Introduction
LightningChart Python
Setting Up Python Environment
Loading and Processing Data
Visualizing Data with LightningChart Python
Conclusion

Air Quality Application Tutorial with LightningChart Python & Open Meteo's API

Tutorial

Assisted by AI

Follow this air quality application tutorial using Open Meteo's API and LightningChart Python data visualization library for advanced data analysis.

Soroush Sohrabian

Software Developer

Air-Quality-Application-Tutorial-Cover

Introduction to the Project

Based on the importance of air pollution in our life and its impact on health, It is essential to monitor air quality in real-time.

This air quality application tutorial with LightningChart Python & Open Meteo focuses on creating a real-time air quality monitoring dashboard using Python. This application gets data from Open-Meteo’s Air Quality API and uses LightningChart Python for visualization.

About the Dataset and Open-Meteo

Open-Meteo is a free weather API that provides historical, real-time, and forecast data on weather conditions, air quality, and environmental parameters. The dataset used in this project includes:

PM10 (Particulate Matter 10)
PM2.5 (Particulate Matter 2.5)
Nitrogen Dioxide (NO₂)
Ozone (O₃)
Carbon Monoxide (CO)
Sulfur Dioxide (SO₂)
UV Index
Temperature, Humidity, and Wind Direction

By integrating Open-Meteo’s API, we can fetch past, current, and future air quality conditions and visualize them using interactive dashboards.

LightningChart Python

LightningChart Python is a high-performance data visualization library that offers real-time and different interactive charting. It allows the creation of 2D and 3D visualizations that makies it ideal for real-time weather monitoring.

LCPython1

Features and Chart Types Used in the Project

In this project, we used several LightningChart components:

Line Charts: To show air quality trends over time.
Bar Charts: For displaying historical, current and forecasted pollutant concentrations.
Polar Charts: To analyze PM2.5 by wind direction.
Radar Charts: For comparing multiple air quality parameters in real-time.
Gauge Charts: To display UV index levels.
3D Charts: To visualize weather and air quality conditions.

Performance Characteristics

LightningChart Python provides:

GPU acceleration that enables smooth visualization even with large datasets.
Real-time updates which makes it ideal for monitoring live weather changes.
Low CPU and memory usage which improves dashboard responsiveness.

Setting Up Python Environment

Before running the project, install Python and the other required libraries using:

pip install requests pandas pytz lightningchart

Overview of Libraries Used

NumPy: For handling and processing weather data.
Pandas: For handling time-series air quality data
Requests: To fetch data from the Open-Meteo API.
Pytz: For handling timezone conversions
Trimesh: For 3D weather condition models.
LightningChart: To create interactive visualizations.

Setting Up Your Development Environment

Set up a virtual environment:

python -m venv rf_analysis_env
source rf_analysis_env/bin/activate  # On Windows: rf_analysis_env\Scripts\activate

Use Visual Studio Code (VSCode) for a streamlined development experience.

Loading and Processing Data

To create this Open Meteo weather application, we will fetch the weather data using the following function:

import requests
import pandas as pd

API_URL = "https://air-quality-api.open-meteo.com/v1/air-quality"
params = {
    "latitude": 60.1699,
    "longitude": 24.9384,
    "hourly": "pm10,pm2_5,nitrogen_dioxide,ozone,carbon_monoxide",
    "past_days": 1,
    "forecast_days": 1,
    "timezone": "auto"
}
response = requests.get(API_URL, params=params)
data = response.json()

Handling and Preprocessing the Data

After fetching data, it is moved into a DataFrame for easier analysis and visualization.

df = pd.DataFrame(data["hourly"])
df["time"] = pd.to_datetime(df["time"])

Visualizing Data with LightningChart Python

Here are some examples that shows how we created key visualizations:

Line chart to display PM10 trends

import lightningchart as lc

dashboard = lc.Dashboard(rows=6, columns=8, theme=lc.Themes.Dark)
chart = dashboard.ChartXY(column_index=0, row_index=0, column_span=8, row_span=4)
chart.set_title("PM10 Levels Over Time")

series = chart.add_line_series(data_pattern="ProgressiveX")
for index, row in df.iterrows():
    series.add([row["time"].timestamp() * 1000], [row["pm10"]])

dashboard.open()

Polar Chart (PM2.5 by Wind Direction

# Create a dashboard
dashboard = lc.Dashboard(rows=1, columns=1)

# Create a Polar Chart
polar_chart = dashboard.PolarChart(column_index=0, row_index=0)
polar_chart.set_title("PM2.5 Concentration by Wind Direction (Scaled)")

