Data for numerical example.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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This book covers the latest uses of this phycocolloid in the pharmaceutical, medical, and technological fields, namely bioink for 3D bioprinting in tissue engineering and regenerative medicine, and the application of artificial intelligence in modern healthcare systems.",isbn:"978-1-78985-642-2",printIsbn:"978-1-78985-641-5",pdfIsbn:"978-1-83968-558-3",doi:"10.5772/intechopen.77849",price:119,priceEur:129,priceUsd:155,slug:"alginates-recent-uses-of-this-natural-polymer",numberOfPages:150,isOpenForSubmission:!1,hash:"61ea5c1aef462684a3b2215631b7dbf2",bookSignature:"Leonel Pereira",publishedDate:"February 5th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8019.jpg",keywords:null,numberOfDownloads:3879,numberOfWosCitations:0,numberOfCrossrefCitations:8,numberOfDimensionsCitations:23,numberOfTotalCitations:31,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 11th 2019",dateEndSecondStepPublish:"April 12th 2019",dateEndThirdStepPublish:"June 11th 2019",dateEndFourthStepPublish:"August 30th 2019",dateEndFifthStepPublish:"October 29th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"279788",title:"Dr.",name:"Leonel",middleName:null,surname:"Pereira",slug:"leonel-pereira",fullName:"Leonel Pereira",profilePictureURL:"https://mts.intechopen.com/storage/users/279788/images/system/279788.jpg",biography:"Dr. Leonel Pereira has a degree in Biology (Scientific branch) and a PhD in Biology (Specialty Cell Biology), by the Faculty of Science and Technology of the University of Coimbra, where he is currently a Professor. In addition to teaching at this university, he is also a Researcher integrated in MARE (Center for Marine and Environmental Sciences). His interests are mainly focused on the areas of Marine Biodiversity (algae), Marine Biotechnology (bioactive compounds from macroalgae) and Marine Ecology (environmental assessment). Since 2008, he is the author and editor of the electronic publication MACOI - Portal Português das Macroalgas (www.uc.pt/seaweeds). He gave more than a hundred lectures and oral communications at several national and international scientific events. In 1998 he received the Francisco de Holanda Award (Honorable Mention) and, more recently, the Mar Rei D. 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Anomaly detection in multivariate time series, such as water quality data, derived from water quality monitoring stations, is considered a non-trivial task. This is mainly due to the implication involved with both false positive and false negative situations.
In the case of false positive, i.e., the water is declared non-drinkable, and an alternative for customers must be found by the relevant water utility. This type of one-time event may be costly, while repetitive mistakes of this kind will eventually cause the monitoring system to be perceived as unreliable.
In the case of false negative, i.e., the monitoring system failed to detect a problem, some health hazard situations may develop and, in the long run, once again, the monitoring system may be considered unreliable.
Several methods have been suggested as a methodology for detecting abnormal events in similar cases. The basic approach for solving such problems may be based on unsupervised machine learning (USL), As described in detail by Celebi and Aydin [1]. One of the first and most fundamental methods of USL is based on clustering. Clustering is a methodology which groups vectors into several similar groups, where the members of each group are as similar as possible and the differences between groups are as great as possible. Clustering may be distance-based or density-based. Examples of distance-based clustering, such as the kMean algorithm, were presented by Knorr and Ng [2, 3] who compute abnormality score by counting neighbors to each point. More updated work in this field was introduced by Angiulli and Pizzuti [4] who compute the anomaly score of a data instance as the sum of its distances from its k-nearest neighbors. Ramaswamy et al. [5] extend this technique to spatial data. Their methods are also based on the kNN algorithm. Bay and Schwabacher [6] introduced the same algorithm with regard to pruning.
Another clustering philosophy is based on density of points. Examples of such cases are Breunig et al. [7, 8], in relation to relative density. Jin et al. [9] showed how some of the calculations can be skipped. Tang et al. [10] further improved clustering by adding the idea of a connectivity-based outlier factor, which refers to the number of connections between points. Jin et al. [9] also introduced improvements by adding the idea of symmetric neighborhood relationship. The main density-based algorithm is known as the EM algorithm.
In both methods - distance or density - the result is a multi-dimensional data structure which contains centroids. A centroid is a center of a group. It is, in the broadest sense, the group’s center of gravity, i.e., the coordinates of the center of the group in each dimension. Once such a data structure exists, each new incoming record is evaluated based on its distance from the most nearest centroid. If the new incoming record is too far from any known centroid, it is declared a suspicious record, one that should be examined. After examination, the new point is classified, either as a True or False event. This classification is added to the model’s learning set.
A second method, which has been used for abnormality detection, is based on a prediction methodology. According to this methodology, one of the variables in a multi-dimensional space is considered to be a dependent variable, whose value or class (in the case of discrete values) is related to the other variables. Given that, a mathematical model is then constructed, which describes the relation between the dependent variable and all other variables. This model can be based, for example, on linear regression, decision trees or neural networks. In this case, each new incoming record is used to generate a prediction. If the predicted value is too far or has a different class value than the actual value of the dependent variable, the new incoming record, is again considered abnormal and must be investigated. An example of such a methodology is given by Stefano et al. [11], Odin and Addison [12], Hawkins et al. [13] and Williams et al. [14].
A third method, which will be the focus of this chapter, is based on examining noise pattern changes, generated by the multi-dimensional data. Several methods have been suggested along this line. A fundamental method has been demonstrated by Cheng et al. [15], which used an RBF1 function to identify abnormal patterns in a moving window.
