Modeling Of Environmental Data Research Articles

Airborne transmission risks of tuberculosis and COVID-19 in schools in South Africa, Switzerland, and Tanzania: Modeling of environmental data.

The COVID-19 pandemic renewed interest in airborne transmission of respiratory infections, particularly in congregate indoor settings, such as schools. We modeled transmission risks of tuberculosis (caused by Mycobacterium tuberculosis, Mtb) and COVID-19 (caused by SARS-CoV-2) in South African, Swiss and Tanzanian secondary schools. We estimated the risks of infection with the Wells-Riley equation, expressed as the median with 2.5% and 97.5% quantiles (credible interval [CrI]), based on the ventilation rate and the duration of exposure to infectious doses (so-called quanta). We computed the air change rate (ventilation) using carbon dioxide (CO2) as a tracer gas and modeled the quanta generation rate based on reported estimates from the literature. The share of infectious students in the classroom is determined by country-specific estimates of pulmonary TB. For SARS-CoV-2, the number of infectious students was estimated based on excess mortality to mitigate the bias from country-specific reporting and testing. Average CO2 concentration (parts per million [ppm]) was 1,610 ppm in South Africa, 1,757 ppm in Switzerland, and 648 ppm in Tanzania. The annual risk of infection for Mtb was 22.1% (interquartile range [IQR] 2.7%-89.5%) in South Africa, 0.7% (IQR 0.1%-6.4%) in Switzerland, and 0.5% (IQR 0.0%-3.9%) in Tanzania. For SARS-CoV-2, the monthly risk of infection was 6.8% (IQR 0.8%-43.8%) in South Africa, 1.2% (IQR 0.1%-8.8%) in Switzerland, and 0.9% (IQR 0.1%-6.6%) in Tanzania. The differences in transmission risks primarily reflect a higher incidence of SARS-CoV-2 and particularly prevalence of TB in South Africa, but also higher air change rates due to better natural ventilation of the classrooms in Tanzania. Global comparisons of the modeled risk of infectious disease transmission in classrooms can provide high-level information for policy-making regarding appropriate infection control strategies.

Mining the Web of Science for African cities and climate change (1991–2021)

This study provides a synthetic overview of thirty years of research devoted to urban climate change in Africa. Which cities in Africa are being researched on the impacts of climate change affecting them? What are the main social and urban issues and how are they linked? Is the development of climate services envisaged for these cities? Related to which local issues? Some answers are drawn by text mining the metadata of more than a thousand articles published in the 1991–2021 period and recorded in the Web of Science. The evidences produced are based on the design and exploitation of a taxonomy of keywords forming a set of issues and on their articulation in a network based on their co-occurrences in the articles' metadata. Forty-eight African countries and 134 cities are cited, Cairo, Dar es Salaam, Cape Town, Accra, Lagos, Durban, Nairobi, Addis Ababa, Kampala and Johannesburg being the cities deferring the largest number of studies. The salient urban climate change issues-health, water, energy, social issues and governance, followed by agriculture and food, mitigation, heat, urban territories, risks and hazards-are generally addressed in their interdependences. Urbanization and the implementation of associated policies, as well as the management of water resources, floods health and energy, and land use and land cover changes to a less extent, are proving to be the most pressing challenges. In view of the intricacy of these issues, climate services appear underdeveloped in African cities and barely confined to the acquisition and modeling of environmental data for decision-making in adaptation planning.

Statistical Power of Goodness-of-Fit Tests for Type~I Left-Censored Data

Type I doubly left-censored data often arise in environmental studies. In this paper, the power of the most frequently used goodness-of-fit tests (Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling) is studied considering various sample sizes and degrees of censoring. Attention is paid to testing of the composite hypothesis that the data has a specific distribution with unknown parameters, which are estimated using the maximum likelihood method. Performance of the tests is assessed by means of Monte Carlo simulations for several distributions, specifically the Weibull, lognormal and gamma distributions, which are among the most frequently used distributions for modelling of environmental data. Finally, the tests are used for identification of the distribution of musk concentrations if fish tissue.

