Investigating the Impacts of Airborne Dust on Herbicide Performance on Redroot Pigweed (Amaranthus retroflexus)

Investigating the Impacts of Airborne Dust on Herbicide Performance on Redroot Pigweed (Amaranthus retroflexus) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigating the Impacts of Airborne Dust on Herbicide Performance on Redroot Pigweed (Amaranthus retroflexus) Firouzeh Sharifi Kalyani, Sirwan Babaei, Yasin Zafarsohrabpour, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3236065/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2024 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Dust pollution poses environmental hazards, affecting agriculture through reduced sunlight exposure, photosynthesis, crop yields, and food security. Also, dust adversely impacts plant physiology and herbicide efficacy, but our study found it can also enhance certain herbicides. A factorial experiment was conducted in 2019 and replicated in 2020 to evaluate the interactive effects of dust and various herbicide applications, including bentazon, sulfosulfuron, tribenuron-methyl, aminopyralid + florasulam, foramsulfuron + iodosulfuron + thiencarbazone, 2,4-D + MCPA, and acetochlor on redroot pigweed ( Amaranthus retroflexus L.) control efficacy. Dust decreased the total chlorophyll 9.2% content of redroot pigweed by 9.2%, while herbicide application reduced the redroot pigweed’s total chlorophyll by 67.5%. The reduction of total chlorophyll content was more pronounced when herbicides were applied in the presence of dust. Herbicides and dust reduced redroot pigweed's leaf, stem weights, and biomass. Finally, the total biomass of plants was reduced by tribenuron-methyl, aminopyralid + florasulam, sulfosulfuron, and foramsulfuron + iodosulfuron + thiencarbazone regardless of dust presence, showing the most significant effect. The study results indicate that herbicides used in the presence of dust could affect redroot pigweed growth, which signifies the presence of dust, resulting in decreased control efficacy or increased rate of herbicide resistance evolution. Earth and environmental sciences/Natural hazards Biological sciences/Ecology/Agri ecology Biological sciences/Plant sciences/Plant stress responses Biological sciences/Plant sciences/Plant physiology bentazon 2 2-dioxo-3-propan-2-yl-1H-2λ6 1 3-benzothiadiazin-4-one environmental hazards herbicide efficacy weed Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Agriculture significantly contributes to the global economy and human health 1 , 2 . The quantity and quality of agricultural products, including cereal, the primary source of human nutrition, are substantially affected by the unwanted growth of weeds in agricultural fields. Weeds may cause a crop yield reduction from 15% to over 70% 3,4 . On the other hand, the agricultural sector should increase production by at least 70% by 2050 to fulfill the nutritional requirement of the increasing human population 5 . In order to meet the need for increased yields, the importance of weed control is also increasing, with a focus on applying effective herbicides at the correct dosage, at optimal environmental conditions, and at weed growth stages. Herbicides are essential for controlling weeds in current intensified agricultural systems worldwide 1 , 6 . The environmental conditions at the time of herbicide application substantially influence the effectiveness of herbicides in controlling weeds. Current studies 7 – 9 show that adverse environmental conditions, such as drought and extreme heat, caused by climate change, can indirectly lead to other environmental problems, e.g., the dispersal of airborne dust to different parts of the world. In recent years, the issue of dust has become a central concern in Middle Eastern countries, including Iran and Iraq 10 – 13 . Dust storms have become a common environmental issue in many parts of the world, and their impact on agricultural productivity cannot be overstated 14 – 16 . Airborne dust often occurs in areas prone to soil erosion and an average annual rainfall of less than 100 mm 17 – 20 . Dust particles are organic and inorganic materials that vary in diameter and size (5.0 ± 0.4 µm). The most important chemical components in dust particles include SiO 2 , CaO, Al 2 O 3 , Fe 2 O 3 , and MgO 21 . Dust storms can carry heavy metals such as Fe, Zn, Cr, Ni, Pb, Cu, Co, and Cd, with the concentration of heavy metals in dust storms in the Middle East during springtime being higher than the exposure thresholds recommended by the World Health Organization 21 – 24 . The literature agrees that dust storms can reduce the efficacy of various herbicide active ingredients 17 , 25 . The adherence of herbicide molecules to dust particles is the primary mechanism by which dust particles reduce the effectiveness of herbicides. The formation of the herbicide-particle complex makes it difficult for the herbicide to penetrate plant tissues, thus reducing its activity 26 , 27 . In addition to the chemical and physical properties of dust and applied herbicides, the leaf characteristics of the weed influence the particles' inhibitory effect 17 , 28 – 30 . Redroot pigweed ( Amaranthus retroflexus L.) has become a persistent problem in corn ( Zea mays L.) fields in regions where dust storms are expected. Chemical (herbicide) control efforts have often been unsuccessful, possibly due to suboptimal management decisions and the presence of dust particles on this weed's application surface, interfering with herbicides' absorption and effectiveness. Unfortunately, there is not enough information regarding the precise effect of dust particles on plant growth and herbicide efficiency. In addition, no study has yet assessed the effectiveness of herbicides in controlling redroot pigweed plants in fields under the influence of dust particles. Therefore, the objective of the current study was to quantify the interactive effects of airborne dust and various herbicide active ingredients on redroot pigweed physiological and morphological traits and control efficacy. 2. Material and methods 2.1 Location and experimental procedure An experiment was conducted in the Agriculture Faculty Research field at the University of Kurdistan in Dehgolan with geographic coordinates of 35.314286, 47.315656, and altitude of 1866 m during the 2019 and 2020 growing seasons. The average annual rainfall of this region is 350 mm, and according to the Amberge method, the region's climate is Mediterranean and semi-arid 31 . Soil physical and chemical properties of the experimental site are described in Table 1. Before the experiment (in 2018 and 2019), redroot pigweed seeds were collected from the fields around the experimental site and stored at room temperature under dry conditions until the experiments started. All the acquisitions were obtained following both national and international protocols, and the plant collection took place under the supervision and authorization of University of Kurdistan. The authors ensure adherence to all local and national guidelines in this regard. A randomized complete block design experiment with a factorial arrangement and three replicates was applied to assess the interactive effects of herbicides and dust particles on redroot pigweed control. The experimental factors were two dust levels (with and without dust) and eight commonly used herbicides (Table 2) in the corn production field. The herbicide selection was based on usage rate and availability for control of broad leaves in corn fields in the dust-affected region. The redroot pigweed seeds were planted (100 m -2 ) in 1.5 x 1.5 m plots at a 2 cm soil depth on May 24 th , 2019, and May 27 th , 2020. , five additional plots were designated to ensure consistent and precise dust application. After the emergence of redroot pigweed seedlings, all plants other than redroot pigweed were eliminated by hand weeding to maintain the redroot plant density of 20 m -2 . 2.2 Dust preparation The dust gathered during the spring seasons in western Iran in 2019 and 2020. Following an official metrological announcement, a moistened sponge collected dust from smooth surfaces such as windows and cars after each dust event passed through Syria and Iraq. The collected dust was then mixed to obtain a uniform sample, as reported by Naghib Alsadati et al. (2020). The mixed sample was analyzed for its properties, including particle composition, size, and elemental content (Table 4). The mineralogical and elemental properties of the dust were measured in the Department of Chemistry, University of Kurdistan. X-ray diffractometry (XRD) technique was applied to measure the dust mineralogical content. The mineral concentrations were standardized and normalized to 100% in the unit 32,33 . It should be noted that this method typically excludes the concentrations of low crystalline and amorphous phases, e.g., organic compounds and volcanic glass. Dust was applied on shoots of the redroot pigweed plants at the 4-6 leaves stage using a windpump at 3 km hr -1 speed. This growth stage was chosen due to the ordinary coincidence of the occurrence of dust at the 4- to 6-leaf stage of Amaranthus spp. in the field. Initially, the windpump was calibrated using wheat flour (flour particles are the same size as dust particles). All plants were washed using a water sprinkler before dust application to prevent any other dust interference. Dust was applied at a 1 g m -2 at the same size (<3 microns diameter) and rate measured on the other plants when a dust storm passed through western Iran from Iraq and Syria. A plastic shield was used to prevent dust infiltration into adjacent plots. To ensure consistent and precise dust application, the plants from the additional plots were harvested 15 minutes after dust application and thoroughly washed with distilled water. The solution from the washed leaves was then taken to the laboratory and subjected to a week-long exposure at 40 ˚C. After the complete evaporation of water inside the containers, the remaining dust was weighed to confirm the uniform and correct application of dust 34 . 2.3 Herbicide application Ten minutes after dust application, herbicides were applied in the recommended doses described in Table 2. The herbicides were applied by a rechargeable electric-knapsack sprayer equipped with a flood-jet nozzle (8002 E, Ag Spray Equipment), delivering 250 L ha -1 at a pressure of 250 kPa. The spraying speed was 5 km hr -1 , and the nozzle height was 35 cm above the top of the plant canopy. Check-basin irrigation was applied regularly in each plot based on plant water requirement calculation 35 , avoiding shoots. Plants were visually assessed two weeks after bentazon application and four weeks after other herbicide treatments based on the EWRC rating scale (Table 3) 36 . The last applied treatment was 2,4-D to avoid the adverse effects of tank spry residual on other plots. 2.4 Measurements To measure the morphological traits of redroot pigweed, including leaf weight, stem weight, plant height, and total biomass, five plants from each plot were randomly selected and were catted at the soil surface 21 days after herbicide application. Also, to assess the physiological features of this weed, leaf samples from the five harvested plants were frozen in liquid nitrogen and transferred to the laboratory. The chlorophyll concentration in leaves was measured by reading the leaf samples' absorbance 663 and 645 nm wavelengths 37 . Bradford's method measured leaves' soluble protein concentration 38 . The absorbance of prepared samples by the Bates et al. (1973) methods was measured at 520 nm using the spectrophotometer instrument to determine leaf proline concentration 39 . The leaf carbohydrate concentration was evaluated according to the previous method (Yemm & Willis, 1954), in which both types of carbohydrates, including those in solution in alcohol and water, can be measured. 2.5 Statistical analysis Data were first evaluated for normality of residuals for both years using PROC Univariate in SAS (SAS Institute, 2015) according to the Shapiro-Wilk test. The result showed that the residuals were average, so data transformations were unnecessary. Data then were subject to analysis of variance (ANOVA) using Proc Mixed in SAS (SAS Institute, 2015), in which dust and herbicide factors were considered fixed effects while year and block were considered random effects. Since there were no differences between the data of both years, the mean comparison of the measured traits was carried out on the pooled data of both years. Fisher's least difference (LSD) test was used at P≤0.05 to consider the difference between the applied treatments. 3. Results and Discussion 3.1 Physiological traits ANOVA was employed to determine the effects of dust and herbicide application on various physiological parameters, such as chlorophyll-a, chlorophyll-b, and total chlorophyll content, protein, proline, and concentrations of water-soluble and alcohol-soluble carbohydrates of redroot pigweed (Table 5). The results of the ANOVA revealed that dust alone meaningfully impacted total chlorophyll, protein, proline, and water- and alcohol-soluble carbohydrates; while herbicides alone significantly impacted all physiological parameters. Moreover, dust and herbicide application interaction also affected chlorophyll-b, proline, and water-soluble and alcohol-soluble carbohydrates (Table 5) . Chlorophyll Chlorophyll-a The results indicated that dust did not affect chlorophyll-a content, causing only a 1.47% reduction compared to the no-dust and no-herbicide control (Ctrl). The herbicides bentazon (BNT), sulfosulfuron (SSN), tribenuron-methyl (TBM), aminopyralid + florasulam (APF), foramsulfuron + iodosulfuron + thiencarbazone (FIT), 2,4-D + MCPA (2,4-D), and acetochlor (ACR) reduced the chlorophyll-a content by 53, 51, 49, 43, 46, 16, and 11% respectively, compared to the Ctrl (Figure 1a). Among the treatments, BNT had the most effect in reducing chlorophyll-a content (Figure 1a). Chlorophyll-b Under dust-free conditions, applying TBM, BNT, SSN, and FIT reduced 57, 53, 43, and 32% in chlorophyll-b content compared to the control without dust (NDC). However, herbicides 2,4-D and ACR did not affect chlorophyll-b content under dusty and non-dusty conditions, compared to the DC and NDC. In dusty conditions, TBM, BNT, SSN, FIT, and APF led to reductions of 63, 33, 44, 43, and 59% in chlorophyll-b content, respectively, compared to the DC. Additionally, under dusty conditions, chlorophyll-b content decreased with the application of APF (50%) and 2,4-D (18%), while it increased with BNT (29%), compared to when the herbicides were applied under dust-free conditions. Among the treatments, TBM had the most substantial impact (63 %) on reducing the chlorophyll-b content of redroot pigweed under both dusty and dust-free conditions. Total chlorophyll content (TCC) Dust reduced the TCC (9%) compared to the non-dust control (NDC; data not shown). Applying TBM, BNT, SSN, FIT, APF, ACR, and 2,4-D reduced TCC by 53, 49, 48, 43, 41, 11, and 9%, respectively, compared to the Ctrl (Figure 1c). The interaction of dust and herbicide application was not significant. Previous studies have shown that dust negatively affects plant pigments, such as chlorophyll, due to damage to plant tissue and reduction of pigment concentration 41–46 . The interactive effect of dust and herbicide on chlorophyll-b may vary by herbicide active ingredient formulation and mechanism of action. A previous study found that dust does not positively or negatively affect TBM efficacy in general 47 . Dust particles can settle on the surface of the leaves and reduce the amount of light that reaches the leaf surface, reducing the herbicide's absorption and translocation. However, this effect is not enough to reduce the overall efficacy of the TBM (Table 7). In addition, TBM is relatively stable and does not break down quickly in the soil or environment 9, 48, 49 . One of the possible reasons for the lack of effect of dust application may be related to the formulation of TBM and SSN as wettable granules (WG). A wettable granule with 50% active ingredient may contain 42% clay, 2% wetting agent, 2% dispersing agent, 4% inert ingredients, and 50% active herbicide 50 . When these herbicides are applied as dust, the particle size and composition may closely resemble that of the WG formulation. This similarity could potentially enhance the efficacy of the herbicides when they settle on the plant's surface. Furthermore, the effects of ACR and 2,4-D were associated with increased chlorophyll-b in dusty conditions. The exact mechanism of this observed increase in chlorophyll-b in the presence of dust may be complex and multifactorial. One possible explanation for this unexpected result could be that the dust provided some level of coverage for the target plants and protected them from environmental stresses, such as excessive heat or radiation 51 . The findings indicated that using BNT led to a considerable reduction in the TCC. The BNT is a photosystem II (PSII) inhibitor (Table 2) that affects the photosynthesis process in plants by disrupting the electron transfer chain in the thylakoid membranes 52 . Dust accumulation on the leaf surface can reduce the amount of light that reaches the leaf, decreasing the photosynthesis rate. Moreover, dust accumulation can also reduce the plant's retention and uptake of the herbicide 13,25 . The TCC indicates the plant's photosynthetic activity 53 and can be affected by the herbicide's mode of action 54 . Since BNT targets photosynthesis, any factor affecting the plant's photosynthetic activity may affect the TCC. Adsorption is a primary mechanism affecting the bioavailability and efficacy of SL herbicides in soil. Herbicide molecules can bind to soil particles, mainly clay, and organic matter, reducing their availability to the targeted plants 55–57 . Soluble protein content (SPC) Dust decreased the SPC (21%) compared to the NDC (Figure 2a). The interaction of dust and herbicide did not affect SPC (Table 4). Herbicides, including SSN, TBM, ACR, and FIT, had the highest impact by 63, 60, 42 and 40% decrease compared to the Ctrl, while the effect of BNT had the lowest effect (20% decrease to control) and finally 2,4-D had no result on SPC (Figure 2b). The APF treatment had an average decrease (28%) of SPC compared to the other herbicides, probably because of the ALS-inhibitor active ingredient florasulam, which comprises one-third of the active ingredient per hectare (g ai ha -1 ) in formulation (Table 2). The impact on protein synthesis may not be as pronounced with the herbicides. It is hypothesized that the dust-induced stress led to an increase in the production of reactive oxygen molecules such as hydroxyl radicals, free oxygen, hydrogen peroxide, and