The methodology suggested in this chapter is based on Brill [16]. This methodology is based on detection and classification changes in noise patterns. Noise is measured based on the distance traveled by an artificial particle located at the normalized coordinates of the multi-dimensional vector. The difference between Cheng et al. [15] and Brill [16] is in the classification type of the abnormal events. While the first uses a True and False classification, the second adds the hazard and non-hazard classification. As will be demonstrated later in this chapter, patterns in this noise can be explained by different events related to the water network.
The aim of this chapter is to describe a different methodology for abnormality detection in water network. The chapter describes the basic model and presents a numerical illustration of the calculation framework. Than it illustrates four different cases which enable the identification of changes in the noise pattern and their related events. The last section concludes the chapter.
The following section presents an overview of the mathematical model used in this chapter. It starts by examining Brownian Motion (BM), which is named after Robert Brown [17], who discovered the typical movement of flowers seeds on the water’s surface. Einstein [18] used the idea of BM in order to provide precise details about the movement of atoms. This explanation was later further validated by Perrin who awarded the physics noble price for 1926.
BM was also used by Louis Bachelier [19] (1900) in his Ph.D. thesis “The Theory of Speculation”, in which he presented a stochastic analysis of the stock and option markets. His work went on to inspire the novel work of Black and Scholes [20], which awarded them the Nobel Prize in economics.
Modern literature gives many examples of the usage of BM in various areas; most are related to biology, chemistry, physics and other fields of life sciences. However, it is rare that such a technique or a similar one is used for the analysis of abnormal water events - the main topic of the current chapter.
One of the central results of BM theory is an estimation of the traveling distance of a particle, which travels using random movement in a given time interval across a multi-dimensional space. According to this theory, if
where D is the mass diffusivity, a term which measures how fast particles of a given type may move in a specific material, in our case, water. The solution of Eq. (1) gives a density function with a first moment, which is seen to vanish, and a second moment given by:
The left side of Eq. (2) expresses the distance at which a particle can be found from its origin, given the elapsed time m and the diffusivity parameter D. Assuming x is distributed normally, the maximum value a particle can travel for a given time can be calculated using (2) with a given confidence interval.
Using Eq. (2), and assuming that the left hand side of (2) is distributed normally, and its standard deviation (S) can be estimated empirically, the probability of a particle to travel a given distance from its origin within m units of time can be calculated by:
where t(α) is the confidence interval factor, based on the level of confidence required (drawn from the student distribution). Thus, if a particle is found within m steps at a distance which is greater than L units from its origin, it is considered an abnormal event.
In the physical or chemical diffusion process, the value of D is determined based on material properties, and the value of m is measured in continuous time. In the current model, these values should be determined using another methodology as explained in the following paragraphs.
Let us denote with vector Xm the set of quality measurements of water at each moment m and, assuming Xm has K dimensions. The value of Xm can be normalized with the following process equation:
where
Let’s also define the distance between two vectors to be denoted by
Note please that unlike in the case of BM where the distance of a particle from its origin increases with time, in the case of the DN, the particle may turn back to its origin.
An illustration of this distance in a normalized two-dimensional space is shown in Figure 1. In this case, Vm is a two-dimensional vector.
BM distance traveling in two dimensions.
In terms of Figure 1, assuming a dataset with M records and two variables in each record (x1 and x2), one may look at this dataset as a description of location for a particle in each time stamp. After normalizing the dataset according to Eq. (4), the Euclidian distance between each two points is the distance this particle travels. If the distance is measured in a five-step gap, the result may be a chart as shown in Figure 1.
Figure 2 shows a schematic chart of
DN over time.
The first is calculated by comparing the value of
This section numerically describes the calculation procedure as described in the previous section. Table 1 contains an example data set with 20 records. The measured variables are Free Chlorine (CL), Turbidity (TU), pH and Conductivity (CO). These are a common water quality indicators.
Data for numerical example.
The first two rows display the minimum and maximum values of each variable. These values were used to normalize the records in the left side of the table to the right side of the table. After normalization, a vector of the Euclidian Distance between each pair of records with a difference of 5 steps was calculated. This vector is the most right column in the table. The first value in this vector (located in row 6) contains the distance between record 6 to record 1. The second contains the distance between record 7 to record 2. The chart in Figure 3 displays relevant DN chart.
Dynamic noise chart.
In the following section, examples from a read data set are used to illustrate the analysis framework of abnormality detection and classification using this methodology.
The following examples refer to a real data set recorded at a field station with measurements, as presented in Table 2. The table’s quality measurements include Free Chlorine (Cl measured in ppm), Turbidity (TU measured in NTU), pH, Conductivity (CO measured in mS), and pressure (PRI measured in bars). For each measurement, the algorithm dynamically calculates the minimum and maximum values of the last 48 hours. (see first two rows of Table 1).
Symbol | Measurement | Units | Minimum | Maximum |
---|---|---|---|---|
Cl | Free Chlorine | mA | 0 | 2 |
TU | Turbidity | NTU | 0 | 2 |
pH | pH | pH | 6 | 9.5 |
CO | Conductivity | mS | 0 | 800 |
PRI | Pressure | Bar | 0 | 15 |
Measurements.
Typical minimum and maximum values are shown in Table 2. Given these minimum and maximum values, the raw measurements are transformed into normalized measurements, as shown by Eq. (4) of Section 2. The normalized measurements are used to calculate the “Dynamic Noise”, as shown in Eq. (5), with a lag difference between the records of 10 timestamps. Figure 4 shows the distribution of the dynamic noise values.
DN histogram.
As can be seen from the histogram, a value which is more than 0.25 is rare (see red arrow). Hence, the threshold for the dynamic noise was set to 0.3. In terms of Section 2 of this chapter, L = 0.3.
