Roof Types Research Articles

Investigation of the causes of land surface temperature changes in Khorramabad

development of remote sensing applications has led to their use in a wide range of environmental studies. One of these aspects is urban studies and especially land surface temperature. In this study, the changes in land surface temperature in the Khorramabad city in Iran were investigated and the causes of land temperature changes were investigated. For this purpose, Landsat satellite images were processed in four periods of 2000, 2007, 2014 and 2021 and were recovered using a single-channel surface temperature algorithm. Temperatures were high in 2000 due to the type of roofs of buildings and the dirt around the city. Decreased in 2007 and 2014 due to the use of roofs that reflect light. In 2021, due to severe manipulations around the city and the destruction of vegetation and change it into built-up bare soil cover caused the temperature to rise again in the suburbs.

CFD Simulations of Snowdrifts on a Gable Roof: Impacts of Wind Velocity and Snowfall Intensity

Roof structures are suffering serious threats caused by unbalanced snow distribution, especially long-span spatial structures, such as gable roofs. The formation of unbalanced snowdrifts on the gable roof is affected by the meteorological condition and the drifting snow. This study was conducted to explore the snowdrift characteristics on gable roofs under different snowfall conditions based on a new Eulerian–Eulerian multiphase approach. To consider the diffusive process of snow in different states, the governing equations of air and snow phases were modified separately according to the actual transport process. Additional terms based on the deposition/erosion process were inserted into the governing equations to consider the processes of snow particles being trapped or ejected by snow surface. The feasibility of the new model for the snowdrift was validated by comparing with a field observation. Then, the snowdrifts characteristics on typical gable roofs were investigated under different wind velocity and snowfall intensity conditions. The formation mechanism of snowdrifts and the influence of meteorological conditions on snowdrifts were clarified by analysis. The results show that the uneven distribution of snow on the gable roof becomes more significant with the increase in wind velocity. Furthermore, the distribution of snow on the roof tends to be more even in the case of heavier snowfall.

A mathematical model for the energy stored in green roofs

A simple mathematical model to estimate the energy stored in a green roof is developed. Analytical solutions are derived corresponding to extensive (shallow) and intensive (deep) substrates. Results are presented for the surface temperature and energy stored in both green roofs and concrete during a typical day. Within the restrictions of the model assumptions the analytical solution demonstrates that both energy and surface temperature vary linearly with fractional leaf coverage, albedo and irradiance, while the effect of evaporation rate and convective heat transfer is non-linear. It is shown that a typical green roof is significantly cooler and stores less energy than a concrete one even when the concrete has a high albedo coating. Evaporation of even a few millimetres per day from the soil layer can reduce the stored energy by a factor of more than three when compared to an equivalent thickness concrete roof.

A method for global potential assessment of roof integrated two-stage solar concentrators (TSSCs) at district scale

This paper proposes a core method for integrating two-stage solar concentrators (TSSCs) as roof-integrated energy supply systems at the district scale. However, the performance of these systems not only depends on good configurations but also on optimal building typologies. The design process of buildings-integrated TSSCs on a district scale reflects a complex multi-criteria problem, where several conflicting concerns from multiple stakeholders need to be addressed. Thus, the proposed method aims to support the multi-criteria decision-making process in the early-stage design of sustainable districts utilizing TSSC technology. The method contributes to the design of the TSSCs relative to district designs. Our method enables design generation according to a set of decision variables related to the district (roof type and slope, orientation, building height, width, and length), and TSSC (type, geometric ratio, separation distance between mirrors, modules number, and solar cell size). The method uses a parametric modeling approach combined with a multi-objective optimization algorithm (NSGA-II) that enables design optimization of the district and TSSCs in two consecutive steps. The two-step design optimization allows finding optimal district designs for maximizing cumulative direct normal irradiance (DNI) and minimizing the total energy demand, and TSSC designs for maximizing average monthly load match index (av.LMI) (a ratio of energy yield to demand) and minimizing the average covered roof area occupied by modules. We validated the proposed method in an illustrative case study of ‘Buckower Felder’, a district in the city of Berlin (Germany). The illustrative application shows that our method enables the performance-driven, generative design of both; district and building-integrated TSSCs and searches for the most appropriate solutions i.e., urban designs for maximum DNI and minimum demand, and TSSC designs for maximum av.LMI and minimum covered roof area. Results indicate that there is a trade-off between objectives, where district design with high DNI (>4500 kW/m2/h/month) reflects high energy demand (8.6e + 05 kWh/month). Similarly, TSSC designs ensuring high av.LMI (>1.0) require a large roof area (>47 %). Given these trade-offs, the method can meaningfully support the decision-making process. Thus, the method allows for large-scale integration of TSSCs with buildings at an urban scale and supports solar energy planning and energy transition policies for sustainable districts.

