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.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region

“…This section discusses various analyses done on the data collected during the considered deployment. Although a single manufacturer developed the PM sensors that were used in the studied Atmos boxes, their performance in measuring PM 10 has not yet been tested in field conditions. First, we checked the sensors for consistency, and then we deployed them in the field next to the inlets of different research-grade PM measuring instruments.…”

“…Total numbers of hourly averaged data points at MRIU, Faridabad from SMPS-OPS, Atmos PM sensor, and SMPS-APS were 717, 1124, and 766, respectively. The overall mean PM 10 After collecting sufficient PM10 data during the deployment period of 21 January 2018-16 March 2018, time series were plotted for the two study sites, as shown in Figure 4a The total number of hourly averaged data points at IITD, New Delhi from SMPS-OPS and the Atmos PM sensor was 832 and 1029, respectively. Mean PM10 concentrations measured by SMPS-OPS and Atmos were 182.3 ± 84.2 μg•m −3 and 181.0 ± 111.5 μg•m −3 , respectively.…”

“…We compare our sensors in measuring PM 10 levels with the co-located SMPS-OPS and SMPS-APS. We collect PM 10 data during an intense measurement campaign period of seven weeks conducted for pollution monitoring at two selected sites in the Delhi National Capital Region (NCR), India. Merging data obtained from the certified reference instruments result in approximate overall PM mass concentrations.…”

“…Merging data obtained from the certified reference instruments result in approximate overall PM mass concentrations. We validate our PM sensors for PM 10 data with the aid of statistical tools.…”

Extensive evaluation and classification of low‐cost dust sensors in laboratory using a newly developed test method

“…Several researchers adopted their own calibration and evaluation procedures to test the low-cost sensors including co-location measurements in ambient environment and lab-scale measurements using standard test particles or ambient airborne particles. 15,18,[20][21][22][23][24][25] However, the accuracy of measurement data has always been questioned by many researchers and scientists, 15,26,27 and the limited number of sensors have been assessed due to the time-consuming evaluation process, which might not be able to represent the overall performance of hundreds of low-cost sensors in the commercial market. Besides, the lack of standardized methods for performance evaluation of low-cost sensors results in difficulties in intercomparisons of the sensors evaluated in different studies.…”