Adaptations of endolithic communities to abrupt environmental changes in a hyper-arid desert

The adaptation mechanisms of microbial communities to natural perturbations remain unexplored, particularly in extreme environments. The extremophilic communities of halite (NaCl) nodules from the hyper-arid core of the Atacama Desert are self-sustained and represent a unique opportunity to study functional adaptations and community dynamics with changing environmental conditions. We transplanted halite nodules to different sites in the desert and investigated how their taxonomic, cellular, and biochemical changes correlated with water availability, using environmental data modeling and metagenomic analyses. Salt-in strategists, mainly represented by haloarchaea, significantly increased in relative abundance at sites characterized by extreme dryness, multiple wet/dry cycles, and colder conditions. The functional analysis of metagenome-assembled genomes (MAGs) revealed site-specific enrichments in archaeal MAGs encoding for the uptake of various compatible solutes and for glycerol utilization. These findings suggest that opportunistic salt-in strategists took over the halite communities at the driest sites. They most likely benefited from compounds newly released in the environment by the death of microorganisms least adapted to the new conditions. The observed changes were consistent with the need to maximize cellular bioenergetics when confronted with lower water availability and higher salinity, providing valuable information on microbial community adaptations and resilience to climate change.

Development of an application for creation and learning of neural networks to utilize in environmental sciences

Machine learning methods originated from artificial intelligence and today are applied in several fields concerning environmental sciences. Thanks to their powerful nonlinear modelling capability, machine learning methods today are utilized in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. Currently, the popularity of neural networks is growing; their areas of application are constantly expanding. In these conditions, the task of choosing a convenient tool for utilizing in environmental science with neural networks becomes urgent. There are many tools for working with neural networks, but each of them has its own drawbacks. So most of the existing tools require users to have programming knowledge; there are no tools to help quickly select the optimal network structure for the problem being solved. The purpose of the research is to simplify the process of choosing the optimal structure of an artificial neural network by developing an application with a graphical user interface with a visual representation of the stages of creating and learning neural networks in environmental sciences. The object of research is artificial feed-forward neural networks. Research work on the study, comparison and analysis of existing tools for the creation, learning and use of artificial neural networks has been carried out. Based on the research results, an application with a graphical interface aimed at solving the assigned tasks has been developed. An application developed to achieve this goal works correctly, without failures, and allows creating and learning feed-forward neural networks without programming knowledge.

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data.

Topic Modeling of Environmental Data on Social Networks Based on ED-LDA

The rapid development in information technology and web technology has facilitated an extreme increase in the collection and storage of digital data. With the development of environmental online monitoring science and internet technology development, more and more environmental data are stored on the Internet and shared by people on social networks. Therefore, there is a growing interest in automatically identifying environmental factors and environmental big data mining that contribute to public environmental risks, such as mining water quality problem, air pollution problem, soil problem on internet. Better understanding of these factors and analysis data will enable more precise prediction of the location and time of high risk events for environmental management. These environmental data from social networks by using WebCrawler in Twitter, Early work research on environmental data analysis focused more on specific filed analysis for traditional data without consider data relationships and data structure on social networks. The traditional environmental data analysis methods have been studied well, but no algorithms are designed for analysis environmental data on social networks. In this paper, this research propose a novel probabilistic generative model based on LDA, it called ED-LDA algorithm model that algorithm model not only consider the traditional environmental data analysis method, but also include the environmental data relationship and structure to help us find out the useful information and analysis to mine the relationship between users and their posted environmental data on social network to better understand data meaning for environmental management. This research present a Gibbs sampling implementation for inference of our model, and find out the environmental data topic on twitters. Besides our model can be used to many other environmental context files. The experimental result shows that Comparing with the traditional LDA clustering algorithm ED-LDA method can effectively mine and classify environmental data. This method can be a powerful computational approach for clustering environmental data on internet.

A Groud and Aerial BattleField Spatio-Temporal Data Unified OrganizeModel and Aplication

Aiming at the requirement of battlefield environment data model in joint operations, this paper proposed a groud and aerial battlefield spatio-temporal data unified organize model based on grid division. The thought of shere grid division is adopt to divide the space, and extend to the time dimension. Also, the spatio-temporal grid coding is designed. The field and object are combined to build the BSTD organization model, where the panet layer unit and panet volume unit are considered as the reference space separately to quantitatively describe the field, and the spatio-temporal grid coding is adopt to describe the space and time attributes of the objects. The relevant experiment is designed in paratroop tactics wargame simulation, and the results verify the feasibility of the model.

