Analysis Of Air Quality Data Research Articles

Domain knowledge-enhanced multi-spatial multi-temporal [formula omitted] forecasting with integrated monitoring and reanalysis data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

Environmental Impacts of the China-Pakistan Economic Corridor (CPEC): An analysis of Climate Change Mitigation and Adaptation Strategies

This article explores the complex environmental impacts of the China-Pakistan Economic Corridor (CPEC) and suggests sustainable development strategies. Our study reveals significant changes in land use patterns, including loss of important habitat, which is contributing to biodiversity decline. Analysis of air quality data shows increased levels of pollutants in urban areas along the corridor, which are exacerbated by construction activity and increased traffic. Water resource assessments highlight issues related to quality and availability, especially in areas with high industrial activity. In response to these findings, we suggest methods for mitigating and adapting to climate change along the corridor. Interpreting these findings, we argue that these assessments are critical for identifying potential environmental problems and ensuring that development initiatives are built with long-term sustainability in mind. Recommendations for mitigating climate change include international cooperation, increasing public awareness, and using resilient design principles. For future development projects, we include sustainable practices, community involvement and compliance with environmentally friendly standards as important elements in economic growth. We draw conclusions by emphasizing the need for future studies to track long-term impacts, evaluate socio-economic implications, and implement environmentally friendly strategies, maintaining a long-term balance between economic development and environmental conservation in CPEC and other international projects.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

INFORMATION AND ANALYTICAL SYSTEM FOR COLLECTING, PROCESSING AND ANALYZING DATA ON AIR POLLUTION

The presented information and analytical system is designed to provide convenient management of the processes of collecting, processing and analyzing air pollution data. The system is a web application built on the basis of the R programming language using the Shiny web service and works with data from Vaisala monitoring stations. The created service receives data from the monitoring stations on various air parameters such as wind speed, temperature, and levels of pollutants such as PM2.5, CO, and NO2.The main functions of the web service include uploading data to a dedicated server, processing data with the ability to round time ranges, interactive data visualization using graphs and charts, building air quality charts, and presenting AQI, which can be used to classify pollution levels and generate recommendations for different sensitivity groups of the population.The system allows users to download data from the server in various formats or access the original data from the Vaisala server. In addition, users can convert data to different units of measurement. The web service can be of interest to both household users and professionals in various fields, including environmental researchers in Ukraine, governmental environmental control organizations, municipal and public organizations interested in a clean environment. By improving the availability and analysis of air quality data, this service contributes to the preservation of the environment and public health.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

DEVELOPMENT OF AN INTERNET OF THINGS BASED AIR QUALITY MONITORING SYSTEM USING MACHINE LEARNING

Air pollution and its negative impacts on human health have become serious concerns in many places throughout the world. The traditional methods of monitoring air quality, such as manual sampling and laboratory analysis, are time-consuming, expensive, and may not provide real-time information. In this study, an IoT-based Air Quality Monitoring System that uses Machine Learning to provide accurate and timely analysis of air quality data is presented. The system collects data from a network of sensors measuring various air quality parameters, processes the data using ML algorithms to identify patterns and predict future conditions, and provides insights into the current state of the environment. The findings showed that the emissions had an inversely proportional impact on air quality in the study region and the system achieved an accuracy of 0.978. This study has the potential to provide accurate and timely analysis of air quality data and regulate air quality in real-time.

Multidimensional Data Analysis of Ambient Air Quality Based on Apache Kylin

While the environmental monitoring department increases the number of monitoring points and the frequency of monitoring, it will also bring about a surge in the amount of monitoring data and computational response events. However, the traditional data statistics methods based on relational databases are ineffective in the face of huge environmental monitoring data. Aiming at ambient air quality data analysis, this paper uses Hadoop, Hive, Kylin, and other tools to build a multi-dimensional analysis platform for ambient air quality big data in a distributed environment, which realizes the unified storage, calculation, and analysis of ambient air quality monitoring data. Compared with the traditional relational database statistical analysis scheme, the proposed solution significantly improves the efficiency of statistical analysis of ambient air quality data under the condition of large data. The response time is shortened by 98%, reaching the sub-second level.

Spatial–Temporal Characteristics and Influencing Mechanisms of Air Quality Index by Considering COVID-19 in Yunnan, Southeastern Tibetan Plateau

Based on the analysis of air quality data in Yunnan Province from 2015 to 2020, combined with spatial interpolation analysis and geographic detector factor analysis, the spatial–temporal evolution characteristics of air quality in Yunnan Province have been studied, and the main driving factors, the mechanisms, and the impact of regional COVID-19 control measures affecting air quality have been discussed. The results show that the air quality in Yunnan Province was generally good (superior rate > 98%) from 2015 to 2020, that the Air Quality Index (AQI) value is better in the wet season than in the dry season, and that the concentration of major pollutants shows a decreasing trend. AQI values are spatially high in the east and low in the west. The relative humidity, precipitation, population density, building construction area, and civil vehicles have a greater degree of explanation for the spatial differentiation of AQI, whereas the synergistic influence (maximum value 0.92) of socio-economic factors and meteorological factors is significantly greater than that of a single factor (maximum value 0.80) by the geographic detector model. The control measures for COVID-19 in 2020 reduced the concentration of major pollutants in the atmosphere to a certain extent. Controlling regional air pollution in urban agglomerations in low-latitude plateau areas can improve their air quality by reducing human activities. However, the control of O3 concentration is more complex, and more restrictive factors need to be considered. The results will provide a scientific basis for the prevention and control of air pollution in plateau cities.

