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Forest fires are disasters that cause extensive damage to the entire world in economic, ecological, and environmental ways. These fires can be caused by natural reasons, such as high temperatures that can create spontaneous combustion of dry fuel such as sawdust, leaves, lightning, etc., or by human activities, such as unextinguished campfires, arson, inappropriately burned debris, etc1. According to research, 90% of the world’s forest fire incidents have occurred as a result of the abovementioned human carelessness1. The increase in carbon dioxide levels in the atmosphere due to forest fires contributes to the greenhouse effect and climate change. Additionally, ash destroys much of the nutrients in the soil and can cause erosion, which may result in floods and landslides.
At earlier times, forest fires were detected using watchtowers, which were not efficient because they were based on human observations. In recent history and even the present day, several forest fire detection methods have been implemented, such as watchtowers, satellite image processing methods, optical sensors, and digital camera-based methods2, although there are many drawbacks, such as inefficiency, power consumption, latency, accuracy and implementation costs. To address these drawbacks, a forest fire detection system using wireless sensor networks is proposed in this paper.Wireless sensor networks (WSNs) are self-configured and infrastructure-free wireless networks that help monitor physical or environmental conditions and pass these data through the network to a designated location or sink where the data can be observed and analyzed3. Efficiency and low power consumption are the major advantages of a WSN. In the proposed detection system, wireless sensor nodes are deployed according to cellular architecture to cover the entire area with sensors to monitor temperature, relative humidity, light intensity level, and carbon monoxide (CO) level using a microcontroller, transceiver module, and power components. The power supply to the sensor node is provided using batteries as the primary power supply, and solar panels are used as the secondary power supply. These sensor nodes are specially designed with a spherical shape to withstand damage caused by environmental conditions as well as animals.The sensor readings for each parameter are checked with a preset threshold ratio and a ratio that is calculated continuously in the node in real time, and only the ratios that exceed the preset ratio are sent from the sensor node to the base station for further analytical processing. The network utilized for this transmission is in the architecture of tree topology considering facts such as low power consumption, reduced latency, less complexity, etc. Cluster heads are used in this network to gather data from several sensor nodes and pass them on to the base station or the gateway node. The gateway node is an interface that connects the network with the secondary analysis process.For the analysis process, a machine learning regression model was used along with threshold ratio analysis to enhance detection accuracy. For the training and testing process of the model, data were collected during the fire and no fire situations in different areas and under different climatic conditions. During the data collection process, 7000 data samples were collected, where a data sample included temperature, relative humidity, light intensity level, and CO level at a particular time. Eighty percent of the collected data were randomly used as training data for the model, and the remaining 20% were used as test data.If the outcome of the machine learning model indicates a fire in a specific area, a text message will be sent to the mobile phone numbers of the authorized officers in responsible units. As this process is designed with a minimum delay, the fire can be detected within the initial stage, and the responsible parties can take necessary actions in a shorter period, which will minimize the damage.Related workForest fire detection has been a focus of many researchers for the last decade because of increased forest fire case reports from all over the world due to severe damage to society and the environment. Many methods have been proposed to detect forest fires, such as camera-based systems, WSN-based systems, and machine learning application-based systems, with both positive and negative aspects and performance figures of detection. Due to the higher probability of accurate and early detection due to the use of multiple sensor sources and deployment of sensor nodes in areas not visible to satellites, wireless sensor networks have a more positive outlook, and they have become the more applicable technology in many fields4.Many researchers have focused on environmental parameters, such as air temperature, relative humidity, barometric pressure, sound, light intensity, soil moisture, and wind speed and direction, along with gases, such as CO, CO2, methane, H2, and hydrocarbons apart from smoke, to detect forest fire