The TRAMO/SEATS program, combined with the Hodrick–Prescott (HP) filter, was used to detect trends and potential change points in time series of dissolved inorganic nitrogen (DIN) at three stations along the Yangtze River. The trend components were extracted, and two change points were successfully detected. The components revealed that DIN has been increasing at all the stations since the 1990s, although variations exist. Changes visible before 2002 illustrate the differences in agriculture development among regions upstream from the stations. The Three-Gorges Dam (TGD), which began to impound in 2003, led to years of different trends. The DIN concentration, which had been trending upward prior to that date, began a slightly downward trend because of NH4+ depletion. Readings at the Yichang station revealed this trend most strongly; those at the Hankou station less so. The Datong station was far enough away from the TGD so that no obvious effects were seen.

This paper seeks to enhance understanding of the data distribution of ground–level ozone time series by analysing their multifractal spectra. Emphasis is placed on the suitability of the box–counting and moments methods for characterizing scaling properties of ozone concentration, enabling us to describe similarities and differences inferred from the multifractal spectra by analysing various types of monitoring stations (urban, suburban and rural monitoring sites) under identical atmospheric conditions. It is herein demonstrated that that multifractal features are to a considerable extent similar for each type of monitoring station under warm atmospheric conditions and high solar radiation, owing to the fact that these weather characteristics homogenize the scaling behaviour of ozone. On the contrary, location and chemical precursors play a more prominent role under low temperatures and solar radiation, highlighting differences among multifractal features of ozone concentrations in the monitoring sites. At rural stations, the absence of anthropogenic emissions promotes less variability in ozone data and homogenization of multifractal behaviour throughout the year. Furthermore, a data shuffling procedure was performed in order to analyse changes occurring in multifractal spectra as time series are subjected to varying degrees of data position disturbance. Results indicate that multifractal analysis is a useful tool for describing the temporal scaling behaviour of ozone time series at different monitoring sites which refines results that have been traditionally provided by statistical analyses on ozone pollution.

Ambient visibility is a complex manifestation arising out of interactions among many atmospheric variables, including ambient aerosol load, and region specific geophysical characteristics. To functionally relate visibility impairment in Delhi region during winter months-months marred with poor visibility conditions–a novel experiment was designed to relate visibility with ambient aerosol load (PM2.5), and relevant meteorological variables: dew point temperature (Dp), height of planetary boundary layer (PBL), ambient temperature (T), relative humidity (RH), wind speed (WS) and wind direction (WD). Time series data sets of Visibility(t) and other variables were subjected to non–linear decomposition using Empirical Mode Decomposition Method (EMD), enabling to obtain total cyclic and acyclic–trend components embedded in all data–sets. Extracted total cyclic visibility components were functionally related with the corresponding components associated with PM2.5 load and meteorological variables. Decomposed acyclic–trend component of the visibility, representing time dependent acyclic trend (AT), was separately related with the corresponding AT components of the considered meteorological variables. The decomposed components of the visibility (total cyclic and AT) were subjected to multiple linear regression to establish a functional relationship between them and a set of variables among the considered variables. The analysis suggests that acyclic–trend associated with Visibility(t) can be predicted better as opposed to the Visibility(t)cyclic component.

Plastic ingestion by seabirds is a growing conservation issue, but there are few time series of plastic ingestion with large sample sizes for which one can assess temporal trends. Common and Thick-billed Murres (Uria aalge and U. lomvia) are pursuit-diving auks that are legally harvested in Newfoundland and Labrador, Canada. Here, we combined previously unpublished data on plastic ingestion (from the 1980s to the 1990s) with contemporary samples (2011–2012) to evaluate changes in murres’ plastic ingestion. Approximately 7% of murres had ingested plastic, with no significant change in the frequency of ingestion among species or periods. The number of pieces of plastic/bird, and mass of plastic/bird were highest in the 1980s, lowest in the late 1990s, and intermediate in contemporary samples. Studying plastic ingestion in harvested seabird populations links harvesters to conservation and health-related issues and is a useful source of large samples for diet and plastic ingestion studies.

