Research article

Methodology for the characterization of the humidity behavior inside CPV modules

  • Received: 10 June 2015 Accepted: 11 October 2015 Published: 20 October 2015
  • In this study the characterization of the humidity behavior inside concentrating photovoltaic (CPV) modules is addressed. To this purpose, several experimental tests have been carried out by using two different CPV modules and three different breathers, collecting in each analyzed case the evolution of temperature, relative and specific humidity of the air volume contained inside the module for many days. Results indicates that, for each of the CPV modules analyzed, it is possible to construct a characteristic curve in the temperature-specific humidity psychrometric chart, that can be used for estimating the specific humidity of the air inside the CPV module as a function of the internal air temperature. The characteristic curve can be also used to estimate the saturation temperature of the air inside the CPV module, and consequently to detect the eventuality of moisture condensation during cloudy days or night-time, namely when the temperature of the air inside the module is low and reaches the external ambient one. This methodology can be used in CPV modules design for the choice of the breather and of the construction materials, in order to obtain a saturation temperature as low as possible.

    Citation: Carmine Cancro, Gabriele Ciniglio, Luigi Mongibello, Antonino Pontecorvo. Methodology for the characterization of the humidity behavior inside CPV modules[J]. AIMS Energy, 2015, 3(4): 666-678. doi: 10.3934/energy.2015.4.666

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  • In this study the characterization of the humidity behavior inside concentrating photovoltaic (CPV) modules is addressed. To this purpose, several experimental tests have been carried out by using two different CPV modules and three different breathers, collecting in each analyzed case the evolution of temperature, relative and specific humidity of the air volume contained inside the module for many days. Results indicates that, for each of the CPV modules analyzed, it is possible to construct a characteristic curve in the temperature-specific humidity psychrometric chart, that can be used for estimating the specific humidity of the air inside the CPV module as a function of the internal air temperature. The characteristic curve can be also used to estimate the saturation temperature of the air inside the CPV module, and consequently to detect the eventuality of moisture condensation during cloudy days or night-time, namely when the temperature of the air inside the module is low and reaches the external ambient one. This methodology can be used in CPV modules design for the choice of the breather and of the construction materials, in order to obtain a saturation temperature as low as possible.


    Burnout is a common psychosocial phenomenon among health care workers. World Health Organization (WHO) defined Burn-out as an occupational phenomenon, “a syndrome conceptualized as resulting from chronic workplace stress that has not been successfully managed” [1]. It is composed of three dimensions: emotional exhaustion (EE), characterized by the sensation of physical and mental overexertion and lack of energy; (ii) depersonalization (DP) characterized by emotional detachment and negative attitudes towards patients and colleagues; and (iii) low personal accomplishment (PA), the degree to which a person perceives doing well on worthwhile tasks [2]. Burnout had negative effects on the employees, by causing different physical and mental problems and also on the organization, by decreasing the quality of care provided for patients and decreasing productivity [3]. A recent meta-analysis study that investigated burnout among nursing found a prevalence of 28% for high emotional exhaustion, 15% for high depersonalization and 31% for low personal accomplishment [4]. Burnout was associated with many sources of stress in the workplace such as work overload, long working hours, lack of resources and conflict with colleagues in addition to sociodemographic characteristics such as gender, age and years of experience. Previous studies in Saudi Arabia have been conducted among nurses in tertiary hospitals but not in the primary health care centers [5][8]. This study aimed to determine the prevalence and associated factors of burnout among nurses working in the primary health care centers in Medina city, Saudi Arabia.

    This observational analytical cross-sectional study was conducted among 200 nurses in the primary health care centers (PHC) in Medina city, Saudi Arabia. Al Madinah was divided into four regions, and three PHC centers were selected randomly from each region. All nurses in each center were approached. Those who had an experience of less than one-year were excluded.

    A self-administered questionnaire consisting of three parts was used in this study. The first part included questions on the sociodemographic characteristics. Level of education was categorized into two categories; Bachelor of Science Nursing (BSN; 4 years study and one-year internship) and Diploma in nursing (DN: three years study and 6 months internship).

