**4.1 Lithium extraction from brine**

*Thermodynamics and Energy Engineering*

maximum evaluation [3].

**3. Lithium resources**

seawater at about 0.17 mg L−1

and petalite containing lithium.

in hard rocks were 12.8 and 30.7 million tons, respectively; while the brine field data were reported as 21.3 and 65.3 million tons, respectively, for the minimum and

In contradistinction to the uses of lithium, it is necessary to discuss the question of responsibility for Li from a variety of sources. The economic efficiency of lithium is found in minerals, clays and brines. High-grade lithium ores and brines are the current sources for all commercial lithium manufacture. **Figure 1(a)** demonstrates the distribution of lithium over different resources. The figure shows that continental brine is the largest resource (59%) for lithium, followed by solid rock (25%). **Figure 1(b)** demonstrates the spread of lithium across countries. The largest of the studied lithium deposits are in Bolivia and Chile. **Figure 1(c)** demonstrates the distribution of lithium production across countries. The main producers and exporters of lithium ores are Chile and Australia. Chile and China have huge resources of lithium ore. Canada, Russia, Serbia and Congo (Kinshasa) have lithium ores of about 1 million tons each, and equal reserve for Brazil is total 180,000 tons [62].

It is estimated that the earth's crust contains an average of about 0.007% lithium. In nature, lithium does not occur freely, but it occurs in small quantities in almost all magmatic breeds and the ocean, in seawater, in the waters of many mineral springs. Of the approximately 20 known minerals containing lithium, only 4, that is, Lepidolite (KLi1.5Al1.5[Si3O10][F,OH]2), Spodumene (LiO2·Al2O3·4SiO2), Petalite (LiO2·Al2O3·8SiO2) and Amblygonite (LiAl[PO4][OH,F]) are known to occur in quantities sufficient for commercial interest as well industrial importance [63–66]. The spodumen (LiAlSi2O6) mineral is the most significant industrial lithium ore mineral. Minerals of lithium also exist as cookeite as (LiAl4(Si3Al)O10(OH)8) in fine hydrothermal veins of quartz. Taeniolite (KLiMg2 Si4O10F2) is present in veins of smoky quartz in recrystallized novaculite, in manganese deposits the appearance of Lithiophorite ((Al, Li) Mn4+O2(OH)) is noted. Pegmatites, Taeniolite, Lithiophorite and Cookeite are considered to be economically inefficient sources of lithium [67–69]. A large part of the lithium is extracted from brine or seawater has a high concentration of lithium carbonate. In the earth's crust, there are brines called continental brines/subsurface brines are the main source for the production of lithium (lithium carbonate). The literature reports that lithium is also present in

brines and geothermal waters. These sources of brine and seawater are considered less expensive than mining from rocks such as spodumene, lepidolite, amblygonite

*The distribution of lithium (a) different natural resources, (b) worldwide distribution, (c) the number of*

. Lithium is found in significant quantities in oil well

**190**

**Figure 1.**

*producers around the world.*

Extracting lithium from brine is an important potential resource. When considered from an economic and scientific perspective, the following points are important to consider lithium recovery from brine: (1) suitability of pond soil and admissibility of the area for solar evaporation; (2) the concentration of lithium in brine; (3) the ratio of alkali metals and alkaline earth elements to lithium and (4) the complexity of the phase chemistry. The resources of brines containing lithium can be divided into three types: evaporative, geothermal and oilfield brines. In the process of evaporation of the brine about 50% of the original natural brine, lithium remains in the residual brine. This expression has been ascribed to the retention of lithium by precipitated salts. Residual brine is highly loaded with Mg2+ as compared with K+ and Na+ , this makes it difficult to extract lithium from this residual brine [70].

The extraction of lithium from brine does not correspond to any general regularity since each process is specific depending on the composition of the brine field. Typical lithium production technology used for lithium extraction by Outotec®, where different methods such as precipitation, solvent extraction and flotation were used (**Figure 2**). Lithium extraction by Outotec® uses a lithium carbocation process to produce lithium [71]. Various lithium separation and purification methods have been reported in the literature, which is discussed below. By Chagnes and Swiatowska the general technological scheme of lithium production from brine and seawater is proposed [72]. In this method, liquid-liquid extraction, ion exchange, electrodialysis and adsorption are important hydrometallurgical processes necessary to concentrate lithium before production [72]. **Table 1** discusses and summarizes the extraction of lithium from both brine and synthetic brine in various ways.

### **4.2 Recovery of lithium from brine by precipitation**

Pelly et al., Epstein et al. and Kalpan et al. it has been reported that lithium recovery as precipitation of lithium aluminate from Dead sea brine and final brine [73–75]. Pelly et al. have reported, it is necessary to control the pH of the brine through dilution to achieve 90% extraction efficiency end brine and Dead sea brine [73]. As indicated, the optimal pH should be in the range of 6.6–7.2 For Dead sea brine and 6.8–7.0 for end brine. The optimum reaction time should be 3 hours at room temperature. AlCl3·6H2O (30–40 g L−1 ) was added to the brine. The negative effect was given by higher temperature, but better yields were obtained at room temperature and the yield decreased with increasing temperature [73]. The importance of extracting lithium from the Dead sea by precipitation as lithium aluminate followed by liquid-liquid extraction to separate lithium from aluminum with economic evaluation was reported [74]. Kaplan et al. reported on the process of extracting lithium by lithium aluminate from Dead sea brine by precipitation [75]. A small amount of lithium, which is mainly present as LiCl, was precipitated as a lithium aluminate precipitate using ammonia and aluminum salt at room temperature. Although subsequent reduction processes both by dissolving lithium in sulfuric acid and followed by precipitation with calcium chloride lithium were reduced as alum [75]. An et al. reported on the process of extracting lithium from brine collected in Salar de Uyuni, Bolivia. Mg and Ca were extracted from the brine as Mg(OH)2 and gypsum (CaSO4·2H2O) using sulfate and lime. Both CAO and MgO were extracted using oxalic acid followed by firing using residual Ca and Mg. In the end, by heating at 80–90°C lithium was recovered in the form of Li2CO3.

**Figure 2.**

*Outotec lithium production technology from brine. Reproduced with permission from Outotec.*

As a result of precipitation processes with a high content of pure (99.55%) and crystalline Li2CO3 were reduced [93].


