Local Climate Research Articles

Fog Harvesting: An Effective Solution to The Water Scarcity Problem

Fog harvesting, also known as fog collection, is a sustainable approach to addressing water scarcity which captures water droplets from fog, providing a renewable water source for water-scarce regions. The aim of this study is to give more background about fog harvesting by introducing fog harvesting systems, their advantages and disadvantages, real world and laboratory projects and efficiency. This study emphasizes fog harvesting's potential in arid regions with frequent fog occurrence, discusses working mechanisms, and explores nature-inspired and nanotechnology-based fog collectors. Local climate data's importance for feasibility assessment is highlighted, along with the vital role of community involvement for long-term success. Fog harvesting offers a promising and environmentally friendly solution to alleviate water scarcity challenges when combined with innovative strategies and community engagement. Real-world projects have shown that fog water collection can be an effective and sustainable solution, particularly in regions with persistent fog and limited water resources. However, more work is needed on innovative fog collectors and advanced materials to increase sustainability.

A mountain pine beetle (Coleoptera: Curculionidae) adult development rate model confirms evolved geographic differences.

Insects live in a wide range of thermal environments and have evolved species- and location-specific physiological processes for survival in hot and cold extremes. Thermally driven dormancy strategies, development rates and thresholds are important for synchronizing cohorts within a population and to local climates and often vary among populations within a species. Mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae),is a widely distributed forest insect native to North America with clinal genetic differentiation in thermally dependent traits. MPB development occurs in Pinus phloem beneath the bark, and its cryptic habitat makes experimentation difficult, particularly for the adult stage. We describe a novel method for modeling MPB adult development following pupation and terminating in emergence from a brood tree. We focus on an Arizona (southern) MPB population with previously described preadult development rates. Field-observed tree attack, adult emergence, and phloem temperature data are combined in a parameterized cohort model and candidate rate curves are evaluated to describe adult emergence timing. Model competition indicates that the Brière rate curve provided the best fit to field data and performed well under cross-validation. Results confirm that the development of Arizona MPB adults is slower than the previously described development rate of more northern Utah adults. Using the estimated adult rate curve in a scenario of increasing mean temperatures, we show that the timing of second-generation adult emergence in the same year would result in cold-intolerant lifestages during winter, limiting the success of bivoltine populations.

Statistical-based spatial analysis on urban water management under changing environments: a case study of Hawassa, Ethiopia

Hawassa characterizes a typical developing city in Ethiopia, owning to rapid urban growth and demographic trends. The combined effect of climate change and urban expansion is increasing the challenge to the environment and the services it provides. Relating changing environments with urban water management (UWM) is required to build resilience in the urban environment. This research analyzed local climate change and urban growth and linked it to UWM. The historical period 1990–2021 of daily rainfall, temperature variables, four satellite imageries, and DEM were analyzed. Changes in rainfall (annual and daily maximum) and temperature (maximum and minimum) trends are detected and projected to 2051 using a statistical-based model. With geospatial techniques sub-watersheds are delineated, and the urban cover change is quantified. The trend detection result implies an upward trend of annual and daily maximum rainfalls however a significance is insufficient (p > 0.05) to associate it with climate change during the study period. Maximum and minimum temperatures change indicate a positive and significant trend. The forecasting result suggests an increment of both temperatures (0.5 °C–1.5 °C) to the projected period compared to historical scenario. The land cover analysis results show the built-up area changed from 11.6 km2 (7.2%) to 42.5 km2 (26.5%) during the historical period, where the rate varies spatially. The surface runoff increased by 30.7% in the urban watersheds. With a growth rate of 8.9% built-up, the urban area will cover 73.6 km2 (45.9%) for the predicted period. The research finding justifies the potential to reorganize the relationship between the spatial effect of climate change and urban growth on UWM. Considering distinct characteristics of urban watershed, exposure to flooding risk, access to water demand and resilient to climate change have spatial variation. Thus, a local-specific planning approach will support effective UWM and climate adaptation for sustainable city development.