superoxide, which then interacted with various biomolecules such as proteins, nucleic acids, and lipids, causing damage and mutations to the DNA and breakdown of carbohydrates and proteins 58 . Furthermore, the plant's response to stress may lead to an increase in the activity of protease enzymes, which are responsible for breaking down proteins, and decreased protein production 25 . Also, dust can accumulate on the surface of plant leaves, forming a physical barrier that hinders the exchange of gases and blocks sunlight 59 . Reduced sunlight availability can limit photosynthesis, which is crucial to producing energy and synthesizing organic compounds, including proteins 60 . Consequently, the plant may experience a decrease in SPC. Furthermore, stomata are tiny openings on the leaf surface that allow the exchange of gases with the atmosphere. When dust particles settle on the stomata, they can clog these openings, impeding the uptake of carbon dioxide (CO 2 ) needed for photosynthesis 61,62 . Without an adequate supply of CO 2 , the plant's ability to produce energy and synthesize proteins can be impaired, leading to decreased SPC 63, 64 . Among the herbicides, TBM, SSN, and FIT work by inhibiting acetolactate synthase (ALS) (Table 2), which is an enzyme involved in the synthesis of the branched-chain amino acids valine, leucine, and isoleucine 65 . As a result, the plants treated with these herbicides experience a disruption in amino acid production. Since amino acids are the building blocks of proteins, the inhibition of ALS can decrease SPC 66 . On the other hand, herbicides like 2,4-D and BNT have different modes of action that do not directly interfere with amino acid synthesis. Soluble leaf proline content (LPC) Dust caused a 14% decrease in the soluble leaf proline content (LPC) compared to the NDC. (Figure 3). Applying TBM, APF, SSN, 2,4-D, BNT, and ACR changed the LPC compared to the NDC. Herbicides in dusty conditions, including BNT, ACR, FIT, SSN, and 2,4-D, reduced the LPC by 56, 55, 50, 44, and 37% compared to herbicides in non-dust conditions. LPC increased in the presence of 2,4-D, TBM, and APF in non-dust conditions by 42%, 17%, and 17% compared to the NDC (Figure 3). All other herbicides exhibited a decrease in LPC in the presence of dust. Plants accumulate proline as a defensive response to regulate osmotic stress conditions 68, 67,69 as observed when herbicides are applied in non-dusty conditions. The increase in proline accumulation under herbicide treatment could be attributed to increased protein breakdown. This increased protein breakdown can result in the accumulation of amino acids, including proline, as a byproduct 58 . Soluble carbohydrates in water (SCW) and in alcohol (SCA) : Herbicides in the presence of the dust, including 2,4-D, FIT, SSN, TBM, APF, and ACR, resulted in a reduction in the SCW by 69, 62, 61, 56, 37, and 43%, respectively, compared to the herbicide application in non-dust condition and 76, 69, 67, 75, 51, and 55% compared to NDC (Figure 4a). Herbicides' effect in dusty conditions was increased (Table 6), and the amount of SCW was severely reduced except for BNT, APF, and ACR, which was not statistically different compared to DC. In dusty conditions, TBM, 2,4-D, SSN, and FIT had the lowest amount of SCW. The herbicides and dust particles and their interaction affected the SCA, and the result was partially similar to the SCW obtained with less sensitivity (Figure 4b). The reduction of soluble carbohydrates in the presence of dust and herbicides could be attributed to several factors. Dust can block sunlight from reaching the plant's leaves. Sunlight is crucial for photosynthesis, providing the energy needed 70 . If the dust layer is thick enough, it can reduce the light reaching the leaves, decreasing photosynthesis and resulting in fewer soluble carbohydrates. Dust particles may contain pollutants, heavy metals, or other harmful substances that can negatively impact plant metabolism. When plants are under stress, their metabolic processes, including carbohydrate synthesis, can be disrupted, decreasing soluble carbohydrates 71, 72 . The enzymes responsible for the regeneration and synthesis of carbohydrates and the Calvin cycle, such as ribulose diphosphate carboxylase, fructose diphosphate phosphatase, NADP glyceraldehyde 3-phosphodihydrogenase, phosphoribulokinase, and pseudoheptulose diphosphate phosphatase, are activated by appropriate light intensity, and the reduction in light due to the dust and shade can negatively impact their function and ultimately reduce the concentration of soluble carbohydrates in water 73 . Meanwhile, dust accumulation on the leaf surface can reduce the amount of light reaching the chloroplasts, where photosynthesis occurs. Consequently, photosynthetic activity can be impaired, leading to decreased production of carbohydrates, including soluble carbohydrates. The herbicides may exacerbate this effect by further compromising photosynthetic processes. This limitation in carbon dioxide availability can decrease the production of soluble carbohydrates. Also, each herbicide has a specific mode of action that affects plant physiology differently. It is possible that the herbicides themselves directly or indirectly influence carbohydrate metabolism, leading to reduced soluble carbohydrate concentrations in water 59,60 . Overall, the combination of dust accumulation, physical stress, altered herbicide efficacy, impaired photosynthesis, and potential herbicide interactions with dust particles likely contribute to the observed reductions in soluble carbohydrates in water in the presence of dust conditions. However, it is essential to note that specific interactions between dust, herbicides, and the physiology of redroot pigweed should be investigated further to gain a more comprehensive understanding of these effects. 3.2 Morphological traits Leaf dry weight (LDW) The result showed that dust, herbicides, and their interactions affected leaf dry weight (LDW), stem dry weight (SDW), and total biomass of redroot pigweed—also, herbicides alone and the herbicide in the presence of the dust interaction affected plant height (Table 5). Herbicides TBM, SSN, FIT, and BNT in the presence of the dust reduced LDW by 46, 43, 64, and 32, while 2,4-D, ACR, and APF had no meaningful effect on LDW compared to DC (Figure 5). Among the applied treatments, 2,4-D did not affect LDW compared to both controls (NDC and DC), while FIT, TBM, SSN, and BNT had the most effect in the presence of dust by reducing 65, 48, 45, and 35% of LDW compared to NDC (Figure 5). Plants can compensate for specific environmental stresses by adjusting their growth patterns 74,75 . Redroot pigweed may have altered its resource allocation in the presence of dust, shifting resources toward maintaining LDW while reducing other growth parameters, as observed in SDW reduction. This compensatory response could help the plant maintain its structural integrity and support survival 76, 77 . Also, dust accumulation on the leaf surface can disrupt the distribution of resources within the plant. The resources redistributed by redroot pigweed may prioritize allocation towards LDW while compromising other growth parameters. This redistribution may have allowed the plant to maintain its overall biomass despite the adverse effects of dust on other growth processes 78 . Each herbicide has a specific mode of action, which determines how it affects plant growth and development. The herbicides FIT, TBM, and SSN may have more potent 79,80 or targeted modes of action (Table 2) that direct impact processes associated with LDW, such as cell division, elongation, or biomass accumulation 81,82 . On the other hand, the mode of action of 2,4-D may be less effective, or hormesis may be affecting LDW, resulting in a minor impact 83–85 . Also, different herbicides can have varying levels of efficacy on different plant species. Redroot pigweed may be more susceptible to the FIT, TBM, and SSN herbicides than 2,4-D 84 . The specific biochemical pathways these herbicides target may be more critical for LDW accumulation in redroot pigweed. Stem dry weight (SDW) The addition of dust decreased SDW by 16% compared to the NDC for the no herbicide control plants. The application of herbicides under non-dusty conditions, such as FIT, TBM, SSN, APF, BNT, 2,4-D, and ACR, resulted in reductions in SDW by 69, 65, 65, 55, 42, 30, and 28%, respectively, compared to the NDC. Furthermore, when TBM, APF, SSN, FIT, BNT, and ACR were applied in the presence of dust, there was a decrease in SDW by 71, 73, 69, 66, 39, and 19%, respectively, compared to the control in dusty conditions (Figure 6). This result indicates that even in the presence of dust, TBM, APF, SSN, and FIT maintained their effectiveness in suppressing redroot pigweed's growth and biomass accumulation, albeit with some variations in the extent of reduction compared to the dust-free conditions. Research has indicated that dust reduces the stems and branches and various plant parts' fresh and dry weight, which could be attributed to decreased chlorophyll content and photosynthetic processes 86,87 . The impact of dust on reducing the SDW is more significant than its impact on the root 88 , possibly because the plant's natural photosynthetic activities can meet the root's needs before the dust stress 88 . However, dust may slow the photosynthetic process, and as a result, the products produced by photosynthesis tend to move more toward the leaves and roots 88 . The variations in the percentage reductions of SDW among different herbicides can be attributed to several factors, including their specific modes of action, effectiveness on redroot pigweed, interactions with dust particles, and potential differences in plant sensitivity to these herbicides. Notably, the percentage reductions in SDW reflect the overall impact on plant growth and biomass accumulation. The observed reductions indicate that the tested herbicides have the potential to control the growth of redroot pigweed, both in non-dust and dusty conditions, although the presence of dust may influence the effectiveness. Plant height and Total biomass The plant height of redroot pigweed was similar in the presence of dust and under dust-free conditions (Table 5). In dust-free conditions, APF, TBM, FIT, and SSN (were not different), BNT and 2,4-D (were in the same group), and ACR (had the slightest effect) caused reductions in plant height of 74, 72, 70, 61, 47, 26, and 26%, respectively, compared to the NDC. Also, in the presence of dust, these herbicides reduced plant height, with significant of 78, 80, 71, 67, 32, 28, and 18%, respectively, compared to the DC (Figure 7). Among the applied treatments, TBM and APF in the presence of dust had more effect, and plant height decreased (8%) compared to when applied in no dusty condition. While FIT, ACR, SSN, 2,4-D, and APF effects did not change in plant height reduction. Furthermore, BNT application in dusty conditions had a lower effect on plant height decline (Figure 7). Adding dust reduced the total biomass of redroot pigweed by 12%, DC, compared to the NDC. Also, in dust-free conditions, FIT, SSN, TBM, APF, BNT, ACR, and 2,4-D decreased total biomass by 93, 84, 83, 79, 70, 34, and 34%, respectively, compared with NDC. However, these herbicides in the presence of the dust had a different reaction and decreased total biomass by 94, 90, 88, 87, 56, 38, and 13%, respectively, compared with dusty condition control (Figure 8). In the presence of dust, TBM, APF, SSN, and ACR decreased biomass compared to NDC, attributed to previously discussed factors, including: a) Increased herbicide retention: Dust particles on the leaf surface can act as physical barriers, potentially enhancing the retention and adherence of herbicide droplets or residues. This increased retention of herbicides in the presence of dust could lead to higher concentrations and prolonged exposure of redroot pigweed to the herbicides, resulting in more suppression of its growth and biomass accumulation. b) Enhanced herbicide efficacy: Dust particles may interact with herbicides and alter their properties, potentially enhancing their effectiveness. These interactions can affect herbicide distribution, uptake, translocation, or metabolism within the plant. The combined effects of dust and herbicides such as TBM, APF, SSN, and ACR may result in synergistic or additive effects, leading to a more pronounced reduction in redroot pigweed biomass than in non-dusty conditions. c) Stress amplification: Dust particles can induce plant stress by causing physical damage, blocking light penetration, and disrupting stomatal function. Combining stress factors and herbicide action can amplify redroot pigweed's response to dust-induced stres s . The cumulative effects of stress from dust particles and herbicides may result in a greater reduction in total biomass than herbicide application in non-dusty conditions. d) Interference with physiological processes: Dust accumulation can interfere with critical physiological processes in plants, such as photosynthesis, water uptake, and nutrient absorption. The presence of dust may exacerbate the impact of herbicides on these processes, further compromising the growth and biomass accumulation of redroot pigweed. This interference with physiological processes can contribute to more decline in total biomass under dusty conditions. As observed in the results, 2,4-D and BNT in the presence of the dust lost their impact on the total biomass of redroot pigweed by 13 and 29% compared with non-dusty condition herbicide application, which could be attributed to several factors, including: a) Reduced herbicide deposition: Dust particles on the leaf surface can interfere with the deposition of herbicide droplets or residues. Dust particles, may create a physical barrier that hinders contact between the herbicide and the target weed. Consequently, some herbicides may not effectively reach the intended target, leading to reduced efficacy. b) Impaired herbicide absorption: Dust particles can impact the absorption of herbicides by the weed. When dust is present, it may interfere with the penetration of herbicide molecules through the leaf cuticle or hinder their movement within the plant tissues. This interference can reduce herbicide absorption by redroot pigweed, decreasing the effectiveness of 2,4-D and BNT in controlling weed growth. c) Dust-induced physiological stress: Dust accumulation on the leaf surface can induce physiological stress on plants. This stress can disrupt various plant processes, including photosynthesis, water balance, and nutrient uptake. Such physiological stress can weaken redroot pigweed's overall vigor and health, making it less responsive to herbicide treatment. The combination of dust-induced stress and herbicide application may reduce the efficacy of 2,4-D and BNT on redroot pigweed. d) Dust-mediated herbicide degradation: Dust particles may contain compounds or microbes that could interact with herbicides and potentially degrade their active ingredients. These interactions may alter the chemical properties or stability of 2,4-D and BNT, decreasing their effectiveness against redroot pigweed. e) Differential susceptibility of redroot pigweed: It is also possible that redroot pigweed exhibits a lower sensitivity or resistance to 2,4-D and BNT under dusty conditions than in non-dusty conditions. Dust-induced stress or other dust-related factors may confer tolerance or reduced susceptibility to these herbicides in redroot pigweed, reducing their efficacy. It is important to note that the specific interactions between dust, herbicides, and redroot pigweed can be complex and influenced by various factors, including the composition of the dust, formulation of the herbicides, plant physiology, and environmental conditions. Further research and experimentation would be required to gain a more comprehensive understanding of the underlying mechanisms responsible for the observed reduction in the efficacy of 2,4-D and BNT in the presence of dust. 4. Conclusion In conclusion, dust significantly impacted redroot pigweed's physiological and morphological traits. While previous research has recognized the negative impact of dust on plant physiology, affecting processes like photosynthesis, nutrient uptake, and growth parameters, which subsequently diminish herbicide efficacy, our recent study has revealed an interesting finding. Despite its harmful effects, dust may also have the unexpected benefit of enhancing the efficacy of certain herbicides. Dust particles reduced chlorophyll content, potentially affecting herbicide efficacy. Herbicides with different modes of action exhibited varied responses in the presence of dust. Dust accumulation led to a decrease in soluble protein content and soluble carbohydrates, likely due to reduced photosynthesis and impaired gas exchange. The plant's compensatory response focused on maintaining leaf and stem dry weight while reducing other growth parameters. The combination of dust and herbicides reduced total biomass, with potential factors including increased herbicide retention, enhanced herbicide efficacy, stress amplification, and interference with physiological processes. However, the effectiveness of 2,4-D and BNT on total biomass was compromised in the presence of dust, potentially due to reduced herbicide deposition, impaired absorption, dust-induced stress, dust-mediated degradation, or the differential susceptibility of redroot pigweed. Farmers in areas prone to dust accumulation should adopt regular dust management techniques, such as pre-herbicide, rain irrigation, and the utilization of adjuvants, to mitigate the efficacy of herbicides against weeds. Researchers and policymakers must also collaborate in developing effective weed management strategies in such environments. Declarations Acknowledgment The University of Kurdistan funded the project. We thank the Research Vice-Chancellor of the University of Kurdistan and Dr. Kayoomars Karami for their support. Funding University of Kurdistan, Iran, supports this research. Grant number: 11/11/23213 Data availability The data that support this study will be shared upon reasonable request to the corresponding author Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Nosratti, I., Sabeti, P., Chaghamirzaee, G. & Heidari, H. Weed problems, challenges, and opportunities in Iran. Crop Protection 134 , 104371 (2020). Alam, Md. F. Bin et al. Analysis of the drivers of Agriculture 4.0 implementation in the emerging economies: Implications towards sustainability and food security. Green Technologies and Sustainability 1 , 100021 (2023). Zhang, Z., Li, R., Zhao, C. & Qiang, S. Reduction in weed infestation through integrated depletion of the weed seed bank in a rice-wheat cropping system. Agron Sustain Dev 41 , 1–14 (2021). Stefan, L., Engbersen, N. & Schöb, C. Crop-weed relationships are context-dependent and cannot fully explain the positive effects of intercropping on yield. Ecol Appl 31 , (2021). Chauhan, B. Grand Challenges in Weed Management. Frontiers in Agronomy 1 , 3 (2020). Kraehmer, H., Laber, B., Rosinger, C. & Schulz, A. Update on Weed Control Agents: Path to Modern Agriculture Herbicides as Weed Control Agents: State of the Art: I. Weed Control Research and Safener Technology: The Path to Modern Agriculture. Plant Physiol 166 , 1119–1131 (2014). Roy, P. et al. Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Science of The Total Environment 849 , 157850 (2022). Hao, Z. et al. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts. Earth Sci Rev 235 , 104241 (2022). Lorestani, E., Babaei, S., Tahmasebi, I. & Sabeti, P. Assessment of Tribenuron Methyl Soil Residual on Crops Germination Properties. Gesunde Pflanzen 75 , 765–773 (2022). Barbastegan, H., Saeidi, M., Nosratti, I., Honarmand, S. J. & Ghobadi, M. Dust particles on corn (Zea mays) photosynthesis, transpiration and yield in Kermanshah, Iran. Philippine Agricultural Scientist 98 , 333–338 (2015). Neira, M. et al. Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions. Environ Res 216 , 114537 (2023). Gholizadeh, H., Zoghipour, M. H., Torshizi, M., Nazari, M. R. & Moradkhani, N. Gone with the wind: Impact of soil-dust storms on farm income. Ecological Economics 188 , 107133 (2021). Sharifi Kaliani, F., Babaei, S. & ZafarSohrabpour, Y. Study of the effects of dusts on the morphological and physiological traits of some crops. Journal of Plant Production Research 28 , 205–220 (2021). Lambert, A. et al. Dust Impacts of Rapid Agricultural Expansion on the Great Plains. Geophys Res Lett 47 , (2020). Pease, R. Dust Bowl 2.0? Rising Great Plains dust levels stir concerns. Science (1979) (2020) doi:10.1126/SCIENCE.ABF3504. Aili, A., Xu, H. & Zhao, X. Health Effects of Dust Storms on the South Edge of the Taklimakan Desert, China: A Survey-Based Approach. Int J Environ Res Public Health 19 , (2022). Asadi-Sabzi, M., Keshtkar, E., Mokhtassi-Bidgoli, A. & Moss, S. R. Quantifying the detrimental effect of airborne dust on herbicide efficacy. Weed Res 60 , 204–211 (2020). Goudie, A. S. Dust storms: Recent developments. J Environ Manage 90 , 89–94 (2009). Weidhuner, A. et al. Tillage impacts on soil aggregation and aggregate-associated carbon and nitrogen after 49 years. Soil Tillage Res 208 , 104878 (2021). Lwanga, E. H. et al. Microplastic appraisal of soil, water, ditch sediment and airborne dust: The case of agricultural systems. Environmental Pollution 316 , 120513 (2023). Al-Dabbas, M. A., Ayad Abbas, M. & Al-Khafaji, R. M. Dust storms loads analyses—Iraq. Arabian Journal of Geosciences 5 , 121–131 (2012). Najafi, M. S. et al. Characteristics of TSP Loads during the Middle East Springtime Dust Storm (MESDS) in Western Iran. Arabian Journal of Geosciences 7 , 5367–5381 (2014). Saurat, D. et al. Determination of glyphosate and AMPA in indoor settled dust by hydrophilic interaction liquid chromatography with tandem mass spectrometry and implications for human exposure. J Hazard Mater 446 , 130654 (2023). Figueiredo, D. M. et al. Pesticides in doormat and floor dust from homes close to treated fields: Spatio-temporal variance and determinants of occurrence and concentrations. Environmental Pollution 301 , 119024 (2022). Zhou, J., Tao, B. & Messersmith, C. G. Soil dust reduces glyphosate efficacy. Weed Sci 54 , 1132–1136 (2006). Tisdale, S. L., Werner, S. L. & Beaton, J. D. Basic soil-plant relationships. Soil Fertility and Fertilizers. in 4th edition: Macmillan Publishing Conference 95–111 (Macmillan , 1985). Havlin, J. L. Soil: Fertility and Nutrient Management. Landscape and Land Capacity 251–265 (2020) doi:10.1201/9780429445552-34. Brian, R. C. The uptake and adsorption of diquat and paraquat by tomato, sugar beet and cocksfoot. Annals of Applied Biology 59 , 91–99 (1967). Harnly, M. E. et al. Pesticides in dust from homes in an agricultural area. Environ Sci Technol 43 , 8767–8774 (2009). Quirás-Alcal, L. et al. Pesticides in house dust from urban and farmworker households in California: An observational measurement study. Environ Health 10 , 1–15 (2011). Mansouri Daneshvar, M. R., Ebrahimi, M. & Nejadsoleymani, H. An overview of climate change in Iran: facts and statistics. Environmental Systems Research 8 , 1–10 (2019). Schreiner, W. N. A standard test method for the determination of RIR values by x-ray diffraction. Powder Diffr 10 , 25–33 (1995). Chung, F. H. Quantitative interpretation of X-ray diffraction patterns of mixtures. I. Matrix-flushing method for quantitative multicomponent analysis. J Appl Crystallogr 7 , 519–525 (1974). Naghib Alsadati, M., Babaei, S., Tahmasebi, I. & Kiani, H. Evaluation of airborne dust effect on the efficiency of Atlantis OD, clodinafop propargyl and 2,4-D+MCPA herbicides on weed control in wheat. Iranian Journal of Field Crop Science 50 , 1–11 (2020). Villalobos, F. J., Testi, L. & Fereres, E. Calculation of Evapotranspiration and Crop Water Requirements. Principles of Agronomy for Sustainable Agriculture 119–137 (2016) doi:10.1007/978-3-319-46116-8_10. Dear, B. S., Sandral, G. A., Spencer, D., Khan, M. R. I. & Higgins, T. J. V. The tolerance of three transgenic subterranean clover (Trifolium subterraneum L.) lines with the bxn gene to herbicides containing bromoxynil. Aust J Agric Res 54 , 203–210 (2003). Arnon, A. N. Method of extraction of chlorophyll in the plants. Agron J 23 , 112–121 (1967). Bradford, M. A Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding. Anal Biochem 72 , 248–254 (1976). Bates, L. S., Waldren, R. P. & Teare, I. D. Rapid determination of free proline for water-stress studies. Plant Soil 39 , 205–207 (1973). Yemm, E. W. & Willis, A. J. The estimation of carbohydrates in plant extracts by anthrone. Biochem J 57 , 508–514 (1954). Irwe, R. R., Sontakke, S. G. & Darade, M. S. Study of dust deposition on leaves of some plant species in GVISH. Campus of Amravati (MS) India . Int. J. of Life Sciences vol. 5 www.ijlsci.in (2017). Rai, P. K. Impacts of particulate matter pollution on plants: Implications for environmental biomonitoring. Ecotoxicology and Environmental Safety vol. 129 120–136 Preprint at https://doi.org/10.1016/j.ecoenv.2016.03.012 (2016). Lin, W., Yu, X., Xu, D., Sun, T. & Sun, Y. Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes. Remote Sensing 2021, Vol. 13, Page 3570 13 , 3570 (2021). Yin, D. et al. Morphological and biochemical studies of Salvia guaranitica St. Hil. under simulated deposition with different amounts of dust. Ecotoxicol Environ Saf 249 , 114404 (2023). Zilaie, M. N., Arani, A. M. & Etesami, H. The importance of plant growth-promoting rhizobacteria to increase air pollution tolerance index (APTI) in the plants of green belt to control dust hazards. Front Plant Sci 14 , 1098368 (2023). Shah, K. et al. Chronic cement dust load induce novel damages in foliage and buds of Malus domestica. Sci Rep 10 , 12186 (2020). Anwar, S. et al. Biodegradation and Subsequent Toxicity Reduction of Co-contaminants Tribenuron Methyl and Metsulfuron Methyl by a Bacterial Consortium B2R. ACS Omega 7 , 19816–19827 (2022). Noshadi M & Dastranj M. Open Acc J of Toxicol Tribenuron-Methyl Herbicide Decontamination using Vetiver Grass and its Distribution through Soil Profile. 2 , (2017). Rachedi, K. et al. Effect of sulfonylurea tribenuron methyl herbicide on soil Actinobacteria growth and characterization of resistant strains. Brazilian Journal of Microbiology 49 , 79 (2018). Zimdahl, R. L. Herbicide Formulation. Fundamentals of Weed Science: Fifth Edition 501–509 (2018) doi:10.1016/B978-0-12-811143-7.00017-2. Kudsk, P. & Kristensen, J. L. Effect of environmental factors on herbicide performance. Proceedings of the First International Weed Control Congress (1992). Zhu, J., Patzoldt, W. L., Radwan, O., Tranel, P. J. & Clough, S. J. Effects of Photosystem-II-Interfering Herbicides Atrazine and Bentazon on the Soybean Transcriptome. Plant Genome 2 , 191–205 (2009). Jovanić, B. R., Radenković, B., Despotović-Zrakić, M., Bogdanović, Z. & Barać, D. Effect of UV-B radiation on chlorophyll fluorescence, photosynthetic activity and relative chlorophyll content of five different corn hybrids. J Photochem Photobiol 10 , 100115 (2022). Dayan, F. E. & Zaccaro, M. L. de M. Chlorophyll fluorescence as a marker for herbicide mechanisms of action. Pestic Biochem Physiol 102 , 189–197 (2012). Gámiz, B., Velarde, P., Spokas, K. A., Celis, R. & Cox, L. Changes in sorption and bioavailability of herbicides in soil amended with fresh and aged biochar. Geoderma 337 , 341–349 (2019). Yu, Y. L. et al. An exploration of the relationship between adsorption and bioavailability of pesticides in soil to earthworm. Environmental Pollution 141 , 428–433 (2006). Wu, X. M. et al. Effects of adsorption on degradation and bioavailability of metolachlor in soil. J Soil Sci Plant Nutr 11 , 83–97 (2011). Suriyan, C. um & Chalermpol, K. Proline Accumulation, Photosynthetic Abilities and Growth Characters of Sugarcane (Saccharum officinarum L.) Plantlets in Response to Iso-Osmotic Salt and Water-Deficit Stress. Agric Sci China 8 , 51–58 (2009). Zia-Khan, S. et al. Effect of dust deposition on stomatal conductance and leaf temperature of cotton in Northwest China. Water (Switzerland) 7 , 116–131 (2015). Lu, D. et al. Light deficiency inhibits growth by affecting photosynthesis efficiency as well as JA and ethylene signaling in endangered plant Magnolia sinostellata. Plants 10 , (2021). Xu, Z., Jiang, Y., Jia, B. & Zhou, G. Elevated-CO2 response of stomata and its dependence on environmental factors. Front Plant Sci 7 , 657 (2016). Najib, R., Houri, T., Khairallah, Y. & Khalil, M. Effect of dust accumulation on Quercus cerris L. leaves in the Ezer forest, Lebanon. IForest 15 , 322 (2022). Lüttge, U. PHOTOSYNTHESIS AND PARTITIONING | CAM Plants. Encyclopedia of Applied Plant Sciences 688–705 (2003) doi:10.1016/B0-12-227050-9/00096-X. Lawlor, D. W. & Tezara, W. Causes of decreased photosynthetic rate and metabolic capacity in water-deficient leaf cells: a critical evaluation of mechanisms and integration of processes. Ann Bot 103 , 561 (2009). Whitcomb, C. E. An introdaction to ALS-inhibiting herbicides. in Toxicology and Industrial Health vol. 15 231–239 (Toxicol Ind Health, 1999). TAYLOR, J. B., LOUX, M. M., HARRISON, S. K. & REGNIER, E. Response of ALS-Resistant Common Ragweed (Ambrosia artemisiifolia) and Giant Ragweed (Ambrosia trifida) to ALS-Inhibiting and Alternative Herbicides 1 . Weed Technology 16 , 815–825 (2002). Verbruggen, N. & Hermans, C. Proline accumulation in plants: A review. Amino Acids vol. 35 753–759 Preprint at https://doi.org/10.1007/s00726-008-0061-6 (2008). Nanos, G. D. & Ilias, I. F. Effects of inert dust on olive (Olea europaea L.) leaf physiological para. Environ Sci Pollut Res Int 14 , 212–214 (2007). Naresh, R. K., Singh, S. P., Dwivedi, A. & Kumar, V. Effects of water stress on physiological processes and yield attributes of different mungbean (L.) varieties. African Journal of Biochemistry Research 7 , 55–62 (2013). Farmer, A. M. The effects of dust on vegetation--a review. Environ Pollut 79 , 63–75 (1993). Thompson, J. R., Mueller, P. W., Flückiger, W. & Rutter, A. J. The effect of dust on photosynthesis and its significance for roadside plants. Environmental Pollution Series A, Ecological and Biological 34 , 171–190 (1984). Karnosky, D., Percy, K. E., Chappelka, A. H. (Arthur H., Simpson, C. M. (Caroline M. & Pikkarainen, J. (Janet). Air pollution, global change and forests in the new millennium. 2003 . (International Union of Forestry Research Organizations). Chaurasia, M., Patel, K., Tripathi, I. & Rao, K. S. Impact of dust accumulation on the physiological functioning of selected herbaceous plants of Delhi, India. Environ Sci Pollut Res Int 29 , 80739–80754 (2022). Li, C., Barclay, H., Roitberg, B. & Lalonde, R. Ecology and Prediction of Compensatory Growth: From Theory to Application in Forestry. Front Plant Sci 12 , 1352 (2021). Lama, A. D., Klemola, T., Tyystjärvi, E., Niemelä, P. & Vuorisalo, T. Physiological and compensatory growth responses of Jatropha curcas (L.) seedlings to simulated herbivory and drought stress. South African Journal of Botany 121 , 486–493 (2019). McNaughton, S. J. Compensatory Plant Growth as a Response to Herbivory. Oikos 40 , 329 (1983). Lyu, S. & Alexander, J. M. Compensatory responses of vital rates attenuate impacts of competition on population growth and promote coexistence. Ecol Lett 26 , 437–447 (2023). Poorter, H. et al. How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytol 208 , 736 (2015). Eizenberg, H., Goldwasser, Y., Achdary, G. & Hershenhorn, J. The Potential of Sulfosulfuron to Control Troublesome Weeds in Tomato1. https://doi.org/10.1614/0890-037X(2003)017[0133:TPOSTC]2.0.CO;2 17 , 133–137 (2003). Zargar, M., Bayat, M. & Protection, T. A. Study of postemergence-directed herbicides for redroot pigweed (Amaranthus retroflexus L) control in winter wheat in southern Russia. J Plant Prot Res 60 , 7–13 (2020). Whitcomb, C. E. An introduction to ALS-inhibiting herbicides. Toxicol Ind Health 15 , 231–239 (1999). Anderson, A. The effects of acetolactate synthase (ALS) inhibiting herbicides on the growth, yield, nodulation and nitrogen fixation of selected legumes. (2001). Brochado, M. G. da S. et al. Impacts of dicamba and 2,4-D drift on ‘Ponkan’ mandarin seedlings, soil microbiota and Amaranthus retroflexus. Journal of Hazardous Materials Advances 6 , 100084 (2022). Meseldžija, M. et al. Is There a Possibility to Involve the Hormesis Effect on the Soybean with Glyphosate Sub-Lethal Amounts Used to Control Weed Species Amaranthus retroflexus L.? Agronomy 2020, Vol. 10, Page 850 10 , 850 (2020). Marques, R. F. et al. Hormesis of 2,4-D choline salt in productive aspects of cotton. J Environ Sci Health B 56 , 977–985 (2021). Rasouli, S. F., Galeshi, S., Pirdashti, H. & Zeinali, E. Investigation of waterlogging stress on some morphologic and physiological traits of rapeseed (Brassica napus L.) in different developmental stages. Journal of Plant Production Research 21 , 69–89 (2014). Fatima, S. et al. Particle composition and morphology over urban environment (New Delhi): Plausible effects on wheat leaves. Environ Res 202 , 111552 (2021). Yang, H. & Liu, Y. Phytoremediation on Air Pollution. in The Impact of Air Pollution on Health, Economy, Environment and Agricultural Sources (IntechOpen, 2011). doi:10.5772/19942. Tables Table 1. Physical and chemical characteristics of the soil at the experimental site Soil texture Organic matter (%) Electrical conductivity (ds m -1 ) Microelements Absorbable potassium (ppm) Absorbable phosphorus (ppm) pH Sand (%) Silt (%) Clay (%) Br (ppm) Fe (ppm) Zn (ppm) 14.2 38.4 47.4 0.76 0.49 0.7 2.2 0.8 320 12.4 7.62 Table 2. Details of herbicides applied in the experiment. Herbicide active ingredient Mode of Action Trade name Formulation g ai ha -1 tribenuron-methyl (TBM) ALS 1 Inhibitor Granstar® 75% DF 4 22.5 aminopyralid + florasulam (APF) Auxinic + ALS Inhibitor Lancelot® 450 WG 5 300 + 150 sulfosulfuron (SSN) ALS Inhibitor Apirus® 75% WG 36 2,4-D + MCPA (2,4-D) Auxinic 2,4-D + MCPA 67.5% SL 6 0.975 + 1300 foramsulfuron + iodosulfuron + thiencarbazone (FIT) ALS Inhibitor MaisTer® Power 25% OD 7 719.2 bentazon (BNT) PS 2 II Inhibitor Basagran® 48% SL 960 acetochlor (ACR) GGPP 3 Inhibitor Surpass® 76% EC 8 900 1 Acetolactate Synthase 5 Wettable Granule 2 Photosystem II 6 Soluble Liquid 3 Geranylgeranyl Pyrophosphate 7 Oil Dispersion 4 Dry Flowable 8 Emulsifiable Concentrate Table 3. European Weed Research Council (EWRC) rating scale used to score the level of plant injury following herbicide application. EWRC score Crop tolerance Efficacy (weed kill) Weed control (%) 1 No effect Complete kill 100 2 Very slight effects; some stunting and yellowing just visible Excellent 99.9–98 3 Slight effects; stunting and yellowing; effects reversible Very good 97.9–95 4 Substantial chlorosis and or stunting; most effects probably reversible Good–acceptable 94.9–90 5 Strong chlorosis/stunting; thinning of stand Moderate but not generally acceptable 89.9–82 6 Increasing severity of damage Fair 81.9–70 7 Increasing severity of damage Poor 69.9–55 8 Increasing severity of damage Very poor 54.9–30 9 Total loss of plants and yield None 29.9–0 Table 4. Properties of the analyzed dust samples in both experimental years. Particle size 2019 2020 Elements (ppm) 2019 2020 Sand (%) 1.3 1.2 Cl 90 95 Silt (%) 65.5 64.3 Sr 212 225 Clay (%) 33.2 34.5 Ba 267 274 Mean size 14.8 15.1 Rb 66 59 Mineralogical compositions (%) Zr 170 165 Quartz 21.2 20.1 Ni 123 111 Albite 4.9 5.1 V 129 118 Orthoclase 2.2 2.6 Co 31 28 Microcline 1.6 1.2 Cu 38 36 Illite 10 9 Zn 90 95 Kaolinite 3.5 3.7 Ga 12 11 Chlorite 4.6 4.6 Br 5 6 Palygorskite 3.6 3.7 Y 24 23 Calcite 35.2 35.3 Nb 14 14 Dolomite 6.2 6.3 Gypsum 0.8 1.2 Halite 0.3 0.3 Table 5. Analysis of variance of dust and herbicides data on physiological traits of Amaranthus retroflexus . Mean Squares Source of Variation d.f. Chlorophyll-a Chlorophyll-b Total chlorophyll Proteins Proline Water-soluble carbohydrates Alcohol-soluble carbohydrates Block 2 0.31 ns 0.95 ns 1.97 ns 0.45 ns 0.00 ns 9.83 ns 15.62** Dust 1 3.74 ns 8.70 ns 37.93 * 5.71 ** 0.011 ** 5182.11 ** 29.53 ** Herbicides 7 77.40 ** 27.12 ** 205.95 ** 5.58 ** 0.029 ** 1943.06 ** 59.95 ** Dust × Herbicides 7 1.37 ns 4.47 * 6.15 ns 0.09 ns 0.007 ** 1401.59 ** 8.64 * Error 30 1.46 3.06 5.25 0.48 0.002 32.13 3.85 Coefficient variation - 10.86 24.49 12.51 24.53 18.77 16.99 12.67 **, *, ns significant at 1 and 5% level and non-significant, respectively. Table 6. Variance analysis of the effect of dust and herbicides on morphological traits in Amaranthus retroflexus. Source of Variation Mean Squares d.f. Leaf weight (g) Stem weight (g) Total biomass (g) Height (cm) Block 2 0.84 ** 0.02 ns 0.001 ns 3.14 ** Dust 1 0.43 ** 1.07 ** 0.29 ** 0.002 ns Herbicides 7 0.74 ** 4.95 ** 9.53 ** 123.07 ** Dust* herbicides 7 0.13 * 0.16 ** 0.16 ** 2.15 ** Error 30 0.05 0.04 0.01 0.56 Coefficient variation - 15.55 10.81 8.90 9.09 **, *, ns significant at 1 and 5% level and non-significant, respectively. Table 7. Visual assessment two week for bentazon and four weeks after herbicide application based on EWRC rating scale. Herbicide active ingredient Dust Efficacy (%) Non-dust Efficacy (%) Control 10 g 0 g tribenuron-methyl (TBM) 95.8 c 92.1 b aminopyralid + florasulam (APF) 95.7 c 90.2 c sulfosulfuron (SSN) 96.5 b 91.8 b 2,4-D + MCPA (2,4-D) 41.2 f 45.4 f foramsulfuron + iodosulfuron + thiencarbazone (FIT) 97.8 a 96.2 a bentazon (BNT) 74.5 d 79.7 d acetochlor (ACR) 53.3 e 58.6 e Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3236065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":225192942,"identity":"56d7fd99-2182-485e-bff9-947ae4da79ad","order_by":0,"name":"Firouzeh Sharifi Kalyani","email":"","orcid":"","institution":"University of Kurdistan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Firouzeh","middleName":"Sharifi","lastName":"Kalyani","suffix":""},{"id":225192943,"identity":"8939f643-e167-427f-b6db-4ae665855e26","order_by":1,"name":"Sirwan Babaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFACHgZmEMnPAKYZGNiI1iLZQKoWBoMDUC0EgXwD78HPBTU2MsY3kg8bMNTYMfBJH8CvxeAAX7L0jGNpPGY30pITGI4lM7DxJRDQwsBjIM3DdhioJcf4AAMbEPEQdBiP8W+ef/95jGeAtPwjQgvDAR4zad62AzwGEjnGCYxtRGgxOMyXZj2zL5lH4syzZINEIIOww9p7D98u+GZnz9+efFjiwzc7OfkeQg5DiYwEUDSNglEwCkbBKKAcAAAt4DNVOC0EeAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kurdistan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sirwan","middleName":"","lastName":"Babaei","suffix":""},{"id":225192944,"identity":"93c73131-a0d1-41ff-b280-3b67b96c27b9","order_by":2,"name":"Yasin Zafarsohrabpour","email":"","orcid":"","institution":"University of Kurdistan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yasin","middleName":"","lastName":"Zafarsohrabpour","suffix":""},{"id":225192945,"identity":"5270acd3-723c-48f3-a217-9521e7966a6c","order_by":3,"name":"Iraj Nosratti","email":"","orcid":"","institution":"Razi University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iraj","middleName":"","lastName":"Nosratti","suffix":""},{"id":225192946,"identity":"da957604-7913-4630-b2b7-ed00480d8ce0","order_by":4,"name":"Karla Gage","email":"","orcid":"","institution":"Southern Illinois University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karla","middleName":"","lastName":"Gage","suffix":""},{"id":225192947,"identity":"c0a171ef-ce56-4eab-8e9d-e5a1c4a11583","order_by":5,"name":"Amir Sadeghpour","email":"","orcid":"","institution":"Southern Illinois University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Sadeghpour","suffix":""}],"badges":[],"createdAt":"2023-08-04 22:59:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3236065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3236065/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-54134-5","type":"published","date":"2024-02-15T15:00:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41535939,"identity":"19dad8f3-61a6-4749-9a8b-27f60346c791","added_by":"auto","created_at":"2023-08-14 13:39:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51336,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of herbicides on chlorophyll-a (a) and total chlorophyll (c), and interaction of dust and herbicides on chlorophyll-b (b), in the redroot pigweed leaves. Means with the same letters are not statistically different (LSD); in figure b, bars show the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC in figure b: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/250df31ba07b57ab7cdee93d.png"},{"id":41535940,"identity":"fb9df0d1-df1f-4a12-a0ae-0ebb1db4ec89","added_by":"auto","created_at":"2023-08-14 13:39:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40941,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of dust and herbicides on soluble proteins content in the redroot pigweed leaves. Means with the same letters are not statistically different (LSD), Means with the same letters are not statistically different (LSD), (Ctrl: no herbicide control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/9cdaa1b43f02f3b16aff7cc2.png"},{"id":41535942,"identity":"56866816-3c79-4d16-94d2-a92a89e41fa2","added_by":"auto","created_at":"2023-08-14 13:39:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37714,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of interaction of dust and herbicides on redroot pigweed leaf proline content\u003cem\u003e. \u003c/em\u003eThe bars indicate the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/cd2bd39ce566d5f157068989.png"},{"id":41534732,"identity":"2f95f3c8-9b7c-4d6d-9018-10778fba601e","added_by":"auto","created_at":"2023-08-14 13:31:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71585,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of interaction of dust and herbicides on soluble carbohydrates content in water (a) and alcohol (b) of redroot pigweed leaves\u003cem\u003e. \u003c/em\u003eThe bars indicate the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/28a8049c4bf687b4da043135.png"},{"id":41534736,"identity":"c13ba7e0-adf8-4228-b34c-e7020c56d0a8","added_by":"auto","created_at":"2023-08-14 13:31:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44067,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of dust and herbicides interaction on redroot pigweed's leaf dry weight. The bars indicate the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/c1906670c4351197e11f0e0f.png"},{"id":41534734,"identity":"24672d4b-4d83-48c8-a9f9-9467c602ffe7","added_by":"auto","created_at":"2023-08-14 13:31:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50878,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of dust and herbicides interaction on redroot pigweed's stem weight. The bars indicate the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/338dbeb4ab9bb1c092d53fc8.png"},{"id":41537070,"identity":"47aa0d91-0dac-4dce-b132-ed9acaa1b0e2","added_by":"auto","created_at":"2023-08-14 13:47:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43171,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of dust and herbicides interaction on the redroot pigweed height. The bars indicate the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/47fee5abd67f5593a9e136e7.png"},{"id":41534739,"identity":"54610616-c0a7-425d-afb7-76ee6a0c8e63","added_by":"auto","created_at":"2023-08-14 13:31:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38399,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of interaction of dust and herbicides on redroot pigweed total biomass. The bars indicate the standard error, and capital letters (non-dust treatments) and lowercase (dusty condition treatments) indicate mean comparison (LSD). * and n.s at 5% level and non-significant between treatments in non-dust and dusty conditions, respectively. (Ctrl: no herbicide control, DC and NDC: dust and non-dust control, TMB: tribenuron-methyl, APF: aminopyralid + florasulam, SSN: Sulfosulfuron, 2,4-D: 2,4-D + MCPA, FIT: foramsulfuron + iodosulfuron + thiencarbazone, BNT: bentazon, ACR: Acetochlor).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/00c16734fd516f94943a6659.png"},{"id":51322784,"identity":"021c859e-ca7c-4514-9653-e6e82cca4b1d","added_by":"auto","created_at":"2024-02-19 15:12:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":797801,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3236065/v1/4b6fe5ff-ac7c-4f66-a8cf-1d71a4611107.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Impacts of Airborne Dust on Herbicide Performance on Redroot Pigweed (Amaranthus retroflexus)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgriculture significantly contributes to the global economy and human health \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The quantity and quality of agricultural products, including cereal, the primary source of human nutrition, are substantially affected by the unwanted growth of weeds in agricultural fields. Weeds may cause a crop yield reduction from 15% to over 70% \u003csup\u003e3,4\u003c/sup\u003e. On the other hand, the agricultural sector should increase production by at least 70% by 2050 to fulfill the nutritional requirement of the increasing human population \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In order to meet the need for increased yields, the importance of weed control is also increasing, with a focus on applying effective herbicides at the correct dosage, at optimal environmental conditions, and at weed growth stages.\u003c/p\u003e \u003cp\u003eHerbicides are essential for controlling weeds in current intensified agricultural systems worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The environmental conditions at the time of herbicide application substantially influence the effectiveness of herbicides in controlling weeds. Current studies \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e show that adverse environmental conditions, such as drought and extreme heat, caused by climate change, can indirectly lead to other environmental problems, e.g., the dispersal of airborne dust to different parts of the world. In recent years, the issue of dust has become a central concern in Middle Eastern countries, including Iran and Iraq \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDust storms have become a common environmental issue in many parts of the world, and their impact on agricultural productivity cannot be overstated \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Airborne dust often occurs in areas prone to soil erosion and an average annual rainfall of less than 100 mm \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Dust particles are organic and inorganic materials that vary in diameter and size (5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 \u0026micro;m). The most important chemical components in dust particles include SiO\u003csub\u003e2\u003c/sub\u003e, CaO, Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, and MgO \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Dust storms can carry heavy metals such as Fe, Zn, Cr, Ni, Pb, Cu, Co, and Cd, with the concentration of heavy metals in dust storms in the Middle East during springtime being higher than the exposure thresholds recommended by the World Health Organization \u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe literature agrees that dust storms can reduce the efficacy of various herbicide active ingredients \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The adherence of herbicide molecules to dust particles is the primary mechanism by which dust particles reduce the effectiveness of herbicides. The formation of the herbicide-particle complex makes it difficult for the herbicide to penetrate plant tissues, thus reducing its activity \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition to the chemical and physical properties of dust and applied herbicides, the leaf characteristics of the weed influence the particles' inhibitory effect\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRedroot pigweed (\u003cem\u003eAmaranthus retroflexus\u003c/em\u003e L.) has become a persistent problem in corn (\u003cem\u003eZea mays\u003c/em\u003e L.) fields in regions where dust storms are expected. Chemical (herbicide) control efforts have often been unsuccessful, possibly due to suboptimal management decisions and the presence of dust particles on this weed's application surface, interfering with herbicides' absorption and effectiveness. Unfortunately, there is not enough information regarding the precise effect of dust particles on plant growth and herbicide efficiency. In addition, no study has yet assessed the effectiveness of herbicides in controlling redroot pigweed plants in fields under the influence of dust particles. Therefore, the objective of the current study was to quantify the interactive effects of airborne dust and various herbicide active ingredients on redroot pigweed physiological and morphological traits and control efficacy.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Location and experimental procedure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn experiment was conducted in the Agriculture Faculty Research field at the University of Kurdistan in Dehgolan with geographic coordinates of 35.314286, 47.315656, and altitude of 1866 m during the 2019 and 2020 growing seasons. The average annual rainfall of this region is 350 mm, and according to the Amberge method, the region\u0026apos;s climate is Mediterranean and semi-arid \u003csup\u003e31\u003c/sup\u003e. Soil physical and chemical properties of the experimental site are described in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore the experiment (in 2018 and 2019), redroot pigweed seeds were collected from the fields around the experimental site and stored at room temperature under dry conditions until the experiments started.\u0026nbsp;All the acquisitions were obtained following both national and international protocols, and the plant collection took place under the supervision and authorization of University of Kurdistan. The authors ensure adherence to all local and national guidelines in this regard. A randomized complete block design experiment with a factorial arrangement and three replicates was applied to assess the interactive effects of herbicides and dust particles on redroot pigweed control. The experimental factors were two dust levels (with and without dust) and eight commonly used herbicides (Table 2) in the corn production field. The herbicide selection was based on usage rate and availability for control of broad leaves in corn fields in the dust-affected region. The redroot pigweed seeds were planted (100 m\u003csup\u003e-2\u003c/sup\u003e) in 1.5 x 1.5 m plots at a 2 cm soil depth on May 24\u003csup\u003eth\u003c/sup\u003e, 2019, and May 27\u003csup\u003eth\u003c/sup\u003e, 2020. , five additional plots were designated to ensure consistent and precise dust application. After the emergence of redroot pigweed seedlings, all plants other than redroot pigweed were eliminated by hand weeding to maintain the redroot plant density of 20 m\u003csup\u003e-2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Dust preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dust gathered during the spring seasons in western Iran in 2019 and 2020. Following an official metrological announcement, a moistened sponge collected dust from smooth surfaces such as windows and cars after each dust event passed through Syria and Iraq. The collected dust was then mixed to obtain a uniform sample, as reported by Naghib Alsadati et al. (2020). The mixed sample was analyzed for its properties, including particle composition, size, and elemental content (Table 4). The mineralogical and elemental properties of the dust were measured in the Department of Chemistry, University of Kurdistan. X-ray diffractometry (XRD) technique was applied to measure the dust mineralogical content. The mineral concentrations were standardized and normalized to 100% in the unit \u003csup\u003e32,33\u003c/sup\u003e. It should be noted that this method typically excludes the concentrations of low crystalline and amorphous phases, e.g., organic compounds and volcanic glass.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDust was applied on shoots of the redroot pigweed plants at the 4-6 leaves stage using a windpump at 3 km hr\u003csup\u003e-1\u003c/sup\u003e speed. This growth stage was chosen due to the ordinary coincidence of the occurrence of dust at the 4- to 6-leaf stage of \u003cem\u003eAmaranthus\u003c/em\u003e spp. in the field. Initially, the windpump was calibrated using wheat flour (flour particles are the same size as dust particles). All plants were washed using a water sprinkler before dust application to prevent any other dust interference. Dust was applied at a 1 g m\u003csup\u003e-2\u003c/sup\u003e at the same size (\u0026lt;3 microns diameter) and rate measured on the other plants when a dust storm passed through western Iran from Iraq and Syria. A plastic shield was used to prevent dust infiltration into adjacent plots. To ensure consistent and precise dust application, the plants from the additional plots were harvested 15 minutes after dust application and thoroughly washed with distilled water. The solution from the washed leaves was then taken to the laboratory and subjected to a week-long exposure at 40 ˚C. After the complete evaporation of water inside the containers, the remaining dust was weighed to confirm the uniform and correct application of dust \u003csup\u003e34\u003c/sup\u003e.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Herbicide application\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen minutes after dust application, herbicides were applied in the recommended doses described in Table 2. The herbicides were applied by a rechargeable electric-knapsack sprayer equipped with a flood-jet nozzle (8002 E, Ag Spray Equipment), delivering 250 L ha\u003csup\u003e-1\u003c/sup\u003e at a pressure of 250 kPa. The spraying speed was 5 km hr\u003csup\u003e-1\u003c/sup\u003e, and the nozzle height was 35 cm above the top of the plant canopy. Check-basin irrigation was applied regularly in each plot based on plant water requirement calculation \u003csup\u003e35\u003c/sup\u003e, avoiding shoots. Plants were visually assessed two weeks after bentazon application and four weeks after other herbicide treatments based on the EWRC rating scale (Table 3) \u003csup\u003e36\u003c/sup\u003e. The last applied treatment was 2,4-D to avoid the adverse effects of tank spry residual on other plots. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo measure the morphological traits of redroot pigweed, including leaf weight, stem weight, plant height, and total biomass, five plants from each plot were randomly selected and were catted at the soil surface 21 days after herbicide application. Also, to assess the physiological features of this weed, leaf samples from the five harvested plants were frozen in liquid nitrogen and transferred to the laboratory. The chlorophyll concentration in leaves was measured by reading the leaf samples\u0026apos; absorbance 663 and 645 nm wavelengths \u003csup\u003e37\u003c/sup\u003e. Bradford\u0026apos;s method measured leaves\u0026apos; soluble protein concentration \u003csup\u003e38\u003c/sup\u003e. The absorbance of prepared samples by the Bates et al. (1973) methods was measured at 520 nm using the spectrophotometer instrument to determine leaf proline concentration \u003csup\u003e39\u003c/sup\u003e. The leaf carbohydrate concentration was evaluated according to the previous method (Yemm \u0026amp; Willis, 1954), in which both types of carbohydrates, including those in solution in alcohol and water, can be measured.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were first evaluated for normality of residuals for both years using PROC Univariate in SAS (SAS Institute, 2015) according to the Shapiro-Wilk test. The result showed that the residuals were average, so data transformations were unnecessary. Data then were subject to analysis of variance (ANOVA) using Proc Mixed in SAS (SAS Institute, 2015), in which dust and herbicide factors were considered fixed effects while year and block were considered random effects. Since there were no differences between the data of both years, the mean comparison of the measured traits was carried out on the pooled data of both years. Fisher\u0026apos;s least difference (LSD) test was used at P\u0026le;0.05 to consider the difference between the applied treatments.