The use of interpolation methods for the modelling of environmental data

The purpose of the study was to provide some practical examples of using interpolation methods for modelling one-dimensional environmental data. Based on the measurement data acquired from the automatic air monitoring station in Warsaw (Poland), simulation of different variants of interpolation of the 1 h average values of air temperature and SO2 concentration was performed. The interpolation was done by three methods, namely, by a linear method with a cubic polynomial and with a cubic spline. The simulation of supplementing the missing values was conducted for test vectors with a varying measurement gap length and the variable margin of extreme adjacent values. Comparison of the obtained modelling results with previously removed actual data was also made. For the assessment of the modelling error, the mean absolute error, the root mean squared error and Willmott’s Index of Agreement were used. High accuracy of modelling was obtained, both for the short and the longer test vector variants, whereas the best environmental data modelling accuracy was obtained in short time intervals. It has been found that the use of interpolation for modelling a given type of environmental data should be preceded by the assessment of the accuracy of the employed methodology. In the framework of this study, a function for carrying out simulation of the accuracy of environmental data modelling was prepared within the MATLAB software program.

A software tool for retrieving land surface temperature from ASTER imagery

Cevresel verinin toplanmasi, analizi ve modellemesinde uzaktan algilama cok onemli ve iyi bilinen bir arac olmasina ragmen, termal kizilotesi (TIR) uzaktan algilamasina gereken onem verilmemistir. Kuresel cevre degisimi, iklim modelleri ve insan-cevre etkilesimleri gibi bircok cevresel calismalar icin yer yuzey sicakligi (LST) onemli bir parametredir. Bu calismada, Visual Basic NET programlama dili kullanilarak ASTER uydu goruntusunden yer yuzey sicakligini hesaplayan bir yazilim araci gelistirilmistir. Bu arac bes modulden olusmaktadir: 1- Dijital Sayilari (DN) Radyansa donusturme, 2- Yuzey yansima degerinin hesaplanmasi, 3- NDVI’in hesaplanmasi, 4- Yayinirligin hesaplanmasi ve 5- Sicakligin hesaplanmasi. Bu yazilim aracinda Jimenez Munoz ve Sobrino’nun ASTER uydu goruntusunden yer yuzey sicakliginin hesaplanmasi icin gelistirdigi Tek Kanal (Single Channel) Algoritmasi kullanilmistir. Bu programin termal cevre uzerine calisan bilim insanlari veya konuyla ilgilenen kisiler icin faydali olabilecegi umit edilmektedir.

A Software Tool for Retrieving Land Surface Temperature from

Remote sensing is a powerful and well-known tool in the collection, analysis, and modeling of environmental data, however, not much attention has been given to the use of thermal infrared (TIR) remote sensing. Land surface temperature (LST) is a key parameter for many environmental studies such as global environmental change, climate models, and human-environment interactions. In this study, a software tool which retrieves LST from Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) imagery has been developed in Visual Basic .NET. The tool consists of five main modules: 1- Converting DN to Radiances, 2- Calculating surface reflectances, 3- Calculating NDVI, 4- Calculating emissivity and 5- Calculating surface temperature. In this tool, Jimenez Munoz and Sobrino’s Single Channel (SCJM&S) Algorithm for ASTER was employed. With this tool, user can be able to calculate LST using (1) atmospheric parameters such as atmospheric transmissivity, upwelling and downwelling atmospheric radiances, (2) atmospheric water-vapor content. Hopefully, this program will be a useful tool for scientists/persons who are interested in studying thermal environments.

Generalized predictive information criteria for the analysis of feature events

This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler divergence measure. The concept of a model selection process that takes into account the special features of the underlying model is introduced using weighted measures. New information criteria are defined using the bias correction of an expected weighted loglikelihood estimator. Using weight functions that match the features of interest in the underlying statistical models, the new information criteria are applied to simulated studies of spline regression and copula model selection. Real data applications are also given for predicting the incidence of disease and for quantile modeling of environmental data.

Validation of air pollution biomonitoring networks and related data modelling: a geostatistical approach

This paper describes a geostatistical approach for environmental data modeling based on value descriptive analysis, variographic analysis, estimation method and validation. It shows an application related to atmospheric pollutant dispersion (ozone) detected by active biomonitoring in the province of Livorno (Italy). This work proposes a two-stage approach based on: the computation of an index on an exploratory and geostatistical data analysis to assess whether a monitoring network is well distributed in space and allows us to get reliable information about the whole study area; the derivation of a continuous representation of the variable (the Cotyledonous Damage Index) from punctual measurements. The geostatistical approach proposed is useful to define in a structured way the possible problems in a monitoring network, to control the data estimation error in the points not sampled. The case study analysed underlines that the biomonitoring network outline does not have a representative sample station distribution of the study area introducing significant errors related to the territorial generalization of the derived information. The proposed approach allows us to elaborate on the obtained data to carry out the data spatialization associating an error to them and to the evaluation model.