Comparative Lifecycle Analysis of Sri Lankan Non-Conventional Roofing Materials

Building materials, especially roofing products, play a major role as they are essential for any building construction. Knowledge of the environmental performance of building materials is vital when building life cycle assessment. However, only a few studies have been conducted in Sri Lanka in this regard. The present study focuses on the environmental life cycle assessment of two different non conventional roofing materials used in Sri Lanka. Acrylonitrile Styrene Acrylate roofing sheets and Calicut-type clay roofing tiles have been selected for the study. In order to measure and calculate environmental impacts of both types of roofing materials, field data was collected quantitatively and noted as per ISO14040 and ISO14044. Using the Cradle-to-Gate LCA technique and OpenLCA software, the environmental impacts as midpoint and endpoint categories were evaluated. As the main output of the study, environmental performance of these two roofing materials were compared, the hotspots of the manufacturing process and the causes were also identified. . Accordingly, the global warming potential of Acrylonitrile Styrene Acrylate roofing sheets and Calicut-type clay roofing tiles is 13.5 kgs of CO2 eq. and 8.95 kgs of CO2 eq. respectively. Further, comparison of the endpoint categories showed the resource depletion was most impactful, indicating 0.814012 points and0.65305 points, respectively for the two roofing material types. Further, kiln firing was identified as the environmental hotspot contributing most to the endpoint and midpoint impact categories for clay roofing tiles, while mixing materials by hot mixer was the hotspot for Acrylonitrile Styrene Acrylate roofing sheets. In addition, the overall results demonstrated how the production process of clay tile influences more than the Acrylonitrile Styrene Acrylate roofing sheet on both the midpoint as well as the endpoint impact categories. KEYWORDS: Acrylonitrile Styrene Acrylate, Calicut tiles, Cradle-to-Gate, End-pint impacts, Mid-point impacts

Reassessment of the potential for centralized and distributed photovoltaic power generation in China: On a prefecture-level city scale

The successful development of solar energy primarily depends on the scientific and effective evaluation of the photovoltaic power generation potential. This study re-estimated the installed potential of centralized large-scale and distributed small-scale photovoltaic power stations in 449 prefecture-level cities in China based on a geographic information system and Google Earth Engine combined with Baidu map data and related geographic information data. The factors considered in selecting the areas suitable for photovoltaic power generation were economy, terrain, environment for the centralized stations; illumination time, roof type, and shadow obstacles of the building for distributed systems. The results showed that a total area of 583 thousand km2 in China can be utilized to install solar panels, with a technical potential of approximately 55.1 TW. In particular, the technical potential of the centralized power stations was approximately 42.8 TW, primarily distributed in Naqu, Tibet and Haixi, Qinghai. The potential of the distributed systems was approximately 12.3 TW (including 4.9 TW for flat roof, 3.5 TW for pitched roof, and 3.9 TW for building facade), primarily concentrated in Yulin and Guigang, Guangxi; and Meizhou, Guangdong. The findings can clarify the spatio-temporal distribution of photovoltaic potential nationwide and provide decision support for regional planning.

Advanced form finding of cable roof structures integral with supporting frames: Numerical methods and case studies

Traditional form-finding methods usually assume that the boundary conditions of cable roofs are fixed constraints without considering the influence of the supporting frame. However, the cable roof interacts with the supporting frame and they work together as a whole. Pre-tensioning for cables will cause the deformation of the supporting frame toward the center, which will lead to the deviation between the actual initial equilibrium state and the target design result, especially the interconnection between the cable roof and the flexible supporting frame. To solve this problem, the interaction mechanism between the cable roof and supporting frame was first analyzed theoretically in this study. A novel form finding method of cable roof structures integral with supporting frames was proposed. The parameters that affect the form-finding result were analyzed, including the prestress, the stiffness of the supporting frame, and the stiffness of the cable. For the convenience of engineering applications, this method was developed using the ABAQUS software and was compiled in the Python language. According to different types of cable roofs, two form-finding schemes and two algorithmic procedures were proposed. The form-finding results of six typical cases were presented. The novel method was confirmed to be feasible and may be valuable for engineering applications.