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Physiological traits\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eANOVA was employed to determine the effects of dust and herbicide application on various physiological parameters, such as chlorophyll-a, chlorophyll-b, and total chlorophyll content, protein, proline, and concentrations of water-soluble and alcohol-soluble carbohydrates of redroot pigweed (Table 5). The results of the ANOVA revealed that dust alone meaningfully impacted total chlorophyll, protein, proline, and water- and alcohol-soluble carbohydrates; while herbicides alone significantly impacted all physiological parameters. Moreover, dust and herbicide application interaction also affected chlorophyll-b, proline, and water-soluble and alcohol-soluble carbohydrates (Table 5)\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChlorophyll\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChlorophyll-a\u003c/p\u003e\n\u003cp\u003eThe results indicated that dust did not affect chlorophyll-a content, causing only a 1.47% reduction compared to the no-dust and no-herbicide control (Ctrl). The herbicides bentazon (BNT), sulfosulfuron (SSN), tribenuron-methyl (TBM), aminopyralid + florasulam (APF), foramsulfuron + iodosulfuron + thiencarbazone (FIT), 2,4-D + MCPA (2,4-D), and acetochlor (ACR) reduced the chlorophyll-a content by 53, 51, 49, 43, 46, 16, and 11% respectively, compared to the Ctrl (Figure 1a). Among the treatments, BNT had the most effect in reducing chlorophyll-a content (Figure 1a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChlorophyll-b\u003c/p\u003e\n\u003cp\u003eUnder dust-free conditions, applying TBM, BNT, SSN, and FIT reduced 57, 53, 43, and 32% in chlorophyll-b content compared to the control without dust (NDC). However, herbicides 2,4-D and ACR did not affect chlorophyll-b content under dusty and non-dusty conditions, compared to the DC and NDC. In dusty conditions, TBM, BNT, SSN, FIT, and APF led to reductions of 63, 33, 44, 43, and 59% in chlorophyll-b content, respectively, compared to the DC. Additionally, under dusty conditions, chlorophyll-b content decreased with the application of APF (50%) and 2,4-D (18%), while it increased with BNT (29%), compared to when the herbicides were applied under dust-free conditions. Among the treatments, TBM had the most substantial impact (63 %) on reducing the chlorophyll-b content of redroot pigweed under both dusty and dust-free conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal chlorophyll content (TCC)\u003c/p\u003e\n\u003cp\u003eDust reduced the TCC (9%) compared to the non-dust control (NDC; data not shown). Applying TBM, BNT, SSN, FIT, APF, ACR, and 2,4-D reduced TCC by 53, 49, 48, 43, 41, 11, and 9%, respectively, compared to the Ctrl (Figure 1c). The interaction of dust and herbicide application was not significant.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that dust negatively affects plant pigments, such as chlorophyll, due to damage to plant tissue and reduction of pigment concentration \u003csup\u003e41\u0026ndash;46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe interactive effect of dust and herbicide on chlorophyll-b may vary by herbicide active ingredient formulation and mechanism of action. A previous study found that dust does not positively or negatively affect TBM efficacy in general \u003csup\u003e47\u003c/sup\u003e. Dust particles can settle on the surface of the leaves and reduce the amount of light that reaches the leaf surface, reducing the herbicide\u0026apos;s absorption and translocation. However, this effect is not enough to reduce the overall efficacy of the TBM (Table 7). In addition, TBM is relatively stable and does not break down quickly in the soil or environment \u003csup\u003e9, 48, 49\u003c/sup\u003e. One of the possible reasons for the lack of effect of dust application may be related to the formulation of TBM and SSN as wettable granules (WG). A wettable granule with 50% active ingredient may contain 42% clay, 2% wetting agent, 2% dispersing agent, 4% inert ingredients, and 50% active herbicide \u003csup\u003e50\u003c/sup\u003e. When these herbicides are applied as dust, the particle size and composition may closely resemble that of the WG formulation. This similarity could potentially enhance the efficacy of the herbicides when they settle on the plant\u0026apos;s surface.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the effects of ACR and 2,4-D were associated with increased chlorophyll-b in dusty conditions. The exact mechanism of this observed increase in chlorophyll-b in the presence of dust may be complex and multifactorial. One possible explanation for this unexpected result could be that the dust provided some level of coverage for the target plants and protected them from environmental stresses, such as excessive heat or radiation \u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe findings indicated that using BNT led to a considerable reduction in the TCC. The BNT is a photosystem II (PSII) inhibitor (Table 2) that affects the photosynthesis process in plants by disrupting the electron transfer chain in the thylakoid membranes \u003csup\u003e52\u003c/sup\u003e. Dust accumulation on the leaf surface can reduce the amount of light that reaches the leaf, decreasing the photosynthesis rate.\u003c/p\u003e\n\u003cp\u003eMoreover, dust accumulation can also reduce the plant\u0026apos;s retention and uptake of the herbicide \u003csup\u003e13,25\u003c/sup\u003e. The TCC indicates the plant\u0026apos;s photosynthetic activity \u003csup\u003e53\u003c/sup\u003e and can be affected by the herbicide\u0026apos;s mode of action \u003csup\u003e54\u003c/sup\u003e. Since BNT targets photosynthesis, any factor affecting the plant\u0026apos;s photosynthetic activity may affect the TCC. Adsorption is a primary mechanism affecting the bioavailability and efficacy of SL herbicides in soil. Herbicide molecules can bind to soil particles, mainly clay, and organic matter, reducing their availability to the targeted plants \u003csup\u003e55\u0026ndash;57\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoluble protein content (SPC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDust decreased the SPC (21%) compared to the NDC (Figure 2a). The interaction of dust and herbicide did not affect SPC (Table 4). Herbicides, including SSN, TBM, ACR, and FIT, had the highest impact by 63, 60, 42 and 40% decrease compared to the Ctrl, while the effect of BNT had the lowest effect (20% decrease to control) and finally 2,4-D had no result on SPC (Figure 2b). The APF treatment had an average decrease (28%) of SPC compared to the other herbicides, probably because of the ALS-inhibitor active ingredient florasulam, which comprises one-third of the active ingredient per hectare (g ai ha\u003csup\u003e-1\u003c/sup\u003e) in formulation (Table 2).\u003c/p\u003e\n\u003cp\u003eThe impact on protein synthesis may not be as pronounced with the herbicides. It is hypothesized that the dust-induced stress led to an increase in the production of reactive oxygen molecules such as hydroxyl radicals, free oxygen, hydrogen peroxide, and superoxide, which then interacted with various biomolecules such as proteins, nucleic acids, and lipids, causing damage and mutations to the DNA and breakdown of carbohydrates and proteins \u003csup\u003e58\u003c/sup\u003e. Furthermore, the plant\u0026apos;s response to stress may lead to an increase in the activity of protease enzymes, which are responsible for breaking down proteins, and decreased protein production \u003csup\u003e25\u003c/sup\u003e. Also, dust can accumulate on the surface of plant leaves, forming a physical barrier that hinders the exchange of gases and blocks sunlight \u003csup\u003e59\u003c/sup\u003e. Reduced sunlight availability can limit photosynthesis, which is crucial to producing energy and synthesizing organic compounds, including proteins \u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConsequently, the plant may experience a decrease in SPC. Furthermore, stomata are tiny openings on the leaf surface that allow the exchange of gases with the atmosphere. When dust particles settle on the stomata, they can clog these openings, impeding the uptake of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) needed for photosynthesis \u003csup\u003e61,62\u003c/sup\u003e. Without an adequate supply of CO\u003csub\u003e2\u003c/sub\u003e, the plant\u0026apos;s ability to produce energy and synthesize proteins can be impaired, leading to decreased SPC \u003csup\u003e63, 64\u003c/sup\u003e. Among the herbicides, TBM, SSN, and FIT work by inhibiting acetolactate synthase (ALS) (Table 2), which is an enzyme involved in the synthesis of the branched-chain amino acids valine, leucine, and isoleucine \u003csup\u003e65\u003c/sup\u003e. As a result, the plants treated with these herbicides experience a disruption in amino acid production. Since amino acids are the building blocks of proteins, the inhibition of ALS can decrease SPC \u003csup\u003e66\u003c/sup\u003e. On the other hand, herbicides like 2,4-D and BNT have different modes of action that do not directly interfere with amino acid synthesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoluble leaf proline content (LPC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDust caused a 14% decrease in the soluble leaf proline content (LPC) compared to the NDC. (Figure 3). Applying TBM, APF, SSN, 2,4-D, BNT, and ACR changed the LPC compared to the NDC. Herbicides in dusty conditions, including BNT, ACR, FIT, SSN, and 2,4-D, reduced the LPC by 56, 55, 50, 44, and 37% compared to herbicides in non-dust conditions. LPC increased in the presence of 2,4-D, TBM, and APF in non-dust conditions by 42%, 17%, and 17% compared to the NDC (Figure 3). All other herbicides exhibited a decrease in LPC in the presence of dust.\u003c/p\u003e\n\u003cp\u003ePlants accumulate proline as a defensive response to regulate osmotic stress conditions \u003csup\u003e68, 67,69\u003c/sup\u003e as observed when herbicides are applied in non-dusty conditions. The increase in proline accumulation under herbicide treatment could be attributed to increased protein breakdown. This increased protein breakdown can result in the accumulation of amino acids, including proline, as a byproduct \u003csup\u003e58\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoluble carbohydrates in water (SCW) and in alcohol (SCA)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eHerbicides in the presence of the dust, including 2,4-D, FIT, SSN, TBM, APF, and ACR, resulted in a reduction in the SCW by 69, 62, 61, 56, 37, and 43%, respectively, compared to the herbicide application in non-dust condition and 76, 69, 67, 75, 51, and 55% compared to NDC (Figure 4a). Herbicides\u0026apos; effect in dusty conditions was increased (Table 6), and the amount of SCW was severely reduced except for BNT, APF, and ACR, which was not statistically different compared to DC. In dusty conditions, TBM, 2,4-D, SSN, and FIT had the lowest amount of SCW. The herbicides and dust particles and their interaction affected the SCA, and the result was partially similar to the SCW obtained with less sensitivity\u0026nbsp;(Figure 4b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reduction of soluble carbohydrates in the presence of dust and herbicides could be attributed to several factors. Dust can block sunlight from reaching the plant\u0026apos;s leaves. Sunlight is crucial for photosynthesis, providing the energy needed \u003csup\u003e70\u003c/sup\u003e. If the dust layer is thick enough, it can reduce the light reaching the leaves, decreasing photosynthesis and resulting in fewer soluble carbohydrates. Dust particles may contain pollutants, heavy metals, or other harmful substances that can negatively impact plant metabolism. When plants are under stress, their metabolic processes, including carbohydrate synthesis, can be disrupted, decreasing soluble carbohydrates \u003csup\u003e71, 72\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe enzymes responsible for the regeneration and synthesis of carbohydrates and the Calvin\u0026nbsp;cycle, such as ribulose diphosphate carboxylase, fructose diphosphate phosphatase, NADP glyceraldehyde 3-phosphodihydrogenase, phosphoribulokinase, and pseudoheptulose diphosphate phosphatase, are activated by appropriate light intensity, and the reduction in light due to the dust and shade can negatively impact their function and ultimately reduce the concentration of soluble carbohydrates in water \u003csup\u003e73\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, dust accumulation on the leaf surface can reduce the amount of light reaching the chloroplasts, where photosynthesis occurs. Consequently, photosynthetic activity can be impaired, leading to decreased production of carbohydrates, including soluble carbohydrates. The herbicides may exacerbate this effect by further compromising photosynthetic processes. This limitation in carbon dioxide availability can decrease the production of soluble carbohydrates. Also, each herbicide has a specific mode of action that affects plant physiology differently. It is possible that the herbicides themselves directly or indirectly influence carbohydrate metabolism, leading to reduced soluble carbohydrate concentrations in water \u003csup\u003e59,60\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, the combination of dust accumulation, physical stress, altered herbicide efficacy, impaired photosynthesis, and potential herbicide interactions with dust particles likely contribute to the observed reductions in soluble carbohydrates in water in the presence of dust conditions. However, it is essential to note that specific interactions between dust, herbicides, and the physiology of redroot pigweed should be investigated further to gain a more comprehensive understanding of these effects.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Morphological traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeaf dry weight (LDW)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe result showed that dust, herbicides, and their interactions affected leaf dry weight (LDW), stem dry weight (SDW), and total biomass of redroot pigweed\u0026mdash;also, herbicides alone and the herbicide in the presence of the dust interaction affected plant height (Table 5). Herbicides TBM, SSN, FIT, and BNT in the presence of the dust reduced LDW by 46, 43, 64, and 32, while 2,4-D, ACR, and APF had no meaningful effect on LDW compared to DC (Figure 5). Among the applied treatments, 2,4-D did not affect LDW compared to both controls (NDC and DC), while FIT, TBM, SSN, and BNT had the most effect in the presence of dust by reducing 65, 48, 45, and 35% of LDW compared to NDC (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlants can compensate for specific environmental stresses by adjusting their growth patterns \u003csup\u003e74,75\u003c/sup\u003e. Redroot pigweed may have altered its resource allocation in the presence of dust, shifting resources toward maintaining LDW while reducing other growth parameters, as observed in SDW reduction. This compensatory response could help the plant maintain its structural integrity and support survival \u003csup\u003e76, 77\u003c/sup\u003e. Also, dust accumulation on the leaf surface can disrupt the distribution of resources within the plant. The resources redistributed by redroot pigweed may prioritize allocation towards LDW while compromising other growth parameters. This redistribution may have allowed the plant to maintain its overall biomass despite the adverse effects of dust on other growth processes \u003csup\u003e78\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEach herbicide has a specific mode of action, which determines how it affects plant growth and development. The herbicides FIT, TBM, and SSN may have more potent \u003csup\u003e79,80\u003c/sup\u003e or targeted modes of action (Table 2) that direct impact processes associated with LDW, such as cell division, elongation, or biomass accumulation \u003csup\u003e81,82\u003c/sup\u003e. On the other hand, the mode of action of 2,4-D may be less effective, or hormesis may be affecting LDW, resulting in a minor impact \u003csup\u003e83\u0026ndash;85\u003c/sup\u003e. Also, different herbicides can have varying levels of efficacy on different plant species. Redroot pigweed may be more susceptible to the FIT, TBM, and SSN herbicides than 2,4-D \u003csup\u003e84\u003c/sup\u003e. The specific biochemical pathways these herbicides target may be more critical for LDW accumulation in redroot pigweed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStem dry weight (SDW)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe addition of dust decreased SDW by 16% compared to\u0026nbsp;the NDC for the no herbicide control plants.\u0026nbsp;The application of herbicides under non-dusty conditions, such as FIT, TBM, SSN, APF, BNT, 2,4-D, and ACR, resulted in reductions in SDW by 69, 65, 65, 55, 42, 30, and 28%, respectively, compared to the NDC. Furthermore, when TBM, APF, SSN, FIT, BNT, and ACR were applied in the presence of dust, there was a decrease in SDW by 71, 73, 69, 66, 39, and 19%, respectively, compared to the control in dusty conditions (Figure 6). This result indicates that even in the presence of dust, TBM, APF, SSN, and FIT maintained their effectiveness in suppressing redroot pigweed\u0026apos;s growth and biomass accumulation, albeit with some variations in the extent of reduction compared to the dust-free conditions.\u003c/p\u003e\n\u003cp\u003eResearch\u0026nbsp;has indicated that dust reduces the stems and branches and various plant parts\u0026apos; fresh and dry weight, which could be attributed to decreased chlorophyll content and photosynthetic\u0026nbsp;processes \u003csup\u003e86,87\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of dust on reducing the SDW is more significant than its impact on the root \u003csup\u003e88\u003c/sup\u003e, possibly because the plant\u0026apos;s natural photosynthetic activities can meet the root\u0026apos;s needs before the dust stress \u003csup\u003e88\u003c/sup\u003e. However, dust may slow the photosynthetic process, and as a result, the products produced by photosynthesis tend to move more toward the leaves and roots \u003csup\u003e88\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe variations in the percentage reductions of SDW among different herbicides can be attributed to several factors, including their specific modes of action, effectiveness on redroot pigweed, interactions with dust particles, and potential differences in plant sensitivity to these herbicides. Notably, the percentage reductions in SDW reflect the overall impact on plant growth and biomass accumulation. The observed reductions indicate that the tested herbicides have the potential to control the growth of redroot pigweed, both in non-dust and dusty conditions, although the presence of dust may influence the effectiveness.