Aspergillus were also isolated from the surface of the rebars. Additionally, spore-forming bacteria of the genus Bacillus immobilized for samples from two years ago. This indicates the high viability of immobilized strains in cold climates. Aboriginal microflora isolated by the enrichment culture technique from the samples was represented by: actinobacteria of the genera Nocardia and Streptomyces; yeast of the genus Rhodotorula; and mold fungi of the genus Penicillium. It was shown that the enrichment culture technique is a highly informative method of diagnosing the bio-infection of polymer composite materials during their operation in extremely low temperatures. The metabolic activity of the cells of cryophilic microorganisms isolated from experimental samples of basalt fiber reinforced plastic rebars was associated with the features of the enzymes and fatty acid composition of the lipid bilayer of cell membranes. In the case of temperature conditions when conventional (mesophilic) microorganisms stop developing vegetative cells, the process of transition of the lipid bilayer of cell membranes into a gel-like state was activated. This transition of the lipid bilayer to a gel-like state allowed the prevention of crystallization and death of the microbial cell when the ambient temperature dropped to negative values and as a result, after thawing, growth resumed and the metabolic activity of the microorganisms was restored. Studies have been carried out on the effect of biodepletion on the elastic strength characteristics, porosity and monolithicity of these materials, while at the same time, after a two year exposure, the strength preservation coefficient was k = 0.82 and the porosity increased by more than two times. The results show that the selected strains affect the properties of polymeric materials in cold climates in relation to the organic components in the structure of polymer composites.For the first time, the possibility of penetration of mold fungi mycelium and spore-forming bacteria into the structure of basalt fiber reinforced plastic rebars has been shown in laboratory and field experiments. Biological contamination at the “fiber-binding” border reveals areas of swelling and penetration of mold fungi mycelium and bacterial spore cells into the binder component. After the exposure of samples at extremely low temperatures, strains of mold fungi of the genus  Damage from corrosion and aging increases due to biological damage to materials in a wide variety of operating conditions . In many cases, biocorrosion is the dominant cause of the destruction. Currently, the study of the biological impact on polymer composite materials (PCM) is one of the key areas not only in microbiological science but in a number of other fundamental and applied disciplines related to the study of the properties of materials and their production ,2,3,4.Laboratory and natural tests search for new environmentally friendly biocidal additives and ways to protect metals, nonmetals and petroleum products from exposure to biopests ,6,7,8.There has been more evidence on microbial contamination and the impact of this process on the destruction of PCM. The incidence of microorganisms is most significant in geographical areas with a relatively high air temperature, high humidity, and abundance of organic dust (tropics and subtropics) ,10,11,12Aspergillus, Penicillium, Trichoderma, Cladosporium, Fusarium and bacteria of the genera Pseudomonas, Streptomyces, Bacillus, and Arthrobacter that destroy polymer compounds under oxidation conditions [The effect of microorganisms on polymeric samples causes their biodegradation to varying degrees. This is due to both the composition of polymeric materials and the different activity of different types of microorganisms with highly active extracellular hydrolases, phosphatases and other enzymes. First of all, these are fungi from the genera nditions ,14. Thisnditions . Neverthnditions .The choice of basalt fiber as a fibrous reinforcing component among various polymer composite materials (PCM) is based on such positive properties as high strength, heat resistance, resistance to chemically active compounds ,20,21,22However, the question of the role of contamination of materials by cryophilic microorganisms and changes in the properties of PCM under the influence of biological effects during their operation in the climatic conditions of the Far North remains poorly understood . The topThe ability of a cryophilic group of microorganisms to adsorb materials from the external environment depends largely on the adhesive properties of microbial cells. Adhesive properties are characteristic of many cryophilic bacterial associations and microscopic molds capable of growing at temperatures from 4–5 to 15–20 °C. Under favorable conditions , the processes of adhesion are replaced by the process of penetration of microorganisms into microcracks. They accumulate their biomass, resulting in internal stresses that lead to the expansion and branching of microcracks. As a result, deterioration of the strength properties of PCM occurs ,27; howeIt is worthwhile investigating the issue of bio-infection of BFRP rebar by cryophilic microorganisms and studying the mechanism of biodamage of basalt fiber reinforced plastic rebars in an extremely cold climate. This will allow us to create a database of microbiological agents of biodeterioration of PCM during their long-term use in environmental conditions. The obtained results can further serve as the basis for the development of compositions for the prevention of biological damage to BFRP rebar and other polymer composite materials (PCM) during long-term storage, operation and for inactivation of biodestructive microflora, which can be stored for hundreds of years buried in the earth and can be discovered and carried by air during construction and earthworks, settling on surfaces, causing the biocontamination and biodeterioration of construction materials, in particular BFRP rebar.The purpose of this investigation was to identify and study the cultural and biochemical characteristics of cryophilic microorganisms capable of bioinfection of PCM, in particular BFRP rebar, at negative temperatures in an open ecosystem and the impact of this process on changes in the properties of the BFRP rebar.The reinforcing member was RBN 13-2400-4S basalt roving with a 13 microns diameter fiber . The nominal linear density of roving was 2400 tex. The breaking load of the roving was not less than 320 mN/tex. The binder was an epoxy resin ED-22 , which was “hot” cured with a suitable amount of hardener in the presence of an accelerator. The hardener, isomethyltetrahydrophthalic anhydride (iso-MTHPA) , was chosen because the systems cured with it have high mechanical properties, excellent waterproofing properties, good electrical properties, and resistance to climatic impact. A 2,4,6-tris(dimethylaminomethyl)phenol (UP 606/2) accelerator was chosen due to its high catalytic activity .Specimens of BFRP rebar with diameters of 6, 8, 12, and 20 mm, and with a height of 1000 mm were manufactured. The main stages of manufacturing the BFRP rebar included: continuous basalt roving  impregnated with thermosetting epoxy resin; some of the impregnated rovings were pulled through the central spinneret forming a forced (8 kgF) inner rebar with a diameter of 4, 8 mm, respectively, then this rebar was cured; some of the impregnated rovings were pulled through the peripheral spinneret forming an outer layer and fixed to the cured inner rebar and after that were wrapped with a bundle of polyamide yarns to increase the adhesion of rebar with mating materials .Technical Specifications 2296-001-86166796-2013 “Non-metallic composite armature made from basalt plastic”.The BFRP rebar was prepared in accordance with the The profile and key of the rebar sizes are shown in 2.The biofouled BFRP rebars were exposed at open stands in the extremely cold climate of the city of Yakutsk for 24 months . YakutskThe study of the impact of biofouling on the properties of BFRP rebar at low temperatures was carried out in three stages.At the first stage, rinse sampling, the enrichment culture technique in the mineral medium and native application of the test samples on nutrient agar were used to isolate microorganisms.For rinse sampling, buffered peptone water was preliminarily prepared with the following composition (gram/liter): proteose peptone-10.00; sodium chloride-5.00; sodium hydrogen phosphate-3.50; and potassium dihydrogen phosphate-1.50. Buffered peptone water was used as a means to increase the efficiency of the diagnostics of BFRP rebar biofouling by cryophilic microorganisms.The ingredients of the above-described composition were stirred in 1 L of distilled water, after which the pH was adjusted to 7.2 ± 0.2 conventional units and poured into sterile glass bottles of 50 mL.Vials of the buffered peptone water were sterilized at 1.1 atmosphere (121 °C) for 15 min.After sterilization, the buffered peptone water vials were cooled at room temperature.Then, the required number of pre-sterilized biological test tubes with cotton swabs were prepared for taking rinse samples from the BFRP rebar samples No. 1–4, exhibited at the climatic test site.In the prepared test tubes with cotton swabs, 2 mL of the cooled sterile buffered peptone water were aseptically poured. Then, the BFRP rebar samples were swabbed, and the samples were immediately brought to the laboratory for microscopic examination for the preliminary express diagnostics for bio-infection.To obtain the enrichment culture of the microorganisms, rinsing liquid residue from BFRP 1–4 samples in the above method were incubated within 24 h at room temperature. After that, they were moved to a meat-peptone agar (MPA) and an industrially prepared nutrient agar of Saburo and Chapeka .To isolate the cryophilic microorganisms, enrichment of the cultures on the prepared nutrient media was carried out in stationary conditions in the refrigerator at 4 ± 1 °C.To diagnose bio-infection by the native method, about 6 mm thick layers of nutrient agar were poured into sterile Petri cups or containers of the required size. After hardening, they were dried in a thermostat TS-80  at 37 °C for 40 min. The nutrient agar was dried using the method described in “On Unification of Microbiological  Methods Used in Clinical Laboratories of Health Care Facilities”. After that, the prepared samples were laid out on