Assessing the impact of flood risk and associated factors on profitability of fish farming operations: evidence from South- South Nigeria

Fish farming plays a crucial role in food security and economic development in Nigeria. However, the country’s susceptibility to floods due to its geographical location and climate change has raised concerns about the profitability and sustainability of fish farming operations. This study aims to assess the impact of flood risk and associated factors on the profitability and sustainability of fish farming operations in South-South, Nigeria. The research employs a purposive random sampling techniques to generate data on flood frequency and severity, associated factor affecting profitability fish farms locations in the three (3) states, and other associated factors affecting profitability of fish farmers, their production figures are collected and analyzed to identify areas prone to flooding and the extent of damage caused to fish farms. Additionally, two hundred and one (180) fish farmers were sampled for the study (Delta 96, Bayelsa45 and Rivers 60 fish farmers) to gather information on flood risk effects, perception and profitability during flood occurrence. Preliminary findings indicate that flood severity is statistically significant at a 5% level (t-ratio: -2.4806) with a negative coefficient (-0.4876). Flood frequency is statistically significant at a 1% level (t-ratio: -4.5934). with a negative coefficient of -3.8035. This suggests that more severe flood events are associated with lower profitability among fish farmers. The result also revealed that at a probability of 0.1%, fish farmers in Delta, Bayelsa and Rivers States were susceptible to flooding with about 92.9% experiencing flood events although at a probability of 0.2%, it was noticed that only 7.1% of fish farmers were facing flood events. The study recommends that adoption of climate- smart farming practices, such as choosing flood resistant fish species and diversifying income sources, can enhance resilience, prioritize conducting thorough risk assessments specific to each fish farming location. These assessments should consider historical flood data, local climate conditions, topography, and the vulnerability of infrastructure. These findings will inform farmers and policy makers about the level of risk and guide mitigation efforts.

Study on Mapping and Identifying Risk Areas for Multiple Particulate Matter Pollution at the Block Scale Based on Local Climate Zones

As China’s urbanization process accelerates, the issue of air pollution becomes increasingly prominent and urgently requires improvement, based on the fact that environmental conditions such as meteorology and topography are difficult to change. Therefore, relevant optimization studies from the perspective of architectural patterns are operable to mitigate pollution. This paper takes the Wenhua Road block in Shenyang, China, as the research object; obtains the concentration data of three kinds of particulate matter through fixed and mobile monitoring; and analyzes the spatial distribution characteristics of Local Climate Zones ( LCZ) and particulate matter in the block based on the ArcGIS platform, identifies high-risk areas, and excavates the influence of LCZ on the concentrations of three kinds of particulate matter. The results show that the spatial distribution characteristics of PM1, PM2.5, and PM10 under the same pollution level are relatively similar, while the spatial heterogeneity of the distribution of the same particulate matter under different pollution levels is higher. The time-weighted results show that the PM1 pollution level in the block ranges from 44 to 51 μg/m³, PM2.5 ranges from 75 to 86 μg/m³, and PM10 ranges from 87 to 99 μg/m³. The pollution hot spots throughout the year are located in the central, eastern and western parts of the study area. In terms of the relationship between the LCZ and particulate matter, with the increase in the particulate matter diameter, the correlation between the three kinds of particulate matter and LCZ are all enhanced. The built-up LCZ always has a larger average concentration of particulate matter than that of the natural LCZ, and building height and building density are the main factors causing the difference. In the optimal design of the risk area, the proportion of natural vegetation or water surface should be increased and the building height should be properly controlled and the building density should be reduced in the renewal of the urban building form. This study will largely improve the spatial refinement of the optimization of urban architectural patterns oriented to mitigate particulate matter pollution.

Hybrid passive cooling and heating system for Mediterranean greenhouses. Microclimate and sweet pepper crop response

The microclimate of low-tech, unheated greenhouses in Mediterranean areas, associated with the local outdoor climate, is often outside the optimal range for most horticultural crops during both the warm and cold growing season. The use of a new hybrid system of passive cooling (evaporative screens) and heating (water-filled sleeves), in combination with an internal movable shading/thermal screen, was evaluated on a representative summer transplanted sweet pepper crop grown in perlite growing bags. The experiment was carried out in two identical greenhouses at the IFAPA La Mojonera research center in Almeria (SE Spain): one greenhouse with the hybrid passive system of cooling and heating, in combination with a shading/thermal screen, and another (reference greenhouse) using common local greenhouse climate management practices. Evaporative screens, in combination with a movable shading screen, improved the greenhouse climate, in particular the air vapour pressure deficit, and increased the leaf area index in the early stages of the crop, which, in turn, increased the early production of leaf and shoot dry matter and marketable fruit, compared to the reference greenhouse crop. In addition, the combined use of water-filled sleeves and thermal screen during the cold growth period increased greenhouse air temperatures, especially at night, and substrate temperatures. Overall, by improving the greenhouse microclimate during the warm and cold growth periods, the hybrid passive cooling/heating system, in combination with the shading/thermal screen, increased the marketable yield of a summer transplanted sweet pepper crop by 25 %, reduced the irrigation water supply by 8 %, and improved the irrigation water use efficiency by 20 % (including the potential water used to humidify the evaporative screens).