    The second part assessed burnout by using the validated Maslach Burnout Inventory-Human Services Survey (MBI-HSS) which is the most commonly used tool for assessing burnout. It consists of 22 items which are divided into three subscales: emotional exhaustion, 9 items (the feelings of being emotionally overrun and exhausted by one's work); depersonalization, 5 items (the tendency to view others as objects rather than as feeling persons) and personal accomplishment, 8 items (the degree to which a person perceives doing well on worthwhile tasks). The items are answered in a 7-point scale ranging from 0 (never) to 6 (every day) [2]. The three scores are calculated for each respondent. High scores for EE and DP indicated higher levels of burnout, while high scores for PA indicated lower levels of burnout. This instrument was validated in many languages including Arabic language [9]. Cronbach's alpha coefficient for the three MBI subscales of the Arabic version were: emotional exhaustion (alpha = 0.88), depersonalization (alpha = 0.78), personal accomplishment (alpha = 0.89) [9] High level of burnout is defined in this study as high score on any of the three subscales of burnout [9],[10]. Sources of stress were assessed by 10 items which were obtained from the literature [10]. These items were headed by the following question: “to which extent dose the following conditions cause stress to you”. Each item was scored from zero (causing no stress) to 4 (causing severe stress) [10].

    Ethical approval was obtained from the Ethics Committee of the Directorate of Health in Al-Madinah. Objectives and benefits of the study were explained to the participants. Participants confidentiality and anonymity were assured. Signed consents were obtained from the participants.

    Analysis was performed using Statistical Package for the Social Sciences (SPSS®) (version 22.0, IBM, Armonk, NY). The 22 items of MBI were summed to obtain the total score of each subscale [2].

    Each subscale was categorized into low, moderate and high according to the recommended cut-off points [9]. Test of normality was performed for each subscale. T-test and analysis of variance (ANOVA) test were used to assess the association between burnout subscales and the sociodemographic variables. Pearson Correlation coefficient was used to assess the association between burnout subscales and the sources of stress. To obtain the significant factors associated with each subscale of burnout, multiple linear regression analysis was employed by using “Backward” technique. Multi-collinearity was checked between the independent variables by using the VIF. The accepted level of significance was below 0.05 (p < 0.05).

    Most participants were females (73.0%), aged ≤35 years (52.0%), married (81.0%) and had >10 years of service. Most of them had no administrative work (80.0%), had diploma (75.0%) and had a monthly income of less than 12 thousand Saudi Rial (SAR) (53.0%) (Table 1).

    Table 1.  Socio-demographic characteristics of the participants.
    n %
    Age
     ≤35 104 52.0
     >35 96 48.0
    Gender
     Male 54 27.0
     Female 146 73.0
    Marital status
     Single 30 15.0
     Married 162 81.0
     Divorced/widower 8 4.0
    Educational level
     Diploma 150 75.0
     Bachelor 50 25.0
    Years of service
     5 or less 32 16.0
     6–10 57 28.5
     >10 111 55.5
    Administrative task
     Yes 40 20.0
     No 160 80.0
    Monthly income (SAR)*
     <12000 106 53.0
     ≥12000 94 47.0

    Note: *1 USD = 3.7 SAR.

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    About 39% had high EE, 38% had high DP and 85.5% had low PA. Forty-five participants (22.5%) scored high on all the three subscale of burnout and 178 scored high at least on one subscale of burnout (89%) (Table 2). The reliability analysis of the three subscales yielded Cronbach alpha of 0.84 for EE, 0.76 for DP and 0.85 for PA.

    Table 2.  Prevalence of burnout among participants.
    Low n (%) Moderate n (%) High n (%)
    EE 72 (36) 50 (25) 78 (39)
    DP 46 (23) 78 (39) 76 (38)
    PA 171 (85.5) 11 (5.5) 18 (9.0)

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    The most important sources of stress were long working hours, work overload, fear of violence and lack of resources (Table 3).

    Table 3.  Sources of stress in the workplace ranked by mean.
    Item Mean
    Long working hours 3.161
    Work overload 2.779
    Fear of violence 2.623
    Lack of resources 2.588
    Work demands affect my personal homelife 2.362
    Fear of making mistake that can lead to serious consequences 2.302
    Working with uncooperative colleagues 2.302
    Poor work environment 2.281
    Office work 1.985
    Cannot participate in decision-making 1.995

