Low-cost sensors have the potential to create dense air monitoring networks that help enhance our understanding of pollution exposure and variability at the individual and neighborhood-level; however, sensors can be easily influenced by environmental conditions, resulting in performance inconsistencies across monitoring settings. During summer 2020, 20 low-cost particulate sensors were deployed with a reference PM2.5 monitor in Denton, Texas in preparation for calibration. However, from mid to late-summer, dust transported by the Saharan Air Layer moved through the North Texas region periodically, influencing the typical monitoring pattern exhibited between low-cost sensors and reference instruments. Traditional modeling strategies were adapted to develop a new approach to calibrating low-cost particulate sensors. In this study, data collected by sensors was split according to a novel dust filter into dust and non-dust subsets prior to modeling. This approach was compared with building a single model from the data, as is typically done in other studies. Random forest and multiple linear regression algorithms were used to train models for both strategies. The best performing split-model strategy, the multiple linear regression models split according to dust and non-dust subsets (combined R2 = 0.65), outperformed the best performing single-model strategy, a random forest model (R2 = 0.49). …

The Dallas-Forth Worth Metroplex (DFW) serves as a diverse resettlement location for globally displaced refugees. While research examines how the nation impacts refugee resettlement, studies that examine the role of the city and community in placemaking are still lacking. In city resettlement investigations, research often focuses broadly on advocacy and political movements rather than the impacts of local-level structures and policies. In this paper, I develop an evaluation model using Jenny Phillimore's categories for successful refugee resettlement that examines how structural barriers, community interactions, and resource accessibility affect space and place for refugee populations. Through an ethnography of Chin and Rohingya refugee communities in DFW, I explore the differences between community-settled and state-settled refugee groups and the idea of an integrated resettlement program. Additionally, I argue that refugees who choose their settlement location in the United States are empowered and thus have a stronger connection to their host community than state-settled refugees. For example, in interviews, the Chin emphasized their ownership of Lewisville and feelings of home, while the Rohingya expressed feelings of placelessness and dispossession in Dallas. As governments push towards an entirely privatized system of refugee resettlement, this research argues for an integrated method that draws upon federal …