End-user perspective of low-cost sensors for outdoor air pollution monitoring

Highlights

• Low-cost sensors can enable high density monitoring of air pollutants.

• We review the performance of low-cost sensors for monitoring air pollution.

• Data quality is a major concern for the measurements from low-cost sensors.

• The sensors should be frequently calibrated under final deployment conditions.

• Sensor aging and manufacturing variability should be accounted during measurements.

Abstract

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.

Introduction

Outdoor air pollution is a major problem in the 21st century, attributing to ~ 3.7 million deaths globally (WHO, 2014). Today, ~ 92% of the world's population lives in regions where air pollutant levels are higher than the WHO-specified limits (WHO, 2016). In addition, air pollution is also responsible for global climate change (Ramanathan and Feng, 2009) and environmental problems such as acid rain (Menz and Seip, 2004), haze (Li and Zhang, 2014, Xu et al., 2013), ozone depletion (Solomon, 1999, Solomon et al., 1986), and damage to crop (Avnery et al., 2011a, Avnery et al., 2011b, Van Dingenen et al., 2009). Thus, there is a global drive to tackle this problem (Fenger, 2009).

Traditionally, air pollution is monitored by measuring concentrations of various pollutants such as carbon monoxide (CO), ozone (O₃), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and particulate matter (PM) at fixed sites by using accurate and expensive instrumentation (Kumar et al., 2014, Mouzourides et al., 2015, Sharma et al., 2013). Monitoring sites in the EU are determined based on the EU Air Quality Directive 2008/50/EC, which clearly defines the minimum number of fixed monitoring stations for each target pollutant based on the air pollution levels, population, and coverage area. Such sites are generally spread in and around cities and provide temporal concentrations (typically hourly) of different pollutants. Cities in developed countries might contain one official monitoring station covering about 100,000 people as opposed to covering millions of people in cities of developing and highly polluted countries. For example, there are around 300 monitoring sites in the UK (DEFRA, 2011) and around 600 in India (CPCB, 2017). However, they are insufficient to provide accurate information about the spatial distribution of pollutants or identify pollution hotspots, and even more so for developing countries. Even though pollutant dispersion models can be used to address this issue, their accuracy is rather limited (Holmes and Morawska, 2006, Kumar et al., 2011, Kumar et al., 2015, Vardoulakis et al., 2003).

Recent advancements in the field of sensors, digital electronics, and wireless communication technology have led to the emergence of a new paradigm for air pollution monitoring (Hagler et al., 2013, Kumar et al., 2015). This paradigm aims to gather high-resolution spatiotemporal air pollution data by using a ubiquitous network of low-cost sensors for monitoring real-time concentrations of different air pollutants, which can be then utilised for a variety of air pollution management tasks such as (i) supplementing conventional air pollution monitoring; (ii) improving the link between pollutant exposure and human health; (iii) emergency response management, hazardous leak detection, and source compliance monitoring; and (iv) increasing community's awareness and engagement towards air quality issues.

Though there is no universally agreed definition of a “low-cost” sensor, anything costing less than the instrumentation cost required for demonstrating compliance with the air quality regulations can be termed as low-cost. However, the cost should be as low as possible to achieve the above-mentioned aims of a sensor-based system for monitoring air pollution, so that widespread deployment is commercially feasible. Thus, in this paper, the term low-cost sensor is used either for designating sensors costing only a few 10's of US dollars or for sensing kits/nodes/platforms costing a few 100's of US dollars. The higher cost of sensing kits is expected since they typically include one or more sensors, microprocessor, data-logger, memory card, battery, and display.

Several review articles have already addressed this emerging area of sensor-based air quality monitoring (Table 1). A majority of these articles focus on the needs, benefits, challenges, and future directions of a sensor-based pollution monitoring paradigm for different applications (Castell et al., 2013, Kumar et al., 2015, Kumar et al., 2016a, Kumar et al., 2016b, Snyder et al., 2013, White et al., 2012). A few others discuss emerging sensor technologies for monitoring gaseous and/or particulate air pollutants (Aleixandre and Gerbolesb, 2012, Bhanarkar et al., 2016, Koehler and Peters, 2015, White et al., 2012, Zhou et al., 2015). On-going air quality management campaigns using sensor networks were reviewed in some other articles (Castell et al., 2013, Thompson, 2016). However, none of them have comprehensively addressed the crucial aspect of performance assessment of low-cost sensors for monitoring different air pollutants vis-à-vis their more expensive counterparts. Jovašević-Stojanović et al. (2015) provided some information about selecting low-cost PM sensors based on their specifications and the monitoring objectives. However, they did not include gaseous sensors, and several new research articles on performance assessment of PM sensors have come up since then. Williams et al. (2014b) provided guidelines regarding sensor selection but these guidelines are open ended and leave it for end-users to carefully review a sensor's performance before purchasing it. Without a proper understanding of the performance characteristics of the available low-cost sensors, the end-users cannot be expected to effectively deploy them for achieving an effective sensor-based management of air pollution (Castell et al., 2017, Jovašević-Stojanović et al., 2015, Judge and Wayland, 2014, Lewis and Edwards, 2016). Addressing this crucial issue forms the motivation for this review article.

We recognise a need for providing scientific guidance to end-users in choosing appropriate low-cost sensors by matching user requirements with sensor performance. Through a comprehensive review of the scientific literature, we assessed the performance of several commercially available low-cost sensors for measuring PM and gaseous pollutants in the outdoor environment, i.e., CO, O₃ and NO₂. We could not review the low-cost sensors for measuring SO₂ due to a dearth of studies on their performance assessment. Additionally, we have provided recommendations for end-users in selecting low-cost sensors for monitoring outdoor air pollutants. Finally, we have outlined the challenges faced by the end-users in deploying low-cost sensors for monitoring air pollution and the future research directions to overcome them.