In laboratory research and clinical practice, externally-applied electric fields have been widely used to control neuronal activity. It is generally accepted that neuronal excitability is controlled by electric current that depolarizes or hyperpolarizes the excitable cell membrane. What determines the amount of polarization? Research on the mechanisms of electric stimulation focus on the optimal control of the field properties  to improve stimulation outcomes. Emerging evidence from modeling and experimental studies support the existence of interactions between the targeted neurons and the externally-applied electric fields. With cell-field interaction, we suggest a two-way process. When a neuron is positioned inside an electric field, the electric field will induce a change in the resting membrane potential by superimposing an electrically-induced transmembrane potential (ITP). At the same time, the electric field can be perturbed and re-distributed by the cell. This cell-field interaction may play a significant role in the overall effects of stimulation. The redistributed field can cause secondary effects to neighboring cells by altering their geometrical pattern and amount of membrane polarization. Neurons excited by the externally-applied electric field can also affect neighboring cells by ephaptic interaction. Both aspects of the cell-field interaction depend on the biophysical properties of the neuronal tissue, including geometric  and electric  attributes of the cells. The biophysical basis of the cell-field interaction can be explained by the electromagnetism theory. Further experimental and simulation studies on electric stimulation of neuronal tissue should consider the prospect of a cell-field interaction, and a better understanding of tissue inhomogeneity and anisotropy is needed to fully appreciate the neural basis of cell-field interaction as well as the biological effects of electric stimulation. In such, deep brain stimulation (DBS) with inserted electrodes is rendered effective for the treatment of Parkinson’s, essential tremor, and dystonia . Transcractivity , 7. Highactivity , 9 for eactivity .th century through the pioneering works of Jacques d’Arsonval (1896) and Silvanus P. Thompson (1910) in human visual sensations, i.e., magnetophosphenes. In 1982, Polson and Barker applied the first successful stimulation of peripheral nerves through the use of magnetic fields [Aside from direct application via stimulating electrodes, electric current can also be delivered by electromagnetic induction with time-varying magnetic field inside the biological tissue, activating the cells and providing non-invasive stimulation . The effc fields . In 1985c fields . Today, c fields , epilepsc fields  and Parkc fields . Magnetic fields , and in Effects of an electric field on neuronal tissue are caused by an establishment of a transmembrane potential. When a neuron is positioned inside an electric field, the electric field will induce a change in the resting transmembrane potential by superimposing an electrically-induced transmembrane potential (ITP). When the electric current penetrates the membrane, the neuronal membrane may be depolarized and/or hyperpolarized from its resting value , which causes excitation or inhibition of the cell. The depolarization may reach the threshold to generate an action potential, which involves the opening of voltage-gated ion channels such as sodium and potassium channels . Under eIn this review, we focus our questions on 1. How is ITP measured and calculated during electric or magnetic stimulation? 2. What might be the impact factors on the establishment of the ITP across the neuronal membrane? We show that interactions between the targeted neurons and the externally-applied electric fields play significant roles in generating ITP. We argue that a better understanding of tissue inhomogeneity and anisotropy is crucial for the full appreciation of neural basis of cell-field interaction, as well as the biological effects of electric stimulation.http://www.neuron.yale.edu/neuron/) capable of stimulating active ion-channel mechanisms.Both modeling and experimental approaches have been used to estimate the ITP during electric stimulation. The mathematical basis of modeling work is to solve Maxwell equations , which dIn parallel to theoretical studies, direct measurements of ITP were reported ranging from hippocampal neurons with sharp electrodes  to neuraEstimations on ITP during magnetic stimulation have been equally successful with a primary focus on axonal responses to the fields . In examAlthough the electric and magnetic stimulations share the same biophysical mechanisms in activating the neurons, they are not interchangeable in neither clinical nor experimental practices. At a macroscopic tissue level, the electric field further from the stimulating electrodes may be significantly dispersed by the non-homogenous tissue conductivity during electric stimulation. In contrast, the induced electric field may be less dispersed during magnetic stimulation because the magnetic field may penetrate through the tissue without significant attenuation. Therefore, with careful coil design and placement around the head, the magnetic field can achieve good stimulation in the brain region such as the motor cortex , 37. At direction, intensity, frequency, and waveform of the field. Amplitude and frequency of the stimulation current are the key parameters in deep brain stimulation [What determines the amount and pattern of cellular polarization in electric stimulation? First, the properties of the externally-applied electric field play significant roles in membrane polarization; these include mulation , 40. Orimulation . Similarmulation , 43. Celmulation , 44.frequency of the externally-applied magnetic field determines the strength of the induced field by the law of electromagnetic induction [magnitude of the magnetic field determines the intensity of the induced electric field. Both these quantities, in turn, affect the extent of polarization in a single cell [orientation of the magnetic field in relationship to the neurons determines the direction of the induced electric field and the geometric pattern of polarization in the neuronal tissue [Similarly, the characteristics of the magnetic field have been tightly related to the effects of stimulation as they define the properties of the induced electric current in the tissue. Among these parameters, three stand out and dictate the bulk of the overall outcomes. The nduction . The maggle cell . Lastly,l tissue , 46. Conl tissue , 47–50.Evidence presented thus far suggests a one-way process in electric stimulation of neuronal tissues, i.e. the cell membrane is passively polarized by the external field. We, however, hereby argue that a two-way interactive process exists between the neuron and the field: the cell - field interaction. We agree that the cell is directly polarized by the external field, but we propose that the electric field is perturbed and re-distributed by the neuronal tissue Fig. . The folWe first define the electric field around a cell. An electric field around a single cell is separated into three major areas: the field in the extracellular medium (extracellular field), the field within the membrane (transmembrane electric field), and the field in the cytoplasm (intracellular field).Electric fields in these distinct regions all possess important physiological significance. Extracellular electric field has been recorded and analyzed in the study of cellular communication and synchronized excitability. Direct measurement and computation of this field has been a focus for research interested in electric stimulation on neuronal tissue. Equally important, the transmembrane electric field maintains the meta-stable membrane structure , and is Initial evidence of the counter-effect of the neuron to the externally-applied field is seen in the significant disruption and redistribution of the local field when the field is sensed by the cell in question. Models with both analytical and numerical methods support this notion. For example, in an evenly distributed DC field, the electric field could be greatly augmented inside the cell membrane . Jerry eA direct consequence of the counter-effects of the cell to the electric field is that electric field alteration by one individual cell may cause secondary effects on neighboring cells during electric stimulation, particularly in the scenario that two or more cells are located in close proximity and in a high-density cell medium. Evidence demonstrated that the presence of a single cell affects ITP in its neighboring cells without direct physical contact between the two cells . Though Since an externally-applied electric field can activate neurons and the presence of neurons can alter extracellular electric field distribution, it is likely that neurons excited by the externally-applied electric field can also affect neighboring cells via ephaptic interaction. The functional significance of direct electric (ephaptic) interaction was largely ignored until the 1960s when an inhibitory action on Mauthner cells in goldfish was discovered . A singlWhat may affect the cell-field interaction during electric stimulation? In the following sections we will present evidence that both aspects of the interaction (the ITP and the re-distributed electric field around the polarized cell) depend on the inhomogeneous properties of the target tissue.At a microscopic level, cell property falls under two categories of measurement: the geometric and the electric parameters of the cell. Geometrical properties include the shape and size of the cell, as well as its orientation and location in the field. Electrical properties include tissue conductivity and dielectricity in the microscopic cellular environment, including the properties of the extracellular media, the cell membrane, and the internal cellular environment.Shape of the target neurons