scholarly journals GNSS Monitoring Network Optimization Case Study: Opak Fault Deformation, Yogyakarta

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Nurrohmat Widjajanti ◽  
Sherly Shinta Emalia ◽  
Parseno Parseno
Keyword(s):  
High Precision ◽  
Network Optimization ◽  
Fault Location ◽  
Monitoring Network ◽  
Parameter Method ◽  
Station Coordinates ◽  
Cofactor Matrix ◽  
Least Squares Adjustment ◽  
Gnss Network ◽  
Monitoring Stations

Opak fault is a fault located in Opak River area, Bantul. The existence of the fault is one of the biggest causes of earthquake in Yogyakarta in 2006. The seismic potential caused by the active fault requires continuous geodynamic monitoring. The GNSS network (TGD, SGY, and OPK) have been developed since 2013 consists of 17 stations and in 2016 there was an additional number of four monitoring stations. Several high-precision monitoring stations distributed at the fault location are needed to monitor the fault movement. Optimal observation network is one of the factors to obtain high precision station coordinates. The GNSS network optimization has been carried out in the previous research partially on each network; namely the segment of TGD, SGY, and OPK. Therefore, this research conducts a thoroughly optimization for 17 monitoring stations either use old or new stations to obtain an optimal network based on the criteria of accuracy and reliability.The network is designed widely from simple to complex combination and to combination between network segments. The computation uses least squares adjustment with parameter method. The value of the cofactor matrix parameter of the adjustment is applied to analyze the network based on the function of the accuracy criteria, namely A-Optimality, D-Optimality, E-Optimality, S-Optimality, and I-Optimality. Meanwhile, the value of the residual cofactor matrix is used for network configuration analysis based on the reliability objective function, namely the individual redundancy, external and internal reliabilities criteria. The result showed that the design of TGD, SGY and OPK network segments are optimized based on the criteria of accuracy and reliability if they use a network design with a complex baseline. The criteria for accuracy and reliability in the design with a combination of segments such as TGD and SGY, TGD and OPK, as well as TGD, SGY, and OPK are not much different from the optimization results performed by each segment. Therefore, if the measurements are carried out with a limited receiver, it is better to use each of segment designs.

scholarly journals Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David de Andrade Costa ◽  
José Paulo Soares de Azevedo ◽  
Marco Aurélio dos Santos ◽  
Rafaela dos Santos Facchetti Vinhaes Assump
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Network Optimization ◽  
Water Quality Index ◽  
Quality Index ◽  
Monitoring Network ◽  
Water Quality Assessment ◽  
Pollution Source ◽  
Components Analysis ◽  
Government Data

AbstractFifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQINSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

scholarly journals Assessment of ambient air pollution in the Waterberg Priority Area 2012-2015

2016 ◽  
Vol 26 (1) ◽  
pp. 21-28 ◽  
Author(s):  
G.T. Feig ◽  
S. Naidoo ◽  
N. Ncgukana
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Local Source ◽  
Ambient Air Quality ◽  
Priority Area ◽  
Hourly Data ◽  
Ambient Concentrations ◽  
Monitoring Stations

The Waterberg Priority Area ambient air quality monitoring network was established in 2012 to monitor the ambient air quality in the Waterberg Air Quality Priority Area. Three monitoring stations were established in Lephalale, Thabazimbi and Mokopane. The monitoring stations measure the concentrations of PM10, PM2.5, SO2, NOx, CO, O3, BTEX and meteorological parameters. Hourly data for a 31 month period (October 2012-April 2015) was obtained from the South African Air Quality Information System (SAAQIS) and analysed to assess patterns in atmospheric concentrations, including seasonal and diurnal patterns of the ambient concentrations and to assess the impacts that such reported pollution concentration may have. Local source regions for SO2, PM10, PM2.5 and O3 were identified and trends in the recorded concentrations are discussed.

Identifying redundant monitoring stations in an air quality monitoring network

2018 ◽  
Vol 190 ◽  
pp. 256-268 ◽  
Author(s):  
Chenchen Wang ◽  
Laijun Zhao ◽  
Wenjun Sun ◽  
Jian Xue ◽  
Yujing Xie
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Stations

Photochemical assessment monitoring stations program adapted for ozone precursors monitoring network in Mexico City

