Identification of Stressors in Nurses Involved in Caring for Patients with COVID-19

Background and aims: The COVID-19 pandemic put a lot of strain on staff and the health care system. The aim of this study was to determine the stressors in nurses involved in caring for COVID-19 patients in the hospitals of Babol University of Medical Sciences. Methods: This cross-sectional study was conducted on nurses working in the care of COVID-19 patients (Rohani and Yahainejad hospitals) in Babol University of Medical Sciences in 2020. The number of samples was estimated using Cochran's statistical formula was 224 people. Then, the proportion of nurses involved in corona in each hospital, stratified sampling of corona departments was done. Data collection tools were a researcher-made questionnaire. The Stressors Questionnaire in Nurses was in five areas (interpersonal, patient care, physical and environmental, individual and managerial) and the questionnaire was about the demographic characteristics. The collected data were analyzed using SPSS software version 23 and parametric statistical tests of one-way analysis of variance and independent t-test were also performed in the absence of non-parametric Kruskal-Wallis and Mann-Whitney tests. Results: Physical and environmental stressors with an average of 29±10.12 and the patient care stressors with 13.38±5.02 had the highest and lowest intensities of stress, respectively. The most important stressors in nurses are insufficient support (68.2%), high workload (63.9%), tight work schedule (63.5%), heaviness and discomfort of the nurse with protective clothing and boots (63.3%), lack of nurses (61.5%), lack of suitable space to change protective clothing (60.2%), etc. Nurses' occupational stressors were significantly associated with gender, work experience and type of hospital, physical and environmental stressors with shift work, interpersonal stressors with education and patient care stressors with overtime (P<0/05). Conclusion: Since physical and environmental factors were recognized as the most source of stressors, with appropriate measures, it is possible to eliminate or correct these factors, to help reduce stress and improve the health of nurses. © 2022 Iran University of Medical Sciences. All rights reserved.

Impact of Stress During COVID-19 Pandemic

The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.

Are memes selfish? How Internet memes reflect crisis–Covid-19 pandemic in Israel

Purpose: The paper aims to explore, using an analysis of the three components of memes content, form and stance – whether and how the memes offer a broad picture of a specific society during the COVID-19 pandemic crisis. Design/methodology/approach: The author collected, from the two largest Facebook groups in Israel, 25 memes with the largest number of likes in each month, beginning from the month in which awareness of COVID-19 increased significantly, between March 2020 and February 2019. A total of 597 memes were collected. The data were analyzed by a quantitative and qualitative analysis. Findings: Findings indicate that meme culture effectively reflects a society's situation and the challenges it faces. Memes also reflect local cultural icons and effects. Meme contents vary across groups. During a crisis, memes do not function as fertile groups for sharp criticism or calls to take action to resolve society's social ills. Practical implications: Memes may serve as a tool to understand and explore an unfamiliar, foreign culture, its state of mind and its history through meme culture. Social implications: Memes may constitute a platform for relieving stress through light-hearted humor, unaccompanied by a true call to action;that is, "slacktivism” which gives a sense of active participation without involvement in actual activities for change. Originality/value: The study reveals that the Israeli meme culture is not activist and rather focuses on humor to relieve stress. Memes may be used as "bread and circuses” or a means of "slacktivism” that fails to call to genuine activism. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2022-0381. © 2023, Emerald Publishing Limited.

Stress Analysis Using Machine Learning

Stress is one of the major health issues of the world and one of the major reasons for committing suicide. Also, it leads to other mental health issues such as depression, anxiety etc., and damage to organs related to respiratory, cardiovascular and nervous systems. In recent years, stress has impacted many individuals due to the pandemic situation. Since the governments across the globe had started to impose lockdowns, the levels of stress significantly raised because of the disturbances led by covid infections, losing loved ones, continuous engagement with laptops and mobiles etc. It is also found that stress has not only disturbed the health condition but also disturbed the relationships and became a self-destruction component. This project is aimed to help those people to understand their stress and consult a psychologist at right time to overcome the situation. Though stress is an active area of research and achieved high performance of models, those were based on signal and speech which were computationally costlier and text-based research work using a state-of-the-art model called the BERT has achieved an f1-score i.e. 80.65%. This project focuses on text-domain and uses open-sourced Stress Analysis on Social Media dataset available on Kaggle which contains 3.6 K samples. In this project, both Machine Learning and Deep Learning Models were trained with 80% of the data and validated with 20% of the data. After, optimization and evaluation of several models, the best model has achieved a benchmark result of 83.74% f1-score on test data using a new network architecture i.e. combination of stacked Transformer Encoder layers with stacked Bi-directional-LSTM. In addition to this, an explainable AI has been implemented for an embedding layer to inspect input attributions in predicting the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

Coping with the Forced Separation of Close Relationships during the COVID-19 Pandemic.

