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The aim of the research is to use an integrated approach – simulation and Six Sigma to improve the ambient air quality.

Integration of simulation and Six Sigma DMAIC methodology in a foundry had been used to improve the ambient air quality. Various elements of the Six Sigma toolkit such as Cause and Effect diagrams and Failure Mode and Effect Analysis have been used to discover the root causes underlying the problem and prioritize action and incorporate cost‐effective solutions. Simulation has been used to improve and control the environmental efficiency by monitoring the performance of the Venturi Scrubber – the pollution control equipment, by running the model under varying conditions.

The integrated application of Six Sigma and simulation has been successful in reducing particulate emissions from 200 milligrams per cubic meter to less than 20 milligrams per cubic meter and sulphur dioxide emissions from 45 milligrams per cubic meter to less than 4.5 milligrams per cubic meter, thus reducing air pollution.

Air pollution is a burning problem in the present scenario and foundry industries are one of the contributors to air quality degradation. The approach described in this paper is a step towards reducing air pollution due to foundry operations.

Integration of Six Sigma DMAIC methodology and simulation provides a novel cost‐effective strategy for monitoring and reducing air pollution resulting from foundry operations. This paper is useful for environmental division of foundry and other manufacturing industries.

Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.

The authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.

The results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.

In the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

This paper investigates the potential contribution of smart city approaches and tools to sustainable urban development in the environment domain. Recent research has highlighted the need to explore the relation of smart and sustainable cities more systematically, focusing on practical applications that could enable a deeper understanding of the included domains, typologies and design concepts, and this paper aims to address this research gap. At the same time, it tries to identify whether these applications could contribute to the “zero vision” strategy, an extremely ambitious challenge within the field of smart cities.

This objective is pursued through an in-depth investigation of available open source and proprietary smart city applications related to environmental sustainability in urban environments. A total of 32 applications were detected through the Intelligent/Smart Cities Open Source (ICOS) community, a meta-repository for smart cities solutions. The applications are analyzed comparatively regarding (i) the environmental issue addressed, (ii) the associated mitigation strategies, (iii) the included innovation mechanism, (iv) the role of information and communication technologies and (v) the overall outcome.

The findings suggest that the smart and sustainable city landscape is extremely fragmented both on the policy and the technical levels. There is a host of unexplored opportunities toward smart sustainable development, many of which are still unknown. Similar findings are reached for all categories of environmental challenges in cities. Research limitations pertain to the analysis of a relatively small number of applications. The results can be used to inform policy making toward becoming more proactive and impactful both locally and globally. Given that smart city application market niches are also identified, they are also of special interest to developers, user communities and digital entrepreneurs.

The value added by this paper is two-fold. At the theoretical level, it offers a neat conceptual bridge between smart and sustainable cities debate. At the practical level, it identifies under-researched and under-exploited fields of smart city applications that could be opportunities to attain the “zero vision” objective.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

Currently, employees are facing information explosion in the presence of disruptive information and communication technologies of industry 4.0. With the prevalent nature of information pollution, employees are suffering to process large volume of information in order to access quality information. The objective of present study is to develop a measurement scale of perceived information pollution in the context of workplace. Furthermore, this study aims to assess the nomological validity of the proposed construct.

This study has employed a sequential exploratory mixed-method design to develop and validate the measurement scale of perceived information pollution. The population of the present study comprised of the employees who work in the operations and credit department of banking sector. The present study has used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to analyze data in AMOS.

The present study has developed the second-order measurement scale of perceived information pollution. The perceived information pollution comprises of five dimensions – accessible, intrinsic, contextual, representational, and distractive information pollution. This study has also confirmed the nomological validity of the information pollution in relation to employee's job satisfaction, work effort, and learning effort.

Management may employ the five dimensions as a benchmark in revealing polluted information as well as enhancing information quality through information processing.

This study has contributed to the literature of information management by providing a five-dimensional scale of perceived information pollution and confirming its nomological validity.