How agricultural producers use local knowledge, climate information, and on-farm “experiments” to address drought risk

How agricultural producers use local knowledge, climate information, and on-farm “experiments” to address drought risk

Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.

Evaluating the applicability of a machine learning methodology to improve TMY weather file generation for different Canadian climate zones

Typical meteorological year (TMY) weather files are used in building energy simulations to represent typical weather conditions for a location. The conventional approach to generating TMY weather files uses a set of universal weighting factors determined based on expert judgment. These universal weighting factors can overlook the distinct weather patterns between different locations and their influence on energy performance. A previous study used machine learning to generate location-specific weighting factors with respect to building energy performance. The objectives of this paper are: (1) To assess the applicability of the previous study across varying Canadian climates; (2) To investigate the feasibility of using standardized climate zone-based weighting factors to reduce the computational time associated with extracting location-based weighting factors while still considering local climate conditions to facilitate wider adoption of the proposed methodology. The investigation is conducted for a medium office building for 18 locations across six Canadian climate zones and generates two weather files, TMYSTATION and TMYCZ, for each location. The TMYSTATION weather files are generated using location-based weighting factors for each location whereas the TMYCZ weather files are generated using climate zone-based weighting factors for each location. The coefficient of variation of the root mean square error (CVRMSE) and normalized mean bias error (NMBE) are used to compare the performance of the proposed weather files with conventional weather files using the energy demands obtained through building simulation. The TMYSTATION and TMYCZ weather files outperform the conventional weather files in predicting the long-term energy performance of buildings. Although the TMYSTATION files marginally outperform the TMYCZ weather files in representing the long-term weather data, the convenience of standardized climate zone-based weighting factors allows the approach to be widely deployed.

Development of an expanded local climate zone scheme to accommodate diversified urban morphological evolution: A case study of Shanghai, China

The local climate zone (LCZ) scheme efficiently associates urban forms with thermal environments. However, confronted with diversified urban fabrics, the initial LCZ scheme has encountered challenges of its restricted classes and intraclass temperature differences. This study aimed to develop an expanded LCZ scheme to accommodate the diversified urban morphological evolution. Thirty-two block prototypes were developed according to the building footprint ratios (BFRs), mean building heights (BHs), and mean sky view factors (SVFs) of the blocks in Shanghai, China. Then, the block prototypes were classified based on their morphological and temperature indices by k-means clustering, an unsupervised clustering method that maximizes the distance between the clusters and minimizes the distance within the cluster. Results indicated that the block prototypes were clustered into 14 classes, the high-rise LCZs were subdivided, and blocks within six classes were not covered by the initial LCZ scheme. Accordingly, an expanded LCZ scheme was developed by introducing four novel LCZ types and adjusting and subdividing the initial LCZ types, which covered 35% more blocks than the initial LCZ scheme. The mean SVF demonstrated the most significant correlation with the temperature of LCZs, whereas the effects of BFR and BH on the temperature depended on LCZ types.

Urban heat island phenomenon in a desert, coastal city: The impact of urbanization

The state of Qatar has been experiencing rapid urbanization with around 85% of its population residing in Doha. The country faces notable challenges related to the Urban Heat Island (UHI) effect, which is exacerbated by its hot and humid desert climate. This study focuses on analyzing the UHI phenomenon in Doha, utilizing observed meteorological data and the Weather Research and Forecasting model (WRF v4.5). Two land use and land cover (LULC) datasets from 2001 and 2018 are employed, with simulations conducted using different urban canopy models. The 2018 LULC data includes 100 m resolution information that categorizes urban areas into 11 local climate zones. Results indicate significant UHI intensity during both winter and summer periods (up to 6.5 °C), with notable differences observed between daytime and nighttime temperatures. In agreement with observations, the model predicts not only a strong UHI effect during nighttime, but also a significant Urban Cool Island effect in Doha (up to −5.8 °C during daytime in summer). The impact of various urban parameterization schemes on simulation accuracy is highlighted. The building energy model demonstrates superior performance in predicting temperature and relative humidity during the summer period. The spatial distribution of heat index illustrates the intensified warming in the city.

