scholarly journals An Environmental Data Collection for COVID-19 Pandemic Research

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 68 ◽  
Author(s):  
Qian Liu ◽  
Wei Liu ◽  
Dexuan Sha ◽  
Shubham Kumar ◽  
Emily Chang ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Storage ◽  
Implementation Strategies ◽  
Viral Disease ◽  
Environmental Data ◽  
Weather Factors ◽  
Mean Values ◽  
Task Forces ◽  
Global Mapping ◽  
The Impact

The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection.

scholarly journals Development of a model for the management of environmental safety of the region, taking into account of the GIS capacity

2018 ◽  
Vol 193 ◽  
pp. 02038 ◽  
Author(s):  
Viacheslav Burlov ◽  
Andrey Andreev ◽  
Fedor Gomazov
Keyword(s):  
Information System ◽  
Control System ◽  
Data Storage ◽  
Storage System ◽  
Practical Interest ◽  
Environmental Safety ◽  
Environmental Data ◽  
System Control ◽  
Results Management ◽  
The Impact

The system of space monitoring (SM) is of great importance, as a means of ensuring environmental safety. This system is based on remote sensing. The structure of SM is a distributed system. This system comprises independent data storage, system control, system of dynamic ratings, capacity and forecasting, control system, information system (IS) processing of monitoring data. As IS it is necessary to choose a geographic information system (GIS). IS monitoring refers to the problem-oriented system. These information systems include specialized databases models. All monitoring systems use sets of models, which allow building complex enterprise models. The peculiarity of the SM is the need to coordinate support of this monitoring and rate of the GIS capacity. Production Manager's decision is the impact on the object of monitoring. Results management and environmental data are received at the monitoring subsystem. Integration of SM and GIS monitoring has led to the creation of geoinformation space monitor (GISM). The operation of the system GISM is designed to provide a guaranteed result taking into account the capacity. Basis – the decision of the decision makers (DM). Therefore, an independent scientific and practical interest is the adequate mathematical model of DM.

scholarly journals The Impact on Geographic Location Accuracy due to Different Satellite Orbit Ephemerides

2009 ◽  
Vol 2009 ◽  
pp. 1-9
Author(s):  
Claudia C. Celestino ◽  
Cristina T. Sousa ◽  
Wilson Yamaguti ◽  
Helio Koiti Kuga
Keyword(s):  
Data Collection ◽  
Geographic Location ◽  
Satellite Orbit ◽  
Environmental Data ◽  
Location Accuracy ◽  
Doppler Shifts ◽  
Precise Ephemeris ◽  
Doppler Data ◽  
Important Input ◽  
The Impact

The current Brazilian System of Environmental Data Collection is composed of several satellites (SCD-1 and 2, CBERS-2 and 2B), Data Collection Platforms (DCPs) spread mostly over the Brazilian territory, and ground reception stations located in Cuiabá and Alcântara. An essential functionality offered to the users is the geographic location of these DCPs. The location is computed by the in-house developed “GEOLOC” program which processes the onboard measured Doppler shifts suffered by the signal transmitted by the DCPs. These data are relayed and stored on ground when the satellite passes over the receiving stations. Another important input data to GEOLOC are the orbit ephemeris of the satellite corresponding to the Doppler data. In this work, the impact on the geographic location accuracy when using orbit ephemeris which can be obtained through several sources is assessed. First, this evaluation is performed by computer simulation of the Doppler data, corresponding to real existing satellite passes. Then real Doppler data are used to assess the performance of the location system. The results indicate that the use of precise ephemeris can improve the performance of GEOLOC by reducing the location errors, and such conclusion can then be extended to similar location systems.

scholarly journals About creation of a hybrid multi-level atmospheric air monitoring system in the Republic of Tatarstan

2020 ◽  
Vol 2 (3) ◽  
pp. 96-103
Author(s):  
Oleg Trofimov ◽  
Andrey V. Rubezhov ◽  
Maria Kraft ◽  
Andrey V. Udaltsov
Keyword(s):  
Data Collection ◽  
Monitoring System ◽  
General Scheme ◽  
Legal Framework ◽  
Air Monitoring ◽  
Environmental Data ◽  
Atmospheric Air ◽  
Multi Level ◽  
The Republic ◽  
The Impact

The existing system of instrumental monitoring of the state of atmospheric air in the Republic of Tatarstan systematically reveals the facts of unsatisfactory quality of atmospheric air, in this connection, special attention has been paid to the work on regulating the impact of emissions on atmospheric air by means of calculated monitoring. The article notes that by now all the necessary legal framework has been created for creating a hybrid multi-level atmospheric air monitoring system in the Republic of Tatarstan. The paper provides a justification for the need to implement a hybrid multi-level atmospheric air monitoring system for three levels: the level "Enterprise" or "group of enterprises", the level" Municipality", the level"Subject of the Russian Federation the Republic of Tatarstan". The General scheme of the organization of environmental data collection in the Republic of Tatarstan and the scheme of the integrated architecture of the environmental data collection system are presented. The results of the implementation of a hybrid multi-level atmospheric air monitoring system in the environmental policy of the Republic of Tatarstan are presented

