Although the vulnerability assessment of forest parks is used to determine the threats they face, a rapid and holistic framework has not been established well. The primary objective of this study is to adopt a framework for rapid assessment of forest parks vulnerability, examined in Lavizan forest park in Tehran (Iran) as the case study. The vulnerability assessment has been conducted, using the evaluation matrix on the basis of landscape and ecological values and threats. In this model, the most important values and threatening factors of the Lavizan forest park have been identified and assessed, based on the intensity of their effect as well as occurrence probability. Finally, this article proposes five strategies to reduce the vulnerability. Results from this research indicate that the most important values have been air purifcation, wildlife, flora and fauna species, environment regulation, mental health, and scientific resources and the most important threats have included reduction of habitat diversity, intensive exploitation of the resources, fire, woodcutting, and reduction of ecological connectivity. Based on these vulnerabilities, the most important strategies propose the use of affordance strategy formulation framework to preserve and enhance ecological and landscape values of the park.

Ambient particles with aerodynamic diameter <0.1 μm (PM₀.₁) have been suggested to have significant health impact. However, studies on the association between long-term PM₀.₁ exposure and human blood lipid metabolism are still limited. This study was aimed to evaluate such association based on multiple lipid biomarkers and dyslipidemia indicators. We matched the 2006–2009 average PM₀.₁ concentration simulated using the neural-network model following the WRF-Chem model with the clinical and questionnaire data of 15,477 adults randomly recruited from 33 communities in Northeast China in 2009. After controlling for social demographic and behavior confounders, we assessed the association of PM₀.₁ concentration with multiple lipid biomarkers and dyslipidemia indicators using generalized linear mixed-effect models. Effect modification by various social demographic and behavior factors was examined. We found that each interquartile range increase in PM₀.₁ concentration was associated with a 5.75 (95% Confidence interval, 3.24–8.25) mg/dl and a 6.05 (2.85–9.25) mg/dl increase in the serum level of total cholesterol and LDL-C, respectively. This increment was also associated with an odds ratio of 1.25 (1.10–1.42) for overall dyslipidemias, 1.41 (1.16, 1.73) for hypercholesterolemia, and 1.90 (1.39, 2.61) for hyperbetalipoproteinemia. Additionally, we found generally greater effect estimates among the younger participants and those with lower income or with certain behaviors such as high-fat diet. The deleterious effect of long-term PM₀.₁ exposure on lipid metabolism may make it an important toxic chemical to be targeted by future preventive strategies.

As the typical range of influence of oil spills surrounds urbanised and economically active areas, it is likely that fragile regions may not be part of the most vulnerable zones. This premise is remediated in this paper with the adoption of a vulnerability approach based on the integration of static and dynamic information, such as oil pollution susceptibility. Susceptibility is a poorly consolidated term and is often used as synonym for environmental sensitivity; it is considered here to be the distribution areas of oil slicks. To test the proposed approach, an integrated estimation of environmental vulnerability is carried out for an environmentally sensitive area in the south of Brazil by merging static data inherent to the medium with information of a dynamic nature related to trajectory, behaviour and the fate of oil at sea. Moreover, the oil pollution intensity and environmental sensitivity data in susceptible areas are addressed. Subsequently, the environmental vulnerability is estimated by integrating hazard maps, concentrations and losses of the mass of the oil slick, oil beaching time and the littoral sensitivity index hierarchy. Results will prove to be useful to highlight critical areas for which the highest levels of severity are expected, which can lead to improvements in decision-making processes to support oil-spill prevention, as well as improve response readiness, especially in developing countries that have historically under-protected their sensitive regions.

Based on the archival data on oil facilities, oil spill incidents, and environmental conditions, we researched the plausible causes of oil spill disasters in the Niger Delta of Nigeria between 2006 and 2019. The data were analyzed for geospatial and statistical patterns, using ArcGIS and R programming platforms, respectively. A fuzzy logic algorithm was employed to generate three oil spill disaster models (hazard, vulnerability, and risk). Ordinary Least Square algorithm was adopted to model the relationships between oil spill and two sets of predictor variables: oil facilities (oil well, flow station, and pipeline) and disaster models. We found that, during the 23 years, the Niger Delta experienced 7940 oil spill incidents, of which 67% occurred onshore. A total of 4,950, 501, 855 episodes were attributed to sabotage, corrosion, and equipment failure, with 87%, 62%, and 45% occurring onshore, respectively. Besides, 81% of the 5320 onshore oil spill cases were attributed to sabotage, while corrosion and equipment failure accounted for mere 6% and 7% of the incidents, respectively. The estimated average risk index (R = 0.20) shows that the risk of an oil spill disaster in the Niger Delta is low. Whereas, 5% of the region is characterized by a high risk of oil spill disaster. Furthermore, the regression model infers that the oil spillages exhibit a positive relationship with disaster models and oil facilities at α = 0.10. However, only 16% of the incidents were explained by disaster models, while the oil facilities account for 23% of the total cases, indicating the influence of other factors. To avert further socio-environmental damage in the Niger-Delta, oil theft and sabotage should be curbed, polluted areas are remediated, and an all-inclusive socio-economic development is prioritized.