Biochar granulation, particle size, and vegetation effects on leachate water quality from a green roof substrate.

Biochar, due to its favourable physiochemical properties, has been promoted as an ideal substrate additive on green roofs, with potential benefits to hydrological function. However, biochar is susceptible to water erosion, which may result in biochar loss and water pollution. The use of granulated biochars or biochars in large particle sizes could potentially alleviate biochar erosion loss, but effects on leachate quality have not been investigated. Also, biochar type and particle size influence plant performance, and effects on discharge quality may vary with vegetation. We assessed the effects of unprocessed and granulated biochars at five (0.25-0.5mm, 0.5-1mm, 1-2mm, 2-2.8mm, 2.8-4mm) and four (1-2mm, 2-2.8mm, 2.8-4mm, and 4-6.3mm) particle size ranges, respectively, on leachate quality on a typical green roof substrate, with presence and absence of vegetation (Agastache foeniculum - a drought-tolerant native forb). We evaluated integrated leachate quality using the CCME Water Quality Index (WQI). Unprocessed biochars reduced nutrient leaching due to increased water retention capacity (WRC) and total porosity. In contrast, granulated biochars, although showing less pronounced mitigation of nutrient leaching, reduced total suspended solids (TSS) and improved WQI in leachate due to enhanced plant performance. In addition, small biochar particles better reduced nutrient leaching and particle loss than large biochar particles, possibly due to increased WRC and formation of water-stable aggregates. The presence of vegetation generally reduced the leaching of nutrients and TSS, consistent with plant nutrient uptake and root substrate stabilization. However, plant biomass was correlated with increased total N leaching, likely due to litter inputs and rapid litter decomposition. We conclude that applications of granulated biochars may best improve discharge quality from green roofs through sorption effects and by enhancing plant performance.

Review on inorganic pollutants in stormwater runoff of non-metal roofs

Metal roofs have always been in the focus of stormwater runoff contamination. However, other roofing materials are also suspected of releasing metals and other inorganic substances with stormwater runoff. Hence, this review focuses on the impact of commonly used non-metal roofs - vegetated and non-vegetated - on stormwater runoff quality. Results from 42 studies were compiled and assessed to gain an overview of substances in runoff from nine roofing types with a special focus on green roofs. Concentrations of 27 substances including nutrients, heavy metals, and other inorganic substances were compared. Results show that the nine roof types that were assessed can be a potential source for inorganic substances in the runoff. Threshold values for groundwater protection are exceeded especially for PO43-, Cu, Pb, and Zn for some roofing materials. As the concentrations vary strongly for different parameters, no roofing material can be identified as clearly superior or inferior to the others. Gravel roofs act as a sink for NH4+ and can retain some heavy metals. Elevated heavy metal concentrations were found in runoff from wood shingle roofs treated with preservative chemicals and in runoff from most roofing types usually due to the used gutter materials. Green roof runoff shows increased concentrations of Ptot, PO43-, Na, K, Ca, and Mg. The concentrations depend strongly on the green roof age, the growth substrate, and applied fertilizers. In addition to the roofing materials, external factors dependent on the location (rural or urban site) influence the runoff quality. Runoff from the analyzed roofs must be seen as a diffuse source of environmental pollution and requires appropriate treatment before it is released into the environment or used for further applications. Overall, there are only a few studies on the topic so it is not possible to make statistically significant statements. More serious in-depth studies are urgently needed.