the surface of the hardened nutrient medium in the form of rebars 20 cm long, pre-prepared from the sample No. 4. The containers were covered with lids, placed in the refrigerator and incubated at 4 ± 1 °C to record the growth of the cryophilic microorganisms and at 25, 30 and 37 ± 1 °C to record mesophilic microorganisms for 5, 3, and 1 day, respectively.To isolate the pure cultures and study their cultural and biochemical properties, the resulting colonies were re-inoculated in Petri dishes on the surface of the MPA with subsequent re-inoculation on agar slanted in biological test tubes and biochemical tests.To determine the relationship of microorganisms and to build phylogenetic trees, the sequences were processed using software from the RDP-II website  and conventional methods of genetic identification ,29,30,31In the second stage, in order to study the process of biodamage of the BFRP rebars by the microorganisms, the 20 cm long test samples of warehouse storage No. 4 were placed in the soil at a depth of 20 cm. The experimental exposure lasted 1 year.A year later, the samples were removed from the soil and microbiological studies of rinsed material taken from the BFRP rebars were carried out. The separation of microorganisms was carried out in the ways described above, followed by identification.A working collection was formed from the dominant cultures that is promising for biotechnological applications and studying selected microorganisms as PCM destructors.At the third stage, biological products were obtained from the working collection of microbial cultures, which were used to cover the BFRP rebars warehouse storage and 1000 mm long contaminated samples were placed for exposure at the climatic test site.The monolithic test method was carried out in accordance with the Russian standard GOST 32486-2015. The test method was designed to assess the integrity of composite reinforcement and establishes the procedure for determining the penetration of liquid with dye in its longitudinal direction. Longitudinal porosity was determined by controlling the penetration of the dyed liquid into the composite reinforcement. Penetration occurs as spots or points displayed on the opposite dry end of the sample. A 0.25% fuchsine solution dissolved in ethyl alcohol according to the standard GOST 18300 was used as a penetrating liquid. Samples No. 4 of 20 mm diameter for warehouse storage after a 28 month exposure at the test site and biofouled ones after a 24 month exposure, were compared. Five samples 5–6 mm long were cut out from the BFRP rebars. The samples were placed in the Petri bowl with the end down on a thin mesh substrate laid on the bottom of the Petri dish in order to fix the sample, evenly moisturize the lower surface of the samples and reduce air involvement. The dye was poured so that the lower end was covered with dye by 1–2 mm. The time was measured until the first spot appeared on the opposite end of the test sample.To determine the open porosity, the method of hydrostatic weighing was used. Due to its high penetrating power, kerosene was used as an impregnating liquid. To carry out the test, samples with lengths of ~30 mm and 5 pieces of each diameter, were cut from the BFRP No. 1 from the warehouse storage and exposed rebars. Before testing, the samples were pre-dried in a ShSV-65 (3.5) vacuum drying cabinet  at a temperature of 50 °C for 5 h. To weigh the samples in air and in kerosene, an analytical balance Vibra HTR-220CE  with a measurement error of 0.1 mg and range of 0.01–220 g, accuracy class I.m is the mass of a dry sample in air; m1 is the mass of the impregnated sample in the liquid; and m2 is the mass of the impregnated sample in air.According to the method of hydrostatic weighing, the formula for determining the open porosity of the coating is as follows:Sample preparation.m) of samples.Measurement of dry mass (Impregnation of samples in kerosene for 1 day.m2) in air.Weighing of impregnated samples (m1) in kerosene.Weighing of impregnated samples Polarizing microscope Axiolab Pol  at ×1000 magnification.(2)Scanning electron microscope G1200 12MP 1-1200X  at ×10 magnification.Micrographs of the samples were obtained using:Tensile tests were carried out on the Z600 Zwick/Roell  universal test machine according to the standard GOST 32492-2015 “Composite polymer reinforcement for concrete structures”, “Methods for determining physical and mechanical characteristics”.Tensile tests were carried out at room temperature +20 °C, at a relative humidity of 40%. The calculated length of the sample during the tests was 200 mm. The preload speed was 10 mm/min. Test speed was 5 mm/min.BFRP No. 1 of a 6 mm diameter from warehouse storage after 28 month exposure at the test site and biofouled ones after a 24 month exposure were used for comparative analysis.Three groups of cryophilic microorganisms were isolated from samples of BFRP No. 1–4 exhibited in winter in an open climate test site at extremely low temperatures: bacteria, actinobacteria and microscopic mold fungi . On the The received rinse material was planted on the surface of the nutrient agar, then it was incubated in a refrigerator at a temperature of plus 4 ± 1 °C. Already after seven days of incubation on the plates with nutrient media, a full growth of microorganisms was noted, which were identified by cultural-biochemical, morphological and microscopic methods .Bacillus, actinobacteria of the genus Nocardia and microscopic mold fungi of the genera Aspergillus and Fusarium were isolated from fragments of the BFRP rebars by the above method of culture accumulation.Bacteria of the genus It was established that the share of viable cells in the rinse fluid was 33%. The isolated microorganisms are distributed in the following ratio: bacteria–23% and mold fungi–33%, actinobacteria capable of forming substrate and air mycelium–44%.At the same time, the method of native application of the BFRP No. 4 test samples onto nutrient agar was studied .Bacillus subtilis and Streptomyces albus in agar-based medium after 72 h. Scale 1:1.Typical fouling of the BFRP No. 4 by colonies of Bacillus, and Pseudomonas; actinobacteria of genera Nocardia, and Streptomyces and microscopic mold fungi of genera Aspergillus and Fusarium  and 0.5–2.0 µm (spore diameter) for Streptomycetes; 0.5–2.0 µm (hyphae diameter) for Nocardia; 2.5–5.0 µm , 8–12 µm (chlamydospores), 30 × 5 µm (macroconidia) for Fusariums; 4–6 µm (mycelium width), 7–10 µm (hyphae), 2–3.5 µm (spores) for Aspergillus.The microscopic method showed that the cell size of bacillary strains from the surface of the BFRP rebar test samples was 0.7–3.0 µm for Aspergillus niger and Aspergillus fumigates, in a ratio of 1:1 with a concentration of at least 1 × 109 cells per 1 cm3, liquid biological products were made, which were used to process the rebars of BFRP samples in order to study their biodamaging properties.Based on the selected mold fungus cultures, Samples 1000 mm long of basalt plastic rebars prepared for the experiment were infected with a liquid biological product by immersion in a bath following the standard, GOST R MEC 60068-2-10-2009 “Tests on external factors. Part 2–10. Tests. J test and manual: Fungal resistance.” The method is easy to use and promotes better adhesion of spores to the surface of the contaminated material. After infection with spores, the rebars of the BFRP rebar were dried by the contact method and exhibited on the territory of the open climate test site in Yakutsk for 12 months.Aspergillus was discovered in a sample of BFRP rebars by microscopy, which indicates their survival in cold climates.A year after exposure, the genus The growth of mycelium was recorded both in the direction along the length between the basalt fibers at the boundaries of the “fiber-binding (hardener)-mycelium of fungi-fiber”, and across the fibers. Additionally, microscopy showed rebar-shaped bacterial cells on the BFRP rebar samples. Some of them had spores at one end, which indicates the preservation of microorganisms within the BFRP rebars after exposure at extremely low temperatures, to minus 42 °C on the day of sampling .The length of the fungal mycelium, which grew through test samples in one year, was recorded at the level of 1.4 to 1.8 μm in microscopic examination, which in terms of the 1 g of sample BFRP rebar under study was about 0.035–0.045 cm or 0.00035–0.00045 m. The recalculation was made using the Johnson and Mollis method .At the second stage, in order to study the process of biodamage of the BFRP rebars by microorganisms, test samples were placed in the soil at a depth of 20 cm. The experimental exposure time was also one year .One year after the removal of samples from the soil, during visual control, the beginning of the destruction process under the influence of mold fungi on the samples had a form of dimming caused by the degradation of the organic components of the polymer matrix.The microscopic method in the dimming zone on damaged areas of the BFRP rebars recorded simmering of the binder inside the samples .Bacillus were isolated from the surface of the samples and the soil taken from the site of the experiments , which confirmed the likelihood of contamination of the rebars by soil bacteria.Polarizing microscopy with a ×1000 magnification showed that areas of binder swelling and rebar-shaped spore cells of bacteria  were visFrom the dominant bacterial and fungal cultures identified during the research, a working collection has been formed that is promising for biotechnological use and studying the selected microorganisms as potential PCM destructors. Maintaining the viability of microorganisms at extremely low temperatures (up to minus 42 °C) and the resumption of their metabolic activity when entering favorable temperature and other conditions, is one of the common signs of cryophilicity for the strains under study, which allows the use of microorganisms selected for bioinfection and for studying the processes of destruction in samples outside the vegetation period .Bacillus +3–5 °C ; and for mold fungi of the genus Aspergillus +3–4 °C .The temperature minimum relevant to the development of vegetative cells and spores of the microorganisms-destructors included in the working collection is presented in the following order: for bacteria of the genus The metabolic activity of selective strain cells at minimum temperatures (+4 ± 1 °C) is associated with the features of enzymes and fatty acid composition