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant height and Total biomass\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant height of\u0026nbsp;redroot pigweed was similar in the presence of dust and under dust-free conditions (Table 5). In dust-free conditions, APF, TBM, FIT, and SSN (were not different), BNT and 2,4-D (were in the same group), and ACR (had the slightest effect) caused reductions in plant height of 74, 72, 70, 61, 47, 26, and 26%, respectively, compared to the NDC. Also, in the presence of dust, these herbicides reduced plant height, with significant of 78, 80, 71, 67, 32, 28, and 18%, respectively, compared to the\u0026nbsp;DC (Figure 7). Among the applied treatments, TBM and APF in the presence of dust had more effect, and plant height decreased (8%) compared to when applied in no dusty condition.\u0026nbsp;While FIT, ACR, SSN, 2,4-D, and APF effects did not change in plant height reduction. Furthermore, BNT application in dusty conditions had a lower effect on plant height decline (Figure 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdding dust reduced the total biomass of redroot pigweed by 12%, DC, compared to the NDC.\u0026nbsp;Also, in dust-free conditions, FIT, SSN, TBM, APF, BNT, ACR, and 2,4-D decreased total biomass by 93, 84, 83, 79, 70, 34, and 34%, respectively, compared with NDC. However, these herbicides in the presence of the dust had a different reaction and decreased total biomass by 94, 90, 88, 87, 56, 38, and 13%,\u0026nbsp;respectively, compared with dusty condition control (Figure 8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the presence of dust, TBM, APF, SSN, and ACR decreased biomass compared to NDC, attributed to previously discussed factors, including:\u003c/p\u003e\n\u003cp\u003ea) Increased herbicide retention: Dust particles on the leaf surface can act as physical barriers, potentially enhancing the retention and adherence of herbicide droplets or residues. This increased retention of herbicides in the presence of dust could lead to higher concentrations and prolonged exposure of redroot pigweed to the herbicides, resulting in more suppression of its growth and biomass accumulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eb) Enhanced herbicide efficacy: Dust particles may interact with herbicides and alter their properties, potentially enhancing their effectiveness. These interactions can affect herbicide distribution, uptake, translocation, or metabolism within the plant. The combined effects of dust and herbicides such as TBM, APF, SSN, and ACR may result in synergistic or additive effects, leading to a more pronounced reduction in redroot pigweed biomass than in non-dusty conditions.\u003c/p\u003e\n\u003cp\u003ec) Stress amplification: Dust particles can induce plant stress by causing physical damage, blocking light penetration, and disrupting stomatal function. Combining stress factors and herbicide action can amplify redroot pigweed\u0026apos;s response to dust-induced stres\u003cem\u003es\u003c/em\u003e. The cumulative effects of stress from dust particles and herbicides may result in a greater reduction in total biomass than herbicide application in non-dusty conditions.\u003c/p\u003e\n\u003cp\u003ed) Interference with physiological processes: Dust accumulation can interfere with critical physiological processes in plants, such as photosynthesis, water uptake, and nutrient absorption. The presence of dust may exacerbate the impact of herbicides on these processes, further compromising the growth and biomass accumulation of redroot pigweed. This interference with physiological processes can contribute to more decline in total biomass under dusty conditions.\u003c/p\u003e\n\u003cp\u003eAs observed in the results, 2,4-D and BNT in the presence of the dust lost their impact on the total biomass of redroot pigweed by 13 and 29% compared with non-dusty condition herbicide application, which could be attributed to several factors, including:\u003c/p\u003e\n\u003cp\u003ea) Reduced herbicide deposition: Dust particles on the leaf surface can interfere with the deposition of herbicide droplets or residues. Dust particles, may create a physical barrier that hinders contact between the herbicide and the target weed. Consequently, some herbicides may not effectively reach the intended target, leading to reduced efficacy.\u003c/p\u003e\n\u003cp\u003eb) Impaired herbicide absorption: Dust particles can impact the absorption of herbicides by the weed. When dust is present, it may interfere with the penetration of herbicide molecules through the leaf cuticle or hinder their movement within the plant tissues. This interference can reduce herbicide absorption by redroot pigweed, decreasing the effectiveness of 2,4-D and BNT in controlling weed growth.\u003c/p\u003e\n\u003cp\u003ec) Dust-induced physiological stress: Dust accumulation on the leaf surface can induce physiological stress on plants. This stress can disrupt various plant processes, including photosynthesis, water balance, and nutrient uptake. Such physiological stress can weaken redroot pigweed\u0026apos;s overall vigor and health, making it less responsive to herbicide treatment. The combination of dust-induced stress and herbicide application may reduce the efficacy of 2,4-D and BNT on redroot pigweed.\u003c/p\u003e\n\u003cp\u003ed) Dust-mediated herbicide degradation: Dust particles may contain compounds or microbes that could interact with herbicides and potentially degrade their active ingredients. These interactions may alter the chemical properties or stability of 2,4-D and BNT, decreasing their effectiveness against redroot pigweed.\u003c/p\u003e\n\u003cp\u003ee) Differential susceptibility of redroot pigweed: It is also possible that redroot pigweed exhibits a lower sensitivity or resistance to 2,4-D and BNT under dusty conditions than in non-dusty conditions. Dust-induced stress or other dust-related factors may confer tolerance or reduced susceptibility to these herbicides in redroot pigweed, reducing their efficacy.\u003c/p\u003e\n\u003cp\u003eIt is important to note that the specific interactions between dust, herbicides, and redroot pigweed can be complex and influenced by various factors, including the composition of the dust, formulation of the herbicides, plant physiology, and environmental conditions. Further research and experimentation would be required to gain a more comprehensive understanding of the underlying mechanisms responsible for the observed reduction in the efficacy of 2,4-D and BNT in the presence of dust.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn conclusion, dust significantly impacted redroot pigweed's physiological and morphological traits. While previous research has recognized the negative impact of dust on plant physiology, affecting processes like photosynthesis, nutrient uptake, and growth parameters, which subsequently diminish herbicide efficacy, our recent study has revealed an interesting finding. Despite its harmful effects, dust may also have the unexpected benefit of enhancing the efficacy of certain herbicides. Dust particles reduced chlorophyll content, potentially affecting herbicide efficacy. Herbicides with different modes of action exhibited varied responses in the presence of dust. Dust accumulation led to a decrease in soluble protein content and soluble carbohydrates, likely due to reduced photosynthesis and impaired gas exchange. The plant's compensatory response focused on maintaining leaf and stem dry weight while reducing other growth parameters. The combination of dust and herbicides reduced total biomass, with potential factors including increased herbicide retention, enhanced herbicide efficacy, stress amplification, and interference with physiological processes. However, the effectiveness of 2,4-D and BNT on total biomass was compromised in the presence of dust, potentially due to reduced herbicide deposition, impaired absorption, dust-induced stress, dust-mediated degradation, or the differential susceptibility of redroot pigweed. Farmers in areas prone to dust accumulation should adopt regular dust management techniques, such as pre-herbicide, rain irrigation, and the utilization of adjuvants, to mitigate the efficacy of herbicides against weeds. Researchers and policymakers must also collaborate in developing effective weed management strategies in such environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe University of Kurdistan funded the project. We thank the Research Vice-Chancellor of the University of Kurdistan and Dr. Kayoomars Karami for their support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e University of Kurdistan, Iran, supports this research. Grant number: 11/11/23213\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The data that support this study will be shared upon reasonable request to the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNosratti, I., Sabeti, P., Chaghamirzaee, G. \u0026amp; Heidari, H. Weed problems, challenges, and opportunities in Iran. \u003cem\u003eCrop Protection\u003c/em\u003e \u003cstrong\u003e134\u003c/strong\u003e, 104371 (2020).\u003c/li\u003e\n\u003cli\u003eAlam, Md. F. Bin \u003cem\u003eet al.\u003c/em\u003e Analysis of the drivers of Agriculture 4.0 implementation in the emerging economies: Implications towards sustainability and food security. \u003cem\u003eGreen Technologies and Sustainability\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 100021 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, Z., Li, R., Zhao, C. \u0026amp; Qiang, S. Reduction in weed infestation through integrated depletion of the weed seed bank in a rice-wheat cropping system. \u003cem\u003eAgron Sustain Dev\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 1\u0026ndash;14 (2021).\u003c/li\u003e\n\u003cli\u003eStefan, L., Engbersen, N. \u0026amp; Sch\u0026ouml;b, C. Crop-weed relationships are context-dependent and cannot fully explain the positive effects of intercropping on yield. \u003cem\u003eEcol Appl\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eChauhan, B. Grand Challenges in Weed Management. \u003cem\u003eFrontiers in Agronomy\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 3 (2020).\u003c/li\u003e\n\u003cli\u003eKraehmer, H., Laber, B., Rosinger, C. \u0026amp; Schulz, A. Update on Weed Control Agents: Path to Modern Agriculture Herbicides as Weed Control Agents: State of the Art: I. Weed Control Research and Safener Technology: The Path to Modern Agriculture. \u003cem\u003ePlant Physiol\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e, 1119\u0026ndash;1131 (2014).\u003c/li\u003e\n\u003cli\u003eRoy, P. \u003cem\u003eet al.\u003c/em\u003e Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e849\u003c/strong\u003e, 157850 (2022).\u003c/li\u003e\n\u003cli\u003eHao, Z. \u003cem\u003eet al.\u003c/em\u003e Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts. \u003cem\u003eEarth Sci Rev\u003c/em\u003e \u003cstrong\u003e235\u003c/strong\u003e, 104241 (2022).\u003c/li\u003e\n\u003cli\u003eLorestani, E., Babaei, S., Tahmasebi, I. \u0026amp; Sabeti, P. Assessment of Tribenuron Methyl Soil Residual on Crops Germination Properties. \u003cem\u003eGesunde Pflanzen\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 765\u0026ndash;773 (2022).\u003c/li\u003e\n\u003cli\u003eBarbastegan, H., Saeidi, M., Nosratti, I., Honarmand, S. J. \u0026amp; Ghobadi, M. Dust particles on corn (Zea mays) photosynthesis, transpiration and yield in Kermanshah, Iran. \u003cem\u003ePhilippine Agricultural Scientist\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 333\u0026ndash;338 (2015).\u003c/li\u003e\n\u003cli\u003eNeira, M. \u003cem\u003eet al.\u003c/em\u003e Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions. \u003cem\u003eEnviron Res\u003c/em\u003e \u003cstrong\u003e216\u003c/strong\u003e, 114537 (2023).\u003c/li\u003e\n\u003cli\u003eGholizadeh, H., Zoghipour, M. H., Torshizi, M., Nazari, M. R. \u0026amp; Moradkhani, N. Gone with the wind: Impact of soil-dust storms on farm income. \u003cem\u003eEcological Economics\u003c/em\u003e \u003cstrong\u003e188\u003c/strong\u003e, 107133 (2021).\u003c/li\u003e\n\u003cli\u003eSharifi Kaliani, F., Babaei, S. \u0026amp; ZafarSohrabpour, Y. Study of the effects of dusts on the morphological and physiological traits of some crops. \u003cem\u003eJournal of Plant Production Research\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 205\u0026ndash;220 (2021).\u003c/li\u003e\n\u003cli\u003eLambert, A. \u003cem\u003eet al.\u003c/em\u003e Dust Impacts of Rapid Agricultural Expansion on the Great Plains. \u003cem\u003eGeophys Res Lett\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003ePease, R. Dust Bowl 2.0? Rising Great Plains dust levels stir concerns. \u003cem\u003eScience (1979)\u003c/em\u003e (2020) doi:10.1126/SCIENCE.ABF3504.\u003c/li\u003e\n\u003cli\u003eAili, A., Xu, H. \u0026amp; Zhao, X. Health Effects of Dust Storms on the South Edge of the Taklimakan Desert, China: A Survey-Based Approach. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eAsadi-Sabzi, M., Keshtkar, E., Mokhtassi-Bidgoli, A. \u0026amp; Moss, S. R. Quantifying the detrimental effect of airborne dust on herbicide efficacy. \u003cem\u003eWeed Res\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 204\u0026ndash;211 (2020).\u003c/li\u003e\n\u003cli\u003eGoudie, A. S. Dust storms: Recent developments. \u003cem\u003eJ Environ Manage\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 89\u0026ndash;94 (2009).\u003c/li\u003e\n\u003cli\u003eWeidhuner, A. \u003cem\u003eet al.\u003c/em\u003e Tillage impacts on soil aggregation and aggregate-associated carbon and nitrogen after 49 years. \u003cem\u003eSoil Tillage Res\u003c/em\u003e \u003cstrong\u003e208\u003c/strong\u003e, 104878 (2021).\u003c/li\u003e\n\u003cli\u003eLwanga, E. H. \u003cem\u003eet al.\u003c/em\u003e Microplastic appraisal of soil, water, ditch sediment and airborne dust: The case of agricultural systems. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e \u003cstrong\u003e316\u003c/strong\u003e, 120513 (2023).\u003c/li\u003e\n\u003cli\u003eAl-Dabbas, M. A., Ayad Abbas, M. \u0026amp; Al-Khafaji, R. M. Dust storms loads analyses\u0026mdash;Iraq. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 121\u0026ndash;131 (2012).\u003c/li\u003e\n\u003cli\u003eNajafi, M. S. \u003cem\u003eet al.\u003c/em\u003e Characteristics of TSP Loads during the Middle East Springtime Dust Storm (MESDS) in Western Iran. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 5367\u0026ndash;5381 (2014).\u003c/li\u003e\n\u003cli\u003eSaurat, D. \u003cem\u003eet al.\u003c/em\u003e Determination of glyphosate and AMPA in indoor settled dust by hydrophilic interaction liquid chromatography with tandem mass spectrometry and implications for human exposure. \u003cem\u003eJ Hazard Mater\u003c/em\u003e \u003cstrong\u003e446\u003c/strong\u003e, 130654 (2023).\u003c/li\u003e\n\u003cli\u003eFigueiredo, D. M. \u003cem\u003eet al.\u003c/em\u003e Pesticides in doormat and floor dust from homes close to treated fields: Spatio-temporal variance and determinants of occurrence and concentrations. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e \u003cstrong\u003e301\u003c/strong\u003e, 119024 (2022).\u003c/li\u003e\n\u003cli\u003eZhou, J., Tao, B. \u0026amp; Messersmith, C. G. Soil dust reduces glyphosate efficacy. \u003cem\u003eWeed Sci\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1132\u0026ndash;1136 (2006).\u003c/li\u003e\n\u003cli\u003eTisdale, S. L., Werner, S. L. \u0026amp; Beaton, J. D. Basic soil-plant relationships. Soil Fertility and Fertilizers. in \u003cem\u003e4th edition: Macmillan Publishing Conference\u003c/em\u003e 95\u0026ndash;111 (Macmillan , 1985).\u003c/li\u003e\n\u003cli\u003eHavlin, J. L. Soil: Fertility and Nutrient Management. \u003cem\u003eLandscape and Land Capacity\u003c/em\u003e 251\u0026ndash;265 (2020) doi:10.1201/9780429445552-34.\u003c/li\u003e\n\u003cli\u003eBrian, R. C. The uptake and adsorption of diquat and paraquat by tomato, sugar beet and cocksfoot. \u003cem\u003eAnnals of Applied Biology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 91\u0026ndash;99 (1967).\u003c/li\u003e\n\u003cli\u003eHarnly, M. E. \u003cem\u003eet al.\u003c/em\u003e Pesticides in dust from homes in an agricultural area. \u003cem\u003eEnviron Sci Technol\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 8767\u0026ndash;8774 (2009).\u003c/li\u003e\n\u003cli\u003eQuir\u0026aacute;s-Alcal, L. \u003cem\u003eet al.\u003c/em\u003e Pesticides in house dust from urban and farmworker households in California: An observational measurement study. \u003cem\u003eEnviron Health\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1\u0026ndash;15 (2011).\u003c/li\u003e\n\u003cli\u003eMansouri Daneshvar, M. R., Ebrahimi, M. \u0026amp; Nejadsoleymani, H. An overview of climate change in Iran: facts and statistics. \u003cem\u003eEnvironmental Systems Research\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1\u0026ndash;10 (2019).\u003c/li\u003e\n\u003cli\u003eSchreiner, W. N. A standard test method for the determination of RIR values by x-ray diffraction. \u003cem\u003ePowder Diffr\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 25\u0026ndash;33 (1995).\u003c/li\u003e\n\u003cli\u003eChung, F. H. Quantitative interpretation of X-ray diffraction patterns of mixtures. I. Matrix-flushing method for quantitative multicomponent analysis. \u003cem\u003eJ Appl Crystallogr\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 519\u0026ndash;525 (1974).\u003c/li\u003e\n\u003cli\u003eNaghib Alsadati, M., Babaei, S., Tahmasebi, I. \u0026amp; Kiani, H. Evaluation of airborne dust effect on the efficiency of Atlantis OD, clodinafop propargyl and 2,4-D+MCPA herbicides on weed control in wheat. \u003cem\u003eIranian Journal of Field Crop Science\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 1\u0026ndash;11 (2020).\u003c/li\u003e\n\u003cli\u003eVillalobos, F. J., Testi, L. \u0026amp; Fereres, E. Calculation of Evapotranspiration and Crop Water Requirements. \u003cem\u003ePrinciples of Agronomy for Sustainable Agriculture\u003c/em\u003e 119\u0026ndash;137 (2016) doi:10.1007/978-3-319-46116-8_10.\u003c/li\u003e\n\u003cli\u003eDear, B. S., Sandral, G. A., Spencer, D., Khan, M. R. I. \u0026amp; Higgins, T. J. V. The tolerance of three transgenic subterranean clover (Trifolium subterraneum L.) lines with the bxn gene to herbicides containing bromoxynil. \u003cem\u003eAust J Agric Res\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 203\u0026ndash;210 (2003).