conditions by considering the variations in these parameters during a fire5,6, and sensors have been selected according to the range, sensitivity, power consumption, and cost7,8.As supplying power to a sensor node is a challenging task in forested areas, utilizing only battery options is difficult because they do not last long, and distributing power using a wire would require a higher cost to deploy in a large forest. Therefore, many researchers have proposed solar-powered systems as secondary power sources along with rechargeable batteries as the main power source4,6, while some researchers have proposed solar batteries because they last longer9. To reduce the power consumption of sensor nodes, techniques such as keeping selected components active while others are deactivated have been proposed10,11,12.Most WSN-based detection systems are centered around a base station due to the memory and processing limitations of the nodes. Important and partially processed data are transferred to the base station through wireless media for processing and enabling relevant actions, while the base station also acts as the gateway between the sensor nodes and the system user4,9.When constructing a WSN, communicating data among the relevant entities is the main objective, and star topology and mesh topology-based networks have been proposed in many papers because of the different attributes in their systems. A mesh topology was chosen over a star topology because of its ability to self-organize, self-configure, and automatically establish among nodes in a network13. As a smaller number of nodes involved for transmission results in minimum energy consumption, concepts based on cluster heads have been used14. To minimize the loss of energy and data packages during transfer, a cluster-tree network topology structure was proposed15. Considering the sensing range of a node, fault tolerance, and energy consumption, a paper has proposed applying the on-demand k-coverage technique that provides event detection using static nodes with variable sensing ranges. This technique utilizes the maximum detection performance with the minimum power consumption for an event16. A survey on rare event detection has mentioned many event detection strategies that deliver maximized detection capability, minimized detection delay and low energy consumption, such as duty cycle, component deactivation, overpopulation/node redundancy, collaboration, and energy harvesting17.To reduce deployment cost and power consumption, a paper proposes a novel localization scheme that divides the whole forest area into different grids and allocates them to respective zones with another 8 neighboring grids. One centroid node from those grids, which is called the initiator node, predicts whether the zone is highly active (HA), medium active (MA), and low active (LA). Here, HA zones send data continuously to the base node through the interior node, MA zones send data periodically, and LA zones do not transfer data in the status that manages power consumption effectively18. Another author proposed obtaining data from the sensors every 2 min if there is the potential of a forest fire or obtaining data every 15 min otherwise to reduce the energy wastage19.To place sensor nodes in the most effective configuration to detect fire conditions, a sensor node was proposed at three different heights to perform different parameter measurements, while some authors have suggested covering sensor nodes to avoid direct sunlight exposure and minimize the false alarm rate4,10. As the network connectivity of service providers in forest areas is not robust, communication techniques that use dedicated network paths such as LoRa, ZigBee, and XBee have been used as the communication infrastructure. When considering attributes such as transmission range, high security, low power consumption (LPWAN protocol), and other relevant configurations, most papers have suggested using the LoRa module for transmission13,19.Most papers have suggested having threshold value-based fire detection on a sensor node, and if the exceeded threshold has remained the same, then a sink determines the location and will send an alarm to the fire department11. Because of the environmental parameter variations according to the place and time, threshold values are configured by the user considering geographic situations, climatic changes, seasonal changes, etc. after sensors obtain the data from the surrounding10.A fusion information process was proposed, where information from multiple sources is considered in making the final decision, which is better than using those sources individually, and two algorithms based on the threshold ratio method and Dempster-Shafer theory were used4. To enhance the detection accuracy, machine learning applications have been proposed in many papers based on different machine learning approaches, such as support vector machine (SVM) classification14 and regression techniques, such as logistic regression. However, applying machine learning techniques to fire detection systems has many limitations, such as the limited amount of energy, the energy required for data processing, the short range of communication and limited computations, the complexity of ML algorithms when executing on sensor nodes, and the difficulty of being distributed on every sensor node20,21. More