Background: Issues regarding close relations became especially prevalent within the context of the pandemic, due to the forced separation of these relationships. This is now seen as a significant stressor that influences emotions and subjective perceptions of the relationship. Objective: The current study aims to investigate the specifics of coping with the forced separation of partners with varying types of closeness. Design: The study was carried out with quantitative and qualitative methods. The sample included 43 women and 42 men (aged 21-47); all were involuntarily separated from their partners by impacts of the pandemic. Results: Three scenarios of romantic relationships were identified based on a balance of love components (intimacy, passion, commitment) and prevalent feelings (closeness or distance). The authors concluded that certain coping strategies (positive reassessment, acceptance, distraction) can help the individual to cope with the separation, but do not contribute to the closeness of the partners and the strengthening of the relationship. Coping primarily through active support was typical for partners with intense and balanced feelings based on closeness (Scenario 3). Ambivalent feelings toward a partner (Scenario 1) were associated with passive coping, which increased the risk of detachment. The moderate manifestation of balanced love components and closeness (Scenario 2) focused on acceptance of the situation, positive reassessing, and distraction, all of which reduce the stress of separation, but cause anxiety regarding the future of the relationship. Conclusion: The type of close relationship has been found to correlate to the coping strategy adopted by the couple following forced separation during the COVID-19 pandemic. However, further studies are required to investigate separation in various social contexts, not only in isolation due to the pandemic, as well as its connection to psychological well-being.

CRISPR/Cas Genome Editing Technologies for Plant Improvement against Biotic and Abiotic Stresses: Advances, Limitations, and Future Perspectives.

Crossbreeding, mutation breeding, and traditional transgenic breeding take much time to improve desirable characters/traits. CRISPR/Cas-mediated genome editing (GE) is a game-changing tool that can create variation in desired traits, such as biotic and abiotic resistance, increase quality and yield in less time with easy applications, high efficiency, and low cost in producing the targeted edits for rapid improvement of crop plants. Plant pathogens and the severe environment cause considerable crop losses worldwide. GE approaches have emerged and opened new doors for breeding multiple-resistance crop varieties. Here, we have summarized recent advances in CRISPR/Cas-mediated GE for resistance against biotic and abiotic stresses in a crop molecular breeding program that includes the modification and improvement of genes response to biotic stresses induced by fungus, virus, and bacterial pathogens. We also discussed in depth the application of CRISPR/Cas for abiotic stresses (herbicide, drought, heat, and cold) in plants. In addition, we discussed the limitations and future challenges faced by breeders using GE tools for crop improvement and suggested directions for future improvements in GE for agricultural applications, providing novel ideas to create super cultivars with broad resistance to biotic and abiotic stress.

The moderating role of emotional intelligence on remote work-related stresses during the COVID-19 pandemic

At the start of the COVID-19 pandemic, many employees were laid off, furloughed, and forced to work remotely with no time to prepare for the drastic shift in routine. Those working from home have sometimes found it challenging to maintain boundaries between work and family, often leading to decreased overall psychological well-being. Research suggests that individuals with high levels of emotional intelligence (EI) may be better equipped to regulate their emotions during stressful times. Informed by EI theory, the purpose of this quantitative study was to investigate the potential for ability EI to influence work stresses associated with stay-at-home orders during the COVID crisis. One hundred and thirty full-time U.S. English-speaking adults, ages 18 to 65+, who worked on-site prior to COVID-19 and then shifted to remote work completed a survey on Momentive, a cloud-based platform. Although no moderating effect was found for the relationship between remote work and job-related stresses, findings revealed that remote work was a significant predictor of work-life balance. Additionally, ability EI was a significant predictor of work engagement, highlighting the value of increased worker satisfaction and productivity. As the pandemic persists and remote work increases, organizational leaders may want to add EI training to the onboarding process for new hires. Increasing employees' EI may effect positive social change by improving individual well-being and quality of life. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Experiences of Nurses in Nursing Homes during the COVID-19 Pandemic in Germany: A Qualitative Study.

