Atmospheric Pollution and Consumption Patterns in Spain: An Input-Output Approach

This paper analyses the relationship between Spanish household consumption patterns and atmospheric pollutant emissions in 2000. Applying an input-output approach we estimate the relative responsibility of different types of households in the emissions of nine different atmospheric pollutants: the six greenhouse gases (CO2, CH4, N2O, SF6, HFCs and PFCs) regulated by the Kyoto protocol and three other gases (SO2, NOx and NH3). We combine input-output tables, national consumer survey statistics and environmental pollution satellite accounts into an environmental extended input-output model. We also analyse the assumptions required in order to apply the model to available data. We find that there is a positive and very high relationship between the level of household expenditure and the direct and indirect emissions generated by household consumption. However, the emission intensities tend to decrease with the expenditure level for the different atmospheric pollutants, with the exception of the synthetic greenhouse gases (SF6, HFCs and PFCs).Input-Output Analysis, Consumption Pattern, Atmospheric Pollution

Estimation of the atmospheric flux of nutrients and trace metals to the Eastern Tropical North Atlantic Ocean

Atmospheric deposition contributes potentially significant amounts of the nutrients iron, nitrogen and phosphorus (via mineral dust and anthropogenic aerosols) to the oligotrophic tropical North Atlantic Ocean. Transport pathways, deposition processes and source strengths contributing to this atmospheric flux are all highly variable in space and time. Atmospheric sampling was conducted during 28 research cruises through the Eastern Tropical North Atlantic (ETNA) over a 12 year period and a substantial dataset of measured concentrations of nutrients and trace metals in aerosol and rainfall over the region was acquired. This database was used to quantify (on a spatial- and seasonal-basis) the atmospheric input of ammonium, nitrate, soluble phosphorus and soluble and total iron, aluminium and manganese to the ETNA. The magnitude of atmospheric input varies strongly across the region, with high rainfall rates associated with the Inter-tropical Convergence Zone contributing to high wet deposition fluxes in the south, particularly for soluble species. Dry deposition fluxes of species associated with mineral dust exhibited strong seasonality, with highest fluxes associated with winter-time low-level transport of Saharan dust. Overall (wet plus dry) atmospheric inputs of soluble and total trace metals were used to estimate their soluble fractions. These also varied with season and were generally lower in the dry north than in the wet south. The ratio of ammonium plus nitrate to soluble iron in deposition to the ETNA was lower than the N:Fe requirement for algal growth in all cases, indicating the importance of the atmosphere as a source of excess iron

A modular radiative transfer program for gas filter correlation radiometry

The fundamentals of a computer program, simulated monochromatic atmospheric radiative transfer (SMART), which calculates atmospheric path transmission, solar radiation, and thermal radiation in the 4.6 micrometer spectral region, are described. A brief outline of atmospheric absorption properties and line by line transmission calculations is explained in conjunction with an outline of the SMART computational procedures. Program flexibility is demonstrated by simulating the response of a gas filter correlation radiometer as one example of an atmospheric infrared sensor. Program limitations, input data requirements, program listing, and comparison of SMART transmission calculations are presented

A parameterisation of single and multiple muons in the deep water or ice

Atmospheric muons play an important role in underwater/ice neutrino detectors. In this paper, a parameterisation of the flux of single and multiple muon events, their lateral distribution and of their energy spectrum is presented. The kinematics parameters were modelled starting from a full Monte Carlo simulation of the interaction of primary cosmic rays with atmospheric nuclei; secondary muons reaching the sea level were propagated in the deep water. The parametric formulas are valid for a vertical depth of 1.5-5 km w.e. and up to 85 deg for the zenith angle, and can be used as input for a fast simulation of atmospheric muons in underwater/ice detectors.Comment: 25 pages, 8 figure

Atmospheric correction of New Zealand Landsat imagery : a thesis presented in partial fulfilment of the requirements for the degree of Master of Philosophy in Earth Science at Massey University