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Symbiont diversity and distributionThe present study provides the first evidence of symbiosis in R. alisae, a species of nudibranchs. This is the most multiple symbiosis that have ever been recorded for marine invertebrates. While many organisms establish an exclusively one-on-one relationship with a single microbial species or microbes belonging to the same functional group5,12, there are also organisms that harbor multiple microbial species, in which symbiont–symbiont and host–symbiont interactions occur. Six phylotypes of chemoautotrophic bacteria were reported for mussel Idas sp. from a cold seep area11 and five extracellular symbionts for the gutless oligochaete worm Olavius algarvensis34. However, in these cases, symbioses involving bacteria and marine invertebrates are either endosymbiotic microbes co-occurring inside the host bacteriocytes5,11 or ectosymbiotic microbes associated with the external surfaces of the animals3,4,9,15,34, with the exception of scaly-foot snail from hydrothermal vents having partnerships simultaneously with epi- and endosymbiontic microbes35.Bacterial symbionts in R. alisae have appeared to be more diverse than was previously known for marine invertebrates. It is evident that the detected symbiont phylotypes differ greatly from all other known symbionts found in marine invertebrates. Labrenzia (Rodobacteriales) and Maritalea (Rhizobiales) have not been recorded as forming symbiotic associations with invertebrates or plants so far, although other members of the families Rodobacteriales and Rhizobiales are well known symbionts14. Strains of Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas are reported as symbionts of plants, interacting with a vast majority of nodulating legume species and efficient in biological nitrogen fixation36. This may be important when considering the nature of these symbionts in the nudibranch. Symbioses between cyanobacteria and marine organisms are commonly found among marine plants, fungi, sponges, ascidians, corals, and protists37,38. Synechococcus, identified as dominant symbiont clones of R. alisae (Table S2), is a unicellular cyanobacterium common in the marine environment, providing a range of beneficial functions including photosynthesis, nitrogen fixation, UV protection, and production of defensive toxins8,9,37. Symbiotic interactions between actinobacteria and their host have been observed in insects, human, animals, and plants, where the bacteria provide the host with protection against pathogens and produce essential nutrients39. However, none of the members of the clade Actinobacteria recorded in R. alisae are known to live symbiotically.Arrangement of symbiotic associationDespite the high diversity of bacteria, they are well organized in the host. Dense clusters of rod-shaped bacteria, Labrenzia, Maritalea, Bradyrhizobium, Burcholderia, Achromobacter, and Stenotrophomonas, were found within host-derived vacuoles, referred to as bacteriocytes, inside epithelial cells of R. alisae (Fig. 3). Although such arrangement differs from that typical of bacteriocytes, which are usually considered as specialized cells of the hosts for harboring bacteria, it resembles that reported for scaly-food snail from hydrothermal vents, which harbor symbionts in the esophageal gland35. Bacteriocytes in the gastropod Lurifax vitreus found near hydrothermal vents also constitute a portion of the mantle epithelium; they have large vacuoles containing many live and dividing bacteria40. Each bacteriocyte was densely packed with certain symbionts, and the bacteriocytes were randomly distributed within the epithelium cells. A distinctly regular distribution pattern was observed in the gill epithelium of the mussel Bathymodiolus sp.: the thiotrophic symbionts occupy the apical region, and the methanotrophic symbionts are more abundant in the basal region of bacteriocytes4. In the mussel Idas sp., however, there is no spatial pattern of the six distinct bacterial phylotypes, and the symbionts are mixed within bacteriocytes11.Synechococcus dominated the cytoplasm of intestinal epithelium and, more rarely, epidermis cells, mainly as specialized cell type referred to as nitrogen-fixing heterocysts. They are visually similar to cyanobacteria from corals and sponges8,37.The phylogenetic diversity and the spatial organization of the symbiotic community in R. alisae were determined by the 16S rRNA analysis, which was consistent with the results of FISH and TEM. Unlike most symbioses of marine invertebrates when bacteria house specialized host cells5,11 or cover epidermis7,15, symbiotic association of R. alisae exhibited spatial partitioning between symbionts, which were unevenly distributed between the tissues (Table S2). It has been established