Polycyclic aromatic hydrocarbons in harvested rainwater in Warri and Agbarho, Nigeria

ABSTRACT. The aim of this research was to provide an overview of current knowledge, by assessing the possible contamination sources of harvested rainwater by polycyclic aromatic hydrocarbons (PAHs), its effects and possible human health risk assessment. Random sampling technique was used to collect eight rainwater samples from different roofing materials made from (asbestos, zinc, aluminium and thatch) in Warri and Agbarho (AGB). The samples were extracted using a liquid-liquid extraction (LLE) system according to the established procedures of United States Environmental Public Agency (USEPA). Under the optimized conditions, the ranges of extraction recoveries and solid-phase extraction (SPE) clean-up of the PAHs in the harvested rainwater were in the range of 71-90%. Gas chromatography-mass spectrometry (GC-MS) was used for the determination of the sixteen priority PAHs in the samples. The total concentration of PAHs for each of the roof types in Warri Refinery Petrochemical Company (WRPC) are: 0.036±0.012, 0.047±0.020, 0.045±0.018 and 0.0023±0.008 ppm for aluminium, zinc, asbestos and thatch roof, respectively. In addition, total concentration of PAHs for each of the roof types in AGB are: 0.046±0.020, 0.024±0.009, 0.023±0.008 and 0.021±0.007 ppm for aluminium, zinc, asbestos and thatch roof. The PAHs analysed exceeded the United States environmental protection agency (USEPA) standards of contamination.
 
 KEY WORDS: AGB, GC-MS, Harvested Rainwater, LLE, PAHs, WRPC
 Bull. Chem. Soc. Ethiop. 2022, 36(4), 779-789. 
 DOI: https://dx.doi.org/10.4314/bcse.v36i4.5

Assessment of ethmoid roof and olfactory fossa variations using Keros classification in a tertiary care hospital

BackgroundFunctional endoscopic sinus surgery is a common surgery done in otorhinolaryngology, but it is also riddled with complications. The most dangerous complication is the injury to the skull base leading to cerebrospinal fluid leak, meningitis or damage to intracerebral vessels. Lateral lamella of the cribriform plate is the most common site of injury. Keros described the depth of the olfactory fossa in detail and divided them into three types.This study aims to assess the anatomical variations of type of ethmoid roof and depth of olfactory fossa as per Keros classificationMethodsDescriptive study where CT scans were evaluated from the radiology department from 29 September 2018 to 20 May 2020. All the CT films were assessed according to the observation checklist.ResultsOur study included 256 patients. Males constituted 54.3% of patients, and 45.7% of patients were females. Type 2 Keros was the most common followed by type 1 and type 3. Type 3 was seen more in males, and it was statistically significant too.ConclusionThe study evaluated the depth categorization of the ethmoid roof and its variations on the basis of age and gender. In our study, the most common type was Keros type 2, but there was a noteworthy difference between the two sides in the same individual. Therefore, extra care must be taken during surgery to note the type of Keros on each side.

Influences of zinc loads in urban catchment runoff: Roof type, land use type, climate and management strategies

The contribution of ecotoxic dissolved metals from metallic roofs into urban waterways is a global issue. Identifying the specific origin of dissolved metals is critical to enabling appropriate stormwater management approaches that can provide the intended outcome of cleaner urban waterways. An event load pollutant model, Modelled Estimates of Discharges for Urban Stormwater Assessments (MEDUSA2.0), was used to predict the zinc load contributed from individual roof surfaces, under a wide range of rainfall conditions. Zinc was chosen as the pollutant of most concern given the extensive area of zinc-based roof surfaces, and the prevalence and mobility of zinc within urban waterways. The model categorized each roof by surface material and condition, and was run for individual rain events across multiple years to illustrate the influences on zinc loads from both surface type and rainfall conditions. Scenarios of future management were also assessed through the model to compare their benefits in terms of load reductions against the current baseline loadings. To understand how the load prediction and scenario modelling can provide valuable guidance for stormwater management decision-makers, the model was applied to a large urban catchment in Christchurch, New Zealand. Seven representative subcatchments of the varying proportions of industrial, commercial and residential land use type were also modelled to compare zinc loads generated. Results showed that an individual catchment's composition of roof types was the main driver of zinc load generation rather than the catchment's land use type. The modelled management scenarios demonstrated that reductions of 30% zinc could be achieved by changing only 4–13% of a subcatchment's unpainted zinc-based roof surfaces.

