Effects of road salt on nitrogen removal by freshwater urban wetlands

Effects of road salt on nitrogen removal by freshwater urban wetlands | 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 Research Article Effects of road salt on nitrogen removal by freshwater urban wetlands Md Moklesur Rahman, Marc Peipoch, Jinjun Kan, Eric Moore, Matthew Sena, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6543627/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Wetlands → Version 1 posted 5 You are reading this latest preprint version Abstract Freshwater urban wetlands are important ecosystems that can naturally filter and remove excess nitrogen (N) through the process of denitrification (DNF). However, anthropogenic inputs such as road salt application may affect the N removal capacity of urban wetlands by affecting the relative rates of DNF and another competing reductive process that retains N – dissimilatory nitrate reduction to ammonium (DNRA). Here, we assessed 13 roadside wetlands in urban/suburban areas of Delaware, USA to determine the effects of road salt sodium (Na + ) on soil physical, chemical, and biological properties and the rates of DNF and DNRA. Based on soil Na + concentrations, wetlands were grouped into three categories: low (Na + < 70 mg kg − 1 ), medium (70 mg kg − 1 < Na + 150 mg kg − 1 ). Rates of DNF and DNRA ranged from 0.8–83 and 0.2–24 µg N L − 1 slurry h − 1 , respectively. DNF was significantly lower in high Na + category wetlands whereas DNRA did not show any significant differences. Similarly, macroaggregates and bioavailable Fe were lowest in the high Na + category, whereas concentrations of soil NH 4 + , NO 3 − , TOC, TN, and microbial metrics (biomass and nosZ and nrfA functional genes) did not reveal any consistent patterns. These findings imply that road salt Na + input exhibited mixed effects on soil properties in these wetlands. Overall, elevated Na + from road salt could undermine the N removal capacity of the roadside urban wetlands. Therefore, strategies should be implemented to reduce the application of road salt or identify effective alternatives. Wetland road salt sodium nitrogen denitrification DNRA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Wetlands are considered the “ kidneys ” of the land because of their unique ability to filter excess nutrients and contaminants from runoff (Mitsch and Gosselink 2015 ). In doing so, wetlands provide valuable ecosystem services that are now under threat from anthropogenic activities, especially in urban and suburban landscapes. Here, we investigate how decades of road salt application, and specifically sodium (Na + ) input, may undermine the processes, functions, and ecosystem services provided by freshwater wetlands in urban/suburban landscapes. Our specific focus is on nitrogen (N) processes like denitrification (DNF) and dissimilatory nitrate reduction to ammonium (DNRA) that influence N removal and retention, but which can be affected by road-salt salinization. Deicing road salt use has tripled in the US since the 1970s and now stands at an annual rate of ~ 20 metric tons/km of two-lane highways (Hintz et al. 2022 ). Nationwide, the predominant (> 90%) form of road salt is “rock salt” or sodium chloride (Hintz et al. 2022 ). Road salt salinization has strong detrimental effects on soil and water quality and current levels of these ions (Na + and Cl − ) exceed the threshold critieria to protect aquatic biota in many aquatic ecosystems across the US (Hintz et al. 2022 ; Kelly et al. 2024 ). Road salt concentrations in some streams and wetlands of urban landscapes exceed sea water salinity (sea water - specific conductivity of ~ 50,000 µS/cm; Huskinson, 2023 ). Under natural conditons, specific conductivity can range between 100 and 250 µS/cm. Extreme and abrupt winter freezing events are likely to further amplify the negative effects of road salt due to large road salt applications prior to the events, followed by flushing of the salt into runoff with subequent warming (Fitch et al. 2005 ; Tao et al. 2018 ; Sherman et al. 2022 ). To date, most of our knowledge about how salinization affects wetland N processing and cycling is derived from estuarine/marine studies investigating the effects of sea level rise on coastal wetlands (Giblin et al. 2013 ; Herbert et al. 2015 ; Zhou et al. 2017 ). In addition to Na + and Cl − , sea water also includes the effects of other cations like calcium (Ca 2+ ) and magnesium (Mg 2+ ) and anions like sulfate (SO 4 2− ). While Ca 2+ and Mg 2+ tend to flocculate/aggregate the clays in soils, Na + counteracts this effect by dispersing the clays because of its single charge and larger atomic radius, and thus reduces oxygen diffusion, and makes the soils more anoxic (Herbert et al. 2015 ) (e.g., conceptual model in Fig. 1 ). Na + can preferentially displace ammonium (NH 4 + ) from soil exchange sites resulting in increased dissolved/soluble NH 4 + concentrations (Weston et al. 2010 ; Ardón et al. 2013 ; Weissman et al. 2021 ). In addition, Na + can suppress microbial abundance by depressing microbial enzymes associated with N removal by DNF (Neubauer et al. 2019 ; Morina and Franklin 2022 ). On the other hand, reductive processes such as DNRA (Pandey et al. 2020 ; Rahman et al. 2019b , c ) that compete with DNF for nitrate (NO 3 − ) (Rahman et al. 2024 ), may be facilitated by elevated Na + , resulting in net N retention as NH 4 + in wetlands. Na + can also displace Fe 2+ off sorption surfaces (Baldwin et al. 2006 ) resulting in increased availability of Fe 2+ for DNF and DNRA (Weston et al. 2010 ). Soil organic carbon (SOC) that serves as an important energy source for reductive micobial N processes could also be affected by road salt. For example, batch experiments (Setia et al., 2013 ) with road salt (Bäckström et al. 2004 ), and field investigations of the freshwater wetland soils (Morina and Franklin 2022 ) demonstrated that increased concentrations of Na + and higher salinity enhanced the release of soil organic carbon (SOC) and subsequently increased the concentration of dissolved organic carbon (DOC) in water by decreasing soil sorption. While estuarine/marine studies have improved our understanding of the effects of salinization, and other studies have investigated the biogeochemical effects of road salt in urban systems (Kaushal et al. 2023 ), only a few studies (Lancaster et al. 2016 ; Craig and Zhu 2018 ; Walker et al. 2021 ) have explicitly investigated the effects of road salt on freshwater wetland N processes. Given that road salt salinization is predominantly due to NaCl, we expect these effects will be more acute and detrimental than seawater due to the stronger negative effects of Na + versus other cations. Furthermore, while many studies have explored the effects of Cl − from road salt, fewer studies have investigated the effects of Na + , particularly on both DNF and DNRA in urban wetlands. Researchers have also cautioned that salt effects on nitrogen processes could be complex due to multiple positive and negative feedbacks which could vary with the magnitude of salt/Na + inputs (Herbert et al. 2015 ; Morina and Franklin 2022 ). To address these key knowledge gaps, we evaluated the following specific questions: 1. How do road salt Na + concentrations affect the rates of DNF and DNRA in freshwater wetland soils and associated N processing? 2. How do Na + concentrations affect the concentrations of NH 4 + , SOC, and Fe 2+ in wetland soils? 3. How do Na + concentrations affect soil aggregation? 4. How are bacterial biomass and N functional genes affected by road salt? We hypothesized that (conceptual model in Fig. 1 ): (a) elevated Na + concentrations will decrease DNF but increase DNRA rates; (b) higher concentrations of Na + will promote the release of soil bound NH 4 + and thus increase dissolved phase NH 4 + concentrations; (c) road salt affected wetland soils will have poor soil aggregation compared to unaffected reference sites; and (d) overall bacterial biomass and DNF functional genes ( nos Z) will be lower, while the fraction of DNRA genes ( nrf A) will be higher, for wetland soils with higher Na + concentrations. 2. Materials and methods 2.1. Study sites The study was conducted in the mid-Atlantic region of USA along the urban/suburban corridor between Newark and Wilmington, Delaware (Figure 2). This region transitions between the Piedmont and Coastal Plain physiographic provinces. Ten wetland sites that were proximal to interstate highways, roads, and paved parking lots were selected for sampling. In addition, we selected three proximal reference sites with minimum or no salt effects, but similar soils and geology. We expected these sites to be impacted to a varying extent by road salt input from the impervious surfaces. The sites were selected randomly based on proximity to the impervious surface, permissions, safe access for sampling, and availability of wetland surface water and soil for sampling. Five of the thirteen study sites were in Justice40 Tracts (Figure 2), which represent disadvantaged communities that are marginalized, underserved, and overburdened by pollution (U.S. Department of Commerce, 2021). Details of each study site including names, identification, coordinates, and distance from nearest roadway are listed in Table 1. Wetland vegetation was primarily Phragmites australis (common reed), Typha spp. (cattails), and Juncus spp. (rushes) in the herbaceous understory, while Fraxinus pennsylvanica (green ash), Liquidambar styraciflua (sweet gum), and Salix nigra (black willow) were the dominant overstory (tree) species. Table 1: Names and identification of the 13 wetlands along with their coordinates and salt categories. Salt affected sites are “W” while reference sites are “R”. Name ID Latitude Longitude Salt category Distance from road (m) 1 UD Campus R1 39°40'03.3"N 75°45'07.9"W Low 30 Glasgow Park R2 39°36'36.7"N 75°43'45.5"W Low 46 Abby Medical Centre R3 39°41'48.3"N 75° 39' 23.9"W Low 10 Brookside W1 39°40'40.0"N 75°41'48.1"W Low 17 2 Dupont Envl Centre W2 39°43'23.6"N 75°33'43.6"W Low 80 Newark Train Station W3 39°40'4.99"N 75°45'12.12"W Low 10 Southbridge Wilmington W4 39°43'55.9"N 75° 33' 8.9"W Low 30 Cooch site W5 39°38'47.0"N 75°44'32.2"W Medium 30 Riverfront Chase Centre W6 39°43'52.2"N 75°33'57.0"W Medium 5 Route 13 Exit 1 Ramp W7 39°42'55.0"N 75°33'27.4"W Medium 12 3 YMCA Pulaski Hwy W8 39°36'24.0"N 75°43'50.6"W High 5 Porter Road Intersection W9 39°35'13.8"N 75°44'23.0"W High 14 4 Southbridge ICS W10 39°43'35.4"N 75° 32' 29.7"W High 160 1. UD: University of Delaware; 2. Envl: Environmental; 3. YMCA: Young Men’s Christian Association; 4. ICS: Intercontinental service 2.2. Soil and water sampling protocols Two water-saturated soil samples (0-20 cm depth below surface) 3-5 m apart were collected from each of the 13 wetlands for a total of 26 samples. Soil samples were collected using a clean shovel and transferred into air-tight and vacuumed (air removed) Ziplock bags and placed on ice in a cooler. One surface water sample was also collected manually from each wetland using 250 mL acid-cleaned Nalgene bottles and filtered using 0.7-micron GFF. Both soil and water samples were stored in the fridge at 4 o C until analysis. 2.3. DNF and DNRA rates using 15 N assays A 10% slurry to measure the rates of DNF and DNRA was prepared by homogenizing the soils and mixing with unfiltered surface/ponded water in an acid-washed glass beaker in the laboratory at room temperature (20-25 ℃). Briefly, following the transfer of 8 mL slurries into 12 mL vials (Exetainer, Labco) with 4 mL headspace, the vials were sealed, purged with helium (high purity) for five minutes and shaken at 120 rpm overnight to eliminate any residual oxygen and create anoxic environment. To determine the potential rates of DNF and DNRA, 15 N-NO 3 - tracer ( 15 N at. 98%, Cambridge Isotope Laboratories, Inc., USA) were added to each vial to a final concentration of 1.5 mg 15 N L -1 . Slurry incubations were terminated by adding 250 μL of 50% (w/v) ZnCl 2 after 0 and 3 h. For DNF, at the end of the experiment, vials were kept upside down and stored under water (to prevent leakage of N 2 ) until injecting air into the headspace following N 2 analysis. For DNRA, slurries were extracted with 2M KCl to bring soil-bound 15 N-NH 4 + into solution and total 15 N-NH 4 + was quantified via hypobromite conversion of 15 N-NH 4 + to 15 N-N 2 (Rahman et al. 2019c; Risgaard-Petersen et al. 1995; Roberts et al. 2014). The detail of hypobromite method for DNRA was previously described (Roberts et al. 2014; Rahman et al. 2019c). 2.4. Sample analysis Physical analysis: Soil texture (% sand, silt and clay) was determined by treating soil with a dispersant followed by the hydrometer method (Gee and Bauder, 1986). Soil micro- (0.053mm) and macroaggregates (>0.25 mm) were determined using the standard wet aggregate stability test (Kemper and Rosenau 1986). Chemical analysis: pH, and electrical conductivity (EC) of water samples were determined using standard calibrated meters (Accumet, Hobo, and YSI). Total carbon and nitrogen for soil and water samples were determined via combustion using an Elementar TC/TN analyzer. For soils, NO 3 - and NH 4 + were determined by KCl extraction followed by colorimetric analysis of the extract. For water, NO 3 - and NH 4 + were determined colorimetrically using a Bran & Luebbe AutoAnalyzer 3 (Bran & Luebbe, Buffalo Grove, IL) while Cl was determined on a calibrated Hach DR3900 meter. Soil Na + were extracted using EPA 3051 and Mehlich-3 (M3) methods (Sikora and Moore 2014). The former provides the total content while the latter only provides the “bioavailable” fractions. Similarly bioavailable Fe was determined via M3 (M3-Fe) and amorphous iron (A-Fe) for dried and ground up soil samples was extracted using acid ammonium oxalate solution at pH 3 (Chao and Zhou 1983) and then analyzed using ICP Spectrometer (iCAP 7000 Series). Potential rates of DNF and DNRA for soil slurries were determined according to Risgaard-Petersen 2004; Meyer et al. 2005; Bernard et al. 2015 (details below). The N 2 from the headspace was analyzed using an Elementar GreenHouse Gas (GHG) analyzer interfaced with an Elementar Precision isotope-ratio mass spectrometer (IRMS) . Rates of DNF were calculated using the linear production of 29 N 2 and 30 N 2 over time resulting from the consumption of 15 N-NO 3 - (Dalsgaard et al. 2000; Nielsen 1992). Rates of DNRA were calculated from the linear production of 15 N-NH 4 + over time. To test the recovery of 15 NH 4 + , a series of standards were prepared in the same matrix. The recovery ranged from 102 ± 2 to 105 ± 3% for all standards. Phospholipid Fatty Acid (PLFA) analysis: PLFA, which provides an estimate of the live microbial and bacterial biomass were measured following Frostegård et al. (2011) at the Regen Ag Lab in Nebraska. 