(1) Background: The aim of this qualitative study was to examine pandemic-related changes in nursing work in nursing homes, the resulting work-related stresses and external as well as internal alleviating measures. (2) Methods: We conducted 10 interviews from March to June 2021 with nurses from eight facilities. Data were analysed according to qualitative content analysis. (3) Results: Nurses faced increased workloads due to regulations and guidelines paired with staffing shortages. Work became more difficult due to personal protective equipment (PPE), conflict with residents' relatives and, in the case of outbreaks, excess death and suffering. Nurse-to-resident care work became more emotionally demanding, with residents more distressed due to the lockdown, while families and social workers were not allowed into the facility. Residents with dementia posed an additional challenge, as they did not remember hygiene and distancing rules. Internal and external measures were not sufficient to alleviate the situation. However, some measures, such as training programmes or existing palliative care concepts, were considered helpful. (4) Conclusions: Facing other possible upcoming pandemics, ways to improve facility administration to prepare for future pandemics are highly needed, such as regular training programmes to prepare for possible lockdown scenarios, PPE use or potential hygiene measures.

Systematic Analysis and Functional Characterization of R2R3-MYB Genes in Scutellaria baicalensis Georgi.

R2R3-MYB transcription factors participate in multiple critical biological processes, particularly as relates to the regulation of secondary metabolites. The dried root of Scutellaria baicalensis Georgi is a traditional Chinese medicine and possesses various bioactive attributes including anti-inflammation, anti-HIV, and anti-COVID-19 properties due to its flavonoids. In the current study, a total of 95 R2R3-MYB genes were identified in S. baicalensis and classified into 34 subgroups, as supported by similar exon-intron structures and conserved motifs. Among them, 93 R2R3-SbMYBs were mapped onto nine chromosomes. Collinear analysis revealed that segmental duplications were primarily responsible for driving the evolution and expansion of the R2R3-SbMYB gene family. Synteny analyses showed that the ortholog numbers of the R2R3-MYB genes between S. baicalensis and other dicotyledons had a higher proportion compared to that which is found from the monocotyledons. RNA-seq data indicated that the expression patterns of R2R3-SbMYBs in different tissues were different. Quantitative reverse transcriptase-PCR (qRT-PCR) analysis showed that 36 R2R3-SbMYBs from different subgroups exhibited specific expression profiles under various conditions, including hormone stimuli treatments (methyl jasmonate and abscisic acid) and abiotic stresses (drought and cold shock treatments). Further investigation revealed that SbMYB18/32/46/60/70/74 localized in the nucleus, and SbMYB18/32/60/70 possessed transcriptional activation activity, implying their potential roles in the regulatory mechanisms of various biological processes. This study provides a comprehensive understanding of the R2R3-SbMYBs gene family and lays the foundation for further investigation of their biological function.

COVID-19 Impact on Online Learning: A Statistical and Machine Learning Model Analysis for Stress Detection

The preventive measure to control the outbreak of COVID-19 pandemic compelled the government across the globe to close the educational premises. In order to fill the academic gap, and to follow the prescribed isolation in this outbreak, a shift of physical classroom interaction to virtual space becomes indispensable. This rushed shift is largely affecting the academicians, groups of students, and institutions. Although, students from different backgrounds may have different psychological impacts of online learning experiences depending upon their usage and comfortability with the e-learning technology. Many researchers reported that online learning has resulted in depression and anxiety disorder among students and in due course resulting in increased stress levels. Therefore, it is vital to comprehend and examine the impact of this unexpected shift in the learning environment on students’ psychology and stress levels. There are numerous studies associated with stress identification in controlled laboratory surroundings, but there is inadequate research related to stress measurement in general (Can et al. in J. Biomed. Inform. 92, 2019). The present study is an attempt to explore a variety of predictive analysis and statistical analysis techniques in identification of stress and also to analyze the perceived stress level of students in online learning experience using cross-sectional research. The data collected is analyzed in depth using the regression analysis and the relationship between various factors is established and other valuable insights are demonstrated. The outcome of this research would be helpful for the educational institutes, policymakers, and government bodies to appraise the challenges and the inadequacies of online teaching platforms and their impact on student’s stress levels. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (-1), and neutral (0) emotions through different visualization. © 2022 Tech Science Press. All rights reserved.