of the lipid bilayer of cell membranes, which are in a liquid–crystalline state in conditions optimal for microorganisms. In the case of temperature conditions, when the development of vegetative cells temporarily stops—in particular from +3–5 °C for strains of the working collection—the process of transition of the lipid bilayer of cell membranes into a gel-like state is activated, which prevents crystallization and death of the microbial cell with a gradual decrease in ambient temperature to negative values, so that after thawing growth resumes and the metabolic activity of the microorganism is restored; however, a sharp transition from an optimal to extremely low ambient temperature can contribute to the destruction and damage of microbial cells. This can happen when refreezing and defrosting selected strains during experiments, or after samples or biologics after storage at room temperature or a temperature of +3–5 °C are sharply taken out for exposure in winter conditions at extremely low temperatures. The death of the immobilized cells of microorganisms on samples can occur due to impaired barrier functions of cell membranes.Bacillus (B. simplex and Bacillus sp.) were selected, in which a targeted study revealed fungicidal activity in relation to mold fungi of the genus Aspergillus (A. niger and A. fumigates).To study fungicidal activity, bacterial strains of the genus Brief characteristics of the strains.Bacillus simplex are Gram-positive rebar cells with centrally located ellipsoid spores. On the IPA forms shiny, wet, large colonies of creamy white color, stretching for a bacteriological loop.Bacteria, Firmicutes, Bacilli, Bacillales, Bacillaceae, Bacillus.Initial screening on the GenBank and RDP-II database showed that the strain under study belongs to the following systematic groups: The results of sequence processing using software located on the RDP-II  website, designed to determine the relationship of microorganisms and build phylogenetic trees, are presented in Bacillus simplex (100%).Analysis of phylogenetic kinship, built using typical strains of closely related bacteria, taking into account the morpho-physiological properties of the strain, shows that the closest to the strain under study is the species Bacillus sp.-Gram-positive rebar-shaped cells with centrally located oval spores. On the IPA forms matte, large, opaque, grayish colonies with an uneven, rough surface and uneven edges with a diameter of up to 5 mm or more.Bacteria, Firmicutes, Bacilli, Bacillales, Bacillaceae, and Bacillus.Initial screening on the GenBank and RDP-II database showed that the strain under study belongs to the following systematic groups: The results of sequence processing using software on the RDP-II website , designed to determine the relationship of microorganisms and build phylogenetic trees, are presented in Bacillus are closest to the strain under study: B. altitudinis (99.89%); B. stratosphericus (99.89%); and B. safensis (99.89%). According to this criterion, the bacterial strain under study is classified as Bacillus sp. (99.89%).Analysis of phylogenetic kinship, built using typical strains of closely related bacteria, taking into account the morpho-physiological properties of the strain, shows that three types of cryophilic strains of the genus The presence of fungicidal activity in the studied bacterial strain was judged by the presence of an inhibition zone of the test strain.The results of the tests of the selected strains for antagonistic activity are presented in Bacillus sp. + Bacillus simplex and Aspergillus niger + Aspergillus fumigatus) than when using the same microorganisms as part of monocultures.It has been established that when testing the relationship between antagonist strains and mold-sensitive test cultures, antagonistic activity manifests itself higher when using a paired composition of cultures (Bacillus (B. simplex and Bacillus sp.) have fungicidal activity to strains of mold fungi of the genus Aspergillus (A. niger and A. fumigatus)  .A common established feature for the strains under study is that they, having cryophilic properties, can develop at low temperatures in conditions of limited oxygen access to the environment. This indicates that selected strains can show their destructive properties in cold climates relative to organic components in the structure of polymer composites when their metabolism is slowed down and there is no air exchange. It is shown that the method of enrichment culture is a highly informative method of diagnosing the bio-infection of polymer composite materials during their operation in extremely low temperatures.In order to study the participation of selected microorganisms in the biodestruction of organic components of PCM in low ambient temperatures, liquid forms of biological products were manufactured, which were used to treat the BFRP rebar of warehouse storage.Bacillus bacteria and the second was based on mold spores of the genus Aspergillus.Two types of biological product were used in the experiment: the first was based on 9 CFU/cm3 for the fungi and bacteria. Exposure of the bio-infected BFRP rebars was carried out in accordance with the standard, GOST 9.708-83, in an extremely cold climate in the city of Yakutsk, at the climate station of the Institute of Physical and Technical Problems of the North, Siberian Branch of the Russian Academy of Sciences, for two years.The concentration of cells in the biologics at equal ratios (1:1) was 1–9 × 10After exposure time, rinses from the BFRP rebar samples were selected for microbiological studies and tensile and porosity tests were carried out.Bacillus remained immobilized on all 43 samples; and strains of mold fungi of the genus Aspergillus were isolated from 10 samples of BFRP rebar, which measured 23%.It has been found that bacteria of the genus Nocardia and Streptomyces; yeast of the genus Rhodotorula; and mold fungi of the genus Penicillium.Aboriginal microflora isolated from the rinse fluid taken from the surface of test samples was presented by: actinobacteria of the genera A set of phenotypic signs and antibiotic resistance of the experimental strains of bacteria and mold fungi served as a marker to confirm the identity of the BFRP rebar immobilized for the samples and isolated from the rinse fluid after two years of exposure.The result of the monolithic studies shows  that on To study the change in strength properties of the rebars during their biodegradation under short-term tension, rebar specimens with a diameter of 6 mm were tested at an ambient temperature of 20 °C and a relative humidity of 40–50%.tR is the ultimate tensile strength (tσ) and porosity (oP), measured after different periods of exposure; R0—initial values of the corresponding indicators. The results of comparative tests of BFRP No. 1 for axial tension and open porosity are shown in To determine the climatic and biogenic impact, the change in the generalized indicator—the relative coefficient of conservation—was calculated:When comparing the values of the ultimate tensile strength for the original samples and biologically contaminated samples exposed for 24 months, one can see a decrease in the ultimate strength by 18% and an increase in porosity by more than two times in a shorter period.The received results show that the selected strains affect the properties of polymeric materials in cold climates in relation to organic components in the structure of polymer composites.As a result of microbiological studies from the surface of samples of basalt plastic composite materials exhibited at the climatic test site in an open ecosystem at extremely low ambient temperatures (up to minus 42 °C), various types of cryophilic microorganisms have been identified; the share of viable cells was 33%, of which were bacteria–23%; mold fungi–33%; and actinobacteria–44%. The metabolic activity of cryophilic microorganism cells isolated from the experimental samples of BFRP rebars is associated with the features of enzymes and fatty acid composition of the lipid bilayer of cell membranes. In conditions optimal for life, microorganisms are in a liquid–crystalline state, and in the case of temperature conditions, when ordinary (mesophilic) microorganisms stop developing vegetative cells, the process of transition of the lipid bilayer of cell membranes into a gel-like state is activated. This allows the prevention of crystallization and death of the microbial cell when the ambient temperature drops to negative values, so that after thawing growth resumes and the metabolic activity of the microorganism is restored.Bacillus and mycelium of mold fungi of the genus Aspergillus were found, which indicates the survival of immobilized microorganisms in a cold climate. In laboratory and field experiments, for the first time, the possibility of penetration of fungal mycelium into the structure of BFRP rebars during their exposure in an open polygon was established. Biological contamination at the “fiber-binding” border reveals areas of blistering and penetration of bacterial spore cells into the binder component.In samples of polymer composites treated with microbial spores, after one and two years of exposure at the climate test site, cells of spore-forming bacteria of the genus Bacillus bacteria remained immobilized on all 43 samples; and strains of mold fungi of the genus Aspergillus were isolated from 10 samples of BFRP rebars, which was 23% of all microflorae preserved in a viable state.Microbiological studies have found that after two years of exposure in extremely low temperatures, Bacillus sp. and Bacillus simplex have fungicidal activity in relation to the strains of mold fungi Aspergillus fumigatus and Aspergillus niger. A common established feature for the strains under study is that they, having cryophilic and osmotolerance properties, can develop at low temperatures in conditions of limited oxygen access in the environment. This indicates that selected strains can show their destructive properties in cold climates relative to the organic components in the structure of polymer composites when their metabolism is slowed down and there is no air exchange. It was shown that the enrichment culture technique is a highly informative method of diagnosing the bio-infection of polymer composite materials during their operation in extremely low temperatures.It has been established that the bacterial strains The results of the studies have expanded the range of microorganisms capable of contaminating polymer composite materials at extremely low temperatures (−42 °C). The formation of a working collection of destructors of polymer composites has begun. The reliability of the taxonomic belonging of most microbial strains included in the working collection is confirmed by a modern molecular genetic method based on the nucleotide sequence of 16S rRNA of the gene.As a result of comparative tests, an impact on the strength properties of basalt fiber reinforcement plastic rebar by cryophilic mold fungi has been established when exposed in cold climates. There was a decrease in the strength limit by 18% and an increase in porosity by more than two times.The results show that the selected strains affect the properties of polymeric materials in cold climates in relation to organic components in the structure of polymer composites.


