Particulate Matter Measurements

Particulate matter is generated from many different activities, and particulates smaller than 2.5 and 10 pm are well-knowm for their association with adverse respiratory and cardiovascular health effects. Most portable particulate matter instruments with real-time sensing use an optical method for measurements (Loh et al. 2017). High-end sensors include lasers, and others use a different photodiode to produce light through which particles are counted as they pass.

Currently, available particulate matter sensors are best suited for stationary rather than portable monitoring. Even outdoor applications may be problematic for these instruments w'here there are higher particulate matter concentrations and relative humidity (Ueberham and Schlink 2018).

Noise Measurements

Hazardous noise is a direct cause for hearing loss, and it causes other negative health effects such as stress, increased blood pressure, and increased heart rate. For these reasons, in addition to interference with voice communications, it is important to verify noise levels are acceptable.

Noise is one of the most easily measured physical stressors. There are apps, many of which are free, available for smartphones as w'ell as inexpensive devices readily available. However, many of these common measurement tools do not meet standard specifications. For example, most microphones in smartphones are aligned to the human voice (300-400Hz and 40-60dB) and may not reliably measure sounds outside those frequencies or sound range (Kardous and Shaw' 2014). Furthermore, many apps and devices may not be calibrated, do not provide weighting options (e.g., dBA), or lack the ability to analyze noise frequency. Despite their limitations, they have been useful for citizen scientists and noise mapping projects (Maisonneuve et al. 2009).

Internet of Things

Portability of sensors enables the ability for measurement data to be integrated into internet-linked mobile devices such as smartphones. With such data uploaded to an internet platform, healthcare professionals and researchers may offer possibilities to a world of information at a scale previously unattainable. Not only could physicians monitor environmental triggers for adverse health outcomes such as asthma in individuals, but public health scientists could ascertain trends in environmental exposures, especially if the data is linked to locations with geographic information systems (GIS).

This concept easily aligns with the IoT realm. One definition for IoT is “the networking capability that allows information to be sent to and received from objects and devices using the Internet” (Merriam-Webster 2019). IoT is a global network infrastructure of numerous connected devices that rely on sensory, communication, networking, and information processing technologies (Tan and Wang 2010). Although a foundational technology for IoT is radio-frequency identification (RFID), wireless sensor networks (WSNs) are also key. Barcodes, smartphones, social networks, and cloud computing support IoT as well (Xu et al. 2014).

Data Management

Current environmental exposure measurement systems largely rely on manual processing and analysis of data, which do not leverage real-time data acquisition and communications. One of the main obstacles in making portable environmental exposure sensor data useful is in processing the potentially large data sets and providing salient information in real time (Bae et al. 2013). The volume of data generated from personal sensors could result in intolerant latency, making a significant challenge to deliver important data in a real-time manner (Mukhopadhyay 2015). In order to optimize such a system, the amount of data chosen would need to be robust enough to avoid blind spots, but not so voluminous for practical storage and processing time.

There are established data analysis algorithms that can find patterns, outliers, and classifications. Statistical algorithms, such as Bayesian Item Response Theory and Item Response Theory, can predict the combined effect of spatial-temporal variables on the susceptibility of an individual to a specific health effect (Bae et al. 2013). Bae et al. (2016) also developed a Voroni map method for estimating environmental exposures based on a probabilistic routing aggregation that copes with uncertainty.

Location Tracking

GIS using location tracking such as global positioning system (GPS) technology is a nearly universal feature in smartphones. Linking sensors with these mobile devices would give a valuable dimension to sensor data analysis. Bae et al. (2013) proposed a system in which sensors are interfaced with Android mobile phones, and corresponding GPS data is collected real time, transferred to servers, stored in an extensible markup language (XML) format, and later integrated with other GIS datasets.

System Architecture

Although an environmental exposure system integrated with mobile internet-enabled devices is not yet a reality, there are certain features the architecture of such a system should include (Bae et al. 2013).

Security, Reliability, and Usability

There are potential security concerns for users, especially when exposure data is connected to an individual’s personal information. Ethical issues may also be raised when considering that data ownership and protection rules are not clear. If sensor data is collected as part of research, participants must have informed consent and clearly understand the terms of participation and how the data will be used. However, companies that own data can typically deal with data privacy as they wish without consulting users, and data protection laws vary among countries.


The IoT universe will continue to grow. An exposome monitoring system will include new algorithms and components that must be accommodated into an existing framework.


In order to be useful, the system must be able to process large data sets and extract meaningful data in a timely fashion. Advances in computing and related disciplines will help usher in this capability.

Simplicity and Organization

Ideally, this system will be reasonably simplicity with a high degree of organization. Otherwise, there is a potential for high complexity without a suitable framework. However, simplicity may be elusive given the lack of a widely accepted IoT platform and heterogeneity of its underlying networks (Xu et al. 2014).

Feasible Systems for Today

The possibilities of networked wearable portable sensors connected to GIS data that provide real-time analysis of the external exposome over the internet is a tantalizing prospect. However, work remains to make this scenario a reality. In the meantime, a monitoring system built around a smaller set of portable sensors as well as stationary or publically available sources is a more feasible scenario (Loh et al. 2017). There have also been demonstrated advancements in wearable sensors for single analytes. For example, Li et al. (2019) developed a wearable IoT aldehyde sensor based on an electrochemical fuel cell and integrated with a cloud-based informatics system.


Personal sensors for monitoring the external exposome are improving in accessibility, reliability and ease of use, and promise to one day be a practical IoT option.

Coupled with future advances in statistics, data mining techniques, computing power, and careful sharing of data resources while protecting personal data, we may realize a sophisticated and powerful network of environmental exposure sensors.


The views expressed are those of the author and do not necessarily reflect the official policy or position of the Air Force, the Department of Defense, or the U.S. Government.

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