PubMed Health⌕ Search

PubMed · 17185253

Cox models for ecologic time-series data?

Abstract

The source did not provide an abstract. Follow the original record for more information.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thomas Lumley, Holly Janes, Lianne Sheppard. 2006. Cox models for ecologic time-series data?. https://doi.org/10.1289/ehp.114-1764157

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Stable chloroform emissions in southeastern China: insights from recent observations.

Chloroform (CHCl3) is a short-lived ozone-depleting substance not currently regulated under the Montreal Protocol. Due to the unique meteorological conditions in East Asia, CHCl3 emitted in this region has a greater potential to reach the stratosphere and contribute to ozone depletion. As an essential component of national CHCl3 emissions, southeastern China has attracted increasing attention. However, long-term observational data in this region remain relatively scarce, with no updates since 2020. In this study, we continuously measured atmospheric CHCl3 concentrations at a remote monitoring station in southeastern China from 1 June 2023 to 31 May 2025. Frequent concentration enhancements were observed during the monitoring period, with mixing ratios ranging from 9.3 to 134.1 ppt and an average value of 36.8 ± 19.7 ppt. Back-trajectory analysis indicated that air masses associated with elevated CHCl3 levels primarily originated from coastal industrial provinces in eastern China. Using the Potential Source Contribution Function and Concentration Weighted Trajectory methods, we identified the Yangtze River Delta (Jiangsu, Anhui, and Zhejiang Provinces) and Jiangxi Province as dominant source regions. Emissions of CHCl3 in southeastern China were estimated using the Tracer Ratio Method to be approximately 30.1 ± 5.3 Gg/yr from 2023-06-01 to 2025-05-31, indicating overall stability relative to earlier estimates and no apparent upward trend. These findings provide updated insights into the current status of CHCl3 emissions in southeastern China and highlight the need for continued monitoring and emission assessment of CHCl3 in East Asia, given its unregulated status and implications for ozone layer recovery.

Air Pollutants↗

Sulfide oxidation at halo-alkaline conditions in a fed-batch bioreactor.

A biotechnological process is described to remove hydrogen sulfide (H(2)S) from high-pressure natural gas and sour gases produced in the petrochemical industry. The process operates at halo-alkaline conditions and combines an aerobic sulfide-oxidizing reactor with an anaerobic sulfate (SO(4) (2-)) and thiosulfate (S(2)O(3) (2-)) reducing reactor. The feasibility of biological H(2)S oxidation at pH around 10 and total sodium concentration of 2 mol L(-1) was studied in gas-lift bioreactors, using halo-alkaliphilic sulfur-oxidizing bacteria (HA-SOB). Reactor operation at different oxygen to sulfide (O(2):H(2)S) supply ratios resulted in a stable low redox potential that was directly related with the polysulfide (S(x) (2-)) and total sulfide concentration in the bioreactor. Selectivity for SO(4) (2-) formation decreased with increasing S(x) (2-) and total sulfide concentrations. At total sulfide concentrations above 0.25 mmol L(-1), selectivity for SO(4) (2-) formation approached zero and the end products of H(2)S oxidation were elemental sulfur (S(0)) and S(2)O(3) (2-). Maximum selectivity for S(0) formation (83.3+/-0.7%) during stable reactor operation was obtained at a molar O(2):H(2)S supply ratio of 0.65. Under these conditions, intermediary S(x) (2-) plays a major role in the process. Instead of dissolved sulfide (HS(-)), S(x) (2-) seemed to be the most important electron donor for HA-SOB under S(0) producing conditions. In addition, abiotic oxidation of S(x) (2-) was the main cause of undesirable formation of S(2)O(3) (2-). The observed biomass growth yield under SO(4) (2-) producing conditions was 0.86 g N mol(-1) H(2)S. When selectivity for SO(4) (2-) formation was below 5%, almost no biomass growth was observed.

Air Pollutants↗

Performance and microbial analysis of defined and non-defined inocula for the removal of dimethyl sulfide in a biotrickling filter.

The performance and microbial communities of three differently inoculated biotrickling filters removing dimethyl sulfide (DMS) were compared. The biotrickling filters were inoculated with Thiobacillus thioparus TK-m (THIO), sludge (HANDS) and sludge + T. thioparus TK-m + Hyphomicrobium VS (HANDS++), respectively. The criteria investigated were length of the start-up period, the maximum elimination capacity, and the effects of intermittent loading rates, low pH, peak loading and very low loading rate on the DMS removal efficiency. The HANDS++ reactor exhibited the best performance considering all treatments. HANDS performed almost equally well as HANDS++, except during the determination of the EC(max), while THIO was generally the least efficient. During stable DMS loading at concentrations of 20 ppmv or lower, all reactors exhibited similar and high removal efficiencies (>99%). Denaturing gradient gel electrophoresis (DGGE) analysis showed the establishment of T. thioparus in the biofilm of all reactors, but not of Hyphomicrobium VS. Quantitative monitoring of the introduced bacterial strains was performed with a newly developed real-time PCR protocol. Initially, the inoculated strains were exclusively found in the reactors in which they were added. Afterwards, however, both strains developed in the biofilm of all three reactors, although T. thioparus attained higher cell densities than Hyphomicrobium. The presence of T. thioparus in THIO was related with the DMS loading rates that were applied, in the sense that intermittent DMS loading and very low DMS loading rates (0.5 ppmv) induced a decrease in gene copy numbers. Real-time PCR and DGGE both gave consistent results regarding the presence of Hyphomicrobium VS and Thiobacillus thioparus TK-m in the reactors. Only real-time PCR could be used to detect bacteria comprising of less than 1.4% of the total bacterial community ( approximately 10(5) copies ring(-1)).

Air Pollutants↗