Analysis of trends and variability of toxic concentrations in the Niagara River using the Hilbert-Huang transform method

Tsai, Christina W.; Treadwell, Huntington

doi:10.1016/j.ecoinf.2019.02.008

Cited by 5 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lake Ontario exhibited the highest number of features with respect to the other lakes likely due in part to the influence of the Niagara River, which passes through an area of concern. The Niagara River is known to transport contaminants from multiple pollution sources from a heavily industrialized area of Smokes Creek near the southern end of Buffalo Harbor to the north to the mouth of the Niagara River at Lake Ontario. , …”

Section: Resultsmentioning

confidence: 99%

“…The Niagara River is known to transport contaminants from multiple pollution sources from a heavily industrialized area of Smokes Creek near the southern end of Buffalo Harbor to the north to the mouth of the Niagara River at Lake Ontario. 40,41 The halogenated mass spectral signatures correspond to potential PBT compounds that are not listed in the monitoring programs, and there is a high probability that these unknown The objective of this study was not to identify molecular formulas of the features isolated. The mass error observed using the APGC-QToF resulted in an elevated uncertainty in assigning unequivocal molecular formulas 42 for the isolated features.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

See 1 more Smart Citation

Decadal Differences in Emerging Halogenated Contaminant Profiles in Great Lakes Top Predator Fish

Baygi

Fernando

Hopke

et al. 2020

Environ. Sci. Technol.

View full text Add to dashboard Cite

Legacy halogenated contaminants have been monitored in the Great Lakes for decades, but there are many additional unknown halogenated contaminants potentially affecting the Great Lakes ecosystem. To address this concern, lake trout (Salvelinus namaycush) were collected in 2005/2006 and 2015/2016 from each lake and screened for previously unidentified compounds. The isotopic profile deconvoluted chromatogram algorithm was used to isolate unknown halogenated components using high-resolution mass spectrometry data files generated by an atmospheric pressure gas chromatography-quadrupole time-of-flight mass spectrometer operated in positive and negative modes. The temporal and spatial differences in the newly detected features were used to isolate new potential contaminants. Decadal differences in the unknown halogenated compounds (or features) were compared with the total polychlorinated biphenyl concentration trends. Greater than 2000 unknown halogenated features were detected. As expected, Lake Superior contained the lowest number of unknown halogenated features, whereas Lake Ontario contained the highest. Unknown features tended to have fewer Cl and/or Br atoms compared to traditional legacy contaminant features typically monitored. Diverse patterns of unknown halogenated compounds between lakes suggested that there continues to be unidentified sources of halogenated contaminants in the Great Lakes missed by current monitoring programs.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: ■ Experimental Sectionmentioning

confidence: 99%

Decadal Differences in Emerging Halogenated Contaminant Profiles in Great Lakes Top Predator Fish

Baygi

Fernando

Hopke

et al. 2020

Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…EMD uses an adaptive dyadic filter bank to decompose input data into several IMFs and extract a residue representing the trend of the original time series. Sifting is completed when the last component is monotonic or constant, as in this case, where no additional IMFs can be extracted [15,19,33]. The flowchart (Figure 2) and equation for the HHT are as follows:…”

Section: Experimental Design and Data Acquisitionmentioning

confidence: 99%

“…Time series analysis is perceived as having the capability to resolve soil loss time series for different time scales [12]. However, traditional methods (such as the Fourier transform) are inadequate, as they can only analyze linear and stationary time series [13][14][15]. An alternative technique, termed the wavelet transform, has become popular in investigations of hydrological data and comprises distinguishing and classifying different sequence processes in a time series [16,17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Correlation between Soil Loss and Natural Rainfall on Sloping Farmland Using the Hilbert–Huang Transform in Southwestern China

