Inconsistent definitions of "urban" result in different conclusions about the size of urban carbon and nitrogen stocks

There is conflicting evidence about the importance of urban soils and vegetation in regional C budgets that is caused, in part, by inconsistent definitions of "urban" land use. We quantified urban ecosystem contributions to C stocks in the Boston (Massachusetts, USA) Metropolitan Statistical Area (MSA) using several alternative urban definitions. Development altered aboveground and belowground C and N stocks, and the sign and magnitude of these changes varied by land use and development intensity. Aboveground biomass (live trees, dbh > or = 5 cm) for the MSA was 7.2 +/- 0.4 kg C/m2 (mean +/- SE), reflecting a high proportion of forest cover. Vegetation C was highest in forest (11.6 +/- 0.5 kg C/m2), followed by residential (4.6 +/- 0.5 kg C/m2), and then other developed (2.0 +/- 0.4 kg C/m2) land uses. Soil C (0-10 cm depth) followed the same pattern of decreasing C concentration from forest, to residential, to other developed land uses (4.1 +/- 0.1, 4.0 +/- 0.2, and 3.3 +/- 0.2 kg C/m2, respectively). Within a land use type, urban areas (which we defined as > 25% impervious surface area [ISA] within a 1-km(2) moving window) generally contained less vegetation C, but slightly more soil C, than nonurban areas. Soil N concentrations were higher in urban areas than nonurban areas of the same land use type, except for residential areas, which had similarly high soil N concentrations. When we compared our definition of urban to other commonly used urban extents (U.S. Census Bureau, Global Rural-Urban Mapping Project [GRUMP], and the MSA itself), we found that urban soil (1 m depth) and vegetation C stocks spanned a wide range, from 14.4 +/- 0.8 to 54.5 +/- 3.4 Tg C and from 4.2 +/- 0.4 to 27.3 +/- 3.2 Tg C, respectively. Conclusions about the importance of urban soils and vegetation to regional C and N stocks are very sensitive to the definition of urban used by the investigators. Urban areas, regardless of definition, are rapidly expanding in their extent; a systematic understanding of how our development patterns influence ecosystems is necessary to inform future development choices.     

An application of multivariate analysis to acid rain data in Northern Italy to discriminate natural and man-made compounds

This paper presents the preliminary results of a study, the aim of which was to analyse the pluviometric and chemical rain data, recorded by a wet only network located in Northern Italy.More in detail, precipitations were collected on a weekly basis and chemical analysis was performed on pH, electric conductivity and Ca, Mg, Na, K, NH₄, NO₃, SO₄, Cl concentrations.The Principal Components Analysis pointed out that the first three components are enough to explain more than 90% of the variability of the parameters. Moreover each component may have a different physical interpretation, that is the first one is mainly related to the precipitation amount, while the second to the man made and natural sources and the last one to the sea/soil contribution.     

Progressive nitrogen limitation of ecosystem processes under elevated CO2 in a warm-temperate forest

A hypothesis for progressive nitrogen limitation (PNL) proposes that net primary production (NPP) will decline through time in ecosystems subjected to a step-function increase in atmospheric CO2. The primary mechanism driving this response is a rapid rate of N immobilization by plants and microbes under elevated CO2 that depletes soils of N, causing slower rates of N mineralization. Under this hypothesis, there is little long-term stimulation of NPP by elevated CO2 in the absence of exogenous inputs of N. We tested this hypothesis using data on the pools and fluxes of C and N in tree biomass, microbes, and soils from 1997 through 2002 collected at the Duke Forest free-air CO2 enrichment (FACE) experiment. Elevated CO2 stimulated NPP by 18-24% during the first six years of this experiment. Consistent with the hypothesis for PNL, significantly more N was immobilized in tree biomass and in the O horizon under elevated CO2. In contrast to the PNL hypothesis, microbial-N immobilization did not increase under elevated CO2, and although the rate of net N mineralization declined through time, the decline was not significantly more rapid under elevated CO2. Ecosystem C-to-N ratios widened more rapidly under elevated CO2 than ambient CO2 indicating a more rapid rate of C fixation per unit of N, a processes that could delay PNL in this ecosystem. Mass balance calculations demonstrated a large accrual of ecosystem N capital. Is PNL occurring in this ecosystem and will NPP decline to levels under ambient CO2? The answer depends on the relative strength of tree biomass and O-horizon N immobilization vs. widening C-to-N ratios and ecosystem-N accrual as processes that drive and delay PNL, respectively. Only direct observations through time will definitively answer this question.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.