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    Table 4.  Relationship between burnout and socio-demographic characteristics.
    Variables Nurses' burnout
    EE
    DP
    PA
    Mean (SD) P value Mean (SD) P value Mean (SD) P value
    Age
     ≤35 21.4 (11.5) 8.9 (5.0) 21.2(5.8)
     >35 24.9 (13.5) 0.041 9.6 (6.4) 0.348 16.1(5.5) 0.002
    Gender
     Male 23.4 (13.4) 9.2 (5.2) 17.9 (6.2)
     Female 22.9 (12.2) 0.807 9.3 (5.1) 0.855 19.0 (5.0) 0.502
    Marital status
     Single 22.2 (13.1) 8.9 (5.2) 20.0(5.4)
     Married 23.2 (12.5) 9.3 (5.7) 18.3(5.1)
     Divorced/widower 22.6 (13.8) 0.910 9.3 (7.2) 0.926 17.7(6.2) 0.794
    Educational level
     Diploma 21.2 (12.8) 9.2 (5.8) 20.8 (5.4)
     University 28.4 (10.3) <0.001 9.4 (5.5) 0.800 14.2 (7.2) 0.001
    Years of service
     5 or less 22.8 (12.1) 8.3 (4.5) 22.8 (4.2)
     6–10 21.9 (11.3) 9.4 (5.3) 19.4 (6.4)
     >10 23.7 (13.3) 0.658 9.4 (6.2) 0.627 17.3 (5.7) 0.060
    Administrative task
     Yes 22.4 (11.2) 8.7 (5.4) 23.2 (6.5)
     No 23.2 (12.9) 0.727 9.4 (5.8) 0.464 17.7 (7.4) 0.021
    Monthly income (SAR)
     <12000 24.6 (11.8) 10.5 (5.5) 26.3 (6.4)
     ≥12000 22.0 (12.9) 0.138 8.4 (5.7) 0.014 25.5 (5.6) 0.921

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    In univariate analysis, emotional exhaustion score was significantly higher among those aged >35 years (24.9 ± 13.5) compared to those aged ≤35 years (21.4 ± 11.5), (p = 0.041), and among those who had Bachelor degree (28.4 ± 10.3) compared to those who had diploma (21.2 ± 12.8), (p < 0.001) (Table 4).

    EE was correlated positively and significantly with all the ten sources of stress (r coefficient ranged from 0.379 to 0.586), (p < 0.001) (Table 5). DP was higher among those who had an income of <12000 SAR (10.5 ± 5.5) compared to those with income of ≥12000 (8.4 ± 3.7), (p = 0.014) (Table 4). DP was correlated positively and significantly with all the ten sources of stress (r coefficient ranged from 0.198 to 0.368), (p < 0.005) (Table 5). PA was significantly lower among those who aged >35 years (16.1 ± 5.5) compared to those aged ≤35 years (21.2 ± 5.8), (p = 0.002), among those who had university degree (14.2 ± 7.2) compared to those who had diploma (20.8 ± 5.4), (p = 0.001) and among those who had not administrative task (17.7 ± 7.4) compared to those who had (23.2 ± 6.5), (p = 0.021) (Table 4).

    Table 5.  Relationship between burnout and sources of stress in the workplace.
    Item EE
    DP
    PA
    Coefficient P value Coefficient P value Coefficient P value
    Work overload 0.495 <0.001 0.204 0.004 −0.106 0.135
    Long working hours 0.379 <0.001 0.198 0.005 −0.007. 0.926
    Fear of violence 0.422 <0.001 0.216 0.002 −0.100 0.161
    Poor work environment 0.586 <0.001 0.368 <0.001 −0.219 0.002
    Lack of resources 0.511 <0.001 0.301 <0.001 −0.086 0.228
    Fear of making mistake that can lead to serious consequences 0.428 <0.001 0.220 <0.001 −0.097 0.174
    Working with uncooperative colleagues 0.362 <0.001 0.197 0.005 −0.064 0.365
    Office work 0.340 <0.001 0.273 <0.001 −0.055 0.436
    Cannot participate in decision-making 0.424 <0.001 0.329 <0.001 −0.028 0.697
    Work demands affect my personal home life 0.525 <0.001 0.249 <0.001 −0.0091 0.200

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    In multivariate analysis, significant predictors of EE were work overload (p = 0.010), poor work environment (p < 0.001), lack of resources (p = 0.033), working with uncooperative colleagues (p = 0.005), work demands affect personal homelife (p < 0.001) and having university education (p < 0.001) (Table 6). Significant predictors of DP were poor work environment (p < 0.001), “cannot participate in decision-making” (p = 0.041) and low income (<12000 SAR) (Table 6). Low personal accomplishment was significantly predicted by age (>35 years) (p=0.001), educational level (university), (p = 0.004) and no administrative task (p = 0.003) (Table 6).