Long time series analysis of air quality data in the Veneto region (Northern Italy) to support environmental policies

The world population is demonstrating an increasing awareness about the ecological impacts of air quality, including impacts on human health. The Veneto region and, more generally, the Po Valley (NE Italy), are characterised by frequent exceedance of limit values for air quality, in particular particulate matter (PM10), which causes these areas to be listed as hotspots; thus, this region has some of the worst air pollution in Europe.The aim of the current research was to analyse a 10 year-long time series of air quality data (2011–2021) in the Veneto region to investigate the influence of selected factors on air quality, such as natural processes (meteorological conditions), environmental policies, and health emergency measures due to the COVID-19 pandemic.Generally, the considered pollutants, PM10, NO, and NO2, presented a decreasing trend during the last ten years. The reduction in nitrogen oxides was clearly improved after the implementation of a specific environmental protocol (the “New Agreement of Po Valley Basin”). Conversely, the PM10 concentration seemed to be affected by other important emission sources, such as domestic heating systems, agricultural activities, and animal farms, which are not as strongly regulated as emission sources such as traffic.The 2020 lockdown mainly influenced nitrogen oxide concentrations.

Analysis of seasonal and spatial distribution of particulate matters and gaseous pollutants around an open cast coal mining area of Odisha, India.

Open cast mining-a predominant method of coal production in India (94.46% of total coal production)-has been found to be a major factor which is responsible for the emission of dust particles and gaseous pollutants, leading to the deterioration of air quality in the coal mining area. Considering the health concerns and environmental impacts of these pollutants, the inhabited villages of Ib valley coalfield area of Orisha, India, were selected for this study. In this regard, various researchers have performed the analysis of air quality data and modeling for the dispersion of pollutants. However, a long-term study on spatial and seasonal variations of air pollutants and their relationship with meteorological parameters were missing in the literature. Accordingly, the spatial and seasonal variations of air pollutants in the area were assessed for a period of sixyears (2014-2020), and concentrations of PM2.5, PM10, and SPM were found to be above the annual national ambient air quality standards (NAAQS) for all the three seasons. The overall mean concentrations of NOx, PM10, PM2.5, SPM, and SO2 during this period were found to be 17.2 ± 9.28, 152.5 ± 99.7, 53.27 ± 37.70, 268.5 ± 158.2, and 12.58 ± 7.47μg/m3, respectively. The analysis of meteorological parameters showed a strong and significant negative correlation of relative humidity with PM2.5 (r = - 0.30, p-value = 5.659 × 10-10), PM10 (r = - 0.36, p-value = 1.97 × 10-13), and SPM (r = - 0.45, p-value = 2.2 × 10-16). Furthermore, the spatial distribution of pollutants was performed using the geographic information system (GIS) and inverse distance weighting (IDW) method, wherein the seasonal distribution of pollutants was shown through thebivariate polar plots. Therefore, the analyses and recommendations provided in this study can help thepolicymakers in developing a long-term air quality improvement strategy around a coal mining area, including the spatial and seasonal variations of air pollutants and their relationship with meteorological parameters.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air Quality Improvement during COVID-19 Lockdown in Colombo, Sri Lanka

This study presents a time series analysis of concentrations of particulate matter less than 2.5 micrometers (PM2.5) measured in a Colombo coastline site to determine whether air quality improves in 2020 and 2021 due to COVID-19 related travel restrictions. A package of data analysis tools that includes dedicated functions for air quality data analysis called “openair” developed on R statistical software is used in this study. When time-series data from 2017 to 2021 are analyzed, a clear reduction in PM2.5 concentration is observed. When the trend of PM2.5 concentration from late 2017 to 2021 is calculated using the Theil-Sen method, it suggests a decrease of PM2.5 concentration at a rate of 10.92 mg/m3per year (CI 8.91-13.18). Colombo is a coastal city that is greatly affected by the southwest monsoon. When monthly pollution rose diagrams were analyzed, a diurnal variation of wind pattern is observed with increasing stormy weather during the southwest monsoon season from May to September, from 2017 to 2021. To exclude the fact that the reduction in pollutant concentration is due to the increase in wind speed during the monsoon season, a cluster analysis technique has been used to separate the effect of dilution of pollutants due to the gusty winds during the monsoon season from non-monsoon seasons. The cluster analysis was performed on the concentration polar plot drawn for late 2017 to 2021, and two clear clusters were identified. Cluster-1 represented data during the monsoon season from May to September, and cluster-2 represented the data during the non-monsoon season. Cluster-1 clearly showed an inverse relationship between wind speed and PM2.5 concentration suggesting the dilution effect of concentration with the increasing wind speed. Cluster-2 clearly showed a directly proportional relationship, implying that pollutants from the city are transported to the site with increasing wind speeds. When trend analysis was performed, both clusters showed a decreasing trend. Results of the study suggest that the increasingly stormy weather during monsoons over the years has reduced the PM2.5 concentration in the study site at a rate of 11.57 mg/m3 per year (CI 9.08-14.55). The reduction in human travel that caused a decrease in traffic-related air pollution over the years has reduced the PM2.5 concentration in the study site at a rate of 7.91mg/m3per year (CI 4.68-11.92).
 Keywords: COVID-19, Lockdown, Colombo, PM2.5 concentrations, Polar plot

How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science.