In this study, the PM2.5 concentration data obtained from 340 PM2.5 ground stations in south-central China were used to analyze the variation patterns of PM2.5 in south-central China at different time periods, and six PM2.5 interpolation models were developed in the region. The spatial and temporal PM2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models , geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R2 of the GWR-EBK model construction was the best , and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM Since 2012, China has built a large number of ground-based PM2.5 stations nationwide. However, China is a vast country, and the number of ground-based PM2.5 stations is still scarce, and therefore they cannot accurately explain all the temporal and spatial characteristics of PM2.5, thus limiting the application of PM2.5 data in a variety of practical applications. Therefore, it is necessary to further study how to obtain continuous and accurate regional PM2.5 distributions on different temporal and spatial scales and how to use limited station-derived data to conduct high-precision regional PM2.5 temporal and spatial interpolations, and these applications have become a research hotspot.PM weather ,2,3. In 2.5 in different regions of China, such as North China [2.5, many scholars have tried to use different PM2.5-related variables to combine analyses to improve the accuracy of PM2.5 simulations. The impact of the population density, GDP and other social or economic activities on PM2.5 concentrations in Chinese cities with significant spatial heterogeneity was explored by Gu et al. [2.5 observation records in the United States to explore the correlations between PM2.5 and meteorological variables, and the results showed that the daily variations in meteorological variables described by MLR can explain up to 50% of PM2.5 changes [2, SO2, CO, O3, PM2.5 and PM10) and meteorological parameter  observations, temporal changes and related analyses, and all pollutants showed obvious seasonal trends under the influence of climatology, topography and human activities [China is a vast country, and many scholars have tried to explore the variation patterns of PMth China , the Yanth China , Heilongth China , and theth China ,9. When u et al. ; Tai et  changes ; Ye et ativities . 2.5 research field; for example, Wen et al. [2.5 and ZWD in Baoding, Hebei, China, and found that the correlation coefficient between the daily average PM2.5 and ZWD was mainly greater than 0.4 in autumn and winter in this region, while the correlation coefficient between the hourly average PM2.5 and ZWD was mainly greater than 0.3. Guo et al. [2.5 hourly sequences. The experimental results showed that it is effective to consider using GNSS-derived ZTD to assist in haze monitoring.With the development of GNSS technology, the global navigation satellite system’s (GNSS) zenith tropospheric delay (ZTD), zenith wet delay (ZWD) and precipitable water vapor (PWV) data are able to reflect certain atmospheric water vapor information and can also reflect changes in meteorological conditions ,14. Thern et al.  exploredo et al.  took the2.5 in space and have achieved good results. Zhou et al. [2.5 data from 283 prefecture-level cities in China combined with the random regressed effects of the population, economy, and technology, and used geographically weighted regression methods to evaluate the impacts of different factors on haze pollution in different regions. Wang et al. [2.5, established a comprehensive explanatory framework consisting of 18 determinants covering natural and social conditions, and determined three major categories of economic factors and urban characteristics. Among all natural variables, elevation has a statistically significant impact on PM2.5 in 95.60% of cities, and is negatively correlated with PM2.5 in 99.63% of cities. The effect of elevation is gradually weakened from eastern China to western China; Zou et al. [2.5 concentrations in California. The results showed that both the GWR and LUR models were able to estimate the PM2.5 concentrations and map the spatial distribution in the study area. Jiang et al. [2.5 in the Yangtze River Delta. Hajilooe et al. [2.5 concentration in Tehran and environmental parameters  using GWR to evaluate the impacts of key parameters on PM2.5 concentrations in winter and summer.Due to the continuous development and improvement of spatial fitting and interpolation models, some scholars have used geographically weighted regression (GWR)  to simulu et al.  establisg et al.  used theu et al.  comparedg et al.  introduce et al.  evaluate2.5, most scholars have begun to study GWR models that are more applicable to PM2.5. Yang et al. [2.5 spatial variation relationships in China by developing a modified GWR model using meteorological, topographical and emission factors observed in 2015. Rui et al. [2.5 concentrations in the Pearl River Delta region of China using an enhanced GWR model by introducing geodetector analysis and principal component analysis (PCA) to enhance the GWR model. Zhai et al. [2.5 concentrations by fully considering the contributions of all potential variables simultaneously, and conducted a one-year experiment comparing the performance of PCA-GWR with that of conventional GWR in the Beijing–Tianjin–Hebei region. The results showed that the PCA-GWR model outperformed the conventional GWR model. Due to the strong spatial and temporal heterogeneity of PMg et al.  analyzedi et al.  analyzedi et al.  develope2.5 and related variables when modeling the experimental data, but did not consider the effect of model residuals. Some scholars introduced the kriging interpolation method to interpolate the GWR residuals for the purpose of correcting the fitted values of the GWR model for the spatial autocorrelation of the residuals after fitting the GWR model, deriving the GWRK model from this; however, this model has been mostly applied to geological field-related research [2.5 spatial interpolations.Most of the abovementioned studies dealt with PMresearch ,27,28. Oresearch  proposedFor the kriging interpolation method, empirical Bayesian kriging (EBK) has been proposed based on this method. Empirical Bayesian kriging differs from other kriging methods in that it accounts for the introduced error by estimating the underlying semivariance function, which has the advantage of predicting standard errors more accurately than other kriging methods, and can accurately predict data that are generally unstable in degree ,31.2.5 by some; Fan et al. [2.5 and its associated influencing factors during the outbreak of new crown pneumonia in China. Yan et al. [2.5 in three major urban agglomerations in the Yangtze River Economic Zone of China using the MGWR model, and found that the total precipitation, wind speed and green coverage had the most significant effects on the PM2.5 distribution.Some scholars have improved the GWR method from the spatial-scale perspective, and the model that is most representative of these improvements is the multiscale geographically weighted regression (MGWR) model, which is more flexible than the GWR model. The model allows different processes to work on different spatial scales , and then et al.  used then et al.  simulate2.5, as described above, strong spatial heterogeneity and spatial nonstationarity exists in PM2.5 distributions, and it is difficult for the above-mentioned models to handle or simultaneously handle these two PM2.5 distribution characteristics. Therefore, to improve the interpolation of PM2.5 concentration values, we compare the changes induced by the differences in each time scale and spatial scale to the model interpolation effect, fully consider the variations in GNSS-derived ZTD and PWV, and two models, the kriging and empirical Bayesian kriging models, are introduced to eliminate the effect of residual spatial correlations on the fitting of the GWR and MGWR models, thus improving the interpolation accuracy.Although both types of models, GWR and MGWR, have performed well in previous spatial studies of PMGeographically weighted regression (GWR) is a spatial analysis technique that belongs to the local spatial analysis model and is a relatively simple and effective method used to detect spatial nonsmoothness. The method allows for the existence of different spatial relationships in different geographic spaces and results in local, rather than global, parameter estimates, thus enabling the detection of the spatial nonsmoothness of spatial data.GWR explores the spatial variability in the study object at a given scale and the associated drivers by establishing local regression equations at each point in the spatial scale. As this method takes into account the local effects of spatial objects, it has the advantage of improved accuracy, and its basic principle can be expressed as follows ,20,21,22The estimation of the regression coefficients in the model is implemented using the least squares method, and the coefficients at each point are represented by the matrix as follows:The spatial weight matrix 2.5 concentration estimated by the GWRK model; 2.5 concentration estimated by the GWR model; 2.5 concentration values are estimated by the GWR model.The geographically weighted regression kriging (GWRK) model uses the kriging model to spatially interpolate the regression residuals obtained from the GWR model; then, the obtained residual interpolation results are superimposed on the GWR regression estimates to correct the fitter GWR-derived values to obtain the GWRK model estimation results ,27,28,29The empirical Bayesian