Redundant Stations Research Articles

Production line balance problem identification and improvement based on decision tree: A case study of commercial air conditioner production line.

In the production of air conditioners, there are various issues such as complex requirements, redundant stations, excessive man-hours, and low production line balance rate. This paper aims to address these problems by analyzing the historical data of H Company's commercial air conditioner production line. The data is categorized into five aspects: station, working hours, standard working hours, labor capacity, and presence of bottleneck processes. To optimize and improve the second production line, this paper applies the production line balance management method based on data mining. It utilizes the decision tree model in data mining and incorporates lean production knowledge from industrial engineering. The goal is to identify crucial factors that affect the balance of the production line and address the issues caused by these factors. The aim is to reduce and eliminate redundant working hours and enhance the balance rate of the production line. By implementing the approach outlined in this paper, the bottleneck time of the second production line was reduced from 96.67 s to 74.6 s, and the production line balance rate increased from 68% to 85%.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

A novel assessment framework for improving air quality monitoring network layout

ABSTRACT Redundant stations in the air quality monitoring network (AQMN), not only cause high maintenance and operation costs, but also affect the performance of air quality assessment. This study presents a novel framework for identifying the redundant stations and selecting the corresponding alternatives in AQMN. The framework composes three main steps. Firstly, we identify the redundant stations by correlation analysis and stepwise regression methods. Secondly, we determine the corresponding alternative stations by cluster analysis and correspondence analysis methods. Finally, the final optimization results are verified by the support vector regression. We perform empirical evaluations of the framework using Shanghai’s AQMN. The results show that Xuhui, Zhangjiang, Shiwuchang, and Pudong New Area are four redundant pollution monitoring stations. Alternatives for each type of pollutant for these redundant stations are proposed and the adjusted layout of AQMN is verified with historical data. The framework proposed in this study can effectively improve the layout of AQMN, which could be applied to other cities or regions to improve the integrity of pollution information and reduce the monitoring costs. Implications: In this study, we set up a comprehensive framework. A case study proves that the framework we proposed can help countries identify redundant stations, so as to reduce the monitoring costs, improve the monitoring efficiency, and provide technical support for governments to implement accurate air quality control measures.Four particularly important aspects were highlighted in this work: (i) A new framework was constructed that combined regression and prediction for the first time to analyze and validate pollutant data; (ii) The framework used Stepwise Regression to improve previous methods for identifying redundant monitoring stations, effectively improving identification efficiency; (iii) The framework used Support Vector Regression to make predictions to verify the final results of the optimized layout, which was ignored in previous studies. (iv) This framework can be applied to any city or region, which has important practical significance for improving the comprehensiveness and accuracy of pollution monitoring in various cities.

Identification of Redundant Air Quality Monitoring Stations using Robust Principal Component Analysis

Air quality monitoring stations are essentials for monitoring air pollutants and, therefore, are essential to protect the public health and the environment from the adverse effects of air pollution. Two or more stations may monitor the same pollutant behavior. In this scenario, the equipment must be reallocated to provide a better use of public resources and to enlarge the monitored area. The identification of redundant stations can be carried out by the application of principal component analysis (PCA) as a grouping technique. The principal component analysis is a set of linear combinations of the original variables constructed to explain the variance–covariance structure of the data. It is well known that outliers affect the covariance structure of the variables. Since the components are computed by using the covariance or the correlation matrix, the outliers also affect the properties of the components. This article proposes a grouping methodology that applies robust PCA to identify air quality monitoring stations that present similar behavior for any pollutant or meteorological measure. To illustrate the usefulness of the proposed methodology, the robust PCA is applied to the management of the automatic air quality monitoring network of the Greater Vitoria Region in Brazil that consists of 8 stations. It was found that four components could explain 84% of the total variability, and it is possible to create a group composed of at least two stations in each one of the components. Therefore, the redundant stations can be installed in a new site to expand the monitored area.

Assessment of air quality monitoring networks using an ensemble clustering method in the three major metropolitan areas of Mexico

The spatial representativeness of air quality stations is a crucial factor in monitoring networks for designing and applying adequate air quality control measures. If redundant stations, which duplicate air quality data, want to be avoided in order to optimize and reduce the operational cost of air quality networks, robust methodologies must be applied for identifying redundant stations. Therefore, this study proposes the use of a clustering ensemble method to recognize similar and redundant stations by combining three clustering techniques: principle component analysis, hierarchical clustering, and k-means. The result of the ensemble method is analyzed based on additional information, such as emission sources and the meteorological and topographical conditions of the area of interest. This methodology is applied to the ozone (O3) and particulate matter with an aerodynamic diameter of less than or equal to 10 μm (PM10) time series data, acquired from the air pollutant monitoring systems located in the three main metropolitan areas of Mexico: Mexico City (MCMA), Monterrey (MMA), and Guadalajara (GMA). The findings show that the GMA has a well distributed air quality network with the fewest number of similar stations, as well as the MMA, which presents the same stations clusters for PM10 and O3. In contrast, in the MCMA, a cluster of possible redundant stations is found. Results confirm that the clustering ensemble method represents a reliable tool for identifying similar stations.

