Relationship between solar radiation and dimethylsulfide concentrations using in situ data for the pristine region of the southern hemisphere

The biological processes have been proposed as climate variability contributors. Dimethylsulfide (DMS) is the main biogenic sulfur compound in the atmosphere; it is mainly produced by the marine biosphere and plays an important role in the atmospheric sulfur cycle. Currently it is accepted that terrestrial biota not only adapts to environmental conditions but also influences them through regulations of the chemical composition of the atmosphere. In the present study we used a wavelet method to investigate the relationship between DMS, Low cloud cover (LCC), Ultraviolet Radiation A (UVA), Total Solar Irradiance (TSI) and Sea Surface Temperature (SST) in the so called pristine zone of the Southern Hemisphere. We found that the series analyzed have different periodicities which can be associated with large scale climatic phenomena such as El Niño (ENSO) or the Quasi-Biennial Oscillation (QBO), and/or to solar activity. Our results show an intermittent but sustained DMS-SST correlation and a DMSUVA anti correlation; but DMS-TSI and DMS-LCC show nonlinear relationships. The time-span of the series allow us to study only periodicities shorter than 11 years, then we limit our analysis to the possibility that solar radiation influences the Earth climate in periods shorter than the 11-year solar cycle. Our results also suggest a positive feedback interaction between DMS and solar radiation.     

Measuring Global Spatial Autocorrelation with Data Reliability Information

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular SA statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, one is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation. Key Words: American Community Survey, Geary ratio, Moran’s I, permutation test, spatial Bhattacharyya coefficient.     

Geostatistical modeling of clay spatial distribution in siliciclastic rock samples using the plurigaussian simulation method

In order to implement secondary and enhanced oil recovery processes in complex terrigenous formations as is usual in turbidite deposits, a precise knowledge of the spatial distribution of shale grains is a crucial element for the fluid flow prediction. The reason of this is that the interaction of water with shale grains can significantly modify their size and/or shape, which in turn would cause porous space sealing with the subsequent impact in the flow. In this work, a methodology for stochastic simulations of spatial grains distributions obtained from scanning electron microscopy images of siliciclastic rock samples is proposed. The aim of the methodology is to obtain stochastic models would let us investigate the shale grain behavior under various physico-chemical interactions and flux regimes, which in turn, will help us get effective petrophysical properties (porosity and permeability) at core scale. For stochastic spatial grains simulations a plurigaussian method is applied, which is based on the truncation of several standard Gaussian random functions. This approach is very flexible, since it allows to simultaneously manage the proportions of each grain category in a very general manner and to rigorously handle their spatial dependency relationships in the case of two or more grain categories. The obtained results show that the stochastically simulated porous media using the plurigaussian method adequately reproduces the proportions, basic statistics and sizes of the pore structures present in the studied reference images.     

Spatial and temporal variations of atmospheric aerosol optical thickness in northwestern Mexico

The purpose of this paper was to study aerosol particles in the Northwestern region of Mexico (NWM) through Aerosol Optical Thickness (AOT) parameter in the atmosphere. This parameter represents one of the extinction coefficients of solar radiation and the rate of suspended particles in the atmosphere. For determination of AOT, we considered the use of remote sensors outside of the atmosphere. In particular, Moderate Resolution Imaging Spectroradiometer (MODIS) which can measure the atmospheric AOT thickness. Data from the MODIS sensor must be validated before they are considered reliable. For this task, we required surface measurements to obtain a correlation with the data acquired with the remote radiometer. The paper describes the validation process performed for data obtained with MODIS through measurements provided by an AErosol RObotic NETwork (AERONET) photometer located in the city of Hermosillo, Sonora, NWM. Additionally, we carried out a temporal analysis based on the behavior of the AOT graphics and spatial analysis supported in maps with sufficient information.     

Geostatistical simulation of spatial variability of convective storms in Mexico City Valley

Precipitation is one of the main components of the hydrological cycle and knowledge of its spatial distribution is fundamental for the prediction of other closely related environmental variables, for example, runoff, flooding and aquifer recharge. Most of the precipitation in Mexico City is due to convective storms characterized by a high spatial variability, implying that modeling its behavior is very complex. In this work stochastic simulation techniques with a geostatistical approach were applied to model the spatial variability of the rainfall of three convective storms. The analysis of the results shows that using the proposed methodology spatial distributions of rain are obtained that reproduce the statistical characteristics present in the available information.     