2.5. Functional genes for DNF (nosZ) and DNRA (nrfA) Real-time PCR (qPCR) was used to quantify the DNF and DNRA genes for each sample (performed in triplicate). Briefly, the nitrite reductase genes ( nrfA ) were amplified using primers nrfA F2awMOD and nrfAR1MOD (Cannon et al. 2019), whereas the nitrous-oxide reductase genes ( nosZ ) were amplified using primers nosF (Kloos et al. 2001) and nos ZR1622 (Throbäck et al. 2004). Using 1X PowerUp Sybr Green Master Mix (Applied Biosystems), 3 µM of each primer, and 0.5 mg mL -1 BSA (Invitrogen), the nrfA genes were amplified in 20 µL reactions. Following an initial denaturation of 50 °C for two minutes and 95 °C for ten minutes, the QuantStudio3 cycling conditions consisted of 45 cycles of 95 °C for 15 seconds, 56 °C for 30 seconds, and 72 °C for 45 seconds. Similarly , the nosZ genes were amplified in 20 µL reactions, with 1X PowerUp Sybr Green Master Mix, 0.5 µM of each primer, and 0.5 mg mL -1 BSA (detailed description of the cycling conditions can be found in Sienkiewicz et al. (2020). For each run, ten-fold dilution series were produced from corresponding plasmids and used as standards. The copy number per gram of soil was computed considering the amplicon size and plasmid DNA concentration used for the standard curves (Einen et al. 2008; Kan 2018). 2.6. Data and statistical analysis Exchangeable sodium percentage (ESP) was computed based on Na + concentrations and CEC of soil to assess the sodicity of the soils (Weil et al. 2017). JMP Pro 17 was used for statistical analysis. Based on a visual assessment of the histogram of M3-Na + concentrations (Figure 3a), sites were classified into “low” (Na + < 70 mg kg -1 ; R1-R3 and W1-W4), “medium” (70 mg kg -1 < Na + 150 mg kg -1 ; W8-W10) categories (Table 1). We used the M3-Na + value for categorization (as opposed to total Na + ) since it is more bioavailable and thus a better representative of the Na + that would affect microbial processes such as DNF and DNRA. Differences in N process rates (DNF and DNRA) and other soil metrics were then assessed for these wetland categories. Significant differences for various categories were determined using one-way ANOVA followed by t tests between individual categories. The test result's statistical significance was established at the α = 0.05 level for the Tukey-Kramer HSD post-hoc test. 3. Results 3.1. Sodium concentrations, soil sodicity and wetland salt categories Total and M3-Na + concentrations varied across the sites with M3 values typically ranging from 23 to 89% of the total Na + concentrations (28–16501 mg kg − 1 ) (Table S1 ). ESP values, which is an indication of soil sodicity, varied from 0.8 to 41%. EC that indicates soil salinity ranged from 0.03 to 23.1 dS/m in these wetland soils (Table S1 ). Soil EC increased with increasing M3-Na + concentrations and there was a strong positive correlation (r = 0.91, p < 0.05) between them. Dissolved Na + and Cl − in ponded surface water ranged between 7–208 and 6–215 mg L − 1 , respectively (Table S2 ). Water EC ranged from 0.1 to 1.3 dS/m whereas pH ranged from 6.2 to 7.7. Wetland soil revealed a gradient in Na + concentrations across the three categories (Fig. 3 A) and while there was no significant difference ( p > 0.05) between the low and medium groups, the high category was significantly greater than the other two (Fig. 3 B). 3.2. Variations in DNF and DNRA rates in relation to Na + concentrations DNF rates varied substantially (0.8–83 µg N L − 1 slurry h − 1 ) across the sampled wetlands (Fig. 4 A). Wetlands with lower Na + content (e.g., sites R1-3 and W1, W2 and W4) showed comparatively higher rates except W3, which is a newly constructed wetland, whereas wetlands with higher Na + concentrations (e.g., W8-10) typically displayed lower rates. However, no significant correlations (p > 0.05) were observed between DNF rates and Na + concentrations. DNRA rates also varied considerably (0.2–24 µg N L − 1 slurry h − 1 ) and the highest DNRA rates were observed from wetlands with low Na + concentrations (e.g., W2 and W4). DNRA rates were generally lower in wetlands with higher Na + contents (e.g., W8-10) (Fig. 4 B). However, similar to DNF, DNRA also did not show any significant correlation with Na + concentrations in wetland soils. Overall, DNF was 2–110 times higher than DNRA and only one (W10-2) out of 26 sites showed higher DNRA than DNF rates (DNF/DNRA < 1 in Fig. 4 C), indicating that DNF dominated the nitrate removal pathway in these wetlands. When assessed by the Na + categories, DNF did not differ significantly for the low and medium categories, but DNF was significantly lower for the highest Na + category (Fig. 5 A). In contrast, no significant difference was observed for DNRA (Fig. 5 B). 3.3. Soil aggregation and bioavailable fractions of iron Soil macroaggregates accounted for 13–88% of the total soil aggregates (Fig. 6 B), while microaggregates made up < 1 to 39% (Fig. 6 A), and these two were inversely related (r = 0.58, p 0.05), but macroaggregates for the highest salt category were significantly less than those for the medium Na + category. Soil M3-Fe and A-Fe concentrations (Fig. 6 C and D) were elevated for the medium Na + category, but significant differences among the categories varied. M3-Fe was significantly lower for the high versus medium category, while for A-Fe the low category was significantly less than the medium Na + category. 3.4. Soil sorbed and water-soluble nitrogen species and OC Contrary to our expectations, soil and dissolved concentrations of NH 4 + and NO 3 − did not yield any significant differences among the low, medium, and high Na + categories (Fig. 7 A-D). The only pattern, although insignificant, was that the highest salt category had the lowest mean NH 4 + and highest mean NO 3 − concentrations. In comparison, soil TOC and TN values were significantly greater for the medium versus the low and the high Na + categories (Fig. 7 E, F). 3.5. PLFA determined total living microbial and bacterial biomass PLFA derived total living microbial and bacterial biomass did not reveal any significant differences among the Na + categories. The variability of both metrics for the medium category was however less than that observed for the other two categories (Fig. 8 ). 3.6. nosZ (DNF) and nrfA (DNRA) microbial functional genes Like DNF (Fig. 5 ), the abundance of nosZ gene initially increased from low to medium but then decreased for the high category wetlands though these variations were non-significant. The abundance of nrfA gene, unlike DNRA process rate, initially increased non-significantly and then decreased with salt gradient. The nosZ/nrfA gene proportion increased gradually with salt gradient, however, these changes were also not significant. 4. Discussion This study revealed a clear gradient from low to high road salt Na + concentrations across the sampled wetland soils. The effects of soil Na + on nitrate reduction process rates, nutrients (TN, TOC, NH 4 + and NO 3 − ), and microbial metrics were, however, mixed with only the highest salt category potentially indicating any detrimental effects of road salt. We elaborate on these responses in the discussion below. Understanding how excess Na + could be affecting the wetland soil health is critical to mitigating the impacts of road salt. 4.1. Magnitude of wetland soil Na + concentrations and comparisons against other studies Total soil Na + concentrations in this study ranged from 28 to 16501 mg kg − 1 , whereas the M3-Na + values ranged between 11-1082 mg kg − 1 (Fig. 3 , Supplemental Table S1 ). The M3-Na + concentrations are more bioavailable and hence have more potential to impact the biogeochemical processes in soils. Therefore, here we compare our M3-Na + values against the ammonium acetate extractable and readily bioavailable Na + values reported from previous studies. For example, the range of M3-Na + values in this study are comparable to Na + values observed for road salt impacted exurban forested wetlands in Coventry, Connecticut, USA (37-1226 mg kg − 1 ) (Walker et al. 2021 ). Na + concentrations in soils of our low Na + category wetlands (11–67 mg kg − 1 ) (Fig. 3 ) were similar to those reported by Robinson et al. ( 2017 ) for a rural site in Wildwood, Missouri, USA and an urban site in Webster Groves, Missouri, USA. The rural site concentrations were 63 mg kg − 1 at 1 m from the road and 0 to 37 mg kg − 1 between 2.5 m and 15.8 m from the road, while the urban site values were71 mg kg − 1 at 1 m from the road; and 6–30 mg kg − 1 further from the road. Similarly, roadside soils in Massachusetts had Na + from road salt ranging between 22 to 309 mg kg − 1 at a distance of 1.5 meters from the road (Bryson and Barker 2002 ). Overall, these comparisons suggest that Na + concentrations measured at our wetland locations are generally in the same range of values reported for other locations in the USA. Soil sodicity quantifies the concentration of Na + in relation to other cations under saline conditions (Rengasamy and Olsson 1991 ; Wong et al. 2010 ; Steele and Aitkenhead-Peterson 2013 ). Based on soil ESP (Table S1 ), the low and medium Na + category wetlands in this study are classified as non-sodic (ESP 15%) (Northcote and Srene 1972 ; Sparks 2003 ; Osman 2018 ). Conversely, based on soil EC, most wetland soils in this study except for W10-1 are non-saline (EC < 2 dS/m) (Sparks 2003 ). High concentrations of Na + can have detrimental environmental implications. For example, a previous study by Czerniawska-Kusza et al. ( 2004 ) reported that protozoa were toxically affected by soils containing 260 mg kg − 1 Na + , while manifestations of salt injury, such as chlorosis and necrosis of the leaf blade margins, occurred at soil concentrations of 132 mg Na + kg − 1 and became more severe at 260 mg Na + kg − 1 due to widespread necrosis and defoliation. Thus, an average M3-Na + concentration of 33 and 110 mg kg − 1 for low and medium category wetlands in our study, respectively, indicate that Na + levels in these wetlands are non-hazardous. However, an average Na + concentration of 383 mg kg − 1 for the high Na + category wetlands suggest that Na + level for this category is above the environmental thresholds and thus can potentially impact both wetland plants and microbes. 4.2. Effects on soil aggregation and Fe species In contrast to medium Na + category, the % of soil macroaggregates for the high category were significantly lower, likely due to the higher sodicity (Fig. 6 ). Adsorbed Na + on soil particles tends to physically separate them due to its comparatively large size, single electrical charge, and hydration state (Warrence et al. 2002 ). Therefore, the main physical process related to elevated Na + concentrations in soils is the dispersion of soil particles (Norrström and Bergstedt 2001 ; Bauder and Brock 2001 ; Walker et al. 2021 ). Thus, the elevated Na + in soils of high category wetlands is likely decreasing macroaggregation due to dispersive effects of Na + . We also observed that concentrations of M3-Fe were significantly lower for the high Na + category wetlands. Elevated soil Na + concentrations can displace heavy metals such as Fe from soil exchange sites reducing their concentrations in the soil (Norrström and Bergstedt 2001 ; Bäckström et al. 2004 ; Baldwin et al. 2006 ; Willmert et al. 2018 ; Bi et al. 2024 ). Therefore, it is very likely that elevated Na + concentrations in the highest category wetland resulted in the low M3-Fe values. 4.3. Effects on soil N and OC Similar to the effects on Fe, excess Na + can result in the cationic displacement of sorbed NH 4 + from soil exchange sites with subsequent increase of NH 4 + in solution phase (Herbert et al. 2015 ). Work by Weston et al. ( 2010 ) and Ardon et al. (2013) indicates that NH 4 + release occurs at ~ salinity levels of 3 parts per thousand or 3000 ppm and increases with salinity. Nevertheless, we did not observe any significant differences in solid and dissolved phase NH 4 + and NO 3 − across various Na + categories (Fig. 7 ). Moderate salinity levels (EC 16.5 dS/m) can inhibit nitrification and reduce NO 3 − (Ardón et al. 2013 ; Zheng et al. 2024 ). Our EC levels were much lower than the thresholds mentioned above. Thus, it is very likely that the Na + concentrations in our wetland soils were not high enough to result in change in N concentrations across the Na + categories, or any released ions were uptake by wetland vegetation. Road salt salinization in wetlands can have a complex effect on soil OC and N pools because of the multiple positive and negative feedback (Herbert et al. 2015 ). Stagnant wetland hydrology and anaerobic conditions can suppress decomposition and thus increase soil OC and N (Neubauer 2013 ). On the other hand, salt associated osmotic effects can reduce plant growth and biomass in salt-affected wetlands (Kinsman-Costello et al. 2023 ; Mazhar et al. 2022 ). Some studies have revealed enhanced soil OC and N mineralization due to salts, whereas others have not documented any effects (Marton et al. 2012 ; Setia et al. 2013 ). According to one of the few long-term studies, soils with higher soil organic matter stocks seemed to lose more carbon in response to salinization than do soils with lower carbon content (Marton et al. 2012 ). We found a mixed pattern for SOC in our study with an increase in concentrations from low to medium salt category followed by a decline for the highest category. We speculate that the decline for the highest salt category could be associated with salt effects (potentially increased C mineralization), but additional future tests will be required to make a conclusive assessment. 