A Sensor-Driven Adaptive Framework for Urban Air-Quality and Microclimate Management Integrating Mechanical and Nature-Based Controls

Rapid urbanization, population growth, and intensifying anthropogenic activity have substantially increased pressures on urban air quality and microclimate stability. Globally, urban regions account for a dominant share of anthropogenic emissions of particulate matter (PM₂.₅ and PM₁₀), carbon dioxide (CO₂), nitrogen oxides (NOₓ), sulfur dioxide (SO₂), and volatile organic compounds (VOCs). These pollutants are strongly associated with adverse public-health outcomes, ecosystem degradation, and climate-related stressors. Epidemiological assessments consistently link PM₂.₅ exposure to increased risks of cardiovascular disease, respiratory morbidity, impaired lung development, and premature mortality, with global burden estimates attributing several million excess deaths annually to ambient air pollution. Gaseous pollutants such as NOₓ and SO₂ contribute both directly to respiratory irritation and indirectly to secondary aerosol formation, acid deposition, and photochemical smog, while CO₂—though not acutely toxic at ambient concentrations—remains the dominant driver of anthropogenic radiative forcing and urban heat accumulation. Conventional urban air-quality management strategies have historically focused on emission reduction at the source, regulatory compliance, and long-term urban planning interventions. These approaches are indispensable for long-term environmental protection and climate mitigation; however, growing empirical evidence indicates that they are often insufficient to address short-term exposure peaks, fine-scale spatial heterogeneity, and rapidly evolving urban environmental conditions. Even in cities with declining average pollutant concentrations, localized exceedances and transient pollution events remain common, particularly near traffic corridors, street canyons, industrial boundaries, and densely populated public spaces. Urban atmospheric environments are inherently dynamic, non-uniform systems, governed by complex interactions among emission sources, meteorological processes, boundary-layer dynamics, built-environment geometry, and human activity patterns. As a result, pollutant concentrations can vary by factors of 2–10 over distances of tens to hundreds of meters, and may fluctuate substantially on time scales of minutes to hours. Such variability is most pronounced at neighborhood and street-canyon scales, where dispersion is constrained and human exposure is highest. Epidemiological and toxicological studies increasingly demonstrate that acute exposure to short-duration concentration peaks, rather than long-term averages alone, plays a disproportionate role in triggering respiratory, cardiovascular, and heat-stress-related health impacts. This evidence highlights a critical gap in prevailing air-quality management paradigms and underscores the need for complementary strategies that actively manage concentration dynamics and exposure in real time, alongside long-term emission reduction goals. Recent advances in environmental sensing technologies, low-power electronics, wireless communication, and data analytics have fundamentally changed the capacity to observe urban atmospheric conditions. Dense sensor networks can now continuously monitor air-quality and microclimatic variables—such as PM₂.₅, PM₁₀, CO₂ concentration, temperature, humidity, and airflow—at spatial resolutions of tens to hundreds of meters and temporal resolutions of seconds to minutes. These developments provide unprecedented empirical insight into urban pollution dynamics and enable detection of transient events, diurnal accumulation cycles, and localized hotspots that are invisible to sparse regulatory monitoring stations. In parallel, advances in control theory, cyber-physical systems, and smart-infrastructure design have demonstrated the feasibility of adaptive, feedback-driven regulation in complex and uncertain environments. Demand-responsive control strategies are now widely used in building systems, clean-room facilities, and energy networks to optimize performance under variable conditions. At the same time, research in urban ecology and climate science has firmly established the role of vegetation in regulating atmospheric composition and microclimates through photosynthetic carbon uptake, particulate interception, evapotranspiration, and shading. Quantitative studies show that urban vegetation can reduce near-surface particulate concentrations by 10–40% in localized settings and lower daytime air temperatures by 1–5 °C, contributing to heat-island mitigation and improved thermal comfort. Despite these advances, technological and biological interventions are typically treated as independent or loosely connected solutions within existing urban environmental frameworks. Sensor networks are often limited to monitoring and reporting; mechanical filtration systems operate on fixed schedules; and green infrastructure is deployed primarily for aesthetic or long-term sustainability benefits, rather than as part of an integrated control strategy. The absence of a unified, operational framework that dynamically coordinates sensing, control, mechanical mitigation, and biological regulation represents a critical limitation in current urban environmental management practice. This research addresses this gap by proposing a unified, systems-level framework for urban climate and air-quality management that integrates distributed environmental sensing, adaptive feedback control, mechanical air-absorption and filtration, and vegetation-assisted regulation into a coherent operational architecture. Rather than treating air quality as a static condition evaluated periodically, the framework conceptualizes the urban atmosphere as a controllable dynamic system, governed by mass-balance principles and responsive to real-time intervention. The proposed approach explicitly emphasizes concentration and exposure management, focusing on the suppression of short-term pollution peaks and thermal stress through anticipatory, sensor-driven control actions. By combining engineered and biological mechanisms within a closed-loop control structure, the framework enables targeted, scalable, and resilient environmental regulation across multiple urban scales—from individual buildings to neighborhoods and entire city districts. Mechanical systems provide rapid, high-intensity responses during acute pollution or heat events; vegetation delivers continuous background regulation and microclimatic stabilization; and sensor-driven adaptive control ensures that each component operates only when and where it is most effective. This coordinated strategy is designed to enhance public-health protection, reduce unnecessary energy consumption, and improve the robustness of urban environmental infrastructure under variable and uncertain conditions. Overall, this study contributes a scientifically rigorous and policy-relevant model for adaptive urban environmental control that bridges atmospheric science, environmental engineering, control systems, and ecosystem-based regulation. By aligning exposure science with modern sensing and control capabilities, the proposed framework offers a practical pathway toward data-driven, resilient, and health-supportive urban environments in the context of accelerating urbanization and climate change....Please check the attachment for details