Real-time, high-resolution quantitative measurement of multiple soil gas emissions: selected ion flow tube mass spectrometry

A new technique is presented for the rapid, high-resolution identification and quantification of multiple trace gases above soils, at concentrations down to 0.01 microL L(-1) (10 ppb). The technique, selected ion flow tube mass spectrometry (SIFT-MS), utilizes chemical ionization reagent ions that react with trace gases but not with the major air components (N2, O2, Ar, CO2). This allows the real-time measurement of multiple trace gases without the need for preconcentration, trapping, or chromatographic separation. The technique is demonstrated by monitoring the emission of ammonia and nitric oxide, and the search for volatile organics, above containerized soil samples treated with synthetic cattle urine. In this model system, NH3 emissions peaked after 24 h at 2000 nmol m(-2) s(-1) and integrated to approximately 7% of the urea N applied, while NO emissions peaked about 25 d after urine addition at approximately 140 nmol m(-2) s(-1) and integrated to approximately 10% of the applied urea N. The monitoring of organics along with NH3 and NO was demonstrated in soils treated with synthetic urine, pyridine, and dimethylamine. No emission of volatile nitrogen organics from the urine treatments was observed at levels >0.01% of the applied nitrogen. The SIFT method allows the simultaneous in situ measurement of multiple gas components with a high spatial resolution of < 10 cm and time resolution <20 s. These capabilities allow, for example, identification of emission hotspots, and measurement of localized and rapid variations above agricultural and contaminated soils, as well as integrated emissions over longer periods.     

Statistical models for evaluating suspected artefacts in long-term environmental monitoring data

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no prominent underlying trend in the series, the shift estimates are rather stable and show less variation. If an artificial shift occurs during a slower downward or upward tendency, it is difficult to separate these two effects and shift estimates can be both biased and have large variation. In the case of a change in method or laboratory, we show that conducting the analyses with both methods in parallel strongly improves estimates of artefact effects on the time series, even if certain problems remain. Due to the difficulties of estimating artificial level shifts, posterior adjustment is problematic and can lead to time series that no longer can be used for trend analysis or other analysis based on the longitudinal structure of the series. Before carrying out a change in analytic method or laboratory, it should be considered if this is absolutely necessary. If changes cannot be avoided, the analysis of the two methods considered, or the two laboratories contracted, should be run in parallel for a considerable period of time so as to enable a good assessment of changes introduced to the data series.