that different members of the microbial community can complement each other in acquisition of various restrictive nutrients, confirming the importance of the functional diversity of symbionts41. Thus, Stenotrophomonas rhizophila and Bradyrhizobium build a beneficial association in the rhizosphere and can act synergistically on promoting growth and nutrient uptake of soybean36. Cyanobacteria can interact synergistically with beneficial members from the endophytic microbiome of rice seedlings42. The location of bacterium in the organism of R. alisae may, in fact, depend on the specific metabolic and ecological roles that the symbionts play, and also on the interaction with bacterium belonging to different physiological groups.Nature of symbiosisSymbiotic associations between microbes and invertebrates are acquired mainly in a nutrient-depleted environment where symbionts usually provide their hosts with essential nutrients and high-energy compounds1. In contrast to known symbioses between microbes and gutless invertebrates, which obtain nutrients exclusively from the bacteria, R. alisae, like most nudibranch species, is a sponge-eating predator. However, due to the lack of adipose tissue, sponges are distinguished by a low lipid content (0.4 to 1.5% of wet weight)43 and also by specific proteinaceous spongin fibers and chitin, a polysaccharide similar to cellulose that can be indigestible for some predators, which together indicate their low nutritional value. Furthermore, R. alisae feeds exclusively on the sponge O. pennata; therefore, in habitats with low prey availability, this nudibranch has to survive starvation while searching for sponge assemblages. We suppose that symbiotic bacteria of R. alisae contribute to the utilization of low-quality food, similarly to symbiotic bacteria from the genera Rhodobacter, Burkholderia, and Aeromonas associated with the detritivorous isopod Asellus aquaticus44.A fatty acid analysis, as a useful approach to clarifying the nature of symbiosis5,20,32, has confirmed the trophic interaction between symbionts and the nudibranch host (Table S2). Among the fatty acids of symbiotic bacteria in R. alisae, OBFA are a major acyl constituent of membranes in Stenotrophomonas45 and also in Actinobacteria, Arthrobacter, Iamia, Ilumatobacter, and Kocuria46. Cis-vaccenic acid is a major component of Maritalea30. Omega-cyclohexyl tridecanoic acid (cyclo19:0) is specific to Bradyrhizobium47, Burkholderia, and Achromobacter48. Linoleic acid is produced by cyanobacteria including marine species of Synecoccocus33; in nudibranch, it obviously serves as a precursor in the synthesis of arachidonic acid (20:4n-6), thus, providing additional evidence for the transfer of fatty acids from symbionts to the host. Mollusks are capable of converting linoleic acid to arachidonic acid, since they have enzymes required for its synthesis21. The presence of these bacteria-specific markers and the abundance of arachidonic acid confirm the metabolic role of symbionts in the nudibranch host.Among nutrients, biologically available nitrogen can be considered a restrictive nutrient for the sponge-eating R. alisae, which can be acquired with the help of nitrogen-fixing symbionts, also referred to as diazotrophs. R. alisae harbors Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas that are efficient in biological nitrogen fixation previously found to be associated with nodulating legume species36. Symbiotic nitrogen fixers are known to be associated with a variety of marine invertebrates such as wood-boring bivalves, corals, sponges, sea urchins, tunicates, and polychaetes7,8,37. Moreover, the protection of the enzyme nitrogenase that catalyzes N2 fixation against oxygen is an important physiological requirement in bacteria such as symbiotic Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas that are located in bacteriocytes and provide this protection. Synechococcus is known as a nitrogen-fixer37,49. It performs N2 fixation in heterocysts where nitrogenase is restricted under oxic conditions. Indeed, heterocysts of Synechococcus are abundant in the intestine cells of R. alisae (Fig. 5B–D).Nitrate assimilation is one of the major processes of nitrogen acquisition by many heterotrophic bacteria and cyanobacteria50,51. Symbionts of R. alisae can play an important role in the process of nitrate utilization through denitrification, dissimilatory nitrate reduction, and assimilatory nitrate reduction as a nitrogen source and synthesize it into organic nitrogen. The nitrate reducers, Labrenzia52, Stenotrophomonas53, Maritalea30, and Rhodobacteraceae29 are widely represented in R. alisae. Synechococcus also utilizes nitrate, nitrite, or ammonium for growth50. Thus, symbiotic bacteria may play a significant role in the N-budget of the nudibranch mollusk.The symbiotic bacteria of R. alisae, including Bradyrhizobium, Maritalea, Labrenzia, Burkholderia, Achromobacter, Stenotrophomonas, Arthrobacter, Iamia, Ilumatobacter, and Kocuria, are known as carboxydotrophic or carbon monoxide (CO) oxidizers54,55. Despite the toxicity