AIAir Monitor: AI Air Quality Monitoring Platform - @ Google AiStudio - Live Demo

Project Overview

AIAir Monitor is an advanced, AI-driven platform developed to provide governmental and industrial regulators with real-time, comprehensive monitoring and deep analysis of city-wide industrial emissions and ambient air quality (AQI). By centralizing vast datasets and leveraging the power of the Gemini API, the platform moves beyond simple reporting to generate detailed, actionable corrective action plans, transforming environmental policy from reactive to predictive.

Technology Stack: React (TypeScript), Tailwind CSS, D3.js (Visualization), Mapbox GL (Geospatial Mapping), Gemini API (Corrective Action Planning & Trend Analysis). 

My Role: Product Designer, AI Integration Lead, Data Visualization Architect.

1. The Problem: Data Fragmentation and Non-Actionable Insights

Environmental and industrial compliance teams face significant challenges in managing the sheer scale and complexity of air quality data:

• Data Fragmentation: Sensor readings (NOx, SO2, PM2.5, Ozone) often come from disparate sources (city sensors, industrial stacks) and require manual correlation to understand their source and impact.

• Slow Intervention: Identifying the root cause of an AQI spike often takes hours or days, allowing pollution events to peak before interventions can be implemented.

• Analysis Paralysis: Raw data is difficult for policymakers and city planners to translate into effective, enforceable policy changes. Simple alerts lack the context needed for immediate, high-impact decisions.

The core challenge was to design a platform that could instantaneously synthesize complex environmental data and provide expert-level, actionable recommendations.

2. The Solution: Predictive Modeling and AI Policy Generation

AIAir Monitor utilizes deep learning and the Gemini API to turn streams of raw pollutant data into a dynamic, city-wide environmental intelligence map.

• Real-Time Geospatial Visualization: A dynamic Mapbox interface displays real-time AQI heat maps and overlays industrial emission data, pinpointing specific facilities or traffic corridors responsible for pollutant spikes.

• Predictive Trend Analysis: AI algorithms identify emerging pollution trends (e.g., smog formation risk based on weather forecasts and industrial output patterns) up to 72 hours in advance.

• AI-Generated Corrective Action Plans (Gemini API): When a regulatory threshold is breached or predicted, the Gemini API is fed the historical data, current readings, weather conditions, and regulatory mandates. It instantly generates a prioritized, step-by-step recommendation report, such as:

  • Example Recommendation: "Temporarily reduce throughput at Plant A by 20% in the next 6 hours and reroute heavy goods vehicle traffic from Sector 4 to 9 between 06:00 and 10:00."

• Unified Data Storytelling: Complex multivariate data is simplified into clear, color-coded widgets and dynamic charts, ensuring rapid comprehension by non-technical stakeholders.

3. The Design Process: Transparency and Critical Hierarchy

The design process focused on ensuring the highest level of trust and operational clarity, especially given the platform's role in public safety and industrial compliance.

• UX Research – Decision Flow Mapping: We mapped the emergency response process to ensure critical alerts (e.g., PM2.5 exceeding dangerous levels) are presented immediately with one-click access to the AI-generated corrective plan.

• Data Visualization (D3.js): Custom visualizations were developed to clearly show the correlation between cause (industrial output, traffic) and effect (AQI spike), building user confidence in the AI’s root-cause analysis.

• Multi-Role Dashboarding: Designed separate, tailored views for different users: a High-Level Policy View (focusing on long-term trends and compliance) and an Operational View (focusing on real-time sensor data and immediate alerts).

• Mobile Responsiveness: Built on Tailwind CSS, the platform ensures regulators can receive and act upon critical alerts and plans instantly, even when away from the main command center.

4. The Final Product: Data-Driven Environmental Governance

AIAir Monitor successfully provides a critical tool for environmental management, transforming data into measurable improvements in air quality.

• 75% Faster Response Time: The AI-driven corrective plans allow regulatory action to be taken in minutes, not hours, significantly limiting the duration and severity of pollution events.

• Data-Driven Policy Enforcement: Provides irrefutable, contextualized data to support industrial compliance actions, improving regulatory effectiveness.

• Improved Public Health Outcomes: By enabling predictive intervention, the platform helps city officials proactively reduce citizen exposure to harmful pollutants.