The first data analysis step with regard to the dynamic noise algorithm is to estimate the normal conditions, i.e., to observe how a dynamic noise curve behavie in case of a normal data flow. Figure 5 shows a normal period of time for the four water quality measurements. Note that pH ranges between 7.70 and 7.79; Free Chlorine ranges between 0.37 and 0.48; Conductivity usually has an average of around 520–530 with short drops to 450; and Turbidity ranges between 0.09 and 0.12.
Normal measurements.
Figure 6 shows the equivalent dynamic noise for the relevant measurements. As can be seen, the values range between 0.03 and 0.25 at the most.
DN for normal measurements.
We will now discuss four different cases, in which the dynamic noise violation threshold is analyzed. Note please that violation of the threshold L triggers an alarm only after a delay time in which the value of the DN is above the level of L. This in order to avoid false alarms caused by short spikes.
Case 4.1: Malfunctioning of sensors
The first case shows a situation in which two of the sensors stopped functioning for a short period of time. As can be seen from Figure 7, the Cl and the pH dropped suddenly to zero for a short period. This may result from communication problems, which are very common with distributed I/O.
Malfunctioning of sensors.
Figure 8 shows the resulting dynamic noise curve of the sensor’s malfunctioning. As can be seen, the drop in the values of Cl and pH causes a sharp increase in the value of the dynamic noise. After a short period, when the sensors resume functioning, the value of the dynamic noise drops back to a level below the red threshold line (0.3).
Noise curve for sensor malfunctioning.
Note also that if the sensors remain non-functional for a long period of time, and the algorithm stops using the values of these sensors as part of Eq. (5), the level of the dynamic noise curve will be lower during steady state, since less sensors are transmitting data.
The gray box around the area of the event depicts the shape of the dynamic noise curve as a rectangle. This is due to the sharp change in the values of certain sensors. This sharp change can only occur during sensor failure . A chemical change in water quality cannot occur within 1 minute.
Case 4.2: Operational change
The second typical change is an operational change. This is defined as a situation in which one of the variables controlled by operators has been changed. Some examples of such variables may be pressure or flow. An operational change may influence the variables’ quality. One such example of this type of situation is shown in Figure 9. The black line shows a change in the pressure (PRI) value. Shortly after this change, a peak in the Turbidity value is recorded (see red line in Figure 9).
Operational change - raw data.
Figure 10 shows the corresponding changes in the dynamic noise curve. The chart indicates that the operational change also results in the violation of the dynamic noise’s “red line”. However, this can be explained by the change in the operational variable.
Operational change - dynamic noise curve.
Since operational changes are also sudden changes, the peak in the dynamic noise curve is immediate. However, the return to normal happens gradually. This is the reason why the gray area has a triangular shape in Figure 10.
Case 4.3: Water source change
The third case illustrates what happens when a change is made to the water source. In this case, if the attributes of the water from the new source are different, the footsteps of the water source change can also be seen in the dynamic change curve.
Figure 11 shows a change in the water source. Conductivity rose from a level of 345 mS to a level of 385 mS (within 6 hours, from 10:00 am to 4:30 pm). Together with this, the pH level dropped from 8.20 to 8.07.
Water source change - raw data.
The effect on the dynamic noise curve can be seen in Figure 12. The average value of the dynamic noise curve has changed. This is due to the change in the noise level of the water measurements from the new source.
Water source change - dynamic noise.
A change in water source, which can take between 30 and 60 minutes and up to several hours, will result in a change in the noise of the dynamic noise curve. Sometimes, it will also cause a change to occur in the average value of the dynamic noise.
Case 4.4: Contamination event
Finally, we have the case of contamination. Figure 13 shows raw data from a typical contamination event. The event starts with a drop in the Free Chlorine (see green line in Figure 13). This is due to chlorine consumption by the contaminator. Shortly after, the Turbidity level starts to rise (see blue line in Figure 13).
Contamination event - raw data.
At the same time, the chlorination dosing system reacts to the situation and the level of Free Chlorine once again rises to a normal level; and due to the diffusion factor, the Turbidity level gradually drops. The result of this event can be seen in the dynamic noise line shown in Figure 14.
Contamination event - dynamic noise.
As seen in Figure 14, when the event starts, the dynamic noise curve rapidly rises above the red line of the threshold. It is fast, but not steep, as in the case of sensor malfunctioning or water source change. Once the maximum level of contamination has been obtained, the level starts to gradually drop, due to the diffusion effect, which causes the contamination to be diluted with the incoming water. This is why the right side of the curve in Figure 14 is not symmetric to the left side of the blue curve. The overall situation creates the shape of non-symmetric triangle, as is illustrated by the gray area in Figure 14. Note that the farther the contamination’s penetration to the system is from the measuring point, the less non-symmetric and the shorter the triangle will be. This is due to the dilution effect.
The current chapter demonstrates how the simple pattern recognition of a curve created by a noise capture process, similar to a random walk, can be used to classify different types of abnormal events. The presented algorithm uses the imaginary center of gravity of the water quality measurements in order to measure the noise of the process captured as traveling distance. It has been shown that the created curve has a maximum value, due to the nature of the process. This threshold is violated when abnormal events occur.
Four different types of abnormal events were examined: malfunctioning of sensors, operational change, water source change and contamination events. Numerical examples based on real data show that each of the events has a different “signature”, which enables the identification of the event’s nature.
The current chapter shows how water analytics can be used as part of the information system which helps operators protect the water system. The above framework can also assist control systems in regard to the automatic classification process of observed events, in order to reduce the level of false alarms in water monitoring systems. For example, this may be achieved by eliminating alarms like the first three types analyzed in this study and notifying operators only in the case of contamination event alarms.
