
G10	Food containers single use foamed or plastic	food and drink	30	0.99	32
G153	Cups, food containers, wrappers (paper)	food and drink	27	0.89	28
G177	Foil wrappers, aluminum foil	food and drink	26	0.86	39
G95	Cotton bud/swab sticks	waste water	23	0.76	39
G923	Tissue, toilet paper, napkins, paper towels	personal items	22	0.73	39
G21	Drink lids	food and drink	20	0.66	28
G156	Paper fragments	packaging non food	18	0.59	42
G152	Cigarette boxes, tobacco related paper/cardboard	tobacco	18	0.59	28
G66	Straps/bands; hard, plastic package fastener	infrastructure	17	0.56	32
G186	Industrial scrap	infrastructure	17	0.56	25
G25	Tobacco; plastic packaging, containers	tobacco	16	0.53	35
G73	Foamed items & pieces (non packaging/insulatio...	recreation	16	0.53	28
G93	Cable ties; steggel, zip, zap straps	infrastructure	15	0.49	17
G3	Plastic bags, carier bags	packaging non food	15	0.49	21
G137	Clothing, towels & rags	personal items	15	0.49	17
G98	Diapers - wipes	waste water	15	0.49	35
G24	Plastic lid rings	food and drink	14	0.46	25
G203	Tableware ceramic or glass, cups, plates, pieces	food and drink	14	0.46	21
G22	Lids for chemicals, detergents (non-food)	infrastructure	13	0.43	17
G922	Labels, bar codes	packaging non food	12	0.40	17
G32	Toys and party favors	recreation	12	0.40	28
G100	Medical; containers/tubes/ packaging	waste water	12	0.40	21
G33	Lids for togo drinks plastic	food and drink	12	0.40	21
G194	Cables, metal wire(s) often inside rubber or p...	infrastructure	11	0.36	21
G50	String < 1cm	recreation	11	0.36	21
G70	Shotgun cartridges	recreation	10	0.33	14
G35	Straws and stirrers	food and drink	10	0.33	21
G198	Other metal pieces < 50cm	infrastructure	10	0.33	21
G211	Swabs, bandaging, medical	personal items	9	0.30	21
G31	Lollypop sticks	food and drink	9	0.30	7
G191	Wire and mesh	agriculture	9	0.30	10
G159	Corks	food and drink	9	0.30	28
G148	Cardboard (boxes and fragments)	packaging non food	8	0.26	10
G146	Paper, cardboard	packaging non food	8	0.26	3
G204	Bricks, pipes not plastic	infrastructure	7	0.23	10
G101	Dog feces bag	personal items	7	0.23	21
G142	Rope , string or nets	recreation	6	0.20	21
G87	Tape, masking/duct/packing	infrastructure	6	0.20	14
G175	Cans, beverage	food and drink	6	0.20	10
G908	Tape; electrical, insulating	infrastructure	6	0.20	10
G914	Paperclips, clothespins, plastic utility items	personal items	6	0.20	17
G23	Lids unidentified	packaging non food	6	0.20	14
G96	Sanitary-pads/tampons, applicators	waste water	5	0.16	14
G90	Plastic flower pots	agriculture	5	0.16	17
G927	String trimmer line, used to cut grass, weeds,...	infrastructure	5	0.16	14
G936	Sheeting ag. greenhouse film	agriculture	5	0.16	10
G135	Clothes, footware, headware, gloves	personal items	5	0.16	14
G157	Paper	packaging non food	5	0.16	14
G943	Fencing agriculture, plastic	agriculture	4	0.13	10
G154	Newspapers or magazines	personal items	4	0.13	7
G34	Cutlery, plates and trays	food and drink	4	0.13	14
G7	Drink bottles < = 0.5L	food and drink	4	0.13	10
G28	Pens, lids, mechanical pencils etc.	personal items	3	0.10	7
G65	Buckets	agriculture	3	0.10	10