\u003c/li\u003e\n\u003cli\u003eArnon, A. N. Method of extraction of chlorophyll in the plants. \u003cem\u003eAgron J\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 112\u0026ndash;121 (1967).\u003c/li\u003e\n\u003cli\u003eBradford, M. A Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding. \u003cem\u003eAnal Biochem\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 248\u0026ndash;254 (1976).\u003c/li\u003e\n\u003cli\u003eBates, L. S., Waldren, R. P. \u0026amp; Teare, I. D. Rapid determination of free proline for water-stress studies. \u003cem\u003ePlant Soil\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 205\u0026ndash;207 (1973).\u003c/li\u003e\n\u003cli\u003eYemm, E. W. \u0026amp; Willis, A. J. The estimation of carbohydrates in plant extracts by anthrone. \u003cem\u003eBiochem J\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 508\u0026ndash;514 (1954).\u003c/li\u003e\n\u003cli\u003eIrwe, R. R., Sontakke, S. G. \u0026amp; Darade, M. S. \u003cem\u003eStudy of dust deposition on leaves of some plant species in GVISH. Campus of Amravati (MS) India\u003c/em\u003e. \u003cem\u003eInt. J. of Life Sciences\u003c/em\u003e vol. 5 www.ijlsci.in (2017).\u003c/li\u003e\n\u003cli\u003eRai, P. K. Impacts of particulate matter pollution on plants: Implications for environmental biomonitoring. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e vol. 129 120\u0026ndash;136 Preprint at https://doi.org/10.1016/j.ecoenv.2016.03.012 (2016).\u003c/li\u003e\n\u003cli\u003eLin, W., Yu, X., Xu, D., Sun, T. \u0026amp; Sun, Y. Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes. \u003cem\u003eRemote Sensing 2021, Vol. 13, Page 3570\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 3570 (2021).\u003c/li\u003e\n\u003cli\u003eYin, D. \u003cem\u003eet al.\u003c/em\u003e Morphological and biochemical studies of Salvia guaranitica St. Hil. under simulated deposition with different amounts of dust. \u003cem\u003eEcotoxicol Environ Saf\u003c/em\u003e \u003cstrong\u003e249\u003c/strong\u003e, 114404 (2023).\u003c/li\u003e\n\u003cli\u003eZilaie, M. N., Arani, A. M. \u0026amp; Etesami, H. The importance of plant growth-promoting rhizobacteria to increase air pollution tolerance index (APTI) in the plants of green belt to control dust hazards. \u003cem\u003eFront Plant Sci\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1098368 (2023).\u003c/li\u003e\n\u003cli\u003eShah, K. \u003cem\u003eet al.\u003c/em\u003e Chronic cement dust load induce novel damages in foliage and buds of Malus domestica. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 12186 (2020).\u003c/li\u003e\n\u003cli\u003eAnwar, S. \u003cem\u003eet al.\u003c/em\u003e Biodegradation and Subsequent Toxicity Reduction of Co-contaminants Tribenuron Methyl and Metsulfuron Methyl by a Bacterial Consortium B2R. \u003cem\u003eACS Omega\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 19816\u0026ndash;19827 (2022).\u003c/li\u003e\n\u003cli\u003eNoshadi M \u0026amp; Dastranj M. Open Acc J of Toxicol Tribenuron-Methyl Herbicide Decontamination using Vetiver Grass and its Distribution through Soil Profile. \u003cstrong\u003e2\u003c/strong\u003e, (2017).\u003c/li\u003e\n\u003cli\u003eRachedi, K. \u003cem\u003eet al.\u003c/em\u003e Effect of sulfonylurea tribenuron methyl herbicide on soil Actinobacteria growth and characterization of resistant strains. \u003cem\u003eBrazilian Journal of Microbiology\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 79 (2018).\u003c/li\u003e\n\u003cli\u003eZimdahl, R. L. Herbicide Formulation. \u003cem\u003eFundamentals of Weed Science: Fifth Edition\u003c/em\u003e 501\u0026ndash;509 (2018) doi:10.1016/B978-0-12-811143-7.00017-2.\u003c/li\u003e\n\u003cli\u003eKudsk, P. \u0026amp; Kristensen, J. L. Effect of environmental factors on herbicide performance. \u003cem\u003eProceedings of the First International Weed Control Congress\u003c/em\u003e (1992).\u003c/li\u003e\n\u003cli\u003eZhu, J., Patzoldt, W. L., Radwan, O., Tranel, P. J. \u0026amp; Clough, S. J. Effects of Photosystem-II-Interfering Herbicides Atrazine and Bentazon on the Soybean Transcriptome. \u003cem\u003ePlant Genome\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 191\u0026ndash;205 (2009).\u003c/li\u003e\n\u003cli\u003eJovanić, B. R., Radenković, B., Despotović-Zrakić, M., Bogdanović, Z. \u0026amp; Barać, D. Effect of UV-B radiation on chlorophyll fluorescence, photosynthetic activity and relative chlorophyll content of five different corn hybrids. \u003cem\u003eJ Photochem Photobiol\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 100115 (2022).\u003c/li\u003e\n\u003cli\u003eDayan, F. E. \u0026amp; Zaccaro, M. L. de M. Chlorophyll fluorescence as a marker for herbicide mechanisms of action. \u003cem\u003ePestic Biochem Physiol\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 189\u0026ndash;197 (2012).\u003c/li\u003e\n\u003cli\u003eG\u0026aacute;miz, B., Velarde, P., Spokas, K. A., Celis, R. \u0026amp; Cox, L. Changes in sorption and bioavailability of herbicides in soil amended with fresh and aged biochar. \u003cem\u003eGeoderma\u003c/em\u003e \u003cstrong\u003e337\u003c/strong\u003e, 341\u0026ndash;349 (2019).\u003c/li\u003e\n\u003cli\u003eYu, Y. L. \u003cem\u003eet al.\u003c/em\u003e An exploration of the relationship between adsorption and bioavailability of pesticides in soil to earthworm. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 428\u0026ndash;433 (2006).\u003c/li\u003e\n\u003cli\u003eWu, X. M. \u003cem\u003eet al.\u003c/em\u003e Effects of adsorption on degradation and bioavailability of metolachlor in soil. \u003cem\u003eJ Soil Sci Plant Nutr\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 83\u0026ndash;97 (2011).\u003c/li\u003e\n\u003cli\u003eSuriyan, C. um \u0026amp; Chalermpol, K. Proline Accumulation, Photosynthetic Abilities and Growth Characters of Sugarcane (Saccharum officinarum L.) Plantlets in Response to Iso-Osmotic Salt and Water-Deficit Stress. \u003cem\u003eAgric Sci China\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 51\u0026ndash;58 (2009).\u003c/li\u003e\n\u003cli\u003eZia-Khan, S. \u003cem\u003eet al.\u003c/em\u003e Effect of dust deposition on stomatal conductance and leaf temperature of cotton in Northwest China. \u003cem\u003eWater (Switzerland)\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 116\u0026ndash;131 (2015).\u003c/li\u003e\n\u003cli\u003eLu, D. \u003cem\u003eet al.\u003c/em\u003e Light deficiency inhibits growth by affecting photosynthesis efficiency as well as JA and ethylene signaling in endangered plant Magnolia sinostellata. \u003cem\u003ePlants\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003cli\u003eXu, Z., Jiang, Y., Jia, B. \u0026amp; Zhou, G. Elevated-CO2 response of stomata and its dependence on environmental factors. \u003cem\u003eFront Plant Sci\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 657 (2016).\u003c/li\u003e\n\u003cli\u003eNajib, R., Houri, T., Khairallah, Y. \u0026amp; Khalil, M. Effect of dust accumulation on Quercus cerris L. leaves in the Ezer forest, Lebanon. \u003cem\u003eIForest\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 322 (2022).\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;ttge, U. PHOTOSYNTHESIS AND PARTITIONING | CAM Plants. \u003cem\u003eEncyclopedia of Applied Plant Sciences\u003c/em\u003e 688\u0026ndash;705 (2003) doi:10.1016/B0-12-227050-9/00096-X.\u003c/li\u003e\n\u003cli\u003eLawlor, D. W. \u0026amp; Tezara, W. Causes of decreased photosynthetic rate and metabolic capacity in water-deficient leaf cells: a critical evaluation of mechanisms and integration of processes. \u003cem\u003eAnn Bot\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 561 (2009).\u003c/li\u003e\n\u003cli\u003eWhitcomb, C. E. An introdaction to ALS-inhibiting herbicides. in \u003cem\u003eToxicology and Industrial Health\u003c/em\u003e vol. 15 231\u0026ndash;239 (Toxicol Ind Health, 1999).\u003c/li\u003e\n\u003cli\u003eTAYLOR, J. B., LOUX, M. M., HARRISON, S. K. \u0026amp; REGNIER, E. Response of ALS-Resistant Common Ragweed (Ambrosia artemisiifolia) and Giant Ragweed (Ambrosia trifida) to ALS-Inhibiting and Alternative Herbicides 1 . \u003cem\u003eWeed Technology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 815\u0026ndash;825 (2002).\u003c/li\u003e\n\u003cli\u003eVerbruggen, N. \u0026amp; Hermans, C. Proline accumulation in plants: A review. \u003cem\u003eAmino Acids\u003c/em\u003e vol. 35 753\u0026ndash;759 Preprint at https://doi.org/10.1007/s00726-008-0061-6 (2008).\u003c/li\u003e\n\u003cli\u003eNanos, G. D. \u0026amp; Ilias, I. F. Effects of inert dust on olive (Olea europaea L.) leaf physiological para. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 212\u0026ndash;214 (2007).\u003c/li\u003e\n\u003cli\u003eNaresh, R. K., Singh, S. P., Dwivedi, A. \u0026amp; Kumar, V. Effects of water stress on physiological processes and yield attributes of different mungbean (L.) varieties. \u003cem\u003eAfrican Journal of Biochemistry Research\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 55\u0026ndash;62 (2013).\u003c/li\u003e\n\u003cli\u003eFarmer, A. M. The effects of dust on vegetation--a review. \u003cem\u003eEnviron Pollut\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 63\u0026ndash;75 (1993).\u003c/li\u003e\n\u003cli\u003eThompson, J. R., Mueller, P. W., Fl\u0026uuml;ckiger, W. \u0026amp; Rutter, A. J. The effect of dust on photosynthesis and its significance for roadside plants. \u003cem\u003eEnvironmental Pollution Series A, Ecological and Biological\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 171\u0026ndash;190 (1984).\u003c/li\u003e\n\u003cli\u003eKarnosky, D., Percy, K. E., Chappelka, A. H. (Arthur H., Simpson, C. M. (Caroline M. \u0026amp; Pikkarainen, J. (Janet). \u003cem\u003eAir pollution, global change and forests in the new millennium. 2003\u003c/em\u003e. (International Union of Forestry Research Organizations).\u003c/li\u003e\n\u003cli\u003eChaurasia, M., Patel, K., Tripathi, I. \u0026amp; Rao, K. S. Impact of dust accumulation on the physiological functioning of selected herbaceous plants of Delhi, India. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 80739\u0026ndash;80754 (2022).\u003c/li\u003e\n\u003cli\u003eLi, C., Barclay, H., Roitberg, B. \u0026amp; Lalonde, R. Ecology and Prediction of Compensatory Growth: From Theory to Application in Forestry. \u003cem\u003eFront Plant Sci\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1352 (2021).\u003c/li\u003e\n\u003cli\u003eLama, A. D., Klemola, T., Tyystj\u0026auml;rvi, E., Niemel\u0026auml;, P. \u0026amp; Vuorisalo, T. Physiological and compensatory growth responses of Jatropha curcas (L.) seedlings to simulated herbivory and drought stress. \u003cem\u003eSouth African Journal of Botany\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 486\u0026ndash;493 (2019).\u003c/li\u003e\n\u003cli\u003eMcNaughton, S. J. Compensatory Plant Growth as a Response to Herbivory. \u003cem\u003eOikos\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 329 (1983).\u003c/li\u003e\n\u003cli\u003eLyu, S. \u0026amp; Alexander, J. M. Compensatory responses of vital rates attenuate impacts of competition on population growth and promote coexistence. \u003cem\u003eEcol Lett\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 437\u0026ndash;447 (2023).\u003c/li\u003e\n\u003cli\u003ePoorter, H. \u003cem\u003eet al.\u003c/em\u003e How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. \u003cem\u003eNew Phytol\u003c/em\u003e \u003cstrong\u003e208\u003c/strong\u003e, 736 (2015).\u003c/li\u003e\n\u003cli\u003eEizenberg, H., Goldwasser, Y., Achdary, G. \u0026amp; Hershenhorn, J. The Potential of Sulfosulfuron to Control Troublesome Weeds in Tomato1. \u003cem\u003ehttps://doi.org/10.1614/0890-037X(2003)017[0133:TPOSTC]2.0.CO;2\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 133\u0026ndash;137 (2003).\u003c/li\u003e\n\u003cli\u003eZargar, M., Bayat, M. \u0026amp; Protection, T. A. Study of postemergence-directed herbicides for redroot pigweed (Amaranthus retroflexus L) control in winter wheat in southern Russia. \u003cem\u003eJ Plant Prot Res\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 7\u0026ndash;13 (2020).\u003c/li\u003e\n\u003cli\u003eWhitcomb, C. E. An introduction to ALS-inhibiting herbicides. \u003cem\u003eToxicol Ind Health\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 231\u0026ndash;239 (1999).\u003c/li\u003e\n\u003cli\u003eAnderson, A. The effects of acetolactate synthase (ALS) inhibiting herbicides on the growth, yield, nodulation and nitrogen fixation of selected legumes. (2001).\u003c/li\u003e\n\u003cli\u003eBrochado, M. G. da S. \u003cem\u003eet al.\u003c/em\u003e Impacts of dicamba and 2,4-D drift on \u0026lsquo;Ponkan\u0026rsquo; mandarin seedlings, soil microbiota and Amaranthus retroflexus. \u003cem\u003eJournal of Hazardous Materials Advances\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 100084 (2022).\u003c/li\u003e\n\u003cli\u003eMeseldžija, M. \u003cem\u003eet al.\u003c/em\u003e Is There a Possibility to Involve the Hormesis Effect on the Soybean with Glyphosate Sub-Lethal Amounts Used to Control Weed Species Amaranthus retroflexus L.? \u003cem\u003eAgronomy 2020, Vol. 10, Page 850\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 850 (2020).\u003c/li\u003e\n\u003cli\u003eMarques, R. F. \u003cem\u003eet al.\u003c/em\u003e Hormesis of 2,4-D choline salt in productive aspects of cotton. \u003cem\u003eJ Environ Sci Health B\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 977\u0026ndash;985 (2021).\u003c/li\u003e\n\u003cli\u003eRasouli, S. F., Galeshi, S., Pirdashti, H. \u0026amp; Zeinali, E. Investigation of waterlogging stress on some morphologic and physiological traits of rapeseed (Brassica napus L.) in different developmental stages. \u003cem\u003eJournal of Plant Production Research\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 69\u0026ndash;89 (2014).\u003c/li\u003e\n\u003cli\u003eFatima, S. \u003cem\u003eet al.\u003c/em\u003e Particle composition and morphology over urban environment (New Delhi): Plausible effects on wheat leaves. \u003cem\u003eEnviron Res\u003c/em\u003e \u003cstrong\u003e202\u003c/strong\u003e, 111552 (2021).\u003c/li\u003e\n\u003cli\u003eYang, H. \u0026amp; Liu, Y. Phytoremediation on Air Pollution. in \u003cem\u003eThe Impact of Air Pollution on Health, Economy, Environment and Agricultural Sources\u003c/em\u003e (IntechOpen, 2011). doi:10.5772/19942.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e\n \u003cp\u003eTable 1. Physical and chemical characteristics of the soil at the experimental site\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eSoil texture\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eOrganic matter (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eElectrical conductivity\u003c/p\u003e\n \u003cp\u003e(ds m\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eMicroelements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAbsorbable potassium (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAbsorbable phosphorus (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSand (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSilt (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClay (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBr (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFe (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eZn (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eTable 2. Details of herbicides applied in the experiment.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHerbicide active ingredient\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMode of Action\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrade name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFormulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eg ai ha\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etribenuron-methyl (TBM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALS\u003csup\u003e1\u003c/sup\u003e Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGranstar\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75% DF\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eaminopyralid + florasulam (APF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAuxinic + ALS Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLancelot\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e450 WG\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300 + 150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esulfosulfuron (SSN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALS Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eApirus\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75% WG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,4-D + MCPA (2,4-D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAuxinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,4-D + MCPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.5% SL\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.975 + 1300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eforamsulfuron + iodosulfuron + thiencarbazone (FIT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALS Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaisTer\u0026reg; Power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25% OD\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e719.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebentazon (BNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePS\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eII Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBasagran\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48% SL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eacetochlor (ACR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGGPP\u003csup\u003e3\u003c/sup\u003e Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurpass\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76% EC\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Acetolactate Synthase \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003csup\u003e5\u0026nbsp;\u003c/sup\u003eWettable Granule\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ePhotosystem II \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003csup\u003e6\u0026nbsp;\u003c/sup\u003eSoluble Liquid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Geranylgeranyl Pyrophosphate \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003csup\u003e7\u003c/sup\u003e Oil Dispersion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003e Dry Flowable \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003csup\u003e8\u003c/sup\u003e Emulsifiable Concentrate\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eTable 3. European Weed Research Council (EWRC) rating scale used to score the level of plant injury following herbicide application.