In the last two decades, developments in the area of biomineralization has yielded promising results making it a potentially environmentally friendly technique for a wide range of applications in engineering and wastewater/heavy metal remediation. Microbially Induced Carbonate Precipitation (MICP) has led to numerous patented applications ranging from novel strains and nutrient sources for the precipitation of biominerals. Studies are being constantly published to optimize the process to become a promising, cost effective, ecofriendly approach when compared with the existing traditional remediation technologies which are implemented to solve multiple contamination/pollution issues. Heavy metal pollution still poses a major threat towards compromising the ecosystem. The removal of heavy metals is of high importance due to their recalcitrance and persistence in the environment. In that perspective, this paper reviews the current and most significant discoveries and applications of MICP towards the conversion of heavy metals into heavy metal carbonates and removal of calcium from contaminated media such as polluted water. It is evident from the literature survey that although heavy metal carbonate research is very effective in removal, is still in its early stages but could serve as a solution if the microorganisms are stimulated directly in the heavy metal environment.

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

Microbial-induced carbonate precipitation (MICP) is gaining attention as an eco-friendly and sustainable method for concrete crack repair. However, key challenges related to its large-scale implementation, regulatory approval, and integration into existing construction standards remain underexplored. This review examines recent advances in MICP, emphasizing its role in circular economy practices and sustainable building solutions. Traditional synthetic sealants contribute to environmental pollution and have limited long-term durability, highlighting the need for greener alternatives. Global research trends reveal an increasing focus on self-healing materials, biomineralization, and durability enhancement, alongside emerging innovations such as encapsulation technologies, marine applications, and bio-based composites. Unlike previous reviews, this study integrates bibliometric analysis to systematically assess research trends, identify key collaboration networks, and evaluate regulatory challenges that impact MICP adoption. While MICP offers significant advantages, including self-healing capabilities and compatibility with industrial by-products, barriers related to cost, scalability, and policy integration persist. This review identifies critical thematic clusters which include microbial action, sustainability, and engineering applications. This helps to provide actionable insights for researchers, engineers, and policymakers. By fostering interdisciplinary collaboration, MICP has the potential to become a transformative solution for resilient and environmentally sustainable infrastructure.

This comprehensive study aimed to evaluate the water quality and sediment contamination in the Chuhe River in Nanjing. The spatial assessment of 10 samples collected in September highlighted that, in surface water, Copper (Cu) > Nickel (Ni) > Zinc (Zn) > Chromium (Cr) > Lead (Pb) > Arsenic (As) > Cadmium (Cd) > Mercury (Hg), whereas in sediments, Zn > Cr > Cu > Pb > Ni > As > Cd > Hg. The coefficient of variation (CV) for Ni and Zn in surface water was >15, whereas As, Cu, Pb, and Ni had a CV that was higher than 15 in sediments, indicating variability in contamination sources. The Pollution Load Index values ranged between 2.16 and 3.05, reflecting varying contamination levels across samples. The Geoaccumulation Index data also showed moderate-to-considerable contamination, especially for elements such as Cd and Cu. Correlation analyses in water and sediments unearthed significant relationships, with notable links between Cu and Pb in the water and strong correlations between As and Cu and between Cr and Ni in sediments. In sediments, Total Nitrogen and Phosphorus were significantly correlated with As, Cu, Pb, and Ni. The Potential Ecological Response Index for sediments indicated that they are at medium to high risk (307.47 ± 33.17) and could be potentially detrimental to aquatic life in the tributary. The tributary, influenced by agricultural runoff, residential areas, and other anthropogenic activities, showed that despite Nemerow pollution index values for water samples being below 1, sediment analysis indicated areas of concern. Principal Component Analysis (PCA) was conducted to identify the potential sources of heavy metal contamination. In surface water, shared negative loadings on PC 1 (60.11%) indicated a unified influence, likely from agricultural runoff, while PC 2 (14.26%) revealed additional complexities. Sediments exhibited a unique signature on PC 1 (67.05%), associated with cumulative agricultural impacts, with PC 2 (18.08%) providing insights into nuanced factors, such as sediment composition and dynamic interactions. These findings offer a complete insight into the Chuhe River tributary’s condition, underlining the urgency for ongoing monitoring and potential remediation measures.

Climate change continues to increase the intensity, frequency and impacts of weather and climate extremes. This work uses bias-adjusted Coupled Model Intercomparison Project Phase six (CMIP6) model datasets to investigate the future changes in temperature extremes over Mediterranean (MED) and Sahara (SAH) regions. The mid- (2041–2070) and far-future (2071–2100) are studied under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5 scenarios. Quantile mapping function greatly improved the performance of CMIP6 by reducing the notable biases to match the distribution of observation data, the Climate Prediction Center (CPC). Results show persistent significant warming throughout the 21st century, increasing with the increase in radiative forcing. The MED will record a higher increase in temperature extremes as compared to SAH. The warming is supported by the projected reduction in cold days (TX10p) and cold nights (TN10p), with the reduction in the number of cold nights exceeding cold days. Notably, warm spell duration index (WSDI) and summer days (SU) have a positive trend in both timelines over the entire study area. There is a need to simulate how climate sensitive sectors, such as water and agriculture, are likely to be affected by projected changes under different scenarios for informed decision making in the choice and implementation of adaptation and mitigation effective measures.