    Table 6.  Factors associated with burnout in multivariate analysis.
    B SE Beta P value VIF
    Emotional exhaustion
    Work overload 1.636 0.63 0.170 0.010 1.688
    Poor work environment 3.134 0.68 0.318 <0.001 1.887
    Lack of resources 1.473 0.68 0.148 0.033 1.853
    Fear of making mistake that can lead to serious consequences 1.038 0.59 0.110 0.082 1.565
    Working with uncooperative colleagues 1.875 0.65 −0.199 0.005 1.907
    Work demands affect my personal home life 2.226 0.56 0.267 <0.001 1.774
    University (reference = diploma) 6.009 1.51 0.206 <0.001 1.052
    Depersonalization
    Poor work environment 1.246 0.35 0.278 <0.001 1.246
    Cannot participate in decision-making 0.702 0.35 0.156 0.041 0.702
    Monthly income less than 12K SAR −1.776 0.76 −0.0152 0.021 −1.776
    Personal accomplishment
    Age (>35) −5.550 1.59 −0.0234 0.001 1.009
    Educational level (Bachelor) −5.354 1.82 −0.0196 0.004 1.001
    No administrative task −5.960 1.98 −0.0201 0.003 1.010

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    The primary aim of this study was to estimate the prevalence of burnout and its associated factors among nurses in the primary health care setting. This study found 89% of the participants scored high at least on one subscale of burnout. Low personal accomplishment was found among 89% of nurses while high EE and high DP were reported by 39%, and 38% respectively. Moderate level of burnout was found among 25% (EE), 39% (DP) and 5.5% (PA). The overall prevalence of burnout in this study was 89%.

    Previous studies among nurses in Saudi Arabia found that 32 % to 71.6% of nurses had high levels of burnout [5][8]. It was found by Al-Turki et al. that 45% of nurses had high EE, 42% had high depersonalization and 71.5% had low personal accomplishment. [5] Another study from Saudi Arabia found that 71.6 % of nurses had high level of burnout. [7] Another study from Saudi Arabia reported that 42% of nurses had moderate level of stress. [8] However, these two previous studies did not define the cut-off point for burnout. All the other mentioned studies used Maslach burnout inventory.

    A recent study from Egypt found that 54.6% of nurses had average levels of emotional exhaustion, 48% scored high on depersonalization, and 77.5% had low personal accomplishment [11]. Another study from Egypt found that 52.8% of nurses experienced high EE, 7.2% had high level of DP and 96.5% had low PA [12]. A study among Iranian nurses found that 25% of the participants had high level of burnout. [13] A study of nurses in Israel reported that 30.8% reported high emotional exhaustion, 5.1% had high depersonalization, and 84.6% had low personal accomplishment [14]. In Jordan 55% of nurses reported high level of emotional exhaustion, 50% reported high level of depersonalization, and 50% reported low personal accomplishment [15]. A recent international meta-analysis study that investigated burnout among nurses found that 28% of nurses had high level emotional exhaustion, 15% had high level of depersonalization and 31% had low personal accomplishment [4]. Regarding factors associated with burnout, this study found that high emotional exhaustion was associated with age group, level of education, and with sources of stress in the work place such as work overload, lack of resources, uncooperative colleagues, and poor working environment. DP was associated with low income, poor working environment and inability to participate in decision-making. Low PA was associated with age group, level of education and no engagement in administrative work.

    While some studies had not found association between burnout and socio-demographic factors, [8] some other studies had found a significant association between burnout and age, marital status and education level [5],[7]. However, there is a great agreement between studies that burnout is associated with stress and sources of stress in the workplace [15][20].

    That sources of stress in the workplace included role conflict, work overload, conflict with colleagues, long working hours, poor working environment and low supervisor support. A previous meta-analysis study found that job insecurity, low job control, low reward, high demands and high work load increased the risk for developing burnout [21].

    Long-term exposure to stressors was found to affect the professional quality of life, leading to cognitive and emotional distress and burnout [22]. Continuous effort in stressful, demanding tasks can have physiological and psychological impacts, such as increased heart rate and prolonged stimulation of the sympathetic nervous system. This is well recognized to be associated with exhaustion, particularly when the workload is high. Long working hours was found to be associated with emotional exhaustion because it produces excessive demands and disrupt family life and ability to trail outside interests [15][17].

    This finding emphasis that any effort to manage burnout should be directed toward the management of sources of stress in the work place. Burnout was also found to be affected by other factors rather than work related factors and stressors in the work place. It was found in the previous studies that Alexithymic personality trait increased likelihood to experience burnout and has a negative effect on the professional quality of life among. radiation oncologists [23],[24]. In addition, emotional intelligence was found to be linked with all the three parts of burnout [25]. Emotional intelligence is defined as the ability to perceive emotion, integrate emotion to facilitate thought, understand emotions, and regulate emotions to promote personal growth [26].