4.4. Microbial biomass and N functional genes Based on our PLFA and qPCR results, we did not observe any significant changes in living microbial biomass and the abundance of microbial functional genes for the Na + categories (Fig. 8 ). Previous studies have reported a negative correlation between salinity and microbial abundance (Simachew et al. 2016 ; Xie et al. 2017 ; Zhao et al. 2020 ; Morina and Franklin 2022 ). Freshwater microbial communities revealed a high degree of resistance to low salinity (3 ppt) (Berga et al. 2017 ), but when exposed to high salinity conditions (14 ppt), the microbial community rapidly restructured (Morina and Franklin 2022 ). Elsewhere, elevated NaCl concentrations ( 10,000–30,000 mg/L) in a sludge system decreased the diversity of microbial communities (Pang et al. 2020 ). These studies suggest that the impact on microbial communities could vary as a function of salt concentrations. Given the lack of significant microbial response for our study, we speculate that the Na + concentrations in our study were likely not high enough to affect the microbial population and functional genes. 4.5 DNF and DNRA process rates Rates of DNF and DNRA observed in this study are comparable to those reported for riparian ecosystems (DNF: 6.7–79 and DNRA: 5.2 − 37.6 µ g N L − 1 slurry h − 1 ) in the mid-Atlantic USA (Rahman et al. 2024 ), urban wetlands (DNF: 0.12–7.39 and DNRA: 0.13–1.94 µ g N g dry soil − 1 h − 1 ) in Phoenix, Arizona, USA (Handler et al. 2022 ), but an order lower than constructed freshwater urban wetlands (DNF: 90–405 and DNRA: 9-165 µ g N L − 1 slurry h − 1 ) in Melbourne, Australia (Rahman et al. 2019a ). Our rates, however, are one or two orders higher than those observed for freshwater wetland soils from Taskinas Creek in Virginia, USA (DNF: 0.3–2.25 and DNRA: 0.03–0.75 µ g N g OM − 1 h − 1 ) under varying saline conditions (0–35 ppt) (Morina and Franklin 2022 ) and wetland sediments collected from the Nansha Wetland Park in the Pearl River Estuary in China (DNF: 0.03–0.21 and DNRA: 0.01–0.1 µ g N g − 1 h − 1 ) and experimented with artificial seawater with a salinity ranging from 0 to 35 ppt (Zheng et al. 2024 ). Thus, overall, the reductive N process rates measured in this study are greater than sites substantially impacted by salinization. DNF and DNRA process rates are dictated by physicochemical conditions, substrate availability, and microbial abundance and diversity, all of which can be affected by salt inputs as highlighted above. Reduced soil oxygen diffusion associated with Na + dispersed soils can enhance hypoxia/anoxia and thus encourage DNF and DNRA (Herbert et al. 2015 ). DNF and macroaggregates followed similar trends across our Na + categories, with a significant drop in both from the medium to the high category. While a cause-and-effect relationship is difficult to establish based on existing data, it is possible that Na + concentrations for the highest category are affecting both responses with possible feedback effects between them. For both DNF and DNRA, NO 3 − is the electron acceptor, whereas OC and Fe can serve as the electron donors (Pandey et al. 2020 ; Rahman et al. 2019b , 2024 ). Thus, concentrations of these substrates could influence the reductive process rates (Ballantine et al. 2014 ; Robertson et al. 2016 ; Rahman et al. 2019b , 2024 ). Concentrations for NO 3 − across the salt categories (Fig. 7 D) clearly did not follow the DNF trend. On the contrary, NO 3 − concentrations displayed a highly variable and non-significant increase in both soil and surface waters from the low to the high category (Fig. 7 B and D). Thus, measured NO 3 − concentrations were likely not a factor for the significant decline in DNF for the highest salt category. In contrast, both soil M3-Fe (Fig. 6 C) and OC (Fig. 7 E) followed a similar pattern as DNF with a significant drop in both variables for the highest salt category. Thus, we argue that these responses are likely influenced by highest Na + concentrations, and these substrate concentrations could potentially also be influencing the DNF process rates for the highest salt category. Finally, DNF and DNRA process rates are also influenced by the microbial communities. For example, Pan et al. ( 2023 ) observed that DNF was the primary nitrogen removal process in coastal farming drainage ditches, but it decreased significantly by about 60% mainly due to a substantial decline in the DNF functional genes abundance under highly saline conditions (15 ppt). In addition, salinity was found to result in incomplete DNF by affecting the abundance of denitrifying nosZ functional genes (Zaghmouri et al. 2018 ; Wang et al. 2018 ; Fu et al. 2019 ; Jiang et al. 2023 ; Pan et al. 2023 ). While the overall patterns in our microbial/bacterial biomass (Fig. 8 ) and nosZ genes (Fig. 9 ) across the salt categories were similar to DNF rates, there were no significant differences among the microbial metrics. This suggests that microbial biomass and genes were likely not an important factor shaping the salt-driven changes in DNF, especially at the highest salt level. It is also possible that although elevated Na + concentrations from road salt did not significantly alter the abundance of denitrifying functional genes, it might adversely affect the necessary functions of the genes resulting in lower DNF. Previous studies have demonstrated that increased salinity can reduce DNF (Seo et al. 2008 ; Lancaster et al. 2016 ), as a result of osmotic stress (Yan et al. 2015 ) and ion-specific toxicity (Macêdo et al. 2019 ) to denitrifying bacteria (Kinsman-Costello et al. 2023 ). For instance, DNRA increased with increasing salinity and was more likely to be preferred over DNF for a study in oligohaline estuarine sediments in Massachusetts, USA (Giblin et al. 2010 , 2015 ). Similarly, DNRA was found positively correlated with salinity in Texas estuaries (Gardner et al. 2006 ). However, Zheng et al. ( 2024 ) reported that DNRA decreased as salinity increased from 0 to 35 ppt, while other research revealed that DNRA bacterial activity and abundances did not vary in response to salinity (4–22 ppt) in the Yellow River Estuary in China (Bu et al. 2017 ) and across the Yellow River Delta wetland (0.22–1.24 psu) in China (Zhou et al. 2021 ). These discrepancies might be due to changes in some DNRA genera caused by salinity-driven alterations to physicochemical factors (Pang et al. 2024 ). Most of these studies were in saltwater coastal environments and had salinity levels that substantially exceeded our freshwater values. Therefore, we speculate that the highest salt levels in this study were likely still not sufficient to impact the DNRA process. 6. Conclusions and environmental implications In this study, we investigated how Na + in road salt impacts the soil physical, chemical, and biological properties of roadside freshwater urban wetlands and thus affects their capacity to process nitrate-nitrogen through DNF and DNRA reductive processes. To our knowledge, this is the first study that has evaluated the effects of Na + on DNF and DNRA. Overall, our study yielded mixed results, with the greatest impacts of Na + for the high category (Na + >150 mg kg − 1 ), but little or no effects for the low (< 70 mg kg − 1 ) and medium (70 mg kg − 1 < Na + < 150 mg kg − 1 ) categories. A significant drop in soil macroaggregates, bioavailable Fe, and DNF rate were observed for the high Na + category. On the other hand, we did not find any significant impacts on wetland DNRA rate, microaggregate values, concentrations of soil NH 4 + , NO 3 − , TOC, TN, and microbial metrics (biomass and nosZ and nrfA functional genes). On the contrary, and in conflict with our hypotheses, some of the parameter/process values increased insignificantly from the low to medium category. These observations confirm that salt effects on urban/suburban wetlands can be highly complex and variable across ecosystem characteristics and processes. Salt effects may not necessarily gradually and monotonically increase but may be subject to threshold and non-linear behavior that may vary with ecosystem processes and characteristics. Alternatively, some processes or characteristics may be more sensitive than others to salt inputs. Understanding and quantifying these thresholds or sensitivity for N and other biogeochemical parameters and cycles is critical for effective salt management and ecosystem protection. The suppression of DNF due to road salts is however a concern given that the wetlands are important natural filters in urban/suburban landscapes and provide a valuable ecosystem service by removing excess N from storm water runoff. Given the increasing use of fertilizers in urban/suburban settings (e.g., use of fertilizers on golf courses and homeowner lawns) loss of this N buffering potential would be particularly detrimental to our environment. Furthermore, urban stormwater wetlands are especially vulnerable because they are subject to: (a) frequent hydrologic stagnation and hypoxia/anoxia (Kinsman-Costello et al. 2023 ); (b) peak flows and salt inputs associated with high impervious surfaces and extreme precipitation/climate events (Inamdar et al. 2018 ); and (c) high/extreme summer temperatures and urban heat island effects that yield warmer surface runoff (Somers et al. 2013 ). All of these effects could, individually or in concert, further amplify the negative effects of road salt salinization for urban and suburban wetlands. Therefore, reducing the application of road salts or identifying and applying other viable alternatives should be actively pursued to protect the water quality filtering services of urban/suburban wetlands. Declarations Acknowledgement We thank DuPont Environmental Education Centre for permission to collect soil and water samples from their site. We also thank Karen Gartley, and Joe Paller for the water quality analysis performed at the University of Delaware Soils Lab and the Regen Ag Lab for the soils analysis. We also appreciate Laura Borecki of the Stroud Water Research Centre for her assistance. Funding: This publication was developed under Assistance Agreement No. CD-95340701 awarded by the United States Environmental Protection Agency (USEPA). The views expressed in this document are solely those of the authors and EPA does not endorse any products or commercial services mentioned in this publication. Competing interests: 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 article. Author contributions Md. Moklesur Rahman: First & corresponding author who designed the study plan, collected samples, implemented the proper methodology, did sample and data analysis, and wrote the initial draft of the manuscript, Marc Peipoch : First co-author who helped in sample analysis and acquisition of data, data analysis and reviewed the manuscript, Jinjun Kan : Assisted in thorough review of the manuscript, Eric Moore: Assisted in site selection, sample collection, experiments and sample analysis Matthew Sena : Assisted in experiments and reviewing the manuscript Mukta Kantak: Assisted in sample collection, experiments and sample analysis Suparn Sharma: Assisted in sample collection and sample analysis Chander Lekha: Assisted in sample analysis Shreeram P lnamdar: Leading in conceptualizing and designing the study, funding acquisition, project administration, supervision, validation, analysis and interpretation of data, reviewing the manuscript critically for important intellectual content. 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Journal of Environmental Sciences 106:39–46. https://doi.org/10.1016/j.jes.2021.01.015 Supplementary Files SupplementaryInformationSITables.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Wetlands → Version 1 posted Reviewers agreed at journal 07 Jul, 2025 Reviewers invited by journal 06 Jul, 2025 Editor invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 First submitted to journal 27 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6543627","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481206209,"identity":"c045bbe4-3885-473d-9d05-6a0ced624320","order_by":0,"name":"Md Moklesur Rahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYJACZhDBz8BgwPCAgYGxgWgtkg1ALQkkaTE4QKwW3WlnH34uzLHLM76RvPFBAoON7IYDBLSY3U43lp65LbnY7EZasUECQ5oxEVrSGKR5tzEnbruRYyaRwHA4kRgtzL95t9Unbp6RY/4jgeE/UVrYgLYADZfIMQN6/wBxWqx5tx1PnHHmWbFEgkGy8UxiHHabd1t1Yn978sYPHyrsZPsIaUEDBqQpHwWjYBSMglGAAwAAZ2lFGIMEiDcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8032-6809","institution":"University of Delaware","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Moklesur","lastName":"Rahman","suffix":""},{"id":481206210,"identity":"854f672b-3e90-4639-a0fa-81adb81e0930","order_by":1,"name":"Marc Peipoch","email":"","orcid":"","institution":"Stroud Water Research Center","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Peipoch","suffix":""},{"id":481206211,"identity":"a4020b5a-ed57-41b7-8fa5-13c2c5c6ac38","order_by":2,"name":"Jinjun Kan","email":"","orcid":"","institution":"Stroud Water Research Center","correspondingAuthor":false,"prefix":"","firstName":"Jinjun","middleName":"","lastName":"Kan","suffix":""},{"id":481206212,"identity":"be13440b-89f2-431e-bb0c-e000f22a42c4","order_by":3,"name":"Eric Moore","email":"","orcid":"","institution":"University of Delaware","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Moore","suffix":""},{"id":481206213,"identity":"637ac7f6-58d6-4a70-a85e-1923e7de5dc6","order_by":4,"name":"Matthew Sena","email":"","orcid":"","institution":"University of Delaware","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Sena","suffix":""},{"id":481206214,"identity":"98516dac-dcb9-47ba-b9c3-c3369860e6ae","order_by":5,"name":"Mukta Kantak","email":"","orcid":"","institution":"The University of Texas at Austin","correspondingAuthor":false,"prefix":"","firstName":"Mukta","middleName":"","lastName":"Kantak","suffix":""},{"id":481206215,"identity":"2da69ddc-60f4-45f7-84bb-fad1a2db4d1f","order_by":6,"name":"Suparn Sharma","email":"","orcid":"","institution":"Charter School of Wilmington","correspondingAuthor":false,"prefix":"","firstName":"Suparn","middleName":"","lastName":"Sharma","suffix":""},{"id":481206216,"identity":"a6bf0129-f024-45de-aaa9-3037d13f9a68","order_by":7,"name":"Chander Lekha","email":"","orcid":"","institution":"University of Delaware","correspondingAuthor":false,"prefix":"","firstName":"Chander","middleName":"","lastName":"Lekha","suffix":""},{"id":481206217,"identity":"9b29b3c4-20d1-4b7f-ac74-fd670a137e6d","order_by":8,"name":"Shreeram P. Inamdar","email":"","orcid":"","institution":"University of Delaware","correspondingAuthor":false,"prefix":"","firstName":"Shreeram","middleName":"P.","lastName":"Inamdar","suffix":""}],"badges":[],"createdAt":"2025-04-28 04:06:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6543627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6543627/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13157-025-02026-3","type":"published","date":"2026-01-20T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86490071,"identity":"c2f62264-e939-46e3-b1aa-da10f03f0940","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":480242,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model highlighting the physical, chemical and microbial effects of road salt salinization on nitrogen (N) processes in urban/suburban wetlands.