cassini-team-todo/eea-river-proximity

EEA Industrial Emissions - River Proximity Dataset

This dataset links 160,576 industrial facilities from the European Environment Agency (EEA) to nearby rivers, with upstream/downstream flow analysis based on HydroRIVERS network data.

Why This Dataset?

Industrial facilities that discharge pollutants into water bodies affect downstream ecosystems and communities. This dataset enables:

• —Impact assessment: Which communities/ecosystems are downstream of polluting facilities?
• —Source tracing: Where does a facility's water supply come from?
• —Regulatory analysis: Mapping industrial emissions to affected river networks
• —Environmental research: Studying relationships between industry and water quality

How It Works

         ┌─────────────┐
         │  UPSTREAM   │  ← Source water (green)
         │  (5 parts)  │     flowing TOWARD facility
         └──────┬──────┘
                │
         ───────●─────── ← Split point (closest to facility)
                │
         ┌──────┴──────┐
         │  FACILITY   │  ← Industrial facility (orange)
         │   (211m)    │     within 1km of river
         └─────────────┘
                │
         ┌──────┴──────┐
         │ DOWNSTREAM  │  → Affected water (red)
         │  (4 parts)  │     flowing AWAY from facility
         └─────────────┘

For each facility:

1. 1.Find the closest river segment within 1km
2. 2.Split the river at the closest point
3. 3.Trace upstream 10km (source water)
4. 4.Trace downstream 10km (potentially affected)
5. 5.Clip water surface polygons to match

Dataset Files

river_data_facilities.geoparquet (2.6 GB)

Main dataset with 160,576 facilities matched to rivers.

river_data_segments.geoparquet (1.7 MB)

River segments with direction labels (28,434 entries).

Usage

Load the dataset

import geopandas as gpd
from shapely import wkb

# Load facilities
facilities = gpd.read_parquet("river_data_facilities.geoparquet")
print(f"Loaded {len(facilities):,} facilities")

# Example: Find facilities in Germany
german = facilities[facilities['countryName'] == 'Germany']
print(f"Germany has {len(german):,} facilities near rivers")

Extract river geometries

# Get a specific facility
facility = facilities[facilities['facilityName'].str.contains('PRECHEZA')].iloc[0]

# Parse WKB geometries
upstream_line = wkb.loads(facility['upstream_line_wkb'])
downstream_line = wkb.loads(facility['downstream_line_wkb'])
upstream_poly = wkb.loads(facility['upstream_poly_wkb'])
downstream_poly = wkb.loads(facility['downstream_poly_wkb'])

print(f"Upstream: {facility['n_upstream']} segments")
print(f"Downstream: {facility['n_downstream']} segments")

Visualize a facility

python visualize_single_facility.py "PRECHEZA"
# Opens facility_map.html in browser

Visualize multiple facilities

python visualize_facilities_rivers.py
# Opens facilities_rivers_map.html with 200 sampled facilities

Scripts

river_proximity.py

Main pipeline that:

1. 1.Loads EEA facilities, HydroRIVERS segments, and EU-Hydro polygons
2. 2.Builds river network graph from NEXT_DOWN field
3. 3.For each facility, finds closest river and splits at nearest point
4. 4.Traces upstream (BFS) and downstream (linear) within distance limits
5. 5.Clips water surface polygons to match river geometries
6. 6.Outputs geoparquet files

visualize_single_facility.py

Creates an interactive Folium map for a single facility showing:

• —Green: upstream river and water surface
• —Red: downstream river and water surface
• —Orange marker: facility location

visualize_facilities_rivers.py

Creates an overview map with sampled facilities and their river associations.

Source Data

Parameters

Statistics

• —Total facilities processed: 254,027
• —Facilities near rivers: 160,576 (63%)
• —Unique upstream segments: 18,498
• —Unique downstream segments: 9,936
• —Average upstream parts: 7.1
• —Average downstream parts: 3.7

Sentinel visibility (EU-Hydro River_Net_p overlap ≥ 30 %)

• —Facilities with `has_sentinel_visible_river = True`: 59,568 / 160,576 (37.1 %)
• —*Facilities where the closest reach is visible*: 23,507 / 160,576 (14.6 %)

Lower-order rates (~20–25 %) are driven by upstream/downstream propagation: a headwater facility may drain into a wider river within the 10 km trace window.

facility_timeseries.parquet (210 MB)

Sentinel-2 water-quality time series for Sentinel-visible industrial facilities (both upstream and downstream polygons detectable in 10 m imagery). Produced by fetching the Sentinel Hub Statistical API in P10D bins over 2017–2023 and merging three shards. One row per (facility, direction, 10-day bin).

Coverage: 9.2 M rows · 23,158 unique facility IDs · 20,755 paired (both directions) · 2017-01-01 → 2023-12-16

facility_anomalies_per_bin.parquet (216 MB)

Per-bin anomaly detection output. One row per paired (facility, 10-day bin) where both upstream and downstream data exist. Produced by scripts/compute_facility_anomalies.py.

Pipeline steps applied:

1. 1.Inner-join upstream + downstream on (facility_id, date)
2. 2.Pixel-count quality flags
3. 3.Raw downstream-minus-upstream delta
4. 4.Spatial detrending: subtract cross-facility median delta per date to remove regional Sentinel-2 artifacts
5. 5.Robust z-score per (facility_id, quarter) using median + MAD × 1.4826
6. 6.Previous-bin z (persistence check)
7. 7.High-confidence anomaly flag (detrended delta > 0, z > 3, prev-z > 1.5)

Coverage: 4.26 M rows · 20,755 facilities · 255 unique dates

facility_anomalies_events.parquet (tiny)

Consolidated pollution events — one row per unique (upstream polygon, downstream polygon, time window) after deduplication. Single-bin events and events where the downstream signal is not worse than upstream are excluded.

Coverage: 42 events · 36 unique polygon pairs · 2017–2023

Thresholds used: z > 3.0, prev-bin z > 1.5, ≥ 2 consecutive bins, detrended delta > 0, seasonal MAD baseline requires ≥ 8 bins per quarter.

License

CC-BY-4.0. See source datasets for their respective licenses.

Citation

If you use this dataset, please cite the source datasets:

• —Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186.
• —European Environment Agency (EEA) Industrial Emissions Database
• —Copernicus EU-Hydro River Network Database