The most common outcome was the general interference of climate change with the overall quality of life and (mental) well-being, including risk perceptions and the influence of the psychological distance of climate change (24–27). This general impairment was often paired with emotions (23, 28, 29) like different levels of happiness (30), grief, worrying (31, 32), concerns, helplessness (33), solastalgia and ecological anxiety (34, 35). Other studies addressed schizophrenia and psychotic experiences (36).

Climate change appeared as a significant concern even at younger age. Thomas et al. (23) identified anger and frustration as predominant emotions, which reflects the burden felt by children facing climate-related challenges. Spence et al. (25) explored the psychological distance of climate change, revealing perceptions about climate change as both globally distant and locally relevant. Lawrance et al. (27), highlighted the distress many young people feel about climate change, suggesting a potential impact on their mental well-being and quality of life.

Mode of commuting, recreation activities and education can influence mental well-being (23, 27). Higher levels of education were negatively associated causing increased concerns about climate change (33). However, higher education enhanced perceptions of climate change among forest owners (24). Figure 6 offers an overview on the addressed environmental and socio-individual determinants and related outcomes.

The diversity of socio-individual determinants reflected a rather holistic approach of many studies. The relative socioeconomic position plays an important role in the mental health and well-being of children (25, 27). For example, Lawrance et al. (27) utilized the Family Affluence Scale-III to gauge relative socioeconomic position and assess psychological responses and mental health. Age and gender were identified to be the most relevant and pathogenic determinants especially for women and elderly. Females as well as older people tended to be more worried (32) and “1.491 times more likely to be concerned about climate change than males (33)”. For children studies considered parental characteristics such as age, education, smoking during pregnancy, and occupation. They acknowledging the potential impact of parental attributes on children’s well-being and cognitive development (23).

Additionally, to the focus on human well-being and quality of life the papers addressed outcomes like different levels of happiness (37), grief and worrying (32), as well as stress (38, 39), anxiety and the morbidity of anxiety disorder and depression (40–46).

In a study of adolescents, a distinction was made between emotional symptoms, behavior (e.g., hyperactivity-inattention), conduct and peer relationship problems (47). Measurements used include health-related quality of life questionnaires like ESPRINT-15 (Cuestionario ESPañol del Calidad de Vida en RINiTis) (48), CASP-19 (The Control, Autonomy, Self-realization, Pleasure) and EQ-5D (47). Muñoz-Cano et al. (48) measured sleep with the Medical Outcomes Study Sleep Scale (MOS Sleep Scale), and anxiety and depression with the Hospital Anxiety and Depression Scale (HAD). Others were the 12-item General Health Questionnaire (GHQ-12) (40), the Mental Health Continuum Short Form (MHC-SF) (49), the 36-Item Short-Form Health Survey (SF-36) (50), SF12 Mental Wellbeing, the General Health Questionnaire score (GHQ) (51) and the Strength and Difficulties Questionnaire (SDQ-R) (47). Rathmann et al. (52) combined self-report assessments of subjective well-being with analysis of Heart Rate, Inter-Beat-Interval and Galvanic Skin Conductance, and Blood Volume Pressure. Triguero-Mas et al. (39) employed self-reports of mood, stress and perceived restoration experience with salivary cortisol tests, and cognitive tests.

The presence, amount and types of green spaces were discussed as well as access, distance, and visibility (38–40, 43, 44, 47, 49, 53, 54). They were mostly measured by the Normalized Difference Vegetation Index (NDVI) using buffers of 100, 300 and/or 500 metres around residence (38, 40, 47, 53). A few papers also included people’s perceptions of green spaces, using an index for perceived amount of nature near home (49) or perceived restorative quality of the neighborhood (Perceived Restorativeness Scale (PRS) (40).

Cruz et al. (44) showed a mix of social and ecological determinants as relevant for the protection of mental health. Higher concentration of air pollutants, higher distance to green spaces with lakes, deprivation, higher numbers of ethnic minorities were identified as pathogenic determinants and increased the prevalence for serious mental diseases. The studies pointed to the importance for well-being but also revealed the complexity of disentangling associations between the physical surroundings and well-being outcomes. Hart et al. (37) showed that neighborhoods with higher aesthetics and more water and green were salutogenic (higher levels of happiness). Combined effects of health satisfaction, social life, ontological security, greenspace proximity, and time in nature also impact well-being positively (46). Living in residences with higher vegetated surroundings over the lifetime reduced the prevalence of experiencing premenstrual symptoms like anxiety and tension, feeling depressed and hopeless (38).