Outdoor activities have many health benefits, such as promoting physical activity and could play a role in stress management and mental health [1, 2]. However, the outdoor environment can be challenging for human physiology. Therefore, preparations are needed before the start of the activity in order to prevent any potential risks. The present chapter will outline the basics human physiological responses to different environmental challenges during outdoor activities and how to better prepare for them in the wild. Most of the research related to environmental physiology and guidelines in outdoor safety were conducted in work-related environments from different occupational health-related studies. This previous research could give us a better understanding of the physiological responses in the environment and may be used to prepare for outdoor activities. However, the physiological responses in a work-related environment could be different in the outdoors, and thus recommendations may have to be adapted for outdoor recreational activities. For this reason, the present chapter will include both work-related and outdoor-related physiological responses and guidelines.
Human body core temperature needs to stay in a short-range of approximately 37 ± 1° C [3]. During an outdoor event, the environment can impact the core temperature. Accordingly, wearing clothes, seeking shelter or exposure time will help keep the homeostasis of a core temperature between 27 and 42°C [4]. However, some outdoor sports involve risks related to cold exposure due to the distance from a shelter, and in turn, this could affect core temperature.
The environment influences human thermoregulation in different ways by four physics principles: radiation, conduction, evaporation and convection. The heat transfer from or to a human body with the environment is based on physics rules. Convection is a transfer of heat by fluid motion (gas or liquid) across the surface. Conduction is a heat transfer from a solid material to another through direct contact, for example, the hot sand on a beach to your feet. Radiation in the environment comes from the sun radiation, which is reflected from the ground or a rock. Nonetheless, the radiation can arise from the human skin that constantly radiates heat in all directions. This radiation can be observed with an infrared device and be used for safety in some work area where workers are exposed to high-temperature variations, such as a frozen food factory. Finally, evaporation is the primary way to dissipate heat from the human body. For example, sweat must evaporate (turn into a gas form) to dissipate the heat. In a situation where the sweat stays in liquid form, it represents a water loss without heat loss for the human body. The interactions between the environment and the human body are illustrated in Figure 1. Some equations are proposed to understand the thermoregulation resulting from these different factors interactions between the human and his environment: the heat balance equation:
Thermal balance between environmental factors as well as behavioral and physiological human responses.
M: metabolism (heat production in Kcal), R: radiation or radiant heat exchange (where the value is negative when the environment temperature is lower than the skin and can be measured with a globe thermometer or infrared), C: heat exchange by convection (where the value is negative when the air temperature is lower than skin temperature that can be measured by a thermometer and hot-wire anemometer), K: heat exchange by conduction (this value is usually negligible, except for activity like swimming or diving, K can be 20 times heat conductive (heat-removing in cold water) than air. Finally, E: heat loss by evaporation can be estimated by weight difference to estimate the sweat amount where some equations can help to convert in heat (energy) loss [5, 6, 7, 8].
From this equation (Eq. (1)), other authors add some parameters: Gagge and Gonzalez [9] added work (+W) to the equation (Eq. (2)); work means any physical activity where a heat production will result from muscle activity. Winslow, Gaggeand, and Herrington [10] also proposed correction due to the storage of body heat (S). It was observed that most tissues are about 0.83. Even if all these equations are from old studies, they are the foundation of many guidelines for outdoor activities.
Some human behavior factors help humans to survive in an environment where the balance will be superior (core temperature increase) or inferior (core temperature decrease) at 0 in the equation’s result. The 0 represent the thermal comfort, and to reach it, one of the planning of outdoor activity is clothing. It is also possible to calculate the thermal comfort from clothes in some environment with the Clo units [11]. One Clo unit represents the thermal isolation to maintain comfort and mean skin temperature at 33°C at rest in an environment with the air temperature of 21°C, relative humidity less than 50% and air velocity at 20 ft./min as defined by Gagge et al. [12]. This way to measure the impact of clothing still use today in occupational health and sports enthusiast [13, 14].
A cold environment can affect a stable thermal balance by inducing physiological changes. Briefly, when the blood and/or the skin temperature decreases, the information goes to the hypothalamus to induce: 1) vasoconstriction in skin blood vessels to reduce heat loss in the environment and, 2) the skeletal muscles are activated to produce heat, causing the shivering. These responses help to increase heat production and in turn increase body temperature to keep homeostasis. These physiological changes are influenced by individual characteristics such as body composition, ethnicity, sex, age, and fitness levels [6]. But other factors could also affect these responses such as medication, caffeine and alcohol [15, 16, 17]. For example, consuming acetaminophen may decrease core temperature and heat production [18]. However, it should be noted that the physiological responses to a cold environment are complex and that the fundamental mechanisms involved in this process are poorly understood.
In a cold environment, the human body responds differently from an acute than repeated exposure to cold. During an acute exposure to cold, the skin receptors are responsible for alerting the human body by some discomfort. Usually, the human body begins to experience some discomfort at a skin temperature under 29°C. However, with acclimatization by repeated exposure to cold, a tolerance is developed partially by a psychological adaptation, by a central nervous system adaptation and lower finger skin temperature loss [19, 20]. During outdoor activity, this acclimatization can help motivate people to enjoy winter outdoor sports, but it can further increase the risk of cold injuries [19, 21].
