GCS Miljø (Environment) Data Catalog

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

Frontend Metrics Supported

| Metric Key | Danish Name | Description | |------------|-------------|-------------| | pesticide_burden | Pesticidbelastning | Total pesticide load | | pesticide_pfas | PFAS i pesticider | PFAS-containing pesticides | | pesticide_glyphosate | Glyphosat | Glyphosate usage | | nitrogen_leaching | Kvælstofudvaskning | Nitrogen leaching estimates | | bnbo_overlap | BNBO-overlap | Fields overlapping drinking water areas | | protected_nature | Beskyttet natur | Protected nature areas | | environmental_compliance | Miljøoverholdelse | Environmental compliance rate | | biodiversity_score | Biodiversitet | Biodiversity indicators |

Available Datasets

Silver Layer

Pesticides (316K rows)

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

| Column | Type | Description | Example | |--------|------|-------------|---------| | cvr_number | string | Company CVR | 31373077 | | pesticide_name | string | Pesticide product name | Roundup | | active_ingredient | string | Active chemical | Glyphosate | | dosage_quantity | float | Amount applied | 2.5 | | dosage_unit | string | Unit of measure | L/ha | | application_date | date | Date applied | 2024-05-15 | | crop_type | string | Target crop | Hvede |

BNBO Status (5.4K rows)

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

| Column | Type | Description | Example | |--------|------|-------------|---------| | geometry | binary | Protection zone polygon (WKB) | - | | area_ha | float | Zone area in hectares | 45.2 | | temanavn | string | Theme name | BNBO | | status_bnbo | string | BNBO status | Indsatsplan vedtaget | | kommunenav | string | Municipality name | København | | anlaegsnav | string | Water facility name | Vandværk Nord | | dgunr | string | DGU number (well ID) | 123.456 |

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

| Column | Type | Description | |--------|------|-------------| | id | int64 | Wetland polygon ID | | gridcode | int | Grid classification code | | toerv_pct | float | Peat percentage | | geometry | binary | Wetland polygon (WKB) |

Soil Types

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

| Column | Type | Description | |--------|------|-------------| | geometry | binary | Soil type polygon (WKB) | | soil_code | string | Soil classification code | | soil_description | string | Soil type description | | clay_content | float | Clay percentage |

Slurry Leaks (Incidents)

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

| Column | Type | Description | |--------|------|-------------| | incident_date | date | Date of leak | | location | binary | Incident location (WKB) | | volume_m3 | float | Volume of spill | | cvr_number | string | Responsible company |

Gold Layer

Pesticide Disaggregation (1.52M rows)

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

| Column | Type | Description | Example | |--------|------|-------------|---------| | DisaggregatedID | int64 | Unique record ID | 12345678 | | cvr_number | string | Company CVR | 31373077 | | PesticideName | string | Pesticide name | Roundup Bio | | PesticideRegistrationNumber | string | Registration number | 1-234 | | DosageQuantity | float | Dosage amount | 2.5 | | DosageUnit | string | Dosage unit | L/ha | | MatchedFieldID | string | Matched field ID | 610341-27-1-0 | | MatchedBlockID | string | Matched block ID | 610341-27 | | AllocatedArea | float | Area allocated (ha) | 5.2 | | field_uuid | string | Field UUID | - | | municipality | string | Municipality code | 0101 |

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

| Column | Type | Description | Example | |--------|------|-------------|---------| | field_id | string | Field identifier | 610341-27-1-0 | | field_uuid | string | Field UUID | - | | block_id | string | Block identifier | 610341-27 | | cvr_number | string | Company CVR | 31373077 | | year | int | Estimate year | 2024 | | area_ha | float | Field area | 12.45 | | crop_type | string | Crop grown | Vårbyg | | soil_code | string | Soil classification | JB3 | | soil_description | string | Soil type name | Lerblandet sandjord | | clay_content | float | Clay percentage | 15.2 | | nitrogen_washout_kg_ha | float | N leaching kg/ha | 42.5 | | percolation_mm | float | Water percolation mm | 285.0 | | data_quality_score | float | Quality indicator | 0.85 |

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

| Column | Type | Description | |--------|------|-------------| | field_id | string | Field identifier | | bnbo_overlap_ha | float | Area overlapping BNBO | | wetland_overlap_ha | float | Area overlapping wetlands | | natura2000_overlap_ha | float | Area overlapping Natura 2000 |

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

| This Dataset | Join Column | Target Dataset | Target Column | |--------------|-------------|----------------|---------------| | pesticide_disaggregation | cvr_number | subsidies | cvr_number | | pesticide_disaggregation | MatchedFieldID | fvm_marker | field_id | | pesticide_disaggregation | field_uuid | field_production | field_uuid | | nles5_nitrogen | field_id | fvm_marker | field_id | | nles5_nitrogen | cvr_number | subsidies | cvr_number | | bnbo_status | geometry | fvm_marker | geometry (spatial) | | wetlands | geometry | fvm_marker | geometry (spatial) |

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