Zock et al. (43) concluded that high diversity in land use may be beneficial for physical and mental health. Presence of greenspace in the neighborhood was weakly associated with less morbidity for anxiety disorder and depression. In Dzhambrov’s (40) cross-sectional analysis the effect of residential green space on mental health was mediated by physical activity and restorative quality. While in the longitudinal analysis more green space exerted a direct effect on mental health. Triguero-Mas et al. (39) showed that perceived psychological restoration experience increased physical activity. Decreased air pollution mediated the effect of green spaces on psycho-physiological health indicators. Two papers had a narrower focus considering a specific green setting together with the activity performed, i.e., participation in community gardening (54), and forest walks (52).

Several papers took a broader perspective, i.e., land use diversity and socio-economic context. Based on multiple Dutch cohort studies (n = 32.487), Generaal et al. (42) found that urbanization level, socioeconomic, physical, and social neighborhood characteristics were associated with prevalence of depression. Distinct urban-rural inequalities were identified with remote rural regions and inner-city areas as environments with poor and more pathogenic health-related features. Cerletti et al. (50) referred to people’s perception of their proximity (in minutes) to supermarkets, local services, restaurants and cafés, public transportation services, sports facilities, parks, green spaces and quiet places. Perceived access to outdoor open space, facilities for walking and cycling plus aesthetics of the built environment were mentioned (37, 55, 56). Residential street improvements in the UK had an impact on elderlies perceptions of street walkability and safety at night, but not on the overall levels of physical activity or wellbeing (55).

The studies further evaluated the significance of physical surroundings, such as urban environmental factors and green spaces, in shaping cognitive functions (57–60). Brons et al. (47) identified a stronger influence of the school environment on the mental health of adolescents than the residential environment. Further the mode of commuting, recreation activities and education can also influence mental well-being (59).

Socio-economic conditions were considered at a local scale, through neighborhood (41, 42, 47) and school socio-economic status (SES) (47). Household determinants (11 studies) included family’s socio-economic capital (53), income (41, 42, 45, 54, 56), SES (38, 46, 51), income deprivation (44) and Index of Multi Deprivation (51). Binter et al. (57) included a deprivation index comprising income level, employment rate, and educational level from each country under study. This indicator reflects the economic status of the regions and helped to assess the impact of economic disparities on children’s cognitive and motor functions. Other studies incorporated neighborhood and family SES, including average income, unemployment rate, parental education and socioeconomic vulnerability. Thereby, the studies highlighted the importance of economic background in understanding how green spaces and air pollution impact mental well-being during adolescence (58, 60).

Social conditions were considered at different socio-spatial scales, the largest scale referred to perceived neighborhood social cohesion, social network, level of trust and safety (37, 40–42, 56) and social engagement (50). The second level referred to family and household. This level included household structure/composition (46, 54), contact with family and friends and social interaction (39, 53). According to Maitre et al. (53) was “a rich social capital of the family [ … ] protective” for behavior problems of children. Family structure and family support (47), marital status, family status (45), children in the household (37) and health status of others (51) were also important. Neighborhoods with more social contacts, a stronger social cohesion and trustworthy neighbours, were linked with higher levels of happiness (37).

Among the personal determinants, age and gender were considered in almost all studies and in 6 studies together with an ethnicity indicator (40, 42–44, 51, 55). Several personal determinants were health-related variables, e.g., body mass index (BMI) (38, 54), menstrual regularity, cycle length (38), restrictions in carrying out activities of daily life (43), illness in the past year (46) and chronic diseases (55). Two studies included self-reported connectedness/relatedness to nature (49, 54).

The potential impact of parental attributes on children’s well-being and cognitive development were also acknowledged, including parental age, education and occupation (57–60). Binter et al. (57), also delved into individual demographic factors like maternal age, educational level, ethnicity, pre-pregnancy body mass index, and others.

