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Low-cost sensors (LCSs) emerge as a popular tool for urban micro-climate studies by offering dense observational coverage. This study evaluates the performance of PurpleAir (PA) sensors for ambient temperature monitoring—a key but underexplored aspect of their use. While widely used for particulate matter, PA sensors’ temperature data remain underutilized and lack thorough validation. For the first time, this research evaluates their accuracy by comparing PA temperature measurements with collocated high-precision temperature data loggers across a dense urban network in a humid subtropical U.S. county. Results show a moderate correlation with reference data (r = 0.86) but an average overestimation of 3.77 °C, indicating PA sensors are better suited for identifying temperature trends but not for precise applications like extreme heat events. We also developed and compared eight calibration methods to create a replicable model using readily available crowdsourced data. The best-performing model reduced RMSE and MAE by 51% and 47%, respectively, and achieved an R2 of 0.89 compared to the uncalibrated scenario. Finally, the practical application of PA temperature data for identifying heat wave events was investigated, including an assessment of associated uncertainties. In sum, this work provides a crucial evaluation of PA’s temperature monitoring capabilities, offering a pathway for improved heat mapping, multi-hazard vulnerability assessments, and public health interventions in the development of climate-resilient cities. 

Abstract Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R²value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health. 

The health risks associated with particulate matter pollution (PM_2.5) highlight the importance of comprehending key drivers of its spatial and temporal variability. While explanatory modeling is widely used for statistical inference in such applications, the role of wind variables (speed, direction) and their interplay with built environment are often overlooked. This study addresses this gap through a mobile air quality campaign in a suburban area over 10 days for each of three distinct seasons. We developed four innovative wind-based buffers to account for the wind factors and assess the impact of LiDAR-derived 3D built environment structure on PM_2.5variability at local and regional scales. Our second objective is to assess the predictive capabilities of wind-based variables. Results indicate that in our low-elevation, low-rise building study area, the built environment does not emerge as a significant factor, contrary to findings in larger, denser urban areas. Wind direction proves more effective in capturing concentration fluctuations than wind speed, and its interaction with pollution source orientation can change pollution dynamics. This study introduces new insights and methodologies for incorporating wind-related factors into the analysis of air pollution dynamics, offering opportunities for further investigation in various urban settings.