EEA quickstart — PM2.5 for a country¶

The European Environment Agency publishes reference-grade European air-quality monitor observations. This notebook pulls PM2.5 for Malta (a small country — quick to download) over June 2022 and plots the station means.

The earthlens EEA backend is tabular and country-granular (see the reference docs): download() returns a long-format pandas.DataFrame.

Needs the [eea_aq] extra. This backend wraps the airbase SDK (pip install earthlens[eea_aq]). No credentials are required. The live cell below runs only when airbase is importable, so the notebook stays runnable without it.

Setup¶

In [ ]:

%matplotlib inline
import importlib.util

import matplotlib.pyplot as plt

from earthlens.core import EarthLens
from earthlens.eea_aq import Catalog

have_airbase = importlib.util.find_spec("airbase") is not None
print("airbase available:", have_airbase)

%matplotlib inline import importlib.util import matplotlib.pyplot as plt from earthlens.core import EarthLens from earthlens.eea_aq import Catalog have_airbase = importlib.util.find_spec("airbase") is not None print("airbase available:", have_airbase)

The pollutant catalog¶

variables names pollutants, mapped to airbase poll notations. No network needed.

In [ ]:

sorted(Catalog().pollutants), Catalog().polls_for(["pm25", "o3"])

sorted(Catalog().pollutants), Catalog().polls_for(["pm25", "o3"])

Build the request¶

We pass country="MT" explicitly (precise and fast); the bbox is only used when country= is omitted.

In [ ]:

client = EarthLens(
    data_source="eea-aq",
    variables=["pm25"],
    start="2022-06-01",  # Verified era (2013-2022) — stable archive
    end="2022-06-30",
    country="MT",
    lat_lim=[35.7, 36.1],
    lon_lim=[14.1, 14.6],
    path="out/eea",
)

client = EarthLens( data_source="eea-aq", variables=["pm25"], start="2022-06-01", # Verified era (2013-2022) — stable archive end="2022-06-30", country="MT", lat_lim=[35.7, 36.1], lon_lim=[14.1, 14.6], path="out/eea", )

Download the observations¶

In [ ]:

df = None
if have_airbase:
    df = client.download(progress_bar=False)
    print(df.shape)
else:
    print("install earthlens[eea_aq] to run the live download cell")

df = None if have_airbase: df = client.download(progress_bar=False) print(df.shape) else: print("install earthlens[eea_aq] to run the live download cell")

Inspect the first rows¶

In [ ]:

if df is not None:
    display(df.head())

if df is not None: display(df.head())

Plot mean PM2.5 per station¶

In [ ]:

if df is not None and not df.empty:
    means = df.groupby("station_id")["value"].mean().sort_values()
    fig, ax = plt.subplots(figsize=(9, 4))
    means.plot.bar(ax=ax)
    ax.set_ylabel("mean PM2.5 (ug.m-3)")
    ax.set_xlabel("sampling point")
    ax.set_title("June 2022 mean PM2.5 by EEA station — Malta")
    fig.tight_layout()
    plt.show()

if df is not None and not df.empty: means = df.groupby("station_id")["value"].mean().sort_values() fig, ax = plt.subplots(figsize=(9, 4)) means.plot.bar(ax=ax) ax.set_ylabel("mean PM2.5 (ug.m-3)") ax.set_xlabel("sampling point") ax.set_title("June 2022 mean PM2.5 by EEA station — Malta") fig.tight_layout() plt.show()