plays a significant role in determining the amplitude and pattern of distribution of ITP during electric and magnetic stimulations. By comparing different ITPs in a spherical cell  versus aCell radius is also important in determining the amplitude of the ITP . Larger Orientation of the cell to the externally-applied field determines both the amplitude and pattern of ITP within the cell , 24. ForIn addition to the geometrical properties of the cell, growing evidence supports that electric properties of the cellular environment affect ITP. For example, ITP induced by a microelectrode on a spherical cell is dependent on extracellular and intracellular media conductance . In a spThe other half of the cell-field interaction, redistribution of the field by the tissue, is also largely reliant on the properties of the tissue.The geometrical property of the cell causes external field redistribution. Numerical modeling results  have shoField distribution in the neuronal tissue also depends heavily on the electrical properties of the target tissue under electric stimulation. At a single cell level, membrane conductance affects the field distribution around and within a single cell during electric stimulation , 58, 72.The existence of cell-field interaction is rooted in the fact that most biological tissue is composed of abundant non-homogenous, anisotropic components such as the cellular/axonal membrane, the internal organelles, and the extracellular medium. The electrical properties  of the tissue vary with its geometrical location within the tissue, even at a subcellular level, causing local accumulation of induced electric charges  that aff2V = 0 in charge-free regions [As charge accumulation is a process that depends on microscopic cellular properties across the interface of two media, ITP and field re-distribution around a cell depend on both the geometrical and electrical properties of the neuron. Many works, including ours, have focused on understanding the neuronal excitation by electromagnetic field with low frequency (including DC). Under electric stimulation, the electric field distributions can be calculated by  regions , 24. Of  regions , which c regions , 23.In magnetic stimulation, two sources contribute to the magnetically-induced transmembrane electric field : \documemponent” , \documee . The tee  has significant experimental implications. Currently, many researchers use invasive methods (patch or microelectrodes) to measure electric field distributions during electric stimulation. Unfortunately, the presence of these electrodes may further perturb the applied field in the tissue. To accurately quantify field distributions and transmembrane potential changes during electric stimulation, less invasive methods should be considered, such as voltage-sensitive dyes, which provide high temporal and spatial resolutions to measure potential changes in the single cells , 83, 84.Parallel to the intensive experimental efforts, many mathematical modeling works have explored the mechanisms underlying the biological effects of electric stimulation. At least two distinguished types of approaches have been taken, and the discrepancy between these approaches has not been fully resolved.The “biophysical modeling” approach simplifies the neuronal tissue with the use of basic geometrical shapes “Engineering modeling” relies on powerful computerized simulation tools to replicate biologically authentic neurons that possess complicated morphologies and active ion channels. Extracellular stimulation of target neurons is a three-step process . FiThese simplifications could potentially cause underestimation of the field generated by the electrode, and introduce potential certain inaccuracy in the modeling. Some investigators have acknowledged this problem in previous studies. Among them, Kotnik  suggesteFuture modeling approaches should encapsulate the biophysical concept of cell-field interaction. This is even more important for the modeling works focusing on the mechanisms of neural control by modern technologies, such as TMS and tDCS, in the treatment of neurological diseases.Preliminary modeling studies that include some aspects of cell-field interaction have yielded a better description of the system and provided more acute simulation results that better represent biological authenticity. In the modeling of DBS of the subthalamic nucleus (STN) and its neighboring regions, Sotiropoulos and Steinmetz  includedWe propose a hybrid method in modeling electric effects by combining “biophysics modeling” and “engineering modeling”. The core of the hereby proposed approach relies on the consideration of cell-field interactions and its dependency on tissue properties, as argued in this review. The following offers several insights into our approach: 1. Because ITP depends on tissue non-homogeneity, a detailed morphological description of the cell geometry and electrical properties at a microscopic cellular level should be implemented in the model to calculate the electric field. 2. ITP depends on the “instant” conductivities in the intracellular space, the membrane, and the extracellular space. In addition, activation of the membrane will change these conductivities through mechanisms such as voltage-gated channel opening and influx/efflux of ions. Therefore, an “instant” update of the tissue conductivities is necessary during simulations. 3. If more than one cell is modeled, as in a high-density cell medium, the interactions between the cells/axons through extracellular electric field coupling must also be considered, as the electric field generated by a polarized neuron may have “secondary” effects on its neighboring neurons/axons and vice versa.Several lines of converging experimental and modeling substantiations have suggested a broader frame of reference in which the interaction between the cells and the electric field might play significant roles in determining the ultimate polarization of the neuronal tissue under electric and magnetic stimulation. The inhomogeneous tissue property seems to affect this interaction and cellular polarization. The emerging methodology of combined physics modeling and engineering modeling, which takes into account the cell-field interaction, will provide a more accurate quantitative understanding of the neuronal activation by electric current. A joint effort of modeling and experimental research can further illustrate the significance of cell-field interaction and its biological effects during electric stimulation.


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. The public concern on air pollution increases significantly due to the serious hazards to the public health, as described in [In order to mitigate the impacts of air pollution on human health, global environment and worldwide economy, governments have put tremendous efforts on air pollution monitoring. With detailed information of the air pollution situation, scientists, policy makers and planners are able to make informed decisions on managing and improving the living environment . CountriTraditionally, air pollution situation is monitored by conventional air pollution monitoring systems with stationary monitors. These monitoring stations are highly reliable, accurate and able to measure a wide range of pollutants by using the conventional analytical instruments, such as gas chromatograph-mass spectrometers .The drawbacks of the conventional monitoring instruments are their large size, heavy weight and extraordinary expensiveness. These lead to sparse deployment of the monitoring stations see . In ordeSuch low spatio-temporal resolution is sufficient for ambient background monitoring but extremely inadequate for the public to be aware of their personal exposure to air pollution and cannot reflect their personal health risks. In , researcIn order to increase the spatio-temporal resolution of the air pollution information, researchers are pushing the air pollution monitoring systems to the limit by combining the low-cost portable ambient sensors and the Wireless Sensor Network (WSN) into one system which is known as The Next Generation Air Pollution Monitoring System (TNGAPMS) . By utilResearchers anticipate that the real-time, high spatio-temporal  air pollution information can help advise the public to take proper actions according to their individual health needs , and raise public awareness about the air pollution that further leads to change of public “unclean” activities .The remainder of this paper is organized as follows. In 2), ground level ozone (O3), sulfur dioxide (SO2), particulate matter (PM) and lead (Pb) [Pollutants are emitted by human activities and natural sources. Hundreds of hazardous pollutants in our living environment have been identified . Howeveread (Pb) . The heaead (Pb) ,27,28,29Governments and organizations have put regulation limits on these pollutants to reduce the risks. The United States Environmental Protection Agency (EPA), the World Health Organization (WHO), the European Commission (EC), the Chinese Ministry of Environmental Protection (MEP) and the Environmental Protecting Department (EPD) of Hong Kong have declared different standard limits for these pollutants see .In order to help the public understand the current air quality easily, the government and organization agencies introduced an indicator called Air Quality Index (AQI). AQI measures the “condition or state of each relative to the requirements of one or more biotic species and/or to any human need or purpose” . In a woTo illustrate the concept of AQI, an AQI example introduced by the Environmental Protection Department (EPD) of Hong Kong  called AConventional air pollution monitoring systems are mainly based on sophisticated and well-established instruments. In order to guarantee the data accuracy and quality, these instruments use complex measurement methods  and a loCurrently, the air pollution data at locations without monitoring stations are obtained by air