Equity rules for burden sharing in the mitigation process of climate change

Although international negotiation on the mitigation of climate change is a process of determining burden-sharing rules between countries, there has been no clear agreement on equity principles for burden sharing. During the negotiating process up to the Kyoto Protocol, various proposals were made on such burden-sharing rules, but an agreement on emission targets for Annex I countries was achieved without explicitly agree-ing to any rules. In the next phase of the negotiation, debates on emission targets are likely to shift from those between developed countries to those between all parties to the convention. In such a phase, debates on burden-sharing rules will be revisited. The purpose of this paper is: (1) to determine implicitly a formula for the rule for burden sharing between Annex I countries that was considered to be underlying the emission targets of the Kyoto Protocol, and (2) to examine plausible emission targets and timing of commitments for non-Annex I countries in the future by using the result of the analysis on the Kyoto Protocol. A multi-regression method is used for this purpose. It was concluded that the burden sharing between Annex I countries in the Kyoto Protocol can mostly be explained by three variables: the increase in the rate of CO₂ emission during the years 1990 to 2010, the increase in the rate of afforestation between 1990 and 1995, and the GDP per capita at the time of negotiation. The timing of future commitments of developing countries and the levels of targets differ widely, depending on which index or formula is agreed as “equitable”. Some of the developing countries would have to start limiting their emissions within several years if GDP per capita or CO₂ per capita were chosen as the burden-sharing indicator. Developing countries would not have to make commitments until the mid-late 21st century if population growth rate were chosen. If the inferred formula of the Kyoto Protocol were applied to developing countries, they would have had to start mild limitation from 1990.     

Heavy metals and soil microbes

Heavy metal pollution is a global issue due to health risks associated with metal contamination. Although many metals are essential for life, they can be harmful to man, animal, plant and microorganisms at toxic levels. Occurrence of heavy metals in soil is mainly attributed to natural weathering of metal-rich parent material and anthropogenic activities such as industrial, mining, agricultural activities. Here we review the effect of soil microbes on the biosorption and bioavailability of heavy metals; the mechanisms of heavy metals sequestration by plant and microbes; and the effects of pollution on soil microbial diversity and activities. The major points are: anthropogenic activities constitute the major source of heavy metals in the environment. Soil chemistry is the major determinant of metal solubility, movement and availability in the soil. High levels of heavy metals in living tissues cause severe organ impairment, neurological disorders and eventual death. Elevated levels of heavy metals in soils decrease microbial population, diversity and activities. Nonetheless, certain soil microbes tolerate and use heavy metals in their systems; as such they are used for bioremediation of polluted soils. Soil microbes can be used for remediation of contaminated soils either directly or by making heavy metals bioavailable in the rhizosphere of plants. Such plants can accumulate 100 mg g^?1 Cd and As; 1000 mg g^?1 Co, Cu, Cr, Ni and 10,000 mg g^?1 Pb, Mn and Ni; and translocate metals to harvestable parts. Microbial activity changes soil physical properties such as soil structure and biochemical properties such as pH, soil redox state, soil enzymes that influence the solubility and bioavailability of heavy metals. The concept of ecological dose (ED₅₀) and lethal concentration (LC₅₀) was developed in response to the need to easily quantify the influence of pollutants on microbial-mediated ecological processes in various ecosystems.     

Polyurethane for removal of organic dyes from textile wastewater

Synthetic organic dyes are extensively used in consumer products from textile to pharmaceuticals. A large amount of organic dyes is ultimately discharged as effluent into water bodies, thus posing a serious threat to environment and life. Therefore, removal of dyes from water bodies is needed. To address this problem, various synthetic and natural materials have been used to adsorb dyes. Here, we review the application of polyurethane for removal of organic dyes. First, we review the application of simple and modified polyurethane as efficient and economic adsorbents for dyes. Secondly, we review the polyurethane-based membranes for separation and adsorption of various dyes. Thirdly, we describe polyurethane composites with improved efficiency of dyes removal. Finally, we review the bioremediation of dyes where polyurethane has been proven as an excellent inert support.     