The main error source in such land cover tasks is diversity within each forest species. Spectral characteristics vary drastically for different tree age and depend on environmental conditions. Therefore, markup adjustment and optimal sampling choice are promising approaches to improve model performance. Another error source is mixed border pixels of neighboring individual stands. In the case of 10 m spatial resolution, even for homogeneous forest stands, spectral characteristics on the border can be affected by other species outside this stand. A possible approach to address this problem for homogeneous stands is to consider just inner pixels remote from the border.

One of the potential limitations is the time and computational cost for markup adjustment model training. In our case, we used the same CNN architecture to perform this stage. We trained the model for markup adjustment and the final segmentation model sequentially. In future studies, an alternative approach can be developed and implemented to perform markup adjustment on the fly for remote sensing tasks.

In this study, we considered forest species classification. However, the proposed approach can be transferred in future studies for other tasks where samples are grouped, and for a group, label distribution is known. The described approach is also applicable for other neural network architectures. Therefore, experiments with new state-of-the-art architectures can be conducted using the same method. Both the sampling and markup adjustment approaches are transferable to new satellite data sources. We considered multispectral Sentinel-2 imagery with a spatial resolution of 10 m. However, it can also be implemented for high-resolution multispectral data such as WorldView or just RGB images such as base maps.

Vegetation indices are significant for environmental tasks as they provide relevant surface characteristics. Therefore, they are widely used as features for classical machine learning methods. However, in the case of deep neural networks, it is assumed that neural networks can learn non-linear connections between raw input data and use prior information for more general characteristics extraction. In our study, we considered only multispectral satellite bands. However, future studies might include vegetation indices or supplementary materials such as digital elevation or canopy height models to achieve higher results and reduce training time.

It is promising to study different augmentation techniques combined with improved markup and the object-wise sampling approach. For example, the object-based augmentation described in [60] can further be implemented to create more variable training samples with different homogeneous stands.

Precise forest species classification can also be implemented in ecological and environmental studies, as large forest patches have been proved to affect human health [61]. Detailed forest characteristics can be helpful for such analysis.

#### **5. Conclusions**

The sampling approach and ground truth markup quality are crucial in forestry tasks involving remote sensing data. In this study, we analyzed the potential of combining CNN and Sentinel-2 images for the task of forest species classification using weak markup with non-homogeneous individual stands. During the first stage, a CNN was trained to find the homogeneous areas within each stand, providing a more accurate markup. During the second stage, the final model was trained to predict four forest species. This

markup adjustment allows us to increase F1-score from 0.74 to 0.76 compared to the initial markup. The experiment confirms the opportunity of finding weak labels and shows promising results for further classification enhancement. We also proposed the CNNbased sampling approach for spatial data in forest species classification. The proposed modification outperformed the prediction quality of a commonly used per-pixel semantic segmentation model (the average F1-metric was increased from 0.68 to 0.74). The described pipeline helps to address the issue of highly imbalanced and not evenly distributed classes. The provided training strategy can help solve forest species classification tasks more precisely, even when the reference data has significant limitations. Further study for other vast territories is promising, and the proposed sampling technique seems to be beneficial in such spatial studies.

**Author Contributions:** Conceptualization, S.I. and V.I.; methodology, S.I. and V.I.; software, S.I.; validation, S.I.; formal analysis, S.I. and A.T.; investigation, S.I.; data curation, V.I. and A.T.; writing original draft preparation, S.I.; visualization, S.I.; supervision, V.I. and I.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Article* **MDIR Monthly Ignition Risk Maps, an Integrated Open-Source Strategy for Wildfire Prevention**

**Luis Santos 1,2,3,\*, Vasco Lopes 1 and Cecília Baptista 1,3**

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +351-967-743-365

**Abstract:** Countries unaccustomed to wildfires are currently experiencing wildfire as a new climatechange reality. Understanding how fire ignition and propagation are correlated with temperature, orography, humidity, wind, and the mixture and age of individual plants must be considered when designing prevention strategies. While wildfire prevention focuses on fire ignition avoidance, firefighting success depends on early ignition detection, meaning that, in either case, ignition plays a major role. The current case study considered three Portuguese municipalities that annually observe frequent fire ignitions (Tomar, Ourém, and Ferreira do Zêzere) as the testing ground for the Modernized Dynamic Ignition Risk (MDIR) strategy, thus evaluating the efficiency of MDIR and the efficacy of the variables used. This methodology uses geographic information systems technology sustained by open-source satellite imagery, along with the Habitat Risk Assessment model from the InVEST software package, as drivers for the MDIR application. The MDIR approach grants frequent update capabilities and fully open-sourced high ignition risk area identification, producing monthly ignition risk maps. The advantage of using this method is the ease of adaptation to any current monitoring strategy, awarding further efficiency and efficacy in reducing ignitions. The approach delivered adequate results in estimating ignitions for the three Portuguese municipalities, achieving, for several months, prediction accuracy percentages of over 70%. For the studied area, MDIR clearly identifies areas of high ignition risk and delivers an average of 62% success in predicting ignitions, thus showing potential for analyzing the impact of policy implementation and monitoring through the strategy design.

**Keywords:** fire ignition; fire hazard; QGIS; InVEST; NDVI; S2 NDWI; risk

### **1. Introduction**

Certain biogeographic regions are historically prone to wildfires; the Mediterranean in Europe, Fynbos and Savannah in Africa, Malee in Australia, Matorral in Chile, and Chaparral in California, are among the most well-known biomes for common wildfire occurrence [1]. Corresponding ecosystems experience cyclic recurrence of wildfires, where ignition and spreading are driven by temperature, orography, humidity, wind, the mixture of plants, and the age of the individual plants [2,3]. The characteristic patchy habitats of such ecosystems exhibit a ratio of dead to live fuel, which varies from species to species, with ratios being much greater in higher succession stages as a result of flammable leaf litter deposition [4,5]. Despite ignition and spread tendencies, hot and dry climate ecosystems require fire: species have evolved to resist wildfires, and in some extreme cases, fire is a mandatory cue for species development [6].