quality models or estimations . HoweverIn the following subsections, the working mechanisms of the low-cost portable ambient sensors that are widely used in TNGAPMS are introduced. As a matter of fact, except the air pollution detecting technologies mentioned in Nowadays, many different technologies for gas detection are available, each with certain advantages and disadvantages. To date, there are five types most suitable and widely used low-cost portable gas sensors, namely electrochemical sensors, catalytic sensors, solid-state (semiconductor) sensors, non-dispersive infrared radiation absorption (NDIR) and photo-ionization detector (PID) sensors see . All of Although, till now there is no low-cost portable gas sensor can achieve the same data accuracy and quality as conventional monitoring instruments. The low-cost portable gas sensors provide a fair enough accuracy and detection range . What is2), ground level ozone (O3) and sulfur dioxide (SO2). Combining the descriptions in [As described in tions in ,84 and tCO: Can be well detected by solid-state and electrochemical sensors.2: Can be well detected by solid-state and electrochemical sensors. Need to consider the interference gas O3. Proper methods can be applied to reduce the interference.NO3: Can be well detected by solid-state and electrochemical sensors. Need to consider the interference gas NO2. Proper methods can be applied to reduce the interference.O2: Can only be well detected by solid-state and electrochemical sensors. It poisons the catalytic sensors. The sensitivity of NDIR sensors is not high enough.SOIn a word, the solid-state and electrochemical sensors are the most suitable types of sensors to monitor these four types of hazard gases in building the TNGAPMS scenario. In fact, these two types of sensors are the basic elements in most of the existing works presented in The working principle of the solid-state ambient gas sensors was discovered when researchers were dealing with the semiconductor p-n junctions, which are sensitive to environmental gases.A solid-state sensor consists of one or several metal oxides like tin oxide or aluminum oxide , and a heating element. The metal oxides can be processed into a paste, which is called bead-type sensor see . The metIn order to increase the reaction rate that results in a strong electrical signal, a heating element is used inside the solid-state ambient gas sensor. The heating element is also used to regular the temperature because the response (conductivity change) of a specific kind of ambient gas is different in different temperature ranges.The working mechanisms of the electrochemical ambient gas sensors are electrochemical reactions  within the sensors. The reaction between the sensor and the ambient gas molecules produces an electrical signal (current) proportional to the concentration of the ambient gas.An electrochemical sensor consists of a Working Electrode (WE) and a Counter Electrode (CE). For sensors requiring an external driving voltage, a Reference Electrode (RE) is needed. These two or three electrodes are separately deployed into the electrolyte within the sensor see .Different sensors may use different types of selective membranes, electrolyte and working electrodes in order to improve the sensor’s selectivity to a specific kind of ambient gas. To allow enough amount of ambient gas to react with the sensor while preventing electrolyte leakage, the ambient gas first goes through a capillary-type opening and a hydrophobic barrier. When the ambient gas reaches the working electrode, the oxidation-reduction reaction occurs. The specifically developed electrode for an interested ambient gas catalyzes these reactions. By measuring the current between the Working Electrode (WE) and the Counter Electrode (CE), researchers are able to deduce the concentration of the target ambient gas. For sensor with Reference Electrode (RE), the reference electrode is used to control the oxidation and reduction reactions and reduce the potential drift on working electrode due to deterioration (may not work when the electrodes are fouled).Note that, most of the electrochemical ambient gas sensors require a small amount of oxygen and humidity to function properly. Also, wind velocity influences the chemical equilibrium on the sensor’s surface and further influences the sensor’s readings .The measurement of particulate matter (PM) is not straightforward and there are many techniques  available for measuring the mass concentrations of PM. Due to the complex nature of PM, different measurement techniques may give different results . Some coThe available techniques for measuring the concentration of PM can be grouped into two categories. One is direct reading instrument which provides continuous measurements  on the concentration of PM in ambient air see . The othThe commonly used continuous measurement techniques of PM in ambient air are listed as follows.3) of PM in ambient air.The TEOM analyzers are widely used in the conventional air pollution monitoring systems. The operation principle of TEOM is that the oscillation frequency of the tapered glass tube is proportional to the mass of the tube. The PMs deposited onto the tube will change the mass and oscillation frequency of the tube. By measuring the oscillation frequency change of the tube and the volume of air sampled, researchers are able to deduce the mass concentration (μg/m10 size selective inlet rejects 50% (no design can reject 100%) of the particles with diameter more than 10 μm and let through particles with diameter of 10 μm and less. In order to eliminate the effect of humidity change, a heating element or a dryer is used.Note that the air is sampled through a size selective inlet. For example, a PMβ-Attenuation Analyzers or β-Attenuation Monitors (BAM) are the most widely used PM measurement instruments in the conventional air pollution monitoring systems. The air is first sampled through a size selective inlet (PM10 or PM2.5) with or without heater/dryer that minimizes the water contained in the air. Then the air goes through a paper filter, which catches the PM. The paper filter with PM is exposed to β-attenuation source. After the measurement interval, researchers are able to deduce the mass of the PM on the filter by measuring the radiation intensity of the filter.The The black smoke technique collects the PM on a paper filter over 24 h period through a size selective inlet. The darkness of the paper filter is then measured by a reflectometer and converted to the PM’s mass concentration. This kind of monitoring equipment is relative simple, robust and cost-efficient. However, the mass concentration is derived by measuring the darkness of the filter and the darkness of PM varies in different locations. This makes the darkness-to-mass coefficient change from time to time and location to location.The optical analyzers utilize the interaction between the ambient PM and the imaging, laser or infrared light. These analyzers are small, lightweight and battery operated. Base on the optical principle, the optical analyzers can be classified into three categories, namely direct imaging, light scatting and light obscuration  analyzers.Light Scatting:i.e., PM2.5, PM5 and PM10). However, the particle counts need to be converted to mass concentration by calculation  and this will introduce errors that further affect the precision and accuracy of the analyzers.This category of optical analyzers uses a high-energy laser as the light source. When a particle passes through the detection chamber that only allows single particle sampling, the laser light is scattered by the particle. A photo detector detects the scatting light. By analyzing the intensity of the scatting light, researchers can deduce the size of the particle. Also, the number of particle counts can be deduced by counting the number of detecting light on the photo detector see . The advDirect Imaging:In a direct imaging particle analyzer, a beam of halogen light illuminates the particles and the shadow of each particle is projected to a high definition, high magnification and high resolution camera. The camera records the passing particles. The video is then analyzed by computer software to measure the PM’s attributes. Both size and counts of the PMs in the ambient air can be obtained. What’s more, the color and the shape of the particles can also be detected.Light Obscuration (Nephelometer):3) measurement method with high precision and low detection limited. A nephelometer is an instrument that measures the size and mass concentration of PM in the ambient air. In a nephelometer, a near infrared LED is used as the light source and a silicon detector is used to measure the total light scattered  by the PMs  are prevalent in these systems ,94,95. TIn order to address these prevalent problems, researchers have put lots of efforts into the concept of TNGAPMS by utilizing the advance sensing techniques, MicroElectroMechanical Systems (MEMS), and Wireless Sensor Networks (WSN).According to the definition of participatory sensing ,97 and vThese existing works greatly improve the spatio-temporal resolution and QoS of the air pollution information compared with that of conventional monitoring systems. However, in TNGAPMSs, it is impossible to use the same high-end measurement instruments as the ones utilized in stationary monitors of Conventional Stationary Monitoring Network (CSMN). Hence, whenever we deal with the TNGAPMSs, we face the same interesting trade-off as shown in etc.In SSN systems, the sensor nodes are typically mounted on the streetlight or traffic light poles, or walls see . By utilCarrier: The sensor nodes are usually mounted on the streetlight or traffic light poles, or carefully selected locations.Related Works:In , the proIn , a WSN b3, NO2, CO and H2S are sensed and transmitted back to the backend server through the GPRS wireless communication link every minute. Authorized air pollution information is