Akwasi Bonsu Asumadu-Sakyi

Bio: Akwasi Bonsu Asumadu-Sakyi is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Thermal comfort & HVAC. The author has an hindex of 5, co-authored 12 publications receiving 322 citations.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Abstract: Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Low-cost PM2.5 sensors: An assessment of their suitability for various applications

Abstract: Recently, there has been a substantial increase in the availability and use of low-cost particulate matter sensors in a wide range of air quality applications. They carry the promise of revolutionising air quality monitoring, yet considerable reservations exist regarding their performance and capabilities, thus hindering the broader-scale utilization of these devices. In order to address these concerns and assess their feasibility and accuracy for various applications, we evaluated six low-cost PM2.5 sensors (the Sharp GP2Y1010AU0F, Shinyei PPD42NS, Plantower PMS1003, Innociple PSM305, Nova SDS011 and Nova SDL607) in laboratory and field conditions using two combustion aerosols, concrete dust and ambient particles. In assessing the performance of these sensors, we focussed on indicators such as the linearity, accuracy and precision, critically differentiating between these qualities, and employed inter-comparison (the coefficient of determination, R2). In the laboratory, all sensors responded well, with an R2 > 0.91 when the PM2.5 concentration was > 50 µg m–3, as measured by the DustTrak. In particular, the PMS1003 demonstrated good accuracy and precision in both laboratory and ambient conditions. However, some limitations were noted for the tested sensors at lower concentrations. For example, the Sharp and Shinyei sensors showed poor correlations (R2

Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)

Abstract: Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair , so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.

Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment

Abstract: Exposure to ambient particulate matter (PM) air pollution is a leading risk factor for morbidity and mortality, associated with up to 8.9 million deaths/year worldwide. Measurement of personal exposure to PM is hindered by poor spatial resolution of monitoring networks. Low-cost PM sensors may improve monitoring resolution in a cost-effective manner but there are doubts regarding data reliability. PM sensor boxes were constructed using four low-cost PM micro-sensor models. Three boxes were deployed at each of two schools in Southampton, UK, for around one year and sensor performance was analysed. Comparison of sensor readings with a nearby background station showed moderate to good correlation (0.61 < r < 0.88, p < 0.0001), but indicated that low-cost sensor performance varies with different PM sources and background concentrations, and to a lesser extent relative humidity and temperature. This may have implications for their potential use in different locations. Data also indicates that these sensors can track short-lived events of pollution, especially in conjunction with wind data. We conclude that, with appropriate consideration of potential confounding factors, low-cost PM sensors may be suitable for PM monitoring where reference-standard equipment is not available or feasible, and that they may be useful in studying spatially localised airborne PM concentrations.

Wildfire and prescribed burning impacts on air quality in the United States.

Abstract: Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM 2.5 ) concentrations for exten...