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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

Environmental variable quantification along an altitudinal gradientThis study area included 22 sampling sites, and 66 samples, classified into three transects, namely Transect 1 (1047–1587 m), Transect 2 (876–3070 m), and Transect 3 (1602–2110 m). Significant differences in soil properties and plant parameters were observed along the three studied altitudinal transects (P  0.05%, while the remaining bacteria were merged into an “others” class. As shown in Fig. 1B, the proportion of Actinobacteria, Alphaproteobacteria and Gammaproteobacteria at each elevation was 45%, whereas Deltaproteabacteria, Acidobacteria_Subgroup_6, and Gemmatimonadetes were prevalent at low levels in most soil samples. At the genus level (Supplementary Fig. 1), 76 genera were detected in the research areas, with the dominant genera including norank_f_67-14_ o_Solirubrobacterales (5.72%), Rubrobacter (4.35%), Solirubrobacter (2.83%), Pseudonocardia (2.26%) and Bradyrhizobium (2.19%) and less than 0.01% of the bacterial genera were classified into others.Figure 1Bacterial community composition variations at the phylum (A) and class (B) levels in soil samples collected at different levels. These were done in R (v3.3.1, http://www.R-project.org).Full size imageBacterial community composition varies along elevation gradientsWe next sought to analyze the differences in relative bacterial abundance at the phylum level among Transects 1–3 (Fig. 2). Significant differences in the relative abundance of Actinobacteria, Proteobacteria, Acidobacteria, Verrucomicrobia, Firmicutes, and Rokubacteria were detected in samples from the different transects (Fig. 2A). The relative abundance of Actinobacteria and Firmicutes in Transect 1 (48.64% and 1.89%, respectively) was significantly higher than in Transect 2 (38.43% and 1.49%, respectively) and Transect 3 (39.63% and 0.98%, respectively) (P  More

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Up to 1 December 2020, 156 countries had exhibited at least one peak and then decay of cases/capita (of which 36 had experienced a second peak and decay), 151 countries had exhibited at least one peak and then decay of deaths/capita (of which 32 had experienced a second peak and decay), and 93 countries had sufficient testing data to determine at least one peak and then decay of cases/tests (of which 23 had experienced a second peak and decay). Time-series for all countries and the three metrics are shown in Supplementary Fig. 1. For resilience, having filtered cases of reasonably exponential decay for further analysis (r2 ≥ 0.8) and included multiple instances of well-fitted recovery occurring in one country in the dataset, we obtain n = 177 decays for cases/capita, n = 159 for deaths/capita, n = 105 for cases/tests. In a few countries a minimum had not yet been reached by 1 December 2020, so the reduction dataset is smaller (cases/capita n = 165, deaths/capita n = 150, cases/tests n = 101).Comparable resilience and reduction of cases and deathsThe relative measures of resilience (rate of decay) and (proportional) reduction of cases should be more reliably estimated than absolute case numbers but could still be biased by variations in testing intensity across time and space. Encouragingly, we find across countries and waves, resilience of cases/capita and cases/tests are strongly positively rank correlated (n = 100, (rho) =0.86, p  More

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