Examining Different Placement Strategies for Indoor Environmental Quality Sensors in Office Environments

Collecting Indoor Environmental Quality (IEQ) data from an occupant's immediate surroundings can provide personalized insights for healthy environmental conditions aligned with occupant preferences, but effective sensor placement for data accuracy and reliability has not been thoroughly explored. This paper explores various positioning of IEQ multi-sensing devices at individual workstations in typical office settings, aiming to identify sensor placements that most accurately reflect the environmental conditions experienced by occupants. We examined five unique positions close to an occupant (above and below the monitor, right side of the desk, ceiling, and chair backrest), two orientations, and three desk locations characterized by different lighting levels, thermal and airflow conditions. Data on temperature, humidity, carbon dioxide (CO2), particulate matters (PM1, PM2.5, PM10), illuminance, and sound were collected over a 2-week longitudinal experiment, followed by short-term experiments simulating common pollution events such as coughing and sneezing. Principal Component Analysis, Spearman's rank correlation, R2, and Mean Absolute Error were applied to identify the position and orientation that best captures the most information and matches breathing zone measurements. It was found that above the monitor position, facing the occupant, best captures the IEQ conditions experienced by the occupant.

An AI-driven framework for rapid and localized optimizations of urban open spaces

As urbanization accelerates, open spaces are increasingly recognized for their role in enhancing sustainability and well-being, yet they remain underexplored compared to built spaces. This study introduces an AI-driven framework that integrates machine learning models (MLMs) and explainable AI techniques to optimize Sky View Factor (SVF) and visibility, key spatial metrics influencing thermal comfort and perceived safety in urban spaces. Unlike global optimization methods, which are computationally intensive and impractical for localized adjustments, this framework supports incremental design improvements with lower computational costs and greater flexibility. The framework employs SHapley Adaptive Explanations (SHAP) to analyze feature importance and Counterfactual Explanations (CFXs) to propose minimal design changes. Simulations tested five MLMs, identifying XGBoost as the most accurate, with building width, park area, and heights of surrounding buildings as critical for SVF, and distances from southern buildings as key for visibility. Compared to Genetic Algorithms, which required approximately 15/30 minutes across 3/4 generations to converge, the tested CFX approach achieved optimized results in 1 minute with a 5% RMSE error, demonstrating significantly faster performance and suitability for scalable retrofitting strategies. This interpretable and computationally efficient framework advances urban performance optimization, providing data-driven insights and practical retrofitting solutions for enhancing usability and environmental quality across diverse urban contexts.

Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates

In rapidly urbanizing regions, designing climate-responsive urban forms is crucial for sustainable development, especially in dry arid-climates where urban morphology has a significant impact on energy consumption and environmental performance. This study advances urban morphology evaluation by combining Urban Building Energy Modeling (UBEM) with machine learning methods (ML) and Explainable AI techniques, specifically Shapley Additive Explanations (SHAP). Using Tehran's dense urban landscape as a case study, this research assesses and ranks the impact of 30 morphology parameters at the urban block level on key energy metrics (cooling, heating, and lighting demand) and environmental performance (sunlight exposure, photovoltaic generation, and Sky View Factor). Among seven ML algorithms evaluated, the XGBoost model was the most effective predictor, achieving high accuracy (R2: 0.92) and a training time of 3.64 seconds. Findings reveal that building shape, window-to-wall ratio, and commercial ratio are the most critical parameters affecting energy efficiency, while the heights and distances of neighboring buildings strongly influence cooling demand and solar access. By evaluating urban blocks with varied densities and configurations, this study offers generalizable insights applicable to other dry-arid regions. Moreover, the integration of UBEM and Explainable AI offers a scalable, data-driven framework for developing climate-responsive urban designs adaptable to high-density environments worldwide.