Nonetheless, circumstances are changing, and countries unaccustomed to wildfires are currently experiencing this new phenomenon. In Europe, countries such as Sweden, Germany, Poland, and Slovakia, among others, are experiencing increased burnt area,

**Citation:** Santos, L.; Lopes, V.; Baptista, C. MDIR Monthly Ignition Risk Maps, an Integrated Open-Source Strategy for Wildfire Prevention. *Forests* **2022**, *13*, 408. https://doi.org/10.3390/f13030408

Academic Editor: Costantino Sirca

Received: 14 December 2021 Accepted: 1 March 2022 Published: 3 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

1 School of Technology, Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal<EMAIL_ADDRESS>(V.L<EMAIL_ADDRESS>(C.B.)

while wildfire hotspot countries, such as Portugal, Spain, Greece, Italy, and France have experienced unusually severe fires, claiming hundreds of lives and resulting in serious economic loss [7,8]. Many argue that this is an indicator of increasing wildfire activity, resulting in destructive megafires under the current climate change scenario [8,9].

Europe's extensive urbanization and expansion trends are blurring the line between urban and rural areas, which under current climate change predictions, will increase fire risk connected with the urban-wildland interface [10,11]. Portugal follows the general European trend, with a turn in the opposite direction. The main culprits are the demographic shift from rural to urban areas, the changes in land use with evident abandonment, a fragmented land ownership that discourages investment in forest management, and poor fire planning strategies, resulting in the increased connectivity of unmanaged forested areas, consequently increasing wildfire severity [12,13].

Although the causes of most wildfires are proven to be accidental or criminally driven [14,15], wildfires can arise naturally from sources such as the spontaneous combustion of dry matter under high temperatures and wind conditions [16], or most commonly, lightning strikes [17]. Ignitions, no matter what underlies their origin, are the first and most important factor in fire prevention [18,19].

Wildfire risk is mainly associated with the number of ignitions that potentially result in large fires, considering that risk is a factor of hazard by the number of elements at risk, ignitions add to hazard, even though most don't result in large fires [20,21]. Ignition prevention associated with an adequate monitoring strategy represents a key element in fire risk reduction [22,23]. Several risk projection maps and techniques are based mostly on meteorological characteristics such as wind, humidity, and temperature; however, vegetation density and leaf litter, along with chlorophyll content and dryness, are factors of major importance, as has been thoroughly addressed in the literature [11,21,24–26].

Wildfire mitigation analysis has evolved significantly since the turn of the century with the development of satellite imagery and remote sensing (RS) analysis, mostly combined with geographic information systems (GIS). GIS offer a panoply of software and applications, both commercial and open-source, widely used to detect and monitor the behavior of operational firefighting activities and facilitate burnt area mapping. These technologies are proven to confer further efficiency and precision when compared with traditional surveying methods, reducing risk, burnt area, and saving human lives [27,28].

While most high-quality satellite imagery is still fee-based, new free satellite technology, providing good resolution imagery through a wide range of cameras, is currently widely available. One of these satellites is the Sentinel-2, which offers a wide wavelength range, 10 m resolution imagery, and free access [29,30].

Fire occurrence, prediction, and propagation models are widely tested and compared in various research papers and literature reviews [18,19,31], offering an indisputable myriad of solutions to fire problems, from statistical methods [18,31] to remote sensing [29,32] and machine learning [31,33]. Despite the many advances in scientific knowledge, such solutions are partly restricted to specialists and are not as readily accessible to the operational agents that play an important role in fire prevention and firefighting.

Adapting and mitigating wildfire risk requires a thorough understanding of all variables involved, both natural and anthropic, thus considering their intricate relationship. The development of suitable planning and fire management policy will only be possible by considering such variables along with the operational firefighting activities [34].

The current study aims to create dynamic monthly risk maps, designed to optimize monitoring strategies as part of the integrated dynamic process involved in fire prevention. The main output is monthly updateable ignition risk maps, which in combination with the current meteorological-based techniques, will enhance firefighting, conferring a more efficient monitoring strategy and enabling its use by both researchers and field operational staff.

### **2. Materials and Methods**

Following the trends within geographic information technology, aided by the plethora of free satellite imagery, the current study applied a combined methodology which used the Habitat Risk Assessment model, one of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) models. The InVEST Habitat and Species Risk Assessment (HRA) model allows the assessment of cumulative risks posed to habitats by human activities and habitat-specific consequences, delivering a final ecosystem risk map for each individual habitat resulting from the contribution of exposure and consequence to overall risk [35].

Satellite imagery was obtained from the European Space Agency Sentinel-2 satellite, equipped with the MultiSpectral Instrument (MSI) sensor including 13 spectral bands with different resolutions covering the spectral regions of the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) ranges (https://sentinel.esa.int/web/sentinel/missions, accessed on 1 November 2020).

The fire ignition risk potential maps were created using the previously published Modernized Dynamic Ignition Risk (MDIR) strategy, which combines the InVEST HRA model engine and the variables of road network, historic ignitions, the Digital Terrain Model (DTM), visibility, the Normalized Difference Vegetation Index (NDVI), and population to produce risk maps. This study improves the previous strategy by including the dynamic variable of the Normalized Difference Water Index (S2-NDWI) [36]. The new MDIR approach uses as dynamic variables the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (S2-NDWI), which in combination with static variables, land occupation, slope, forest road network, historical fire ignitions, and visualization basins, enabled the new fire ignition prevention methodology.

All geographic variables within the habitat risk assessment model (HRA) were correlated with each other, with different weights assigned to each distinct land occupation habitat class, whereas the remaining variables were assigned as stressors contributing to fire risk. All weights were attributed based on the research noted in the bibliography [37].

### *2.1. Study Area*

The study area belongs to the biogeographical transitional region situated between the floodplains of the Tagus River and the mountain range of Montejunto-Estrela in central Portugal (Figure 1).

Administratively, the region comprises the municipalities of Tomar, Ourém, and Ferreira do Zêzere belonging to the Santarém district, Nomenclature of Territorial Units for Statistical Purposes (NUTS) level II and III; the municipalities are inserted in the Central region and in the sub-region of the Middle Tagus. Biogeographically, the municipalities under study are included in the Mediterranean bioclimate, exhibiting a pluvio-seasonal oceanic bio-climatic region [38] with an average altitude ranging from 30 to 650 m ASL, mean annual precipitation of 660 mm, and a mean annual temperature of 14 ◦C. Temperature peaks and heatwave duration are major forest fire ignition drivers; data from http://portaldoclima.pt/en/ (accessed on 1 September 2020), indicates that on average, the yearly number of days with a temperature ≥35 ◦C is 12, where heatwave duration with temperatures ≥35 ◦C is 4 to 6 days. This region is severely affected by recurrent forest fires, where the main anthropic causes are associated with the misuse of fire, land abandonment, aging population, desertification, and the choice of forestry species (*Eucalyptus globulus*) influenced by economic yields.