Using heritage building registers to characterise unreinforced masonry buildings of Brisbane, Australia

ABSTRACT Worldwide, unreinforced masonry (URM) buildings are present in abundance with many holding cultural and heritage value. URM buildings have a known history of poor performance during past earthquakes because of the weak tensile properties and lack of mechanical connections between elements. Information about the construction of URM buildings for a region is important because it can help engineers and policy makers to perform seismic assessment, risk and loss studies. In this regard, this study includes a characterisation of the heritage URM buildings in Brisbane City, Australia, and its surrounding suburbs using the Queensland Heritage Register (QHR). The QHR includes a total of 1775 heritage sites, of which 323 are identified as URM buildings in Brisbane City and surrounding suburbs. The studied building characteristics obtained from QHR included the architectural styles, construction year, function/use, number of storeys, roof material and load-bearing wall materials. Other features such as roof types (shapes), plan geometry, isolated or interconnected buildings, verandas and openings, and finally the presence of seismic hazards such as gable-end walls, chimneys, and parapets have been obtained using online 3D maps. It was concluded that heritage registers in combination with online digital resources can be successfully utilised to characterise URM buildings.

Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments.

Analysis of combined low-level indicators for the hot-season performance of roof components

A single performance indicator, the solar transmittance factor (STF), has been proposed in previous works, together with the derived solar transmittance index (STI). It is aimed at evaluating the summer performance of the roofing system and allowing the selection of the most effective mix of surface and mass properties. It is easily calculated from low-level indicators such as U-value, module of periodic thermal transmittance, and solar reflectance. In the present work, the correlation between STF and the cooling energy demand, integrated over a reference period, was studied, as well as the peak of ceiling temperature increase with respect to the indoor temperature, relevant for thermal comfort. In particular, the thermal behavior of different roof types with variable insulation was calculated numerically by TRNSYS 17 for a wide set of locations and environmental conditions. Unlike other commonly used indicators, to which the analysis has been extended, a strong correlation with STF was found for both cooling energy demand and ceiling temperature rise.

Screening criteria of underground coal gasification (UCG): a case study from Mangunjaya Area, South Sumatra Basin, Indonesia

Underground coal gasification (UCG) is a technology that can increase the value of coal. This technology turns coal into gas below the surface through the gasification process. UCG offers many applications for power generation, heating, and the chemical industry. The Mangunjaya area is one prospecting area, located in the South Sumatra Basin, which has been actively producing coal for a long time. This study focused on determining the potential location of UCG in Mangunjaya. Screening criteria were extracted from previous studies including coal thickness, depth of the coal seam, depth and thickness ratio, coal rank, the dip of a coal seam, roof and floor rock type and thickness, and geological structure. Thirteen boreholes were examined to determine their potential, namely MJ1 to MJ13. The evaluation results show that the Mangus coal seam at a depth of 260,4 meters in the MJ2 borehole and the surrounding area in 15 km2, matches the criteria and has the most potential coal for UCG development.

A Guideline for Developing Resilient Office Buildings using Nanotechnology Applications

Resilient buildings have become a widely researched topic in the engineering discipline. A building has enabled connectivity between building systems, including air conditioning, ventilation, safety and security system. They are even more important with growing pandemic risks and the spread of airborne viruses such as Covid-19. The literature indicates that nanotechnology applications can have a great role in the designing resilient office buildings. Nanotechnologies play a major role in architectural design; building materials combined with nanotechnology became smaller, lighter, and more efficient than conventional materials. Thus, this research aims to develop design guidelines for resilient office buildings in Egypt using nanotechnology applications. The methodology applied includes a review on nanotechnology and resilient office buildings key design features, following a simulation using design builder program where conducted to modify the nanomaterials in resilient office buildings recorded through quantified data. After applying the nanotechnology concept on walls, the roof and glazing types by inserting nanomaterials. The research concluded that the use of vacuum insulation panel, Nano-gel glass and Nano-painting had the highest impact on decreasing the total energy consumption by 14.5% and carbon dioxide emissions by 42%.

