Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 μg/m3 to 73 μg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

Outcome of single-stage transanal endorectal pull through for short segment Hirschsprung's disease in neonates and infants

Hirschsprung's disease (HD) is developmental disorder of the enteric nervous system. Diagnosis can be confirmed by rectal biopsy. The surgical management of HD includes so many procedures. The purpose of this study was to evaluate the out come of single-stage transanal endorectal pull-through operation for short segment HD in neonates and infants. This prospective study was carried out in the Department of Pediatric Surgery, Dhaka Shishu (Children) Hospital, Dhaka, Bangladesh during the period from January 2004 to December 2009. The study included neonates & infants (age 1 day to 1 year) of both sex, who were clinically suspected and biopsy proven HD with barium enema suggesting rectosigmoid HD. We excluded patients with short segment HD who had associated anomalies, and with hugely dilated proximal colon, operated cases with less than 6 months' follow up. During study period, single-stage transanal endorectal pull through (TEP) operation done in 68 neonates and infants patients (8 excluded) of short segment HD. The mean operative time, postoperative hospital stay, followed up time were 120 minutes, 7.94 days and 12 months respectively. Transeverse colostomy was needed in 4 patients because of anastomotic leakage and they were complicated with anastomotic stenosis, however managed by progressive dilatation. Overall parents' satisfaction was achieved. Single-stage primary transnanl endorectal pull-through for short segment HD is feasible in neonates and infants. It preserves all the advantages of minimally invasive surgery. TEP is a better option in terms of comfort, operative success, postoperative morbidity, cosmetic appearance and parents' satisfaction.