The influence of regional climate change (CC) on agricultural production variance in the Mediterranean region has been discussed based on the assessment of the last decade. Most of the Mediterranean region has experienced frequent natural disasters, expanding population, increase in temperature, and increase in the surface of the Mediterranean Sea. Furthermore, the temperature in the Mediterranean area is rising 25% faster than the rest of the globe, and in the summer, it is warming 40% faster than the global average. Climate change can alter the food supply, restrict access to food, and degrade food quality. Temperature rises, changes in precipitation patterns, changes in severe weather events, and decreased water availability, for example, might all result in lower agricultural production. The fact that most Mediterranean nations rely on imported basic foodstuffs adds to the severity of the situation. Instability and insecurity of agricultural supply in the region might lead to massive population movement, transforming most Mediterranean nations into a global source of instability. Based on the experience of similar geographical locations, the article has highlighted the essential elements affecting crop productivity and the five domains of water, ecosystems, food, health, and security. Despite the region’s complexity, the Mediterranean region has been offered an overall assessment that predicts the best strategy for the best solution. Such an attempt describes a methodical integration of scientific discoveries to understand better the combined hazards illustrated by the fact that CC has affected food production, resulting in widespread insecurity. Utilizing current technologies in agricultural production has been recommended to support regional nations in reaching higher yields. The significance of this study could be realized by mitigating climatic shocks through a sustainable food production system to accomplish development goals in vulnerable nations.

During the COVID-19 pandemic, key policies aimed at reducing exposure to the virus from social distancing, restrictions on travel through to strongly enforced lockdowns. However, COVID-19 restrictions required people to spend more time at home so the exposure to air pollutants shifted to being derived from that of domestic interiors, rather than outdoors or the workplace environment. This study aims to characterise the influence of lockdown intervention on the balance of indoor and outdoor PM2.5 exposure in a Malaysian suburb. We also calculate the potential health risk from exposure to both indoor and outdoor PM2.5 to give context to personal exposure assessment in different microenvironments during the COVID-19 lockdown, known locally as Movement Control Orders (MCO). The implementation of the MCOs slightly reduced daily average of outdoor PM2.5 concentrations (median of 12.63 μg m-3 before and 11.72 μg m-3). In the Malaysian apartment considered here, cooking led to a substantial increase in exposure from increasing concentrations in PM2.5 during a COVID-19 lockdown (maximum average concentration at 52.2 µg m-3). The estimated excess risk to health was about 25% for lung cancer from staying indoor. Thus, there seems a potential for greater exposure to fine particles indoors under lockdown, so it is likely premature to suggest that more lives were saved through a reduction of outdoor pollutants than lost in the pandemic. Unfortunately, little is known about the toxicity of indoor particles and the types of exposures that result where people increase the amount of time they spend working from home or staying indoors, especially during periods of lockdown.

Malaysian tropical forest vegetation cover type has experienced a continuous degradation and defragmentation during the past two decades. This study examined the tropical forest land cover density and conditions using Normalised Difference Vegetation Index (NDVI) observations derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries specifically in Pahang, Malaysia. Prior to classification, the images were geometrically corrected to a common map projection. A set of 1294 random spatially distributed points was analysed to investigate the accuracy assessment of the NDVI classification. The results show that 96% of the total area were classified as the forest cover type in 2002 and only 87% in 2015. It was found that a total of 8.9% (0.22 mHa – 250m) and 6.6% (0.16 mHa – 1km) forest were degraded from the study area, mostly happens at the southern region. The four images classifications are having 70 – 90 % accuracy (Kappa coefficient = 0.4 to 0.5) and shows that imagery with higher resolution (250-m) has a better assessment. In conclusion, MODIS imagery can obtain information about the forest vegetation and can be more broadly applicable with various method combinations for forest degradation investigation.