Mapping Asbestos-Cement Corrugated Roofing Tiles with Imagery Cube via Machine Learning in Taiwan

Locating and calculating the number of asbestos-cement corrugated roofing tiles is the first step in the demolition process. In this work, archived image cubes of Taiwan served as the fundamental data source used via machine learning approach to identify the existence of asbestos-cement corrugated roofing tiles with more than 85% accuracy. An adequate quantity of ground-truth data covering all the types of roofs via aerial hyperspectral scan was the key to success for this study. Twenty randomly picked samples from the ground-truth group were examined by X-ray refraction detection to ensure correct identification of asbestos-cement corrugated roofing tiles with remote sensing. To improve the classifying accuracy ratio, two different machine learning algorithms were applied to gather the target layers individually using the same universal training model established from 400 ground-truth samples. The agreement portions within the overlapping layers of these two approaches were labeled as the potential targets, and the pixel growth technique was performed to detect the roofing boundary and create the polygon layer with size information. Exacting images from aerial photos within the chosen polygon were compared to up-to-date Sentinel-1 images to find the temporal disagreements and remove the mismatched buildings, identified as non-asbestos roofs, from the database to reflect the actual condition of present data. This automatic matching could be easily performed by machine learning to resolve the information lag while using archived data, which is an essential issue when detecting targets with non-simultaneous acquired images over a large area. To meet the 85% kappa accuracy requirement, the recurring processes were applied to find the optimal parameters of the machine learning model. Meanwhile, this study found that the support vector machine method was easier to handle, and the convolution neuro network method offered better accuracy in automatic classification with a universal training model for vast areas. This work demonstrated a feasible approach using low-cost and low-resolution archived images to automatically detect the existence of asbestos-cement corrugated roofing tiles over large regions. The entire work was completed within 16 months for an area of 36,000 km2, and the detected number of asbestos-cement corrugated roofing tiles was more than three times the initial estimation by statistics method from two small-area field surveys.

Association between Environmental Factors and the Incidence of Acute Respiratory Infection in Children under Five in East Nusa Tenggara, Indonesia

Introduction: ARI (Acute Respiratory Infection) is one of the main causes of death among children under five in many countries worldwide. In Indonesia, 10% of ARI cases occur in East Nusa Tenggara, which is the highest prevalence of ARI in this country, with 90% of them leading to under-five mortality. There are various factors that cause the prevalence of ARI. In East Nusa Tenggara itself, environmental factors are suspected to be the risk factors for this disease. This study aims to analyse the association between environmental factors and the incidence of ARI among children under five years old in East Nusa Tenggara. Methods: A cross-sectional study was conducted by analysing secondary data sets obtained from DHS, with a total of 337 subjects examined in this study. Multivariate logistic regression was applied to investigate the association between ARI and various environmental factors. Results: This study found that the prevalence of ARI among children under five years old in East Nusa Tenggara was 9.04% of all subjects. ARI was significantly associated with the types of residence (OR 2.2, 95%CI 1.1-4.3) and passive smoking status (OR 4.7, 95%CI 1.8-12.4) as its risk factors. In addition, there was strong association between ARI and the types of roof (OR 3.3, 95%CI 1.3-8.9) as its protective factor. Conclusion: Based on the findings of this study, it can be concluded that the incidence of ARI in children under five years old was closely associated with residential area, smoke exposure, and roof materials.

Can microbial inoculants boost soil food webs and vegetation development on newly constructed extensive green roofs?

Green roofs are a key to providing nature-based solutions in cities. However, most green roofs installed in the Northern hemisphere are shallow, stonecrop planted systems (“extensive” green roofs), which have been shown to support limited biodiversity and could be more effective at providing ecosystem services. One issue with this type of extensive green roof is that rootzones are almost sterile on construction, relying on natural colonisation to provide a soil food web. This is a slow process, meaning plant growth can also be slow. Our aim was to determine if a soil food web could be introduced when the green roof is built. We applied microbial inoculants (mycorrhizal fungi and bacteria (Bacillus spp.)) to a new green roof and monitored plant growth and the soil food web (bacteria, mycorrhizal fungi and microarthropods). Different inoculants altered the composition of microarthropod communities, potentially impacting later succession. In particular, bacterial inoculants increased microarthropod populations. This is one of the first studies to demonstrate that the addition of microbial inoculants impacts not only plant growth, but also faunal components of the soil food web, which could have implications for long-term resilience. Bacteria were effective at aiding mycorrhizal colonisation of plants roots, but this colonisation had no impact on the growth of our selected stonecrops, Sedum album, Petrosedum reflexum and Phedimus spurius. We suggest that if a beneficial mycorrhiza could be found to promote the growth of these specific species on green roofs, bacteria could be effective “helper” species to aid colonisation. This study enables green roof researchers and the industry to justify further exploration of the impact of microbial inoculants on green roofs.