Atmósfera ◽  
2016 ◽  
Vol 29 (2) ◽  
pp. 169 ◽  
Author(s):  
Mónica Del Carmen Jaimes Palomera ◽  
Humberto Bravo Álvarez ◽  
Rodolfo Sosa Echeverria ◽  
Elías Granados Hernández ◽  
Pablo Sánchez Álvarez ◽  
...  
Keyword(s):  
Mexico City ◽  
Atmospheric Chemistry ◽  
Control Strategies ◽  
Monitoring Network ◽  
Ozone Precursors ◽  
The Us ◽  
Us Epa ◽  
Photochemical Modeling ◽  
Urban Ozone ◽  
Monitoring Stations

The purpose of this study is to select a number of stations from the existing Sistema de Monitoreo Atmosférico (Atmospheric Monitoring System, SIMAT) of Mexico City to serve as an equivalent to the Photochemical Assessment Monitoring Stations according to the US-EPA criteria, in order to improve the study of urban ozone occurrence. The results indicate that four existing SIMAT stations meet the criteria to form such network. The relevance of this study is to present an ozone precursors monitoring network with continuous measurements for future trustful studies on air quality for ozone, considering the atmospheric chemistry and photochemical modeling for the design control strategies appropriate for the particular conditions of Mexico City.

scholarly journals Time-lapse gravity and levelling surveys reveal mass loss and ongoing subsidence in the urban subrosion-prone area of Bad Frankenhausen, Germany

Solid Earth ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 599-619 ◽  
Author(s):  
Martin Kobe ◽  
Gerald Gabriel ◽  
Adelheid Weise ◽  
Detlef Vogel
Keyword(s):  
Mass Loss ◽  
Soil Water ◽  
High Precision ◽  
Urban Areas ◽  
Quantitative Estimation ◽  
Monitoring Network ◽  
Time Lapse ◽  
Ground Subsidence ◽  
Special Focus ◽  
Precision Time

Abstract. We present results of sophisticated, high-precision time-lapse gravity monitoring that was conducted over 4 years in Bad Frankenhausen (Germany). To our knowledge, this is the first successful attempt to monitor subrosion-induced mass changes in urban areas with repeated gravimetry. The method provides an approach to estimate the mass of dissolved rocks in the subsurface. Subrosion, i.e. leaching and transfer of soluble rocks, occurs worldwide. Mainly in urban areas, any resulting ground subsidence can cause severe damage, especially if catastrophic events, i.e. collapse sinkholes, occur. Monitoring strategies typically make use of established geodetic methods, such as levelling, and therefore focus on the associated deformation processes. In this study, we combine levelling and highly precise time-lapse gravity observations. Our investigation area is the urban area of Bad Frankenhausen in central Germany, which is prone to subrosion, as many subsidence and sinkhole features on the surface reveal. The city and the surrounding areas are underlain by soluble Permian deposits, which are continuously dissolved by meteoric water and groundwater in a strongly fractured environment. Between 2014 and 2018, a total of 17 high-precision time-lapse gravimetry and 18 levelling campaigns were carried out in quarterly intervals within a local monitoring network. This network covers historical sinkhole areas but also areas that are considered to be stable. Our results reveal ongoing subsidence of up to 30.4 mm a−1 locally, with distinct spatiotemporal variations. Furthermore, we observe a significant time-variable gravity decrease on the order of 8 µGal over 4 years at several measurement points. In the processing workflow, after the application of all required corrections and least squares adjustment to our gravity observations, a significant effect of varying soil water content on the adjusted gravity differences was figured out. Therefore, we place special focus on the correlation of these observations and the correction of the adjusted gravity differences for soil water variations using the Global Land Data Assimilation System (GLDAS) Noah model to separate these effects from subrosion-induced gravity changes. Our investigations demonstrate the feasibility of high-precision time-lapse gravity monitoring in urban areas for sinkhole investigations. Although the observed rates of gravity decrease of 1–2 µGal a−1 are small, we suggest that it is significantly associated with subterranean mass loss due to subrosion processes. We discuss limitations and implications of our approach, as well as give a first quantitative estimation of mass transfer at different depths and for different densities of dissolved rocks.

scholarly journals Groundwater nitrate monitoring network optimization with missing data

2004 ◽  
Vol 40 (2) ◽  
Author(s):  
L. M. Nunes ◽  
E. Paralta ◽  
M. C. Cunha ◽  
L. Ribeiro
Keyword(s):  
Missing Data ◽  
Network Optimization ◽  
Monitoring Network

A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

2020 ◽  
Author(s):  
Woo-Sik Jung ◽  
Woo-Gon Do
Keyword(s):  
Air Pollution ◽  
Cluster Analysis ◽  
Air Quality ◽  
Monitoring Network ◽  
Hierarchical Cluster ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Monitoring Stations

<p><strong>With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.</strong></p><p><strong>This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).</strong></p>

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