Abdul Ghafoor Nizamani

• Alazawi, Amer Abdullah; Alchalabi, Oday Qusay Abdulqader; Alhafody, Ashraf Ibrahim & Nizamani, Abdul Ghafoor (2026). Aesthetics of Interruption: Professional Disconnection and Façade Transformation in Post-2017 Mosul Residential Design. Architecture. 6(3), s. 103–103. doi: 10.3390/architecture6030103. Fulltekst i vitenarkiv Vis sammendrag

  Post-conflict reconstruction research has examined façade materiality and symbolism, yet the process conditions under which aesthetic specifications are systematically overridden during construction remain neglected. This study investigates why designed architectural aesthetics fail to survive implementation in post-2017 Mosul, Iraq. A mixed-methods design combined formal visual analysis of 12 recently completed residential façades with a structured survey of 45 practicing architects. Survey data reveal that designers are excluded from construction supervision in 76% of projects and that clients intervene in material and color selection in 70% of cases. Visual analysis identifies a sophisticated design language—orthogonal massing articulated through contrasting materials—that is rarely realized in built form. Where designers retain supervisory authority, projects most consistently achieve material–form coherence. The study advances the concept of an aesthetics of interruption (the systematic degradation of designed form–material relationships through the fragmentation of professional authority during construction). Exclusion produces four distinct pathologies: material substitution, execution degradation, language override, and ornamental hollowing. The findings demonstrate that aesthetic degradation in post-conflict reconstruction stems not from design incapacity but from broken process structures. Safeguarding architectural quality requires contractual frameworks mandating designer supervision and material-substitution protocols that protect design intent.

• Nizamani, Abdul Ghafoor; Torgersen, Geir & Edvardsen, Magdalena (2026). Building-Integrated Green Walls in Cold Climate: Operational Requirements and Maintainability. SSRN Electronic Journal. doi: 10.2139/ssrn.7518015. Fulltekst i vitenarkiv Vis sammendrag

  Green walls are increasingly integrated into building envelopes, but their long-term operation depends on maintaining both living and technical components. This study examines maintenance practices, operating-cost evidence and post-installation responsibility for building-integrated green-wall systems in Norway. An exploratory multiple-case study used six built green-wall projects, four semi-structured interviews with professionals in maintenance, supply, municipal practice and research, and case-specific documentation supplemented by relevant standards, certification and policy material. Operational requirements varied substantially between systems and settings. Exposed outdoor systems followed a marked seasonal cycle involving irrigation shutdown, spring recommissioning, vegetation maintenance and system inspection, whereas the indoor case operated year-round. Across the documented cases and interview examples, maintenance frequencies ranged from approximately every two weeks to one or two scheduled visits per year. Wall-specific cost data were limited and heterogeneous, and operating figures could not be compared reliably per square metre because service scope, access requirements, system complexity, scale and seasonal workload differed between projects. Interviews further indicated that practical operating knowledge often remained with suppliers and specialist providers while longer-term responsibility shifted towards owners and building operators. Maintainable access to technical components, usable operation and maintenance documentation, monitoring arrangements and post-installation service were therefore important during the transition to routine operation. The findings indicate that maintainability is partly determined by decisions made during design and procurement. Irrigation design, seasonal operation, maintenance access, expected operating requirements and transfer of technical knowledge should therefore be considered alongside installation requirements.

• Nizamani, Abdul Ghafoor; Pregnolato, Maria & Torgersen, Geir (2026). Operational stormwater performance of building integrated green walls | Research Square. Research Square. ISSN 2693-5015. doi: 10.21203/rs.3.rs-10841067/v1. Fulltekst i vitenarkiv Vis sammendrag

  Green walls offer a building-integrated option for managing roof runoff in dense urban areas, but their operational retention depends on wall architecture, wall-to-roof ratio and pre-storm storage. We combined full-scale experiments on tank-type and porous-substrate walls with a parsimonious two-...

