The rise of low-cost sensing for managing air pollution in cities
01 Feb 2015-
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
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TL;DR: In this article , the authors demonstrate the use of a network of low-cost particulate matter (PM) sensors to gather indoor and outdoor PM2.5 data from seven locations in the urban Seattle area, along with a personal exposure monitor worn by a resident living in one of these locations during the 2020 Washington wildfire event.
10 citations
TL;DR: In this article , the authors evaluated the transferability of calibration models developed at co-location sites to the rest of an air pollution monitoring network, even after appropriate cross-validation, and provided a series of transferability metrics for calibration metrics that can be used in other LCS networks and some suggestions as to which calibration model would be most useful for achieving different end-goals.
Abstract: Abstract. Ambient fine particulate matter (PM2.5) pollution is a major health
risk. Networks of low-cost sensors (LCS) are increasingly being used to
understand local-scale air pollution variation. However, measurements from
LCS have uncertainties that can act as a potential barrier to effective
decision making. LCS data thus need adequate calibration to obtain good
quality PM2.5 estimates. In order to develop calibration factors, one
or more LCS are typically co-located with reference monitors for short or
long periods of time. A calibration model is then developed that
characterizes the relationships between the raw output of the LCS and
measurements from the reference monitors. This calibration model is then
typically transferred from the co-located sensors to other sensors in the network.
Calibration models tend to be evaluated based on their performance only at
co-location sites. It is often implicitly assumed that the conditions at the
relatively sparse co-location sites are representative of the LCS network
overall and that the calibration model developed is not overfitted to the
co-location sites. Little work has explicitly evaluated how transferable
calibration models developed at co-location sites are to the rest of an LCS
network, even after appropriate cross-validation. Further, few studies have
evaluated the sensitivity of key LCS use cases, such as hotspot detection, to
the calibration model applied. Finally, there has been a dearth of research
on how the duration of co-location (short-term or long-term) can impact these
results. This paper attempts to fill these gaps using data from a dense
network of LCS monitors in Denver deployed through the city's “Love My Air”
program. It offers a series of transferability metrics for calibration
models that can be used in other LCS networks and some suggestions as to
which calibration model would be most useful for achieving different end
goals.
9 citations
TL;DR: In this paper , the authors presented data-driven techniques for sensor calibration techniques, which can be used in designing similar low-cost sensor-based monitoring systems, and the experimental results showed that calibration with AH has better performance as compared with RH.
Abstract: With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.
9 citations
TL;DR: In this paper , the authors employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM2.5), NO2, and O3 over a 6 month period for selected urban centers in three of the four macroregions in Uganda.
Abstract: Air pollution is prevalent in cities and urban centers in developing countries including sub-Saharan Africa, but ground monitoring data on local pollution remain inadequate, hindering effective mitigation. We employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM2.5), NO2, and O3 over a 6 month period for selected urban centers in three of the four macroregions in Uganda. PM2.5 diurnal profiles exhibited consistent patterns across all monitoring locations with higher pollution levels manifesting from 18:00 to 00:00 and from 06:00 to 09:00; while the periods from 00:00 to 05:00 and from 09:00 to 17:00 had the lowest levels. Daily PM2.5 varied widely between 34 and 107 μg/m3 over a 7 day period, well within unhealthy levels (55.5-150.4 μg/m3) for short-term exposure. The inconsistent daily trend are instructive for multiple pollutant assessment to aid specific policy initiatives. The results also show inverse relations between seasonal particulate levels and precipitation, that is, R (correlation coefficient) = -0.93 and -0.62 for Kampala and Wakiso, R = -0.49 and -0.44 for the Eastern region, and R = -0.65 and -0.96 for the Western region. NO2 monthly concentrations replicated PM2.5 spatial levels, whereas O3 exhibited inverse relations probably due to a higher retention time in less-urbanized environments. Both PM2.5 and NO2 correlated positively with the resident population. Our findings show significant spatiotemporal variations and exceedances of health guidelines by about 4-6 times across most study locations (with two exceptions) for longer-term exposure. This paper demonstrably highlights the practicability and potential of low-cost approaches for air quality monitoring, with strong prospects for citizen science. This paper also provides novel information regarding air pollution that is needed to improve control strategies for reducing exposures.