Identification of air quality redundant stations through a clustering ensemble method

The main purpose of this study is to assess the performance of three air quality monitoring networks of Mexico. Emphasis is placed on an ensemble method to combine the results of the different clustering techniques: Principle Component Analysis, Hierarchical Clustering and k-means. The specific objectives of this paper are: (i) finding similar and redundant stations using the ensemble method and (ii) giving a physical meaning to groups of similar stations by evaluating additional information like emission sources, meteorology and topography of the area of interest. The study was applied on time series data of particulates that have aerodynamic diameters less than or equal to 10 μm (PM10) and ozone (O3), acquired from the air pollutant monitoring systems in the metropolitan areas of Mexico City (MCMA), Monterrey (MMA) and Guadalajara (GMA). These three conurbations are characterized by diverse meteorological and geographical conditions. The findings show that the GMA has a well distributed air quality network with the fewest number of similar stations. The MMA presents the same clusters of stations for PM10 and O3, while in the MCMA a cluster of possible redundant stations is found. Results confirm that the clustering ensemble method is a confidence tool to identify similar stations.

Optimization of water quality monitoring stations using dynamic programming approach, a case study of the Mond Basin Rivers, Iran

Due to the importance of reassessing water quality monitoring stations, we employed the performance of the dynamic programming approach (DPA) to locate the monitoring stations and their numbers at the Mond Basin Rivers, located in the south of Fars and Bushehr Province, Iran. Firstly, the present monitoring stations were classified and prioritized using the Strahler stream order system and geographic information system, and then, the objective function was developed in terms of irrigation and drinking purposes in the study area. The catchment area was broken into five subbasins, and each station separately was assigned a score between 10 and 90. The DPA showed a reduction in the existing stations from 16 to 12 for the irrigation concern and from 16 to 11 for drinking concern. The optimum number of stations in the study area was predicted to be 12 to satisfy the drinking and irrigation purposes together. The majority of retained stations were located in the southern part of the basin, which demonstrated that the river water quality conspicuously might be changed in comparison with the northern part. The compatibility between the DPA results, the ongoing catchment area situation and literature represented the suitability of DPA in optimizing the water quality monitoring stations and cutting redundant monitoring stations.

Black Hole Spin Measurement Based on Time-domain VLBI Observations of Infalling Gas Clouds

Abstract The black hole spacetime is described by general relativity and characterized by two quantities: the black hole mass and spin. Black hole spin measurement requires information from the vicinity of the event horizon, which is spatially resolved for the Galactic center Sagittarius A* (Sgr A*) and the nearby radio galaxy M87 by means of very long baseline interferometry observations with the Event Horizon Telescope (EHT). In this paper, we simulate EHT observations for a gas cloud intermittently falling onto a black hole and construct a method for spin measurement based on its relativistic flux variation. The light curve of the infalling gas cloud is composed of peaks formed by photons which directly reach a distant observer and by secondary ones reaching the observer after more than one rotation around the black hole. The time interval between the peaks is determined by a period of photon rotation near the photon circular orbit which uniquely depends on the spin. We perform synthetic EHT observations for Sgr A* under the more realistic situation where a number of gas clouds intermittently fall toward the black hole with various initial parameters. Even for this case, the black hole spin dependence is detectable in correlated flux densities which are accurately calibrated by baselines between sites with redundant stations. The synthetic observations indicate that our methodology can be applied to EHT observations of Sgr A* from 2017 April.

Development of an Entropy Method for Groundwater Quality Monitoring Network Design

A new approach is presented based on the entropy theory to design an optimal groundwater quality monitoring network. First, the unrecorded values of Total Dissolved Solids (TDS) are estimated in the study area using Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) methods, and then, using Differential Evolution (DE) optimization algorithm aimed at maximizing the pure information gained by stations. The number and location of monitoring stations are selected optimally from the active wells located in the study area. The spatial distribution of chosen wells provides adequate coverage of the whole aquifer and, at the same time, gives the maximum useful information about the water quality status. It also avoids selecting any redundant station considering the operational and temporal costs. The proposed entropy method is compared with “error minimization” and “K-Means clustering” methods and is shown to be superior. The proposed approach is used for optimizing Eshtehard aquifer monitoring network in central part of Iran. As a result of its implementation, 20 wells are selected among 79 active monitoring wells in the study area, so that by sampling the wells, the maximum qualitative information can be obtained without needing to sample all the wells.

