Estimating streamwater solute loads is a central objective of many water‐quality monitoring and research studies, as loads are used to compare with atmospheric inputs, to infer biogeochemical processes, and to assess whether water quality is improving or degrading. In this study, we evaluate loads and associated errors to determine the best load estimation technique among three methods (a period‐weighted approach, the regression‐model method, and the composite method) based on a solute's concentration dynamics and sampling frequency. We evaluated a broad range of varying concentration dynamics with stream flow and season using four dissolved solutes (sulfate, silica, nitrate, and dissolved organic carbon) at five diverse small watersheds (Sleepers River Research Watershed, VT; Hubbard Brook Experimental Forest, NH; Biscuit Brook Watershed, NY; Panola Mountain Research Watershed, GA; and Río Mameyes Watershed, PR) with fairly high‐frequency sampling during a 10‐ to 11‐yr period. Data sets with three different sampling frequencies were derived from the full data set at each site (weekly plus storm/snowmelt events, weekly, and monthly) and errors in loads were assessed for the study period, annually, and monthly. For solutes that had a moderate to strong concentration–discharge relation, the composite method performed best, unless the autocorrelation of the model residuals was <0.2, in which case the regression‐model method was most appropriate. For solutes that had a nonexistent or weak concentration–discharge relation (model R² < about 0.3), the period‐weighted approach was most appropriate. The lowest errors in loads were achieved for solutes with the strongest concentration–discharge relations. Sample and regression model diagnostics could be used to approximate overall accuracies and annual precisions. For the period‐weighed approach, errors were lower when the variance in concentrations was lower, the degree of autocorrelation in the concentrations was higher, and sampling frequency was higher. The period‐weighted approach was most sensitive to sampling frequency. For the regression‐model and composite methods, errors were lower when the variance in model residuals was lower. For the composite method, errors were lower when the autocorrelation in the residuals was higher. Guidelines to determine the best load estimation method based on solute concentration–discharge dynamics and diagnostics are presented, and should be applicable to other studies.

Mercury (Hg) is deposited from the atmosphere to remote areas such as forests, but the amount of Hg in trees is not well known. To determine the importance of Hg in trees, we analyzed foliage, bark and bole wood of eight tree species at four sites in the northeastern USA (Huntington Forest, NY; Sleepers River, VT; Hubbard Brook, NH; Bear Brook, ME). Foliar concentrations of Hg averaged 16.3 ng g^-1 among the hardwood species, which was significantly lower than values in conifers, which averaged 28.6 ng g^-1 (p < 0.001). Similarly, bark concentrations of Hg were lower (p < 0.001) in hardwoods (7.7 ng g^-1) than conifers (22.5 ng g^-1). For wood, concentrations of Hg were higher in yellow birch (2.1–2.8 ng g^-1) and white pine (2.3 ng g^-1) than in the other species, which averaged 1.4 ng g^-1 (p < 0.0001). Sites differed significantly in Hg concentrations of foliage and bark (p = 0.02), which are directly exposed to the atmosphere, but the concentration of Hg in wood depended more on species (p < 0.001) than site (p = 0.60). The Hg contents of tree tissues in hardwood stands, estimated from modeled biomass and measured concentrations at each site, were higher in bark (mean of 0.10 g ha^-1) and wood (0.16 g ha^-1) than in foliage (0.06 g ha^-1). In conifer stands, because foliar concentrations were higher, the foliar pool tended to be more important. Quantifying Hg in tree tissues is essential to understanding the pools and fluxes of Hg in forest ecosystems.