X-ECGNet: An Interpretable DL model for Stress Detection using ECG in COVID-19 Healthcare Workers

COVID-19 pandemic erupted in December 2019, spreading extremely fast and stretching the healthcare infras-tructure of most countries beyond their capacities. This impacted the healthcare workers (HCW) adversely because 1) they were pressured to work almost round the clock without a break;2) they were in close contact with the COVID-19 patients and hence, were at high risk;and 3) they suffered from the fear of spreading COVID to their families. Hence, many HCWs were stressed and burnout. It is known that stress directly affects the heart and can lead to serious cardiovascular problems. Currently, stress is measured subjectively via self-declared questionnaires. Objective markers of stress are required to ascertain the quantitative impact of stress on the heart. Thus, this paper aims to detect stress contributing factors in HCWs and determine the changes in the ECG of stressed HCWs. We collected data from multiple hospitals in Northern India and developed a deep learning model, namely X-ECGNet, to detect stress. We also tried to add interpretability to the model using the recent method of SHAP analysis. Deployment of such models can help the government and hospital administrations timely detect stress in HCWs and make informed decisions to save systems from collapse during such calamities. © 2021 IEEE.

Personalized Stress Monitoring AI System for Healthcare Workers

In the current COVID-19 pandemic scenario, healthcare workers, in particular nurses, face prolonged exposure to stress. This intense duress takes a toll on their health overtime, affects their quality of life, and in turn impacts the quality of care provided to the patients. Hence, real-time detection and monitoring of stress is extremely important for early detection of stress patterns, prevention of burnouts and chronic conditions in healthcare workers as well as facilitate improved patient-care outcomes. In this paper, we present a proof-of-concept case study using machine learning (ML) and artificial intelligence (AI)-based stress detection model that determines a personalized assessment of stress level using heart rate, heart rate variability, and physical activity of the users. We used wearable electrocardiogram and inertial sensor to record heart activity and physical activity of nurses during their shifts. Our preliminary results indicate that the proposed stress tracking model can effectively predict any stress occurrences. This study is a pivotal attempt to emphasize the significance of stress-detection and relief for healthcare workers and provide them a tool for an effective assessment of personalized stress levels. © 2021 IEEE.

Stress Analysis and Care Prediction System for Online Workers

Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers. © 2021 IEEE.

Wearable Sensing Systems for Monitoring Mental Health.

Wearable systems for monitoring biological signals have opened the door to personalized healthcare and have advanced a great deal over the past decade with the development of flexible electronics, efficient energy storage, wireless data transmission, and information processing technologies. As there are cumulative understanding of mechanisms underlying the mental processes and increasing desire for lifetime mental wellbeing, various wearable sensors have been devised to monitor the mental status from physiological activities, physical movements, and biochemical profiles in body fluids. This review summarizes the recent progress in wearable healthcare monitoring systems that can be utilized in mental healthcare, especially focusing on the biochemical sensors (i.e., biomarkers associated with mental status, sensing modalities, and device materials) and discussing their promises and challenges.

Psychological and Occupational Pressure Among Female Teachers in Light of the Coronavirus Disease Pandemic and Coping Strategies

This study aims at identifying the level of psychological and professional stress in light of the Coronavirus disease (COVID-19) pandemic. It adopted descriptive and analytical methodologies. Two scales were prepared: One scale for psychological and professional stress and the other for coping with psychological and professional pressures. The study sample consisted of 608 female teachers at public schools in Al-Kharj province, Kingdom of Saudi Arabia (KSA). The results show a moderate level of psychological and professional stresses. The findings show statistically significant differences in teaching experience variable. The differences were in favour of female teachers with experience of less than 5 years. However, no statistically significant differences were found on the variable of academic qualification. It was found that female teachers had followed strategies to cope with psychological and professional stresses including resorting to faith, connecting with other teachers and students, planning different strategies, seeking help from others and managing time. The study recommends arranging training courses for female teachers to develop remote teaching skills and using various technologies and applications in this field particularly during the ongoing COVID-19 pandemic. © 2022 Eurasian Society of Educational Research. All rights reserved.

Mycorrhizal Fungi Inoculation Improves Capparis spinosa’s Yield, Nutrient Uptake and Photosynthetic Efficiency under Water Deficit

Agricultural yields are under constant jeopardy as climate change and abiotic pressures spread worldwide. Using rhizospheric microbes as biostimulants/biofertilizers is one of the best ways to improve agro-agriculture in the face of these things. The purpose of this experiment was to investigate whether a native arbuscular mycorrhizal fungi inoculum (AMF-complex) might improve caper (Capparis spinosa) seedlings’ nutritional status, their morphological/growth performance and photosynthetic efficiency under water-deficit stress (WDS). Thus, caper plantlets inoculated with or without an AMF complex (+AMF and −AMF, respectively) were grown under three gradually increasing WDS regimes, i.e., 75, 50 and 25% of field capacity (FC). Overall, measurements of morphological traits, biomass production and nutrient uptake (particularly P, K+, Mg2+, Fe2+ and Zn2+) showed that mycorrhizal fungi inoculation increased these variables significantly, notably in moderate and severe WDS conditions. The increased WDS levels reduced the photochemical efficiency indices (Fv/Fm and Fv/Fo) in −AMF plants, while AMF-complex application significantly augmented these parameters. Furthermore, the photosynthetic pigments content was substantially higher in +AMF seedlings than −AMF controls at all the WDS levels. Favorably, at 25% FC, AMF-colonized plants produce approximately twice as many carotenoids as non-colonized ones. In conclusion, AMF inoculation seems to be a powerful eco-engineering strategy for improving the caper seedling growth rate and drought tolerance in harsh environments.