Recent work suggesting that fisheries depletions have turned the corner is misplaced because analysis was based largely on fisheries from better-managed developed-world fisheries. Some indicators of status show improvements in the minority of fisheries subjected to formal assessment. Other indicators, such as trophic level and catch time series, have been controversial. Nevertheless, several deeper analyses of the status of the majority of world fisheries confirm the previous dismal picture: serious depletions are the norm world-wide, management quality is poor, catch per effort is still declining. The performance of stock assessment itself may stand challenged by random environmental shifts and by the need to accommodate ecosystem-level effects. The global picture for further fisheries species extinctions, the degradation of ecosystem food webs and seafood security is indeed alarming. Moreover, marine ecosystems and their embedded fisheries are challenged in parallel by climate change, acidification, metabolic disruptors and other pollutants. Attempts to remedy the situation need to be urgent, focused, innovative and global.

It can be argued that the intensity of monitoring of coastal marine environments lags behind the equivalent terrestrial environments. This results in a paucity of long-term time series of key environmental parameters such as turbidity. This lack of management information of the sources and sinks, and causes and impacts of stressors to the coastal marine environment, along with a lack of co-ordination of information collection is compromising the ability of environmental impact assessments of major coastal developments to discriminate between local and remote anthropogenic impacts, and natural or background processes. In particular, the quasi outsourcing of the collection of coastal information can lead to a perverse incentive whereby in many cases nobody is actively or consistently monitoring the coastal marine environment effectively. This is particularly the case with regards to the collection of long-term and whole-of-system scale data. This lack of effective monitoring can act to incentivise poor environmental performance.

Large scale environmental remediation projects applied to sea water always involve large amount of capital investments. Rigorous effectiveness evaluations of such projects are, therefore, necessary and essential for policy review and future planning. This study aims at investigating effectiveness of environmental remediation using three different Seemingly Unrelated Regression (SUR) time series models with intervention effects, including Model (1) assuming no correlation within and across variables, Model (2) assuming no correlation across variable but allowing correlations within variable across different sites, and Model (3) allowing all possible correlations among variables (i.e., an unrestricted model). The results suggested that the unrestricted SUR model is the most reliable one, consistently having smallest variations of the estimated model parameters. We discussed our results with reference to marine water quality management in Hong Kong while bringing managerial issues into consideration.

Water turbidity and suspended sediment concentration (SSC) are commonly used as part of marine monitoring and water quality plans. Current management plans utilise threshold SSC values derived from mean-annual turbidity concentrations. Little published work documents typical ranges of turbidity for reefs within open coastal waters. Here, time-series turbidity measurements from 61 sites in the Great Barrier Reef (GBR) and Moreton Bay, Australia, are presented as turbidity exceedance curves and derivatives. This contributes to the understanding of turbidity and SSC in the context of environmental management in open-coastal reef environments. Exceedance results indicate strong spatial and temporal variability in water turbidity across inter/intraregional scales. The highest turbidity across 61 sites, at 50% exceedance (T50) is 15.3 NTU and at 90% exceedance (T90) 4.1 NTU. Mean/median turbidity comparisons show strong differences between the two, consistent with a strongly skewed turbidity regime. Results may contribute towards promoting refinement of water quality management protocols.

Many studies have used valuation techniques to predict the potential effect of environmental improvements on human use of coastal areas, but there is a lack of post hoc empirical evidence that these policies indeed affect the way people use coastal areas. A panel data approach is developed to statistically determine how storm drain diversions affected attendance at 26 beaches in Southern California. This study uses a 10-year time series of data to conduct a statistical analysis of attendance at beaches with and without diversions and before and after the diversions were installed, while controlling for all observable, confounding factors. Results indicate that beach attendance increased at beaches with diversions compared to those that did not have diversions (between 350,000 and 860,000 visits annually at a 95% confidence interval). Establishing this link between mitigation policies and human use patterns can lead to better management of coastal areas.

In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH⁴⁺–N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH⁴⁺–N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing–refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH⁴⁺–N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering “real” data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.