Background: Inappropriate dry-weather misconnections into storm drainage system are a demanding environmental problem worldwide, which leads to unexpected dry-weather discharge into surface waters. It often costs a large amount of manpower and resources to identify the source of misconnections and estimate its contributions. In this study, we evaluated the possibility of quantifying proportional source contribution in a storm drainage system with dry-weather misconnections from domestic sewage and river water inflow, using rapid and low-cost fluorescence spectroscopy methods. For this purpose, samples of both misconnection sources and outflows of storm drainage system were collected and analyzed in a downtown catchment of Shanghai, China. Results: Results showed that fluorescent peak intensity of tryptophan-like T₁ in domestic sewage (802 ± 126 a.u.) was significantly higher than that in urban river water (57 ± 12 a.u.), while fluorescent peak intensities of tryptophan-like T₂ in urban river water (732 ± 304 a.u.) was much higher than that in domestic sewage (261 ± 64 a.u.) due to increased algal activity in the local river and upstream inflow chemistry. However, only peak T₂ passed the conservative behavior test in the incubation experiments, which could be used as a fingerprint for quantitatively identifying the misconnections. We further developed a Bayesian fluorescence mass balance model (FMBM) to infer the percentage of dry-weather misconnections into the storm drainage system as a function of fluorescence intensities of peak T₂ in the samples of sources and outflow. It was found that the maximum posteriori probability estimate of the percentage of river water intrusion into the storm drains was up to 20.8% in this site, which was validated by the results of on-site investigation. Conclusion: Our findings implied that in situ fluorescent sensors and Bayesian FMBM for the fingerprint fluorescence peak could be applied to fast track inappropriate dry-weather misconnections into storm drainage system qualitatively and quantitatively with low costs.

Mercury (Hg) in tree wood has been overlooked, in part because concentrations are so low as to be below detection limits of some analytical methods, but it is potentially important to forest ecosystem processes and budgets. We tested methods for the preparation and determination of Hg in tree wood by analysing samples of four tree species at the Hubbard Brook Experimental Forest, New Hampshire, USA, using thermal decomposition, catalytic conversion, amalgamation and atomic absorption spectrophotometry (USEPA Method 7473). Samples that were freeze-dried or oven-dried at 65°C were suitable for determination of Hg, whereas oven-drying at 103°C resulted in Hg losses, and air-drying resulted in Hg gains, presumably due to sorption from indoor air. Mean (±SE) concentrations of Hg tree bole wood were 1.75 ± 0.14 ng g⁻¹ for American beech, 1.48 ± 0.23 ng g⁻¹ for sugar maple, 3.96 ± 0.19 ng g⁻¹ for red spruce and 4.59 ± 0.06 ng g⁻¹ for balsam fir. Based on these concentrations and estimates of wood biomass by species based on stand inventory, we estimated the Hg content of wood in the reference watershed at Hubbard Brook to be 0.32 g ha⁻¹, twice the size of the foliar Hg pool (0.15 g ha⁻¹). Mercury in wood deserves more attention and is feasible to measure using appropriate techniques.

Core Ideas: Detecting change depends on soil variability, sampling effort and desired confidence. We illustrate detectable rates of change using data from the Calhoun Long‐Term Soil‐Ecosystem experiment. The sampling intensity needed to detect change varied with soil depth and chemical element. Experience at this LTSE can be used to improve long‐term soil monitoring designs. Quantifying rates of change in soil carbon and nutrients is essential to understanding the global carbon cycle as well as to guiding local management decisions. However, change in soils can be nonlinear, difficult to detect, and dependent on the depth of soil examined. The purpose of this study was to demonstrate how to quantify uncertainty in detection of soil change, which has major implications for the design of soil monitoring projects. We analyzed soils collected over five decades from a long‐term soil‐ecosystem experiment in the South Carolina Piedmont of the United States. We estimated the minimum detectable change (MDC) using a dataset of soil carbon, nitrogen, calcium, magnesium, and potassium concentrations at four soil depths (0–7.5, 7.5–15, 15–35, and 35–60 cm). Changes over time in the log‐transformed concentration at each soil depth were fit with one of three response shapes (linear, exponential, and quadratic) with the best model fit determined by a corrected AIC. The MDC analyses were conducted using repeated‐measures models. For soil depths in which elemental change was quadratic, we conducted MDC analyses separately on the decreasing and increasing limbs. We also used paired t tests to analyze the MDCs based on only the first and last sample dates. Greater sampling intensity was needed to detect equivalent proportional changes in elements at intermediate depths than at shallower or deeper depths. Our study demonstrates the importance of considering the pattern and rate of the expected change, and how that may vary by element and depth, when designing and evaluating soil monitoring studies.