Drivers of Urban Growth: Cellular Automata–Markov–Analytic Hierarchy Process Modeling of Land Use Change in Amman City, Jordan

2026-01-14T04:24:03+00:00

Over the past two decades, rapid urban growth significantly altered land-use patterns in Amman, raising critical concerns regarding sustainability and food security. This study utilized an integrated Cellular Automata–Markov (CA–Markov) model, in combination with the Analytical Hierarchy Process (AHP), to simulate land-use and land-cover (LULC) changes and project future scenarios for 2031 and 2040. The CA–Markov model quantified temporal land-use transitions and simulated spatial growth patterns, while AHP served as a multi-criteria decision-making tool to determine the relative influence of key driving factors on urban growth. Landsat imagery from 2004, 2013, and 2022 was classified into three main categories: built-up areas, agricultural land, and barren land. The simulation framework incorporated key driving factors, including GDP per capita, population density, road accessibility, elevation, and slope. Model validation against actual 2022 LULC data yielded a high accuracy of 91.4% and a Kappa index of 0.89, demonstrating the reliability of the predictive framework. The results projected that built-up areas would increase from 257.35 km² (32.3%) in 2022 to 309.18 km² (38.9%) in 2031 and 349.17 km² (43.9%) by 2040, accompanied by a consistent decline in both agricultural and barren lands. Spatial analysis revealed that districts with higher population density, intense economic activity, and superior road accessibility were particularly susceptible to rapid urbanization. These findings highlighted the urgent need for proactive urban planning policies to protect agricultural land and manage growing infrastructure demands. While the CA–Markov model effectively replicated historical patterns, its reliance on past trends limited its capacity to anticipate sudden policy shifts or environmental shocks. Future research should prioritize integrating higher-resolution datasets, such as QuickBird imagery and detailed cadastral or infrastructure data, to improve the spatial accuracy of LULC simulations. In addition, the development of policy-driven and scenario-based models should incorporate urban growth boundaries, agricultural land protection policies, and transportation expansion plans. This would enable more realistic forecasting of land-use dynamics and provide stronger decision-support tools for resilient and sustainable urban development.

Occupational Exposure to Engineered Nanomaterials: Pathways, Risk Assessment and Regulations

2026-05-04T01:47:05+00:00

Occupational exposure to engineered nanomaterials (ENMs) has emerged as a critical concern due to their unique physicochemical properties, which influence their behavior, bioavailability, and toxicity. This review synthesizes current knowledge on occupational exposure pathways, risk assessment strategies, regulatory frameworks, and key challenges associated with ENMs. Occupational exposure occurs predominantly during manufacturing and handling processes, with inhalation identified as the primary route, although dermal and incidental ingestion pathways are also relevant. Exposure characterization remains limited, particularly across the full lifecycle of nano-enabled products, as transformation processes such as dissolution, aggregation, and surface modification can alter exposure profiles. Advances in risk assessment have led to the development of control banding tools, Bayesian networks, weight-of-evidence frameworks, and computational models such as nano-quantitative structure–activity relationship (nano-QSAR) models. Grouping and read-across strategies have also been proposed to address data gaps and reduce testing requirements. However, these approaches remain constrained by insufficient standardized data, variability in dose metrics, and limited regulatory acceptance. Existing lifecycle–based decision support systems offer promising integrated frameworks but remain dependent on data availability and methodological harmonization. This review integrates occupational exposure pathways, emerging risk assessment methodologies, and regulatory developments into a unified lifecycle-oriented perspective. It further offers a critical perspective on how predictive modeling, grouping strategies, and safe-by-design concepts can collectively support preventive rather than reactive nanosafety governance. Despite regulatory progress in the European Union, the United States, and the Asia-Pacific regions, inconsistencies in definitions, data requirements, and nanospecific provisions continue to hinder global harmonization and effective risk management of ENMs.

Environmental Research and Planetary Health

Drivers of Urban Growth: Cellular Automata–Markov–Analytic Hierarchy Process Modeling of Land Use Change in Amman City, Jordan

Occupational Exposure to Engineered Nanomaterials: Pathways, Risk Assessment and Regulations