GCS Miljø (Environment) Data Catalog

GCS Miljø (Environment) Data Catalog

Environmental data including pesticides, nitrogen leaching, protected areas, and soil.

Frontend Metrics Supported

Available Datasets

Silver Layer

Pesticides (316K rows)

Path: gs://$GCS_BUCKET/​silver/​pesticides/​*/​data.parquet

BNBO Status (5.4K rows)

Path: gs://$GCS_BUCKET/​silver/​bnbo_status/​*/​data.parquet

Schema (introspected):

geometry: binary (WKB)
area_ha: double
temanavn: string
status_bnbo: string
kommunenav: string
anlaegsnav: string
dgunr: string
[30 columns total]

Wetlands (1.7M rows)

Path: gs://$GCS_BUCKET/​silver/​wetlands/​*/​data.parquet

Soil Types

Path: gs://$GCS_BUCKET/​silver/​soil_types/​*/​data.parquet

Slurry Leaks (Incidents)

Path: gs://$GCS_BUCKET/​silver/​slurry leaks/​*/​data.parquet

Gold Layer

Pesticide Disaggregation (1.52M rows)

Path: gs://$GCS_BUCKET/​gold/​pesticide_disaggregation_{year}/​*/​data.parquet

Schema (introspected):

DisaggregatedID: int64
cvr_number: string
PesticideName: string
PesticideRegistrationNumber: string
DosageQuantity: double
DosageUnit: string
MatchedFieldID: string
MatchedBlockID: string
AllocatedArea: double
field_uuid: string
municipality: string
[17 columns total]

NLES5 Nitrogen Estimates (500K rows)

Path: gs://$GCS_BUCKET/​gold/​nles5_nitrogen_{year}/​*/​data.parquet

Schema (introspected):

field_id: string
field_uuid: string
block_id: string
cvr_number: string
year: int64
area_ha: double
crop_type: string
soil_code: string
soil_description: string
clay_content: double
nitrogen_washout_kg_ha: double
percolation_mm: double
data_quality_score: double
[20 columns total]

Field Intersections (Environmental Overlaps)

Path: gs://$GCS_BUCKET/​gold/​field_analysis_{year}_intersections_*/​*/​data.parquet

Common Queries

Get Pesticide Usage by CVR

import pyarrow.parquet as pq
from google.cloud import storage
import io

client = storage.Client()
bucket = client.bucket('$GCS_BUCKET')

# Read pesticide disaggregation data
blob = bucket.blob('gold/​pesticide_disaggregation_2024/​2025-01-10/​data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
df = pq.read_table(buffer).to_pandas()

# Filter by CVR
company_pesticides = df[df['cvr_number'] == '31373077']
print(f"Total pesticide records: {len(company_pesticides)}")
print(f"Unique pesticides used: {company_pesticides['PesticideName'].nunique()}")

# Summarize by pesticide
usage_summary = company_pesticides.groupby('PesticideName').agg({
    'DosageQuantity': 'sum',
    'AllocatedArea': 'sum'
}).reset_index()

Find Fields in BNBO Protection Zones

import geopandas as gpd

# Read BNBO zones
blob = bucket.blob('silver/​bnbo_status/​2025-01-10/​data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
bnbo = gpd.read_parquet(buffer)
bnbo = bnbo.set_crs('EPSG:4326')

# Read field boundaries
blob = bucket.blob('silver/​fvm_marker_2024/​2025-01-10/​data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
fields = gpd.read_parquet(buffer)
fields = fields.set_crs('EPSG:4326')

# Spatial join to find overlapping fields
fields_in_bnbo = gpd.sjoin(fields, bnbo, how='inner', predicate='intersects')
print(f"Fields overlapping BNBO zones: {len(fields_in_bnbo)}")

Calculate Nitrogen Leaching by Municipality

# Read nitrogen data
blob = bucket.blob('gold/​nles5_nitrogen_2024/​2025-01-10/​data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
nitrogen = pq.read_table(buffer).to_pandas()

