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Abstract Particulate matter poses significant risks to respiratory and cardiovascular health. Monitoring ambient particulate matter concentrations can provide information on potential exposures and inform mitigation strategies, but ground-based measurements are sparse. Data fusion approaches that integrate data from multiple sources can complement existing observation networks and reveal insights that single-sensor data might miss to better manage pollutant exposure risks. However, data fusion approaches face multiple challenges, including incompatible measurement units, varying data resolutions, and differing levels of uncertainty. As a result, the optimal method for data fusion remains an open question. Here, we propose a probabilistic spatiotemporal model, based on the stochastic advection–diffusion (SAD) equation, as a data fusion method to process multimodal air quality data to predict hourly concentrations of fine particulate matter (PM_2.5). We employ a variational inference method to calibrate the probabilistic model using ground-level observations and the numerical output of two simulation models. We then evaluate the prediction performance of our model for two scenarios: (1) incorporating simulation outputs and ground-level observations from sparse regulatory-grade stations and (2) using ground-level observations from both low-cost and regulatory-grade stations. For the first scenario, the data fusion method reduces prediction error by 14% compared to the nearest regulatory-grade air monitor located 20 km away. For the second scenario, error is reduced by 40% compared to the nearest regulatory-grade monitor and 11% compared to the nearest low-cost sensor located approximately 1 km away. The model captures 78% of observed data within a 75% confidence interval across both scenarios, demonstrating its ability to accurately represent uncertainty. Our findings demonstrate that the proposed SAD model can effectively integrate multimodal data to provide improved prediction of particulate matter concentrations at high spatial resolution. Model outputs can inform individual and community-level decision-making to mitigate air pollutant exposures. 

Cities in coastal regions are particularly prone to experiencing environmental impacts arising from both natural and human causes. Additionally, climate change imposes stressors on communities along shorelines. Smart city concepts can assist communities in informed decision-making, building on technology-based approaches to measure and evaluate various aspects of everyday life in cities. While smart city concepts have gained significant momentum over past decades, this study presents an approach to integrate the human factor from the early stages of developing smart cities. The active engagement of residents underscores the pursuit of data accessibility and equity within urban governance. This study outlines a comprehensive participatory framework integrating local knowledge and stakeholder engagement into designing and implementing an environmental monitoring data dashboard for coastal communities. By leveraging insights from multiple disciplines – including urban design & planning, civil engineering, computer science, and public policy – this research seeks to create a sociotechnical network that effectively addresses the complex interplay between technology and human factors. To do so, this study follows the Participatory Action Research paradigm, deploying a mixed-methods approach for developing a data dashboard tailored to the specific needs of communities and their environmental challenges. The Texas Coastal Bend Region serves as a case-study to demonstrate the development and application of a six-step participatory framework, developing a sociotechnical monitoring network on flooding, air quality, and water quality. The outcomes of this study serve as a guide for engaged scholars and designers in developing participatory frameworks for designing data dashboards addressing academic and non-academic constituents, residents seeking informed insights, and decision-makers entrusted with the stewardship of urban development in a vulnerable context. 

As human industrial technology advances, coastal communities face threats from both nature and industry. Rising tropical storms and sea levels lead to disruptive floods, endangering residents, industries, and vital infrastructure. While economic benefits come from new industrial facilities, expanded shipping, and water desalination, they also bring increased emissions, habitat destruction, and altered hydrology, harming air, water, and land resources. To be able to effectively and affordably monitor the air and water quality in coastal communities is vital. In this paper, we present an environmental monitoring system for coastal communities with low-cost sensors and capabilities to integrate and present data from multiple sources. The sensing system, powered by regenerative and city electricity sources, uses LoRa Wanto wirelessly transmit seawater and air quality data to LoRaWAN gateways, where it will be further forwarded to a server system for storage, analysis, and visualization. A proof of concept monitoring system is deployed in a coastal community in Texas. We present some of the data gathered and provide analysis on the cost benefits. 

Various techniques in computer vision have been proposed for water level detection. However, existing methods face challenges during adverse conditions including snow, fog, rain, and nighttime. In this paper, we introduce a novel approach that analyzes images for water level detection by incorporating a deblurring process to increase image clarity. By employing real-time object detection technique YOLOv5, we show that the proposed approach can achieve significantly improved precision, during both daytime and nighttime under under challenging weather circumstances. 

Coastal flooding is a severe and recurring problem, as evidenced by recent disasters that have caused significant damage for coastal communities. A community’s ability to mitigate the effects of coastal flooding depends on the local context and its adaptive capacity. Although past research has highlighted the important role that non-governmental organizations play in building adaptive capacity to support effective adaptation, few studies have focused specifically on rural, community-based nonprofits. To fill this gap, we employ a mixed-methods approach to evaluate the role of the Ingleside on the Bay Coastal Watch Association (IOBCWA), a resident-led, community-based nonprofit, in building adaptive capacity to coastal flooding in the City of Ingleside on the Bay (IOB; pop. 800), located in the Coastal Bend Region of Texas. By applying a grounded theory framework, we show that IOBCWA has improved the adaptive capacity of IOB through five primary activities: engaging in community organizing, boosting advocacy and outreach, implementing evidence-based data collection, building capacity among residents, and developing regional communication networks. Our findings are further examined using the Regional Fingerprint tool (Hirschfeld et al., 2020) to assess progress toward building regional adaptive capacity. We identify a need for more formalized policies, enhanced regional partnerships, and broader inclusion of socially vulnerable groups to address environmental challenges. Overall, this work highlights the important role that small, community-based nonprofits like IOBCWA play in building community adaptive capacity and suggests the need for a more comprehensive regional approach with participation from multiple stakeholders to address challenges related to coastal flooding. 

In the process of developing a smart city framework, sensor data are crucial to enable cities and communities to make informed decisions on future plans. Involving community-based organizations and residents is an integral component of this process to ensure equity and accessibility of data. This study aims to develop a sociotechnical network to (1) identify vulnerability zones; (2) measure data on flooding, air, and water quality; and (3) inform community members and decision-makers through a data dashboard. A small coastal town in the Texas Coastal Bend region is utilized as a case study. Methodologically, this study utilizes participatory action research to frame a mixed-methods approach toward developing a data dashboard. This research project is a practical guide for engaged scholars in the social sciences, engineering, and urban design fields. The outcomes include recommendations for the engaged community and provide a data-dashboard targeting academic and non-academic audiences, residents, and decision-makers.