available to the public through the customized Web and mobile Apps. A solar panel was utilized to solve the power constraint issue of the sensor nodes (stationary).In , an outdIn , an inno3, CO and NO2) and a ZigBee wireless communication link based on the Libelium’s [In , an outdbelium’s  gas sensIn , a WSN bIn , an indoAdavantages:Loose constraint on energy consumption No locating device (The location of a sensor node is known once it was deployed since the sensor node is stationary.)Loose limitations on weight and size (The carrier of the sensor node is able to carry sufficient enough weight.)Multiple sensors per node Accurate and reliable data (Sensor node can integrate with assisting tools because of the loose limitations on weight and size.)Guaranteed network connectivity Well calibrated and maintained sensors Disadvantages or Challenges:Careful placement of sensor nodes requirement (This is because of the location dependence of air pollutants.)Large number of sensor nodes requirement Resource wasting in certain level (The stationary sensor nodes are in sleep mode most of the time because continuously updating data at one location is pointless .)Inconveniences of calibration and maintenance 2-Dimensional data acquisition Customized network requirement (A customized wireless or wired network is required when the cellular network is not utilized.)etc.In CSN (or Participatory Sensing) systems, the sensor nodes are typically carried by the users see . By utilCarrier: The sensor nodes are carried by the public or professional users, usually by volunteers or people who are keen on air quality.Related Works:3 sensor and an off-the-shelf smart phone. The sensor communicates with the smart phone through the USB port. Data (tagged with location information from the build-in GPS module) are uploaded to the server through the cellular network. Authorized information is available to the public through the customized Web and mobile Apps. Two methods were proposed and implemented to improve the data quality of the sensor. This paper claimed that air pollution information with high spatial resolution can be achieved by the community-driven sensing infrastructure like OpenSense [In , a low-ppenSense .2, CO, VOCs, H2, temperature and relative humidity) and a Bluetooth link. Data are acquired by the sensors and transmitted to the smart phone through the Bluetooth link. The smart phone then uploaded the data to the server through the cellular network. Authorized air pollution information is available to the public through the customized Web and mobile Apps. Several research issues that need to be addressed before practical deployment of the P-sense system were also pointed out.In , an outd2, CO, O3, temperature and relative humidity sensors) and a Bluetooth link communicating with the smart phone. The smart phone further forwards the data to the server using a build-in Wi-Fi module, which was also utilized for localization. Authorized air pollution information is available to the public through the customized Web and mobile Apps. Three novel techniques were proposed and implemented to improve the data accuracy and energy efficiency of the system.In , a persoX, temperature and Bluetooth) to a GPS-enabled cellphone, the raw air pollution data, which help understand the impacts of air pollution on both individuals and communities, are gathered. The sensor node communicates with the cellphone through the Bluetooth wireless link. Note that, this paper didn’t focus on the implementation but the design of the sensing platform. Several research challenges like unpredictable user behaviors and movements, and user privacy problems were discussed in this paper.In , a hardwIn , an outdIn , a VolatAdavantages:Cost efficiency Coupled data generators and consumers Public-driven property Automatic gathering property Mobility of sensor nodes (The mobility of the cellphones or users enlarges the sensor node’s geographic coverage.)Public behaviors acquisition ability Disadvantages or Challenges:Low data accuracy and reliability .Privacy issues (The users may not want to make their location information public for privacy issues).Badly calibrated and maintained sensors .Serious constraint on energy consumption .Uncontrolled or semi-controlled mobility .2-Dimensional data acquisition .Serious limitations on weight and size .etc.In VSN systems, the sensor nodes are typically carried by the public transportations like buses or taxis see . By utilCarrier: The sensor nodes are carried by the public transportations  or specially equipped cars.Related Works:3, CO and NO2. Each sensor node utilizes a GPS module for acquiring time and location information and a Bluetooth link for communicating with the laptop inside car. Collected data are then uploaded to the server through the laptop’s Wi-Fi link. Authorized air pollution information is accessible through the sensor node’s LCD display or the SensorMap Web App.In , the Mob2, NOX, O3 and VOCs) and a wireless link (ZigBee or Wi-Fi or others). The MS nodes are mounted on the public transportations and transmitting data to the SS nodes. The SS nodes are able to perform data acquisition and fusion, and further forward the data (from MS nodes and SS nodes) to the central server. Currently, the air pollution information is only available to the researchers. A distributed data mining-algorithm for identifying the relationships between the urban transport and the environment was also proposed.In , a distr2 sensor is mounted outside the car to monitor the concentration of CO2. A ZigBee intra-vehicle wireless network is utilized to communicate with the inside-car processing unit, which equipped with a micro-controller, a GSM short message module and a GPS module. Data are then sent to the GSM base stations and further forwarded to the monitoring server. Authorized air pollution information is available to the public through a Web App. In order to balance the accuracy of sensed data and the cost of communication, an on-demand approach that adjusts vehicles’ reporting rates was proposed.In ,116, a v2 and VOCs) for detecting the pollutants’ concentrations, a GPS module for collecting the location information, and a GPRS or Wi-Fi module for communicating with the server. Analyzed data are available to the public through a Web App. This paper claimed that the proposed system demonstrated higher spatial coverage at the expense of lower temporal resolution compared with the SSN systems.In , a mobil2, CO2, CO and O3), a GPS module, and a ZigBee wireless link. Data acquired by the sensor nodes are transmitted back to the central computer for further analysis through the static ZigBee accessing points. These sensor nodes are mounted on the public transportations, like buses. By utilizing the mobility of the public transportations, even with a few sensor nodes, the urban air pollution information with fine-grained  level was achieved. In this paper, the air pollution information is only available to the researchers.In , a low-cIn , a fine-2 and SO2), a GPS and GPRS modules. Data with location information are sent to the server through the cellular network (GPRS) for further processing and analysis. Authorized air pollution information is available to the public through the customized Web App.In , a GPRS Adavantages:Loose constraint on energy consumption (The sensor nodes are powered by the vehicles’ batteries.)Loose limitations on weight and size (The carrier of the sensor node is able to carry sufficient enough weight.)Multiple sensors per node Accurate and reliable data (Sensor node can integrate with assisting tools because of the loose limitations on weight and size.)High mobility of sensor nodes (The highly mobile vehicles significantly enlarge the sensor node’s geographic coverage.)Feasibility in maintenance Well calibrated and maintained sensors (This is because of the feasibility in maintenance of the VSN systems.)Automatic gathering property Disadvantages or Challenges:Uncontrolled or semi-controlled mobility Redundant sampling issues Cost inefficiency on carriers Locating and communication devices requirement Customized network requirement (A customized wireless network is required when the cellular network is not utilized. The network connectivity may not be guaranteed due to the mobility of vehicles.)2-Dimensional data acquisition Spatial-to-Temporal resolution trade-off In this section, 20 state-of-the-art TNGAPMSs are discussed and classified into three categories, namely the SSN, CSN and VSN. Summary information  of these systems is shown in Ranking , Reasons (reasons for why we choose this property for comparison) and Explanation (detail explanation of the ranking).In this section, the comparisons between SSN, CSN and VSN are presented. The six properties for comparison are listed as follows. Each property is described in detail with respect to the Ranking: VSN > CSN > SSNReasons:The mobility of the carrier enables a sensor node to cover sufficient large geographic areas within a short period of time. Higher spatial resolution of the sensed data can be achieved and fewer number of sensor nodes are required compared with systems using stationary carriers.Explanation:The sensor nodes carried by the public transportations in VSN systems are with the highest mobility among the three types of sensor networks. Following is the sensor nodes carried by the public users in CSN systems because the users travel much slower than the vehicles and the users spend most of time indoor or inside cars . The staRanking: SSN > VSN > CSNReasons:One of the objectives of TNGAPMS is to increase the temporal resolution of the acquired air pollution information. And the air pollution information from all TNGAPMSs has much higher temporal resolution than that from the conventional monitoring systems. However, the temporal resolutions of the acquired pollution information in SSN, CSN and VSN are slightly different due to several reasons.Explanation:In terms of building