# Read field data with municipality
blob = bucket.blob('silver/​fvm_marker_2024/​2025-01-10/​data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
fields = pq.read_table(buffer).to_pandas()

# Join and aggregate
nitrogen_with_muni = nitrogen.merge(
    fields[['field_id', 'municipality']],
    on='field_id',
    how='left'
)

muni_stats = nitrogen_with_muni.groupby('municipality').agg({
    'nitrogen_washout_kg_ha': 'mean',
    'area_ha': 'sum'
}).reset_index()
muni_stats.columns = ['municipality', 'avg_n_leaching', 'total_area']

Glyphosate Usage Analysis

# Filter for glyphosate products
glyphosate = df[
    df['PesticideName'].str.contains('glyph|roundup', case=False, na=False)
]

# Summarize by municipality
glyph_by_muni = glyphosate.groupby('municipality').agg({
    'DosageQuantity': 'sum',
    'AllocatedArea': 'sum'
}).reset_index()

# Calculate intensity
glyph_by_muni['dose_per_ha'] = glyph_by_muni['DosageQuantity'] / glyph_by_muni['AllocatedArea']

Wetland Overlap Analysis

import geopandas as gpd

# Read wetlands
blob = bucket.blob('silver/​wetlands/​2025-01-10/​data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
wetlands = gpd.read_parquet(buffer)
wetlands = wetlands.set_crs('EPSG:4326')

# High peat content areas
high_peat = wetlands[wetlands['toerv_pct'] > 50]
print(f"High peat wetland polygons: {len(high_peat)}")

Join Keys

Data Quality Notes

Pesticide Disaggregation

• Update frequency: Annual (after reporting deadline)
• Coverage: Disaggregated to field level from company-level reports
• Caveat: Allocation is modeled based on crop types

NLES5 Nitrogen

• Update frequency: Annual
• Model: NLES5 model output from Aarhus University
• Coverage: All agricultural fields
• Quality: data_quality_score indicates confidence

BNBO Status

• Update frequency: Quarterly
• Coverage: All designated drinking water protection zones
• Source: Danish EPA (Miljøstyrelsen)

Wetlands

• Update frequency: Annual
• Source: Danish Environmental Portal
• Note: toerv_pct indicates peat soil percentage

Related Skills

• landbrugsareal/​ - Field boundaries for spatial joins
• okonomi/​ - Subsidies potentially affected by environmental compliance
• husdyr/​ - Livestock density affecting nitrogen loading
• medarbejdere/​ - Environmental compliance inspections

GCS Paths Reference

# List pesticide disaggregation years
gsutil ls gs://$GCS_BUCKET/​gold/ | grep pesticide

# List NLES5 nitrogen years
gsutil ls gs://$GCS_BUCKET/​gold/ | grep nles5

# List BNBO status snapshots
gsutil ls gs://$GCS_BUCKET/​silver/​bnbo_status/

# List wetland data
gsutil ls gs://$GCS_BUCKET/​silver/​wetlands/

# List field intersection analyses
gsutil ls gs://$GCS_BUCKET/​gold/ | grep intersections

Spatial Analysis Tips

CRS Handling

All geometry stored in EPSG:4326 (WGS84). For Danish projections:

# Convert to Danish UTM
gdf = gdf.to_crs('EPSG:25832')

# Calculate areas in meters
gdf['area_m2'] = gdf.geometry.area

Buffer Analysis

# Create 100m buffer around BNBO zones
bnbo_buffered = bnbo.copy()
bnbo_buffered = bnbo_buffered.to_crs('EPSG:25832')  # UTM for meters
bnbo_buffered['geometry'] = bnbo_buffered.buffer(100)
bnbo_buffered = bnbo_buffered.to_crs('EPSG:4326')  # Back to WGS84