Smart Air Quality Monitoring and Pollution Mapping System for Nepal

A smart air quality monitoring and pollution mapping system is a strong IoT final-year project for engineering students in Nepal.

The project can use ESP8266, ESP32, or Raspberry Pi to collect environmental data such as particulate matter, CO₂, temperature, and humidity and send the readings to a custom server for storage, visualization, mapping, and analysis.

Instead of building only:

Air Quality Sensor -> ESP32 -> Display

a stronger Bachelor-level project can include:

Multiple Monitoring Nodes
        ↓
Wi-Fi / GSM / LoRa
        ↓
Custom API / Server
        ↓
Custom Database
        ↓
Web Application
        ↓
Mobile Application
        ↓
Pollution Map + Historical Analysis + Alerts

Project Objective

The main objective is to measure air quality from one or more locations and make the data available through a digital monitoring platform.

Possible parameters include:

• PM2.5
• PM10
• CO₂
• temperature
• humidity
• optional VOC or gas measurements

The system can show both the current condition and historical pollution trend.

Why This Project Is Relevant in Nepal

Air pollution is an important environmental and public-health concern in many parts of Nepal, particularly in growing urban areas, traffic corridors, construction zones, and valley environments.

A student project can be designed for locations such as:

• Kathmandu Valley
• Biratnagar
• Pokhara
• Nepalgunj
• Birgunj
• university campuses
• roadside locations
• industrial areas
• schools and hospitals

The same architecture can also be used globally for community air-quality monitoring, research, smart-city applications, and environmental sensing.

Suggested System Architecture

A complete project can use several distributed monitoring nodes.

PM2.5 / PM10 Sensor
CO₂ Sensor
Temperature / Humidity Sensor
        |
        v
ESP8266 / ESP32
        |
        v
Wi-Fi / GSM / LoRa
        |
        v
Custom API / Server
        |
        v
Custom Database
        |
   +----+---------+
   |              |
   v              v
Web App       Mobile App
   |
   v
Pollution Map
Historical Graphs
Alerts
Reports

For a larger project, a Raspberry Pi can be added as an edge gateway between the sensor nodes and cloud/database layer.

ESP8266, ESP32 and Raspberry Pi Options

ESP8266

ESP8266 is suitable for a low-cost monitoring node when the system mainly requires:

• Wi-Fi
• one or two sensors
• periodic data upload
• basic local processing

It is useful for small prototypes and indoor or campus deployments with reliable Wi-Fi.

ESP32

ESP32 is generally the stronger choice for a final-year project because it provides:

• more GPIO
• more serial interfaces
• more processing capability
• easier multi-sensor integration
• better expansion options
• Wi-Fi and Bluetooth

It is well suited for an outdoor or multi-sensor air-quality node.

Raspberry Pi

A Raspberry Pi can be used when the project requires:

• Linux
• Python-based data processing
• local database storage
• edge analytics
• local dashboard hosting
• communication with several ESP32/ESP8266 nodes
• temporary operation when internet connectivity is unavailable

A stronger architecture can be:

ESP8266 / ESP32 Sensor Nodes
            ↓
       Wi-Fi / MQTT
            ↓
      Raspberry Pi
       Edge Gateway
            ↓
 Local Processing / Buffer
            ↓
      Cloud / Server
            ↓
        Database
            ↓
   Web + Mobile Apps

Hardware Components

A typical monitoring node can include:

Component	Purpose
ESP8266 or ESP32	Main sensor-node controller
Raspberry Pi	Optional edge gateway
PM2.5 / PM10 sensor	Measures particulate pollution
CO₂ sensor	Measures carbon-dioxide concentration
Temperature/humidity sensor	Measures environmental conditions
GPS module	Optional node location
Display	Optional local readings
Wi-Fi/GSM/LoRa module	Communication
Power supply	Powers the monitoring node
Solar and battery system	Optional outdoor deployment

For field deployment, sensor choice, airflow, enclosure design, power stability, and calibration should be considered carefully.

Data Collection

Each monitoring node can periodically create a record such as:

Device ID
Timestamp
Latitude
Longitude
PM2.5
PM10
CO2
Temperature
Humidity
Signal Strength
Battery Voltage

Example:

AQ-NODE-03
2026-08-12 13:30
27.7172
85.3240
42 µg/m³
68 µg/m³
620 ppm
29.4 °C
61 %
-69 dBm
4.02 V

The data can then be sent to the backend using HTTP, MQTT, or another suitable protocol.

Custom Database

A custom database makes the project significantly stronger than relying only on a ready-made IoT dashboard.

The database can store:

• node information
• sensor readings
• location
• timestamps
• device status
• battery level
• alerts
• calibration information
• maintenance records

Possible technologies include PostgreSQL, MySQL, MongoDB, or another suitable database.

A basic structure can be:

devices
sensor_readings
locations
alerts
maintenance
users

This allows the same data to be used by both the web and mobile applications.

Pollution Mapping

One of the most important features of this project is displaying sensor readings geographically.

Each monitoring node can have:

Latitude
Longitude
Device ID
Current PM2.5
Current PM10
Status
Last Update

The web application can then display the nodes on a map.

Example:

Green  -> Lower pollution
Yellow -> Moderate condition
Orange -> High pollution
Red    -> Very high pollution

The exact thresholds should be defined according to the air-quality standard or index selected for the project.