Identifying redundant monitoring stations in an air quality monitoring network

The monitoring information provided by air quality monitoring networks (AQMNs) supports air quality assessment and improvement planning in urban environments. The existence of redundant monitoring stations in an AQMN not only determines the cost of pollution monitoring, but also determines the integrity of pollution information monitoring and the accuracy of air quality assessment. In this study, we developed a method of identifying redundant monitoring stations using a combination of correlation analysis, principal components analysis, an assignment method, the cluster analysis, and correspondence analysis. We used (i) correlation analysis to determine whether the pollution information captured by a monitoring station was correlated with that of other stations, so as to determine whether the AQMN had redundant monitoring stations; (ii) principal components analysis to classify the pollution information and initially identify potentially redundant monitoring stations using the assignment method; and (iii) cluster analysis to identify alternative combinations of redundant monitoring stations. Finally, we verified the alternative combinations by means of a correspondence analysis to determine the final stations which were redundant. We used data on six pollutants (particulates smaller than 2.5 μm (PM2.5), 10 μm (PM10) in diameter, sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO)) at 13 environmental monitoring stations in Xi'an city, China, in 2016. The analysis identified three redundant stations (Xiao Zhai, Guang Yun Tan, and the Hi-Tech West Zone), and alternatives for each of the redundant stations. Therefore, the method described in this paper can effectively optimize the layout of an AQMN, thereby improving the integrity of the monitoring information and decreasing the cost.

Information theory‐based decision support system for integrated design of multivariable hydrometric networks

AbstractAdequate and accurate hydrologic information from optimal hydrometric networks is an essential part of effective water resources management. Although the key hydrologic processes in the water cycle are interconnected, hydrometric networks (e.g., streamflow, precipitation, groundwater level) have been routinely designed individually. A decision support framework is proposed for integrated design of multivariable hydrometric networks. The proposed method is applied to design optimal precipitation and streamflow networks simultaneously. The epsilon‐dominance hierarchical Bayesian optimization algorithm was combined with Shannon entropy of information theory to design and evaluate hydrometric networks. Specifically, the joint entropy from the combined networks was maximized to provide the most information, and the total correlation was minimized to reduce redundant information. To further optimize the efficiency between the networks, they were designed by maximizing the conditional entropy of the streamflow network given the information of the precipitation network. Compared to the traditional individual variable design approach, the integrated multivariable design method was able to determine more efficient optimal networks by avoiding the redundant stations. Additionally, four quantization cases were compared to evaluate their effects on the entropy calculations and the determination of the optimal networks. The evaluation results indicate that the quantization methods should be selected after careful consideration for each design problem since the station rankings and the optimal networks can change accordingly.

Optimized GNSS Station Selection to Support Long-Term Monitoring of Ionospheric Anomalies for Aircraft Landing Systems

Differential global navigation satellite systems (GNSS)-based aircraft precision approach and landing systems require the development of ionospheric threat models to insure that users are sufficiently protected against ionospheric anomalies. The long-term ionospheric anomaly monitor (LTIAM) is being used to build ionospheric threat models for ground-based augmentation systems (GBAS) and to continuously monitor ionospheric behavior over the life cycle of GBAS. While LTAIM exhaustively detects all potential anomalies, the use of poor-quality GNSS data degrades the accuracy of ionospheric delay estimates and produces many faulty anomaly candidates, thus adding a great burden to LTIAM processing. To select GNSS reference stations with high-quality data, an optimized set of thresholds for data quality metrics are established. The high-quality station selection method maximizes the elimination of spurious gradients while minimizing unnecessary station removals. When applied to the continuously operating reference stations (CORS) network in the Conterminous U.S. (CONUS), this method discards 90% of faulty candidates while only excluding 14% of the over 1600 CORS stations. The well-distributed subnetwork selection method is also proposed to remove geographically redundant stations in dense regions. The number of CORS stations in CONUS is reduced to 48% of total stations when a desired baseline constraint is 100 km. The results demonstrate that the optimal GNSS station section methods are applicable to a wide range of GNSS station networks that will be used for ionospheric monitoring.

Evaluation of Air Pollution Monitoring Networks in Seoul Metropolitan Area using Multivariate Analysis

The adequacy of urban air quality monitoring networks in the largest metropolitan city, Seoul was evaluated using multivariate analysis for , , CO, PM10, and . Through cluster analysis for 5 air pollutants concentrations, existing monitoring stations are seen to be clustered mostly by geographical locations of the eight zones in Seoul. And the stations included in the same cluster are redundantly monitoring air pollutants exhibiting similar atmospheric behavior, thus it can be seen that they are being operated inefficiently. Because monitoring stations groups representing redudancy were different depending on measurement items and several pollutants are being measured at the same time in each air monitoring station, it is seemed to be not easy to integrate or transmigrate stations. But it may be proposed as follows : the redundant stations can be integrated or transmigrated based on ozone of which measures are increasing in recent years and alternatively the remaining pollutants other than the pollutant exhibiting similar atmospheric behavior with nearby station's can be measured. So it is considered to be able to operate air quality monitoring networks effectively and economically in order to improve air quality.