Enhanced degradation of the antibiotic tetracycline by heterogeneous electro-Fenton with pyrite catalysis

There is actually increasing concern about the accumulation of antibiotics, such as tetracycline, in soil and water bodies. There is therefore a need for efficient methods to degrade antibiotics and thus clean waters. Here we tested the degradation of tetracycline using the heterogeneous electro-Fenton-pyrite method and compared the results with the conventional electro-Fenton method. The reaction was performed with a boron-doped diamond or Pt anode and carbon-felt cathode allowing electrogeneration of H₂O₂ from O₂ reduction. Results show an increasing tetracycline mineralization using the following methods: anodic oxidation with electrogenerated H₂O₂, electro-Fenton and then electro-Fenton-pyrite using boron-doped diamond. Ion-exclusion HPLC revealed the complete removal of malic malonic, succinic, acetic, oxalic and oxamic acids. Nitrogen present in tetracycline was mainly mineralized in NH₄ ⁺. The higher efficiency of electro-Fenton-pyrite is explained by self-regulation of soluble Fe²⁺ and pH to 3.0 from pyrite catalyst favoring larger ^·OH generation from Fenton’s reaction.     

H and O isotopic differences in typhon and urban-induced heavy rain in Tokyo

Stable isotope ratios of hydrogen and oxygen of water are useful tracers of the hydrological cycle. For example, isotopes monitor the evapotranspiration in vegetated areas, local snow ice processes and stream water flow processes. δ¹⁸O and δD in rainwater reflect the processes of evaporation, condensation and precipitation. Heavy rains thus modify the stable isotope ratio of ground water, stream water and transpiration water vapor. However, the controlling factors of δ¹⁸O and δD are not clear. Here we analyzed the inorganic ion concentration and stable isotope ratio in 38 normal rainwater and 15 heavy rainwater samples were collected in Shinjuku, Tokyo, Japan, during four years from October 2012 to December 2015. Results show a decrease in δ¹⁸O and δD values with the total rainfall amount, thus highlighting the amount effect. δ¹⁸O and δD volume-weighted mean values in typhoon heavy rain were higher than the values estimated from amount effect, whereas δ¹⁸O and δD volume-weighted mean values in urban-induced heavy rain were lower. Typhoon heavy rain has high Na⁺ ratio and stable isotope ratios, while urban-induced heavy rain has low Na⁺ ratio and stable isotope ratio.     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls

The Deepwater Horizon oil spill occurred in the Gulf of Mexico on April 20, 2010. Considered the largest accidental marine oil spill in history, oil flowed for three months and approximately five million barrels of oil spilled through by mid-July 2010. In this article, we analyze bird data to assess the impact of the oil spill on the Gulf wildlife. Particularly, we want to determine in which regions Laughing Gulls were mostly affected by the oil spill, and whether those regions spatially shifted throughout the year 2010. Though our data sets have some limitations to apply statistical analysis methods, we obtained very interesting results. Our analyses showed the general consistency of the results based on two population data sets (from the 2011 Data Expo and from the eBird community) and justified the use of both publicly available data sets. We showed that the closer the surface oil spill area approached to the Laughing Gulls habitats, the more significant clusters of bird cases were observed.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.     

Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines

Temperature is an important physical factor that is known to strongly affect biodiversity as well as ecosystems and their functioning. However, research in this area is still relatively limited; this may also be attributed to the multitude of influencing factors and the complexity of the statistics involved. This study analyzes the differences between the surface temperature of three Central European broadleaf tree species. A better understanding of these differences may help to elucidate the role of microclimate in biodiversity. We consider a time series of high-resolution thermal images taken from a meteorological observation tower and calculate mean canopy leaf temperatures for beech, ash and maple (Fagus silvatica, Fraxinus excelsior and Acer pseudoplatanus). In a first step, comparable image areas are extracted from the thermal image sections of the crown of each tree species avoiding shadow areas, branches, etc. We used an automatic segmentation technique, the Otsu thresholding. Extracted canopy leaf temperature values were then processed and the resulting temperature profiles estimated by O’Sullivan penalized splines. For comparing the differences in canopy leaf temperature over time, we propose the construction of simultaneous confidence bands. The analyses show that there are significant—though small—differences in canopy surface temperature between the three tree species.