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Understanding coastal communities' awareness and risk perceptions of climate change impact is essential in developing effective risk communication tools and mitigation strategies to reduce the vulnerability of these communities. In this study, we examined coastal communities' climate change awareness and risk perceptions of climate change impact on the coastal marine ecosystem, sea level rise impact on the mangrove ecosystem and as a factor affecting coral reefs and seagrass beds. The data were gathered by conducting face-to-face surveys with 291 respondents from the coastal areas of Taytay, Aborlan and Puerto Princesa in Palawan, Philippines. Results showed that most participants (82%) perceived that climate change is happening and a large majority (75%) perceived it as a risk to the coastal marine ecosystem. Local temperature rise and excessive rainfall were found to be significant predictors of climate change awareness. Sea level rise was perceived by most participants (60%) to cause coastal erosion and to affect the mangrove ecosystem. On coral reefs and seagrass ecosystems, anthropogenic drivers and climate change were perceived to have a high impact, while marine livelihoods had a low impact. In addition, we found that climate change risk perceptions were influenced by direct experiences of extreme weather events (i.e., temperature rise and excessive rainfall) and climate-related livelihood damages (i.e., declining income). Climate change risk perceptions were also found to vary with household income, education, age group and geographical location. The results suggest that addressing poverty and effectively communicating climate change risks can improve climate change awareness and risk perceptions.

The aim of this study is to obtain knowledge about which cultivable bacterial species are present in indoor air in homes, and whether the concentration and diversity of airborne bacteria are associated with different factors. Measurements have been performed for one whole year inside different rooms in five homes and once in 52 homes. Within homes, a room-to-room variation for concentrations of airborne bacteria was found, but an overlap in bacterial species was found across rooms. Eleven species were found very commonly and included: Acinetobacter lowffii, Bacillus megaterium, B. pumilus, Kocuria carniphila, K. palustris, K. rhizophila, Micrococcus flavus, M. luteus, Moraxella osloensis and Paracoccus yeei. The concentrations of Gram-negative bacteria in general and the species P. yeei were significantly associated with the season with the highest concentrations in spring. The concentrations of P. yeei, K. rhizophila and B. pumilus were associated positively with relative humidity (RH), and concentrations of K. rhizophila were associated negatively with temperature and air change rate (ACR). Micrococcus flavus concentrations were associated negatively with ACR. Overall, this study identified species which are commonly present in indoor air in homes, and that the concentrations of some species were associated with the factors: season, ACR and RH.

Tracking and measuring national carbon footprints is key to achieving the ambitious goals set by the Paris Agreement on carbon emissions. According to statistics, more than 10% of global transportation carbon emissions result from shipping. However, accurate tracking of the emissions of the small boat segment is not well established. Past research looked into the role played by small boat fleets in terms of greenhouse gases, but this has relied either on high-level technological and operational assumptions or the installation of global navigation satellite system sensors to understand how this vessel class behaves. This research is undertaken mainly in relation to fishing and recreational boats. With the advent of open-access satellite imagery and its ever-increasing resolution, it can support innovative methodologies that could eventually lead to the quantification of greenhouse gas emissions. Our work used deep learning algorithms to detect small boats in three cities in the Gulf of California in Mexico. The work produced a methodology named BoatNet that can detect, measure and classify small boats with leisure boats and fishing boats even under low-resolution and blurry satellite images, achieving an accuracy of 93.9% with a precision of 74.0%. Future work should focus on attributing a boat activity to fuel consumption and operational profile to estimate small boat greenhouse gas emissions in any given region.

Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City, Taytay and Aborlan, and facilitate future predictions for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988-2020 were used for this research. The support vector machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2693 ha) decrease was recorded during 1988-1998 and an 8.6% increase in 2013-2020 to 4371 ha. In Puerto Princesa City, a 95.9% (2758 ha) increase was observed during 1988-1998 and 2.0% (136 ha) decrease during 2013-2020. The mangroves in Taytay and Aborlan both gained an additional 2138 ha (55.3%) and 228 ha (16.8%) during 1988-1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov chain model in the context of ecological sustainability involving policy intervention. However, as this research did not capture the environmental factors that may have influenced the changes in mangrove patterns, it is suggested adding cellular automata in future Markovian mangrove modelling.

The impact of the coronavirus 2019 (Covid-19) pandemic on different countries and populations is well documented in quantitative studies, with some studies showing stable mental health symptoms and others showing fluctuating symptoms. However, the reasons behind why some symptoms are stable and others change are under-explored, which in turn makes identifying the types of support needed by participants themselves challenging. To address these gaps, this study thematically analysed 925 qualitative responses from five open-ended responses collected in the UCL-Penn Global COVID Study between 17 April and 31 July 2021 (Wave 3). Three key themes that comprised 13 codes were reported by participants across countries and ages regarding the impact of Covid-19 on their health, both mental and physical, and livelihoods. These include: (1) Outlook on self/life, (2) Self-improvement, and (3) Loved ones (friends and family). In terms of support, while 2.91% did not require additional support, 91% wanted support beyond financial support. Other unexpected new themes were also discussed regarding vulnerable populations suffering disproportionately. The pandemic has brought into sharp focus various changes in people's mental health, physical health and relationships. Greater policy considerations should be given to supporting citizens' continued access to mental health when considering pandemic recovery.

Instilling a collaborative approach can widen participation to a range of stakeholders, enabling the diffusion of sustainability and increasing local capacity to meet decarbonisation targets to mitigate against climate change. Dingle Peninsula 2030 has emerged as an international case study of a collaborative regional sustainability project, whereby a wide range of initiatives, beyond the initial remit of the project, have emerged in the area. This holistic scale of action is required for effective climate action. Using the Sustainable Development Goals (SDGs) as a framing, the interrelated nature of climate action has been shown through this study. In setting out to undergo energy projects a wide range of new initiatives emerged as community members became engaged in the process. Initiatives have emerged related to energy, transport, agriculture, education, tourism and employment, in what we have coined the 'diffusion of sustainability'.

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual's health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from Wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalisable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). To do so, data from Wave 2 of the UK lockdown (n = 263) was used to conduct a graphical inspection of the week-by-week distribution of self-perceived loneliness scores. In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between weeks 3 and 7 of Wave 1 of the UK national lockdown. Furthermore, although the sample size by week in Wave 2 was too small to have a meaningful statistical insight, a graphical U-shaped distribution between weeks 3 and 9 of lockdown was observed. Consistent with past studies, these preliminary results suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

In this study we surveyed families' experiences with parental depression, stress, relationship conflict and child behavioural issues during 6 months of the coronavirus (Covid-19) pandemic through the Covid-19: Global Social Trust and Mental Health Study. The current analyses used data collected from online surveys completed by adults in 66 countries from 17 April 2020 to 13 July 2020 (Wave I), followed by surveys 6 months later at Wave II (17 October 2020-31 January 2021). Analyses were limited to 175 adult parents who reported living with at least one child under 18 years old at Wave I. Parents reported on children's level of externalising and internalising behaviour at Wave I. At Wave II, parents completed self-reported measures of stress, depression and inter-partner conflict. Child externalising behaviour at Wave I significantly predicted higher levels of parental stress at Wave II, controlling for covariates. Child internalising behaviour at Wave I did not predict parental stress or depression, controlling for covariates. Neither child externalising nor internalising behaviour predicted parental relationship conflict. The overall findings demonstrate that child behaviour likely influenced parental stress during the Covid-19 pandemic. Findings suggest that mental health interventions for children and parents may improve the family system during times of disaster.