""" Carbon Emission Maps - Visualization of CO2 emissions across Delhi Similar to AQI maps but showing emission sources and concentrations """ import folium import os import time import random import pandas as pd from pathlib import Path # Delhi center coordinates DELHI_LAT = 28.7041 DELHI_LON = 77.1025 # Emission hotspots with sector-based CO2 values (in tonnes/day per zone) # Categories: Industrial, Transport Hub, Residential, Power, Commercial EMISSION_HOTSPOTS = [ # Industrial Areas (Highest emissions) {"name": "Bawana Industrial Area", "lat": 28.8, "lon": 77.03, "emission": 45.2, "sector": "Industry", "icon": "🏭"}, {"name": "Okhla Industrial Area", "lat": 28.5365, "lon": 77.2803, "emission": 42.8, "sector": "Industry", "icon": "🏭"}, {"name": "Mundka Industrial", "lat": 28.6794, "lon": 77.0284, "emission": 38.5, "sector": "Industry", "icon": "🏭"}, {"name": "Wazirpur Industrial", "lat": 28.6967, "lon": 77.1658, "emission": 36.2, "sector": "Industry", "icon": "🏭"}, {"name": "Narela Industrial", "lat": 28.85, "lon": 77.1, "emission": 34.8, "sector": "Industry", "icon": "🏭"}, {"name": "Patparganj Industrial", "lat": 28.612, "lon": 77.292, "emission": 32.5, "sector": "Industry", "icon": "🏭"}, # Transport Hubs {"name": "IGI Airport", "lat": 28.5562, "lon": 77.1, "emission": 48.5, "sector": "Aviation", "icon": "✈️"}, {"name": "ISBT Kashmere Gate", "lat": 28.6682, "lon": 77.2284, "emission": 28.4, "sector": "Transport", "icon": "🚌"}, {"name": "ISBT Anand Vihar", "lat": 28.6508, "lon": 77.3152, "emission": 26.8, "sector": "Transport", "icon": "🚌"}, {"name": "New Delhi Railway Station", "lat": 28.6421, "lon": 77.2194, "emission": 22.5, "sector": "Transport", "icon": "🚂"}, {"name": "ITO Junction", "lat": 28.6294, "lon": 77.241, "emission": 21.2, "sector": "Transport", "icon": "🚗"}, {"name": "Connaught Place", "lat": 28.6315, "lon": 77.2167, "emission": 24.6, "sector": "Transport", "icon": "🚗"}, {"name": "Chandni Chowk", "lat": 28.656, "lon": 77.231, "emission": 23.8, "sector": "Transport", "icon": "🚗"}, # Power Plants / Substations {"name": "Badarpur Power Plant", "lat": 28.48, "lon": 77.35, "emission": 52.3, "sector": "Power", "icon": "⚡"}, {"name": "Rajghat Power House", "lat": 28.6567, "lon": 77.2519, "emission": 38.7, "sector": "Power", "icon": "⚡"}, {"name": "Indraprastha Power", "lat": 28.6125, "lon": 77.2372, "emission": 35.4, "sector": "Power", "icon": "⚡"}, # Residential Dense Areas {"name": "Rohini Residential", "lat": 28.7019, "lon": 77.0984, "emission": 18.5, "sector": "Residential", "icon": "🏠"}, {"name": "Dwarka Sector-8", "lat": 28.5656, "lon": 77.067, "emission": 16.8, "sector": "Residential", "icon": "🏠"}, {"name": "Jahangirpuri", "lat": 28.716, "lon": 77.1829, "emission": 15.2, "sector": "Residential", "icon": "🏠"}, {"name": "Shahdara", "lat": 28.681, "lon": 77.305, "emission": 17.4, "sector": "Residential", "icon": "🏠"}, {"name": "Lajpat Nagar", "lat": 28.5697, "lon": 77.253, "emission": 14.8, "sector": "Residential", "icon": "🏠"}, {"name": "Punjabi Bagh", "lat": 28.673, "lon": 77.1374, "emission": 15.6, "sector": "Residential", "icon": "🏠"}, {"name": "RK Puram", "lat": 28.5505, "lon": 77.1849, "emission": 14.2, "sector": "Residential", "icon": "🏠"}, # Commercial Areas {"name": "Nehru Place", "lat": 28.5485, "lon": 77.2513, "emission": 19.8, "sector": "Commercial", "icon": "🏢"}, {"name": "Saket Mall Area", "lat": 28.521, "lon": 77.219, "emission": 17.2, "sector": "Commercial", "icon": "🏢"}, {"name": "Gurgaon Border", "lat": 28.495, "lon": 77.08, "emission": 22.4, "sector": "Commercial", "icon": "🏢"}, ] # Sector colors for visualization SECTOR_COLORS = { "Industry": "#dc2626", # Red "Aviation": "#f97316", # Orange "Transport": "#eab308", # Yellow "Power": "#8b5cf6", # Purple "Residential": "#22c55e", # Green "Commercial": "#3b82f6", # Blue } def get_emission_color(emission_value): """Get color based on emission level (tonnes/day).""" if emission_value < 15: return "#22c55e" # Green - Low elif emission_value < 25: return "#84cc16" # Lime elif emission_value < 35: return "#eab308" # Yellow elif emission_value < 45: return "#f97316" # Orange else: return "#dc2626" # Red - High def add_delhi_boundary_to_map(m): """Helper to fetch and add Delhi boundary to a map.""" import requests try: geojson_url = "https://raw.githubusercontent.com/datameet/Municipal_Spatial_Data/master/Delhi/Delhi_Boundary.geojson" folium.GeoJson( geojson_url, name="Delhi Boundary", style_function=lambda x: { 'fillColor': 'none', 'color': 'black', 'weight': 3, 'dashArray': '5, 5', 'opacity': 0.6 } ).add_to(m) return True except Exception as e: print(f"Error adding boundary: {e}") return False def generate_emission_heatmap_html(): """Generates a Folium map with Grid Heatmap for CO2 emissions (Clipped to Delhi).""" m = folium.Map( location=[DELHI_LAT, DELHI_LON], zoom_start=10, control_scale=True ) import requests from shapely.geometry import shape, Point from shapely.ops import unary_union add_delhi_boundary_to_map(m) delhi_polygon = None has_boundary = False try: geojson_url = "https://raw.githubusercontent.com/datameet/Municipal_Spatial_Data/master/Delhi/Delhi_Boundary.geojson" resp = requests.get(geojson_url) data = resp.json() features = data.get('features', []) shapes = [shape(f['geometry']) for f in features] delhi_polygon = unary_union(shapes) has_boundary = True except Exception as e: print(f"Error processing boundary for clipping: {e}") # Grid config lat_min, lat_max = 28.40, 28.88 lon_min, lon_max = 76.85, 77.35 step = 0.015 # Approx 1.5km grid size def get_color(value): """Blue-to-red emission heatmap colors.""" if value < 15: return "#eff6ff" # Very Light Blue if value < 25: return "#bfdbfe" # Light Blue if value < 35: return "#93c5fd" # Medium Blue if value < 45: return "#60a5fa" # Blue if value < 55: return "#3b82f6" # Strong Blue if value < 65: return "#f97316" # Orange return "#dc2626" # Red lat = lat_min while lat < lat_max: lon = lon_min while lon < lon_max: center_point = Point(lon + step/2, lat + step/2) if has_boundary and not delhi_polygon.contains(center_point): lon += step continue # Calculate emission based on nearby hotspots base_emission = 20.0 # Baseline # Add contribution from nearby hotspots for hotspot in EMISSION_HOTSPOTS: dist = ((lat - hotspot['lat'])**2 + (lon - hotspot['lon'])**2)**0.5 if dist < 0.05: # ~5km radius of influence contribution = hotspot['emission'] * (1 - dist/0.05) * 0.5 base_emission += contribution # Add random variation val = base_emission + random.uniform(-5, 5) val = max(10, min(80, val)) # Clamp color = get_color(val) folium.Rectangle( bounds=[[lat, lon], [lat + step, lon + step]], color=None, fill=True, fill_color=color, fill_opacity=0.6, tooltip=f"CO₂: {val:.1f} t/day" ).add_to(m) lon += step lat += step return m.get_root().render() def generate_emission_hotspots_html(): """Generates a map with sector-wise emission sources.""" m = folium.Map(location=[DELHI_LAT, DELHI_LON], zoom_start=11) add_delhi_boundary_to_map(m) for spot in EMISSION_HOTSPOTS: emission = spot['emission'] sector = spot['sector'] color = SECTOR_COLORS.get(sector, "#6b7280") # Size based on emission level radius = 8 + (emission / 5) folium.CircleMarker( location=[spot['lat'], spot['lon']], radius=radius, popup=f"{spot['icon']} {spot['name']}
Sector: {sector}
CO₂: {emission} t/day", color=color, fill=True, fill_color=color, fill_opacity=0.7 ).add_to(m) # Add emission value label folium.Marker( location=[spot['lat'], spot['lon']], icon=folium.DivIcon( html=f'
