Air Pollution Monitoring and Mining Based on Sensor Grid in London

Air Pollution Management: A Multivariate Analysis of Citizens' Perspectives and Their Willingness to Use Greener Forms of Transportation

The present research aims to understand how air pollution can be managed by public authorities, both central and local, starting from citizens' perspectives on the issue. Air quality is a real problem, affecting people at multiple levels. Thus, we introduced the following variables to better understand the problem and to be able to formulate theoretical and practical implications for public management: the involvement of authorities in reducing air pollution; the involvement of citizens in reducing air pollution; financial incentives for citizens and companies for adopting behaviors that reduce air pollution; green investments in the city; the impact of air pollution on the community; and the need for independent bodies to monitor air pollution. The research methodology used is partial least squares structural equation modelling (PLS-SEM) and the required data were gathered from issuing a survey to citizens from the most important cities in Romania where pollution poses important challenges for the community and for the authorities. The results are useful to public managers in local and central institutions for creating better strategies meant to reduce air pollution, increase air quality, and improve the quality of the citizens' lives.

Multiparametric Sensor Node for Environmental Monitoring Based on Energy Harvesting

The heterogeneity and levels of chemicals released into the environment have dramatically grown in the last few years. Therefore, new low-cost tools are increasingly required to monitor pollution and follow its trends over time. Recent approaches in electronics and wireless communications permit the expansion of low-power, low-cost, and multiparametric sensor nodes that are limited in size and communicate untethered in small distances. For such a monitoring system to be ultimately feasible, a suitable power source for these nodes must be found. The present research falls within the frame of this global effort. The study sits within the context discussed above with the particular aim of developing groundbreaking technology-based solutions by means of efficient environmentally powered wireless smart sensors. This paper presents a multiparametric sensor node for indoor/outdoor air quality monitoring, able to work without battery and human intervention, harvesting energy from the surrounding environment for perpetual operation. The complete system design of the sensor and experimental results are reported. The evaluation of the energy-harvesting blocks with a budget allocation of the power consumption is also discussed.

Investigation of Low-Cost and Optical Particulate Matter Sensors for Ambient Monitoring

This article presents a long-term evaluation of low-cost particulate matter (PM) sensors in a field measurements campaign. Evaluation was performed in two phases. During the first five months of the campaign, two PM sensors were simultaneously compared with the results from the reference air quality monitoring station in various atmospheric conditions—from the days with freezing cold (minimum temperature below −10 °C) and high relative humidity (up to 95%) to the days with the maximum temperature above 30 °C and low relative humidity (at the level of 25%). Based on the PM10 measurements, the correlation coefficients for both devices in relation to the reference station were determined (r = 0.91 and r = 0.94, respectively), as well as the impact of temperature and relative humidity on measurements from the low-cost sensors in relation to the reference values. The correction function was formulated based on this large set of low-cost PM10 measurements and referential values. The effectiveness of the corrective function was verified during the second measurement campaign carried out in the city of Nowy Sącz (located in southern Poland) for the same five months in the following year. The absolute values of the long-term percentage errors obtained after adjustment were reduced to a maximum of about 20%, and the average percentage errors were usually around 10%.

Field Demonstration of a Distributed Microsensor Network for Chemical Detection

We have developed the ABEAM-15, a custom-built multiplexed reflectance device for the detection of vapor phase and aerosolized chemical plumes. The instrument incorporates fifteen individual sensing elements, has wireless communications, offers support for a battery pack, and is capable of both live and fully autonomous operation. Two housing options have been fabricated: a compact open housing for indoor use and a larger weather-sealed housing for outdoor use. Previously developed six-plex analysis algorithms are extended to 15-plex format and implemented on a laptop computer. We report the results of recent outdoor field trials with this instrument in Denver, CO in a stadium security scenario. Through software, the wireless modules on each instrument were configured to form a six-instrument, star-point topology, distributed microsensor network with live reporting and real-time data analysis. The network was tested with aerosols of methyl salicylate.