Few studies have analyzed the health effects of temperature variability (TV) accounting for both interday and intraday variations in ambient temperature. In this study, TV was defined as the standard deviations of the daily minimum and maximum temperature during different exposure days. Distributed lag non-linear Poisson regression model was used to examine the city-specific effect of TV on mortality in 31 Chinese municipalities and provincial capital cities. The national estimate was pooled through a meta-analysis based on the restricted maximum likelihood estimation. To assess effect modification on TV-mortality association by individual characteristics, stratified analyses were further fitted. Potential effect modification by city characteristics was performed through a meta-regression analysis. In total, 259 million permanent residents and 4,481,090 non-accidental deaths were covered in this study. The effect estimates of TV on mortality were generally increased by longer exposure days. A 1 °C increase in TV at 0–7 days' exposure was associated with a 0.60% (95% CI: 0.25–0.94%), 0.65% (0.24–1.05%), 0.82% (0.29–1.36%), 0.86% (0.42–1.31%), 0.98% (0.57–1.39%) and 0.54% (−0.11-1.20%) increase in non-accidental, cardiovascular, IHD, stroke, respiratory and COPD mortalities, respectively. Those with lower levels of educational attainment were significantly susceptible to TV. Cities with dense population, higher mean temperatures, and relative humidity and lower diurnal temperature ranges also had higher mortality risks caused by TV. This study demonstrated that TV had considerable health effects. An early warning system to alert residents about large temperature variations is recommended, which may have a significant impact on the community awareness and public health.

Ozone concentration spatial patterns remain largely uncharacterized across the extensive wilderness areas of the Sierra Nevada, CA, despite being downwind of major pollution sources. These natural areas, including four national parks and four national forests, contain forest species that are susceptible to ozone injury. Forests stressed by ozone are also more vulnerable to other agents of mortality, including insects, pathogens, climate change, and ultimately fire. Here we analyze three years of passive ozone monitor data from the southern Sierra Nevada and interpolate landscape-scale spatial and temporal patterns during the summer-through-fall high ozone concentration period. Segmentation analysis revealed three types of ozone exposure sub-regions: high, low, and variable. Consistently high ozone exposure regions are expected to be most vulnerable to forest mortality. One high exposure sub-region has been documented elsewhere as being further vulnerable to increased drought and fire potential. Identifying such hot-spots of forest vulnerability has utility for prioritizing management.

Seagrasses are globally important, but their extent is decreasing due to the impact of human activities and changing climatic conditions. Seagrass meadows provide vital services, but their condition and distribution are not yet well known, particularly in many small tropical Pacific islands. In 2018, observations and samples were collected from intertidal seagrasses of Efate Island, Vanuatu (South Pacific). Observations included canopy height, percentage cover, growth rate and species variety. Water quality samples were also collected in some locations. Our seagrass metrics suggested more challenging conditions for the seagrasses near Port Vila, the main urban area in the island, where water quality analyses indicated higher levels of dissolved inorganic nitrogen and suspended solids.

Increasing maritime traffic in the Arctic has heightened the oil spill-related risks in this highly sensitive environment. To quantitatively assess these risks, we need knowledge about both the vulnerability and sensitivity of the key Arctic functional groups that may be affected by spilled oil. However, in the Arctic these data are typically scarce or lacking altogether. To compensate for this limited data availability, we propose the use of a probabilistic expert elicitation methodology, which we apply to seals, anatids, and seabirds. Our results suggest that the impacts of oil vary between functional groups, seasons, and oil types. Overall, the impacts are least for seals and greatest for anatids. Offspring seem to be more sensitive than adults, the impact is greatest in spring, and medium and heavy oils are the most harmful oil types. The elicitation process worked well, yet finding enough skilled and motivated experts proved to be difficult.

Jakarta Bay, next to the Jakarta Metropolitan Area with around 30 million inhabitants, is facing extreme pollution. Although local coral reefs are degraded and marine resources heavily exploited, they provide livelihoods for millions of people. This study investigates anthropogenic pressures on local fisheries resources and associated livelihoods. Questionnaire surveys were conducted in 15 coastal communities (10 coastal neighborhoods in Jakarta Bay on the mainland and 5 of the offshore Thousand Islands). The most economically valuable species were Caesio cuning (Redbelly yellowtail fusilier) on the islands and Rastrelliger kanagurta (Indian mackerel) on the mainland. Over 80% of all interviewed fishermen regarded the current state of marine resources as declining, mainly due to pollution and overexploitation. While perceptions of declining resources were equally high on the islands and the mainland, pollution was listed as the principal cause of degradation significantly more on the mainland. Findings are discussed in the context of coastal livelihood vulnerability.

This work gives an assessment of the hazard faced by Sicily coasts regarding potential offshore surface oil spill events and provides a risk assessment for Sites of Community Importance (SCI) and Special Protection Areas (SPA). A lagrangian module, coupled with a high resolution finite element three dimensional hydrodynamic model, was used to track the ensemble of a large number of surface trajectories followed by particles released over 6 selected areas located inside the Sicily Channel. The analysis was carried out under multiple scenarios of meteorological conditions. Oil evaporation, oil weathering, and shore stranding are also considered. Seasonal hazard maps for different stranding times and seasonal risk maps were then produced for the whole Sicilian coastline. The results highlight that depending on the meteo-marine conditions, particles can reach different areas of the Sicily coast, including its northern side, and illustrate how impacts can be greatly reduced through prompt implementation of mitigation strategies.