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1. Spatial and Seasonal Variations of Disinfection Byproduct (DBP) Precursors in Relation with Total Organic Carbon (TOC)

   https://doi.org/10.1061/9780784483466.058  

   Ejjada, M. ; Gerrity, D. ; Marti, E.J. ( May 2021 , World Environmental and Water Resources Congress 2021)

   Disinfection is an essential process for both potable water and wastewater treatment plants. However, disinfection byproducts (DBPs) like trihalomethanes (THMs), haloacetonitriles (HANs), and nitrosamines (NOAs) are formed when organic matter precursors react with disinfectants such as chlorine, chloramine, and ozone. Formation of DBPs is strongly associated with the type of water source, type of disinfectant, and organic matter concentration, which can have seasonal variation. In this study, water samples were collected from 20 different intra-watershed locations, which included urban runoff (with and without the influence of unsheltered homeless populations), wastewater effluent discharges, and a large, terminal reservoir that serves as the local drinking water source. Samples were collected on dry and rainy days, which represent seasonal samples. DBP formation potential (FP) tests were conducted at consistent pH, contact time, and temperature. THMs, NOAs, and HANs were analyzed by gas chromatography-mass spectrometry (GC-MS). The FP tests performed on these water samples revealed that chlorine formed the highest THM concentrations, while THM concentrations were low for the ozone FP test as expected. Chloramine produced the greatest HAN concentrations, with dichloroacetonitrile representing the highest concentration. With respect to sample type, more DBPs were formed at the non-wastewater-impacted runoff sites as compared to the wastewater effluent discharge sites. With respect to TOC levels, rain event samples for all locations had higher TOC concentrations compared to dry sampling days. Similarly, rain event samples showed increased DBP formation; a significant amount of precursors for THMs was found in runoff waters that were influenced by wastewater effluent discharges and unsheltered homeless locations (concentration of total THMs for chlorine FP test was >200 μg/L). Therefore, urban runoff waters should be considered as potential sources of DBP precursors to drinking water source waters, and runoff water is prone to seasonal variation. 

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2. Phytoplankton Drivers of Dissolved Organic Material Production in Colorado Reservoirs and the Formation of Disinfection By-Products

   https://doi.org/10.3389/fenvs.2021.673627  

   Khan, Alia L. ; Sokol, Eric R. ; McKnight, Diane M. ; Saunders, James F. ; Hohner, Amanda K. ; Summers, R. Scott ( July 2021 , Frontiers in Environmental Science)

   null (Ed.)

   Previous studies have shown that algal-derived dissolved organic matter (DOM) has a strong influence on the formation of disinfection byproducts (DBPs) during the treatment of drinking water. In the summer of 2010, we evaluated the role of nitrogen and phosphorus loading and phytoplankton abundance as drivers of the concentrations and quality of DOM and the associated DBP formation in 30 reservoirs in the mountains and plains of the State of Colorado. Optical properties such as Specific Ultraviolet Absorbance at 254 nm (SUVA 254 ) and fluorescence spectroscopy were used to characterize DOM quality. Nutrient concentrations such as total nitrogen were also assessed and were associated with high concentrations of chlorophyll a (Chl-a). In turn, high total organic carbon (TOC) concentrations were associated with high concentrations of Chl-a, and the DOM in these reservoirs had a fluorescence signature indicative of contributions from phytoplankton growth. The reservoirs with TOC concentrations above 4 mgC/L were predominantly located in the plains and many are impacted by agricultural runoff and wastewater discharges, rather than in the mountains and are characterized by warm water conditions and shallow depths. For a subset of fourteen reservoirs, we characterized the composition of the phytoplankton using a rapid imaging microscopy technique and observed a dominance by filamentous Cyanobacteria in reservoirs with TOC concentrations above 4 mgC/L. The combination of high TOC concentrations with microbial characteristics resulted in high potential for production of two major classes of regulated DBPs, trihalomethanes and haloacetic acids. While fluorescence spectroscopy was useful in confirming the contribution of phytoplankton growth to high TOC concentrations, evaluation of predictive models for DBP yields found that all equally predictive models included SUVA 254 and some of these models also included fluorescence indices or logTOC. These findings provide a limnological context in support of the recent guidelines that have been implemented for protection of high-quality drinking water supplies in the State of Colorado. 

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3. Rapid, high-sensitivity analysis of oxyhalides by non-suppressed ion chromatography-electrospray ionization-mass spectrometry: application to ClO 4 − , ClO 3 − , ClO 2 − , and BrO 3 − quantification during sunlight/chlorine advanced oxidation

   https://doi.org/10.1039/d0ew00429d  

   Young, Tessora R. ; Cheng, Shi ; Li, Wentao ; Dodd, Michael C. ( August 2020 , Environmental Science: Water Research & Technology)

   null (Ed.)