    This study found that 89% of the participants scored high at least on one subscale of burnout. Burnout was associated mainly with work related sources of stress. A comprehensive interventional approach is needed to minimize and prevent burnout among nurses in the primary health care centers. There were three types of interventions to manage burnout: individual-focused, organizational, and combine interventions. Individual-focused interventions included self-care workshops, stress management skills, communication skills training, yoga, mindfulness, meditation and coping programs. Organizational interventions aimed mainly to reduces stress and to mitigate the impact of stressors in the workplace; they included workload or schedule-rotation, stress management training program, access to peer mentoring, help and guidance from experienced work colleagues and teamwork/transitions. Individual and organizational interventions should be combined to effectively reduce burnout among healthcare providers. It would be also of great interest if future studies investigate which personality factors are associated with burnout in nursing working in primary health care centers. This will help to prioritize intervention to focus on nurses with high risk personality trait. Interventions to improve emotional intelligence are also recommended. Poulsen & Poulsen (2018) proposed a Self-Determination Theory and they suggested two steps to prevent burnout during early career. The first step was to educate trainers and trainees about times when individuals may be vulnerable to work stress. Learning how to recognize the warning signs of burnout and being aware of vulnerability is a vital first step. Education about the need for self-awareness and importance of self-care would occur in the early stages of training. The next step involved alerting practitioners regarding the extent and accessibility of information regarding evidence-based strategies that can be employed to address exhaustion and prevent disengagement [27].

    [1] Hülsmann P, Weiss KA (2015) Simulation of water ingress into PV-modules: IEC-testing versus outdoor exposure. Sol Energ 115: 347-353. doi: 10.1016/j.solener.2015.03.007
    [2] Hülsmann P, Weiß KA, Köhl M (2012) Temperature-dependent water vapour and oxygen permeation through different polymeric materials used in photovoltaic-modules. Progress in Photovoltaics: Res Appl 22: 415-421.
    [3] Kempe MD (2006) Modeling of rates of moisture ingress into photovoltaic modules. Sol Energ Mater Sol Cell 90: 2720-2738. doi: 10.1016/j.solmat.2006.04.002
    [4] Hülsmann P, Philipp D, Köhl M (2009) Measuring temperature-dependent water vapor and gas permeation through high barrier films. Rev Sci Instrum 80: 113901. doi: 10.1063/1.3250866
    [5] Hülsmann P, Heck M, Köhl M (2013) Simulation of Water Vapor Ingress into PV-Modules under Different Climatic Conditions. J Mater 2013. Available from: http://dx.doi.org/10.1155/2013/102691.
    [6] Vogt A, Peharz G, Jaus J, et al. (2006) Degradation Studies on FLATCON® Modules and Assemblies, 21st European Photovoltaic Solar Energy Conference, Dresden, Germany.
    [7] Kempe M, Dameron A, Reese M (2013) Evaluation of moisture ingress from the perimeter of photovoltaic modules. Prog Photovol Res Appl 22: 1159-1171.
    [8] Reisner E, Stollwerck G, Peerlings H, et al. (2006) Humidity in a solar module - horror vision or negligible, 21st European Photovoltaic Solar Energy Conference, Dresden, Germany.
    [9] Kempe MD (2005) Control of Moisture Ingress into Photovoltaic Modules, 31st IEEE Photovoltaics Specialists Conference and Exhibition Lake Buena Vista, Florida.
    [10] Mikami R, Toya K, Saito K, et al. (2015) CPV module's reliability test results and the degradation by glass dimming, Proceeding of 11th International Conference on Concentrator Photovoltaic Systems-Aix Les Bains.
    [11] Kennedy CE, Terwilliger K, Jorgensen GJ (2007) Further analysis of accelerated exposure testing of thin-glass mirror matrix, Proceedings of ES2007 Energy Sustainability 2007, Long Beach, California.
    [12] Fucci R, Cancro C, Graditi G, et al. (2011) ENEA's activities on C-PV technology, Proceeding of International Conference in Clean Electrical Power, Ischia, Italy.
    [13] Cancro C, Graditi G, Fucci R, et al. (2015) Characterization And Validation Tests On Ecosole C-Modules First Prototypes, Proceeding of 11th International Conference on Concentrator Photovoltaic Systems-Aix Les Bains.
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