of CO for multicellular organisms, numerous aerobic and anaerobic microorganisms can use CO as a source of energy and/or carbon for cell growth56. The marine worm Olavius algarvensis establishes symbiosis with chemosynthetic bacteria using CO, a substrate previously not known to play a role in symbiotic associations with marine invertebrates, as an energy source57. We do not rule out that the R. alisae symbionts also might exploit CO as carbon and energy source. Despite this, assumption may seem impossible taking in account the CO toxicity, but, since many invertebrates (mollusks, tube worm, etc.) use toxic sulfate, thiosulfate, and methane as an energy source1,15, this hypothesis is worth to be addressed.An important component of skeleton in marine sponges of the family Microcionidae, including O. pennata, is the structural polysaccharide chitin58. Some bacteria are capable of hydrolyzing chitin via the activity of chitinolytic enzymes and can utilize chitin as a source of carbon, nitrogen, and/or energy59. Chitinase activity was documented for strains of Labrenzia60, Burkholderia61, Arthrobacter62, Achromobacter63, Stenotrophomonas64, Alcaligenes65, and actinobacteria59 associated with R. alisae. Thus, these bacteria can work synergistically to digest chitin and spongin, contributing to feeding success of the host nudibranch which depends solely on low-quality, nitrogen- and carbon-deficient food available.Furthermore, direct evidence has confirmed that many bioactive compounds from invertebrates are produced by symbiotic microorganisms66,67. Many biologically active compounds including toxic and deterrent secretions have been identified in nudibranchs of the family Discodorididae68. Symbiotic bacteria may exhibit toxic activity to provide the host nudibranch with chemical defense against predators and environment. Bacteria, especially actinobacteria, living in a symbiotic relationship with R. alisae may help the host in defense, since nudibranch lack a shell, and secondary metabolites of bacteria can provide them with chemical defense against predators and environment, as has been reported for some marine invertebrates2,9,10.In complex associations, the integration and coexistence of symbionts depend on supplementary partnerships and mutual contribution to the host’s metabolism41. The most intensively studied cases are highly specialized associations, where both partners can only exist in close relationship with one another. The relatively high diversity of microbes in R. alisae complicates understanding the complex pattern of molecular and cellular interactions between the host and its symbionts. More

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Description of study areasIGR plant (33° 42/N, 46° 13/E) is located along the edge of the mountains of Zagros forests and 25 km from Ilam city. Its main activity, to supply gas to the western provinces of Iran, started in 2007. It converts sour gas to sweet gas and also produces various products such as pastil sulfur, ethane, and liquefied gas. The refinery has two chimneys, which release waste gases into the atmosphere. Oak trees are the main tree species of the Zagros forests around the refinery; these are exposed to various air pollutants and different elements from this source. Based on random analysis of exhaust emissions, sulfur dioxide and sulfide hydrogen are the major pollutants emitted from the flare gases of this refinery plant34. The sampling points have an average altitude of about 1000–1250 m and a slope of less than 20%. The climate of the region is semiarid and influenced by Mediterranean winds. The predominant wind direction was west and southwest. The highest and lowest air temperatures were 41.4 °C and − 11.3 °C, respectively. The average annual rainfall was 71.94 mm (http://www.amarilam.ir).Samples collection and analysesAll methods were carried out in accordance with the relevant institutional, national, and international guidelines and legislation. Besides they were discussed and approved by the Research Ethics Committee of Tarbiat Modares University. The formal identification of the Quercus brantii Lindl. was performed by H. Dadkhah-Aghdash based on colorful Flora of Iran35. The permissions or licenses to collect Brant oak (Quercus brantii Lindl.) trees in Zagros forests were obtained. A voucher specimen of Brant oaks were collected and deposited at the Herbarium of department of Plant Biology of Tarbiat Modares University.We studied different distances (1000, 1500, 2000, 2500, and 10,000 m [control]) in an easterly direction from the gas refinery. The map of study area was drawn by software of ArcGIS version of 10.5, https://desktop.arcgis.com (Fig. 5). At each distance, three soil samples taken from the depth of 0–20 cm with a plastic gardening shovel, 30 healthy and mature leaves were collected from a certain height (nearly the middle of the canopy) and the outer canopy of three Brant oak trees in the late spring, summer, and autumn of 2019. These trees with average height and diameter at breast height of 5.5 m and 45 cm were selected