Spatiotemporal dynamic relationships and simulation of urban spatial form changes and land surface temperature: a case study in Chengdu, China.

Exploring the spatiotemporal dynamic evolution of local climate zones (LCZ) associated with changes in land surface temperature (LST) can help urban planners deeply understand urban climate. Firstly, we monitored the evolution of 3D urban spatial form in Chengdu City, Sichuan Province, China from 2010 to 2020, used the ordinary least squares model to fit the dynamic correlation (DR) between the changes in urban spatial patterns and changes in LST, and revealed the changes of urban spatial patterns closely related to the rise in LST. Secondly, the spatiotemporal patterns of LST were examined by the integration of the Space-Time Cube model and emerging hotspot analysis. Finally, a prediction model based on curve fitting and random forest was integrated to simulate the LST of study area in 2025. Results show the following: the evolution of the urban spatial form consists of three stages: initial incremental expansion, midterm incremental expansion and stock renewal, and late stock renewal and ecological transformation. The influence of the built environment on the rise of LST is greater than that of the natural environment, and the building density has a greater effect than the building height. The overall LST shows a warming trend, and the seven identified LST spatiotemporal patterns are dominated by oscillating and new hotspots patterns, accounting for 51.99 and 11.44% of the study area, respectively. The DR between urban spatial form and LST varies across different time periods and built environment types, whereas the natural environment is always positively correlated with LST. The thermal environment of the city will warm up in the future, and the area affected by the heat island will shift to the central of the city.

Seasonal storage for space heating using solar DHW surplus

Due to the seasonality of solar energy, achieving 100 % of annual solar fraction for domestic hot water (DHW) production is only possible by greatly oversizing the collector area of a solar system, thus creating a significant energy surplus in summer. This simulation study investigates the possibility of using this surplus to promote space heating during winter, in a moderate South European climate, to try achieving a total solar fraction of 100 %. Priority is given to the DHW reservoir, diverting the excess heat to an additional large-capacity seasonal thermal energy storage (STES) reservoir. The best configuration for the number of collectors and STES tank volume was assessed through a parametric study, to reach a compromise between a high solar fraction and a reasonable system efficiency. The results showed that a system with 10 m2 of solar collectors and a 30 m3 STES tank or, alternatively, 20 m2 of collectors and a 20 m3 tank achieved the desired solar fraction and efficiency for the chosen building and local climate conditions. A comparison with the literature shows that this strategy can achieve better results, requiring less collector area and storage volume.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Surface energy balance in a suburban area of the megacity of São Paulo - Seasonal variation and closure

The study addressed the seasonal variation and closure of the Surface Energy Balance (SEB) in a suburban area of the Megacity of São Paulo. This area consists of a combination of buildings and vegetation classified into local climate zone typologies 5 (38.5%), 6 (26.7%), and 8 (11.2%), along with areas exclusively with vegetation representing 12.1%. The investigation is based on high-quality measurements taken during February (rainy summer) and August (dry winter) between 2013 and 2015. The greatest seasonal variation was observed in latent heat flux, reaching 64.7% for daily values and 63.9% for daytime values. The smallest was observed by sensible heat flux, reaching 20.3% and 24.1%, respectively. This suggests that seasonal variation in temperature and humidity plays a crucial role in energy distribution, generating uncertainties in the closure of the energy balance. The largest difference between energy input and output was seen in August, reaching 25% for daily values and 45% for daytime values. The gap saw in closure is consistently larger in winter. The results contributed to improving understanding of energy exchanges in the surface-canopy-atmosphere system, being essential in the development of more correct meteorological and hydrological models.

Regional and seasonal partitioning of water and temperature controls on urban dryness variability in China

Rapid urban expansion can significantly alter urban microclimate. In recent years, the phenomenon of urban dry islands (UDIs) has attracted increasing attention, but there remains a lack of understanding regarding the impact of urbanization on UDIs in different urban agglomerations (UAs). In this study, vapor pressure deficit (VPD) was used as the humidity indicator, and meteorological data from 20 UAs were used to explore how atmospheric temperature and humidity control urban dryness variability in China. Our analysis revealed that VPD trends are increasing with a median rate of 0.121 hPa/decade and 0.103 hPa/decade for urban and rural areas, respectively. The urbanization effect is more significant in coastal UAs. Furthermore, the difference in VPD between urban and rural areas obviously varied on a seasonal scale, with the largest difference observed in summer (i.e., −0.63 hPa on average and up to 2.66 hPa). Notably, our findings suggest that changes in local atmospheric water content primarily control the response of VPD to urbanization. In summer, the average contribution of temperature is 0.118 hPa, while the average contribution of relative humidity is 0.723 hPa. Overall, these results have important implications for our understanding of the impacts of urbanization on local climate.

