Transformational IoT sensing for air pollution and thermal exposures

Abstract

Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.

Closing the loop between environment, brain and mental health: how far we might go in real-life assessments?

“…In this section, we focus on the question, which environmental aspects are of interest for mental health and how can we capture them with high spatio-temporal granularity using “real-life” approaches integrating sensor-aided ESM and moving beyond. Pantelic et al [39 ▪▪ ] suggested a three-level approach for assessing environmental factors, starting with data on a human scale (biometric and behavioral data; for example, ESM, see section 2.1 [40]), extended by a building (indoor sensors [41]) and urban scale (outdoor sensors [42]). This approach can be seen as an extension of the sensor-aided ESM assessments and calls for entire ecosystems of interconnected sensors [43,44 ▪ ,45], from smart homes [46 ▪ ,47], digital contexts [48 ▪▪ ], to smart cities [49,50 ▪ ].…”

From COVID-19 Crisis to Digital Health Care Innovation

“…The sensor detects the presence of airborne SARS-CoV-2 using a combined optical and thermal sensor; to date, the sensor can differentiate the difference between two different coronaviruses (Qiu et al, 2020). A company, Senseware, has developed a distributed network of sensors for use in buildings to detect and mitigate SARS-CoV-2 (Pantelic, 2020). The sensors use the following four approaches: 1) continuous measurement of CO 2 as a ventilation proxy, 2) air-handling filters with continuous monitoring of particulate size, 3) relative humidity monitoring and humidity regulation between 40 percent and 60 percent, and 4) ion generators to kill COVID-19 viruses with continuous monitoring of ozone levels to ensure safe oxygen levels.…”

The impact of automated control of indoor air pollutants on cardiopulmonary health, environmental comfort, sleep quality in a simulated apartment: A crossover experiment protocol

“…During the day, a person can be exposed to air pollution in the residential setting (Assimakopoulos et al, 2018;Tran et al, 2021), at the workplace (Saraga et al, 2014), during the commute (Karanasiou et al, 2014;Good et al, 2016), walking (Li et al, 2020) and in other indoor and outdoor environments (Levy et al, 2002). Personal exposure can be assessed with stationary environmental sensors on urban and building scale (Pantelic et al, 2022), but available air pollution level information might be far away from the actual exposure location (Pantelic et al, 2022). Personal air pollution exposures can be more accurately measured using portable air quality monitoring devices (Meng et al, 2009;de Kluizenaar et al, 2017;Sagona et al, 2018;Koehler et al, 2019), but personal monitors are relatively bulky and noisy; therefore, considering some of their limitations, this type of measurement is not feasible over long periods (Steinle et al, 2013).…”

“…Recent advancements in sensing technology have enabled the application of sensors like TVOC, PM 2.5 , O 3 , and NO 2 , with high granularity for monitoring (Kumar et al, 2016;Derbez et al, 2018;Schieweck et al, 2018;Demanega et al, 2021;Omidvarborna et al, 2021;Shen et al, 2021;Yang et al, 2021). A recent literature review of IoT-enabled technologies showed the potential for environmental control with IoT-enabled sensors and devices but also showed that regardless of the technical possibilities, no studies investigated their application and effectiveness (Pantelic et al, 2022). In the current study, we developed the air quality control ecosystem consisting of IoT-enabled air quality interventions and sensing.…”