Stephen A. Jubb
Bio: Stephen A. Jubb is an academic researcher from University of Sheffield. The author has contributed to research in topics: Air quality index & Smart grid. The author has an hindex of 2, co-authored 4 publications receiving 55 citations.
TL;DR: The low-cost sensors offer a more affordable alternative for providing real-time high-resolution spatiotemporal air quality and meteorological parameter data with acceptable performance.
Abstract: Traditional real-time air quality monitoring instruments are expensive to install and maintain; therefore, such existing air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant maps. More recently, low-cost sensors have been used to collect high-resolution spatial and temporal air pollution data in real-time. In this paper, for the first time, Envirowatch E-MOTEs are employed for air quality monitoring as a case study in Sheffield. Ten E-MOTEs were deployed for a year (October 2016 to September 2017) monitoring several air pollutants (NO, NO2, CO) and meteorological parameters. Their performance was compared to each other and to a reference instrument installed nearby. E-MOTEs were able to successfully capture the temporal variability such as diurnal, weekly and annual cycles in air pollutant concentrations and demonstrated significant similarity with reference instruments. NO2 concentrations showed very strong positive correlation between various sensors. Mostly, correlation coefficients (r values) were greater than 0.92. CO from different sensors also had r values mostly greater than 0.92; however, NO showed r value less than 0.5. Furthermore, several multiple linear regression models (MLRM) and generalised additive models (GAM) were developed to calibrate the E-MOTE data and reproduce NO and NO2 concentrations measured by the reference instruments. GAMs demonstrated significantly better performance than linear models by capturing the non-linear association between the response and explanatory variables. The best GAM developed for reproducing NO2 concentrations returned values of 0.95, 3.91, 0.81, 0.005 and 0.61 for factor of two (FAC2), root mean square error (RMSE), coefficient of determination (R2), normalised mean biased (NMB) and coefficient of efficiency (COE), respectively. The low-cost sensors offer a more affordable alternative for providing real-time high-resolution spatiotemporal air quality and meteorological parameter data with acceptable performance.
01 Apr 2019
TL;DR: The core aim is to structure an integrated AQMN in urban areas, which will lead to the collection of AQ data with high spatiotemporal resolution, and the use of LCS in the proposed network provides a cheaper option for setting up a purpose-designed network for greater spatial coverage, especially in low- and middle-income countries.
Abstract: Air pollution in large urban areas has become a serious issue due to its negative impacts on human health, building materials, biodiversity and urban ecosystems in both developed and less-wealthy nations. In most large urban areas, especially in developed countries air quality monitoring networks (AQMN) have been established that provide air quality (AQ) data for various purposes, e.g., to monitor regulatory compliance and to assess the effectiveness of control strategies. However, the criteria of structuring the network are currently defined by single questions rather than attempting to create a network to serve multiple functions. Here we propose a methodology supported by numerical, conceptual and GIS frameworks for structuring AQMN using social, environmental and economic indicators as a case study in Sheffield, UK. The main factors used for air quality monitoring station (AQMS) selection are population-weighted pollution concentration (PWPC) and weighted spatial variability (WSV) incorporating population density (social indicator), pollution levels and spatial variability of air pollutant concentrations (environmental indicator). Total number of sensors is decided on the basis of budget (economic indicator), whereas the number of sensors deployed in each output area is proportional to WSV. The purpose of AQ monitoring and its role in determining the location of AQMS is analysed. Furthermore, the existing AQMN is analysed and an alternative proposed following a formal procedure. In contrast to traditional networks, which are structured based on a single AQ monitoring approach, the proposed AQMN has several layers of sensors: Reference sensors recommended by EU and DEFRA, low-cost sensors (LCS) (AQMesh and Envirowatch E-MOTEs) and IoT (Internet of Things) sensors. The core aim is to structure an integrated AQMN in urban areas, which will lead to the collection of AQ data with high spatiotemporal resolution. The use of LCS in the proposed network provides a cheaper option for setting up a purpose-designed network for greater spatial coverage, especially in low- and middle-income countries.
••01 Sep 2021
TL;DR: In this paper, the authors introduce a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies, and show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.
Abstract: Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT.