kriging (EBK) method is a geostatistical interpolation method that automatically performs the most difficult steps in the construction of an effective kriging model. EBK automatically calculates the model parameters by constructing subsets and simulating the process.Empirical Bayesian kriging differs from other kriging methods in that it accounts for the error introduced by estimating the underlying semivariance function. Other kriging methods calculate a semivariance function from known data locations and use this single semivariance function to make predictions at unknown locations; this process implicitly assumes that the estimated semivariance function is the true semivariance function in the interpolated region. By not accounting for the uncertainty in the estimation of the semivariance function, other kriging methods underestimate the standard error of the prediction.EBK predicts standard errors more accurately than other kriging methods for data that are generally unstable, especially for small data sets [GWR-EBK model estimate of the PM2.5 concentration, 2.5 concentration estimated by the GWR model, and EBK interpolation of the regression residuals from the GWR model after estimating the PM2.5 concentration values.ata sets ,31, and The specific bandwidth of each variable in the MGWR model can be used as an indicator of the spatial scale of each spatial processing action. The MGWR model is calculated as follows ,33,34:YiMGWR uses the same Gaussian kernel function as the previous GWR. We mainly use the MGWR2.2 software .MGWRK model uses the kriging model to spatially interpolate the regression residuals obtained from the MGWR model; then, the obtained residuals are interpolated to correct the MGWR estimates:MGWRK model estimate of the PM2.5 concentration, 2.5 concentration estimated by the MGWR model, and MGWR model after estimating the PM2.5 concentration values.The MGWR model; then, the obtained residuals are interpolated to correct the MGWR estimates:MGWR-EBK model estimate of the PM2.5 concentration, 2.5 concentration estimated by the MGWR model, and EBK interpolation of the regression residuals from the MGWR model after the PM2.5 concentration values are estimated.The MGWR-EBK model uses an empirical Bayesian kriging model to spatially interpolate the regression residuals obtained from the The five provinces of south-central China  are located within longitudes of 104°28′–117°19′ E and latitudes of 20°13′–36°22′ N. These provinces cover an area of nearly one million square kilometers and have a resident population of nearly 400 million people; the terrain is high in the west and low in the east. Due to the large north–south span and the very different folklore and geographical styles of each province, the region is an important economic and cultural development area in China; thus, the monitoring and prevention of environmental pollution and other events should be the focus in the process of development. 2.5, CO, SO2, NO2, and O3) collected at PM2.5 ground stations in five provinces in south-central China from 2017 to 2019 and quarterly average air quality parameter values from December 2016 to November 2019 .The data obtained from ground monitoring stations have the advantage of high accuracy, so this paper mainly uses various types of station data for experiments and analysis. The missing data from the observation stations in the five south-central provinces were screened and eliminated. Finally, annual average air quality parameter data .GNSS -derived data (PWV and ZTD) were collected from 16 sounding stations and 21 GNSS sites in south-central China for annual average values from 2017 to 2019 and quarterly average values from December 2016 to November 2019, respectively b. , and the digital elevation model (DEM)-derived  elevation values of the region were used as the experimental data. To simplify the expression, all meteorological parameters used in this paper are abbreviated: PRE stands for pressure, TEM stands for temperature, and WIN stands for wind speed. Annual averages from 2017 to 2019 and seasonal averages from December 2016 to November 2019 for all types of meteorological data  for South Central China from 97 meteorological stations c in the PWV) method using a ground-based GPS . PWV is mainly calculated from the ZWD as follows [ZTD refers to zenith tropospheric delay; for space geodesy, this delay causes signal propagation errors, which affect the observation accuracy and are a kind of error source. In 1992, Bevis et al.  proposed follows ,14:PWV=Π2.5 ground stations. To better obtain data on ZTD, PWV, and meteorological parameters  with a corresponding time scale to that obtained at the PM2.5 ground stations, three interpolation methods, IDW (inverse distance weighting), OK (ordinary kriging) and TSF (tension spline function), were used, and their interpolation effects were compared when interpolating different variables at annual and seasonal scales in the study region.The number of GNSS sites, sounding stations, and meteorological stations is much lower than the number of PMInverse distance weighting (IDW) is an interpolation method that weights the distance between the interpolation point and the sampling point. The method is simple and effective; the closer the interpolation point is, the larger the weight is, and the contribution of the weight is inversely proportional to the distance . The calThe ordinary kriging model is expressed as the following equation .Zv*(x)=The tension spline function (TSF) is a kind of radial basis function interpolation method that is fast, and its range of estimated sizes is not limited. The basic principle of the tension spline function is shown in the following equation:RMSE (root mean square error) values of the data observed at the corresponding stations were calculated for the accuracy assessment from three models:RMSE results are shown in The 2.5 ground stations and GNSS-derived PWV and ZTD data, the inverse distance weighted interpolation method is uniformly used for regional interpolation processing.By looking at the information in 2.5 is a very important part of the experiments conducted in this study. To better explore the variation in regional PM2.5 values, the PM2.5 data obtained from each air quality monitoring station were interpolated with the inverse distance weighting method to obtain a regional PM2.5 distribution map, which was analyzed at two temporal resolutions: annual and seasonal.Investigating the spatial and temporal distributions of PM2.5 values in the central and southern regions range from roughly 20 μg/m3 to 80 μg/m3, with a relatively smooth change.As shown in 2.5 values vary below 60 μg/m3.As we can see from 2.5 in the south-central region, a cluster analysis of PM2.5 values in the region was conducted to obtain the spatial clustering information of elements with high or low values to achieve a deeper understanding of PM2.5 distribution characteristics in the south-central region.After exploring the spatial distribution pattern of PM2.5 in the five study provinces in south-central China, where the spatial relationship of elements was defined by choosing the inverse distance, and the distance between each element and the adjacent elements was calculated by choosing the euclidean distance; the results are shown in Cluster analysis reveals how high or low variables are clustered in a region and is one of the most important tools used to explore spatial patterns. We used the anselin local moran’s I statistic in Arcgis 10.4 to perform a clustering analysis of PM2.5 in the south-central region mainly occurs in the Henan and Hubei regions, while low clustering is mainly concentrated in south-central Guangdong province, and there is almost no clustering in the Guangxi or Hunan regions at the annual average scale.As shown in Additionally, as shown in 2.5 in the five studied south-central provinces at different scales, a correlation analysis of the multiple variables considered in the experiment was also conducted, before the modeling analysis was performed, to ensure that all the variables considered had a certain correlation with PM2.5 and were thus suitable for subsequent modeling work.After exploring the spatial and temporal variation patterns of PM2.5 [2.5 with PWV and ZTD at different time scales at each station in the five provinces of south-central China, data corresponding to 340 PM2.5 ground stations were mapped and analyzed.Satellite-derived tropospheric data have a strong correlation with PM2.5 . To expl2.5.As seen in 2.5 and ZTD at the seasonal average scale are consistent with those at the annual mean scale. The overall trends of PM2.5 and ZTD at the seasonal mean scale are consistent with the negative-correlation phenomenon observed at the annual mean scale.From 2.5 recorded in spring and autumn range between 20 mm and 45 mm, and the variation pattern between PWV and PM2.5 is consistent with the negative correlation phenomenon observed under the annual average scale.As seen in Since Spearman’s correlation coefficient determination rule does not require a given distribution of original variables and has a wide range of applications , this meBecause of the large number of explanatory variables considered in the experiments, this paper presents the various types of correlation analysis in a uniform way using line graphs, so that the five time scales of annual average, winter, spring, summer and autumn can be clearly visualized.2.5, reflecting the previous conclusion.The results shown in On the annual average scale, the correlation level of the wind speed is the lowest, the correlation levels of the four air pollutants are maintained at approximately 0.5, and the correlation level of the DEM is between 0.3 and 0.4. Among the meteorological factors, the correlation levels of the wind speed and air pressure are low, while the correlation level of temperature is high and stable, with values maintained above 0.8.3 is higher than those of the other pollutants, and the correlation level of the DEM is the highest compared with the annual average and with other seasons; in autumn, the correlation level of O3 drops sharply compared with that measured in summer.On a seasonal scale, temperature, ZTD and PWV still maintain high negative correlations in winter; in spring, the correlation levels of all air pollutants are roughly the same as the annual average values; in summer, the correlation level of O2.5, which is a class of variables with strong spatial