COVID-19-Related Concerns and Symptoms of Anxiety: Does Concern Play a Role in Predicting Severity and Risk?

Objective Rates of anxiety have increased during the coronavirus disease 2019 (COVID-19) pandemic, partially attributable to the experience of COVID-19 related concerns. It remains pivotal to determine the implications of such concerns on the severity of anxiety as they may represent opportune targets for public health preventative or therapeutic efforts. The current study evaluated COVID-19 related concerns as predictors of anxiety symptom severity. It also assessed the relative risk associated with sub-types of COVID-19 concerns, the role of age, sex, and minority status as potential moderators; and the unique contribution of COVID-19 concerns beyond sociodemographics, perceived stress, and self-reported general mental health. Methods The data source was obtained from the publicly available "Crowdsourcing: Impacts of COVID-19 on Canadians-Your Mental Health survey" conducted by Statistics Canada. Participants were Canadians aged 15 and older living in ten provinces or three territories. Only participants that completed the self-reported sociodemographics, COVID-19 concerns, and general anxiety symptoms measures were included (n = 44549). Multivariate linear regression was used to evaluate continuous reports of anxiety symptoms, and the relative risk of meeting anxiety cut-off levels was determined using chi-square non-parametric testing. Results Within the sample, 29.1% met cut-off levels of anxiety. Levels of coping and security (R2 = 0.205, p < 0.001), distal (R2 = 0.043, p < 0.001), and proximal concerns (R2 = 0.122, p < 0.001) were found to predict the severity of anxiety experiences, which was determined to be robust to statistical control for sociodemographics, perceived stress and self-reported general mental health (ΔR2 = 0.0625, p < 0.001). Minority status and sex were significant moderating variables, although the interaction accounted for less than 0.1% of the observed variance. Family stress from confinement, support during and after the crisis and personal health concerns significantly predicted more than a 200% increase in the risk of meeting anxiety cut-off levels. Conclusion The current study represents a novel examination of COVID-19-related concerns as risk factors for the experience of anxiety amongst a sizeable Canadian cohort. Coping and security-related concerns represented robust predictors of anxiety symptom experiences. Participants who experienced concerns relating to their proximal social groups were two times more at risk for meeting cut-off anxiety levels than individuals without such concerns. Longitudinal and evidence synthesis remains essential for identifying therapeutic targets and developing pandemic-related public health prevention and care.

Interactive Effects of Drought and Saline Aerosol Stress on Morphological and Physiological Characteristics of Two Ornamental Shrub Species

Effects of drought and aerosol stresses were studied in a factorial experiment based on a Randomized Complete Design with triplicates on two ornamental shrubs. Treatments consisted of four levels of water container (40%, 30%, 20%, and 10% of water volumetric content of the substrate) and, after 30 days from experiment onset, three aerosol treatments (distilled water and 50% and 100% salt sea water concentrations). The trial was contextually replicated on two species: Callistemon citrinus (Curtis) Skeels and Viburnum tinus L. ‘Lucidum’. In both species, increasing drought stress negatively affected dry biomass, leaf area, net photosynthesis, chlorophyll a fluorescence, and relative water content. The added saline aerosol stress induced a further physiological water deficit in plants of both species, with more emphasis on Callistemon. The interaction between the two stress conditions was found to be additive for almost all the physiological parameters, resulting in enhanced damage on plants under stress combination. Total biomass, for effect of combined stresses, ranged from 120.1 to 86.4 g plant−1 in Callistemon and from 122.3 to 94.6 g plant−1 in Viburnum. The net photosynthesis in Callistemon declined by the 70% after 30 days in WC 10% and by the 45% and 53% in WC 20% and WC 10% respectively after 60 days. In Viburnum plants, since the first measurement (7 days), a decrease of net photosynthesis was observed for the more stressed treatments (WC 20% and WC 10%), by 57%. The overall data suggested that Viburnum was more tolerant compared the Callistemon under the experimental conditions studied.