"Photovoltaic Environmental and Air-quality Records for PV Learning (PEARL)"

"The increasing adoption of photovoltaic systems in urban regions is significantly impacted by environmental stressors such as dust deposition, air pollution and variable meteorological conditions. Existing datasets often emphasize electrical performance or limited weather observations, creating a gap in capturing the combined influence of particulate matter, soiling and atmospheric variability on PV yield. Prior research has shown that air pollutants and suspended particulates reduce solar irradiance and accelerate performance degradation, yet publicly available datasets that comprehensively record these effects remain scarce. To address this gap, we present the Photovoltaic Environmental and Air-quality Records for PV Learning (PEARL), a multivariate time-series dataset collected from a grid-connected PV installation at Netaji Subhas University of Technology (NSUT), Delhi, India. PEARL integrates meteorological variables (temperature, solar radiation, wind speed), air-quality indicators (PM2.5, PM10, NO2), electrical performance parameters (AC\/DC power, current, voltage) and dust concentration indices, recorded at fixed intervals using calibrated sensors and inverter monitoring systems. The dataset is standardized, quality-checked and openly accessible, enabling research in PV yield forecasting, performance loss attribution and environmental impact assessment. By releasing PEARL, this work contributes a validated resource to advance reproducibility, data-driven innovation and collaborative research in sustainable urban energy systems."