During winter outdoor activity, other considerations need to be taken regarding energy intake and exercise performance. Metabolism increases 2-5 times by the sympathetic system at rest, but at exercise, it will depend on the intensity and can reach 10-20 times higher. For the moment, the literature is not consistent with the oxygen consumption at the same intensity in a cold environment. Some authors will report a higher oxygen consumption, and others reported a similar consumption [22]. These different results can be explained by different temperature expositions, the exercise intensity and, the participants’ fitness levels [22, 23]. A similar controversy can be observed with the heart rate at submaximal exercise that can be explained by the exercise intensity. Oska et al. [23] observed that the heart rate was lower at the cold environment for a moderate exercise intensity (50%VO2max), but was higher during very light exercise intensity (25%VO2max). Nonetheless, the authors agreed on the decrease of maximal oxygen consumption and the decrease of maximal cardiorespiratory capacity [4, 22]. Finally, other physiological responses can affect exercise performance during outdoor activity in a cold environment such as: a decrease in strength, a decrease of maximal power, coordination lost a decrease of dexterity, higher reaction time and decision-making that can increase the accident risks for some sport like climbing [6, 8, 20].
Some individual characteristics need to be taken into consideration with the planning of outdoor activities. As mentioned earlier, body composition, ethnicity, sex, health status and age can influence the physiological responses to cold. Body fat percentage and the ratio body surface on body mass can affect the thermoregulation efficiency. That is, subcutaneous fat is an excellent insulator due to reduced conductor proprieties by the lower quantity of blood vessels and water. This creates an excellent resistance to heat exchange towards the environment in which individuals with higher fat percentage will conserve more heat in a cold environment. Skinfold measurements may be used as a clinical tool to quantify subcutaneous fat thickness and could be used as an indicator of tolerance to cold. The ratio body surface to body mass also influences the rate of heat loss. In fact, a larger individual with a smaller surface area will increase their tolerance to cold. These two anthropometric characteristics explain why older people, adolescents and children are more at risk than an adult. Usually, these populations have lower subcutaneous fat and a disadvantaged ratio. Furthermore, older individuals often have medication intake that can play a role in thermoregulation, and this may increase the risks in a cold environment [4]. Ethnicity can also influence thermoregulation responses. For example, research has shown that Caucasians have a different oxygen consumption, skin temperature and rectal temperature responses to acute cold exposure than African American [24]. These differences seem to be explained by several adaptation patterns (by genotypes and/or phenotype) across other various ethnicity groups such as Eskimos, Alacaluf Indians and Australian Aborigines [25].
For the sex influence, the estrogen secretion influences the hypothalamus where even if women have generally a higher percentage of body fat, they have a lower tolerance to cold and lower skin temperature for the same effort and same environmental conditions than men [26, 27]. This implies postmenopausal women have a higher cold tolerance than premenopausal women. It was also observed by Kaikaew and al. [26], that women will start shivering at a higher environmental temperature than men. This sex different should be taken into consideration during preparation and during outdoor activity.
Frostbite and hypothermia can have important consequences, which include superficial tissue loss to loss of consciousness. The Wilderness Medical Society has provided some simple guidelines in a cold environment [28]:
Be aware of any underlying factors that increase risks in a cold environment, such as medical problems like Raynaud’s syndrome or diabetes, substance intakes like medication, alcohol or drugs that can decrease peripheral perfusion, individual’s characteristic like age and sex;
Bring spare dry clothes, mostly for the peripheral members (e.g., socks, mitts);
Bring chemical or electric hand and foot warmers;
Locate shelters during the activity if needed;
Maintain hydration and energy needs. Exercise is a great way to produce heat, but the energy and hydration needs will increase too;
Be careful of moisture from perspiration or activities in the snow; wet clothes increase heat loss;
Minimize blood flow restriction such as tight clothing or footwear, backpack, and hiking carriers for babies;
Verify regularly for pain/cold discomfort and numbness or venous return if possible.
Physiological responses to high temperatures have the same goal as the cold environment: keep a body temperature balance inside the vital range. Similarly to cold temperature, when the core temperature increase the blood activate signals to the hypothalamus. These signals activate sweat glands in order to increase evaporation and vasodilation in the skin blood vessels to increase heat loss and regulate core temperature for homeostasis. Similarly to cold temperature, responses can be different from one individual to another regarding various characteristics such as ethnicity, sex or age. Furthermore, due to the changes in the blood flow by peripheral vasodilatation for sweating and the increase of cardiac output, medication uptake can affect the thermoregulation responses and increase hyperthermia risks. Another response to a warm environment is an increase in heart rate at rest and during exercise. People at risk using medication that affects cardiovascular parameters need to be supervised closely and carefully.
The thermoregulation needed in a hot environment increases metabolism and sweat, while hydration and energy/mineral uptake can be a challenge due to this higher need [13, 29, 30]. Some guidelines in occupational health propose to drink around 0.23-0.25 L of cold water every 15-20 min [31]. Other authors are more precise like the US Army table, in which water intake is adapted according to the following factors: temperature, activity intensity and duration. The guidelines vary from 3.8 cups/hour at over 90°F WBGT (wet-bulb globe temperature index) for 10 min of hard work (600 W) to 1.9 cup/hour for an unlimited time at easy work (250 W) at 78-81.9°F WBGT [32]. Nonetheless the guidelines you follow, drink content should also be taken into consideration due to risks of hyponatremia. Plasma sodium can rapidly drop below 130 mmol/L during high-intensity exercise in a hot environment. It is for this reason, the ACSM recommends to drink during exercise over 2% of body weight loss from water deficit and after exercise consuming meals, snacks and beverages with sodium to help the recovery [33]. To help you plan the need of hydration, occupational health Institution, like the IRSST (Institut de recherche Robert-Sauvé en Santé Sécurité au Travail) proposed calculators in their website to plan rest, activities, hydration and other recommendations to reduce risks in a hot environment. It should be noted that these recommendations are specific to workers, thus, they may not be appropriate for all outdoor activities.