Studies demonstrated a strong correlation between air pollution and the outcomes, mainly cognitive, behavioral and well-being (67, 82, 83). These include impacts on cognitive development in children/adolescents and cognitive decline in the elderly (57, 59, 80, 84–86). Mortamais et al. (67) and Oudin et al. (87) demonstrated that prenatal exposure to PM_2.5, particularly during the last trimester, may induce structural brain changes in children. This enhanced the risk for developing autism spectrum disorder (ASD) and attention deficit hyperactive disorder (ADHD). Similarly, Rivas et al. (83) showed that exposure to PM_2.5 during early life was negatively associated with fundamental cognitive abilities at school age, such as working memory and the attentional conflict network. PM from traffic was associated with important reductions in cognitive growth and increased risk of ASD (80, 81, 84). Furthermore, exposure to air pollution can lead to neurodevelopmental deceleration, leading to cognitive dementia, such as inattentiveness, verbal memory, reasoning, and verbal influence [e.g., (88–92)]. However, some studies found no evidence for the association of air pollution with an increased risk for neurodevelopmental disorders in children (62, 93, 94). Behavioral issues were linked to air pollution, particularly in children (6 papers). Studies reported a higher incidence of ASD, ADHD, behavioral development and other behavioral problems (53, 62, 93–96). Bloemsma et al. (58) revealed that higher exposure to ambient air pollution was related to poorer mental well-being in adolescents. However, this association attenuated after adjustment for green space. Conversely, Jorcano et al. (97) and Midouhas et al. (98), presented conflicting evidence, suggesting no significant association between air pollution exposure and emotional or behavioral problems in children.

Rittner et al. (72), supported that reduction of PM_2.5 emissions would have a substantial impact on mortality and adverse health effects, improving overall quality of life. Several studies revealed a concerning link between air pollution and adverse psychological outcomes in children (68, 70, 77, 99, 100). These studies observed a correlation between childhood exposure to ambient air pollution and an increased risk of depression onset, psychopathology, and overall psychiatric illness in late adolescence. Additionally, Newbury et al. (36), demonstrated that socioenvironmental risk for schizophrenia during upbringing is correlated with a genetic risk (e.g. family psychiatric history). This correlation can be seen in Figure 7 where most outcomes are related to personal determinants.

Further, air pollution was closely associated with depression in 10 studies [e.g., (42, 45, 65, 77)], anxiety in 9 studies [e.g., (43, 70, 97, 101)], and stress in 2 studies (102, 103). Additionally, 6 studies found a correlation with sleep quality (53, 70, 104–107), insomnia (104) and sleep duration (106, 107), leading to reduced life satisfaction. Long-term effects of air pollution on well-being were significant, with chronic exposure leading to sustained mental health issues. This includes chronic depression and anxiety, highlighted by Orru et al. (108) and Ronaldson et al. (109).

Allergens and allergies (4 paper), can be a severe personal predisposition for a person’s quality of life, causing limited quality of life (physical activity) with differences between perennial and seasonal allergies (48, 110–113). Additionally, specific mental health aspects (47) such as stress (114), anxiety (48, 114), sleep quality disturbances (48, 113, 115), depression (48) and overall satisfaction with life were identified. The reported confounders in studies were inconsistent. Highlighted were sleep quality and duration with quality of life and cognitive outcomes (40, 82, 115), depression connected to cognition and schizophrenia (36, 79, 86) and satisfaction with life (quality of life) (46).

The data sources varied, as many (41 studies) relied on self-reported questionnaires [e.g., (45–47, 58, 65)]. This included general health questionnaires, behavioural and cognitive tests, depression and anxiety scales, sleep quality surveys, and other health-related questionnaires. Six studies used clinical tests and screenings to assess cognitive functions, mental health, and other health-related outcomes (57, 59, 85, 107) as well as the SF-12 Health Survey and the 31-item Rhinosinusitis Outcome Measure (RSOM-31) (110), the Paediatric Asthma Quality of Life Questionnaire (PAQLQ), Paediatric Asthma Caregiver’s Quality of life Questionnaire (PACQLQ) (111). Others utilised data from national surveys and statistic databases (29 studies) such as UK Biobank, Lithuanian Health Information Centre databases, Betula [e.g., (74, 79, 89, 109)]. Verheyen et al. (103) measured hair cortisol concentrations to assess stress levels and their correlation with environmental determinants.