{spot["emission"]:.0f}
' ) ).add_to(m) # Add legend legend_html = '''
Emission Sources (t/day)
● Industry
● Aviation
● Transport
● Power
● Residential
● Commercial
''' m.get_root().html.add_child(folium.Element(legend_html)) return m.get_root().render() def generate_forecast_emission_hotspots_html(year: int): """Generates emission hotspots adjusted for forecast year.""" m = folium.Map(location=[DELHI_LAT, DELHI_LON], zoom_start=11) add_delhi_boundary_to_map(m) # Get scaling factor try: forecast_path = Path(__file__).parent / "emission_forecast_3years_full.csv" if forecast_path.exists(): df = pd.read_csv(forecast_path) df['Date'] = pd.to_datetime(df['Date']) df['Year'] = df['Date'].dt.year baseline_2025 = df[df['Year'] == 2025]['Total_Emission'].mean() year_avg = df[df['Year'] == year]['Total_Emission'].mean() if not pd.isna(baseline_2025) and not pd.isna(year_avg) and baseline_2025 > 0: scaling_factor = year_avg / baseline_2025 else: scaling_factor = 1.0 else: scaling_factor = 1.0 except Exception as e: print(f"Error calculating scaling: {e}") scaling_factor = 1.0 print(f"Emission hotspots for {year}: scaling factor = {scaling_factor:.4f}") for spot in EMISSION_HOTSPOTS: emission = spot['emission'] * scaling_factor sector = spot['sector'] color = SECTOR_COLORS.get(sector, "#6b7280") radius = 8 + (emission / 5) folium.CircleMarker( location=[spot['lat'], spot['lon']], radius=radius, popup=f"{spot['icon']} {spot['name']}
Sector: {sector}
CO₂: {emission:.1f} t/day ({year} Forecast)", color=color, fill=True, fill_color=color, fill_opacity=0.7 ).add_to(m) folium.Marker( location=[spot['lat'], spot['lon']], icon=folium.DivIcon( html=f'
{emission:.0f}
' ) ).add_to(m) # Add legend with year legend_html = f'''
{year} CO₂ Sources (t/day)
● Industry
● Aviation
● Transport
● Power
● Residential
● Commercial
''' m.get_root().html.add_child(folium.Element(legend_html)) return m.get_root().render() def generate_sector_heatmap_html(sector: str): """Generates a Folium map with Grid Heatmap for a specific sector's emissions.""" m = folium.Map( location=[DELHI_LAT, DELHI_LON], zoom_start=10, control_scale=True ) import requests from shapely.geometry import shape, Point from shapely.ops import unary_union add_delhi_boundary_to_map(m) delhi_polygon = None has_boundary = False try: geojson_url = "https://raw.githubusercontent.com/datameet/Municipal_Spatial_Data/master/Delhi/Delhi_Boundary.geojson" resp = requests.get(geojson_url) data = resp.json() features = data.get('features', []) shapes = [shape(f['geometry']) for f in features] delhi_polygon = unary_union(shapes) has_boundary = True except Exception as e: print(f"Error processing boundary for clipping: {e}") # Filter hotspots by sector sector_hotspots = [h for h in EMISSION_HOTSPOTS if h['sector'] == sector] base_color = SECTOR_COLORS.get(sector, "#6b7280") # Grid config lat_min, lat_max = 28.40, 28.88 lon_min, lon_max = 76.85, 77.35 step = 0.015 def get_sector_color(value, max_val=50): """Generate color gradient based on sector color.""" import colorsys # Convert hex to RGB hex_color = base_color.lstrip('#') r, g, b = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) # Vary opacity/lightness based on value intensity = min(1.0, value / max_val) # Blend with white for lower values r = int(255 - (255 - r) * intensity) g = int(255 - (255 - g) * intensity) b = int(255 - (255 - b) * intensity) return f"#{r:02x}{g:02x}{b:02x}" lat = lat_min while lat < lat_max: lon = lon_min while lon < lon_max: center_point = Point(lon + step/2, lat + step/2) if has_boundary and not delhi_polygon.contains(center_point): lon += step continue # Calculate emission based on nearby sector-specific hotspots base_emission = 5.0 # Lower baseline for sector-specific for hotspot in