Spatial and Sectoral Patterns of Firm Entry in China

This article provides new empirical evidence on the patterns of firm entry across sectors and space. Based on Pavitt’s four manufacturing industry categories—supplier-dominated, scale-intensive, specialized suppliers, and knowledge-based—we use China’s firm-level database, covering all manufacturing firms with sales above 5 million RMB from 1998 to 2008, to investigate the relation between spatial patterns of new entry firms and industrial structure change and firm heterogeneity. Based on our analysis, there are three key findings. First, industrial structure change plays an important role in influencing the sectoral pattern of new firm entry. Second, the transformation from a supplier-dominated industry to a scale-intensive industry from 1998 to 2008 in China has largely influenced spatial patterns of new entry firms (mainly spatial agglomeration and entry similarity between regions). Third, the heterogeneity of firms (mainly firm ownership, firm size, and technological relatedness) affected the location choice of new entry firms. Based on our findings, we conclude that the importance of industrial structure change and the heterogeneity of firms in revealing the spatial pattern of new entry firms should receive more attention. Key Words: China, firm entry, firm heterogeneity, industrial structure change, sectoral and spatial pattern.     

Daubechies wavelet coefficients: a tool to study interplanetary magnetic field fluctuations

We have studied a set of 41 magnetic clouds (MCs) measured by the ACE spacecraft, using the discrete orthogonal wavelet transform (Daubechies wavelet of order two) in three regions: Pre-MC (plasma sheath), MC and Post-MC. We have used data from the IMF GSM-components with time resolution of 16 s. The mathematical property chosen was the statistical mean of the wavelet coefficients (〈Dd1 〉). The Daubechies wavelet coefficients have been used because they represent the local regularity present in the signal being studied. The results reproduced the well-known fact that the dynamics of the sheath region is more than that of the MC region. This technique could be useful to help a specialist to find events boundaries when working with IMF datasets, i.e., a best form to visualize the data. The wavelet coefficients have the advantage of helping to find some shocks that are not easy to see in the IMF data by simple visual inspection. We can learn that fluctuations are not low in all MCs, in some cases waves can penetrate from the sheath to the MC. This methodology has not yet been tested to identify some specific fluctuation patterns at IMF for any other geoeffective interplanetary events, such as Co-rotating Interaction Regions (CIRs), Heliospheric Current Sheet (HCS) or ICMEs without MC signatures. In our opinion, as is the first time that this technique is applied to the IMF data with this purpose, the presentation of this approach for the Space Physics Community is one of the contributions of this work.     

Hydroelectric Infrastructure and Potential Groundwater Contamination in the Brazilian Amazon: Altamira and the Belo Monte Dam

Heavy investments in hydroelectric development are occurring throughout the Amazon Basin, which holds 42.2 percent of Brazil’s hydroelectric potential. The Belo Monte dam is the most recent and largest project in this region. The prevalence of septic systems in the Amazon, coupled with the widespread use of water wells and rising water table from filling the reservoir, create sanitation and health concerns for upstream communities. Using spatial analytical data and terrain analyses, we identify high-risk locations within the most densely populated neighborhoods in Altamira, Belo Monte’s host city. The purpose of this research is to develop a heuristic for identifying areas susceptible to groundwater and well contamination in relation to existing and proposed hydroelectric projects. Altamira’s city center persists as a high-risk location for contamination of wells because of its population density and relatively low elevation compared to other parts of the city. The methods, tools, and analyses presented in this article provide a framework that can be used to identify vulnerability to groundwater and drinking well contamination. The results presented here can guide implementation of public health and sanitation efforts in areas affected by large hydroelectric projects to avoid future water quality crises. Key Words: Belo Monte, groundwater contamination, hydroelectric infrastructure, risk assessment, spatial and terrain analyses.     

Spatial Correlation between Traffic and Air Pollution in Beijing

Many studies have attempted to model the sophisticated influence of traffic emissions on air pollution, but most models only calculate the contribution of traffic emissions near monitoring sites. It is difficult to observe the near-surface dynamics such as wind, rain, and human activities and precisely distinguish traffic emissions. These obstacles make model simulation very expensive in practice. The regional distribution patterns that can help adjust policies and actions taken remain unknown. Therefore, this article proposes a grid-oriented geostatistics-based approach to overcome these obstacles. We chose central Beijing as the study area. An experiment implemented the approach on data collected from Global Positioning System navigation software, car rental companies, and meteorology stations. The results suggest that the northwest area of Beijing has high traffic-related air pollution (TRAP) and the southeast area has low TRAP. Unlike modeling-based methods, this work uses geostatistical methods to directly study the spatiotemporal connections between traffic and PM_2.5 (particulate matter with diameter less than 2.5?μm) from the phenomenon. The calculation is conducted under no hypotheses and has little risk of producing results contradictory to facts. This work provides a reference for future TRAP research on directly learning from the phenomenon and assists decision makers with seamless spatiotemporal heat maps of TRAP distribution. Key Words: Beijing, geospatial statistics, PM_2.5, spatial correlation, traffic-related air pollution.