Various studies revealed a multifaceted set of determinants. Studies included green space [e.g., (44, 47, 59, 82)], noise level (41, 45, 64, 70, 109), traffic noise annoyance (40, 95), traffic density (62), urbanization grade (42), and proximity to major roads (103). These variables were significant in determining the level of noise pollution, air quality, and the amount of green space. Dadvand et al. (59), Hiscock et al. (46) and Orru et al. (116) mentioned that the primary mode of commuting contributes, too.

Studies highlighted that education is linked to SES and influences lifestyle choices, which influence mental well-being (104). Years of education (42, 45, 64, 74, 82, 105) and educational qualification (115, 117) played a role in determining mental health outcomes. Smoking, alcohol intake, and proximity to fast-food outlets (101) was additionally related to lifestyle choices.

The living environment, including indoor air quality and housing conditions, was crucial to mental health. Studies indicated that living areas (urban/rural) (62, 65, 71, 86, 117), house value (42), household size (53, 61) and home satisfaction (46) were also significant environmental determinants.

Determinants such as physical activity [e.g., (40, 76, 79, 82, 104, 109, 118)], time spent outside (46, 85), and engagement in recreational activities (101) were salutogenic as they are related to stress relief, mood enhancement, and overall well-being. The amount and frequence of the addressed determinants can be seen in S5.

Numerous studies (16 studies) underscored the pivotal role of household income as an economic determinant [e.g., (42, 61, 119–121)]. Generaal et al. (41) indicated that lower-income individuals might face more severe adverse effects due to constraints in accessing cleaner living environments or protective resources. Interestingly, Oudin et al. (91) discovered an increased susceptibility to asthma linked to air pollution among children from higher-income families. Additionally, Abed Al Ahad et al. (119) and Shiue (71) highlighted financial situations as crucial, significantly influencing the impact of air pollution.

Critical social determinants identified by 16 studies in the context of air pollution’s impact on well-being included marital status (45, 59, 83, 84, 116) family-related factors such as household composition (46), family support (47), parental education (67, 83, 95, 98), siblings at birth (95), and ethnicity (40, 122). Likewise, SES and community-related factors like social cohesion and safety (41, 43), civil status (105), social benefits (41, 82) and social isolation (79) were highlighted. Shiue (71) further investigated the influence of social participation, the quality of social services, and the social exclusion index on the effects of air pollution on adult mental health. Collectively, these studies underlined the multifaceted nature of social determinants in mediating the mental health consequences of air pollution.

Personal determinants, like demographic attributes, including age, sex, and ethnicity, were identified as key influencers of individual responses to air pollution (44, 47, 58, 88, 122). Furthermore, low birth weight, health conditions, particularly chronic diseases and asthma, significantly modified the impact [e.g., (66, 86, 95, 104, 123)]. Individuals with pre-existing health issues were found to be more adversely affected (74, 108, 124). Lifestyle choices, such as alcohol consumption, smoking habits (65, 88, 107), and BMI (75, 76, 101, 103, 109, 118), also played a crucial role.

Genetic factors, notably the presence of the APOEϵ4 allele, is a well-defined risk factor for Alzheimer’s disease. In children with the APOEϵ4 allele, exposure to traffic-related pollution was linked to higher behavioral problem scores, less cognitive improvement, and smaller brain volumes (88). Adults exposed to air pollution showed reductions in grey matter volume and increases in white matter volume in areas associated with Alzheimer’s progression (64). Long-term exposure to PM_2.5 was also linked to an increased risk of dementia, regardless of APOE genotype (90). Additionally, a study involving the UK Biobank (74) found that a combination of air pollutants and the APOEε4 alleles increased the risk of dementia. Ethnicity did not significantly impact dementia risk factors, despite differences in APOE allele frequencies (79). Lastly, the impact of air pollution on cognitive function was partly mediated by lung function, with non-APOEε4 carriers showing a slightly higher mediation (86).

