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.

It has been shown that the exposure to airborne particulate matter is one of the most significant environmental risks people face. Since indoor environment is where people spend the majority of time, in order to protect against this risk, the origin of the particles needs to be understood: do they come from indoor, outdoor sources or both? Further, this question needs to be answered separately for each of the PM mass/number size fractions, as they originate from different sources. Numerous studies have been conducted for specific indoor environments or under specific setting. Here our aim was to go beyond the specifics of individual studies, and to explore, based on pooled data from the literature, whether there are generalizable trends in routes of exposure at homes, schools and day cares, offices and aged care facilities. To do this, we quantified the overall 24h and occupancy weighted means of PM, PM and PN - particle number concentration. Based on this, we developed a summary of the indoor versus outdoor origin of indoor particles and compared the means to the WHO guidelines (for PM and PM) and to the typical levels reported for urban environments (PN). We showed that the main origins of particle metrics differ from one type of indoor environment to another. For homes, outdoor air is the main origin of PM and PM but PN originate from indoor sources; for schools and day cares, outdoor air is the source of PN while PM and PM have indoor sources; and for offices, outdoor air is the source of all three particle size fractions. While each individual building is different, leading to differences in exposure and ideally necessitating its own assessment (which is very rarely done), our findings point to the existence of generalizable trends for the main types of indoor environments where people spend time, and therefore to the type of prevention measures which need to be considered in general for these environments.

No abstract

There has been considerable scientific interest in personal exposure to ultrafine particles (UFP). In this study, the inhaled particle surface area doses and dose relative intensities in the tracheobronchial and alveolar regions of lungs were calculated using measured 24-h UFP time series of school children personal exposures. Bayesian hierarchical modeling was used to determine mean doses and dose intensities for the various microenvironments. Analysis of measured personal exposures for 137 participating children from 25 schools in the Brisbane Metropolitan Area showed similar trends for all participating children. Bayesian regression modeling was performed to calculate the daily proportion of children's total doses in different microenvironments. The proportion of total daily alveolar doses for home, school, commuting, and other were 55.3%, 35.3%, 4.5%, and 5.0%, respectively, with the home microenvironment contributing a majority of children's total daily dose. Children's mean indoor dose was never higher than the outdoor's at any of the schools, indicating there were no persistent indoor particle sources in the classrooms during the measurements. Outdoor activities, eating/cooking at home, and commuting were the three activities with the highest dose intensities. Children's exposure during school hours was more strongly influenced by urban background particles than traffic near the school.