Modeling and design of solar+storage-powered community resilience hubs across California.

Distributed clean, reliable energy resources like solar plus battery storage (solar+storage) can reduce harmful emissions while supporting resilience. Solar+storage-powered resilience hubs provide energy for critical services during disasters while increasing human adaptive capacity year round. We studied where utility rates, local climate, and historical injustice make solar+storage resilience hubs more valuable and more challenging. We modeled the economic and climate impacts of outfitting candidate hub sites across California with solar+storage for everyday operations and identified designs and costs required to withstand a range of outages considering weather impacts on energy needs and availability. We integrated sociodemographic data to prioritize the siting of resilience hubs, to focus potential policy and funding priorities on regions where solar+storage for resilience hubs is hard or expensive, and where populations are most in need. We identified almost 20,000 candidate buildings with more than 8 GW of total rooftop solar potential capable of reducing CO2 emissions by 5 million tons per year while providing energy for community resilience. Hub capacity for one of the most challenging missions-providing emergency shelter during a power outage and smoke event-could have a statewide average lifetime cost of less than $2000 per seat. We identified regional challenges including insufficient rooftop solar capacity in cities, low sunlight in northern coastal California, and high costs driven by utility rate structures in Sacramento and the Imperial Valley. Results show that rates and net metering rules that incentivize solar+storage during everyday operations decrease resilience costs.

Меры российских властей по развитию здравоохранения на Северо-Западном Кавказе в 20–30-е годы XIX века

The measures taken by Russian authorities to solve epidemic problems in the North-West Caucasus in the 1820s-1830s are considered. The landscape and climate features of the region, the difficulties of acclimatization of the personnel of the Russian Army, and the unstable military and political situation hindered the development of healthcare in this territory. The losses of the Russian Army personnel from diseases were greater than on the battlefields. To overcome the difficulties that arose, the authorities took a set of measures aimed at creating medical institutions, pharmacies, and a quarantine system. Methods for disease prevention were developed and implemented. First of all, attention was paid to sanitary and hygienic procedures, nutrition appropriate to the needs of a weakened organism, introduction of clothing and footwear suitable for the local climate. An extensive network of quarantine outposts was created to prevent the spread of epidemics. Doctors were sent to the region to provide assistance to those in need, regardless of their nationality. All this made it possible, if not to completely normalize the epidemiological situation, then to significantly reduce the risk of the spread of virulent diseases.

Analysis of Local Climate Change Impacts on the Rainfall Patterns and Dry Spells in Banda Aceh and Aceh Besar, Indonesia

Analysis of Local Climate Change Impacts on the Rainfall Patterns and Dry Spells in Banda Aceh and Aceh Besar, Indonesia

Bioinspired nanostructured hydroxyapatite-polyelectrolyte multilayers for stone conservation

Stone-built cultural heritage faces threats from natural forces and anthropogenic pollutants, including local climate, acid rain, and outdoor conditions like temperature fluctuations and wind exposure, all of which impact their structural integrity and lead to their degradation. The development of a water-based, environmentally-friendly protective coatings that meet a combination of requirements posed by the diversity of the substrates, different environmental conditions, and structures with complex geometries remains a formidable challenge, given the numerous obstacles faced by current conservation strategies. Here we report the structural, electrical, and mechanical characterization, along with performance testing, of a nanostructured hydrophilic and self-healing hybrid coating based on hydroxyapatite (HAp) nanocrystals and polyelectrolyte multilayers (PEM), formed in-situ on Greek marble through a simple spray layer-by-layer surface functionalization technique. The polyelectrolyte-hydroxyapatite multilayer (PHM) structure resembled the design of naturally forming trabecular bone, attained at a short procedural time. It exhibited chemical affinity, aesthetical compatibility and resistance to weathering while offering reversibility. The proposed method is able to generate micron-sized coatings with controlled properties, such as adhesion and self-healing, leading to less weathered surfaces. Our results show that the PHM is a highly effective protective material that can be applied for stone protection and other similar applications.