GIS integration model for geothermal exploration and well siting

The work involved in identifying geothermal fields can be simplified by means of a Geographical Information System (GIS), a decision-making tool used to determine the spatial association between exploration and environmental thematic maps. This methodology has been applied to part of the Sabalan geothermal field in northwestern Iran, and to the siting of exploration wells. The datasets used in the analyses consist of geological, geochemical and geophysical information. Boolean and Index Overlay knowledge-driven models were developed for site selection; the findings from these two models correlated well with the exploration data modeling. The results of the Index Overlay model were combined with those of an environmental suitability analysis for final selection of well sites. The results of exploration and environmental data modeling were combined to select and prioritize the sites of three exploratory wells in the study area.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Air quality monitoring using heterogeneous networks

AbstractIn this paper, we consider some approaches to spatio‐temporal modeling of environmental data obtained from an heterogeneous network.Besides discussing modeling details for spatio‐temporal dynamics and calibration of different instruments, we consider crossvalidation issues and extensions to the monitoring network assessment based on sensitivity analysis.We then consider a case study based on the heterogeneous network related to fine particulate matter (PM10) coming from the Po Valley, North Italy. Copyright © 2007 John Wiley & Sons, Ltd.

Modelling spatio-temporal environmental data

A conceptual model for environmental data is presented with special emphasis on the ability to store spatio-temporal references of the data. Other aspects of the model are the ability to handle hierarchical data and semantics of the measurements. The model was tested with an implementation on an object-relational database management system. As a part of the test implementation, a forestry data set covering 75 years and 4900 hectares was loaded onto the database.

Prediction of nonlinear spatial functionals

Spatial statistical methodology can be useful in the arena of environmental regulation. Some regulatory questions may be addressed by predicting linear functionals of the underlying signal, but other questions may require the prediction of nonlinear functionals of the signal. For example, in order to be in regulatory compliance, air-pollution levels have to fall within specified limits over some geographic region; whether or not they are in compliance and where they are out of compliance are nonlinear functionals. We propose a spatial empirical Bayes model for environmental data collected over a region. Further, we propose a predictor, based on the kriging methodology with extra constraints, that implies useful unbiasedness properties in predicting nonlinear spatial functionals. This predictor, called covariance-matching constrained kriging, is an optimal linear predictor that matches not only first moments but second moments (including specified covariances) as well.

Web‐based tools for environmental management

Contemporary urban environmental management calls for dissemination of environmental information among the public. This can be achieved by using a simple, while at the same time efficient, environmental management system platform, that integrates the monitoring and modelling of environmental data. The use of such a platform enables the urban planner and the environment‐related decision maker to have direct access to the relevant monitoring data, while a library of environmental management scenarios is developed and can be enriched. This article discusses the results of the IRENIE Environmental Telematics project in Athens, Greece, including the Web‐based geographical information system and its configuration. This specific project involves the application possibilities mentioned above, while integrating environmental monitoring, modelling and mapping over the Internet. The results discussed here demonstrate a significant increase in the capabilities of relevant authorities to advance their environmental management potential and to better inform the sensitive members of the public on potential health hazards.

Mathematical modeling of environmental data

Environmental pollution studies conducted to monitor ambient levels and to quantify the concentration of various pollutants entering a given environmental area are of great interest for possible adverse-health effects. Of particular importance in environmental data analysis is to select appropriate probability models. The previous studies indicate that none of the probability models, including the classical log-normal, has been identified to be superior to others in a general sense. To address this problem, the purpose of this paper is twofold. First, we introduce a generalized log-logistics distribution as a general model in fitting environmental pollutant data. The family of the generalized log-logistics distribution includes several well-known distributions in modelling data of environmental pollutant concentrations, such as log-normal, Weibull, and gamma as special cases. Second, by applying the proposed model to some environmental data sets, we explore the possibilities of using this model as a general probability model for fitting environmental-quality data.