The region, once prosperous with *Pinus pinaster* plantations, due to the last century's forestry policy, and speckled with olive orchards and lowland agriculture, is currently transformed into a mosaic of *Eucalyptus globulus* plantations, with remnants of *Pinus pinaster* and shrubland dominated by *Ulex* spp., *Erica* spp., *Pistacia* sp., *Myrtus* sp., and *Rubus* spp. where agriculture once thrived, confirming the abandonment trend that is further observed as we move northeastward. According to the data of the Official Administrative Chart of Portugal (DGT-2019), the three municipalities cover an area of approximately

95,823 ha, divided as follows: Tomar (35,120 ha), Ourém (41,666 ha), and Ferreira do Zêzere (19,037 ha) [36].

**Figure 1.** The geographical location of the study region.

### *2.2. Methods*

The applied methodology derives from the previously published Modernized Dynamic Ignition Risk (MDIR) model [36] which correlates stressors, both static and dynamic, and resilience variables with land occupation type, returning potential rural fire ignition maps. The MDIR approach is driven by the InVEST Habitat Risk Assessment (HRA) model using the process described in Figure 2. The InVEST HRA was not primarily designed to evaluate fire ignitions; however, it utilizes a well-supported exposure-consequence framework that assesses spatial variation as a cumulative risk from multiple human activities across diverse conditions and land use patterns. The cumulative aspect of ignitions is addressed by various authors [39,40] who, in some cases, consider exposure-consequence [41] as important indicators of ignition risk in habitats strongly influenced by human activities.

The condition of a habitat is a key model determinant; as anthropogenic stressors continue to diversify and intensify, so too does the need for quick, clear, and repeatable ways of assessing risks to habitats, both now and under future management scenarios. These factors award HRA the necessary characteristics to adequately estimate ignition risk and deliver updateable risk maps. Considering that, this model explores the consequences of human activities through the assessment of cumulative risk; the ignition risk probability can be assessed in such a cumulative manner that exposure to risk is positively correlated with the number of stressors affecting a particular area.

**Figure 2.** Modernized Dynamic Ignition Risk (MDIR) conceptual methodology.

The conceptual model is built using land use and occupation data (COS2018) obtained from the Directorate General of the Territory of Portugal (http://mapas.dgterritorio.pt/ DGT-ATOM-download/COS\_Final/COS2018v1.0-NUT3/COS2018-V1-PT16I\_Medio\_Tejo. zip; accessed on 7 September 2020). Land use data is available with 9 classes of land occupation: artificialized territories, agriculture, pastures, agroforestry areas, forests, bush, uncovered bare soil or with little vegetation, wetlands, and surface water masses. The land use data was reclassified using the QGIS software to produce individual habitat maps, where some pairs of classes were joined, "artificialized territories" with "uncovered bare soil or with little vegetation," "agroforestry areas" with "forests," and "wetlands" with "surface water masses." These classes were grouped due to land occupation similarities, which in case of fire ignition, does not significantly influence results [14,20].

The model also considers a set of slow variables: Digital Terrain Model (MDT), population density, roads network, and viewshed.

The Digital Terrain Model (MDT) from Porto University Faculty of Sciences, (https: //www.fc.up.pt/pessoas/jagoncal/srtm/; accessed on 7 September 2020) was used to calculate the slope. The data was edited using the QGIS (2020) software slope function, followed by a reclassification into two classes, ≥10 and ≤10 degrees, where the layer from 0 to 10 degrees will be used as a stressor in the InVEST risk assessment model, since low slopes are considered ignition-prone areas according to the last 10-year records for this region. However, for fire spread, the authors refer to high slopes as favorable to fire spread [31].

Population density (inhabitants per km2) was determined using the geographic and alphanumeric data from the 2011 census from the Portuguese National Statistics Institute (http://mapas.ine.pt/download/index2011.phtml, https://censos.ine.pt/xportal/xmain? xpid=CENSOS&xpgid=censos\_lugar; accessed on 7 September 2020). QGIS software was used to delimitate the stressor layer of population density, which corresponds to areas below 250 inhabitants per km2, and thus awarded an increased risk of ignition, due to desertification and elderly population, with little or no possibilities to maintain forested or agricultural spaces [42–44].

For the road network stressor, the municipal plans forest fire defense road network from the Portuguese Institute of Nature Conservation and Forests (https://fogos.icnf.pt/ infoPMDFCI/PMDFCI\_PUBLICOlist.asp; accessed on 7 September 2020), was analyzed using the QGIS buffer function of 20 m from the road network, which was selected as a

high risk, considering that most ignitions start in the vicinity of roads and attributed to negligent or criminal acts [43,45].

The viewshed QGIS map entered as a stressor in the InVEST model were the shadow areas, or areas not visible by at least three lookout posts/towers, corresponding to an additional risk. Early ignition detection improves early intervention and therefore, firefighting success. The viewshed analysis was calculated by applying the QGIS software plugin, using the MDT available (https://www.fc.up.pt/pessoas/jagoncal/srtm/; accessed on 7 September 2020), and the National Network of Lookout Posts made available by the Nature and Environment Protection Service of the Republican National Guard (SEPNA/GNR).

Fire ignition history is considered a dynamic stressor in the model obtained from the Nature and Environment Protection Service of the Republican National Guard (SEPNA/GNR) which, in Portugal, holds the responsibility to investigate and determine the ignition cause and starting point of fires. The stressor is determined with the QGIS Hotspot function, being assigned a radius of 1000 m at each point; only fire ignitions that occurred from the first day of the calendar year to the date of the satellite images were used for the calculation of the MDIR, hence the dynamic classification. This variable derives from the ignition trend of proximity to a known occurrence, attributed to both natural and anthropic causes [36].