a pollution map, the pollution information from SSN systems has the highest temporal resolution. Then comes the pollution information from VSN systems, followed by that from CSN systems. The ranking is based on the assumptions that the sensor nodes’ sensing rates are identical in different systems and the sensors have a limited effective coverage . In a siIn terms of monitoring personal exposure, the pollution information’s temporal resolution for people wearing the sensor nodes in CSN systems is the highest. For people without carrying the sensor nodes, the temporal resolution of the pollution data on personal exposure depends on the pollution map.Ranking: CSN > VSN > SSNReasons:The air pollution situation in rapid industrializing countries is much more critical than that in industrialized countries . SeveralExplanation:In sensor node level, the CSN systems have the highest cost efficiency, followed by the SSN systems and the VSN systems. In CSN systems, the users’ cellphones are fully utilized, including the build-in GPS and wireless communication modules, and the computational powers. The sensor nodes in CSN systems typically require no locating, communicating and computing devices and hence the cost efficiency is enhanced. In SSN systems, the stationary sensor nodes require no locating device but the communicating and computing devices because the location of a specific sensor node is known once it is deployed. For the sensor nodes in VSN systems, the GPS modules are essential due to the mobility of the carriers. Also, the communicating and computing devices are needed. Hence, the cost efficiency of VSN systems is the lowest in sensor node level.In system level, the vast majority of the system cost is contributed by the acquisition of sensor nodes. Moreover, if the number of sensor nodes in system enlarges, a larger database for data storage and management, a faster wireless sensor network for data transmission and a more powerful computing center for data processing and decision making in real-time are required. As described in The final ranking is achieved by averaging the rankings in sensor node level and system level.Ranking: SSN > VSN > CSNReasons:etc.) that help improve the data quality.The endurance of the sensor nodes is a major property for comparison because it will further influence the Maintenance property and the Data Quality property. A sensor node with energy constraint  requires replacing battery frequently, which increases the burden of maintenance. Moreover, the energy constraint of sensor nodes limits the use of conditioning appliances  of the sensor nodes are extraordinary serious. These sensor nodes are typically light weight and small size, and impossible to equip with assisting instruments like temperature and humidity controllers. Moreover, the sensor nodes are not well maintained and may be put into bags or pockets that further lower the data quality. In VSN systems, these constraints are not as critical as that in CSN systems. Adding assisting tools to the sensor nodes is possible and the sensor nodes are well maintained by the professionals. However, the high mobility of the sensor nodes becomes a major factor affecting the accuracy of the sensor readings due to the varying air flow around the sensor head . In SSN After we have an in-deep understanding of these six comparison properties described above, we graded these properties of SSN, CSN and VSN systems using the grading code defined as: `0’ means ‘None’; ‘1’ means `Low/Short/Inconvenient’; ‘2’ means ‘Medium’ and ‘3’ means ‘High/Long/Convenient’. The final grade of each comparison property of SSN, CSN and VSN systems following the descriptions above are shown in Air pollution is an essential environmental issue due to the tremendous impacts on public health, global environment, and worldwide economy. Urban air pollution with non-uniform distribution trend arises the necessity for pollution monitoring with high spatio-temporal resolution, which the conventional air pollution monitoring systems cannot provide because of the limited data availability and non-scalability of the systems. By utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN), researchers are pushing the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) to the limit and have achieved great progresses. Many of state-of-the-art air pollution monitoring systems have been implemented and tested. All of these systems evidence that an air pollution monitoring system with high spatio-temporal resolution, cost and energy efficiency, deployment and maintenance feasibility, convenient accessing ability for the public or professional users are achievable. However, from Lack of 3-Dimensional Data Acquisition Ability: All the systems presented in Infeasibility of Active Monitoring: The sensor nodes in SSN, CSN and VSN systems presented are all passive monitoring sensor nodes . We believe that active monitoring (users can fully control the sensor network including the formation and routes of the sensor nodes) provides higher flexibility and QoS.Uncontrolled or Semi-Controlled Carriers: The carriers in SSN, CSN and VSN systems are uncontrolled or semi-controlled because they are either stationary or with pre-determined routes. We anticipate that fully controlled carriers have higher mobility and make active monitoring possible. By utilizing the fully controlled carriers , feasibility in deployment, cost efficiency of systems and convenience in maintenance can be achieved.In fact, all the abilities or characteristics of these state-of-the-art TNGAPMSs need to be carried forward and some of them can be improved in future systems.Mobility of Carriers: The mobility of the carriers enables one sensor node to cover a sufficient large geographic area within a short period of time. The number of sensor nodes required is reduced and the system cost and maintenance are relaxed. In fact, the multi-rotors (There are regulations for drones in some areas and we put aside this issue for a moment in this paper.) will not be trapped into traffic-jams or stop by unreachable areas as the carriers in VSN systems. The multi-rotors have much higher mobility than the carriers in VSN systems.Feasibility of Maintenance: The system’s feasibility on maintenance need to be carried forward and enhanced because it affects the data quality and cost efficiency of the system. If the fully controlled UAVs were utilized in the system, professional maintenance on large number of sensor nodes can be performed simultaneously by driving all UAVs to a specific location. In this case, the quality of the sensed data is guaranteed while the time and manpower for maintenance are saved.Add-on Sensor Ability: We note that all the sensor nodes in the existing TNGAPMSs are with no add-on ability. Reconfigurations on the hardware and software of the sensor nodes are needed whenever the sensing species are modified. In real-life large-scale applications, there could be hundreds or even thousands of sensor nodes in the system. Sensor nodes with add-on (the sensor node is able to identify the type of sensor mounted and chooses the suitable program to handle the sensing data) ability are essential in this case. Properties like modifiable sensing and transmitting intervals, remote programmable ability, cost and energy efficiencies and failure check feature are also essential.Last but not least, all the existing state-of-the-art TNGAPMSs claim that they have a better spatio-temporal resolution than the conventional air pollution monitoring systems (which is obvious). However, none of them has ever considered how good they are, not to mention the comparisons among the SSN, CSN and VSN systems, with respect to real-time performance, spatio-temporal resolution and QoS. And this will be a major direction of our future works.


A genetic screen has revealed one of the molecules that allow choanoflagellates, the closest unicellular relative of animals, to form colonies, which could help researchers to answer questions about the earliest days of animal evolution. Related research article Levin TC, Greaney AJ, Wetzel L, King N. 2014. The rosetteless gene controls development in the choanoflagellate S. rosetta. eLife3:e04070. doi: 10.7554/eLife.04070Image Choanoflagellates need a protein (cyan) encoded by the rosetteless gene to form coloniesThe central question in the field of developmental biology is: how can a single fertilised egg cell develop into a living organism made of millions of specialised cells? The evolutionary flip side of this question is: how did a multicellular animal first evolve from a single-celled microorganism? For decades evolutionary biologists have tried to answer this question by analysing the fossil record, by studying the species that represent the earliest branches in the animal family tree and, lately, by exploring the closest unicellular relatives of the animal kingdom.Choanoflagellates are considered to be the closest single-celled cousins of animals that are alive today. Choanoflagellates are unicellular eukaryotes that live in fresh and marine waters, and each beats its tail-like flagellum to catch bacterial prey. The choanoflagellates' close relationship with the animals was first hypothesised as early as two centuries ago, and was based on their similarity to a specific cell type found in one of the simplest animals, the sponges . InteresMonosiga brevicollis and Salpingoeca rosetta  are known to be involved in cell-cell adhesion in the multicellular aggregative stage of the amoeba Capsaspora owczarzaki (Regardless of whether the czarzaki .Finding common tools involved in the multicellular life cycles of animals and their unicellular cousins is key to unravelling how complex the last common single-celled ancestor of animals was. Knowing this will allow us to answer the decisive question: was the origin of multicellular animals one giant leap in eukaryotic evolution or one small step for a colony-forming microorganism?