In this study, MODIS data for New Zealand was downloaded and evaluated as input to the 6S atmospheric correction model. Data for one year were downloaded for aerosols, water vapour and ozone and trends of this data were studied. The sensitivity of retrieved reflectance of several targets to changes in the atmospheric components as seen in the MODIS data were also analysed. Several methods were developed for using this data for atmospheric correction and the output compared to a commercial atmospheric correction package (ATCOR 2). In addition, ground measurements were used to confirm the accuracy of the MODIS data. This involved both data obtained from NIWA and readings taken with a hand held MICROTOPS instrument. These readings showed that the MODIS data has some inaccuracies. This can result in a significant error in the retrieved reflectance, especially for darker targets, such as forest. Therefore caution should be exercised when using aerosol values from MODIS in an atmospheric correction. However, the results for water vapour and ozone were reasonably close, giving confidence for using MODIS ozone and water vapour in atmospheric correction. Ground measurements were also taken of targets with a GER 2600 Spectroradiometer and these readings compared to the atmospheric corrections of the same targets. This confirmed the accuracy of the atmospheric correction methods

Investigating bias in the application of curve fitting programs to atmospheric time series

The decomposition of an atmospheric time series into its constituent parts is an essential tool for identifying and isolating variations of interest from a data set, and is widely used to obtain information about sources, sinks and trends in climatically important gases. Such procedures involve fitting appropriate mathematical functions to the data. However, it has been demonstrated that the application of such curve fitting procedures can introduce bias, and thus influence the scientific interpretation of the data sets. We investigate the potential for bias associated with the application of three curve fitting programs, known as HPspline, CCGCRV and STL, using multi-year records of CO2, CH4 and O3 data from three atmospheric monitoring field stations. These three curve fitting programs are widely used within the greenhouse gas measurement community to analyse atmospheric time series, but have not previously been compared extensively. The programs were rigorously tested for their ability to accurately represent the salient features of atmospheric time series, their ability to cope with outliers and gaps in the data, and for sensitivity to the values used for the input parameters needed for each program. We find that the programs can produce significantly different curve fits, and these curve fits can be dependent on the input parameters selected. There are notable differences between the results produced by the three programs for many of the decomposed components of the time series, such as the representation of seasonal cycle characteristics and the long-term (multi-year) growth rate. The programs also vary significantly in their response to gaps and outliers in the time series. Overall, we found that none of the three programs were superior, and that each program had its strengths and weaknesses. Thus, we provide a list of recommendations on the appropriate use of these three curve fitting programs for certain types of data sets, and for certain types of analyses and applications. In addition, we recommend that sensitivity tests are performed in any study using curve fitting programs, to ensure that results are not unduly influenced by the input smoothing parameters chosen. Our findings also have implications for previous studies that have relied on a single curve fitting program to interpret atmospheric time series measurements. This is demonstrated by using two other curve fitting programs to replicate work in Piao et al. (2008) on zero-crossing analyses of atmospheric CO2 seasonal cycles to investigate terrestrial biosphere changes. We highlight the importance of using more than one program, to ensure results are consistent, reproducible, and free from bias

Income growth and atmospheric pollution in Spain: an Input-Output approach

The relationships between economic growth and environmental pressures are complex. Since the early nineties, the debate on these relationships has been strongly influenced by the Environmental Kuznets Curve hypothesis, which states that during the first stage of economic development environmental pressures increase as per capita income increases, but once a critical turning-point has been reached these pressures diminish as income levels continue to increase. However, to date such a delinking between economic growth and emission levels has not happened for most atmospheric pollutants in Spain. The aim of this paper is to analyse the relationship between income growth and nine atmospheric pollutants in Spain. In order to obtain empirical outcomes for this analysis, we adopt an input-output approach and use NAMEA data for the nine pollutants. First, we undertake a structural decomposition analysis for the period 1995-2000 to estimate the contribution of various factors to changes in the levels of atmospheric emissions. And second, we estimate the emissions associated with the consumption patterns of different groups of households classified according to their level of expenditureinput-output analysis; atmospheric pollution; income growth, Environmental Kuznets Curve; Spain.

Comparison of modelled and empirical atmospheric propagation data

The radiometric integrity of TM thermal infrared channel data was evaluated and monitored to develop improved radiometric preprocessing calibration techniques for removal of atmospheric effects. Modelled atmospheric transmittance and path radiance were compared with empirical values derived from aircraft underflight data. Aircraft thermal infrared imagery and calibration data were available on two dates as were corresponding atmospheric radiosonde data. The radiosonde data were used as input to the LOWTRAN 5A code which was modified to output atmospheric path radiance in addition to transmittance. The aircraft data were calibrated and used to generate analogous measurements. These data indicate that there is a tendancy for the LOWTRAN model to underestimate atmospheric path radiance and transmittance as compared to empirical data. A plot of transmittance versus altitude for both LOWTRAN and empirical data is presented