The article gives an overall presentation of the demonstrator implemented, in the framework of the REMSSBOT project, in Attica. The application site is the Ministry of Environment, Physical Planning and Public Works of Greece and fields of application are the domains of air pollution, water quality and solid waste. The objectives of the demonstrator are examined, under the specific Greek conditions in environmental information management, together with the users and data providers involved. The functionality of the demonstrator is also presented, including the technical and physical environment, as well as the information made available through the system. Last, the expected benefits of the demonstrator and the future prospectives are outlined.

The purpose of this paper is to present a more objective evaluation method for a mining area ecological environment. It proposes the weight of indices with respect to the total goal, and provides a new evaluation method which generalizes the weighted‐average method.

First, the authors describe the evaluation grades by fuzzy variables in credibility space, and the degree of an index belonging to a grade is transformed to be the credibility measure of a fuzzy event by the credibility inversion theorem. Second, a new algorithm which generalizes the weighted‐average method is proposed to realize the transformation from the credibility degree of the underlying indices to the credibility degree of the total target.

The method proposed in this paper can obtain the correct results illustrated with a numerical evaluation example. The comparison with two other methods shows that the proposed method has stronger classification ability.

The new method presented in this paper generalizes the weighted‐average method, and provides methods for other evaluation systems with a hierarchical structure.

This article aims at giving a general overview of the telematics role in environmental information management in Greece. The main scope is to present the existing situation, the current problems and needs, as well as the possibilities and perspectives for the future. The topics of the article include first of all a description of current environmental information management in Greece and the production of information, as well as the presentation of the EPPER programme for the environment, together with the telematics applications used for environmental management in Greece. A brief introduction to the scope and objectives of the National Air Quality Network and National Environment Information Network is also given, together with an analysis of the needs for a National Catalogue of Data Sources. Last, the role of YPEXODE in the REMSSBOT project is presented, with the benefits obtained from this participation, as well as future perspectives for developments in the field of telematics and environment in Greece.

This paper describes a practical approach to implement computational steering for problem solving environments (PSEs) by using WBCSim as an example. WBCSim is a Web based simulation system designed to increase the productivity of wood scientists conducting research on wood‐based composites manufacturing processes. WBCSim serves as a prototypical example for the design, construction, and evaluation of small‐scale PSEs.

Various changes have been made to support computational steering across the three layers – client, server, developer – comprising the WBCSim system. A detailed description of the WBCSim system architecture is presented, along with a typical scenario of computational steering usage.

The set of changes and components are: design and add a very simple steering module at the legacy simulation code level, provide a way to monitor simulation execution (alert users when it is time to steer), add an interface to access and visualize simulation results, and perhaps to compare intermediate results across multiple steering attempts. These simple changes and components have a relatively low cost in terms of increasing software complexity.

The novelty lies in designing and implementing a practical approach to enable computational steering capability for PSEs embedded with legacy simulation code.

This study aims to examine the impact of economic policy uncertainty (EPU) on firm investment in corporate social responsibility (CSR)’s environmental, social and governance (ESG) dimensions. Additionally, the study examines whether firm size moderates the EPU–CSR relationship.

The sample includes 2,017 US. firms from 2002 to 2018. Data on ESG scores are drawn from the Asset-4 database in Thomson Reuters to measure CSR investment. ordinary least square regression, including fixed effects at the year and industry level, is used as the main econometric specification. Moreover, the study employed the two-step system Generalized Method of Moments to address the endogeneity concerns.

The findings reveal that firms increase their CSR investment in response to high EPU. The results are consistent in all the three ESG/CSR dimensions: ESG. Moreover, the positive association between EPU and CSR is driven by firm size, indicating that large-sized firms have the resources and incentives to invest more in CSR. Our main findings remain consistent after addressing the endogeneity concerns and controlling for the effect of omitted variable biasness.

Using a unique sample of US firms, this study empirically contributes to the current literature on the association between EPU and CSR investment. Moreover, firm size plays a vital role in moderating this relationship.