The adoption of pro-environmental behaviours reduces anthropogenic environmental impacts and subsequent human health effects. This study developed composite indices measuring adoption of pro-environmental behaviours at the household level in Canada.The 2007 Households and the Environment Survey conducted by Statistics Canada collected data on Canadian environmental behaviours at households' level. A subset of 55 retained questions from this survey was analyzed by Multiple Correspondence Analysis (MCA) to develop the index. Weights attributed by MCA were used to compute scores for each Canadian province as well as for socio-demographic strata. Scores were classified into four categories reflecting different levels of adoption of pro-environmental behaviours.Two indices were finally created: one based on 23 questions related to behaviours done inside the dwelling and a second based on 16 questions measuring behaviours done outside of the dwelling. British Columbia, Quebec, Prince-Edward-Island and Nova-Scotia appeared in one of the two top categories of adoption of pro-environmental behaviours for both indices. Alberta, Saskatchewan, Manitoba and Newfoundland-and-Labrador were classified in one of the two last categories of pro-environmental behaviours adoption for both indices. Households with a higher income, educational attainment, or greater number of persons adopted more indoor pro-environmental behaviours, while on the outdoor index, they adopted fewer such behaviours. Households with low-income fared better on the adoption of outdoors pro-environmental behaviours.MCA was successfully applied in creating Indoor and Outdoor composite Indices of pro-environmental behaviours. The Indices cover a good range of environmental themes and the analysis could be applied to similar surveys worldwide (as baseline weights) enabling temporal trend comparison for recurring themes. Much more than voluntary measures, the study shows that existing regulations, dwelling type, households composition and income as well as climate are the major factors determining pro-environmental behaviours. A significant source of pollution to our natural environment comes from domestic activities and behaviours. For example household-generated waste in Canada accounts for around a third of total waste and household energy use and municipal water consumption for 17% and 57%, respectively Municipal waste can impact the environment in various ways including soil and water contamination from leachate in landfills disposal and the production of greenhouse gas emissions (GHG) and air pollution, either from landfills or the incineration process. When solid waste are recycled or composted instead of being landfilled or incinerated, the demand for energy and new-resources can be reduced significantly The production of energy can impact the environment in various ways, depending on the technology. In Canada, energy production and consumption accounts for around 80% of all GHG emission Water shortages are happening worldwide and one way to limit their occurrence is through water conservation behaviours. In most homes, more than 60% of water use comes from toilet flushing, showers and baths, making water-saving devices like low-flow shower head an efficient way of reducing water consumption. In summer, water use can increase by 50% for yard activities such as watering the lawn. There are behaviours that households can implement to decrease their water consumption in summer time like using sprinklers with a timer or adopting the use of a rain barrel It thus becomes clear that addressing sustainability concerns has to take into account not only industry or agriculture, but also household behaviours, their impacts on ecosystems and ultimately on human health. Monitoring trends of household behaviours can inform policy and research agendas on the development of incentives or other mechanisms such as information campaigns to reduce domestic pollution and facilitate adaptative measures to minimize related health risks. The adoption of several pro-environmental behaviours, i.e. actions that contribute to the preservation of the environment, should be encouraged to significantly reduce the anthropic impact on the environment.In Canada, the Households and the Environment Survey (HES) was designed to measure household behaviours with respect to the environment. The HES is a periodic survey conducted by Statistics Canada, the federal government statistical agency, and administered across Canadian provinces. The survey covers 12 broad themes including energy use and heating, water use, transportation decisions, motor vehicle use, recycling and composting  [7]. WhiIt is difficult to follow up on such a wide array of relevant behaviours and their trends over time, unless they are summarized in some way. A composite index is a tool which can be useful to that purpose as it incorporates several aspects of an issue and allow for monitoring across several themes simultaneously, thus facilitating the measurement of trends This study thus sets out to develop a composite index that summarizes pro-environmental behaviours at the household level across Canadian provinces based on the HES (2007) given the periodicity and geographical coverage of the survey. Pro-environmental behaviours are defined as actions that contribute to the preservation of the environment and can have a positive impact on the health of the population. This study will serve as baseline of the trend of the composite index over time, given the periodicity and geographical coverage of the survey.This research did not require the approval of an ethics review board as we used an existing and anonymized database made available to universities by Statistics Canada. Statistics Canada obtained consent previous to survey administration. No new data was collected for this study.The Households and the Environment Survey (HES) is conducted by Statistics Canada. It was designed to address the needs of the Canadian Environmental Sustainability Indicators project. The project reports on air quality, water quality and greenhouse gas emissions in Canada using indicators to identify areas of importance to Canadians and monitor progress The survey aimed Canadian households with at least one person aged 18 year or older. The HES covers all 10 of the provinces and excludes the 3 northern territories, Indian reserves and members of the Canadian Armed Forces. The survey was first conducted in 1991 and since 2005 has been carried out biennially. In the present study, the 2007 HES database was used in its Public Use Microdata Files format (PUMF) From the 40 584 households selected in the 2007 CCHS, a sub-sample of 29 957 households were selected for the HES. Of those, 21 690 households responded to the survey resulting in an overall response rate of 72%. The survey is representative of 12 932 350 households, corresponding to 97% of all Canadian households The person with the best knowledge of environmental household practices was asked to respond on behalf of the household. The main questionnaire covered 12 themes and included 121 questions  [16]. AmThe PUMF was used for the analysis and unlike the master file, applies privacy measures to protect personal information Sampling weights were applied to ensure that any derived composite index is representative of the study population. They were used when proportions and averages were estimated and to weight the relative frequencies of the Burt matrix in the MCA .This study focuses on everyday pro-environmental behaviours, defined as actions that contribute to the preservation of the environment and can have a positive impact on the health of the population. For example, air pollutants can be reduced when households adopt behaviours that decrease their energy consumption such as the use of energy-efficient appliances or when they use more sustainable transport options such as public or active transport.Based on the above definition, a panel of four environmental health experts applied progressive development consensus after iterations, based on a nominal group technique Given that the data was mostly categorical, the indices in this paper were developed by multiple correspondence analysis (MCA) MCA was conducted using the ‘ca’ package of the R statistical software Then, an exploratory MCA was performed to project data onto maps where potential outliers were identified and excluded from subsequent analyses. MCA was then applied again to determine the most relevant factorial axes that would serve to build the composite index. There are no universal rules for the determination of the number of dimensions to retain in MCA. However, since the first factorial axis captures the most important part of the total inertia, it plays a central role in the computation of a composite index.As recommended by Asselin The computation of the index score was performed as follows: first, the score of any household was obtained by taking the average of its category-weights generated by the MCA. Then for each province we took the average over all household scores as the value of its composite index. The sampling weight was used in this final step. Coordinates were missing for excluded responses.The 10 average provincial scores were grouped into categories reflecting different levels of adoption of pro-environmental behaviours. First we applied a cluster analysis and then we used a dendrogram plot using SAS version 9.2  to determine such groups. The categories limits generated for the provincial index were used as reference categories for indices on other socio-demographic variables.Finally, others indices based on various socio-demographic variables were constructed . HousehoOf the 121 questions in the survey, 55 were kept by the Expert Panel for use in the MCA. These represented 285 response possibilities. On the MCA map projection there was a clear opposition between the missing data  located far from the map center and the other responses which gathered close to the center . ExcludiWe then screened the projected responses to identify questions following an ordinal structure, ). Twenty-three such questions with pro-environmental responses deteriorating from left to right on the first axis (group A), and 16 questions with opposite ordinal structure (group B) were identified. The remaining 16 (of 55) questions were excluded from the analysis because their responses were not sufficiently discriminating . As well the majority of these questions (10/16) had at least two responses with a contribution of zero to the first axis .These exploration steps led us to consider two separate composite indices. Group A included 96 responses but after excluding missing data, 52 responses were used in the MCA. The majority of excluded responses had frequencies lower than 2.0% and two responses had frequencies of 4.6% and 4.7%. After exclusion of missing data, some responses still looked like extreme values on the map . They weFor group