01 Nov 2017
TL;DR: This paper is the first to present the concept and the philosophy on which SGOBs are based, along with initial results, demonstrating how a building can adjust its loads to reduce stress on the grid.
Abstract: Smart Grid Optimised Building (SGOB) can be thought of as meeting its service obligations to its occupants and minimising its operational cost and footprint to its owner while actively engaging with the electricity provider, enabling in this way the best use of the available resources. SGOBs differ from Smart Buildings, regarding their aim and objectives, as their design and energy systems are optimised for the needs of the Smart Grid. Conceptually, they must have an active interaction with the energy network through responses to dynamic electricity prices and carbon emissions, similarly to Active Buildings. Instead of being considered as a passive element of the energy equation like conventional buildings, SGOBs follow an original and innovative approach and have the capacity to transform to prosumers, with the deployment of on-site renewable energy sources and by participating in a 2-direction power exchange with the Network Operator. The current literature and research have followed an ad-hoc approach by focusing on conventional strategies on existing buildings, such as increasing the building energy efficiency or reducing the current energy loads. On the other hand, SGOBs are expected to consist of several optimised design elements, including thermal mass, shape, orientation, insulation and glazing. Furthermore, SGOBs can meet their energy loads with electricity, either directly from the grid or using their incorporated energy storage systems e.g. batteries. Electricity can be stored at times of low demand when the electricity tariffs are cheaper, and used on the following day to cover part of the peak load. Another possibility includes the load-levelling service, where the building is notified by the Network Operator to maintain its consumption below a power limit for a specific time period. This paper is the first to present the concept and the philosophy on which SGOBs are based, along with initial results, demonstrating how a building can adjust its loads to reduce stress on the grid.
TL;DR: In this paper , a path loss model that accurately predicts the path loss with low computational complexity considering environmental factors is presented, and the results show that the root mean square error (RMSE) of the proposed model is 1.4 dB smaller than the widely used log-distance model.
Abstract: We present a path loss model that accurately predicts the path loss with low computational complexity considering environmental factors. In the proposed model, the entire area under consideration is recognized and divided into regions from a raster map, and each type of region is assigned with a path loss exponent (PLE) value. We then extract the model parameters via measurement in a suburban area to verify the proposed model. The results show that the root mean square error (RMSE) of the proposed model is 1.4 dB smaller than the widely used log-distance model.
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.
TL;DR: The study showed the effectiveness of calibration in improving low-cost IoT sensor data quality and also demonstrated the convenience of feature selection and the ability of data fusion to provide more consistent, accurate and reliable information for calibration models.
Abstract: Environmental monitoring has become an active research area due to the current rise in the global climate change crises. Current environmental monitoring solutions, however, are characterized by high cost of acquisition and complexity of installation; often requiring extensive resources, infrastructure and expertise. It is infeasible to achieve with these solutions, high density in-situ networks such as are required to build refined scale models to facilitate robust monitoring, thus, leaving large gaps within the collected dataset. Low-Cost Sensors (LCS) can offer high-resolution spatiotemporal measurements which could be used to supplement existing dataset from current environmental monitoring solutions. LCS however, require frequent calibration in order to provide accurate and reliable data as they are often affected by environmental conditions when deployed on the field. Calibrating LCS can help to improve their data quality and ensure they are collecting accurate data. Achieving effective calibration, however, requires identifying factors that affect sensor’s data quality for a given measurement. This study evaluates the performance of three Feature Selection (FS) algorithms including Forward Feature Selection (FFS), Backward Elimination (BE) and Exhaustive Feature Selection (EFS) in identifying factors that affect data quality of low-cost IoT sensors in environmental monitoring networks. Applying the concept of data fusion, sensors data were merged with environmental factors and integrated into a single calibration equation to calibrate cairclipO3/NO2 and cairclipNO2 sensors using Linear Regression (LR) and Artificial Neural Networks (ANN). The study showed the effectiveness of calibration in improving low-cost IoT sensor data quality and also demonstrated the convenience of feature selection and the ability of data fusion to provide more consistent, accurate and reliable information for calibration models. The analysis showed that the cairclipO3/NO2 sensor provided measurements that have good correlation with reference measurements whereas the cairclipNO2 sensor showed no reasonable correlation with the reference data. Calibrating the cairclipO3/NO2 yielded good improvement in its measurement outputs when compared to reference measurements (R2=0.83). However, calibrating the cairclipNO2 sensor data yielded no significant improvement in its data quality.