ASSESSMENT OF THE SPATIAL VARIATION AND SOURCE APPORTIONMENT OF AIR POLLUTION BASED ON CHEMOMETRIC TECHNIQUES: A CASE STUDY IN THE PENINSULAR MALAYSIA

This study aims to investigate the spatial variation in the source of air pollution, identify the percentage contribution of each pollutant and apportion the mass contribution of each source category using chemometric techniques. Hierarchical agglomerative cluster analysis (HACA) successfully grouped the five air monitoring sites into three groups (cluster 1, 2 and 3). Principal component analysis (PCA) was used to spot out the sources of air pollution which are attributed to anthropogenic activities. Multiple linear regression (MLR) was used to develop an equation model that explains the contribution of pollutants in each cluster. However, it was observed that particulate matter (PM10) and Ozone (O3) are the most significant pollutants influencing the value of air pollutant index (API). Meanwhile, the source apportionment indicates that cluster 1 is influenced by gas and non-gas pollutants to a degree of 84%, weather condition 15% and 1% by gas and secondary pollutants. Cluster 2 is affected by gas and secondary pollutants to a tune of 87% and 13% by weather condition while cluster 3 is apportioned with 98% secondary gas and non-gas pollutants and 2% weather condition. This study reveals the usefulness of chemometric technique in modeling and reducing the cost and time of monitoring redundant stations and parameters.

A performance assessment and adjustment program for air quality monitoring networks in Shanghai

Abstract In this study, we evaluated the performance of Shanghai's air quality monitoring network (AQMN) using principal components analysis, an assignment method, and cluster analysis. Our goal was to improve the utilization of monitoring stations and evaluate Shanghai's air quality more comprehensively and accurately. Specifically, we (i) identified similar pollution sources or behaviors in the monitoring areas; (ii) identified redundant monitoring stations and re-evaluated the AQMN's performance without them; and (iii) proposed adjustments to the AQMN. We used data on particulates less than 2.5 μm (PM2.5) and 10 μm (PM10) in diameter, sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) at stations in and around Shanghai from 1 January to 22 August 2014. For each pollutant, we grouped the monitoring stations into clusters based on their different pollution behaviors, revealing redundancy and inefficiency in the current AQMN that resulted from the concentrated station distribution and similarity of the monitoring environments. The analysis results showed that there exist redundant stations in the current AQMN of Shanghai. Furthermore, we proposed adjustments to Shanghai's AQMN: transfer four redundant stations and build a new station in the directions of the Taicang Experimental Primary School, Kunshan Zhenchuan Middle School, Suzhou Industrial Park, Wujiang Industrial Zone, and Jiaxing Monitoring Station. Our analysis suggests that, in addition to industrial, transportation, construction, and population influences inside Shanghai, external pollutants significantly affect Shanghai's air quality. Therefore, it is necessary to jointly prevent and control regional air pollution both in Shanghai and in neighboring cities.

Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

AbstractRainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well‐designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging‐based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.

Mean seasonal and spatial variability in gauge‐corrected, global precipitation

AbstractUsing traditional land‐based gauge measurements and shipboard estimates, a global climatology of mean monthly precipitation has been developed. Data were obtained from ten existing sources, screened for coding errors, and redundant station records were removed. The edited data base contains 24,635 spatially independent terrestrial station records and 2223 oceanic grid‐point records. A procedure for correcting gauge‐induced biases is presented and used to remove systematic errors caused by wind, wetting on the interior walls of the gauge, and evaporation from the gauge. These ‘corrected’ monthly precipitation observations were then interpolated to a 0·5° of latitude by 0·5° of longitude grid using a spherically based interpolation procedure. Bias‐corrected spatial distributions of the annual mean and intraannual variance are presented along with a harmonic decomposition of the intra‐annual variance.

Mean seasonal and spatial variability in global surface air temperature

Using terrestrial observations of shelter-height air temperature and shipboard measurements, a global climatology of mean monthly surface air temperature has been compiled. Data were obtained from ten sources, screened for coding errors, and redundant station records were removed. The combined data base consists of 17 986 independent terrestrial station records and 6 955 oceanic grid-point records. These data were then interpolated to a 0.5° of latitude by 0.5° of longitude lattice using a spherically-based interpolation algorithm. Spatial distributions of the annual mean and intra-annual variance are presented along with a harmonic decomposition of the intra-annual variance.