The Influence of Marine Traffic on Particulate Matter (PM) Levels in the Region of Danish Straits, North and Baltic Seas

The aim of the study was to determine air pollution over the sea surface (North Sea and Baltic Sea) compared to the situation in ports, as well as to examine the impact of ships on the level of particulate matter (PM) concentration. The measurements, made during the two-week cruise of the tall ship Fryderyk Chopin, demonstrated that the principal source of PM emission over the sea surface are passing ships equipped with internal combustion engines, including quite numerous units powered by marine oil. The highest pollution levels were observed in locations distant from the coast, with increasing concentrations when other ships were approaching. During the cruise, at least two places were identified with increased PM concentration (18–28 μg/m3 for PM10 and 15–25 μg/m3 for PM2.5) caused by passing ships. The share of PM2.5 fraction in the general PM concentration in these places increased from 70–72% to 82–85%, which means that combustion emission dominated. In turn, measurements made in ports (Copenhagen and Kołobrzeg) showed lower levels of air pollution and indicated a typical variability of the PM concentrations characteristic for land areas. The results confirm the need for determining suitable solutions for sustainable sea transport.

Evaluation of Microclimatic Detection by a Wireless Sensor Network in Forest Ecosystems

Timely and accurate detection of microclimates is extremely valuable for monitoring and stimulating exchanges of mass and energy in forest ecosystems under climate change. Recently, the rapid growth of wireless sensor networks (WSNs) has provided a new approach for detecting microclimates in a complex environment at multiple temporal and spatial scales. However, applications of wireless sensors in forest microclimate monitoring have rarely been studied, and the corresponding observation accuracy, error sources and correction methods are not well understood. In this study, through field experiments in two typical subtropical forest ecosystems in Zhejiang Province, China, the accuracy of the temperature and humidity observed by the wireless sensors was evaluated against standard meteorological data. Furthermore, the observation error sources were analyzed and corresponding correction models were established. The results showed that the wireless sensor-based temperature and humidity values performed well within the total observation accuracy. However, the observation errors varied with season, daily periodicity and weather conditions. For temperature, the wireless sensor observations were overestimated during the daytime while they were underestimated during the nighttime. For humidity, the data observed by the wireless sensors generally appeared as overestimates. Adopting humidity as the corrected factor, correction models were established and effectively improved the accuracy of the microclimatic data observed by the wireless sensors. Notably, our error analysis demonstrated that the observation errors may be associated with the shell material of the wireless sensor, suggesting that shading measures for the wireless sensors should be considered for outdoor work.

Mapping Rural Road Networks from Global Positioning System (GPS) Trajectories of Motorcycle Taxis in Sigomre Area, Siaya County, Kenya

Effective transport infrastructure is an essential component of economic integration, accessibility to vital social services and a means of mitigation in times of emergency. Rural areas in Africa are largely characterized by poor transport infrastructure. This poor state of rural road networks contributes to the vulnerability of communities in developing countries by hampering access to vital social services and opportunities. In addition, maps of road networks are incomplete, and not up-to-date. Lack of accurate maps of village-level road networks hinders determination of access to social services and timely response to emergencies in remote locations. In some countries in sub-Saharan Africa, communities in rural areas and some in urban areas have devised an alternative mode of public transport system that is reliant on motorcycle taxis. This new mode of transport has improved local mobility and has created a vibrant economy that depends on the motorcycle taxi business. The taxi system also offers an opportunity for understanding local-level mobility and the characterization of the underlying transport infrastructure. By capturing the spatial and temporal characteristics of the taxis, we could design detailed maps of rural infrastructure and reveal the human mobility patterns that are associated with the motorcycle taxi system. In this study, we tracked motorcycle taxis in a rural area in Kenya by tagging volunteer riders with Global Positioning System (GPS) data loggers. A semi-automatic method was applied on the resulting trajectories to map rural-level road networks. The results showed that GPS trajectories from motorcycle taxis could potentially improve the maps of rural roads and augment other mapping initiatives like OpenStreetMap.