Other notable socio-individual determinants included neighbourhood-level, family SES [e.g., (58, 70, 77, 83, 85, 86, 100)], school/home/urban vulnerability index (41, 79, 90), and area-level deprivation index (51, 57, 65, 67, 103, 123), highlighting the importance of the broader socio-economic environment. Besides, Gao et al. (117) and Ma et al. (74) considered the Townsend deprivation index as significant contributor.

The primary outcomes from exposure to floods were the deterioration of quality of life, mental health in general for people affected (127, 129, 130, 134, 139–141), and the higher prevalence of stress-related symptoms, PTSD, anxiety and depression [e.g., (126, 136, 142–144)]. Lamond et al. (143) suggested, that “households experiencing one symptom frequently are more likely to experience other and multiple psychological impacts on a regular basis”, which refers to a high-level co-morbidity.

In studies exploring the impact of particular flood events, quality of life included various aspects (e.g. mental health, resilience, or well-being) and was assessed via different scales, such as the Satisfaction with Life Scale (125), the health-related EQ-5D-5L (145), or via questionnaires/surveys (138, 140, 141). Qualitative interviews (130, 139), art-based and participatory inquiries were used to explore experiences in-depth (127).

Stress, anxiety, PTSD, depression were investigated via questionnaires/surveys using different scales, e.g. Impact of Event Scale-Revised (IES-R) (131), PTSD Checklist civilian version (PCL-C) [e.g., (132, 136, 142)], Generalized Anxiety Disorder Scale (GAD-2) (136, 142, 144, 146) and the Patient Health Questionnaire (PHQ-2) for depression (e.g. 136, 146).

Most of the studies investigated the impacts of the flood after the event (few months or years retrospective). Motreff et al. (134) analyzed the acute effect of a storm event in France via psychotropic drug deliveries. Three weeks after the flood event, drug deliveries showed an increase. Bunyan et al. (33) suggested that a lack of understanding about the interplay between climate change and flooding contributed to concerns among residents, including children and adolescents. The temporal pattern of psychological distress was an essential aspect of understanding the impacts: Hieronimi et al. (139) noted “interviewees mentioned two points of time of greatest psychological stress: the time of the flooding, and the time when the physical exhaustion decreased, and the processing [ … ] began”.

Jermacane et al. (142), Bakic and Ajdukovic (125), and several others showed that negative effects of flooding can persist for years after the actual event, leading to increased risk of psychological morbidity, PTSD, depression, and anxiety among affected individuals. Mental health issues can continue for at least 3 years post-flooding [significantly for depression and PTSD (146)], with a decrease in prevalence over time. Respondents also reported experiencing problems with performing usual activities following flooding (145). Even six years after the flooding, “some young people in Simbach am Inn still struggle with recurring anxiety during heavy rain” (139). Displacement due to flooding also had a significant impact on mental health, with displaced individuals experiencing worse outcomes compared to those who remained at home (136). Especially children/adolescents who may struggle with detachment from familiar places and resources (material and social) suffer more (127). Even those not directly affected by flooding can experience increased psychological morbidity (144). O’Neill et al. (138) demonstrated that flood risk perception has more impact on the emotional component of floods than the actual location.

The three most frequently addressed determinants were education/learning (14 papers), housing/living (15 papers) and working (12 papers) (compare S5).

Results showed that level of education might have no or little influence on the outcome (e.g. 131, 136). Waite et al. (144) highlighted the disruption to education caused the increase of psychological morbidity in the disrupted population. Bakic and Ajdukovic (125) found that higher education was related to fewer symptoms of PTSD after the flooding. According to Bunyan et al. (33), respondents with lower levels of education were less likely to be concerned about flooding and climate change.

Since flooding has a profound adverse impact on people’s homes, housing and changes in living conditions were highly important determinants and tend to be rather pathogenic. Disaster Property Loss was scored in Wind et al. (132) as an indicator of the severity of the individual disaster experience concerning the damage and loss of possessions. Experiencing storm- or flood-related exposure and damage in the home increased the risk of having common mental disorder (126). This was in line with findings of Jermacane et al. (142) and Mulchandani et al. (146), showing that those experiencing persistent damage to their homes had greater odds of psychological morbidity. Determinants associated with increased odds included the depth of floodwater, loss of utility, or more than 24h of flooding of the home (144).