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/d9ba86ca444c18300e9be450.png"},{"id":86490080,"identity":"0cd2391c-c837-4c7d-804a-e7e6f0cb62e4","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":350886,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the 10 (indicated by W) roadside freshwater urban wetlands located in Newark and Wilmington, Delaware, USA affected by road salts to different levels categorized as low, medium and high sodium category. Three reference sites are indicated by R.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/41d098a8af001a2f3735fdb9.png"},{"id":86490069,"identity":"44db7278-390b-483d-b36e-8478626df948","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31669,"visible":true,"origin":"","legend":"\u003cp\u003eConcentrations of Na\u003csup\u003e+\u003c/sup\u003e in soils from 26 sites of 13 wetlands categorized as low, medium and high salt category (a) and a comparison of their group means (b). Similar letters indicate no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05), while different letters indicate a significant difference (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) between wetland categories. Sample size for low category, n = 14, medium category, n = 6, and high category, n = 6. Boxes, empty square, inside line, and whiskers indicate interquartile ranges, mean, median, 25th and 75th percentiles, respectively. Outliers are not shown for these box plots.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/c5ab970865d4cf3adcc52806.png"},{"id":86490479,"identity":"daf043fd-5259-4554-a38a-3c12cd9b9d9c","added_by":"auto","created_at":"2025-07-11 09:02:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31388,"visible":true,"origin":"","legend":"\u003cp\u003eRates of DNF (a), DNRA (b), and the DNF/DNRA ratio (c) from all 26 sites of 13 selected wetlands. A ratio of 1 (DNF = DNRA) is indicated by the horizontal line in figure c.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/fc6a891901e66b69d51ef6ac.png"},{"id":86491341,"identity":"f9a708f2-e0b5-40c4-93c3-0b33fb959e00","added_by":"auto","created_at":"2025-07-11 09:10:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25932,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the group means for DNF (a) and DNRA (b) of different wetland categories. Similar letters indicate no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05), while different letters indicate a significant difference (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) between wetland categories. Boxes, empty square, inside line, and whiskers indicate interquartile ranges, mean, median, 25th and 75th percentiles, respectively. Outliers are not shown for these box plots.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/43438f9a85dd3213a7b04e4c.png"},{"id":86490074,"identity":"0fe67206-7bbc-4959-a9d6-ab90ad58fec4","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53757,"visible":true,"origin":"","legend":"\u003cp\u003eSoil microaggregates (a), macroaggregates (b), M3-Fe (c), and A-Fe (d) in three salt category wetlands. Similar letters indicate no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05), while different letters indicate a significant difference (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) between wetland categories. Boxes, empty square, inside line, and whiskers indicate interquartile ranges, mean, median, 25th and 75th percentiles, respectively. Outliers are not shown for these box plots.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/aae2e677d5529fcf9b3862a3.png"},{"id":86491342,"identity":"ccb233f4-851e-4608-8b0d-ddc0ea3a9c48","added_by":"auto","created_at":"2025-07-11 09:10:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49458,"visible":true,"origin":"","legend":"\u003cp\u003eSoil sorbed NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (a), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e (b), water soluble NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (c), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e (d) and soil TOC (e) and TN (f). Similar letters indicate no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05), while different letters indicate a significant difference (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) between wetland categories. Boxes, empty square, inside line, and whiskers indicate interquartile ranges, mean, median, 25th and 75th percentiles, respectively. Outliers are not shown for these box plots.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/d540a9043317c237f0a86479.png"},{"id":86490079,"identity":"e723528f-5a9a-4fb2-b7ae-649a41652724","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41299,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance of total living microbial (a) and bacterial biomass (b) in soils from wetlands of three different categories. Similar letters indicate no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05), while different letters indicate a significant difference (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) between wetland categories. Boxes, empty square, inside line, and whiskers indicate interquartile ranges, mean, median, 25th and 75th percentiles, respectively. Outliers are not shown for these box plots.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/1bc543e0b2264af47e9e388f.png"},{"id":86490083,"identity":"900b1860-b2fc-467a-9307-c96664babfc2","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33350,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance of \u003cem\u003enosZ\u003c/em\u003e (a), \u003cem\u003enrfA\u003c/em\u003e (b) functional genes and the \u003cem\u003enosZ/nrfA\u003c/em\u003eproportion (c) in soils from wetlands of three different categories. Similar letters indicate no significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05), while different letters indicate a significant difference (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05) between wetland categories. Boxes, empty square, inside line, and whiskers indicate interquartile ranges, mean, median, 25th and 75th percentiles, respectively. Outliers are not shown for the box plots.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/75be9898101fc107c93f8289.png"},{"id":101151759,"identity":"e1fe54f4-9f2d-4909-ae8a-c95da64d8cba","added_by":"auto","created_at":"2026-01-26 16:04:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2167221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/146649f0-e71a-45eb-99b1-f70a23f1b670.pdf"},{"id":86490077,"identity":"10ea27af-b56e-4a72-87ed-588b3cd5d044","added_by":"auto","created_at":"2025-07-11 08:54:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25345,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationSITables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6543627/v1/374476ab117c03a609b4f7fd.docx"}],"financialInterests":"","formattedTitle":"Effects of road salt on nitrogen removal by freshwater urban wetlands","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWetlands are considered the \u0026ldquo;\u003cem\u003ekidneys\u003c/em\u003e\u0026rdquo; of the land because of their unique ability to filter excess nutrients and contaminants from runoff (Mitsch and Gosselink \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In doing so, wetlands provide valuable ecosystem services that are now under threat from anthropogenic activities, especially in urban and suburban landscapes. Here, we investigate how decades of road salt application, and specifically sodium (Na\u003csup\u003e+\u003c/sup\u003e) input, may undermine the processes, functions, and ecosystem services provided by freshwater wetlands in urban/suburban landscapes. Our specific focus is on nitrogen (N) processes like denitrification (DNF) and dissimilatory nitrate reduction to ammonium (DNRA) that influence N removal and retention, but which can be affected by road-salt salinization.\u003c/p\u003e\u003cp\u003eDeicing road salt use has tripled in the US since the 1970s and now stands at an annual rate of ~\u0026thinsp;20 metric tons/km of two-lane highways (Hintz et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nationwide, the predominant (\u0026gt;\u0026thinsp;90%) form of road salt is \u0026ldquo;rock salt\u0026rdquo; or sodium chloride (Hintz et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Road salt salinization has strong detrimental effects on soil and water quality and current levels of these ions (Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e) exceed the threshold critieria to protect aquatic biota in many aquatic ecosystems across the US (Hintz et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kelly et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Road salt concentrations in some streams and wetlands of urban landscapes exceed sea water salinity (sea water - specific conductivity of ~\u0026thinsp;50,000 \u0026micro;S/cm; Huskinson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Under natural conditons, specific conductivity can range between 100 and 250 \u0026micro;S/cm. Extreme and abrupt winter freezing events are likely to further amplify the negative effects of road salt due to large road salt applications prior to the events, followed by flushing of the salt into runoff with subequent warming (Fitch et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tao et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sherman et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo date, most of our knowledge about how salinization affects wetland N processing and cycling is derived from estuarine/marine studies investigating the effects of sea level rise on coastal wetlands (Giblin et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Herbert et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition to Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, sea water also includes the effects of other cations like calcium (Ca\u003csup\u003e2+\u003c/sup\u003e) and magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e) and anions like sulfate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e). While Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e tend to flocculate/aggregate the clays in soils, Na\u003csup\u003e+\u003c/sup\u003e counteracts this effect by dispersing the clays because of its single charge and larger atomic radius, and thus reduces oxygen diffusion, and makes the soils more anoxic (Herbert et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (e.g., conceptual model in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Na\u003csup\u003e+\u003c/sup\u003e can preferentially displace ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) from soil exchange sites resulting in increased dissolved/soluble NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e concentrations (Weston et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ard\u0026oacute;n et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Weissman et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, Na\u003csup\u003e+\u003c/sup\u003e can suppress microbial abundance by depressing microbial enzymes associated with N removal by DNF (Neubauer et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Morina and Franklin \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, reductive processes such as DNRA (Pandey et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003ec\u003c/span\u003e) that compete with DNF for nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) (Rahman et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), may be facilitated by elevated Na\u003csup\u003e+\u003c/sup\u003e, resulting in net N retention as NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e in wetlands. Na\u003csup\u003e+\u003c/sup\u003e can also displace Fe\u003csup\u003e2+\u003c/sup\u003e off sorption surfaces (Baldwin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) resulting in increased availability of Fe\u003csup\u003e2+\u003c/sup\u003e for DNF and DNRA (Weston et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Soil organic carbon (SOC) that serves as an important energy source for reductive micobial N processes could also be affected by road salt. For example, batch experiments (Setia et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with road salt (B\u0026auml;ckstr\u0026ouml;m et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and field investigations of the freshwater wetland soils (Morina and Franklin \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that increased concentrations of Na\u003csup\u003e+\u003c/sup\u003e and higher salinity enhanced the release of soil organic carbon (SOC) and subsequently increased the concentration of dissolved organic carbon (DOC) in water by decreasing soil sorption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhile estuarine/marine studies have improved our understanding of the effects of salinization, and other studies have investigated the biogeochemical effects of road salt in urban systems (Kaushal et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), only a few studies (Lancaster et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Craig and Zhu \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Walker et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have explicitly investigated the effects of road salt on freshwater wetland N processes. Given that road salt salinization is predominantly due to NaCl, we expect these effects will be more acute and detrimental than seawater due to the stronger negative effects of Na\u003csup\u003e+\u003c/sup\u003e versus other cations. Furthermore, while many studies have explored the effects of Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e from road salt, fewer studies have investigated the effects of Na\u003csup\u003e+\u003c/sup\u003e, particularly on both DNF and DNRA in urban wetlands. Researchers have also cautioned that salt effects on nitrogen processes could be complex due to multiple positive and negative feedbacks which could vary with the magnitude of salt/Na\u003csup\u003e+\u003c/sup\u003e inputs (Herbert et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Morina and Franklin \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address these key knowledge gaps, we evaluated the following specific questions: 1. How do road salt Na\u003csup\u003e+\u003c/sup\u003e concentrations affect the rates of DNF and DNRA in freshwater wetland soils and associated N processing? 