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feeds: first draft basel air quality

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tomasr
2026-07-29 13:16:42 +02:00
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commit 7f674b7ad3
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resspublica/baselLuftQualitat.py
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@@ -0,0 +1,410 @@
from datetime import date
import pandas as pd
import geopandas as gpd
from shapely import wkb
import matplotlib.pyplot as plt
import logging
logger = logging.getLogger("resspublica")
from .translations import *
from .utils import *
station_coordinates = {
"12500": (47.558, 7.588), # https://luftqualitaet.ch/messnetz/station/blSIB
"12450": (47.500, 7.620), # https://luftqualitaet.ch/messnetz/station/blSIB
"12510": (47.450, 7.780), # https://luftqualitaet.ch/messnetz/station/blMUT
}
def generateBaselLuftqualitat(ASSETS):
logger.info("Generating Luftqualitat map in Basel feed...")
logger.info("Preparing data...")
urls = [
"https://data.bs.ch/api/v2/catalog/datasets/100048/exports/parquet",
"https://data.bs.ch/api/v2/catalog/datasets/100050/exports/parquet",
"https://data.bs.ch/api/v2/catalog/datasets/100093/exports/parquet",
"https://data.bs.ch/api/v2/catalog/datasets/100049/exports/parquet",
"https://data.bs.ch/api/v2/catalog/datasets/100178/exports/parquet",
"https://data.bs.ch/api/v2/catalog/datasets/100158/exports/parquet",
"https://data.bl.ch/api/v2/catalog/datasets/12500/exports/parquet",
"https://data.bl.ch/api/v2/catalog/datasets/12450/exports/parquet",
"https://data.bl.ch/api/v2/catalog/datasets/12510/exports/parquet"
]
# ---------------------------------------------------------
# Load data
# ---------------------------------------------------------
dataframes = []
for url in urls:
logger.debug(f"Querying {url}...")
df = pd.read_parquet(url)
station_id = url.split("/")[-3]
df["station_id"] = station_id
# -----------------------------------------------------
# Normalize datetime to Swiss time
# -----------------------------------------------------
possible_dates = [
"datum_zeit",
"timestamp",
"anfangszeit",
"messbeginn"
]
date_column = next(
(
col
for col in possible_dates
if col in df.columns
),
None
)
if date_column is None:
logger.warning(
f"No date column found in {url}, skipping"
)
continue
df["date_time"] = pd.to_datetime(
df[date_column],
errors="coerce",
utc=True
)
df["date_time"] = (
df["date_time"]
.dt.tz_convert("Europe/Zurich")
.dt.tz_localize(None)
)
# -----------------------------------------------------
# Add coordinates for BL datasets
# -----------------------------------------------------
if station_id in station_coordinates:
lat, lon = station_coordinates[station_id]
df["latitude"] = lat
df["longitude"] = lon
# -----------------------------------------------------
# Convert long format datasets
# -----------------------------------------------------
if (
"parameter" in df.columns
and "messwert" in df.columns
):
df = df.pivot_table(
index=[
"date_time",
"geo_point_2d"
],
columns="parameter",
values="messwert",
aggfunc="mean"
).reset_index()
# -----------------------------------------------------
# Pollutant normalization
# -----------------------------------------------------
pollutant_mapping = {
"pm10": [
"pm10",
"pm10_stundenmittelwerte_ug_m3"
],
"pm2_5": [
"pm2_5",
"pm2.5",
"pm25",
"pm2_5_stundenmittelwerte_ug_m3",
"g107_pm25",
"g125_pm25",
"g131_pm25",
"a2hard_pm25",
"feldbergstr2_pm25",
"stjohann2_pm25"
],
"no2": [
"no2",
"no2_stundenmittelwerte_ug_m3",
"g107_no2",
"g125_no2",
"g131_no2",
"a2hard_no2",
"feldbergstr2_no2",
"stjohann2_no2"
],
"o3": [
"o3",
"o3_stundenmittelwerte_ug_m3",
"g107_o3",
"g107_03",
"g125_o3",
"g131_o3",
"a2hard_o3",
"feldbergstr2_o3",
"stjohann2_o3"
]
}
# -----------------------------------------------------
# Melt everything into:
# date_time | station_id | pollutant | value | geometry
# -----------------------------------------------------
parts = []
for pollutant, candidates in pollutant_mapping.items():
for column in candidates:
if column not in df.columns:
continue
keep = [
"date_time",
"station_id",
column
]
for extra in [
"geo_point_2d",
"latitude",
"longitude"
]:
if extra in df.columns:
keep.append(extra)
tmp = df[keep].copy()
tmp = tmp.rename(
columns={
column: "value"
}
)
tmp["pollutant"] = pollutant