• Nizamani, Abdul Ghafoor; Pregnolato, Maria & Torgersen, Geir (2026). Assessing the Stormwater Retention Capacity of Green Walls: lab experiments and modelling. SSRN Electronic Journal. doi: 10.2139/ssrn.7268562. Fulltekst i vitenarkiv Vis sammendrag

  Vertical green infrastructure could help manage roof runoff in dense cities, but reported retention values from individual storms are difficult to transfer because wall architecture, antecedent wetness and measurement methods are rarely considered together. This study combines full-scale green wall experiments with a parsimonious event model and design-scenario analysis for roof-runoff-fed systems. A two-zone bucket model was parameterised and evaluated against observations from the Green Sponge Buildings Laboratory for two contrasting modular wall architectures: a tank-type wall (GW1) and a porous-substrate wall (GW2). The model represents wall-specific retention capacity, drainage onset and detention routing. It reproduced retained volumes with Nash–Sutcliffe efficiency ≈ 0.99, R² ≈ 0.99 and percent bias ≈ -0.9% for GW1, and Nash–Sutcliffe efficiency ≈ 0.97, R² ≈ 0.98 and percent bias ≈ +2.1% for GW2. Using Oslo 60-min intensity–duration–frequency design storms as a demonstration case, dry-start retention was governed mainly by wall storage capacity and wall-to-roof ratio. Latin Hypercube sampling (N = 500) identified the derived pre-storm storage fraction as the dominant explanatory state variable, with single-predictor R² values of 0.967 for GW1 and 0.968 for GW2. For the 10-year design storm at a wall-to-roof ratio of λ = 1.0, dry-start assumptions overstated median retention by 89 percentage points for GW1 and 30 percentage points for GW2. Green walls can therefore contribute to urban stormwater management, but their operational effectiveness depends on wall architecture, wall-to-roof ratio and antecedent wetness. Green wall performance claims and design guidance should therefore explicitly report wall architecture, wall-to-roof ratio and antecedent storage condition.

• Nizamani, Abdul Ghafoor; Vu, Dung Thi; Nizamani, Rafeeque Ahmed & Li, Cunrong (2026). Physics-informed neural network calibration and application for urban cooling. Sustainable Cities and Society (SCS). ISSN 2210-6707. 147, s. 107525–107525. doi: 10.1016/j.scs.2026.107525. Fulltekst i vitenarkiv
• Nizamani, Abdul Ghafoor; Vu, Dung Thi; Nizamani, Rafeeque Ahmed & Torgersen, Geir (2026). Urban resilience through nature-based solutions in an era of environmental extremes: A global systematic analysis. Sustainable Cities and Society (SCS). ISSN 2210-6707. 139. doi: 10.1016/j.scs.2026.107192. Fulltekst i vitenarkiv
• Nizamani, Abdul Ghafoor; Vu, Dung Thi; Nizamani, Rafeeque Ahmed & Li, Cunrong (2025). Smart Urban Cooling: Mapping and Mitigating Heat Islands with Physics-Informed Machine Learning. SSRN Electronic Journal. doi: 10.2139/ssrn.5954787. Fulltekst i vitenarkiv Vis sammendrag

  Mitigating the adverse effects of urban heat islands (UHI) is critical for enhancing climate resilience, public health, and energy sustainability in cities. Traditional modelling approaches, however, often face a trade-off between physical fidelity and the flexibility to integrate diverse geospatial data. This paper presents a novel Physics-Informed Neural Network (PINN) framework that bridges this gap by embedding the heat diffusion equation directly into a neural network, ensuring predictions adhere to thermodynamic principles while learning from complex urban data patterns. The framework includes a systematic data-fusion workflow that unifies multi-format geospatial inputs, such as land surface temperature, vegetation indices, and vector-based urban morphology, into a cohesive gridded structure, enabling scalable and reproducible urban climate analysis. Validated through a case study of Paris, the model achieves exceptional predictive accuracy (R2 of 0.9741, RMSE = 0.5019°C) and reliably simulates the city’s thermal patterns. Beyond diagnosis, it functions as a practical scenario-planning tool, quantifying the cooling effects of green infrastructure interventions such as green roofs. By integrating physical simulation with data-driven analytics, this research provides urban planners and policymakers with a scientifically rigorous, scalable resource to support the design of smarter, cooler, and more sustainable cities.