heterogeneity, also has stratified heterogeneity, and the q statistic of a geodetector can be used to measure this stratified heterogeneity, detect its explanatory factors, and analyze the interactive relationships among variables. The q value is calculated as follows [Stratified heterogeneity is one of the basic characteristics of geographical phenomena and the spatial expression of natural and socioeconomic processes, and refers to the fact that the value of an attribute varies among different regions. PM follows :q=1−1Nσ2h = 1, …, L layers, i.e., L subregions; N and q value has a clear physical meaning: the magnitude of the q-value indicates the percentage of variance in attribute y that is explained by stratification x.The region is divided into 2.5 and to detect the extent to which a given factor explains the stratified heterogeneity of PM2.5, geographic probes were used to detect the q-values of each variable; the results are shown in Therefore, in an effort to investigate the stratified heterogeneity of PM2.5, while atmospheric pollutants and the DEM have the second-strongest abilities to explain the stratified heterogeneity of PM2.5. Among them, SO2, which has a strong correlation with PM2.5 as well as high exploratory regression significance, has a weaker ability to explain the stratified heterogeneity of PM2.5.2.5 is significant, we then conducted an exploratory regression experiment, in which all possible combinations of the input candidate explanatory variables were evaluated to obtain the ratio of the number of times each alternative explanatory variable was statistically significant to its statistical significance, where the explanatory variables with stronger effects were always significant, which could help to find the variables that were always strong explanatory factors. The results of the exploratory regression for the significance of each variable are shown in To further investigate whether the effect of each variable on PM2.5 changes; the significance of PWV on PM2.5 is 100% in winter time, and that of ZTD on PM2.5 in summer time also almost remained at 100%, so these two types of factors showed good correlations with PM2.5 changes in the five studied south-central provinces.From It is important to test for multicollinearity in the considered variables before constructing a geographically weighted class model. The combination of variables that are suitable for modeling is selected according to the diagnostic results, facilitating the smooth expression of the model. Therefore, here, a multicollinearity test was performed for all the variables preconsidered in this paper, and the results are shown in 3, CO, NO2, SO2, DEM, wind, and the barometric pressure are not sufficient to trigger multicollinearity. Different degrees of multicollinearity problems exist among the temperature, PWV, and ZTD at the annual scale and at seasonal scales other than summer, especially in winter. The reason for this result may be that the temperature, PWV and ZTD in the five south-central provinces maintain high correlations with PM2.5 on both the annual and seasonal scales, so these three types of variables also show strong correlations with each other, and the subsequent modeling experiments must model and discuss these three types of variables separately. The modeling variable combinations can then be divided into three separate categories, which are applied separately for the subsequent model construction; their variable combinations are shown in We generally consider a VIF (variance inflation factor) value less than 10 to indicate a small amount of multicollinearity, which does not affect the modeling results. Therefore, R2 is used as the basis for comparison, and is calculated as shown in Equation:R2 is between 0 and 1, and the larger the value is, the better the model fitting effect is. The R2 value of each model for each scale is listed in Six types of models  were analyzed on annual and seasonal scales for each of the three combinations of variables. To quantitatively compare the overall simulation effect of each model at each scale, the decidability factor R2 values above 0.9 using the three variable schemes for modeling, and show good fitting effects. Among the six models analyzed using variables such as temperature, the lowest R2 values are obtained for the two models MGWR and MGWRK, with values of 0.922 recorded, and the highest value is obtained for the GWR-EBK model, with an R2 value of 0.962 recorded. The lowest R2 when modeling with variables such as PWV is obtained for MGWR at 0.929. This lowest value is slightly higher than the lowest R2 value of the model obtained when considering variables such as temperature, while the highest R2 value is 0.962 for the GWR-EBK model, which is the same as the highest value obtained when considering variables such as temperature. When modeling with variables such as ZTD, the lowest value is 0.914 for the MGWR model, and the highest value is still 0.962 for the GWR-EBK model; therefore, on the annual average scale, the best overall model fit is found for the GWR-EBK model, and the worst is the MGWR model.The information in R2 values of the MGWR-class models all drop below 0.9, while the GWR-EBK model fit is the same as that obtained when considering PWV-like variables.From the analysis of the seasonal average scale, the GWR-EBK model effect reaches 0.964 in winter when the six models consider variables such as temperature due to the annual average scale of 0.962, while the fitting effect of MGWR drops to 0.891. When considering variables such as PWV, the fit of the GWR-EBK model improves again compared to the combination of temperature variables, reaching 0.966, while the MGWR model decreases again to 0.879. When considering variables such as ZTD, the R2 values of all six models decrease in spring compared to winter, and the GWR-EBK model still performs best in spring among all six models. The effect of the combination of both variables considering temperature and ZTD outperformed the effect of the combination of variables considering PWV, reaching 0.928. In contrast, the MGWR model with a combination of temperature-class variables had the worst simulation fit, with an R2 of 0.838. The R2 values of 0.846 and 0.856 obtained when considering the combination of two types of variables, PWV and ZTD, respectively, are better than the modeling effect obtained via the combination of temperature variables.The 2.5 values change to the lowest values measured among the four seasons, the R2 values of the six models continue to fall, all to below 0.9, with the highest R2 value of 0.874 obtained for the GWR-EBK model when considering the combination of ZTD variables, and the lowest R2 value of 0.754 obtained for the MGWR model.In summer, when PMR2 are observed for the six models. Among them, the GWR-EBK model has an R2 value higher than 0.9 for all three variable combinations modeled, with the highest value being modeled using the ZTD variable combination (0.910), and the lowest R2 value of 0.792 obtained for the MGWR model built using the PWV variable combination.In autumn, appropriate increases in R2 value is 0.962 for all three variable combinations at the annual average scale, and the effect of considering the combination of ZTD variables is better than the effects of considering the other two types of variable combinations, temperature and PWV, at the seasonal average scale. The possible reason for the change in the model R2 values with the changing seasons is that when the PM2.5 value is lower in a given season, the decrease ratio of the sum of squares of the difference between the true value and the mean value may be greater than the decrease ratio of the sum of squares of the residuals, so the increase in the sum of squares of the residuals divided by the sum of squares of the difference between the true value and the mean value leads to a decrease in the R2 value.In summary, in terms of the overall model goodness of fit, the model effects are annual > winter > spring > autumn > summer, the best performance of the goodness of fit is obtained with the GWR-EBK model, and the worst performance is obtained with the MGWR model. Compared with the results obtained with single models such as GWR and MGWR, the quadratic treatment of single-model-regression residuals using interpolation methods such as kriging and empirical Bayesian kriging can effectively improve the fit of the original data. From the choice of variable combinations, the highest We calculated and summarized the RMSEs of the interpolation results obtained from the various constructed models; RMSE is a good indicator for testing the accuracy of an interpolation and can be used to measure the deviation between the interpolation results and the true values. The RMSE results are shown in As seen from the results in 2.5 values, with the lowest value obtained for the GWR-EBK model built with the combination of PWV variables (an RMSE of 4.5 μg/m3) and the highest obtained for the MGWR model built with the combination of ZTD variables (an RMSE of 10.4 μg/m3). The RMSE of the GWR-EBK model built by the combination of ZTD variables and temperature variables was optimal and equal in spring and autumn, with RMSEs of 3.2 μg/m3 and 3.1 μg/m3 obtained, respectively; the PM2.5 values were the lowest in summer, so the RMSEs of the model interpolation were also the lowest in this season; the RMSE of the GWR-EBK model obtained using the combination of three types of variables was the lowest and was equal to 2.7 μg/m3.On the seasonal average scale, the RMSEs of the models also show a larger phenomenon in winter due to the larger PMThe model effects used for the comparison of the RMSE results are quite similar to those used for the comparison of the goodness of interpolation results. The GWR-EBK model performs the best, and at the annual average scale, the combined results of the two comparisons show that the GWR-EBK model constructed by considering the combination of ZTD variables works best. At the seasonal average scale, the GWR-EBK model constructed by considering the combination of PWV variables performed best in winter, while the GWR-EBK model constructed by the combination of ZTD variables had the highest accuracy in spring, summer and autumn.2.5 is a variable with strong spatial and temporal heterogeneity. To further analyze the spatial effect of the model interpolation for PM2.5 in depth, the model effect is further explored from two perspectives: the DEM-derived elevation and the province.PM2.5 ground stations in the five south-central provinces of China were roughly divided into three categories based on their DEM-derived elevations and the number of stations, with 117 stations below 35 m, 111 stations between 35 and 90 m, and 112 stations greater than 90 m.The 340 PMThe accuracy of the model interpolation for each local elevation interval was quantified using RMSE, and the obtained precisions were compared.3 and 2.7 μg/m3, respectively. In the interval of 35–90 m . From the elevation interval effect, it can be seen that although the GWR-EBK model established by considering the combination of temperature variables and ZTD variables in the overall accuracy evaluation of the model in the previous section has an equal RMSE value, after refining the elevation interval, it can be found that the effect of considering the combination of temperature variables .Through The best results were obtained for the GWR-EBK model constructed by combining two types of variables, PWV and ZTD, in Henan province a; for thIn winter, the GWR-EBK model constructed from the combination of PWV variables had the best applicability in all five provinces, and combined with the results of the previous analysis, this proves that the model has the strongest generalizability during winter in the five south-central provinces.In two provinces, Hubei b and GuaR2 values of the summer models, these results show that the GWR-EBK model considering ZTD is the best overall. The GWR-EBK model constructed by the combination of three variables in Hubei province  > meteorological factors, and the results of the stratified heterogeneity of geodetectors again reflected the significant correlation between GNSS parameters and PM2.5, providing a guide for constructing a regional PM2.5 model when meteorological data are missing and PWV and ZTD data can be used as a substitute.The experiments show that considering GNSS-derived PWV and ZTD data in the construction of a regional PM2.5, the interpolation effect of the model changes depending on the choice of variables, the time scale, and the spatial scale. The GWR model has a stronger ability to estimate PM2.5 than the MGWR model and is more efficient in south-central China. Compared with the interpolation effect of a single geographically weighted regression-type model for PM2.5, the combined model shows a stronger advantage, and the overall best performance in this area is obtained with the GWR-EBK model, indicating that the empirical Bayesian kriging method is better for the explanation and interpretation of GWR residuals; further, the GWR-EBK model can improve the accuracy by 14.74% more than the GWR model.In constructing a regional model of PM2.5 values to the seasonal average scale is that when the PM2.5 values are lower due to seasonal changes, the reduction ratio of the sum of squares of the difference between the true value and the mean value may be greater than the reduction ratio of the sum of squares of the residuals, as shown by the formula used to calculate the R2 values. This explains why the R2 value obtained in winter is greater than those obtained in spring and autumn, while the R2 value in summer is the smallest.The largest reason for the inverse ratio of the seasonal PM2.5 in the five provinces of south-central China in 2017–2019 on two time scales ; introduced two types of variables, GNSS-derived PWV and ZTD variables, to participate in the construction of the regional model of PM2.5 in the region; analyzed a total of six types of models constructed by the combination of three variables, and obtained the following conclusions:This article analyzed the spatial and temporal characteristics of PM(1) The IDW interpolation method is most suitable for the regional interpolation of meteorological parameters and GNSS-derived PWV and ZTD data for the five studied provinces in south-central China.2.5 concentration values in the five south-central provinces show clear characteristics of high values in the north and low values in the south, as well as a seasonal variation pattern of high values in winter and low values in summer; clustering phenomena mainly occur in Henan and Guangdong provinces, with high clustering in Henan province and low clustering in Guangdong province recorded.(2) The PM2.5 in the five provinces in south-central China, so it is effective and feasible to consider GNSS-derived PWV and ZTD in the construction of a regional model of PM2.5, and the experiments show that the interpolation is better than the regional model constructed by considering only atmospheric pollutants and meteorological and elevation factors with many iterations at both the annual and seasonal average scales.(3) The GNSS-derived PWV and ZTD data show a strong negative correlation with PM2.5, the combined model shows a stronger advantage, and the best overall performance in the five south-central provinces of China is obtained with the GWR-EBK model, indicating that the empirical Bayesian kriging method has a stronger ability to explain the GWR residuals and a better interpolation effect.(4) Compared with a single geographically weighted regression-type model for PM(5) In the five provinces of south-central China, the applicability of the model in higher-elevation areas is not as good as that in lower-elevation areas, and the applicability of the model in higher-latitude areas is also worse than in lower-latitude areas.


The Reporting Items for Practice Guidelines in Healthcare (RIGHT) checklist was used to assess the reporting quality of 2009–2019 clinical practice guidelines (CPGs) regarding gout and hyperuricemia, aimed to improve the reporting quality of future guidelines.We searched PubMed, the Chinese Biomedical Literature database, the Wanfang Database, and the China National Knowledge Infrastructure from January 2009 to June 2019 for guidelines regarding gout and hyperuricemia. We also searched the websites of guideline development organizations ). Furthermore, supplementary guidelines reported in included articles were systematically searched, as well as Google Scholar.Seventeen guidelines were included, of which one was in Chinese and 16 were in English. The mean reporting rate of the 35 items specified was 14.9 (42.5%); only five CPGs (29.4%) had a reporting rate >50%. Of the 35 items, three were very frequently reported. The reporting proportion of the seven domains  were 64.7%, 36.8%, 50.6%, 42.9%, 8.82%, 33.8%, and 31.4%, respectively.The reporting quality of the present guidelines for gout and hyperuricemia is relatively poor. We suggest that the RIGHT reporting checklist should be used by CPG developers to ensure higher reporting quality of future guidelines.The online version contains supplementary material available at 10.1186/s13643-021-01645-1. Gout is a group of heterogeneous diseases caused by long-term disturbance of purine metabolism, which results in a high serum uric acid concentration. The prevalence of gout is >1% in most developed countries, and with the recent improvement in living standards, the prevalence of gout is likely to increase . FurtherWith recent advances in pharmaceutical therapies, auxiliary diagnostic methods, and novel treatment approaches, a number of clinical practice guidelines (CPGs) have been developed to standardize the diagnosis and treatment of gout –9. On thAGREE II  assesseshttps://osf.io/z4evs/), and search strategy is listed in Supplementary file Table: We searched PubMed, the Chinese Biomedical Literature database (CBM), the Wanfang Database, and the China National Knowledge Infrastructure (CNKI) from January 2009 to June 2019 for the relevant guidelines. We also searched the websites of the organizations responsible for guideline development: the Guidelines International Network (GIN), the National Institute for Health and Clinical Excellence (NICE), the American College of Rheumatology (ACR), and the European League Against Rheumatism (EULAR). Furthermore, supplementary guidelines reported in the included articles were systematically searched, and we also searched Google Scholar for additional material , 9, 16; “P” gout and/or hyperuricemia; “I” any intervention; “C” any comparator or comparison, no “key” CPG content is of interest; “A” 2009 to 2019, English or Chinese; “R” no recommendation is of interest [(1) Old versions of guidelines, if an updated version was available; (2) interpretations and translations of guidelines; (3) repeatedly published guidelines; and (4) guideline for which full text was still not available after contacting authors.Two researchers (Zhen Liu and Can Wang) searched the database; all retrieved studies were screened using EndNote X8. After eliminating duplicates, the titles and abstracts were first screened according to the inclusion criteria, and the reasons for exclusion were recorded. Next, full text of the literature that met the inclusion criteria were screened again and determined whether the retrieved guidelines met the study criteria. Then, independent screening and cross-checking of the guidelines were carried out. Differences between reviewers were resolved through discussion or consultation with a third party (Yaolong Chen and his team).The RIGHT tool  supplem was usedA total of 765 records were identified through database searching. Four other records were identified via the guideline development organization websites and Google Scholar. Of these, 17 guidelines that met the criteria were analyzed , of which four were developed by EULAR (23.5%); six originated from the USA (35.3%), of which three were developed by ACR (17.6%); and three (17.6%) by universities . The remaining two (11.8%) were developed by the Evidence, Expertise, and Exchange initiative. The features of each are summarized (Table The mean reporting rate of the 35 items was 14.9 42.5%), and it ranged from nine (25.7%) to 22 (62.9%) for the 17 CPGs , #12 (describe the approach used to assess the certainty of the body of evidence), and #13c (indicate the strength of the recommendations and the certainty of the supporting evidence), which were reported in 16 (94.1%) guidelines. These were followed by #13a , which was reported in 15 (88.2%) CPGs. Items #8b (describe the setting(s) for which the guideline is intended, such as primary care, low- and middle-income countries, or inpatient facilities) and #17  were reported in none of the CPGs  were 64.7%, 36.8%, 50.6%, 42.9%, 8.82%, 33.8%, and 31.4%, respectively.According