Acclimation to heat at exercise can be done with different varieties of frequency, intensity, duration, and environmental temperature. Adaptation time varies between 4 and 14 days [34, 35, 36]. This acclimation needs to be maintained to keep performance capacities (submaximal performance, VO2max and power). The acclimatization induced a higher and more sustained (decrease sodium and chloride concentrations) sweat rate when needed and improve thermal comfort [30, 34]. Also, the physiological acclimation from repeated exposure to heat induces an increase in sweating and skin blood flow responses, a plasma volume extension, a better fluid balance, better cardiovascular stability (a lowered heart rate but higher stoke volume to induce a better sustained cardiac output and blood pressure), and a lower metabolic rate [30, 34]. All these acclimations help sustain activities in heat environment with a better tolerance, a lesser need for minerals and better sweat evaporation. These acclimations can stay up to a month [37].
Like the cold environment (Figure 2), individual characteristics can play a role in the thermoregulation response. The sex will contribute to some variations. Women will have a body temperature that will fluctuate about 0.5-0.8°Celsius during her menstruation cycle, and the progesterone and estrogen levels with oral contraceptives can influence the thermoregulatory control of skin blood flow and body temperature during physical activity and rest [27]. Furthermore, women have lower sweating rates than men for the same heat loss demand (metabolic demand and environmental temperature) at high temperature (40°Celsius) [38]. Finally, age, genetic and % body fat can influence the thermoregulation in a heated environment for a similar reason as explain for cold exposure. It is for this reason; the individual’s characteristics need to be taken into consideration during the outdoor activity preparation.
Summary of physiological responses to thermoregulation during acute and repeated exposures (cold and hot environment).
Heat environment should be avoided if possible. If the context is impossible to control, it is essential to prepare consequently by having enough water and heat acclimatize to the participant in the outdoor activity. Do not forget that thermal stress relies on multiple factors such as sex, age, ethnicity, anthropometry, substance intake, and health history.
Hyperthermia and other heat-related illnesses can have important consequences, from heat, cramps to heatstroke. The Wilderness Medical Society provided some simple guidelines in heat environment [39]:
Be aware of any underlying factors that increase risks in heat environment, such as medical problems high blood pressure, substance intakes like medication (beta-blockers, amphetamines…), alcohol or drugs, individual’s characteristic like age and sex;
Bring enough water or plan where to refill your bottle, hydrate before during and after the activity, the hydration status is an high-risk factor;
Promote acclimatization: 8 days, 1-2 h per day heat exposure and regular physical activity before the outdoor event if it plans to be in a heated environment;
Use WBGT (wet-bulb globe temperature index) temperature to assess the heat risks;
Wearing the right clothing for the right temperature.
The challenge to climb high mountains and enjoy the view from the summit is probably an outdoor activity goal for many hikers. Altitude environment could drastically challenge human physiology, where appetite, sleep, cardiorespiratory responses and metabolism are affected. Some physiological responses can be affected as low as 1000 m above sea level (a.s.l.) and begin to decrease performance around 2000 m a.s.l. [40]. During hiking ascent, the barometric pressure decreases and the partial pressure of oxygen (PO2) in the arterial from the cardiorespiratory system too. This lower pressure affects the O2 diffusion between alveoli and the arterial blood, and between the arterial blood with the tissues like the muscles. This lower oxygen diffusion in blood arteries and consequently, a decrease of oxygen saturation can be monitored with a pulse oximeter device. An immediate adjustment during the initial rise will be the activation by chemoreceptor in the aortic arch and carotid arteries, stimulated by hypoxia (low PO2), inducing a ventilation increase to compensate [41, 42]. A few hours later, plasma volume decreases frequently due to water loss through ventilation and increase of urine production [43]. This change increases the hemoglobin concentration in the blood flow, increasing the oxygen exchanges to the muscles. After a few hours in altitude, the erythropoietin (EPO) will increase the release by the renal system. The EPO regulates the red blood cell production and increases the blood hemoglobin concentration further. Also, the cardiac output (the function of the heart rate X stroke volume) will increase during rest and exercise; due to the plasma decrease (decrease of cardiac stroke volume). The heart rate will disproportionately increase [43, 44]. This increasing cardiac output, consequently, increases the blood pressure, and people at risks should be monitored for safety. The amount of fluid loss can be a challenge to stay hydrated during ascent; a general rule is to consume at least 3 L of fluid by day in high altitude [7]. The physiological responses to altitude and susceptibility to altitude illness vary between individual, this explains why monitoring often during rising will help you to recognize normal responses to abnormal responses.
Between 1850 m and 5895 m, some people (22-77% of travelers) can experience acute mountain sickness (AMS) [43]. This sickness includes headaches with one or more of the following symptoms: vomiting, lethargy, nausea, loss of appetite, insomnia but with a normal mental status [40, 43]. Usually, around 80% of individuals experiencing AMS will resolve their symptoms when stopping ascension and resting at the current altitude for 2-7 days [45]. Be aware, more severe altitude illness can occur and can be fatal if not treated in time, like the High-Altitude Cerebral Edema or the High-Altitude Pulmonary edema, where monitoring your group is vital for their safety.
