Smart Governance Framework for Automated Urban AirQuality Decision Support

Abstract

Urban centers in tropical developing nations face severe air pollution crises, yet a critical policy inertia gap persists between real-time sensor data acquisition and dynamic municipal enforcement. This study aims to develop and evalu- ate the Smart Air Quality Governance (SAQG) framework, an automated, artificial intelligence (AI)-enhanced Environmental Decision-Support System (EDSS) designed to bridge passive monitoring with legally binding adminis- trative action. Employing a qualitative policy gap analysis and semi-structured key informant interviews (n = 12) with municipal environmental, transporta- tion, and public health authorities in a tropical metropolitan area (Jakarta, In- donesia), this paper examines the institutional bottlenecks delaying emergency responses. Unlike traditional passive monitoring platforms, the novelty of the SAQG framework lies in its active execution engine, which directly links real- time PM2.5 sensor networks to a three-tiered automated policy matrix, enforc- ing immediate interventions such as adaptive signal timing, industrial emission caps, and mandatory work-from-home orders. The results demonstrate that em- bedding AI-driven predictive triggers into local government regulatory architec- tures significantly reduces administrative response latency during atmospheric crises. This study provides local authorities with a practical blueprint to tran- sition from reactive observation to proactive urban resilience, directly advanc- ing UN Sustainable Development Goals (SDG 3: Good Health and Well-Being, SDG 11: Sustainable Cities and Communities, and SDG 13: Climate Action).