2. How do Na\u003csup\u003e+\u003c/sup\u003e concentrations affect the concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, SOC, and Fe\u003csup\u003e2+\u003c/sup\u003e in wetland soils? 3. How do Na\u003csup\u003e+\u003c/sup\u003e concentrations affect soil aggregation? 4. How are bacterial biomass and N functional genes affected by road salt? We hypothesized that (conceptual model in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (a) elevated Na\u003csup\u003e+\u003c/sup\u003e concentrations will decrease DNF but increase DNRA rates; (b) higher concentrations of Na\u003csup\u003e+\u003c/sup\u003e will promote the release of soil bound NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and thus increase dissolved phase NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e concentrations; (c) road salt affected wetland soils will have poor soil aggregation compared to unaffected reference sites; and (d) overall bacterial biomass and DNF functional genes (\u003cem\u003enos\u003c/em\u003eZ) will be lower, while the fraction of DNRA genes (\u003cem\u003enrf\u003c/em\u003eA) will be higher, for wetland soils with higher Na\u003csup\u003e+\u003c/sup\u003e concentrations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003ch2\u003e\u003cstrong\u003e\u003cem\u003e2.1. Study sites\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in the mid-Atlantic region of USA along the urban/suburban corridor between Newark and Wilmington, Delaware (Figure 2). This region transitions between the Piedmont and Coastal Plain physiographic provinces. Ten wetland sites that were proximal to interstate highways, roads, and paved parking lots were selected for sampling. In addition, we selected three proximal reference sites with minimum or no salt effects, but similar soils and geology. We expected these sites to be impacted to a varying extent by road salt input from the impervious surfaces. The sites were selected randomly based on proximity to the impervious surface, permissions, safe access for sampling, and availability of wetland surface water and soil for sampling. Five of the thirteen study sites were in Justice40 Tracts (Figure 2), which represent disadvantaged communities that are marginalized, underserved, and overburdened by pollution (U.S. Department of Commerce, 2021). Details of each study site including names, identification, coordinates, and distance from nearest roadway are listed in Table 1. Wetland vegetation was primarily \u003cem\u003ePhragmites australis\u0026nbsp;\u003c/em\u003e(common reed), \u003cem\u003eTypha\u003c/em\u003e spp. (cattails), and \u003cem\u003eJuncus\u003c/em\u003e spp. (rushes) in the herbaceous understory, while \u003cem\u003eFraxinus pennsylvanica\u0026nbsp;\u003c/em\u003e(green ash), \u003cem\u003eLiquidambar styraciflua\u003c/em\u003e (sweet gum), and \u003cem\u003eSalix nigra\u003c/em\u003e (black willow) were the dominant overstory (tree) species. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eNames and identification of the 13 wetlands along with their coordinates and salt categories. Salt affected sites are \u0026ldquo;W\u0026rdquo; while reference sites are \u0026ldquo;R\u0026rdquo;.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalt category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance from road (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eUD Campus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;40\u0026apos;03.3\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;45\u0026apos;07.9\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eGlasgow Park\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;36\u0026apos;36.7\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;43\u0026apos;45.5\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eAbby Medical Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;41\u0026apos;48.3\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg; 39\u0026apos; 23.9\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eBrookside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;40\u0026apos;40.0\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;41\u0026apos;48.1\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDupont Envl Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;43\u0026apos;23.6\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;33\u0026apos;43.6\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eNewark Train Station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;40\u0026apos;4.99\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;45\u0026apos;12.12\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eSouthbridge Wilmington\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;43\u0026apos;55.9\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg; 33\u0026apos; 8.9\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eCooch site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;38\u0026apos;47.0\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;44\u0026apos;32.2\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eRiverfront Chase Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e\n \u003cp\u003e39\u0026deg;43\u0026apos;52.2\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;33\u0026apos;57.0\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003eRoute 13 Exit 1 Ramp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e\n \u003cp\u003e39\u0026deg;42\u0026apos;55.0\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;33\u0026apos;27.4\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eYMCA Pulaski Hwy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;36\u0026apos;24.0\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;43\u0026apos;50.6\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003ePorter Road Intersection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;35\u0026apos;13.8\u0026quot;N\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg;44\u0026apos;23.0\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1493%;\"\u003e\n \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eSouthbridge ICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30949%;\"\u003e\n \u003cp\u003eW10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1073%;\"\u003e39\u0026deg;43\u0026apos;35.4\u0026quot;N\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.818%;\"\u003e\n \u003cp\u003e75\u0026deg; 32\u0026apos; 29.7\u0026quot;W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1742%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4417%;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1. UD: University of Delaware; 2. Envl: Environmental; 3. YMCA: Young Men\u0026rsquo;s Christian Association; 4. ICS: Intercontinental service\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2. Soil and water sampling protocols\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo water-saturated soil samples (0-20 cm depth below surface) 3-5 m apart were collected from each of the 13 wetlands for a total of 26 samples. Soil samples were collected using a clean shovel and transferred into air-tight and vacuumed (air removed) Ziplock bags and placed on ice in a cooler. \u0026nbsp;One surface water sample was also collected manually from each wetland using 250 mL acid-cleaned Nalgene bottles and filtered using 0.7-micron GFF. Both soil and water samples were stored in the fridge at 4 \u003csup\u003eo\u003c/sup\u003eC until analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3. DNF and DNRA rates using \u003csup\u003e15\u003c/sup\u003eN assays\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 10% slurry to measure the rates of DNF and DNRA was prepared by homogenizing the soils and mixing with unfiltered surface/ponded water in an acid-washed glass beaker in the laboratory at room temperature (20-25 ℃). Briefly, following the transfer of 8 mL slurries into 12 mL vials (Exetainer, Labco) with 4 mL headspace, the vials were sealed, purged with helium (high purity) for five minutes and shaken at 120 rpm overnight to eliminate any residual oxygen and create anoxic environment. To determine the potential rates of DNF and DNRA, \u003csup\u003e15\u003c/sup\u003eN-NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e tracer (\u003csup\u003e15\u003c/sup\u003eN at. 98%, Cambridge Isotope Laboratories, Inc., USA) were added to each vial to a final concentration of 1.5 mg \u003csup\u003e15\u003c/sup\u003eN L\u003csup\u003e-1\u003c/sup\u003e. Slurry incubations were terminated by adding 250 \u0026mu;L of 50% (w/v) ZnCl\u003csub\u003e2\u003c/sub\u003e after 0 and 3 h. For DNF, at the end of the experiment, vials were kept upside down and stored under water (to prevent leakage of N\u003csub\u003e2\u003c/sub\u003e) until injecting air into the headspace following N\u003csub\u003e2\u003c/sub\u003e analysis. For DNRA, slurries were extracted with 2M KCl to bring soil-bound \u003csup\u003e15\u003c/sup\u003eN-NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e into solution and total \u003csup\u003e15\u003c/sup\u003eN-NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e was quantified via hypobromite conversion of \u003csup\u003e15\u003c/sup\u003eN-NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e to \u003csup\u003e15\u003c/sup\u003eN-N\u003csub\u003e2\u003c/sub\u003e (Rahman et al. 2019c; Risgaard-Petersen et al. 1995; Roberts et al. 2014). The detail of hypobromite method for DNRA was previously described (Roberts et al. 2014; Rahman et al. 2019c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4. Sample analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical analysis:\u003c/strong\u003e Soil texture (% sand, silt and clay) was determined by treating soil with a dispersant followed by the hydrometer method (Gee and Bauder, 1986). Soil micro- (\u0026lt;0.25mm but \u0026gt;0.053mm) and macroaggregates (\u0026gt;0.25 mm) were determined using the standard wet aggregate stability test (Kemper and Rosenau 1986).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChemical analysis:\u003c/strong\u003e pH, and electrical conductivity (EC) of water samples were determined using standard calibrated meters (Accumet, Hobo, and YSI). Total carbon and nitrogen for soil and water samples were determined via combustion using an Elementar TC/TN analyzer. For soils, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e were determined by KCl extraction followed by colorimetric analysis of the extract. For water, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e were determined colorimetrically using a Bran \u0026amp; Luebbe AutoAnalyzer 3 (Bran \u0026amp; Luebbe, Buffalo Grove, IL) while Cl was determined on a calibrated Hach DR3900 meter. \u0026nbsp; Soil Na\u003csup\u003e+\u003c/sup\u003e were extracted using EPA 3051 and Mehlich-3 (M3) methods (Sikora and Moore 2014). The former provides the total content while the latter only provides the \u0026ldquo;bioavailable\u0026rdquo; fractions. Similarly bioavailable Fe was determined via M3 (M3-Fe) and amorphous iron (A-Fe) for dried and ground up soil samples was extracted using acid ammonium oxalate solution at pH 3 (Chao and Zhou 1983) and then analyzed using ICP Spectrometer (iCAP 7000 Series).\u003c/p\u003e\n\u003cp\u003ePotential rates of DNF and DNRA for soil slurries were determined according to Risgaard-Petersen 2004; Meyer et al. 2005; Bernard et al. 2015 (details below). The N\u003csub\u003e2\u003c/sub\u003e from the headspace was analyzed using an Elementar GreenHouse Gas (GHG) analyzer interfaced with an Elementar Precision isotope-ratio mass spectrometer (IRMS)\u003cem\u003e.\u003c/em\u003e Rates of DNF were calculated using the linear production of \u003csup\u003e29\u003c/sup\u003eN\u003csub\u003e2\u003c/sub\u003e and \u003csup\u003e30\u003c/sup\u003eN\u003csub\u003e2\u003c/sub\u003e over time resulting from the consumption of \u003csup\u003e15\u003c/sup\u003eN-NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e (Dalsgaard et al. 2000; Nielsen 1992). Rates of DNRA were calculated from the linear production of \u003csup\u003e15\u003c/sup\u003eN-NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e over time. To test the recovery of \u003csup\u003e15\u003c/sup\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, a series of standards were prepared in the same matrix. The recovery ranged from 102 \u0026plusmn; 2 to 105 \u0026plusmn; 3%\u0026nbsp;for all standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhospholipid Fatty Acid (PLFA) analysis:\u003c/strong\u003e PLFA, which provides an estimate of the live microbial and bacterial biomass were measured following Frosteg\u0026aring;rd et al. (2011) at the Regen Ag Lab in Nebraska.