   A rapid and sensitive method is described for measuring perchlorate (ClO 4 − ), chlorate (ClO 3 − ), chlorite (ClO 2 − ), bromate (BrO 3 − ), and iodate (IO 3 − ) ions in natural and treated waters using non-suppressed ion chromatography with electrospray ionization and tandem mass spectrometry (NS-IC-MS/MS). Major benefits of the NS-IC-MS/MS method include a short analysis time (12 minutes), low limits of quantification for BrO 3 − (0.10 μg L −1 ), ClO 4 − (0.06 μg L −1 ), ClO 3 − (0.80 μg L −1 ), and ClO 2 − (0.40 μg L −1 ), and compatibility with conventional LC-MS/MS instrumentation. Chromatographic separations were generally performed under isocratic conditions with a Thermo Scientific Dionex AS16 column, using a mobile phase of 20% 1 M aqueous methylamine and 80% acetonitrile. The isocratic method can also be optimized for IO 3 − analysis by including a gradient from the isocratic mobile phase to 100% 1 M aqueous methylamine. Four common anions (Cl − , Br − , SO 4 2− , and HCO 3 − /CO 3 2− ), a natural organic matter isolate (Suwannee River NOM), and several real water samples were tested to examine influences of natural water constituents on oxyhalide detection. Only ClO 2 − quantification was significantly affected – by elevated chloride concentrations (>2 mM) and NOM. The method was successfully applied to quantify oxyhalides in natural waters, chlorinated tap water, and waters subjected to advanced oxidation by sunlight-driven photolysis of free available chlorine (sunlight/FAC). Sunlight/FAC treatment of NOM-free waters containing 200 μg L −1 Br − resulted in formation of up to 263 ± 35 μg L −1 and 764 ± 54 μg L −1 ClO 3 − , and up to 20.1 ± 1.0 μg L −1 and 33.8 ± 1.0 μg L −1 BrO 3 − (at pH 6 and 8, respectively). NOM strongly inhibited ClO 3 − and BrO 3 − formation, likely by scavenging reactive oxygen or halogen species. As prior work shows that the greatest benefits in applying the sunlight/FAC process for purposes of improving disinfection of chlorine-resistant microorganisms are realized in waters with lower DOC levels and higher pH, it may therefore be desirable to limit potential applications to waters containing moderate DOC concentrations ( e.g. , ∼1–2 mg C L −1 ), low Br − concentrations ( e.g. , <50 μg L −1 ), and circumneutral to moderately alkaline pH ( e.g. , pH 7–8) to strike a balance between maximizing microbial inactivation while minimizing formation of oxyhalides and other disinfection byproducts. 

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4. Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

   https://doi.org/10.5061/dryad.8sf7m0cpx  

   Hussain, Mir Zaman ; Hamilton, Stephen ; Robertson, G. Philip ; Basso, Bruno ( January 2021 , Dryad)

   Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 

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5. Emerging investigators series: the efficacy of chlorine photolysis as an advanced oxidation process for drinking water treatment

   https://doi.org/10.1039/C6EW00029K  

   Remucal, C. K. ; Manley, D. ( January 2016 , Environmental Science: Water Research & Technology)

   null (Ed.)

   The photolysis of hypochlorous acid (HOCl) and hypochlorite (OCl − ) produces a suite of reactive oxidants, including hydroxyl radical (˙OH), chlorine radical (Cl˙), and ozone (O 3 ). Therefore, the addition of light to chlorine disinfection units could effectively convert existing drinking water treatment systems into advanced oxidation processes. This review critically examines existing studies on chlorine photolysis as a water treatment process. After describing the fundamental chemistry of chlorine photolysis, we evaluate the ability of chlorine photolysis to transform model probe compounds, target organic contaminants, and chlorine-resistant microorganisms. The efficacy of chlorine photolysis to produce reactive oxidants is dependent on solution and irradiation conditions ( e.g. , pH and irradiation wavelengths). For example, lower pH values result in higher steady-state concentrations of ˙OH and Cl˙, resulting in enhanced contaminant removal. We also present the current state of knowledge on the alteration of dissolved organic matter and subsequent formation of disinfection by-products (DBPs) during chlorine photolysis. Although the relative yields of DBPs during chlorine photolysis are also dependent on solution conditions ( e.g. , higher organic DBP yields at low pH values), there is conflicting evidence on whether chlorine photolysis increases or decreases DBP production compared to thermal reactions between chlorine and dissolved organic matter in the dark. We conclude the review by identifying knowledge gaps in the current body of literature. 

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