A, the first dimension explained 32.6% of the inertia while the second explained 16.1% . Given tThe second group of 16 questions (group B) consisted of 86 responses, 41 of which were missing values. The 45 remaining responses used for the MCA had frequencies of 7.0% or higher while excluded responses had frequencies lower than 3.5%. For group B, the first dimension explained 62.1% of the inertia while the second explained only 12.4% . Again, Because two distinct behavioural categories resulted from the MCA, two composite indices were created instead of one. The first index (group A) is named the ‘Indoor Index’ and the second one (group B) the ‘Outdoor Index’. Questions included for each index are presented in supporting information, The map representations of the final coordinates generated by the MCA are shown in For the Indoor Index, the households belonging to a province with negative coordinates tend to adopt more pro-environmental behaviours than those of a province with positive coordinates. In contrast, for the Outdoor Index provinces with positive coordinates adopt more outdoor pro-environmental behaviours than those with negative coordinates.The cluster analysis and dendogram plot resulted in the classification of each province into one of four categories reflecting different levels of adoption of pro-environmental behaviours: 1) adopting the most; 2) adopting slightly fewer; 3) adopting much fewer and; 4) adopting the fewest. The provincial coordinates and the categories generated from the cluster analysis are shown in None of the 10 provinces were classified in both indices as adopting the most pro-environmental behaviours. For the Indoor Index, Ontario (ON), Prince Edward Island (PEI) and Nova Scotia (NS) rated in the top category, British Columbia (BC) and Québec (QC) in the next, the three Prairie provinces and New Brunswick (NB) in the third and Newfoundland and Labrador (NL) in “adopting the fewest” category . For theFour provinces  were classified in the top two categories for both indices while four provinces  were classified for both indices, in the two lower categories.The coordinates and the classification for the six comparison variables are shown in This study sought to develop a composite index which measures the overall adoption of pro-environmental behaviours among Canadian households. MCA, our main analytical technique, was used to aggregate survey data and to provide weights to the responses in the construction of the index. Our approach is similar to other studies in different fields The MCA generated two indices based on 39 of the 55 behavioural questions, an Indoor Index and an Outdoor Index, each reflecting environmental behaviours for 5 of the 12 survey themes. Retaining both indices allowed for better representation of the survey; together they cover 9 themes out of 12 (water use is in both) whereas one single index would have covered only 5, excluding important environmental themes such as fertilizer and pesticide use. As well, because the provincial classifications were different for each index and varied as well in the classification by socio-demographics factors for each index  it was deemed justifiable to keep both indices.Most (19/23) questions included in the Indoor Index were asked to all households with the exception of questions on recycling where only those households with access to a program were asked to respond. For the Outdoor Index, most questions (11/16) concerned watering of the lawn or the garden, and the use of fertilizers or pesticides. These (11) questions were answered only by households having a yard. However, even if households living in an apartment did not have to answer these questions, they were still recorded in the Index as households adopting pro-environmental behaviours .One likely explanation for PEI and NS being classified in the top category for the Indoor Index is that nearly 100% of their households recycle and the proportion that compost is substantially above the Canadian average, as reported by Statistics Canada. In these two provinces, households are obligated by law to recycle and compost Recycling and composting are also common in ON but its good ranking is also related to the proportion of households that adopt water conservation behaviours  QC's good classification in the Indoor Index is in part due to its proportion of households adopting recycling behaviours being higher than the Canadian average. There were four questions on recycling which contributed significantly to the first dimension, thus contributing to QC's classification. Despite QC having the lowest proportion of households that compost In AB, MN, SK and NB, the proportion of households that adopted indoor pro-environmental behaviours is below the Canadian average (data not shown), explaining their lower classification in the Indoor Index. NL had only a few variables above the Canadian average and had most often the lowest proportion of all provinces. For example, the proportion is below the average for all four questions on water conservation and for all questions on recycling. In this province, there is no provincial plumbing code requiring the use of water-saving fixtures in new buildings Results for the Outdoor Index show a pattern with respect to Coastal proximity, with coastal provinces, with the exception of NL, rating in the two higher categories, and the continental provinces in the two lower categories with the two lowest rated provinces situated in the Prairies. The climate of the Prairies grasslands is characterized by hot summers combined with low precipitation and periodic drought. The climatic region of the Maritimes however is the one with the greatest annual precipitation Also, there was an important difference in the proportion of households that used fertilizers and pesticides and QC had, by far, the lowest proportion. QC was the first province to adopt a provincial law in 2006 prohibiting the sale of pesticides for cosmetic purposes It should also be noted that QC and BC have the highest proportion of households living in an apartment For most socio-demographic variables, there were oppositions in the classification of the modalities, which means that it is not the same households that adopt pro-environmental behaviours on both indices. Higher income households may be more able to maintain and repair their housing and also invest in environmentally friendly products such as water and energy efficient appliances or fixtures, which can be more expensive than their regular counterparts In Canada, income is usually positively associated to educational attainment Studies have shown that water meters with appropriate pricing are an incentive to reduce water consumption There is a wide variety of measures or instruments than can be introduced by governments to influence households behaviours, from economic instruments to direct regulation, labeling, information campaigns and provision of environment-friendly public goods such as public transportation or bicycle paths This study has identified factors which seem to influence the uptake of beneficial environmental behaviours at the household level. Investment in infrastructure is one of them. The physical or material possibility to act pro-environmentally must indeed be available Regulation is frequently used to efficiently influence the environmental impacts of household decision-making To encourage a reduction in water consumption, both price and non-price policies should be used. Volumetric water charges are associated with both water-saving behaviours and adoption of water-efficient devices Other than governmental measures, household characteristics may play a role in the adoption of environment-related behaviours such as income, household composition and dwelling characteristics We used data from a survey that has been created to address the needs of Statistics Canada and the federal government. Thus, we were limited to its content. The questionnaire does not cover all behaviours that can impact the environment and public health. Also, the indices developed here measure the behaviours available in the survey and retained after the analysis by an expert group for their potential positive impacts on health, and not all existing pro-environmental behaviours. The classification could have been different if other behaviours had been included.Three themes of the survey were not covered by the indices, namely dwelling characteristics, motor vehicle and indoor environment. However, we believe they would not have much impact in the indices. First, there were no behaviours measured in the dwelling characteristics theme and some of the characteristics were included as passive variables in the indices (e.g. year the building was built). The same happened for the motor vehicle theme (focused on the characteristic of the car), yet we used another theme to include the number of vehicles owned by the households in the outdoor index. For the indoor environment theme, only 2 of the 5 questions measured behaviours and they both concerned the type of chemical products used to clean windows and the dwelling. Although every small action is important for the environment, those questions were excluded as some other practices, such as agriculture, use similar products in much larger quantities A good standing in the classification does not mean that there is no place for improvement. Indeed, a high proportion of households that adopt pro-environmental behaviours on one question can compensate for a lower proportion on another question of the same index. Also, the provinces were compared to each other and not classified in relation to a gold standard.Furthermore, it was the MCA that attributed the weight for each modality. Thus, a modality with a higher weight has more impact in the index. For instance, all four recycling questions had the highest contribution to the indoor index. Further studies should investigate if the inclusion of only one of those recycling question or a composite index of those four questions would be more appropriate. The same reasoning should also be applied to questions related to the watering of the lawn or of the garden. Households without a garden or a lawn are rewarded for every question on that subject which at the end can impact greatly the province classification. For example, they were not only rewarded for not watering their lawn, but they were also rewarded for the question concerning the watering duration and the number of time they water. Because the MCA attributed the weights, those household without a garden or lawn had a higher ‘reward’ that households with a garden or lawn that did not water them.One general limit of MCA is that the first dimension usually explains a low proportion of the total inertia in the data set and the other dimensions explain less than the first Because of the study design, based on households, it was also not possible to evaluate the impact of personal attributes, like age and