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5. Functional genes for DNF (nosZ) and DNRA (nrfA)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReal-time PCR (qPCR) was used to quantify the DNF and DNRA genes for each sample (performed in triplicate). Briefly, the nitrite reductase genes (\u003cem\u003enrfA\u003c/em\u003e) were amplified using primers \u003cem\u003enrfA\u003c/em\u003eF2awMOD and nrfAR1MOD (Cannon et al. 2019), whereas the nitrous-oxide reductase genes (\u003cem\u003enosZ\u003c/em\u003e) were amplified using primers nosF (Kloos et al. 2001) and \u003cem\u003enos\u003c/em\u003eZR1622 (Throb\u0026auml;ck et al. 2004). Using 1X PowerUp Sybr Green Master Mix (Applied Biosystems), 3 \u0026micro;M of each primer, and 0.5 mg mL\u003csup\u003e-1\u003c/sup\u003e BSA (Invitrogen), the \u003cem\u003enrfA\u003c/em\u003e genes were amplified in 20 \u0026micro;L reactions. Following an initial denaturation of 50 \u0026deg;C for two minutes and 95 \u0026deg;C for ten minutes, the QuantStudio3 cycling conditions consisted of 45 cycles of 95 \u0026deg;C for 15 seconds, 56 \u0026deg;C for 30 seconds, and 72 \u0026deg;C for 45 seconds. Similarly , the \u003cem\u003enosZ\u003c/em\u003e genes were amplified in 20 \u0026micro;L reactions, with 1X PowerUp Sybr Green Master Mix, 0.5 \u0026micro;M of each primer, and 0.5 mg mL\u003csup\u003e-1\u003c/sup\u003e BSA (detailed description of the cycling conditions can be found in Sienkiewicz et al. (2020). For each run, ten-fold dilution series were produced from corresponding plasmids and used as standards. The copy number per gram of soil was computed considering the amplicon size and plasmid DNA concentration used for the standard curves (Einen et al. 2008; Kan 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.6. Data and statistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExchangeable sodium percentage (ESP) was computed based on Na\u003csup\u003e+\u003c/sup\u003e concentrations and CEC of soil to assess the sodicity of the soils (Weil et al. 2017). JMP Pro 17 was used for statistical analysis. Based on a visual assessment of the histogram of M3-Na\u003csup\u003e+\u003c/sup\u003e concentrations (Figure 3a), sites were classified into \u0026ldquo;low\u0026rdquo; (Na\u003csup\u003e+\u003c/sup\u003e \u0026lt; 70 mg kg\u003csup\u003e-1\u003c/sup\u003e; R1-R3 and W1-W4), \u0026ldquo;medium\u0026rdquo; (70 mg kg\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e\u0026lt; Na\u003csup\u003e+\u003c/sup\u003e \u0026lt; 150 mg kg\u003csup\u003e-1\u003c/sup\u003e; W5-W7) and \u0026ldquo;high\u0026rdquo; (Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; 150 mg kg\u003csup\u003e-1\u003c/sup\u003e; W8-W10) categories (Table 1). We used the M3-Na\u003csup\u003e+\u003c/sup\u003e value for categorization (as opposed to total Na\u003csup\u003e+\u003c/sup\u003e) since it is more bioavailable and thus a better representative of the Na\u003csup\u003e+\u003c/sup\u003e that would affect microbial processes such as DNF and DNRA. Differences in N process rates (DNF and DNRA) and other soil metrics were then assessed for these wetland categories. Significant differences for various categories were determined using one-way ANOVA followed by t tests between individual categories. The test result\u0026apos;s statistical significance was established at the \u0026alpha; = 0.05 level for the Tukey-Kramer HSD post-hoc test.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Sodium concentrations, soil sodicity and wetland salt categories\u003c/h2\u003e\u003cp\u003eTotal and M3-Na\u003csup\u003e+\u003c/sup\u003e concentrations varied across the sites with M3 values typically ranging from 23 to 89% of the total Na\u003csup\u003e+\u003c/sup\u003e concentrations (28\u0026ndash;16501 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). ESP values, which is an indication of soil sodicity, varied from 0.8 to 41%. EC that indicates soil salinity ranged from 0.03 to 23.1 dS/m in these wetland soils (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Soil EC increased with increasing M3-Na\u003csup\u003e+\u003c/sup\u003e concentrations and there was a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between them. Dissolved Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e in ponded surface water ranged between 7\u0026ndash;208 and 6\u0026ndash;215 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Water EC ranged from 0.1 to 1.3 dS/m whereas pH ranged from 6.2 to 7.7. Wetland soil revealed a gradient in Na\u003csup\u003e+\u003c/sup\u003e concentrations across the three categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and while there was no significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the low and medium groups, the high category was significantly greater than the other two (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Variations in DNF and DNRA rates in relation to Na\u003csup\u003e+\u003c/sup\u003e concentrations\u003c/h2\u003e\u003cp\u003eDNF rates varied substantially (0.8\u0026ndash;83 \u0026micro;g N L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e slurry h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) across the sampled wetlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Wetlands with lower Na\u003csup\u003e+\u003c/sup\u003e content (e.g., sites R1-3 and W1, W2 and W4) showed comparatively higher rates except W3, which is a newly constructed wetland, whereas wetlands with higher Na\u003csup\u003e+\u003c/sup\u003e concentrations (e.g., W8-10) typically displayed lower rates. However, no significant correlations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were observed between DNF rates and Na\u003csup\u003e+\u003c/sup\u003e concentrations. DNRA rates also varied considerably (0.2\u0026ndash;24 \u0026micro;g N L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e slurry h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the highest DNRA rates were observed from wetlands with low Na\u003csup\u003e+\u003c/sup\u003e concentrations (e.g., W2 and W4). DNRA rates were generally lower in wetlands with higher Na\u003csup\u003e+\u003c/sup\u003e contents (e.g., W8-10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). However, similar to DNF, DNRA also did not show any significant correlation with Na\u003csup\u003e+\u003c/sup\u003e concentrations in wetland soils.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, DNF was 2\u0026ndash;110 times higher than DNRA and only one (W10-2) out of 26 sites showed higher DNRA than DNF rates (DNF/DNRA\u0026thinsp;\u0026lt;\u0026thinsp;1 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), indicating that DNF dominated the nitrate removal pathway in these wetlands. When assessed by the Na\u003csup\u003e+\u003c/sup\u003e categories, DNF did not differ significantly for the low and medium categories, but DNF was significantly lower for the highest Na\u003csup\u003e+\u003c/sup\u003e category (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In contrast, no significant difference was observed for DNRA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Soil aggregation and bioavailable fractions of iron\u003c/h2\u003e\u003cp\u003eSoil macroaggregates accounted for 13\u0026ndash;88% of the total soil aggregates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), while microaggregates made up \u0026lt;\u0026thinsp;1 to 39% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and these two were inversely related (r\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Road salt had no significant effects on soil microaggregation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but macroaggregates for the highest salt category were significantly less than those for the medium Na\u003csup\u003e+\u003c/sup\u003e category. Soil M3-Fe and A-Fe concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D) were elevated for the medium Na\u003csup\u003e+\u003c/sup\u003e category, but significant differences among the categories varied. M3-Fe was significantly lower for the high versus medium category, while for A-Fe the low category was significantly less than the medium Na\u003csup\u003e+\u003c/sup\u003e category.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Soil sorbed and water-soluble nitrogen species and OC\u003c/h2\u003e\u003cp\u003eContrary to our expectations, soil and dissolved concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e did not yield any significant differences among the low, medium, and high Na\u003csup\u003e+\u003c/sup\u003e categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-D). The only pattern, although insignificant, was that the highest salt category had the lowest mean NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and highest mean NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations. In comparison, soil TOC and TN values were significantly greater for the medium versus the low and the high Na\u003csup\u003e+\u003c/sup\u003e categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5. PLFA determined total living microbial and bacterial biomass\u003c/h2\u003e\u003cp\u003ePLFA derived total living microbial and bacterial biomass did not reveal any significant differences among the Na\u003csup\u003e+\u003c/sup\u003e categories. The variability of both metrics for the medium category was however less than that observed for the other two categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6. nosZ (DNF) and nrfA (DNRA) microbial functional genes\u003c/h2\u003e\u003cp\u003eLike DNF (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the abundance of \u003cem\u003enosZ\u003c/em\u003e gene initially increased from low to medium but then decreased for the high category wetlands though these variations were non-significant. The abundance of \u003cem\u003enrfA\u003c/em\u003e gene, unlike DNRA process rate, initially increased non-significantly and then decreased with salt gradient. The \u003cem\u003enosZ/nrfA\u003c/em\u003e gene proportion increased gradually with salt gradient, however, these changes were also not significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study revealed a clear gradient from low to high road salt Na\u003csup\u003e+\u003c/sup\u003e concentrations across the sampled wetland soils. The effects of soil Na\u003csup\u003e+\u003c/sup\u003e on nitrate reduction process rates, nutrients (TN, TOC, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), and microbial metrics were, however, mixed with only the highest salt category potentially indicating any detrimental effects of road salt. We elaborate on these responses in the discussion below. Understanding how excess Na\u003csup\u003e+\u003c/sup\u003e could be affecting the wetland soil health is critical to mitigating the impacts of road salt.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Magnitude of wetland soil Na\u003csup\u003e+\u003c/sup\u003e concentrations and comparisons against other studies\u003c/h2\u003e\u003cp\u003eTotal soil Na\u003csup\u003e+\u003c/sup\u003e concentrations in this study ranged from 28 to 16501 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas the M3-Na\u003csup\u003e+\u003c/sup\u003e values ranged between 11-1082 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The M3-Na\u003csup\u003e+\u003c/sup\u003e concentrations are more bioavailable and hence have more potential to impact the biogeochemical processes in soils. Therefore, here we compare our M3-Na\u003csup\u003e+\u003c/sup\u003e values against the ammonium acetate extractable and readily bioavailable Na\u003csup\u003e+\u003c/sup\u003e values reported from previous studies. For example, the range of M3-Na\u003csup\u003e+\u003c/sup\u003e values in this study are comparable to Na\u003csup\u003e+\u003c/sup\u003e values observed for road salt impacted exurban forested wetlands in Coventry, Connecticut, USA (37-1226 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Walker et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Na\u003csup\u003e+\u003c/sup\u003e concentrations in soils of our low Na\u003csup\u003e+\u003c/sup\u003e category wetlands (11\u0026ndash;67 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were similar to those reported by Robinson et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for a rural site in Wildwood, Missouri, USA and an urban site in Webster Groves, Missouri, USA. The rural site concentrations were 63 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 1 m from the road and 0 to 37 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e between 2.5 m and 15.8 m from the road, while the urban site values were71 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 1 m from the road; and 6\u0026ndash;30 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e further from the road. Similarly, roadside soils in Massachusetts had Na\u003csup\u003e+\u003c/sup\u003e from road salt ranging between 22 to 309 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at a distance of 1.5 meters from the road (Bryson and Barker \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Overall, these comparisons suggest that Na\u003csup\u003e+\u003c/sup\u003e concentrations measured at our wetland locations are generally in the same range of values reported for other locations in the USA. Soil sodicity quantifies the concentration of Na\u003csup\u003e+\u003c/sup\u003e in relation to other cations under saline conditions (Rengasamy and Olsson \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Wong et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Steele and Aitkenhead-Peterson \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Based on soil ESP (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), the low and medium Na\u003csup\u003e+\u003c/sup\u003e category wetlands in this study are classified as non-sodic (ESP\u0026thinsp;\u0026lt;\u0026thinsp;15%), whereas the high category wetlands are classified as sodic (ESP\u0026thinsp;\u0026gt;\u0026thinsp;15%) (Northcote and Srene \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Sparks \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Osman \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, based on soil EC, most wetland soils in this study except for W10-1 are non-saline (EC\u0026thinsp;\u0026lt;\u0026thinsp;2 dS/m) (Sparks \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). High concentrations of Na\u003csup\u003e+\u003c/sup\u003e can have detrimental environmental implications. For example, a previous study by Czerniawska-Kusza et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) reported that protozoa were toxically affected by soils containing 260 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Na\u003csup\u003e+\u003c/sup\u003e, while manifestations of salt injury, such as chlorosis and necrosis of the leaf blade margins, occurred at soil concentrations of 132 mg Na\u003csup\u003e+\u003c/sup\u003e kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and became more severe at 260 mg Na\u003csup\u003e+\u003c/sup\u003e kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e due to widespread necrosis and defoliation. Thus, an average M3-Na\u003csup\u003e+\u003c/sup\u003e concentration of 33 and 110 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for low and medium category wetlands in our study, respectively, indicate that Na\u003csup\u003e+\u003c/sup\u003e levels in these wetlands are non-hazardous. However, an average Na\u003csup\u003e+\u003c/sup\u003e concentration of 383 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the high Na\u003csup\u003e+\u003c/sup\u003e category wetlands suggest that Na\u003csup\u003e+\u003c/sup\u003e level for this category is above the environmental thresholds and thus can potentially impact both wetland plants and microbes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Effects on soil aggregation and Fe species\u003c/h2\u003e\u003cp\u003eIn contrast to medium Na\u003csup\u003e+\u003c/sup\u003e category, the % of soil macroaggregates for the high category were significantly lower, likely due to the higher sodicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Adsorbed Na\u003csup\u003e+\u003c/sup\u003e on soil particles tends to physically separate them due to its comparatively large size, single electrical charge, and hydration state (Warrence et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Therefore, the main physical process related to elevated Na\u003csup\u003e+\u003c/sup\u003e concentrations in soils is the dispersion of soil particles (Norrstr\u0026ouml;m and Bergstedt \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bauder and Brock \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Walker et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, the elevated Na\u003csup\u003e+\u003c/sup\u003e in soils of high category wetlands is likely decreasing macroaggregation due to dispersive effects of Na\u003csup\u003e+\u003c/sup\u003e. We also observed that concentrations of M3-Fe were significantly lower for the high Na\u003csup\u003e+\u003c/sup\u003e category wetlands. Elevated soil Na\u003csup\u003e+\u003c/sup\u003e concentrations can displace heavy metals such as Fe from soil exchange sites reducing their concentrations in the soil (Norrstr\u0026ouml;m and Bergstedt \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; B\u0026auml;ckstr\u0026ouml;m et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Baldwin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Willmert et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, it is very likely that elevated Na\u003csup\u003e+\u003c/sup\u003e concentrations in the highest category wetland resulted in the low M3-Fe values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Effects on soil N and OC\u003c/h2\u003e\u003cp\u003eSimilar to the effects on Fe, excess Na\u003csup\u003e+\u003c/sup\u003e can result in the cationic displacement of sorbed NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e from soil exchange sites with subsequent increase of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e in solution phase (Herbert et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Work by Weston et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Ardon et al. (2013) indicates that NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e release occurs at ~\u0026thinsp;salinity levels of 3 parts per thousand or 3000 ppm and increases with salinity. Nevertheless, we did not observe any significant differences in solid and dissolved phase NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e across various Na\u003csup\u003e+\u003c/sup\u003e categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Moderate salinity levels (EC\u0026thinsp;\u0026lt;\u0026thinsp;16 dS/m) have been reported to stimulate nitrification and increase NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, while high levels (EC\u0026thinsp;\u0026gt;\u0026thinsp;16.5 dS/m) can inhibit nitrification and reduce NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (Ard\u0026oacute;n et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our EC levels were much lower than the thresholds mentioned above. Thus, it is very likely that the Na\u003csup\u003e+\u003c/sup\u003e concentrations in our wetland soils were not high enough to result in change in N concentrations across the Na\u003csup\u003e+\u003c/sup\u003e categories, or any released ions were uptake by wetland vegetation.\u003c/p\u003e\u003cp\u003eRoad salt salinization in wetlands can have a complex effect on soil OC and N pools because of the multiple positive and negative feedback (Herbert et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Stagnant wetland hydrology and anaerobic conditions can suppress decomposition and thus increase soil OC and N (Neubauer \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On the other hand, salt associated osmotic effects can reduce plant growth and biomass in salt-affected wetlands (Kinsman-Costello et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mazhar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some studies have revealed enhanced soil OC and N mineralization due to salts, whereas others have not documented any effects (Marton et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Setia et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to one of the few long-term studies, soils with higher soil organic matter stocks seemed to lose more carbon in response to salinization than do soils with lower carbon content (Marton et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We found a mixed pattern for SOC in our study with an increase in concentrations from low to medium salt category followed by a decline for the highest category. We speculate that the decline for the highest salt category could be associated with salt effects (potentially increased C mineralization), but additional future tests will be required to make a conclusive assessment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Microbial biomass and N functional genes\u003c/h2\u003e\u003cp\u003eBased on our PLFA and qPCR results, we did not observe any significant changes in living microbial biomass and the abundance of microbial functional genes for the Na\u003csup\u003e+\u003c/sup\u003e categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Previous studies have reported a negative correlation between salinity and microbial abundance (Simachew et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Morina and Franklin \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Freshwater microbial communities revealed a high degree of resistance to low salinity (3 ppt) (Berga et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), but when exposed to high salinity conditions (14 ppt), the microbial community rapidly restructured (Morina and Franklin \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Elsewhere, elevated NaCl concentrations ( 10,000\u0026ndash;30,000 mg/L) in a sludge system decreased the diversity of microbial communities (Pang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies suggest that the impact on microbial communities could vary as a function of salt concentrations. Given the lack of significant microbial response for our study, we speculate that the Na\u003csup\u003e+\u003c/sup\u003e concentrations in our study were likely not high enough to affect the microbial population and functional genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.5 DNF and DNRA process rates\u003c/h2\u003e\u003cp\u003eRates of DNF and DNRA observed in this study are comparable to those reported for riparian ecosystems (DNF: 6.7\u0026ndash;79 and DNRA: 5.2\u0026thinsp;\u0026minus;\u0026thinsp;37.6 \u003cem\u003e\u0026micro;\u003c/em\u003eg N L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e slurry h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in the mid-Atlantic USA (Rahman et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), urban wetlands (DNF: 0.12\u0026ndash;7.39 and DNRA: 0.13\u0026ndash;1.94 \u003cem\u003e\u0026micro;\u003c/em\u003eg N g dry soil\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in Phoenix, Arizona, USA (Handler et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but an order lower than constructed freshwater urban wetlands (DNF: 90\u0026ndash;405 and DNRA: 9-165 \u003cem\u003e\u0026micro;\u003c/em\u003eg N L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e slurry h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in Melbourne, Australia (Rahman et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Our rates, however, are one or two orders higher than those observed for freshwater wetland soils from Taskinas Creek in Virginia, USA (DNF: 0.3\u0026ndash;2.25 and DNRA: 0.03\u0026ndash;0.75 \u003cem\u003e\u0026micro;\u003c/em\u003eg N g OM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) under varying saline conditions (0\u0026ndash;35 ppt) (Morina and Franklin \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and wetland sediments collected from the Nansha Wetland Park in the Pearl River Estuary in China (DNF: 0.03\u0026ndash;0.21 and DNRA: 0.01\u0026ndash;0.1 \u003cem\u003e\u0026micro;\u003c/em\u003eg N g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and experimented with artificial seawater with a salinity ranging from 0 to 35 ppt (Zheng et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, overall, the reductive N process rates measured in this study are greater than sites substantially impacted by salinization.\u003c/p\u003e\u003cp\u003eDNF and DNRA process rates are dictated by physicochemical conditions, substrate availability, and microbial abundance and diversity, all of which can be affected by salt inputs as highlighted above. Reduced soil oxygen diffusion associated with Na\u003csup\u003e+\u003c/sup\u003e dispersed soils can enhance hypoxia/anoxia and thus encourage DNF and DNRA (Herbert et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). DNF and macroaggregates followed similar trends across our Na\u003csup\u003e+\u003c/sup\u003e categories, with a significant drop in both from the medium to the high category. While a cause-and-effect relationship is difficult to establish based on existing data, it is possible that Na\u003csup\u003e+\u003c/sup\u003e concentrations for the highest category are affecting both responses with possible feedback effects between them. For both DNF and DNRA, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e is the electron acceptor, whereas OC and Fe can serve as the electron donors (Pandey et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, concentrations of these substrates could influence the reductive process rates (Ballantine et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Robertson et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Concentrations for NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e across the salt categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) clearly did not follow the DNF trend. On the contrary, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations displayed a highly variable and non-significant increase in both soil and surface waters from the low to the high category (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB and D). Thus, measured NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations were likely not a factor for the significant decline in DNF for the highest salt category. In contrast, both soil M3-Fe (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) and OC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE) followed a similar pattern as DNF with a significant drop in both variables for the highest salt category. Thus, we argue that these responses are likely influenced by highest Na\u003csup\u003e+\u003c/sup\u003e concentrations, and these substrate concentrations could potentially also be influencing the DNF process rates for the highest salt category.