The model also assumes another two dynamic variables, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (S2-NDWI), evolving from the previously published MDIR model [36] which considered only one dynamic variable, NDVI. Indexes were calculated using 75 Multi Spectral Instrument (MSI) sensor Sentinel-2 satellite images from the months of May, June, July, August, and September for the years 2016 to 2020 (https://scihub.copernicus.eu/dhus/#/home, accessed on 1 November 2020). Downloaded study region satellite images, level 2A, were atmospherically, radiometrically, and geometrically corrected for the period under analysis, without the presence of clouds. Selected data values up to 0.4 [46] were considered for both indexes, taking into account the loss of chlorophyll and the absence of humidity as dynamic variables that influence the ignition of rural fires [47,48]. Sentinel-2 satellite imagery corresponding to bands 4 and 8, with 10 m resolution and band 12, with 20 m resolution, were analyzed using the QGIS Raster Calculator to produce chlorophyll NDVI (Equation (1)) and pre-fire vegetation humidity S2-NDWI (Equation (2)) maps (https://foodsecurity-tep.net/node/214 accessed on 1 November 2020).

$$\text{NDVI} = \frac{\text{NIR} - \text{RED}}{\text{NIR} + \text{RED}} \tag{1}$$

**Theorem 1.** *Normalized difference vegetation index (NDVI), Sentinel-2 Band 4 (RED), red region (Red: 650–680 nm, 10 m resolution); Band 8 (NIR) near infrared (NIR: 785–899 nm, 10 m resolution).*

$$\text{S2\\_NDWI} = \frac{\text{NIR} - \text{SWIR2}}{\text{NIR} + \text{SWIR2}} \tag{2}$$

**Theorem 2.** *Normalized Difference Water Index (S2-NDWI) formulae using Sentinel-2 Band 8 (NIR) near infrared (NIR: 785–899 nm, 10 m resolution) and Band 12 (SWIR 2) Shortwave infrared (SWIR: ~1610–~2190 nm, 20 m resolution).*

Finally, the MDIR methodology involves the application of the Habitat Risk Assessment model (HRA) from InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) suite of models. The process involves grouping all produced data layers in a "host" folder, which will be subdivided into two other folders, one referring to the land occupation files representing the habitats, and the other where the different types of stressor maps are lodged; all files are used in vector format, which favors easy applicability and update. The main "host" folder will hold the vector files with the sub-regions of the study area, in

vector format, and two CSV files, where the weights and characteristics of the relationship between habitats and stressors are assigned. Special attention should be given to the need for a direct match between the assigned names in the stressors data table and the stressor folder map, as they should match.

Tables were produced using Microsoft Excel and later saved as CSV files. The Habitat Stressor Info table (Appendix A, Figure A1) contains rows corresponding to the habitat's characteristics and stressors, respectively, with four columns:

The file (exposure\_consequence\_criteria.csv) corresponds to the Score of Exposure and Consequence criteria that must also be in the "host" input folder. This file contains information on the impact of each stressor on each type of land use/occupation (i.e., exposure (B) and consequence (A) scores) for the different types of land occupation and stressors under analysis. The classification should be awarded an odd scale, which for this study, the scale varied from 1 to 5, since the fire risk maps are standardized in 5 classes.

(A) The Consequence criteria consist of the risk observed in certain areas being exposed to a stressor, i.e., the Consequences are determined by the sensitivity of a type of land occupation to a specific stressor and by the natural resilience that occupation holds to resist and recover from disturbances in general.

By default, the model includes two specific measures of sensitivity (frequency of disturbance, change in classification) and four measures of natural resilience (average growth, rotation rate, connectivity rate, natural recovery time). Each of these measures is described as:

(B) Exposure criteria consists of the increasing risk to which certain areas are exposed when accumulating certain characteristics. The characteristics, from a temporal and spatial nature, consider that aspects such as management, overlap, and neighborhood contribute cumulatively to the aggravation of the risk factor.

For each type of land occupation, it is necessary to have a classification and the assignment of a Data Quality (DQ) and a Weight. For each criterion it is necessary to specify whether it corresponds to a Consequence or Exposure. These criteria are characterized as:

The InVEST HRA model considered the Multiplicative Risk calculation (Equation (3)), which, for the current study, considered the Cumulative Risk (Equation (4)) to the Ecosystem from Multiple Stressors as an integrative index of risk across all habitats in a grid cell (The Natural Capital Project, 2021) (Appendix A, Figure A1).

$$\mathcal{R}\_{ijkl} = E\_{jkl} \cdot \mathcal{C}\_{jkl} \tag{3}$$

**Theorem 3.** *Multiplicative Risk calculation where risk to habitat "j" caused by stressor "k" in cell "l" is calculated as the product of the exposure and consequence scores.*

$$R\_I = \sum\_{j=1}^{I} R\_{jl} \tag{4}$$

**Theorem 4.** *The cumulative risk for habitat "j" in cell "l" is the sum of all risk scores for each habitat. To assess the influence of multiple activities, the cumulative risk of all stressors was quantified for each habitat "l" as the sum of all risk scores for each combination of habitat and activity "j" as "R\_jl", and annual and monthly results are presented on Figure 3.*

291

The data ranks and variable valuation in the HRA model were attributed according to literature review and local regional characteristics, expressed as sensitivity and natural resilience measures. All attributed ranks were also discussed thoroughly by a transdisciplinary team of experts holding in-depth knowledge of the regional characteristics. The literature supported the choice of variables and ranked values defined for MDIR, forest road network [15,22,45,49], slope [20,26,50], population density [21,22,41], visualization basins [33], and fire history [5,51,52] combined with NDVI [4,30,53,54] and S2-NDWI [4,55] indices.

Since results are delivered as model maps with spatial risk distribution, the variable validation will undergo a sequential individual variable removal to determine the contribution of variables and interactions of the strategy.

### **3. Results and Discussion**

The results obtained for the study region shown as potential ignition risk maps using the InVEST HRA model within the Modernized Dynamic Ignition Risk strategy (MDIR) are illustrated on Figure 3. The model connected seven different stressors: forest road network, slope, population density, visualization basins, and fire history, combined with NDVI and S2-NDWI indices. The time interval used in the analysis process comprises the months of May to September for the years 2016 to 2020. The results consider five risk classes, minor, guarded, elevated, major, and severe.

The ignition potential mapping (Figure 3) reveals that the area percentages in each risk class do not change significantly from year to year when considering homologous periods; however, they vary geographically from month to month and year to year, most noticeable for the years 2017 and 2018, when major fire events occurred. This may be attributed to the continuous ecological succession of recently fire-affected habitats, land use change, or natural succession processes. The stressors that contribute to the total gross areas class oscillations are mainly represented through the geographical dispersion of dynamic variables, namely, the NDVI and S2-NDWI together with the cumulative fire ignitions in each year, since weather conditions modify the response of vegetation to ignition probability. The vegetation index is sensitive to vegetation and water content, which is complementary with NDVI, where high values of S2-NDWI exhibit a high water content of the vegetation [4,56].