Walker-Springett et al. (129) indicated that flood events can alter residents’ sense of place as the once private home becomes less secure and no longer a place of refuge after being invaded by water. After repeated floods in Germany, Kuhlicke et al. (140) found that with each flood event experienced, households developed certain flood-related capacities. These helped them to reduce damage and restore buildings faster than after the previous flood. In their study, Bunya et al. (33) found that residents of Portsmouth living outside the city centre were less likely to perceive helplessness in tackling climate change and flooding, both individually and through local collective action. Poorer people were more prone to flood damage due to location of their homes (higher risk areas) and lack of ownership or insurance. They became more vulnerable to permanent dislocation or lack of resources for refurbishment (127). Employment status did not play a significant role in the outcome, rather it seemed to have an indirect deteriorating effect, in terms of financial difficulties (126).

Social determinants such as community and interpersonal resources, social support and perceived social support (125, 132, 133),, community resilience (130), dislocation, family separation (127, 139), social capital, collective efficacy (132, 133), social resilience, network and community, community support and community relation (129) were used. Personal determinants included were e.g., age, gender, individual resources, war veteran status, ethnical background, general health, previous illnesses, trust and personal resilience. Economic determinants covered mostly the household income and the level of deprivation (126).

Bunyan et al. (33) identified gender to play a multifaceted role in increasing concern and perception of risk about flooding and climate change for females. At the same time females were less likely to perceive themselves and the city council to be helpless. This is in line with Graham et al. (126) findings. Other rather pathogenic determinants besides being female were aged 16–24, living in a deprived neighbourhood, being in debt arrears and having poorer general health. Furthermore, females were at higher risk for peritraumatic and posttraumatic stress but had lower risk perception (131). Motreff et al. (134) similarly found women to be at higher risk for mental disorders after the storm event.

As Bakic and Ajdukovic (125) found, individual resources, community economic development, and trust in community leadership played an important role in improving community mental health after a disaster. Individual resilience and interpersonal resources (support from family, friends, and partners) had direct salutogenic effects on mental health and life satisfaction. Social capital, relational networks, perceptions of agency, self-efficacy and capacities for sense-making positively influenced post-flooding well-being. Existing social capital was important, nevertheless, flood events can result in new networks to form (129). Children and adolescents’ mental well-being was specifically dependent on the social support and their parents’ well-being, preparedness and coping mechanisms (127, 139).

Trust towards local community was also more helpful to recreate life after the event when there was less trust towards professional organized support, or it was not accessible (130). On the other side, fracturing community networks, separation due to displacements, loss of access to social services and capital showed to be pathogenic and evidently increased the prevalence of mental disorders [e.g., (132, 133)]. Interestingly, Wind et al. (133) found that social capital and the feelings of cohesiveness may protect against depression (salutogenic). However, participation in social structures may increase the prevalence of anxiety disorders. In addition, more social support was related to higher prevalence of depression.

Mental health deterioration was negatively correlated to household income (lower income levels) (143). Other determinants were the amount of timely (at least 12 h) warning before the flood events and displacement as being a protective salutogenic determinant against mental disorders (136). Concerns about health, problems with relationships and loss of sentimental items were associated with poor psychological outcomes (128).

The main outcomes addressed were PTSD, anxiety or depression after exposure to wildfires, primarily found through questionnaires (self-reported) or interviews. Examples of standardized tools used for these studies include: the Children’s Revised Impact of Event Scale (CRIES-13) (148); 25-item Strengths and Difficulties Questionnaire (SDQ), Quality of life test: Kidscreen-10 (150); or clinical psychiatric interviews protocols for anxiety disorders (151). The research was conducted maximum 6 months after wildfire exposure. Fonseca et al. (149) explored the psychometric properties of the Portuguese version of the Child Post-traumatic Cognitions Inventory, revealing that children and adolescents, exposed to a wildfire disaster, exhibited significantly higher scores on stress-related cognitions. Pereira et al. (150) further investigated the dimensionality and measurement invariance of the CRIES-13 in a sample of Portuguese children and adolescents. They found a link between wildfire events and increased stress, particularly PTSD symptoms. The Greek study by Papadatou et al. (148) highlighted adolescents, experiencing elevated levels of stress and depression. Pre-disaster life events and post-disaster losses contributed to higher symptoms of both PTSD and depression.