\u003c/p\u003e\u003cp\u003eFinally, DNF and DNRA process rates are also influenced by the microbial communities. For example, Pan et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that DNF was the primary nitrogen removal process in coastal farming drainage ditches, but it decreased significantly by about 60% mainly due to a substantial decline in the DNF functional genes abundance under highly saline conditions (15 ppt). In addition, salinity was found to result in incomplete DNF by affecting the abundance of denitrifying \u003cem\u003enosZ\u003c/em\u003e functional genes (Zaghmouri et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the overall patterns in our microbial/bacterial biomass (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) and \u003cem\u003enosZ\u003c/em\u003e genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) across the salt categories were similar to DNF rates, there were no significant differences among the microbial metrics. This suggests that microbial biomass and genes were likely not an important factor shaping the salt-driven changes in DNF, especially at the highest salt level. It is also possible that although elevated Na\u003csup\u003e+\u003c/sup\u003e concentrations from road salt did not significantly alter the abundance of denitrifying functional genes, it might adversely affect the necessary functions of the genes resulting in lower DNF. Previous studies have demonstrated that increased salinity can reduce DNF (Seo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lancaster et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as a result of osmotic stress (Yan et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and ion-specific toxicity (Mac\u0026ecirc;do et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to denitrifying bacteria (Kinsman-Costello et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, DNRA increased with increasing salinity and was more likely to be preferred over DNF for a study in oligohaline estuarine sediments in Massachusetts, USA (Giblin et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, DNRA was found positively correlated with salinity in Texas estuaries (Gardner et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, Zheng et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that DNRA decreased as salinity increased from 0 to 35 ppt, while other research revealed that DNRA bacterial activity and abundances did not vary in response to salinity (4\u0026ndash;22 ppt) in the Yellow River Estuary in China (Bu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and across the Yellow River Delta wetland (0.22\u0026ndash;1.24 psu) in China (Zhou et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These discrepancies might be due to changes in some DNRA genera caused by salinity-driven alterations to physicochemical factors (Pang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Most of these studies were in saltwater coastal environments and had salinity levels that substantially exceeded our freshwater values. Therefore, we speculate that the highest salt levels in this study were likely still not sufficient to impact the DNRA process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions and environmental implications","content":"\u003cp\u003eIn this study, we investigated how Na\u003csup\u003e+\u003c/sup\u003e in road salt impacts the soil physical, chemical, and biological properties of roadside freshwater urban wetlands and thus affects their capacity to process nitrate-nitrogen through DNF and DNRA reductive processes. To our knowledge, this is the first study that has evaluated the effects of Na\u003csup\u003e+\u003c/sup\u003e on DNF and DNRA. Overall, our study yielded mixed results, with the greatest impacts of Na\u003csup\u003e+\u003c/sup\u003e for the high category (Na\u003csup\u003e+\u003c/sup\u003e \u0026gt;150 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), but little or no effects for the low (\u0026lt;\u0026thinsp;70 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and medium (70 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026lt; Na\u003csup\u003e+\u003c/sup\u003e \u0026lt; 150 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) categories. A significant drop in soil macroaggregates, bioavailable Fe, and DNF rate were observed for the high Na\u003csup\u003e+\u003c/sup\u003e category. On the other hand, we did not find any significant impacts on wetland DNRA rate, microaggregate values, concentrations of soil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, TOC, TN, and microbial metrics (biomass and \u003cem\u003enosZ\u003c/em\u003e and \u003cem\u003enrfA\u003c/em\u003e functional genes). On the contrary, and in conflict with our hypotheses, some of the parameter/process values increased insignificantly from the low to medium category. These observations confirm that salt effects on urban/suburban wetlands can be highly complex and variable across ecosystem characteristics and processes. Salt effects may not necessarily gradually and monotonically increase but may be subject to threshold and non-linear behavior that may vary with ecosystem processes and characteristics. Alternatively, some processes or characteristics may be more sensitive than others to salt inputs. Understanding and quantifying these thresholds or sensitivity for N and other biogeochemical parameters and cycles is critical for effective salt management and ecosystem protection.\u003c/p\u003e\u003cp\u003eThe suppression of DNF due to road salts is however a concern given that the wetlands are important natural filters in urban/suburban landscapes and provide a valuable ecosystem service by removing excess N from storm water runoff. Given the increasing use of fertilizers in urban/suburban settings (e.g., use of fertilizers on golf courses and homeowner lawns) loss of this N buffering potential would be particularly detrimental to our environment. Furthermore, urban stormwater wetlands are especially vulnerable because they are subject to: (a) frequent hydrologic stagnation and hypoxia/anoxia (Kinsman-Costello et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); (b) peak flows and salt inputs associated with high impervious surfaces and extreme precipitation/climate events (Inamdar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and (c) high/extreme summer temperatures and urban heat island effects that yield warmer surface runoff (Somers et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). All of these effects could, individually or in concert, further amplify the negative effects of road salt salinization for urban and suburban wetlands. Therefore, reducing the application of road salts or identifying and applying other viable alternatives should be actively pursued to protect the water quality filtering services of urban/suburban wetlands.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank DuPont Environmental Education Centre for permission to collect soil and water samples from their site. We also thank Karen Gartley, and Joe Paller for the water quality analysis performed at the University of Delaware Soils Lab and the Regen Ag Lab for the soils analysis. We also appreciate Laura Borecki of the Stroud Water Research Centre for her assistance.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication was developed under Assistance Agreement No. \u0026nbsp;CD-95340701 awarded by the United States Environmental Protection Agency (USEPA). The views expressed in this document are solely those of the authors and EPA does not endorse any products or commercial services mentioned in this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have \u003cem\u003eno known competing financial interests\u003c/em\u003e \u003cem\u003eor personal relationships\u003c/em\u003e that could have appeared to influence the work reported in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eMd. Moklesur Rahman:\u003c/strong\u003e First \u0026amp; corresponding author who designed the study plan, collected samples, implemented the proper methodology, did sample and data analysis, and wrote the initial draft of the manuscript,\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eMarc Peipoch\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e First co-author who helped in sample analysis and acquisition of data, data analysis and reviewed the manuscript,\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eJinjun Kan\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Assisted in thorough review of the manuscript,\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026nbsp;Eric Moore:\u0026nbsp;\u003c/strong\u003eAssisted in site selection, sample collection, experiments and sample analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003eMatthew Sena\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eAssisted in experiments and reviewing the manuscript\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"6\"\u003e\n \u003cli\u003e\u003cstrong\u003eMukta Kantak:\u003c/strong\u003e Assisted in sample collection, experiments and sample analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003e\u003cstrong\u003eSuparn Sharma:\u003c/strong\u003e Assisted in sample collection and sample analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"8\"\u003e\n \u003cli\u003e\u003cstrong\u003eChander Lekha:\u003c/strong\u003e Assisted in sample analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"9\"\u003e\n \u003cli\u003e\u003cstrong\u003eShreeram P lnamdar:\u003c/strong\u003e Leading in conceptualizing and designing the study, funding acquisition, project administration, supervision, validation, analysis and interpretation of data, reviewing the manuscript critically for important intellectual content. Final approval of the version to be submitted.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArd\u0026oacute;n M, Morse JL, Colman BP, Bernhardt ES (2013) Drought-induced saltwater incursion leads to increased wetland nitrogen export. Glob Chang Biol 19:2976\u0026ndash;2985. https://doi.org/10.1111/gcb.12287\u003c/li\u003e\n \u003cli\u003eB\u0026auml;ckstr\u0026ouml;m M, Karlsson S, B\u0026auml;ckman L, et al (2004) Mobilisation of heavy metals by deicing salts in a roadside environment. Water research 38:720\u0026ndash;732\u003c/li\u003e\n \u003cli\u003eBaldwin DS, Rees GN, Mitchell AM, et al (2006) The short-term effects of salinization on anaerobic nutrient cycling and microbial community structure in sediment from a freshwater wetland. 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In: Osman KT (ed) Management of Soil Problems. Springer International Publishing, Cham, pp 255\u0026ndash;298\u003c/li\u003e\n \u003cli\u003ePan Y, She D, Shi Z, et al (2023) Salinity and high pH reduce denitrification rates by inhibiting denitrifying gene abundance in a saline-alkali soil. Sci Rep 13:2155. https://doi.org/10.1038/s41598-023-29311-7\u003c/li\u003e\n \u003cli\u003ePandey CB, Kumar U, Kaviraj M, et al (2020) DNRA: a short-circuit in biological N-cycling to conserve nitrogen in terrestrial ecosystems. Science of the Total Environment 738:139710\u003c/li\u003e\n \u003cli\u003ePang H, Xin X, He J, et al (2020) Effect of NaCl Concentration on Microbiological Properties in NaCl Assistant Anaerobic Fermentation: Hydrolase Activity and Microbial Community Distribution. 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Journal of Environmental Sciences 106:39\u0026ndash;46. https://doi.org/10.1016/j.jes.2021.01.015\u003c/li\u003e\n\u003c/ol\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wetland, road salt, sodium, nitrogen, denitrification, DNRA","lastPublishedDoi":"10.21203/rs.3.rs-6543627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6543627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFreshwater urban wetlands are important ecosystems that can naturally filter and remove excess nitrogen (N) through the process of denitrification (DNF). However, anthropogenic inputs such as road salt application may affect the N removal capacity of urban wetlands by affecting the relative rates of DNF and another competing reductive process that retains N \u0026ndash; dissimilatory nitrate reduction to ammonium (DNRA). Here, we assessed 13 roadside wetlands in urban/suburban areas of Delaware, USA to determine the effects of road salt sodium (Na\u003csup\u003e+\u003c/sup\u003e) on soil physical, chemical, and biological properties and the rates of DNF and DNRA. Based on soil Na\u003csup\u003e+\u003c/sup\u003e concentrations, wetlands were grouped into three categories: low (Na\u003csup\u003e+\u003c/sup\u003e \u0026lt; 70 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), medium (70 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026lt; Na\u003csup\u003e+\u003c/sup\u003e \u0026lt; 150 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and high (Na\u003csup\u003e+\u003c/sup\u003e \u0026gt;150 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Rates of DNF and DNRA ranged from 0.8\u0026ndash;83 and 0.2\u0026ndash;24 \u0026micro;g N L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e slurry h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. DNF was significantly lower in high Na\u003csup\u003e+\u003c/sup\u003e category wetlands whereas DNRA did not show any significant differences. Similarly, macroaggregates and bioavailable Fe were lowest in the high Na\u003csup\u003e+\u003c/sup\u003e category, whereas concentrations of soil NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, TOC, TN, and microbial metrics (biomass and \u003cem\u003enosZ\u003c/em\u003e and \u003cem\u003enrfA\u003c/em\u003e functional genes) did not reveal any consistent patterns. These findings imply that road salt Na\u003csup\u003e+\u003c/sup\u003e input exhibited mixed effects on soil properties in these wetlands. Overall, elevated Na\u003csup\u003e+\u003c/sup\u003e from road salt could undermine the N removal capacity of the roadside urban wetlands. Therefore, strategies should be implemented to reduce the application of road salt or identify effective alternatives.\u003c/p\u003e","manuscriptTitle":"Effects of road salt on nitrogen removal by freshwater urban wetlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 08:54:16","doi":"10.21203/rs.3.rs-6543627/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-07T14:05:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-06T06:38:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2025-04-28T19:45:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T08:27:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2025-04-28T00:05:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1d92eb80-d409-4dd1-a1db-aad4caf1234b","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:01:28+00:00","versionOfRecord":{"articleIdentity":"rs-6543627","link":"https://doi.org/10.1007/s13157-025-02026-3","journal":{"identity":"wetlands","isVorOnly":false,"title":"Wetlands"},"publishedOn":"2026-01-20 15:58:19","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-07-11 08:54:16","video":"","vorDoi":"10.1007/s13157-025-02026-3","vorDoiUrl":"https://doi.org/10.1007/s13157-025-02026-3","workflowStages":[]},"version":"v1","identity":"rs-6543627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6543627","identity":"rs-6543627","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}