One of the observed trends in Figure 3 is the monthly decrease in lower risk class percentages associated with the increase in temperature and reduction of humidity, represented in the model by NDVI and S2-NDWI, and also correlating with regional weather conditions that cumulatively increase the higher risk categories. Another interesting fact is the percentage value of lower risk classes in the initial month of the critical season (May) whose values are associated with the reduction level in the very critical months of August and September, representing the difference between a harsher and calmer fire ignition season, which in turn is correlated with plant water content. Therefore, the amount of water in plants in the beginning of the season will dictate whether or not the year will exhibit recurrent ignitions and possibly larger fires, like those observed in 2017, a critical year for forest fires across the Portuguese territory [1,41].

Although the number of fire ignitions plays an active role in the MDIR model, it may also contribute to the success of model analysis, enhancing the ability to predict high ignition risk areas, particularly for areas exhibiting considerable anthropic modifications. Figure 4 exhibits the number of monthly ignitions for the years 2016–2020, 1025 in total, emphasizing the high number of ignitions in the month of July, as well as the exceptional year of 2017, in which the month of June observed a high incidence of 87 ignitions for this region, hence the importance of early season conditions.

**Figure 4.** The number of monthly critical season ignitions for the years 2016–2020.

Considering the evolution of ignitions throughout the critical period of the five-year timeline (Figure 4), in general, the month of May exhibits the lowest number, with ignitions peaking during the month of July and decreasing during the month of August, concluding with a year-dependent variation in September. This trend may be interpreted as a result of plant water content and associated chlorophyll values; the sustainability during the critical period dictates a dryness threshold variability earlier or later within the critical season [55].

The variation during the month of September after 2017 may be attributed to a policy change in the Portuguese Civil Protection system, in which prior approval, by formal request through communication with the authorities, was required for slash-and-burn, and other forestry and agricultural practices; otherwise, the offense was punishable by law; hence, the reduction in ignitions is observed.

All in all, the 1025 ignitions observed between 2016 and 2020 are most common in forest habitats, making up 51% of the total; the ignitions occurring on agricultural habitats make up 35%, followed by the urban, bush, and pasture habitats exhibiting 9%, 3%, and 2% of the total, respectively (Figure 5).

**Figure 5.** The percentage of ignitions per land use class.

The model's quality was analyzed using the number of ignitions that occurred since the last monthly satellite imagery update of NDVI and S2-NDWI. Pursuing this objective, the number of ignitions since the production of the initial MDIR until the new MDIR was calculated (monthly) and the percentage of ignitions that fell in each of the five risk classes was determined as a validation test (Figure 6).

**Figure 6.** The monthly ignitions since the last MDIR model for the five risk classes during each year of the five-year study.

The monthly ignitions for the five-year analysis period regarding the MDIR model award a higher percentage of ignitions in the lower risk classes due to the high occurrence of these classes in the study area. Despite the lower representativity of the high-risk class areas, the number of ignitions follows a monthly trend, where a higher number of ignitions is observed in the months of June, July, and August, in accordance with the data in the literature [13,52].

The MDIR delivers a larger area of lower-risk classes, when compared to higher-risk classes (Figure 3); therefore, the representativity of ignitions is rather misleading regarding the number of ignitions occurring in the higher-risk classes. The relative representativeness of ignitions per area class was calculated using Equation (5), delivering a proportional percentage of occurrence per area class (Figure 7).

$$P\_{\%} = \left( \left( \frac{rA}{rNi} \right) / At \right) \* 100 \tag{5}$$

**Figure 7.** The rational representative percentage of ignitions per associated representative area.

**Theorem 5.** *The representative percentage of ignitions per associated representative area (P%) equals the relative area of each risk class (rA) divided by the relative number of ignitions in each class (rNi), divided by the total area of classes (At), and multiplied by 100.*

Using the class representative percentage (Equation (5)) allows a clearer interpretation of the success of the MDIR model (Figure 7), where the higher-risk classes "elevated," "major," and "severe" show a noticeable increase during the months of June, July, and

August, with over 50% of the ignitions occurring within these classes. The months of May and September, prone to weather variability, and concurring with the agricultural and forestry practices of slash-and-burn, may deliver a bias to the efficiency of the model.

Focusing on the representative area ignitions (Figure 7), the average model success, considering the higher-risk classes "elevated," "major," and "severe," is of 63%, 70%, 65%, 54%, and 55% for the years 2016, 2017, 2018, 2019, and 2020, respectively. The monthly success variability of each year can be attributed to weather conditions reflected in the model by the NDVI and S2-NDWI; the years evidencing most variability are 2019 and 2020, where additional policy measures may have played a relevant role. The total analysis of average success for the five-year period is 62%, which, if considered the preventive surveillance objective, should confer a significant reduction in the number of ignitions through early detection attributed to proximity monitoring [22].

The importance of the chosen variables was also analyzed by removing each individual variable/stressor from the MDIR model. The model's results for each individual run are presented in Appendix B, Figure A2. The averaged 2016–2020 comparative evaluation of variance from the final model's higher-risk classes "elevated," "major," and "severe" is expressed in Figure 8.

**Figure 8.** The area variance of the individual variable removal when compared with the MDIR model.

The variables "v. basins," "slope," "population d.," and "NDWI," when individually removed from the model, will reduce both the total area and the area with ignitions when compared with the model with the complete set of variables. "V. basins" and "slope" behave in a similar contributive way, where the total area input will be larger than that of the area with ignitions. Population density exhibits a larger contribution than the remaining variables, revealing the fragmented characteristics of the landscape with small, disperse, and numerous urban areas. The NDWI is the only variable where the area with ignitions increases more than the total area and whose contribution towards the model is rather positive. The contribution of NDWI towards the model's success derives from the fact that water content varies considerably throughout the season for this particular region where plants are well adapted to drought. The NDVI chlorophyll content alone may be misleading, whereas the cumulative characteristics of the model and the overlap of NDVI and NDWI will better assess the risk, hence the increase in the area with ignitions.

The considerable increase in the area with ignitions observed with the removal of "road networks," when compared with the modest increase in the total area, emphasizes the specificity of this variable in accounting for social ignition causes. Removing the "ignitions" proximity area from the model displays a balanced increase in both the total area and the area with ignitions, once again bringing to evidence the representativeness of this variable. Curiously, when removing "ignitions," all remaining variables behave in a similar unbalanced manner, either increasing or decreasing both the total area and the area with ignitions, once again revealing the disruptive influence of the social impact.