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003eEWRC score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\"\u003e\n \u003cp\u003eCrop tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\"\u003e\n \u003cp\u003eEfficacy (weed kill)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eWeed control\u0026nbsp;\u003cbr\u003e\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eComplete kill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eVery slight effects; some stunting and yellowing just visible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eExcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;99.9\u0026ndash;98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eSlight effects; stunting and yellowing; effects reversible\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eVery good\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.9\u0026ndash;95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eSubstantial chlorosis and or stunting; most effects probably reversible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eGood\u0026ndash;acceptable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e94.9\u0026ndash;90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eStrong chlorosis/stunting; thinning of stand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eModerate but not generally acceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.9\u0026ndash;82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eIncreasing severity of damage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eFair\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e81.9\u0026ndash;70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eIncreasing severity of damage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003ePoor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e69.9\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eIncreasing severity of damage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eVery poor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e54.9\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.179487179487179%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.794871794871796%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal loss of plants and yield\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.358974358974358%\" valign=\"bottom\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.9\u0026ndash;0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eTable 4. Properties of the analyzed dust samples in both experimental years.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eParticle size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eElements (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSand (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSilt (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eClay (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMean size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMineralogical compositions (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eZr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQuartz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAlbite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOrthoclase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMicrocline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIllite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eKaolinite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eChlorite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePalygorskite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCalcite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDolomite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGypsum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHalite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Analysis of variance of dust and herbicides data on physiological traits of \u003cem\u003eAmaranthus retroflexus\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.457831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.016064257028113%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.52610441767068%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eMean Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003ed.f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003eChlorophyll-a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003eChlorophyll-b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003eTotal chlorophyll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003eProteins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003eProline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003eWater-soluble carbohydrates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol-soluble carbohydrates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eBlock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e0.31 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003e1.97 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003e0.45 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003e9.83 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e15.62**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eDust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e3.74 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e8.70 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003e37.93 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003e5.71 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003e0.011 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003e5182.11 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e29.53 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e77.40 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e27.12 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003e205.95 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003e5.58 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003e0.029 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003e1943.06 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e59.95 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eDust \u0026times; Herbicides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e1.37 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e4.47 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003e6.15 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003e0.09 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003e0.007 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003e1401.59 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e8.64 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003e32.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.438502673796792%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.010695187165775%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e10.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.70053475935829%\" valign=\"top\"\u003e\n \u003cp\u003e24.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.294117647058824%\" valign=\"top\"\u003e\n \u003cp\u003e12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.893048128342246%\" valign=\"top\"\u003e\n \u003cp\u003e24.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.759358288770054%\" valign=\"top\"\u003e\n \u003cp\u003e18.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.433155080213904%\" valign=\"top\"\u003e\n \u003cp\u003e16.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.235294117647058%\" valign=\"top\"\u003e\n \u003cp\u003e12.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e**, *, ns significant at 1 and 5% level and non-significant, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Variance analysis of the effect of dust and herbicides on morphological traits in \u003cem\u003eAmaranthus retroflexus.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.8933092224231465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.15732368896926%\" rowspan=\"2\"\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75.76853526220614%\" colspan=\"6\"\u003e\n \u003cp\u003eMean Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18083182640144665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.669724770642202%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.697247706422019%\"\u003e\n \u003cp\u003ed.f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3302752293578%\"\u003e\n \u003cp\u003eLeaf weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.01834862385321%\"\u003e\n \u003cp\u003eStem weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.08256880733945%\"\u003e\n \u003cp\u003eTotal biomass (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.743119266055047%\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.45871559633027525%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.8933092224231465%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.15732368896926%\" valign=\"top\"\u003e\n \u003cp\u003eBlock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.22242314647378%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.817359855334537%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.359855334538878%\" valign=\"top\"\u003e\n \u003cp\u003e0.02 ns\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u0026nbsp;ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.200723327305607%\" valign=\"top\"\u003e\n \u003cp\u003e3.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3616636528028933%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.787709497206706%\" valign=\"top\"\u003e\n \u003cp\u003eDust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.497206703910615%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.318435754189945%\" valign=\"top\"\u003e\n \u003cp\u003e0.43 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.877094972067038%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.553072625698324%\" valign=\"top\"\u003e\n \u003cp\u003e0.29 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594040968342645%\" valign=\"top\"\u003e\n \u003cp\u003e0.002 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.37243947858473%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.787709497206706%\" valign=\"top\"\u003e\n \u003cp\u003eHerbicides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.497206703910615%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.318435754189945%\" valign=\"top\"\u003e\n \u003cp\u003e0.74 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.877094972067038%\" valign=\"top\"\u003e\n \u003cp\u003e4.95 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.553072625698324%\" valign=\"top\"\u003e\n \u003cp\u003e9.53 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594040968342645%\" valign=\"top\"\u003e\n \u003cp\u003e123.07 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.37243947858473%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.787709497206706%\" valign=\"top\"\u003e\n \u003cp\u003eDust* herbicides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.497206703910615%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.318435754189945%\" valign=\"top\"\u003e\n \u003cp\u003e0.13 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.877094972067038%\" valign=\"top\"\u003e\n \u003cp\u003e0.16 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.553072625698324%\" valign=\"top\"\u003e\n \u003cp\u003e0.16 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594040968342645%\" valign=\"top\"\u003e\n \u003cp\u003e2.15 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.37243947858473%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.787709497206706%\" valign=\"top\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.497206703910615%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.318435754189945%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.877094972067038%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.553072625698324%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594040968342645%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.37243947858473%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.8933092224231465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.15732368896926%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.22242314647378%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.817359855334537%\" valign=\"top\"\u003e\n \u003cp\u003e15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.359855334538878%\" valign=\"top\"\u003e\n \u003cp\u003e10.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.200723327305607%\" valign=\"top\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3616636528028933%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e**, *, ns significant at 1 and 5% level and non-significant, respectively.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTable 7. Visual assessment two week for bentazon and four weeks after herbicide application based on EWRC rating scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003eHerbicide active ingredient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003eDust\u003c/p\u003e\n \u003cp\u003eEfficacy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eNon-dust\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEfficacy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e10 g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0 g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003etribenuron-methyl (TBM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e95.8 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e92.1 b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003eaminopyralid + florasulam (APF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e95.7 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e90.2 c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003esulfosulfuron (SSN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e96.5 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e91.8 b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003e2,4-D + MCPA (2,4-D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e41.2 f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e45.4 f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003eforamsulfuron + iodosulfuron + thiencarbazone (FIT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e97.8 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e96.2 a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003ebentazon (BNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e74.5 d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e79.7 d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.479729729729726%\"\u003e\n \u003cp\u003eacetochlor (ACR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.52027027027027%\"\u003e\n \u003cp\u003e53.3 e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e58.6 e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bentazon, 2,2-dioxo-3-propan-2-yl-1H-2λ6,1,3-benzothiadiazin-4-one, environmental hazards, herbicide efficacy, weed","lastPublishedDoi":"10.21203/rs.3.rs-3236065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3236065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDust pollution poses environmental hazards, affecting agriculture through reduced sunlight exposure, photosynthesis, crop yields, and food security. Also, dust adversely impacts plant physiology and herbicide efficacy, but our study found it can also enhance certain herbicides. A factorial experiment was conducted in 2019 and replicated in 2020 to evaluate the interactive effects of dust and various herbicide applications, including bentazon, sulfosulfuron, tribenuron-methyl, aminopyralid\u0026thinsp;+\u0026thinsp;florasulam, foramsulfuron\u0026thinsp;+\u0026thinsp;iodosulfuron\u0026thinsp;+\u0026thinsp;thiencarbazone, 2,4-D\u0026thinsp;+\u0026thinsp;MCPA, and acetochlor on redroot pigweed (\u003cem\u003eAmaranthus retroflexus\u003c/em\u003e L.) control efficacy. Dust decreased the total chlorophyll 9.2% content of redroot pigweed by 9.2%, while herbicide application reduced the redroot pigweed\u0026rsquo;s total chlorophyll by 67.5%. The reduction of total chlorophyll content was more pronounced when herbicides were applied in the presence of dust. Herbicides and dust reduced redroot pigweed's leaf, stem weights, and biomass. Finally, the total biomass of plants was reduced by tribenuron-methyl, aminopyralid\u0026thinsp;+\u0026thinsp;florasulam, sulfosulfuron, and foramsulfuron\u0026thinsp;+\u0026thinsp;iodosulfuron\u0026thinsp;+\u0026thinsp;thiencarbazone regardless of dust presence, showing the most significant effect. The study results indicate that herbicides used in the presence of dust could affect redroot pigweed growth, which signifies the presence of dust, resulting in decreased control efficacy or increased rate of herbicide resistance evolution.\u003c/p\u003e","manuscriptTitle":"Investigating the Impacts of Airborne Dust on Herbicide Performance on Redroot Pigweed (Amaranthus retroflexus)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-14 13:31:34","doi":"10.21203/rs.3.rs-3236065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-10-17T07:03:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-18T07:22:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a275e565-0063-45df-abb1-dab09f6076e3","date":"2023-09-15T02:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-14T21:49:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-11T18:30:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-08-09T11:06:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-08-09T10:54:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-08-04T22:44:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f046d49-d07c-4df3-8b6d-b96ab08a1d76","owner":[],"postedDate":"August 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":23918841,"name":"Earth and environmental sciences/Natural hazards"},{"id":23918842,"name":"Biological sciences/Ecology/Agri ecology"},{"id":23918843,"name":"Biological sciences/Plant sciences/Plant stress responses"},{"id":23918844,"name":"Biological sciences/Plant sciences/Plant physiology"}],"tags":[],"updatedAt":"2024-02-19T15:03:24+00:00","versionOfRecord":{"articleIdentity":"rs-3236065","link":"https://doi.org/10.1038/s41598-024-54134-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-02-15 15:00:50","publishedOnDateReadable":"February 15th, 2024"},"versionCreatedAt":"2023-08-14 13:31:34","video":"","vorDoi":"10.1038/s41598-024-54134-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-54134-5","workflowStages":[]},"version":"v1","identity":"rs-3236065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3236065","identity":"rs-3236065","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}