randomly. The leaf and soil samples were put into polyethylene bags and transported to the laboratory for analysis36.Figure 5Locations of collection sites of soil samples and Brant oak leaves at five different distances (1000, 1500, 2000, 2500 and 10,000 m [control]) from the gas refinery (drawn by H. Dadkhah-Aghdash using software of ArcGIS Desktop. version of 10.5. ESRI, California, US. https://desktop.arcgis.com).Full size imageIn the lab, firstly the leaves were categorized into two types: unwashed leaves and leaves washed with ethylenediaminetetraacetic acid (EDTA) solution to remove some atmospheric dusts and particles deposition. The leaf and soil samples were dried for 10 days until they reached a constant weight at lab temperature. The leaves were grinded and homogenized, soils were sieved with ASTM mesh (DAMAVAND, Iran) with a diameter of 2 mm and homogenized.To determine the pH and electrical conductivity (EC) of soils, 2 g of the soil samples were shaken in 10 ml of double-distilled water with a ratio of 1:5; after 1 h, the pH and electrical conductivity (EC) of the solution were measured by a digital pH meter (Fan Azma Gostar Company, Iran) and EC meter (Sartorius, PT-20, USA). The analysis of the particle sizes of the soil was carried out using the hydrometer method and texture class was determined with a soil texture triangle37.According to different U.S.EPA protocols that were modified by following references, the soil and leaf samples were prepared and dissolved. The digestion of soil samples was conducted with a mixture of concentrated HF–HClO4–HNO338. Approximately 0.5 g of dry soil sample was digested with 10 mL of HCl on a hot plate at ~ 180 °C until the solution was reduced to 3 mL. Approximately 5 mL of HF (40%, w/w), 5 mL of HNO3 (63%, w/w), and 3 mL of HClO4 (70%, w/w) were then added and the solution was digested. This process was continued with adding 3 mL of HNO3, 3 mL of HF, and 1 mL of HClO4 until the silicate minerals had fully disappeared. This solution was transferred to a 25 mL volumetric tube, and 1% HNO3 was added to bring the sample up to a constant volume for the element’s determinations. After filtering the digested samples, the concentrations of sulfur (S), arsenic (As), chromium (Cr), copper (Cu), lead (Pb), zinc (Zn), manganese (Mn), and nickel (Ni) were measured via inductively coupled plasma mass spectrometry (ICP-MS,7500 CS, Agilent, US). The procedures of quality assurance and quality control (QA/QC) were performed.To quantify element contents from soil samples, external standards with calibration levels were used. The precision and the repeatability of the analysis were tested on the instrument by analyzing three replicate samples.According to Liang et al.39 leaf samples were acid digested and sieved powder samples were placed in the acid-washed tubes and 10 mL of 65% nitric acid was added to it. The solution was placed at room temperature overnight (12 h) after that, it was placed for 4 h at 100 °C and then 4 h at 140 °C until the solution color was clear. After cooling, the solution was diluted by deionized water to 50 mL and then passed through Whatman filter paper until 25 mL of the filtrate volume was provided. Each sample was digested three times and the average of measurements is reported. Total plant elements were measured by using the ICP-MS (7500 CS, Agilent, US). A control sample was also used beside each sample to determine the background pollution during digestion. To confirm the accuracy of the methodology and to ensure the extraction of trace elements from the leaf samples, the standard solution of each studied elements was used.Measuring of pollution levels of different elements in soils and leavesFor assessment of contamination levels (concentration) of different elements in soils and trees, common indices of pollution including geoaccumulation index (Igeo), pollution index (PI), pollution load index (PLI), enrichment factor of plants (EFplant), bioconcentration factor (BCF), air originated metals (AOM ), metal accumulation index (MAI) were used.Igeo was calculated using the following (Eq. 1):$${text{I}}_{{{text{geo}}}} = log_{2} left[ {{text{C}}_{{text{n}}} / 1.5{text{ B}}_{{text{n}}} } right]$$
(1)
where Cn is the measured concentration of the element n, Bn is the geoaccumulation background for this element and 1.5 is a constant coefficient used to eliminate potential variations in the baseline data40. The Igeo classifies samples into seven grades:  5 for extremely polluted30.The first PI is expressed as (Eq. 2):$${text{PI }} = {text{ C}}_{{text{i}}} /{text{S}}_{{text{i}}}$$
(2)
where Ci is the concentration of element i in the soil (mg kg−1) and Si is the soil quality standard or reference value for element i (mg kg−1). The PLI for different elements is calculated via the (Eq. 3):$${text{PLI}} = left( {{text{PI}}_{{1}} times {text{ PI}}_{{2}} times {text{ PI}}_{{3}} times cdots times {text{PI}}_{{text{n}}} } right)^{{{1}/{text{n}}}}$$
(3)
The PLI of soils is classified as follows: PLI  More

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