# Generate random wind direction and PM2.5 concentration data
for i in range(8):
    sector = polar_chart.add_sector()
    wind_direction = i * 45  # Simulated wind directions
    pm2_5_val = random.uniform(5, 50)  # Simulated PM2.5 values
    sector.set_name(f"PM2.5 {pm2_5_val:.1f} μg/m³")
    sector.set_amplitude_start(0)
    sector.set_amplitude_end(pm2_5_val / 5)  # Scaled
    sector.set_angle_start(wind_direction - 10)
    sector.set_angle_end(wind_direction + 10)
    sector.set_color(lc.Color(0, 207, 255))
    sector.set_stroke(color=lc.Color(0, 161, 255), thickness=1)

dashboard.open()

Radar (Spider) Chart (European Air Quality Indicators)

# Create a dashboard
dashboard = lc.Dashboard(rows=1, columns=1)

# Create Radar Chart
radar_chart = dashboard.SpiderChart(column_index=0, row_index=0)
radar_chart.set_title("European Air Quality Indicators")

# Define AQI Components
aqi_components = {
    "PM2.5": 30,
    "PM10": 22,
    "NO₂": 15,
    "O₃": 18,
    "SO₂": 10
}

# Add axis labels
for key in aqi_components.keys():
    radar_chart.add_axis(key)

# Add data series
series_radar = radar_chart.add_series()
series_radar.set_name("Current AQI Data")
series_radar.add_points([{"axis": key, "value": value} for key, value in aqi_components.items()])

dashboard.open()

Gauge Chart (UV Index)

# Create a dashboard
dashboard = lc.Dashboard(rows=1, columns=1)

# Create Gauge Chart
gauge_chart = dashboard.GaugeChart(column_index=0, row_index=0)
gauge_chart.set_title("Current UV Index")

# Configure gauge properties
gauge_chart.set_angle_interval(start=225, end=-45)
gauge_chart.set_interval(start=0, end=12)
gauge_chart.set_bar_thickness(15)
gauge_chart.set_value(5)  # Example value
gauge_chart.set_value_indicators([
    {"start": 0, "end": 2, "color": lc.Color("green")},
    {"start": 2, "end": 5, "color": lc.Color("yellow")},
    {"start": 5, "end": 7, "color": lc.Color("orange")},
    {"start": 7, "end": 10, "color": lc.Color("red")},
    {"start": 10, "end": 12, "color": lc.Color("darkred")},
])

dashboard.open()

Bar Chart (Air Quality Components like PM10, PM2.5, NO2)

# Create a dashboard
dashboard = lc.Dashboard(rows=1, columns=1)

# Create a Bar Chart
bar_chart = dashboard.BarChart(column_index=0, row_index=0, vertical=True)
bar_chart.set_title("PM2.5 and PM10 Levels")

# Sample data
categories = ["PM2.5", "PM10", "NO2", "O3", "CO"]
values = [random.uniform(5, 50) for _ in categories]

# Add data to bar chart
data_list = [{"category": cat, "value": val} for cat, val in zip(categories, values)]
bar_chart.set_data(data_list)

dashboard.open()

3D Chart (Air Quality Models)

# Create a dashboard
dashboard = lc.Dashboard(rows=1, columns=1)

# Create 3D Chart
chart_3d = dashboard.Chart3D(row_index=0, column_index=0)
chart_3d.set_title("Air Quality 3D Visualization")

# Load a 3D model (Example: air quality model)
object_air_quality = trimesh.load("path/to/air_quality.obj")  # Replace with actual file path

# Extract model geometry
vertices = object_air_quality.vertices.flatten().tolist()
indices = object_air_quality.faces.flatten().tolist()
normals = object_air_quality.vertex_normals.flatten().tolist()

# Add model to the 3D chart
model_air_quality = chart_3d.add_mesh_model()
model_air_quality.set_model_geometry(vertices=vertices, indices=indices, normals=normals)
model_air_quality.set_scale(1.5).set_model_location(0, 0.3, 0)

dashboard.open()

Real-Time and the Next Six Hours Forecasting Dashboard

Weekly Forecasting Dashboard

Customizing the Open Meteo Weather Application Visualizations

Color Palettes: Used different gradient colors to show better data intensities.
Legends & Labels: Improved readability of weather parameters.

Conclusion

This air quality application tutorial demonstrates how to integrate Open-Meteo’s Air Quality API with LightningChart Python to create real-time environmental monitoring dashboard. By visualizing historical, real-time and forecasted data, users can track air quality trends and make decisions about outdoor activities based on pollution control dashboard.

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