Shi

et al. 2023

Agronomy

View full text Add to dashboard Cite

The Hilbert–Huang transform (HHT) has been used as a powerful tool for analyzing nonlinear and nonstationary time series. Soil loss is controlled by complicated physical processes and thus fluctuates with nonlinearity and nonstationarity over time. In order to further clarify the relationship between rainfall, surface runoff, and sediment yield, this study adopted the HHT to analyze these characteristics through multiple time scales and investigated their relationship through time-dependent intrinsic correlation (TDIC) in the time series. A six-year study (2015–2020) was conducted on sloping farmlands to explore the relationships between soil loss and rainfall in southwest China. Time series of soil loss and rainfall were identified as the relevant characteristics at different time scales based on the method of HHT. Local correlation between the soil loss and runoff was carried out by the method of TDIC. The original time series of the rainfall, runoff, and soil loss were decomposed into eight intrinsic mode functions (IMFs) and a residue by ensemble empirical mode decomposition (EEMD). The residue indicated that the rainfall and runoff increased and then decreased during the maize-growing season from 2015 to 2020, whereas the soil loss gradually decreased. IMF1 and IMF2 accounted for nearly 80% of the temporal variations in rainfall, runoff, and soil loss, indicating that the variables varied the most at short time scales. The TDIC analysis showed that strong and positive correlations between the soil loss, rainfall, and runoff prevailed over the entire time domain at the scales of IMF1 and IMF2, indicating the rapid response of the soil loss to rainfall and runoff at short time scales. Time-varying correlations were observed at the IMF3–IMF5 scales. At the IMF7 scale, an evident switchover in the nature of the correlation was identified during the years 2018 and 2019; this could be related to a sudden rainstorm under low vegetation coverage conditions. The EEMD-based TDIC tool is an effective means to clarify the relationship between soil loss, rainfall, and runoff. Our results provide a better understanding of the relationship between soil loss and rainfall varied with time at multiple time scales. Short-term heavy rainfall and rapid surface runoff are the important factors causing serious soil and water loss on a short time scale in a mountainous region with yellow soil, which is of great significance for the construction of a regional soil erosion prediction model.

show abstract

“…Rwanda’s first assessment of contaminants in its soil found levels of aldrin in the range of 0.38–0.59 μg⋅kg –1 ⋅dw ( Umulisa et al, 2020 ). A decline in dieldrin concentrations in the Niagara River was detected using a Hilbert–Huang transform analysis ( Tsai and Treadwell, 2019 ). Dieldrin has been found in water, fish, sediments, and large parasites in Okobaba, Lagos Lagoon, Nigeria ( Bamidele et al, 2019 ).…”

Section: Toxicity Of Aldrin and Dieldrinmentioning

confidence: 99%

Microbial Degradation of Aldrin and Dieldrin: Mechanisms and Biochemical Pathways

Pang

Lin

et al. 2022

Front. Microbiol.

View full text Add to dashboard Cite

As members of the organochlorine group of insecticides, aldrin and dieldrin are effective at protecting agriculture from insect pests. However, because of excessive use and a long half-life, they have contributed to the major pollution of the water/soil environments. Aldrin and dieldrin have been reported to be highly toxic to humans and other non-target organisms, and so their use has gradually been banned worldwide. Various methods have been tried to remove them from the environment, including xenon lamps, combustion, ion conversion, and microbial degradation. Microbial degradation is considered the most promising treatment method because of its advantages of economy, environmental protection, and convenience. To date, a few aldrin/dieldrin-degrading microorganisms have been isolated and identified, including Pseudomonas fluorescens, Trichoderma viride, Pleurotus ostreatus, Mucor racemosus, Burkholderia sp., Cupriavidus sp., Pseudonocardia sp., and a community of anaerobic microorganisms. Many aldrin/dieldrin resistance genes have been identified from insects and microorganisms, such as Rdl, bph, HCo-LGC-38, S2-RDLA302S, CSRDL1A, CSRDL2S, HaRdl-1, and HaRdl-2. Aldrin degradation includes three pathways: the oxidation pathway, the reduction pathway, and the hydroxylation pathway, with dieldrin as a major metabolite. Degradation of dieldrin includes four pathways: oxidation, reduction, hydroxylation, and hydrolysis, with 9-hydroxydieldrin and dihydroxydieldrin as major products. Many studies have investigated the toxicity and degradation of aldrin/dieldrin. However, few reviews have focused on the microbial degradation and biochemical mechanisms of aldrin/dieldrin. In this review paper, the microbial degradation and degradation mechanisms of aldrin/dieldrin are summarized in order to provide a theoretical and practical basis for the bioremediation of aldrin/dieldrin-polluted environment.

show abstract

Analysis of trends and variability of toxic concentrations in the Niagara River using the Hilbert-Huang transform method

Cited by 5 publications

References 15 publications

Decadal Differences in Emerging Halogenated Contaminant Profiles in Great Lakes Top Predator Fish

Decadal Differences in Emerging Halogenated Contaminant Profiles in Great Lakes Top Predator Fish

Multi-Scale Correlation between Soil Loss and Natural Rainfall on Sloping Farmland Using the Hilbert–Huang Transform in Southwestern China

Microbial Degradation of Aldrin and Dieldrin: Mechanisms and Biochemical Pathways

Contact Info

Product

Resources

About