9 citations
TL;DR: In this article , the authors investigated the impacts of green infrastructure deployment on the indoor and outdoor air quality and thermal environments of elderly care centres (ECCs) and presented the approaches for integrating green infrastructure into the building environment design.
Abstract: The elderly population is relatively vulnerable to air pollution and thermal stress due to their low mobility and high prevalence of chronic disorders. Appropriate green infrastructure (GI) deployment can improve both the indoor and outdoor air quality and thermal environments of elderly care centres (ECCs), yet a systematic review on this topic area is lacking. This review aims to fill this gap by investigating the impacts of GI on ECC building environment and presents the approaches for integrating GI into the building environment design. We discussed the significance of linking air quality with the thermal environment to ECCs and the effects of GI on the elderly's physical health. We investigated the key design considerations for GI in ECC buildings (e.g., spatial layout, species, aesthetics and fire prevention). Also, the diversity of monitoring and modelling approaches for evaluating the benefits of GI in indoor and outdoor environments was assessed. Finally, we evaluated the associated challenges and provided design recommendations for improving the environments in and around the ECC buildings (e.g., bedrooms, indoor gardens, green roofs and courtyards). The quantitative evidence for linking GI with indoor and outdoor air pollution and extreme heat around the ECC buildings are limited. However, this evidence-base is important for providing generic advice to the building designers and the elderly. Further studies such as the evaluation criteria and monitoring standard are required to develop holistic design recommendations for ECC buildings. The empirical research about the social and economic impacts is also necessary to facilitate the sustainable development of the ageing societies.
7 citations
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TL;DR: The history of research in sensor networks over the past three decades is traced, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs.
Abstract: Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.
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TL;DR: This article surveys existing mobile phone sensing algorithms, applications, and systems, and discusses the emerging sensing paradigms, and formulates an architectural framework for discussing a number of the open issues and challenges emerging in the new area ofMobile phone sensing research.
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TL;DR: In this article, the authors provide a frame model that deals with all contributions involved in conduction within a real world sensor, and then summarize the contributions together with their interactions in a general applicable model for real world gas sensors.
Abstract: Tin dioxide is a widely used sensitive material for gas sensors. Many research and development groups in academia and industry are contributing to the increase of (basic) knowledge/(applied) know-how. However, from a systematic point of view the knowledge gaining process seems not to be coherent. One reason is the lack of a general applicable model which combines the basic principles with measurable sensor parameters. The approach in the presented work is to provide a frame model that deals with all contributions involved in conduction within a real world sensor. For doing so, one starts with identifying the different building blocks of a sensor. Afterwards their main inputs are analyzed in combination with the gas reaction involved in sensing. At the end, the contributions are summarized together with their interactions. The work presented here is one step towards a general applicable model for real world gas sensors.
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TL;DR: This Review highlights the recent developments and reflects the impact of nanoscience on sensor technology, which can be improved and novel sensor concepts based on bottom-up approaches show that the sensor properties can be controlled by molecular design.
Abstract: Sensor technology is one of the most important key technologies of the future with a constantly increasing number of applications, both in the industrial and in the private sectors. More and more gas sensors are used for the control of technical processes, in environment monitoring, healthcare, and automobiles. Consequently, the development of fast and sensitive gas sensors with small cross sensitivity is the subject of intense research, propelled by strategies based on nanoscience and -technology. Established systems can be improved and novel sensor concepts based on bottom-up approaches show that the sensor properties can be controlled by molecular design. This Review highlights the recent developments and reflects the impact of nanoscience on sensor technology.
1,194 citations