LESSONS LEARNED: THE CASE FOR DATA OPTIMIZATION BETWEEN RESPONSE AND NRDA

2017 ◽  
Vol 2017 (1) ◽  
pp. 3134-3150
Author(s):  
Laurie Benton ◽  
Linda Cook ◽  
Bob Haddad ◽  
Paul Boehm
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Oil Spill ◽  
Spatial Scales ◽  
Lessons Learned ◽  
Environmental Data ◽  
Third Party ◽  
Multiple Uses ◽  
The Impact ◽  
Data Optimization

ABSTRACT 2017-284: Over the past decade, three realizations have evolved from our collection and analysis of oil spill data. First is that more response data are being collected than ever before, including field and laboratory measurements in addition to observational data. To process this diverse information, we use sophisticated computer-based systems that allow us to integrate, analyze, and visualize satellite imagery, real-time weather and ship locations, field notes (e.g., shoreline cleanup assessment technique [SCAT] data), chemistry data, and photos. The second is the increased political and social interest in spills. Increased use of social media and the impact of these information pathways on the public’s perception of the spill response can drive real political decisions; making spill communications based on timely and high data quality critical. Lastly is the growing linkages between the collection, management, and uses of environmental data, not only for spill response, but also for NRD assessment (NRDA), determination of civil penalties (e.g., the Clean Water Act [CWA]), and third party legal claims. For example, observational and remote sensing data collected for response actions will ultimately be used to understand questions about contaminant pathways and exposures inherent to NRDA. Similarly, data collected as part of response mitigation and cleanup needs often provides our earliest understanding of the potential and actual natural resource damage issues, which are important for NRDA, third party claims, and CWA penalty mitigation. Historically, the inherent differences in temporal and spatial scales over which oil spill data are collected and used, coupled with the requirements of data quality, usability, and/or provenance, diminishes the ability to effectively optimize collection and uses of these data. Data optimization recognizes that data can/will have multiple uses and thus requires all data, whether response or NRDA-related to be of high and equivalent quality and be based on compatible, if not identical, data quality objectives (DQOs). In this paper, we review several examples that underscore the need for data optimization in environmental data collection. Specifically, we will explore how a focus on the long view and the need for data optimization can drive the collection of appropriate and multipurpose data, as well as inform the structure of data management systems. Using specific examples, we will demonstrate the value of embracing a data optimization framework in developing a common sample/data collection imperative that facilitates multiple uses.

Lessons learned from working with windstorm-related impact data

2021 ◽  
Author(s):  
Ilona Láng ◽  
Antti Mäkelä
Keyword(s):  
Data Collection ◽  
Early Warning Systems ◽  
Lessons Learned ◽  
Environmental Data ◽  
Strong Wind ◽  
Civil Protection ◽  
Worst Case ◽  
Socioeconomic Impacts ◽  
Impact Data ◽  
The Impact

<p>Strong wind related to extratropical cyclones causes severe socioeconomic impacts every year in Europe. Especially in highly forested countries, such as Finland, the civil protection, insurance companies and energy sector are strongly affected by windstorms. Falling trees cause damage to the properties and transmission lines, interrupt the traffic and in the worst case can even cause fatalities. With better preparedness measures, such as highly developed early warning systems (EWSs), windstorm impacts can be reduced significantly.</p><p>For better preparedness and mitigation of storm impacts, it is essential to understand the windstorm and environmental features which contribute to the damages. Wind speed and gusts alone do not always explain why the windstorm is or is not causing disturbances in the society. To increase the understanding of the processes that lead to windstorm impacts, it is crucial to use additional data alongside the traditional meteorological data sources. There is high potential in combining wind impact data (e.g. electricity interruption records or emergency calls) with meteorological parameters to develop tools, for instance as a part of EWSs for crisis decision making. Such tools can help the civil protection or energy companies to prepare for the windstorm with sufficient human resources and other precautionary measures, which ultimately reduces impacts and increases the resilience of the society. Additionally, impact database can benefit the forecasters in their daily work with weather warnings or researchers with easier access to impact data. </p><p>Impact database development has been done for instance on a national scale in SILVA project (2020-2021, Finnish National Emergency Supply Agency and Finnish Meteorological Institute) and on a pan-European scale in LODE project (2018-2021, the European Commission – DG ECHO). In this work we aim to share the lessons we learned in the impact data collection and processing, and the possibilities to connect the socioeconomic impacts with windstorms. We highlight especially how the quality and comprehensiveness of the impact data are the key factors in the development of wind impact tools. For example, to be able to identify significant trends in windstorm impacts, a sufficient temporal coverage and data homogeneity of the datasets are essential. The centralisation of the data collection is an additional important aspect: a centralised impact database maintained by one research organisation can be a solution to store and combine different types of impact data and connect it with the relevant meteorological or environmental data (e.g. forest or land use data).</p>

