To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

Accurate reporting of annual site-level methane emissions is increasingly required under emerging regulatory and voluntary frameworks in the oil and gas (O&G) sector. In this study, we present an event-based framework for estimating annual methane emissions from upstream O&G facilities. The framework applies the Emission Event Data Model (EEDM) to spatiotemporally aggregate multi-scale emissions data into discrete events using the concept of Allen’s interval algebra and spatial proximity. Following the creation of events, emissions are categorized into three groups: resolved (with a known emission rate and duration), partially resolved (with a known emission rate but an unknown duration), and unresolved (with an unknown emission rate and duration) to facilitate various management and emissions estimation approaches. To estimate emissions and its associated uncertainties using events, we developed three Monte Carlo-based approaches, which are (1) estimating durations for partially resolved events using null detections and leak generation and natural repair processes; (2) estimating emissions from unresolved events based on the minimum detection limit of deployed technologies; and (3) estimating emissions from unresolved events using probabilistic occurrence and best-fit rate and duration distributions. This framework enables emissions to be reported and verified at a uniform scale rather than at the individual observation scale. To demonstrate the estimation of emissions using this framework, we created two case studies. In both studies, we performed emissions estimation using emission observations synthesized from real emissions data from an upstream O&G site. The proposed framework and methodologies can be implemented in voluntary initiatives such as Veritas 2.0 and the Oil & Gas Methane Partnership 2.0 and the event data model can be applied as a data management framework for the Measurement, Monitoring, Reporting, and Verification (MMRV) framework.