Depletion-of-Battery Attack: Specificity, Modelling and Analysis

The emerging Internet of Things (IoT) has great potential; however, the societal costs of the IoT can outweigh its benefits. To unlock IoT potential, there needs to be improvement in the security of IoT applications. There are several standardization initiatives for sensor networks, which eventually converge with the Internet of Things. As sensor-based applications are deployed, security emerges as an essential requirement. One of the critical issues of wireless sensor technology is limited sensor resources, including sensor batteries. This creates a vulnerability to battery-exhausting attacks. Rapid exhaustion of sensor battery power is not only explained by intrusions, but can also be due to random failure of embedded sensor protocols. Thus, most wireless sensor applications, without tools to defend against rash battery exhausting, would be unable to function during prescribed times. In this paper, we consider a special type of threat, in which the harm is malicious depletion of sensor battery power. In contrast to the traditional denial-of-service attack, quality of service under the considered attack is not necessarily degraded. Moreover, the quality of service can increase up to the moment of the sensor set crashes. We argue that this is a distinguishing type of attack. Hence, the application of a traditional defense mechanism against this threat is not always possible. Therefore, effective methods should be developed to counter the threat. We first discuss the feasibility of rash depletion of battery power. Next, we propose a model for evaluation of energy consumption when under attack. Finally, a technique to counter the attack is discussed.

A systematic review of data mining and machine learning for air pollution epidemiology

Background

Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology.

Methods

We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed.

Results

Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology.

Conclusions

We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology.

The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

An evaluation tool kit of air quality micro-sensing units

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.

RAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems

Air quality information such as the concentration of PM_2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.

A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

Monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (WSN) framework

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment

We propose a new approach to assess the impact of traffic-related air pollution on public health by mapping personal trajectories using mobile phone tracking technology in an urban environment. Although this approach is not based on any empirical studies, we believe that this method has great potential and deserves serious attention. Mobile phone tracking technology makes it feasible to generate millions of personal trajectories and thereby cover a large fraction of an urban population. Through analysis, personal trajectories are not only associated to persons, but it can also be associated with vehicles, vehicle type, vehicle speed, vehicle emission rates, and sources of vehicle emissions. Pollution levels can be estimated by dispersion models from calculated traffic emissions. Traffic pollution exposure to individuals can be estimated based on the exposure along the individual human trajectories in the estimated pollution concentration fields by utilizing modelling tools. By data integration, one may identify trajectory patterns of particularly exposed human groups. The approach of personal trajectories may open a new paradigm in understanding urban dynamics and new perspectives in population-wide empirical public health research. This new approach can be further applied to individual commuter route planning, land use planning, urban traffic network planning, and used by authorities to formulate air pollution mitigation policies and regulations.

Can pervasive sensing address current challenges in global healthcare?

Important challenges facing global healthcare include the increase in the number of people affected by escalating healthcare costs, chronic and infectious diseases, the need for better and more affordable elderly care and expanding urbanisation combined with air and water pollution. Recent advances in pervasive sensing technologies have led to miniaturised sensor networks that can be worn or integrated within the living environment without affecting a person’s daily patterns. These sensors promise to change healthcare from snapshot measurements of physiological parameters to continuous monitoring enabling clinicians to provide guidance on a daily basis. This article surveys several of the solutions provided by these sensor platforms from elderly care to neonatal monitoring and environmental mapping. Some of the opportunities available and the challenges facing the adoption of such technologies in large-scale epidemiological studies are also discussed.

Odour detection methods: olfactometry and chemical sensors

The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective "analytical instrument" for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through both the techniques is finally reviewed in order to show the complementary responses of human and instrumental sensing.

Testbed Implementation of a Pollution Monitoring System Using Wireless Sensor Network for the Protection of Public Spaces

Air pollution is an important environmental issue that has a direct effect on human health and ecological balance. Factories, power plants, vehicles, windblown dust and wildfires are some of the contributors of to pollution. Reasonable simulation tools exist for evaluating large scale sensor networks, ; however, they fail to capture significant details of node operation or practical aspects of wireless communication. Real life testbeds, capture the realism and bring out important aspects for further research. In this paper, we present an implementation of a wireless sensor network testbed for automatic and real-time monitoring of environmental pollution for the protection of public spaces. The paper describes the physical setup, the sensor node hardware and software architecture for “anytime, anywhere” monitoring and management of pollution data through a single, Web-based graphical user interface. The paper presents practical issues in the integration of sensors, actual power consumption rates and develops a practical hierarchical routing methodology.