sector_hotspots: dist = ((lat - hotspot['lat'])**2 + (lon - hotspot['lon'])**2)**0.5 if dist < 0.08: # Larger radius for sector contribution = hotspot['emission'] * (1 - dist/0.08) * 0.6 base_emission += contribution val = base_emission + random.uniform(-3, 3) val = max(2, min(60, val)) color = get_sector_color(val) folium.Rectangle( bounds=[[lat, lon], [lat + step, lon + step]], color=None, fill=True, fill_color=color, fill_opacity=0.6, tooltip=f"{sector}: {val:.1f} t/day" ).add_to(m) lon += step lat += step return m.get_root().render() def generate_sector_hotspots_html(sector: str, year: int = None): """Generates a map showing only hotspots from a specific sector.""" m = folium.Map(location=[DELHI_LAT, DELHI_LON], zoom_start=11) add_delhi_boundary_to_map(m) # Get scaling factor if year provided scaling_factor = 1.0 if year: try: forecast_path = Path(__file__).parent / "emission_forecast_3years_full.csv" if forecast_path.exists(): df = pd.read_csv(forecast_path) df['Date'] = pd.to_datetime(df['Date']) df['Year'] = df['Date'].dt.year baseline_2025 = df[df['Year'] == 2025]['Total_Emission'].mean() year_avg = df[df['Year'] == year]['Total_Emission'].mean() if not pd.isna(baseline_2025) and not pd.isna(year_avg) and baseline_2025 > 0: scaling_factor = year_avg / baseline_2025 except Exception as e: print(f"Error calculating scaling: {e}") # Filter and display sector hotspots sector_hotspots = [h for h in EMISSION_HOTSPOTS if h['sector'] == sector] color = SECTOR_COLORS.get(sector, "#6b7280") for spot in sector_hotspots: emission = spot['emission'] * scaling_factor radius = 10 + (emission / 4) label = f"{spot['name']}" if year: label += f" ({year})" folium.CircleMarker( location=[spot['lat'], spot['lon']], radius=radius, popup=f"{spot['icon']} {spot['name']}
CO₂: {emission:.1f} t/day{' (' + str(year) + ' Forecast)' if year else ''}", color=color, fill=True, fill_color=color, fill_opacity=0.8 ).add_to(m) folium.Marker( location=[spot['lat'], spot['lon']], icon=folium.DivIcon( html=f'
{emission:.0f}
' ) ).add_to(m) # Sector-specific legend sector_icons = { "Industry": "🏭", "Aviation": "✈️", "Transport": "🚗", "Power": "⚡", "Residential": "🏠", "Commercial": "🏢" } icon = sector_icons.get(sector, "📍") legend_html = f'''
{icon} {sector} Emissions
● {len(sector_hotspots)} locations
{f'Year: {year}' if year else 'Baseline Data'}
''' m.get_root().html.add_child(folium.Element(legend_html)) return m.get_root().render() def render_map_to_png(html_content, output_path): """Renders the HTML content to a PNG image using Selenium headless Chrome.""" from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.chrome.service import Service from webdriver_manager.chrome import ChromeDriverManager import uuid unique_id = uuid.uuid4() tmp_html_path = os.path.abspath(f"temp_emission_map_{unique_id}.html") with open(tmp_html_path, "w", encoding="utf-8") as f: f.write(html_content) chrome_options = Options() chrome_options.add_argument("--headless") chrome_options.add_argument("--window-size=800,600") chrome_options.add_argument("--disable-gpu") chrome_options.add_argument("--no-sandbox") driver = None try: service = Service(ChromeDriverManager().install()) driver = webdriver.Chrome(service=service, options=chrome_options) driver.get(f"file:///{tmp_html_path}") time.sleep(2) driver.save_screenshot(output_path) print(f"Emission map image saved to {output_path}") except Exception as e: print(f"Error rendering map to PNG: {e}") import traceback traceback.print_exc() finally: if driver: driver.quit() if os.path.exists(tmp_html_path): os.remove(tmp_html_path) if __name__ == "__main__": print("Generating emission hotspots map...") html = generate_emission_hotspots_html() render_map_to_png(html, "emission_map_test.png")