• Nizamani, Abdul Ghafoor & Torgersen, Geir (2025). Climate as the Primary Moderator: Towards Context-Driven Design and Implementation of Vertical Greenery Systems for Stormwater Management. Environmental Research Letters. 20(12). doi: 10.1088/1748-9326/ae2b86. Fulltekst i vitenarkiv Vis sammendrag

  Urbanisation intensifies stormwater management challenges by expanding impervious surfaces, increasing flood risk and degrading water quality. Vertical Greenery Systems (VGS) are increasingly promoted as nature-based solutions for space-constrained cities, yet their performance remains highly variable and context-dependent. This systematic review, conducted in accordance with PRISMA guidelines and synthesising evidence from 34 peer-reviewed studies, demonstrates that VGS hydrological efficacy is not a universal constant but is fundamentally moderated by local climatic conditions. Climate acts as the primary filter, shaping system function across tropical, temperate, Mediterranean, arid, and cold zones, each presenting distinct opportunities and constraints. Beyond climate, performance is refined by system typology (green façades versus living walls), plant functional traits (notably root architecture), and substrate composition, which together determine trade-offs between runoff delay, retention, and water quality improvement. The review further reveals a stark divergence in adoption barriers: in the Global North, high costs, regulatory complexity, and data gaps create institutional inertia, whereas in the Global South, limited awareness, misaligned priorities, and weak policy frameworks relegate stormwater management to a low priority. To address these challenges, we propose a five-pillar implementation framework centred on climate-responsive design protocols, advanced economic valuation of co-benefits, adaptive governance, long-term monitoring, and context-sensitive community engagement. This review provides actionable, evidence-based guidance for integrating VGS as resilient, equitable, and multifunctional components of sustainable urban water management, tailored not to global templates, but to local environmental and socio-institutional realities.

• Vu, Dung Thi; Nizamani, Rafeeque Ahmed & Nizamani, Abdul Ghafoor (2025). Smart Urban Design with Physics-Informed Neural Networks: Quantifying Temperature Reductions from Green Infrastructure Using Satellite Thermal Data. EarthArXiv preprint platform. doi: 10.31223/x51t9d. Fulltekst i vitenarkiv Vis sammendrag

  Urban Heat Islands (UHIs), characterised by elevated temperatures in densely built environments, pose critical challenges to urban sustainability, public health, and energy resilience. Mitigating UHIs requires precise quantification of the cooling effects of green infrastructure; however, existing models often fail to integrate high-resolution geospatial data with physical laws. This study presents a novel Physics-Informed Neural Network (PINN) framework that addresses this gap by unifying satellite-derived data, including land surface temperature (LST), normalised difference vegetation index (NDVI), and emissivity, with vector-based urban features within a single predictive model. The framework innovatively embeds thermodynamic principles, such as heat diffusion equations, directly into the neural network's loss function, while a seamless GeoJSON (Geographic JavaScript Object Notation) to tensor format enables the integration of heterogeneous geospatial datasets. Applied to case studies in Bologna, Italy, and Washington, D.C., USA, the model accurately identified optimal locations for green infrastructure. A targeted intervention prioritising green roofs on buildings near railways in identified LST hotspots achieved a 99% reduction in the spatial extent of identified hotspots in Bologna and 98.7% in D.C., with an area-weighted average temperature decrease of 1.31°C and 1.30°C, and maximum localised cooling of 6.13°C and 8.48°C, respectively. A more comprehensive scenario incorporating green roofs near railways and new trees along main roads yielded even greater cooling, with hotspot reductions of 97.4% and 98.3%, and area-weighted average temperature drops of 1.51°C and 1.80°C for the two cities, respectively. Validation against Landsat 8 thermal imagery confirmed high predictive accuracy, with R-squared values of 0.926 and 0.937, demonstrating performance superior to conventional machine learning baselines. By bridging physics-constrained machine learning with geospatial analytics, this work provides a scalable, data-driven tool for smart urban design, offering actionable insights to combat UHIs through targeted, climate-adaptive planning.

• Nizamani, Abdul Ghafoor (2022). The impacts of conventional energies on environmental degradation: does Pakistan’s economic and environmental model follow the Kuznets curve? Environmental Science and Pollution Research. ISSN 0944-1344. doi: 10.1007/s11356-022-22611-x. Fulltekst i vitenarkiv
• Nizamani, Abdul Ghafoor (2021). Influences of the optimized air curtain at subway entrance to reduce the ingress of outdoor airborne particles. Energy and Buildings. ISSN 0378-7788. doi: 10.1016/j.enbuild.2021.111028. Fulltekst i vitenarkiv

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