The spatio-temporal structures of society: modernity and ecological modernization as restructurations of time and space

Approaches in the social sciences have experienced a shift toward the themes of time and space, at least over the past three decades. This shift was clearly announced in the invitation made by Anthony Giddens in the early 1980s to retreat from the considerations of time and space as simple containers of social action. Furthermore, several other authors have pointed out at least three shortfalls of the status quo before 1980s: i) the lack of the temporal dimension in the sociological explanation of modernity, ii) the dismissal of the spatial particularity in the accounts of social change, and iii) the need to temporalize the geographical inquiries. How are social sciences accounts and social actions affected by transformation to the spatio-temporal structures of society? That is the general inquiry that inspired this thesis. The notion of spatio-temporal restructuration is introduced to capture the processes of restructuration that are taking place in the social sciences and in social life. Consequently, the study of the spatio-temporal structures of society includes epistemological and phenomenological research. A reorganization of social science spatio-temporal explanatory frameworks is proposed through epistemological research. A phenomenological investigation refers to the dialogical relationship between spatio-temporal arrangements and regimes, which together define the spatio-temporal structures of society. These two conditions of the research in spatio-temporal restructuration –epistemological and phenomenological- explain the twofold structure of the thesis

Time-Series Mapping of PM 10

This paper presents space-time kriging within a multi-Gaussian framework for time-series mapping of particulate matter less than 10 μm in aerodynamic diameter (PM10) concentration. To account for the spatiotemporal autocorrelation structures of monitoring data and to model the uncertainties attached to the prediction, conventional multi-Gaussian kriging is extended to the space-time domain. Multi-Gaussian space-time kriging presented in this paper is based on decomposition of the PM10 concentrations into deterministic trend and stochastic residual components. The deterministic trend component is modelled and regionalized using the temporal elementary functions. For the residual component which is the main target for space-time kriging, spatiotemporal autocorrelation information is modeled and used for space-time mapping of the residual. The conditional cumulative distribution functions (ccdfs) are constructed by using the trend and residual components and space-time kriging variance. Then, the PM10 concentration estimate and conditional variance are empirically obtained from the ccdfs at all locations in the study area. A case study using the monthly PM10 concentrations from 2007 to 2011 in the Seoul metropolitan area, Korea, illustrates the applicability of the presented method. The presented method generated time-series PM10 concentration mapping results as well as supporting information for interpretations, and led to better prediction performance, compared to conventional spatial kriging

Mapping the geogenic radon potential for Germany by machine learning

The radioactive gas radon (Rn) is considered as an indoor air pollutant due to its detrimental effects on humanhealth. In fact, exposure to Rn belongs to the most important causes for lung cancer after tobacco smoking. Thedominant source of indoor Rn is the ground beneath the house. The geogenic Rn potential (GRP) - a functionof soil gas Rn concentration and soil gas permeability - quantifies what“earth delivers in terms of Rn”and rep-resents a hazard indicator for elevated indoor Rn concentration. In this study, we aim at developing an improvedspatial continuous GRP map based on 4448field measurements of GRP distributed across Germany. Wefittedthree different machine learning algorithms, multivariate adaptive regression splines, random forest and supportvector machines utilizing 36 candidate predictors. Predictor selection, hyperparameter tuning and performanceassessment were conducted using a spatial cross-validation where the data was iteratively left out by spatialblocks of 40 km*40 km. Thisprocedure counteracts the effectofspatial auto-correlation in predictorand responsedata and minimizes dependence of training and test data. The spatial cross-validated performance statistics re-vealed that random forest provided the most accurate predictions. The predictors selected as informative reflectgeology, climate (temperature,precipitation and soil moisture), soil hydraulic, soilphysical (field capacity, coarsefraction) and soil chemical properties (potassium and nitrogen concentration). Model interpretation techniquessuch as predictor importance as well as partial and spatial dependence plots confirmed the hypothesized domi-nant effect of geology on GRP, but also revealed significant contributions of the other predictors. Partial and spa-tial dependence plots gave further valuable insight into the quantitative predictor-response relationship and itsspatial distribution. A comparison with a previous version of the German GRP map using 1359 independent testdata indicates a significantly better performance of the random forest based map

Water quality and pollutant dynamics in the Three Gorges Reservoir on the Yangtze River, China = Wasserqualität und Schadstoffdynamik im Drei-Schluchten-Reservoir am Yangtze, China

This dissertation provides scientific contributions to a sustainable management of the newly created ecosystem along the TGR on the Yangtze River in China. Measurements with the in situ and online multi-sensor system MINIBAT provide detailed insights into water quality and ecosystem processes in both spatial and temporal resolution. Based on the findings about pollutant sources and transportation as well as eutrophication and algal blooms, possible mitigation measures have been derived

Health-relevant, compound ozone and temperature burden in Europe: statistical modeling and climate change projections

Air pollution represents the major environmental health threat in Europe, with exposure to surface, ground-level ozone posing a major risk to the European population. Until the end of the 21st century, the health burden induced by ground-level ozone is expected to worsen due to ongoing climate change. Compound occurrences of health-relevant surface ozone and air temperature levels are of particular interest to environmental health science and projection studies, as there is evidence of an even intensified resulting health risk when both health stressors occur at the same time. The overall aim of the dissertation is to improve our current understanding and knowledge regarding recent and future health-relevant occurrences of ground-level ozone alone or alongside elevated levels of surface air temperature in Europe

Spatial dependence of body mass index and exposure to night-time noise in the Geneva urban area

In this study, we calculated the night-noise mean (SonBase 2014, compatible with the EU Environmental Noise Directive) for the 5 classes obtained after computation of Local Indicators of Spatial Association (LISA; Anselin et al 1995) on the BMI of the participants in the Bus Santé study, a cohort managed by the Geneva University Hospitals (N=15’544; Guessous et al 2014). We expected the mean of dBs to be significantly higher in the group showing spatial dependence of high BMI values (high-high class). We ran an ANOVA and multiple T-tests to compare the dB means between LISA clusters. The approach was applied to the participants of the whole State Geneva cohort, and to a reduced set of individuals living in the urban environment of the municipality of Geneva only

Land Use Conflict Detection and Multi-Objective Optimization Based on the Productivity, Sustainability, and Livability Perspective

Land use affects many aspects of regional sustainable development, so insight into its influence is of great importance for the optimization of national space. The book mainly focuses on functional classification, spatial conflict detection, and spatial development pattern optimization based on productivity, sustainability, and livability perspectives, presenting a relevant opportunity for all scholars to share their knowledge from the multidisciplinary community across the world that includes landscape ecologists, social scientists, and geographers. The book is systematically organized into the optimization theory, methods, and practices for PLES (production–living–ecological space) around territorial spatial planning, with the overall planning of PLES as the goal and the promotion of ecological civilization construction as the starting point. Through this, the competition and synergistic interactions and positive feedback mechanisms between population, resources, ecology, environment, and economic and social development in the PLES system were revealed, and the nonlinear dynamic effects among subsystems and elements in the system identified. In addition, a series of optimization approaches for PLES is proposed