Urban Air Quality Sensor Network

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(333)

# Urban air quality monitoring
n_sensors = 100

# City grid with some vertical spread (buildings)
x = np.random.uniform(0, 10, n_sensors)  # km
y = np.random.uniform(0, 8, n_sensors)   # km
z = np.random.exponential(15, n_sensors) + 2  # meters above ground

# PM2.5 values (higher near ground, varies by location)
pm25 = 50 - z * 0.5 + np.random.exponential(20, n_sensors)
pm25 = np.clip(pm25, 5, 150)

# Size by uncertainty
uncertainty = np.random.uniform(20, 80, n_sensors)

fig = plt.figure(figsize=(10, 8), facecolor='#ffffff')
ax = fig.add_subplot(111, projection='3d', facecolor='#ffffff')

# Color: green=good, yellow=moderate, red=unhealthy
scatter = ax.scatter(x, y, z, c=pm25, cmap='RdYlGn_r', s=uncertainty, 
                     alpha=0.7, edgecolors='#374151', linewidths=0.5)

cbar = plt.colorbar(scatter, ax=ax, shrink=0.6, pad=0.1)
cbar.set_label('PM2.5 (μg/m³)', color='#1f2937', fontsize=10)
cbar.ax.tick_params(colors='#6b7280')

ax.set_xlabel('X (km)', color='#1f2937', fontsize=10)
ax.set_ylabel('Y (km)', color='#1f2937', fontsize=10)
ax.set_zlabel('Height (m)', color='#1f2937', fontsize=10)
ax.set_title('Urban Air Quality Sensor Network', color='#1f2937', fontsize=14, fontweight='bold', pad=20)

ax.tick_params(colors='#6b7280', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#e5e7eb')
ax.yaxis.pane.set_edgecolor('#e5e7eb')
ax.zaxis.pane.set_edgecolor('#e5e7eb')

ax.view_init(elev=25, azim=35)
plt.tight_layout()
plt.show()