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This research investigates the impact of sea level rise (SLR) on the Indus Delta, a vital ecosystem increasingly vulnerable to climate change repercussions. The objective of this study is to comprehensively assess the flooded areas under various shared socioeconomic pathway (SSP) scenarios based on the Intergovernmental Panel on Climate Change's (IPCC) 6th Assessment Report. The study employs a GIS-based bathtub model, utilizing historical (1995–2014) and IPCC-projected (2020–2150) tide gauge data from Karachi, Kandla, and Okha stations to identify potential inundated areas threatened by coastal flooding. Additionally, it analyzes LANDSAT-derived multispectral images to identify coastal erosion hotspots and changes in the landscape. A supervised random forest classifier is used to classify major landforms and understand alterations in land cover. Furthermore, neural network-based cellular automata simulations are applied to predict future land cover for 2050, 2100, and 2150 at risk of inundation. The results indicate that under different SSP scenarios, the estimated inundated land area varies from 307.36 km2 (5 % confidence on SSP1-1.9) to 7150.8 km2 (95 % confidence on SSP5-8.5). By 2150, the region will lose over 550 km2 of agricultural land and 535 km2 of mangroves (mean SLR projection). This work emphasizes identifying sensitive land cover for SLR-induced coastal flooding. It might fuel future policy and modeling endeavors to reduce SLR uncertainty and build effective coastal inundation mitigation methods.

Abstract The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5–2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data.

This study evaluated the concentrations of tropospheric nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and Aerosol Index (AI) as well as different meteorological parameters, including atmospheric temperature (AT), relative humidity (RH), and wind speed (WS) in nine Nigerian cities: Aba, Benin, Ibadan, Kaduna, Kano, Lagos, Onitsha, Port Harcourt, and Umuahia. The role of socioeconomic activities on air pollution was also investigated using nighttime light radiance as a surrogate measure. Welch's one-way ANOVA test was performed for the variance study to evaluate the concentration dynamics of the selected tropospheric air pollutants and their association with nighttime light radiance. The mean concentration of CO and AI was highest in Kano, while that of O3 was the lowest. A similar pattern was also observed in the spatiotemporal transition of the selected pollutants and the nighttime light during the study period. Further, the correlation analysis revealed that AT is positively correlated with NO2 except in Aba and Benin cities, while it is positively correlated with CO and AI for all cities. The principal component analysis (PCA) revealed that O3 and RH have strong negative factor loadings, whereas CO, AI, and AT have strong positive factor loadings. This study will guide future researchers by associating the level of pollution with socioeconomic activities, especially during the absence of permanent air quality stations.

The Red Sea, a significant ecoregion and vital marine transportation route, has experienced a consistent rise in air pollution in recent years. Hence, it is imperative to assess the spatial and temporal distribution of air quality parameters across the Red Sea and identify temporal trends. This study concentrates on utilizing multiple satellite observations to gather diverse meteorological data and vertical tropospheric columns of aerosols and trace gases, encompassing carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). Furthermore, the study employs the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze the backward trajectory of air mass movement, aiding in the identification of significant sources of air pollutants. A principal component analysis (PCA) with varimax rotation is applied to explore the relationship and co-variance between the aerosol index (AI), trace gas concentrations, and meteorological data. The investigation reveals seasonal and regional patterns in the tropospheric columns of trace gases and AI over the Red Sea. The correlation analysis indicates medium-to-low positive correlations (0.2 < r < 0.6) between air pollutants (NO2, SO2, and O3) and meteorological parameters, while negative correlations (−0.3 < r < −0.7) are observed between O3, aerosol index, and wind speed. The results from the HYSPLIT model unveil long-range trajectory patterns. Despite inherent limitations in satellite observations compared to in situ measurements, this study provides an encompassing view of air pollution across the Red Sea, offering valuable insights for future researchers and policymakers.