Human physiology responses acclimate to altitude exposure from a few days to weeks. Some changes help humans to perform at high altitude. These physiological changes should, theoretically, improve O2 transport to the active muscles during exercise, and it is why athlete use altitude training acclimatization to improve their performance [42, 46]. However, some individuals can be affected differently during the acclimatization period when exercising. The maximal aerobic capacity decreases in altitude and by consequence, the exercise intensity will need to decrease too at the beginning of the acclimatization process. Basal metabolic rate will increase, but appetite decreases, which can lead to a daily energy deficit that can affect exercise tolerance and, during long expeditions, muscle mass loss [7, 40, 47]. Furthermore, many individuals experience sleep disturbances and/or sleep apnea; the hypoxia can induce central sleep apnea and affect people at risk of cardiovascular disease [41, 48]. Moreover, sleep is important to recover and perform hiking during the long ascent and can affect the time and performance during long expedition in altitude. Finally, after 4-6 weeks at high elevation, climbers can experience a decrease in total mass area, a capillary density decrease, mitochondrial dysfunction and a decrease in glycolytic activity, which can impact their capacities [40, 44]. However, some other acclimatization will help exercise tolerance after a few days to some weeks, depending on individuals [42, 49]. Cardiac output and peripheric blood flow will return to normal, reducing cardiac work and improving the diffusion time for oxygen extraction by tissues [49]. Also, continuous residence in altitude improves exercise tolerance compared to initial exposition [42].
Hiking in altitude environment can be a beautiful outdoor recreational activity with great landscape pictures opportunities. However, preparing this kind of activity will need to consider long periods of rest and acclimatization and regularly to monitor your group.
Altitude is a challenging environment and can have consequences, from mild headaches to fatal altitude illness. The scientific literature provides some simple guidelines to prevent altitude illness [42, 43, 50]:
Prepare a slow and gradual ascent, a general recommendation is a daily ascent around 500 m altitude gain including a night’s sleep, with an extra acclimatization day every 1000 m;
Using Acetazolamide or Dexamethasone can prevent AMS, but can have side effects and have limits to considerate;
The descent is the best treatment for acute altitude illness, prepare plans if some people need to return at normobaric environment quickly;
Use different tools to assess and monitor your group: Lake Louise AMS score [51] and pulse oximeter devices;
If you can provide repeated exposure to hypoxia (normobaric or hypobaric) before your excursion or spending a week at moderate altitude (2200 m-3000 m) before exposure to high altitude, this can improve exercise tolerance and decrease risks of AMS;
Plan enough water and food, knowing that altitude will increase your needs.
Outdoor activity can be a repetitive action like walking for hikers or rowing for canoes. Repetitive movement for a long period of time increases the risk of musculoskeletal injuries [52]. Musculoskeletal injuries are multifactorial; some authors have proposed underling hypotheses [53]. During repetitive motion some motor units in the muscles are recruited continuously, they are called the Cinderella fibers, increasing risks of injuries by a sustained contraction for a long period of time [52, 53, 54]. Other mechanisms like neural pathomechanisms, reperfusion injures, impaired heat shock responses and mitochondrial damages was also proposed to explain musculoskeletal injuries in repetitive workload (Forbes 2002). Rest and changing the activity (movement) can help to reduce the risks, ergonomic papers propose to plan sufficient rest to avoid injuries [55]. It is for this reason; rest time and recovery need to be planned for this kind of activity. Even if it can be difficult to use occupational health guidelines for rest, outdoor activities should plan regularly rest time and changing movement if possible in order to avoid musculoskeletal injuries.
Hiking energy cost will depend on many factors such as the load, the slope, the environmental conditions, the surfaces, the clothing and footwear and the individual capacities. Walking velocity has an impact on energy expenditure and fatigue; individuals have their own threshold where running would be more cost-effective than walking and will naturally shift from walking to running when it is more cost-efficient. This speed is usually around 7 km/h [56]. The slope impact on this energy cost was introduced as a concept of mechanical efficiency by Margaria [57]. This means you will need to assess your group to find the more effective speed or separate your group in subgroups consequently.
Furthermore, the physical workload can be planned to prevent fatigue. Usually, occupational studies recommend (eight hours a day, all week) that physical demands should be in a range of 33-50% of VO2max. However, backpack weight, altitude, temperature, and distances to travel during hiking expeditions can easily overtake this guideline. It is for this reason, allow enough rest period, assess the group mood and perception. The backpack choice (padded hip belt, size, fitting on the shoulder…) and load weight and his distribution are factors that can influence your participant’s risks relative to their capacity [58]. Among the risks, dorsal back pain, knee pain, rucksack paralysis, stress fractures, low back injuries, metatarsalgia and foot blisters were reported in the literature [58, 59]. The hip belt suggests a more natural movement and stability at the hip, but factors like walking velocity, body weight and posture, hip-belt size, distribution and weight of the load in a backpack in the function of the participant center of mass position can affect the hip belt [59]. Furthermore, shifting load from shoulder to hip decrease discomfort and decrease load impact on the spine by decreasing risks of low back pain or injury [60]. Another reason to be careful with the shoulder forces is the macrovascular and microvascular hemodynamic that can be compromised and lead to neurological dysfunction and probably a loss of hands fine motor control [61] and consequently possibly increase frostbite risk in cold environment.
Fatigue means different things for different individuals. In sports medicine, fatigue is often quantified by a decrement in muscular performance to produce force with/or perception of tiredness or pain [62, 63, 64]. Many factors can contribute to fatigue such as the intensity, the duration and the nature of the activity, the sex and individual capacities, but also the equipment used [62, 63, 64]. Rest induces physiological changes after exercise to improve physical capacities, however; improper recovery may cause residual fatigue, where systematic, can cause overload and increase injury risks, overtraining syndrome or decrease physical performance [65]. Fatigue is often separate as peripheric fatigue, from the muscle capacity (i.e., energy production, contractile mechanism) and central fatigue, from the central nervous system [65]. To reduce risk, plan your activity intensity progressively and according to the individuals or group capacity. If possible, plans an outdoor training before a longer expedition to build higher capacity and decrease the rest needed to recover.