The most common determinants (see S5) included relocation, household damages, or household close to the flames. However, Papadatou et al. (148) stated that it was not the presence in the area or the proximity to the wildfire itself, which was associated with the prevalence of PTSD, it was the subjective threat of one’s own or significant others life and property. Other determinants were education and occupation (148–151). For firefighters seasonal employment was also a determinant (151).

Most studies (147–150) identified age and gender as determinants in the connection to wildfires. Theleritis and colleagues (151) found that coping strategies like risk minimisation or blame (e.g., blaming others or the government for the events) were associated with greater likelihood of PTSD in firefighters. Other determinants included perceived social support, which simultaneously was associated with a higher prevalence of PTSD symptoms (greater perceived social support), decrease in depression symptoms (lower perceived social support) and previous experience with wildfires (148). Children seemed to have higher stress and poorer life quality if they perceive themselves as fragile, which could cause maladaptation and lower prosocial behaviour (149).

Studies highlighted outcomes such as: reduced quality of life/well-being, years of life loss, mortality (152, 156), increase in heat stress (153–155), psychosis (157), depression and mania (158), cognition problems like dementia (157) and emotional problems (159). The outcome data source varied from national survey, statistical data (4 studies), hospital records (3 studies), and self-reported questionnaires (3 studies). Most studies (8) did not investigate biological mechanisms. Three studies discussed that uncomfortable weather conditions during summer can trigger psychiatric hospitalizations (158). Others reported linkages between serotonin production and exposure to bright sunlight and the role of sleep as mediator between some meteorological factors and psychiatric admissions (160). Specifically, this study associated more continuous sleep patterns with high barometric pressure, less precipitation, and lower temperatures. Exposure to temperature extremes was positively associated with a reduced life expectancy (152) or likewise in daily years of life disparities (156) and relative risk of death (157). High temperatures were also associated with an increasing risk of mental health hospitalizations (160, 161). The association of adverse outcomes with other meteorological stressors was less significant, inconclusive or inconsistent (160), although an association between barometric pressure and mania was found (158). Further Mikutta et al. (124) suggested that external meteorological stressors, such as foehn winds, could alter the mental status of the most vulnerable populations.

10 of 16 papers addressed determinants but only 4 discussed their role (see S5). Interestingly, the reported determinants varied across the studies. Specifically, McWilliams et al. (160) analysed the effects of determinants related with the working conditions and work dynamics, while Pogacar et al. (153) analyzed the number of hours spent working outdoor and educational level. Page et al. (157) analyzed the effects of living conditions, particularly the residential region. Finally, Peisker (159) analyzed physical surroundings, educational level, employment condition and presence of children in households. Results suggested that alcohol consumption increases the possibility to suffer from mental diseases, and the level of education and working-class occupation is positively associated with higher environmental concerns. The number of hours spent working outdoor did not seem as relevant. More important for the negative outcome was the potential and degree of outdoor workers acclimatization.

The most common personal determinants were sex/gender and age (10), one study included the presence of chronic diseases (153). Social determinants were family dynamics (160), share of age under 35, urban population and urban community (159). Peisker (159) considered also economic determinants and found that gross domestic product, energy prices, consumer price inflation, unemployment rate, industrial greenhouse gas intensity and income inequality were identified as drivers of concern. Benmarhnia et al. (156) analyzed the effects of neighborhood SES. Age was positively correlated with a higher risk of adverse outcomes from temperature extremes, but also with reduced environmental concerns. One study (157) also suggested that people with mental illness represent a vulnerable group during heat, particularly younger patients with alcohol and substance misuse living in specific environmental settings. Furthermore, previous medication intake, impacting the physiological homeostasis can be related to higher mortality and morbidity in the context of temperature extremes (161). Environmental concerns rise also in correspondence with working class, SES and educational level, and type of environmental surroundings, which significantly increased the vulnerability to extreme temperatures. Messeri et al. (154) analyzed the ethnic differences in heat stress perception and received information about heat protection by native and migrant workers in Italy.