All the variables display a correlational trend between the total area and the area with ignitions, where area variance, as expected, influences the number of ignitions overlap. The geographic area representativeness of each variable certainly affects the model, a fact observed when "ignitions", "r. network," and "NDVI" variables are individually removed, increasing both the total and ignition areas, meaning that their presence will restrict the higher-risk areas, as expected from the model. The remaining variables, when individually removed, will decrease both areas, apparently indicating that their inclusion is not as favorable to the model, since their presence will increase the areas, most likely due to the higher geographic area representativeness, awarding areas with less representativity extra weight. Towards the current model's ambition of pinpointing specific high ignition risk areas for monitoring purposes, ultimately reducing these ignitions, results indicate that specific restricted geographic monitoring/intervention areas are of major importance, hence the representativeness towards model's success.

### **4. Conclusions**

The integrated analysis of spatial, slow, and dynamic variables, shaped by the QGIS software in combination with the InVEST HRA model, allowed the production of geographically specific MDIR risk maps, which favor the implementation of a facilitated monitoring effort and consequent reduction of monitoring costs. Considering the General Public License (GPL) of all software's used in the production of MDIR, along with the detailed description of the process, means that this approach may be replicated by researchers worldwide. This may be of particular interest to developing countries with restricted or no access to licensed software and quality imagery [36].

Sentinel-2 satellite imagery is a valuable, free monitoring tool, with acceptable resolution, for assessing the variation in chlorophyll and water contents. This asset allows a dynamic approach to risk maps, enabling near-real estimate analysis. While NDVI and S2-NDWI are correlated, S2-NDWI positively influences NDVI; the opposite is not mandatory, and hence, the importance of including both as model variables. Furthermore, S2-NDWI grants the necessary balance with other variables in the model that are mostly socially driven.

The MDIR approach grants the identification of high ignition-risk areas, and can easily be adapted to current monitoring strategies. The use of this methodology confers further efficiency and efficacy in reducing fire ignitions, thus granting early critical season evaluation for determining if a particular year presents a higher ignition risk. The approach delivered adequate results in estimating ignitions, achieving, in several month, accuracy percentages of over 70%. For the current case study, the MDIR model, within its domain of applicability, returns a satisfactory range of accuracy consistent with the intended monitoring application.

Understanding that many anthropic factors are difficult to estimate, MDIR produced quality maps identifying areas of high ignition risk, delivering a 62% rate of success in predicting ignitions. From another angle, the importance of possessing a free, easily applicable approach such as MDIR is the possibility to study the impact of new policy implementation or an increased monitoring effort when analyzing the model's accuracy.

Despite the interesting results obtained for determining risk areas, the MDIR model would also benefit from the analysis of the ignition causes that gave rise to large fires in order to test other dimensions and capabilities of this model.

**Author Contributions:** Conceptualization, L.S., C.B. and V.L.; methodology, L.S. and V.L.; software, L.S. and V.L.; validation, L.S., V.L. and C.B.; formal analysis, L.S.; investigation, L.S.; resources, V.L.; data curation, V.L.; writing—original draft preparation, L.S.; writing—review and editing, L.S. and C.B.; supervision, L.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data used in the current study is under access via the public Internet and GEANT networks, with committed reliability and performance on https://sentinels.copernicus. eu/web/sentinel/home, accessed on 1 November 2020.

**Acknowledgments:** We would like to thank the Tomar Center for Nature Protection and the Republican National Guard for granting access to the fire ignition data and for the precious territorial and fire ignition-related knowledge shared.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Appendix A**

**Figure A1.** *Cont.*

**Figure A1.** The Habitat Stressor Info Table.

### **Appendix B**

**Figure A2.** The comparative model results of the analysis of the variables influencing the model.

### **References**

14. Fernandes, P.M. Fire-smart management of forest landscapes in the Mediterranean basin under global change. *Landsc. Urban Plan.* **2013**, *110*, 175–182. [CrossRef]

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel. +41 61 683 77 34 Fax +41 61 302 89 18 www.mdpi.com

*Forests* Editorial Office E-mail<EMAIL_ADDRESS>www.mdpi.com/journal/forests

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel: +41 61 683 77 34

www.mdpi.com

ISBN 978-3-0365-4864-7


**5. Creatinine kinase**

Creatine Kinase (CK) is an intracellular enzyme present primarily in skeletal muscle, myocardium, and brain. Disruption of cell membranes due to hypoxia or other injury releases CK from cytosol to systemic circulation. CK is a dimeric molecule composed of 2 subunits, namely M and B. Combinations of these subunits form the isoenzymes CK-MM, CK-MB, and CK-BB. A significant concentration of CK-MB isoenzyme is found almost exclusively in the myocardium, and therefore elevations in CK-MB levels in serum is highly specific and sensitive for myocardial injury. Normal reference values for CK-MB range from 3 to 5% of total CK, or 5 to 25 IU/L. Creatine Kinase as a marker of myocardial injury has been largely replaced by troponin in clinical practice. As with troponins, several mechanisms explain the elevated cardiac markers in severe COVID-19: viral myocarditis, cytokinedriven myocardial damage, microangiopathy, and unmasked CAD. Myocardial ACE2 receptors are targets for SARS-CoV-2. A hypothesis is that SARS-CoV-2 induces indirect cardiovascular injury through activation of the immune system. The virus attaches to the pattern recognition receptors (PRRs), that initiate hostimmune defense. This host-immune defense system, in turn, induces inflammatory reactance that culminates in a cytokine storm. The cytokine storm is caused by the release of reactive oxygen species (ROS), endogenous nitric oxide (NO), and damage-associated molecular proteins (DAMPs) by the injured myocardium that ultimately leads to myocardial injury. Cytokines and host-immune dysregulation cause direct and indirect cardiac injury, leading to an increase in troponin and CK-MB. A meta-analysis showed that when compared with mortality, COVID-19 patients who died had significantly higher biomarkers, including CK-MB. Another meta-analysis showed that there was a significantly higher CK level in patients who died compared to patients who survived, whereas the patients who were critically ill did not have significantly higher CK levels compared to the patients who were not critically ill.