TL;DR: In this paper, a review of the development of low-cost sensor networks over the last 15 years is presented, highlighting trends and future opportunities for a diverse range of environmental applications.
Abstract: The use of low-cost sensor networks (LCSNs) is becoming increasingly popular in the environmental sciences and the unprecedented monitoring data generated enable research across a wide spectrum of disciplines and applications. However, in particular, non-technical challenges still hinder the broader development and application of LCSNs. This paper reviews the development of LCSNs over the last 15 years, highlighting trends and future opportunities for a diverse range of environmental applications. We found air quality, meteorological and water-related networks were particularly well represented with few studies focusing on sensor networks for ecological systems. Furthermore, we identified bias towards studies that have direct links to human health, safety and livelihoods. These studies were more likely to involve downstream data analytics, visualisations, and multi-stakeholder participation through citizen science initiatives. However, there was a paucity of studies that considered sustainability factors for the development and implementation of LCSNs. Existing LCSNs are largely focussed on detecting and mitigating events which have a direct impact on humans such as flooding, air pollution or geo-hazards, while these applications are important there is a need for future development of LCSNs for monitoring ecosystem structure and function. Our findings highlight three distinct opportunities for future research to unleash the full potential of LCSNs: (1) improvement of links between data collection and downstream activities; (2) the potential to broaden the scope of application systems and fields; and (3) to better integrate stakeholder engagement and sustainable operation to enable longer and greater societal impacts.
TL;DR: In this paper, a systematic review of the existing air quality guidelines and standards implemented by different agencies, which include the Ambient Air Quality Standards (NAAQS), the World Health Organization (WHO), the Occupational Safety and Health Administration (OSHA), the American Conference of Governmental Industrial Hygienists (ACGIH); the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE); the National Institute for Occupational safety and Health (NIOSH); and the California ambient air quality standards (CAQS).
Abstract: The existence of indoor air pollutants—such as ozone, carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, particulate matter, and total volatile organic compounds—is evidently a critical issue for human health. Over the past decade, various international agencies have continually refined and updated the quantitative air quality guidelines and standards in order to meet the requirements for indoor air quality management. This paper first provides a systematic review of the existing air quality guidelines and standards implemented by different agencies, which include the Ambient Air Quality Standards (NAAQS); the World Health Organization (WHO); the Occupational Safety and Health Administration (OSHA); the American Conference of Governmental Industrial Hygienists (ACGIH); the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE); the National Institute for Occupational Safety and Health (NIOSH); and the California ambient air quality standards (CAAQS). It then adds to this by providing a state-of-art review of the existing low-cost air quality sensor (LCAQS) technologies, and analyzes the corresponding specifications, such as the typical detection range, measurement tolerance or repeatability, data resolution, response time, supply current, and market price. Finally, it briefly reviews a sequence (array) of field measurement studies, which focuses on the technical measurement characteristics and their data analysis approaches.
TL;DR: Assessment of the performance of LCS against industry-grade instruments and development of calibration models for LCS indicates that SVR can be considered as a promising approach for LCS calibration.
Abstract: Observation of air pollution at high spatio-temporal resolution has become easy with the emergence of low-cost sensors (LCS). LCS provide new opportunities to enhance existing air quality monitoring frameworks but there are always questions asked about the data accuracy and quality. In this study, we assess the performance of LCS against industry-grade instruments. We use linear regression (LR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF) regression for development of calibration models for LCS, which were Smart Citizen (SC) kits developed in iSCAPE project. Initially, outdoor colocation experiments are conducted where ten SC kits are collocated with GRIMM, which is an industry-grade instrument. Quality check on the LCS data is performed and the data is used to develop calibration models. Model evaluation is done by testing them on 9 SC kits. We observed that the SVR model outperformed other three models for PM2.5 with an average root mean square error of 3.39 and average R2 of 0.87. Model validation is performed by testing it for PM10 and SVR model shows similar results. The results indicate that SVR can be considered as a promising approach for LCS calibration.