to the geographical regions of the sponsors or participants, nine CPGs were participated in or initiated by European countries, and 6 CPGs originated from American countries. Firstly, the mean number of reporting items of the 9 CPGs developed by European countries was 15.2, and only 3 CPGs had a compliance rate of >50% (33.3%); four of these 9 CPGs were EULAR-generated; the mean number of reporting items in these 4 CPGs was 15.8. Secondly, as for the six CPGs developed in the USA, the mean number of reporting item was 14.8. Two of these 6 CPGs were prepared by the ACR, with a mean number of reporting items of 16.5, and two were published on NGC , reported only nine items. Twenty-two items were reported in the American College of Physicians (ACP) CPG, which was the guideline with the largest number of reporting items among the 17 included in the analysis.Eight CPGs were published between 2009 and 2014, which had a mean of 13.9 reporting items, and only one (12.5%) guideline had a compliance rate of more than 50%. Between 2015 and 2019, a total of nine CPGs were published, with a mean of 15.8 items being reported, but only four (44.4%) had a compliance rate of >50%.With regard to the basic information, most of the CPGs could be judged by their title (82.4%), and the contact information for at least one author could be found (82.4%). The classification of the guidelines could be easily discerned in 76.5% of the CPGs, and a summary was presented in 70.5%. However, only 29.4% of the CPGs reported a publication date. Forty-one-point-two percent of the CPGs defined new terms and gave corresponding acronyms.The 17 CPGs did not comprehensively describe the background. More than half reported the epidemiology of gout (52.9%). Six (35.3%) CPGs reported the overall objectives of the guidelines and the specific objectives they were designed to achieve. Most of the guidelines described the main target population (76.5%), but only 17.6% described the subgroups that should be considered. Fewer than half (47.1%) of the guidelines described the key users and other potential users of the guidelines, and none described the specific target environment. Six (35.3%) CPGs listed all the contributors and their roles, and six (35.3%) gave the titles and affiliations of all the participants in their development, but only four (23.5%) contained both sets of information.Seven (41.2%) of the CPGs described the key findings on which the recommendations were based, but only two described the selection and classification of outcomes, and only one reported both. Sixteen (94.1%) guidelines described whether the systematic reviews on which the guidelines were based had been recently completed, but only two (11.8%) presented references, described how they were retrieved and evaluated, and stated whether they had been updated. Only one CPG did not describe the method of evaluation and grading of the quality of evidence.Most of the CPGs  provided clear, accurate, and enforceable recommendations. Sixteen (94.1%) CPGs described the strength of the recommendation and the quality of the evidence supporting it, but only four provided recommendations for subgroups. Four CPGs took into account the preferences and values of the target group, two (11.8%) took into account cost and resource utilization, and two (11.8%) took into account fairness, feasibility, and acceptability, when creating their recommendations. Most of the CPGs  described the decision-making process and the methods used by the working group that created the guideline.Only three (17.6%) CPGs described whether they had been sent for review or not, and none described the level of supervision involved.More than half of the CPGs  described the sources of funding at each stage of their development, but only one described the role of the sponsor in the different stages of guideline formulation, as well as in the dissemination and implementation of the recommendations. Similarly, most of the CPGs  described the types of conflict of interest associated, but only two (11.8%) described the evaluation and management of these conflicts of interest and how users of the guidelines could obtain this information.Eleven (64.7%) CPGs described where the guidelines, corresponding attachments, and other related documents could be obtained. However, only three (17.6%) articles described the differences between current practice and that recommended by research evidence, and/or provided recommendations for future research. In addition, only two CPGs described all the limitations associated with the formulation of the guidelines and their possible impact on the effectiveness of the recommendations.The quality of CPGs about gout and hyperuricemia were assessed before with AGREE II , whereasAmong the seven sections of the RIGHT tool, the reporting rate of basic information was the highest (64.7%), followed by evidence (50.6%), but the reporting rate of the other sections was <50%. The most poorly reported section was the declaration and management of funds and interests, with only 8.8% of CPGs reporting these items. This suggests that the guideline creators are generally good at including basic information, but that review and quality assurance are easily ignored.Most of the CPGs were written by European and American organizations, and the overall completeness of the guidelines was slightly better in Europe than that in the USA. However, the scores for the CPGs written by the ACR and ACP, which are influential guideline-writing organizations in the USA, were higher than those for EULAR-generated CPGs. This shows that there are differences in reporting quality of the CPGs written by different organizations in different geographical regions. With regard to the relationship between publication date and score, the compilation of guidelines has greatly improved in recent years. The guideline writers have aimed to correct the deficits of the previous CPGs with regard to the practical applications of the guidelines, but the emergence of guideline-based systematic reviews in recent years have also made them consider writing their guidelines in accordance with certain norms.Most of the CPGs had a high reporting rate for basic information, but there were still some in which the year of publication was not clear and there was ambiguity in the definitions of acronyms. These issues are very important for researchers and practitioners who wish to know how up-to-date guidelines are, and it is necessary to interpret vague terms and acronyms for more accurate understanding of the guidelines.In the background of the CPGs, the writers usually described the main target groups for the guidelines and discussed the epidemiology of the problems described. However, the other items were frequently not well reported. For example, the inclusion of 8a (the main users of the guide should be described) and 8b (the specific environment for which the guide should be described) demonstrate that the application of the guidelines will vary for different users and different environments. For example, for low- and middle-income people, it is necessary to consider the economic benefits . TherefoAbout half of the items in the evidence section were reported, and items 10b (the methodology for the selection and classification of outcomes should be described) and 11b  had low reporting rates. Items 10b and 11b can greatly help researchers and practitioners to understand the evidence and assess the accuracy of the guidelines. Furthermore, they are important for peer review because they permit shortcomings in the CPGs to be found and remedied. Items 10b and 11b can also be supplemented as new evidence accumulates over time.Most of the guidelines did not make recommendations for subgroups, suggesting that the writers did not regard subgroups as important. Most writers did not consider items 14a , 14b (whether to consider cost and resource utilization), or 14c , or they once considered these issues but did not included them in the CPGs. This means that healthcare professionals cannot easily adapt the CPGs according to different clinical situations.Items in this section were described in only a small number of CPGs, such that the reporting rate was the lowest for all the sections. Some of the guidelines may have been independently reviewed and quality-controlled, but the absence of such information is likely to make guide users and peer reviewers doubt the quality of the guidelines.The RIGHT checklist showed that the quality of the published CPGs was low with regard to this aspect, suggesting that the writers did not pay enough attention to it. A lack of information of this section may lead to the inference that the recommendations of these CPGs could have been influenced by multiple interested parties, which would lower the credibility of the guidelines, especially with regard to specific treatment recommendations.Most of the CPGs failed to differentiate evidence obtained from practice and research or to provide recommendations for future research (or both). They also failed to describe the limitations in the formulation of the CPGs and the possible impact of these limitations on the effectiveness of the recommendations. Such content could have provided a reference for guide users to appropriately use the recommendations and provided guidance for future updates and other researches.This is the first study using the RIGHT tool to evaluate the reporting quality of CPGs in the field of gout and hyperuricemia. In addition, the methods of systematic retrieval, screening, and evaluation were adopted, and the quality of the outcome measurements was strictly controlled.First, only CPGs written in Chinese and English were included; this might cause language bias. Second, only CPGs published in journals and online regarding gout and hyperuricemia were included; CPGs published in the form of books or government documents were not analyzed.In conclusion, most of the current CPGs in the field of gout and hyperuricemia had relatively low scores, when evaluated by using the RIGHT checklist. CPGs with poor reporting quality might mislead users and lead to wrong diagnosis and/or treatment, resulting in a waste of medical resources and/or delay of the disease. We suggest organizations that participate in reporting of CPGs regarding gout and hyperuricemia to use RIGHT tool, so as to improve standardization of reporting, making the reporting clear, complete, and transparent.Additional file 1.