gender, on the adoption of environmental behaviours. The association between environmental behaviours and age is not clear. Studies have observed all possible trends, from older people adopting more pro-environmental behaviours to the opposite trends or no trend at all Because the survey was not meant to measure attitudes or values, we cannot associate the classification of the province to any difference in values or perception. However, others studies have showed cultural differences across Canadian provinces Attitudes and values can also be different in immigrants compared to the native born. The former usually have a smaller ecological footprint Despite those limits, the indices still give a good idea of the global adoption of pro-environmental behaviours with potential positive impacts on health in Canada and remain easy to explain and understand. The main sectors in which households can have an impact are covered by the indices, like air and soil quality as well as water conservation. The weighting methods used (i.e. MCA) are also more appropriate to assign weights as opposed to an equal weights or expert opinion approach that are often criticized for being arbitrary or simplistic MCA was successfully applied in creating Indoor and Outdoor composite Indices of environmental health relevance based on a readily available periodic Statistics Canada dataset. The Indices cover a good range of environmental themes at the household level and the analysis, particularly the indices weights obtained in the MCA, could be applied to similar surveys worldwide (as baseline weights) enabling temporal trend comparisons for recurring themes. Results uncovered provincial patterns of pro-environmental behaviours adoption with certain provinces scoring consistently higher and others consistently lower, as well as the associations between socio-demographic factors and the indices. Much more than voluntary measures, this study shows that existing regulations, dwelling type, household composition and income as well as climate are the major factors determining pro-environmental behaviours.Figure S1Map representation of the MCA results on the 55 questions with extreme responses.(TIFF)Click here for additional data file.Table S1Legend: N/A: Results are not available for supplementary variables. Qlt: Quality (i.e. the sum of the squared correlations for the first two dimensions in this case). Inr: Inertias. K: Principal coordinates for the first dimension. Cor: Squared correlation with the first dimension. Ctr: Contributions of the modality to the explained inertia of the first dimension. All cells are multiplied by 1000. Results are the same on rows and on column when a Burt table is used.Results of the MCA on the 55 questions without extreme responses . (XLS)Click here for additional data file.Table S2Legend: N/A: Results are not available for supplementary variables. Qlt: Quality (i.e. the sum of the squared correlations for the first two dimensions in this case). Inr: Inertias. K: Principal coordinates for the first dimension. Cor: Squared correlation with the first dimension. Ctr: Contributions of the modality to the explained inertia of the first dimension. All cells are multiplied by 1000. Results are the same on rows and on column when a Burt table is used.Results of the MCA for the Indoor Index, 2007. (XLS)Click here for additional data file.Table S3Legend: N/A: Results are not available for supplementary variables. Qlt: Quality (i.e. the sum of the squared correlations for the first two dimensions in this case). Inr: Inertias. K: Principal coordinates for the first dimension. Cor: Squared correlation with the first dimension. Ctr: Contributions of the modality to the explained inertia of the first dimension. All cells are multiplied by 1000. Results are the same on rows and on column when a Burt table is used.Results of the MCA for the Outdoor Index, 2007. (XLS)Click here for additional data file.Table S4Questions and responses selected for the Indoor Index, 2007.(DOC)Click here for additional data file.Table S5Questions and responses selected for the Outdoor Index, 2007.(DOC)Click here for additional data file.


In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation. Wireless mesh networks (WMNs) based on orthogonal frequency division multiple access (OFDMA) have been viewed with considerable interest because they can increase the data rates and flexibility of resource allocation while avoiding the interference between multichannels . OFDMA-bIn , closed-Recently, several distributed synchronization techniques were developed for OFDMA-based WMNs with TDoAs. In , a timinBio-inspired algorithms, which explore the behavioral principles of living organisms, have been successfully applied in various fields, including communication networks . A princThe paper is organized as follows: a preliminary discussion of an OFDMA-based WMN with a TDoA and the flocking algorithm are presented in In this paper, we consider an OFDMA-based WMN without a cluster head. To avoid co-channel interference among the distributed nodes, we assume that all the resources in the 1-hop neighbor nodes are allocated orthogonally in the frequency domain. We are primarily concerned with the initialization stage of the network synchronization, which occurs prior to the data transmission. For theThe arrival time of a signal transmitted from a neighbor node, sampled with a period ofA TDoA may occur because of different propagation delays even when all the nodes in the WMN are synchronized . In an OFDMA-based WMN, such interferences caused by the neighbor nodes with a TDoA result in a loss of orthogonality, significantly degrading the performance even with orthogonal resource allocation. A simple technique for mitigating the interferences caused by the TDoA in an OFDMA-based WMN is to use an extended CP that is larger than the sum of the maximum TDoA and maximum excess channel delay. However, this decreases the spectral efficiency, as the extended CP should be inserted in every OFDM symbol for all nodes.i.e., the estimated starting point of the OFDM symbol is prior to the end of the (lagged) channel response to the previous OFDM symbol), the orthogonality among the subcarrier components is destroyed by the previous symbol, yielding ISI and ICI. Therefore, it is always better to synchronize the symbol timing as closely as possible with the exact timing instance or make it slightly earlier than the exact timing instance.Although the extended CP can be used for a large coverage, it is always preferable that the estimated starting point of the OFDM symbol is precisely or slightly earlier than the exact timing instance. If the estimated starting point of the OFDM symbol is before the exact point but after the end of the (lagged) channel response to the previous OFDM symbol, the symbol does not overlap with the previous OFDM symbol and does not incur any ISI due to the previous symbol. In this case, the phase offset caused by the STO can be easily compensated by a single-tap frequency-domain equalizer. However, if the estimated starting point of the OFDM symbol is too much earlier than the exact timing instance  and (5), it is assumed that the neighbors’ velocities can be detected instantly by observing the movements of neighboring individuals. In real wireless communication channels, unlike the C-S model, there is always a propagation delay between nodesIn Equation (7), the error term given by the average of the propagation delays is accumulated in the distributed time-synchronization process. To use the modified C-S model in a practical situation, the estimation and compensation of the propagation delays for all the neighbor nodes are needed; that is, each node must estimate the propagation delays from its neighbor nodes and request TAs to its neighbor nodes through feedback channels. This precompensation technique has been applied in centralized cellular systems by using an initial ranging signal or periodic ranging signal. However, it is inadequate for distributed WMNs wherein the mesh topology may frequently change. It also requires a large amount of resources, such as time, bandwidth, and network delay, because of the frequent requests through the feedback channels. In this paper, a distributed time-synchronization technique for achieving fast synchronization without a feedback channel in an OFDMA-based WMN with a TDoA is described.In Equations (8) and (9), each nodeThe termz-domain. If we regard the arrival time of the signal from a neighbor node as the input and the transmission time of the nodez-domain as:When there is no propagation delay between the nodes, the formula with the parametersz-plane, the proposed updating formula are always asymptotically stable. To reduce the accumulated error termNote thatIf edit 1 In the FFT window update step, the starting point of the FFT window is updated with the signals received from the neighbor nodes. The updating formula for the FFT window position of the nodeIt is possible to reach the maximum bound of the TDoA in Equation (15) when the transmission times of all the nodes are synchronized using a GPS. In this case, the arrival time from a neighbor node is determined only by the propagation delay between the nodes; hence, the maximum bound of the TDoA is given by the right-hand side of Equation (17). When a 1-hop neighbor node is located far away, the maximum TDoA after the convergence may be larger than the CP duration. In this case, the WMN violates the convergence condition , causing a loss of orthogonality. The WMN can then be reorganized (self-organization) by updating the weights in Equation (10) as follows:The weight between the nodesIn this section, the performance of the proposed distributed time-synchronization technique for an OFDMA-based WMN with TDoAs is evaluated by a simulation. The parameters used for the simulation are taken from the profiles in the IEEE 802.16e standard (mobile WiMAX) . RMa LoSy-axis represents the arrival times of the signals from the neighbor Nodes 1 and 2 at the Node 3 whenIn To evaluate the performance of the proposed synchronization technique in multi-hop mesh networks, a simulation is performed for the scenario shown in In this paper, it was shown that the modified C-S model  cannot be directly applied for distributed time-synchronization in an OFDMA-based WMN with a TDoA, owing to the error-accumulation effect. By considering the propagation delays between the nodes, the proposed algorithm achieves time and frequency synchronization in a distributed manner using only the received signals from neighbor nodes. The proposed technique was shown to achieve a fast synchronization using a simple algorithm because it does not require any precompensation processes for the propagation delays through feedback channels. The information obtained from the self-organization step can be effectively used to construct 1-hop neighbor nodes in an OFDMA-based WMN, especially with a large number of nodes.