Students should clearly document which standard they use rather than inventing their own health categories.

Web Application

The custom web application can provide the main monitoring dashboard.

Useful pages include:

Live Monitoring

Show:

• current PM2.5
• current PM10
• CO₂
• temperature
• humidity
• node status

Pollution Map

Display all monitoring nodes geographically with current readings.

Historical Data

Allow users to select:

• monitoring location
• parameter
• date
• time range

and view historical graphs.

Node Management

Show:

• device ID
• location
• last communication time
• battery voltage
• signal strength
• sensor status

Reports

Generate daily, weekly, or monthly summaries.

Mobile Application

A mobile application can provide quick access for researchers, students, field teams, or users.

Possible features include:

• current air-quality readings
• nearby monitoring stations
• map view
• historical graphs
• pollution alerts
• device status
• notifications
• favorite monitoring locations

The mobile application should use the same backend API and custom database as the web application.

Alerts and Notifications

The system can generate alerts when:

PM2.5 exceeds configured threshold
PM10 exceeds configured threshold
CO2 becomes unusually high
Sensor stops responding
Monitoring node goes offline
Battery becomes low

Notifications can be delivered through:

• mobile push notification
• web dashboard
• email
• SMS
• messaging integration

The alert threshold should be configurable from the backend or administration dashboard.

Multi-Location Monitoring

A single sensor gives information about only one point.

A stronger final-year project uses multiple sensor nodes.

For example:

Node 1 -> Campus Gate
Node 2 -> Main Road
Node 3 -> Residential Area
Node 4 -> Construction Area
Node 5 -> Open Field

The readings can then be compared to investigate how pollution varies by location and time.

This makes the project suitable for pollution mapping and spatial analysis, rather than only basic sensor monitoring.

Historical Data Analysis

Once the system has collected enough data, students can analyze:

• hourly pollution trends
• morning vs evening conditions
• weekday vs weekend differences
• location-to-location differences
• relationship between PM and humidity
• seasonal changes
• unusual pollution events

A Raspberry Pi or backend server can also perform basic local analytics before data is shown on the dashboard.

Optional Edge Processing with Raspberry Pi

A Raspberry Pi can make the project more advanced.

It can receive data from several ESP32 or ESP8266 nodes and perform:

• local MQTT brokering
• temporary data storage
• filtering
• averaging
• validation
• anomaly detection
• local visualization
• internet-failure buffering

Example:

5 ESP32 Sensor Nodes
        ↓
      MQTT
        ↓
 Raspberry Pi Gateway
        ↓
Local Database / Processing
        ↓
Internet Available?
   /             \
 Yes             No
  |               |
  v               v
Cloud Upload    Store Locally
                  |
                  v
            Upload Later

This is useful for demonstrating edge computing together with IoT.

Communication Options

Wi-Fi

Suitable for campuses, laboratories, offices, or locations with reliable internet access.

GSM/LTE

Useful for monitoring stations where local Wi-Fi is unavailable.

LoRa

Useful when several low-data sensor nodes must communicate over a wider area to one gateway.

The communication technology should be selected according to the actual deployment location rather than only because the module is available.

Suggested Development Stages

Stage 1: Single Sensor Node

Connect PM, CO₂, temperature, and humidity sensors to ESP8266 or ESP32.

Stage 2: Data Transmission

Send readings using HTTP or MQTT.

Stage 3: Custom Database

Store all readings with timestamps and device IDs.

Stage 4: Web Dashboard

Display live readings and historical graphs.

Stage 5: Pollution Map

Add geographic locations and map-based visualization.

Stage 6: Mobile Application

Develop a mobile interface using the same backend.

Stage 7: Multiple Nodes

Deploy several monitoring nodes and compare locations.

Stage 8: Raspberry Pi Edge Gateway

Optionally add a Raspberry Pi for local buffering, processing, or gateway operation.

Stage 9: Testing and Evaluation

Evaluate sensor consistency, communication reliability, database performance, uptime, and differences between locations.

Possible Final-Year Research Questions

Students can strengthen the academic side of the project by investigating questions such as:

• How does PM2.5 vary between different locations in the selected Nepal study area?
• How closely do multiple low-cost monitoring nodes agree with each other?
• How does humidity affect particulate-sensor readings?
• Which communication method is most reliable for the selected deployment?
• Can a Raspberry Pi edge gateway reduce data loss during internet outages?
• What daily or seasonal pollution patterns can be identified from historical data?

If comparison with an official or reference-grade station is available, students should clearly distinguish comparison/validation from formal traceable calibration.

Final Project Outcome

A complete final-year project can demonstrate:

Environmental Sensors
        ↓
ESP8266 / ESP32 Nodes
        ↓
Wi-Fi / GSM / LoRa
        ↓
Optional Raspberry Pi Edge Gateway
        ↓
Custom Backend / API
        ↓
Custom Database
        ↓
Web Application
        +
Mobile Application
        ↓
Live Monitoring
Pollution Mapping
Historical Analysis
Alerts
Reports

For Nepali engineering students, this project combines embedded systems, environmental sensing, IoT communication, databases, web development, mobile development, mapping, and data analysis in one practical system.

It is strongly relevant to Nepal while remaining applicable to air-quality monitoring projects anywhere in the world.