INDONESIA LANGUAGE INTERFERENCE FORM IN BASIC PPL LEARNING THAILAND UNHASY COLLEGE SYUDENTS

2020 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Siti Mariana Ulfa
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Data Collection ◽  
Teaching Practice ◽  
Data Presentation ◽  
Analysis Techniques ◽  
Thai Students ◽  
The Impact ◽  
Collection Methods ◽  
Recording Techniques

AbstractHumans on earth need social interaction with others. Humans can use more than one language in communication. Thus, the impact that arises when the use of one or more languages is the contact between languages. One obvious form of contact between languages is interference. Interference can occur at all levels of life. As in this study, namely Indonesian Language Interference in Learning PPL Basic Thailand Unhasy Students. This study contains the form of interference that occurs in Thai students who are conducting teaching practices in the classroom. This type of research is descriptive qualitative research that seeks to describe any interference that occurs in the speech of Thai students when teaching practice. Data collection methods in this study are (1) observation techniques, (2) audio-visual recording techniques using CCTV and (3) recording techniques, by recording all data that has been obtained. Whereas the data wetness uses, (1) data triangulation, (2) improvement in perseverance and (3) peer review through discussion. Data analysis techniques in this study are (1) data collection, (2) data reduction, (3) data presentation and (4) conclusions. It can be seen that the interference that occurs includes (1) interference in phonological systems, (2) interference in morphological systems and (3) interference in syntactic systems. 

scholarly journals Assessing population collapse of Drupella spp. (Mollusca: Gastropoda) 2 years after a coral bleaching event in the Republic of Maldives

Hydrobiologia ◽  
2021 ◽  
Author(s):  
L. Saponari ◽  
I. Dehnert ◽  
P. Galli ◽  
S. Montano
Keyword(s):  
Coral Bleaching ◽  
Prey Availability ◽  
Coral Cover ◽  
Environmental Data ◽  
High Plasticity ◽  
Bleaching Event ◽  
Coral Recovery ◽  
The Republic ◽  
The Impact ◽  
Line Intercept

AbstractCorallivory causes considerable damage to coral reefs and can exacerbate other disturbances. Among coral predators, Drupella spp. are considered as delayer of coral recovery in the Republic of Maldives, although little information is available on their ecology. Thus, we aimed to assess their population structure, feeding behaviour and spatial distribution around 2 years after a coral bleaching event in 2016. Biological and environmental data were collected using belt and line intercept transects in six shallow reefs in Maldives. The snails occurred in aggregations with a maximum of 62 individuals and exhibited a preference for branching corals. Yet, the gastropods showed a high plasticity in adapting feeding preferences to prey availability. Drupella spp. were homogenously distributed in the study area with an average of 9.04 ± 19.72 ind/200 m2. However, their occurrence was significantly different at the reef scale with the highest densities found in locations with higher coral cover. The impact of Drupella spp. appeared to be minimal with the population suffering from the loss of coral cover. We suggest that monitoring programs collect temporal- and spatial-scale data on non-outbreaking populations or non-aggregating populations to understand the dynamics of predation related to the co-occurrence of anthropogenic and natural impacts.

scholarly journals Technology Entrepreneurship and the Performance of Enterprises in the Conditions of Covid-19 Pandemic: The Fuzzy Set Analysis of Waste to Energy Enterprises in Poland

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3891
Author(s):  
Piotr Kordel ◽  
Radosław Wolniak
Keyword(s):  
Fuzzy Set ◽  
High Performance ◽  
Qualitative Comparative Analysis ◽  
Flexible Structures ◽  
Implementation Strategies ◽  
Waste To Energy ◽  
Energy Industry ◽  
Technology Entrepreneurship ◽  
Research Results ◽  
The Impact

This article’s aim is to explain the impact of technology entrepreneurship phenomenon on waste management enterprise performance in the conditions of COVID-19 pandemic. The concept of technology entrepreneurship according to the configuration approach and the category of high-performance organization are the theoretical bases of empirical investigation. For the implementation of empirical research, Fuzzy set Qualitative Comparative Analysis (FsQCA) was adopted. The research sample included a group of producers of Refused Derived Fuel (RDF) as a central part of the waste to energy industry located in Poland. The research results showed that the waste to energy sector is highly immune to pandemic threats. While during COVID-19, the basic economic parameters (i.e., sales, profitability and employment) of the entire industry in Poland clearly decreased, the same parameters in the case of the waste to energy industry remained at the same level. The research results allow the formulation of two high-performance models of technology entrepreneurship in the waste to energy industry under COVID-19 conditions. The first model is based on traditional technologies and hierarchical organizational structures, and the second is using innovative technologies and flexible structures. Both technology entrepreneurship models are determined by their emergence as complementary to implementation strategies and the opportunity-oriented allocation of resources within business model portfolios.

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