**5. Film formation and stability**

Recently, some efforts on spiro‐OMeTAD molecules used as HTM are under attention of researchers in view of their increased hole conductivities [86, 87]. For example, lithium bistrifluoromethanesulfonimide (Li‐TFSI) has been found out to increase the hole conductivity when doped to spiro‐OMeTAD, within some researches [87]. Using various techniques, spiro‐ OMeTAD has widely been used for enhancements in conductivity through increasing the hole

Within the cell, the holes are injected from the perovskite into the spiro‐OMeTAD and electrons are passed from perovskite to the mesoporous TiO2 layer. Here, therefore, the TiO2 is an active counterpart of the photovoltaic conversion event which makes it a main element of the PSC. Lately, it was fortunately shown that TiO2 scaffold has leaded a PCE of up to almost 10% [20, 74]. In some studies, spiro‐OMeTAD has also been used while coupled with other scaffolds (i.e., Al2O3). Where it is mixed with perovskites and good PCE has been attained in [30, 74]. These are famed as meso‐super‐structured SCs where the CH3NH3PbX3 is served as an electron transport material (ETM) in addition to its basic sensitizer duty. Besides with all that, the metal oxide only acts as an insulator with a wide band gap and therefore do not take part in charge transport. Hence, the metal oxide becomes an inactive scaffold here. For that, these are referred as passive scaffolds in the PSC literature. It is good to say that a more uniform perovskite layer formation is applicable with a wide thickness in both of the scaffolds which improves the light

Besides all, new kinds of small molecules as for HTM are continuously introduced to the literature. For example, a Trux‐OMeTAD small molecule is reported where an excellent hole mobility is achieved [90]. Here, the constructed PSC has given efficiency as close as to 19%.

*Inorganic HTMs:* The inorganic HTMs are rather low cost materials; on the other hand, they have progressed less in recent years due to the poor existence of such materials. The most known inorganic HTMs are listed as CuI, CuSCN, and NiO with respective PCEs of around 6, 12, and 11%. These inorganic HTMs are evaluated in mesoporous‐type PSCs. The main advantage is their stability yielding property in ambient conditions compared to the organic HTMs. On the other hand, they yield a low‐quality performance compared to the organic

*Polymer HTMs:* Compared to the inorganic counterparts, organic‐based HTMs have tunable oxidization potentials and surface morphology. In addition, high Voc values can be attained together with advanced HOMO levels [78, 91–93]. Basically, polymer HTMs have some drawbacks related to filling the pores which was tested in solid‐state DSSCs before. Some kinds of polymer HTMs are known to be PTAA, P3HT, and PANI. It has been shown that they have generally good hole mobility and satisfies a better film formation. Among them, PTAA and P3HT have yielded an efficiency level of around 12 and 6%, respectively [31]. The efficiency of PTAA has later been reported to reach more than 16% using a mixed structure with perovskite material [31, 94, 95]. There has been a study where P3HT is used together with carbon nanotube and formed a novel composite [96] in which the conductivity of the new structure is increased

mobility and charge density in HTM layers [88, 89].

290 Nanostructured Solar Cells

absorption of PSCs, thus lead an increase in efficiency [74].

HTMs which makes them disadvantageous against others, as well.

**4.3. Inorganic and polymer HTM**

The optical and electrical properties of perovskite films show varieties depending on the film formation conditions, such as atmospheric conditions and materials' ratios, which affect the device performance significantly. Up to the present time, several film techniques for fabrica‐ tions of perovskite have been reported. Generally, these are grouped as (1) solution process and (2) vapor deposition process. The solution processes can be carried out either as a (a) one‐ step coating technique, or (b) sequential two‐step coating technique. The vapor deposition processes are considered through two types of production as well, using (a) the dual‐source evaporation process and (b) vapor‐assisted solution process (see **Figure 6**).

**Figure 6.** Fabrication of perovskite cells through (a) one‐step solution‐based, (b) two‐steps solution‐based, (c) two‐step solution and deposition‐based, and (d) one‐step deposition‐based processes.

#### **5.1. Solution processed methods**

Two solution process methods have been developed in order to create perovskite film onto substrates: (1) one‐step coating and (2) sequential (two‐step) coating methods.The first deposition method is the most widespread film coating method for PSCs because of its ease of processing and low production cost. In general, the precursor solution contains the mixture of CH3NH3X (or other OMH perovskites) and PbX2 (where X: I, Br, Cl) at 1:1 or 3:1 molar ratios. This mixture is dissolved in a polar aprotic solvent, such as *N*,*N*‐dimethylformamide (DMF), *γ*‐butyrolactone (GBL), or dimethyl sulfoxide (DMSO). Nevertheless, it is still a challenging issue to form a homogeneous pinhole‐free perovskite film using the one‐step deposition process. Therefore, the device efficiency is significantly reduced by a poorly coated perovskite layer which causes decreased light absorption and also poor shunting path for charge recom‐ bination process [6, 97].

The second deposition method is another kind of solution‐based coating process for PSCs. This is first introduced by Mitzi et al. [98], and first used by Burschka et al. [82] for PSC fabrication. In this method, PbX2 is coated on TiO2 layer under optimal conditions that is adjusted through spin‐coating speed and solution concentration. The optimal coating conditions should yield full penetration of this material into the mesoporous layer. Subsequently, the perovskite layer is obtained either by dipping the substrate into a solution of CH3NH3X/isopropanol [82] or by spin‐coating of CH3NH3X molecules on to the substrate [99]. Here, the perovskite films coated by using the two‐step process have a cuboid‐like crystal structure, while the one‐step method provides a shapeless morphology. Among them the process can be much better controlled to form perovskite morphology by using the two‐step method. Namely, better PbX2 confinement into the nanoporous network of TiO2 is obtained using this method. Moreover, the two‐step sequential coating process provides more uniform and dense perovskite films compared to the single‐step coating process. Hence, high efficiency perovskite devices can be fabricated using this process. In addition to all, the two‐step process is both well controlled and provides a reproducible treatment.

In the two‐step coating process, the perovskite grain size can be controlled by changing molar concentration of the CH3NH3X solution. Nevertheless, there are some drawbacks of this coating method related to the trade‐off between surface smoothness and perovskite grain size. The perovskite films which have larger grain size also have poor surface morphology which may negatively affect the device efficiency.

The incomplete perovskite conversion is another challenging issue within the two‐step coating process. This problem can be overwhelmed using some developed device engineering techniques expressed in the study performed by Song et al. [100]. Recently, the PCE of a perovskite SC has reached 20% through the second‐coating method [101].