parts.append(tmp)
if not parts:
logger.warning(
f"No pollutants found in {url}"
)
continue
df = pd.concat(
parts,
ignore_index=True
)
dataframes.append(df)
# ---------------------------------------------------------
# Combine datasets
# ---------------------------------------------------------
dataframe = pd.concat(
dataframes,
ignore_index=True,
sort=False
)
# ---------------------------------------------------------
# Geometry
# ---------------------------------------------------------
def safe_load(x):
try:
return wkb.loads(x)
except Exception:
return None
dataframe["geometry"] = None
if "geo_point_2d" in dataframe.columns:
dataframe["geometry"] = dataframe[
"geo_point_2d"
].apply(
safe_load
)
# Fill missing geometry from coordinates
missing_geometry = dataframe["geometry"].isna()
dataframe.loc[
missing_geometry,
"geometry"
] = gpd.points_from_xy(
dataframe.loc[missing_geometry, "longitude"],
dataframe.loc[missing_geometry, "latitude"]
)
geo = gpd.GeoDataFrame(
dataframe,
geometry="geometry",
crs="EPSG:4326"
)
# ---------------------------------------------------------
# Time filter
# ---------------------------------------------------------
start = pd.Timestamp(
"2026-07-29 00:00:00"
)
end = pd.Timestamp(
"2026-07-29 23:59:59"
)
geo = geo[
(geo["date_time"] >= start)
&
(geo["date_time"] <= end)
]
# ---------------------------------------------------------
# Average per station
# ---------------------------------------------------------
averaged = (
geo
.groupby(
[
"station_id",
"pollutant",
"geometry"
],
as_index=False
)
["value"]
.mean()
)
averaged = gpd.GeoDataFrame(
averaged,
geometry="geometry",
crs="EPSG:4326"
)
# ---------------------------------------------------------
# Boundaries
# ---------------------------------------------------------
gdb = (
ASSETS /
"swissBOUNDARIES3D_1_5_LV95_LN02.gdb"
)
cantons = gpd.read_file(
gdb,
layer="TLM_KANTONSGEBIET"
)
basel_stadt = cantons[
cantons["KANTONSNUMMER"] == 12
].to_crs(averaged.crs)
basel_land = cantons[
cantons["KANTONSNUMMER"] == 13
].to_crs(averaged.crs)
# ---------------------------------------------------------
# Plot
# ---------------------------------------------------------
pollutants = [
"pm10",
"pm2_5",
"no2",
"o3"
]
fig, axes = plt.subplots(
1,
len(pollutants),
figsize=(20, 5)
)
for ax, pollutant in zip(
axes,
pollutants
):
subset = averaged[
averaged["pollutant"] == pollutant
]
if subset.empty:
ax.set_visible(False)
continue
subset.plot(
ax=ax,
column="value",
cmap="hot",
legend=True,
markersize=80
)
basel_land.plot(
ax=ax,
facecolor="none",
edgecolor="black",
linewidth=2
)
basel_stadt.plot(
ax=ax,
facecolor="none",
edgecolor="black",
linewidth=1
)
for _, row in subset.iterrows():
ax.annotate(
row["station_id"],
(
row.geometry.x,
row.geometry.y
),
fontsize=8
)
ax.set_title(
pollutant
)
ax.axis("off")
plt.tight_layout()
plt.show()
resspublica/main.py
+4
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@@ -10,12 +10,14 @@ from .federalInitiativesFeeds import *
from .translations import *
from .bernAsianHornet import *
from .bernReligionMap import *
from .baselLuftQualitat import *
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--verbose", action="store_true", help="Enable debug logging")
parser.add_argument("--gen_federalInitiatives", action="store_true", help="Generate federal popular initiatives feed")
parser.add_argument("--gen_bernAsianHornets", action="store_true", help="Generate Asian hornets sightings in Bern feed (only Mondays)")
parser.add_argument("--gen_baselLuftqualitat", action="store_true", help="Generate Basel air quality feed")
parser.add_argument("--force_gen_bernAsianHornets", action="store_true", help="Generate Asian hornets sightings in Bern feed even when not Monday")
parser.add_argument("--gen_bernReligionMap", action="store_true", help="Generate map of religions in Bern (only start of trimesters)")
parser.add_argument("--force_gen_bernReligionMap", action="store_true", help="Generate map of religions in Bern even when not start of trimester")
@@ -40,4 +42,6 @@ def main():
generateBernAsianHornetFeed(ASSETS, CACHE)
if ( args.gen_bernReligionMap and date.today().day == 1 and date.today().month in [1, 5, 9]) or args.force_gen_bernReligionMap:
generateBernReligionMap(ASSETS, CACHE)
if args.gen_baselLuftqualitat:
generateBaselLuftqualitat(ASSETS)
logging.info("Done")