Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollut

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data

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

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.

Mapping urban air quality in near real-time using observations from low-cost sensors and model information.

Deriving high-resolution urban air pollution maps using mobile sensor nodes

The availability of novel, high-resolution pollution maps enables a wide range of new application scenarios, which were not possible before. In this paper, we combine high-resolution pollution maps available for the city of Zurich, Switzerland, with road network data to analyze how much urban dwellers can reduce their exposure to air pollution by not taking the shortest path between origin and destination but a healthier and slightly longer alternative route. We introduce a new weight function to assess the exposure on each street segment and evaluate the benefits of the healthier path. Finally, we efficiently implement the algorithm as stand-alone application for iOS and Android devices. The app helps city residents to understand and reduce their exposure to air pollutants.

Health-optimal routing in urban areas

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Deriving high-resolution urban air pollution maps using mobile sensor nodes

Health-optimal routing in urban areas