Quality of sleep can be impacted by outdoor environments like altitude and temperature. Furthermore, altitude can cause central sleep apnea, insomnia, pulmonary or brain edema during sleep [66]. Sleep deprivation can impact your outdoor activities by a decrease of capacities, fatigue perception and even bad mood [66, 67]. However, the natural environment can also increase sleep quality for some individuals due to the light pollution and the physical activity (melatonin regulation). Light pollution and lack of exposition to natural light can impact sleep circadian rhythm [68]. The natural environment exposure can influence sleep quality positively by regulating melatonin secretion, reducing artificial light exposure, promoting exercise and reduce some city noises [69].
Load and repetitive movements can increase musculoskeletal injuries risks. The scientific literature provides some simple guidelines to prevent some risks [54, 63]:
Decrease the load as much as possible and increase physiological capacity before a long hiking journey;
Plan many rest breaks during your activity;
Change activity or movement if possible after repetitive tasks like walking or canoeing.
Marine activities can be an outdoor activity with some specific risks to avoid. Epidemiology articles have examined some typical injuries for different nautical sports. Twenty six percent of injuries from wakeboarding involved the head, back and ribs [70], and surfing and water skiing reported many cases of craniomaxillofacial laceration [71]. These injuries are only some example of dangers beyond drowning.
Water safety and aquatic recreation precaution are not always well understood. Moran and Ferner [72] reported that New Zealand residents and tourist surfers had a poor understanding of rip current (powerful and narrow channels, seaward flowing current along gulf, coast, beach and lakes), which is that can frequently occurring hazard. Rip current are also responsible of rescues and drowning with many swimmers, where public education and awareness strategies need to be implement in your activity preparation [73]. Lack of awareness of alcohol consumption and downing risk was also reported as a major safety problem in aquatic activities [74, 75]. From fatal and non-fatal drowning, an average of 49.5% and 34.9% respectively involved alcohol consumption [74]. This lack of understanding and knowledge dangers can also arise from marine envenomation, where tourists are often not aware of venomous creatures like starfish, sea urchins and stingrays [76]. These can also include infectious water area, where pathogens can contaminate the swimmer, surfer, or another nautical activity practitioner. One of these infectious diseases contracted in water is the Legionellosis disease, a severe bacterial infection with similar symptoms of pneumonia; this pathogen can be found in freshwater, natural water or artificial water and some cases were reported in Greece among hotels [76]. Finally, decompression illness (DCI) is probably the most common risk for an unexperimented diver. During DCI, microbubbles are formed by the nitrogen during an inadequate decompression in tissues and intravascular, damaging blood vessel (including pulmonary circulation), distorting tissues and initiate inflammatory response [76, 77]. Most of these risks are the result of lack of knowledge and/or awareness and could be prevented by a proper preparation: wearing the appropriate safety equipment, all practitioner should have proper training and education of the activity and the environment and having adequate communication tools for emergency situation.
Seasickness is a form of motion sickness, a common physiological response to unnatural motion stimuli where the vestibular system (semi-circular canals and otoliths) information from linear (mostly vertical) or angular acceleration of the head mismatching the eyes signals [78, 79]. This conflict can cause nausea, vomiting, cold sweating, pallor, dizziness, drowsiness and headache [80]. Seasickness can affect up to 25% of passengers on a large ship and will increase with a smaller boat and bad weather conditions [81]. Individual difference susceptibility can be difficult to predict who will have seasickness. However, a questionnaire the Motion sickness susceptibility questionnaire (MSSQ) and the short MSSQ can help to identify some people that could be susceptible to seasickness [82]. Furthermore, children aged 6-12 years old, women and lack of sleep can contribute to higher susceptibility to seasickness [81, 82, 83].
Exposure to motion environment (water) can gradually help prevent motion sickness. Accordingly, a few (1-3) days on a boat is usually enough to acclimatize depending on the individual [80, 81, 84]. The following recommendations [84] should help if you have an individual in your group with seasickness:
Ask the passenger(s) with seasickness to look forward at a fixed point on the horizon and not close their eyes;
If possible, gradually increase the number of motion stimuli, start the travel in calm waters;
The passengers with seasickness should avoid close work (cooking, read inside, looking at computer…), avoid space without the horizon view, avoid alcoholic drink, and an empty stomach (eat before traveling);
If the passage can actively synchronize the body with the motion;
The passenger with seasickness should change their mind, like listen to music;
Use a motion sickness susceptibility questionnaire to plan your activity in consequence; for example, people susceptible to seasickness could take medication, like scopolamine, before traveling combine to behavioral strategies (above).
Outdoor activities need some preparation to decrease risks for the participants. Knowledge of physiological responses may help better understand the principal preparation tasks and risks that can occur during outdoor activities. However, some physiological mechanisms remain unknown, and some are controversial, thus, more research is needed to fully understand human physiology in an outdoor environment and propose better prevention tools and guidelines. Furthermore, there is evidence to suggest that natural environment activities are associated with health benefits, but the mechanisms still not fully understood [85].
One of these health benefits is that outdoor activities can increase physical activity time [85, 86, 87], that can help decrease cardiovascular disease, metabolic complications and some cancer risks and improve physical capacity [88]. In a society where physical inactivity and sedentarily time is a major health problem, like sitting at a desk for long hours, outdoor recreative activities can be beneficial even for a short bout like walking in a park during breaks [88, 89, 90]. Furthermore, several studies have observed the positive effects of nature on health, stress and recovery [85, 86, 87]. This exposure to nature, even from a picture, could promote recovery and restore stress levels [91]. To conclude, all these benefits can help society and promote outdoor activities and green spaces. Also, further research is needed to better understand the underlying pathways of these benefits. Moreover, specific guidelines for the outdoor reality are needed in order to provide tools for outdoor professionals.
Authors would like to acknowledge Antony Karelis for general comment and English revision on the manuscript.
The authors declare no conflict of interest.
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