G106	Plastic fragments angular <5mm	micro plastics (< 5mm)	3	0.10	7
G931	Tape-caution for barrier, police, construction...	infrastructure	3	0.10	3
G928	Ribbons and bows	personal items	3	0.10	7
G919	Nails, screws, bolts etc.	infrastructure	3	0.10	3
G170	Wood (processed)	agriculture	3	0.10	10
G901	Mask medical, synthetic	personal items	3	0.10	10
G125	Balloons and balloon sticks	recreation	3	0.10	10
G903	Hand sanitizer containers & packets	personal items	3	0.10	7
G36	Bags/sacks heavy duty plastic for 25 Kg or mor...	agriculture	3	0.10	7
G208	Glass or ceramic fragments > 2.5 cm	unclassified	3	0.10	7
G26	Cigarette lighters	tobacco	3	0.10	10
G182	Fishing; hooks, weights, lures, sinkers etc.	recreation	2	0.07	7
G136	Shoes	personal items	2	0.07	7
G53	Nets and pieces < 50cm	recreation	2	0.07	3
G201	Jars, includes pieces	food and drink	2	0.07	7
G906	coffee capsules aluminum	food and drink	2	0.07	3
G904	Plastic fireworks	recreation	2	0.07	3
G40	Gloves household/gardening	personal items	2	0.07	7
G165	Ice cream sticks, toothpicks, chopsticks	food and drink	2	0.07	3
G195	Batteries - household	personal items	2	0.07	3
G49	Rope > 1cm	recreation	2	0.07	3
G19	Car parts	unclassified	2	0.07	3
G155	Fireworks paper tubes and fragments	recreation	1	0.03	3
G925	Packets: desiccant/ moisture absorbers, plasti...	packaging non food	1	0.03	3
G138	Shoes and sandals	personal items	1	0.03	3
G99	Syringes - needles	personal items	1	0.03	3
G131	Rubber bands	personal items	1	0.03	3
G929	Electronics and pieces; sensors, headsets etc.	personal items	1	0.03	3
G128	Tires and belts	unclassified	1	0.03	3
G141	Carpet	unclassified	1	0.03	3
G933	Bags, cases for accessories; glasses, electron...	personal items	1	0.03	3
G134	Other rubber	unclassified	1	0.03	3
G939	Flowers, plants plastic	personal items	1	0.03	3
G167	Matches or fireworks	recreation	1	0.03	3
G942	Plastic shavings from lathes, CNC machining	unclassified	1	0.03	3
G145	Other textiles	personal items	1	0.03	3
G52	Nets and pieces	recreation	1	0.03	3
G107	Cylindrical pellets < 5mm	micro plastics (< 5mm)	1	0.03	3
G132	Bobbers (fishing)	recreation	1	0.03	3
G6	Bottles and containers, plastic non food/drink	packaging non food	1	0.03	3
G62	Floats for nets	unclassified	1	0.03	3
G2	Bags	packaging non food	1	0.03	3
G197	Other metal	infrastructure	1	0.03	3
G41	Glove industrial/professional	agriculture	1	0.03	3
G124	Other plastic or foam products	unclassified	1	0.03	3
G115	Foamed plastic <5mm	micro plastics (< 5mm)	1	0.03	3
G902	Mask medical, cloth	personal items	1	0.03	3
G92	Bait containers	recreation	1	0.03	3
G129	Inner tubes and rubber sheets	unclassified	1	0.03	3
G905	Hair clip, hair ties, personal accessories pl...	personal items	1	0.03	3
G907	coffee capsules plastic	food and drink	1	0.03	3
G172	Other wood > 50cm	agriculture	1	0.03	3
G91	Biomass holder	waste water	1	0.03	3
G4	Small plastic bags; freezer, zip-lock etc.	packaging non food	1	0.03	3
G37	Mesh bags	personal items	1	0.03	3
G918	Safety pins, paper clips, small metal utility ...	personal items	1	0.03	3

Ticino

Contents