To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.

Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

Medium-term air quality assessment, benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes. By using daily and monthly averaged data, medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives. By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark (CLAB). The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator. Such a benchmark can provide an overall air quality assessment for areas of interest. A case study is presented for Dallas County (U.S.A.) applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019. Six air pollutants considered are: ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, benzene and particulate matter less than 2.5 micrometres. These pollutants are assessed individually and in terms of CLAB, and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019. Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years. The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs. Conducting CLAB analysis on an ongoing basis, using a relevant nearpast time interval for benchmarking that covers several years, can reveal useful monthly, seasonal and annual trends in overall air quality. This type of medium-term, benchmarked air quality data analysis is well suited for ecosystem monitoring.

Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

Differential health and economic impacts from the COVID-19 lockdown between the developed and developing countries: Perspective on air pollution

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations in 20 (reduced PM2.5 concentrations of −7.4–29.1 μg m−3) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM2.5 concentrations caused by the control measures differed between the developed (95% confidence interval (CI): −2.7–5.5 μg m−3) and developing countries (95% CI: 8.3–23.2 μg m−3). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM2.5 concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM2.5 related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM2.5. In particular, the concentrations of primary PM2.5 (e.g., BC) in cities of developing countries were 3–45 times higher than those in developed countries, so the mass concentration of PM2.5 was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM2.5 in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

Integration of air quality model with GIS for the monitoring of PM2.5 from local primary emission at a rural site.

Local primary emissions of air pollutants are responsible for public health, decreasing productivity, and cultural activities in local residential areas. In this study, an integrated air quality observation and modeling system with a geographical information system (GIS) was developed to characterize the air pollution caused by local primary emission sources. This integrated system could provide air quality monitoring, data analysis, and visualization results that reflect air pollutant concentration data in a study area containing a local rural village (LRV) and an asphalt manufacturing facility (AMF). Additionally, the model was used to estimate the contributions of air quality from an emission source at the receptor and determine the control factor for the emission rate or meteorological changes. From the forward and backward modeling results, we found that the concentrations of particulate matter smaller than 2.5μm (PM2.5) concentrations in the village were affected by the unique meteorological and emission conditions. The PM2.5 concentration was significantly increased for the cases with a slow wind speed of 1m/s or high wind speed of 3m/s, with an emission rate of 10g/s. The contribution of AMF emissions was explained by contribution factor analysis. During the study period of December 2014-December 2015, the incoming contribution of PM2.5 at the LRV measurement station was approximately 47.6%. These results suggest that the proposed method can be useful for understanding adverse air quality conditions and estimating the emissions of air pollutants from primary sources for local environmental and public health authorities.

Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network

An analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data problems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on ad hoc exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance.

AiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

Recent technological advances in collecting data on emission sources, meteorological conditions and concentration of air pollutants in urban areas, offer invaluable opportunities for the better understanding of air quality problems. However, processing large sets of data to extract statistically valid evidence poses many challenges from both the conceptual and technical viewpoints. Air quality data acquisition, cleaning and authentication are necessary and crucial preliminary phases to support descriptive, predictive and prescriptive models and to ensure that aggregated and high-quality information is delivered to the central and local governments, decision makers and citizens. Automated software tools can facilitate drawing conclusions based on the information contained in the data, limiting subjective judgment and providing repeatability. However, the costly state-of-the-art software applications developed by major vendors are inaccessible to many cities and townships in the developing world. Moreover, their usage creates dependency on proprietary solutions, which can hinder the possibility of evolving the data processing and analysis protocols. We present an open-source web application for air quality data analysis and visualization, called aiRe, based on the R statistical framework and Shiny web package. aiRe has been developed in collaboration with the Colombian environmental authorities, and implements best practices validated by experts in air quality. We believe that the process of developing aiRe was extremely valuable with the ultimate purpose of supporting cities in air quality management, while strengthening local capabilities to improve urban air pollution. This open-access tool simplifies and makes air quality data analysis and visualization accessible, with the desirable effect of removing ownership costs, fostering appropriation by non-expert users and ultimately promoting informed decision-making for the general public and the local government authorities. We present the performance of this tool over a series of examples of open data collected by the air quality monitoring network of Bogotá, Colombia.

Retraction: Analysis of air quality data in the city of Bogotá through clustering techniques (IOPConf. Series: Materials Science and Engineering 872 012027)

Published 15 September 2020This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation.This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct.Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded.IOP Publishing Limited request any citations to this article be redirected to the original work (1).Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.