Playing Chemical Plant Environmental Protection Games with Historical Monitoring Data

Improving Behavioral-Based Safety Training in Using Verbal Commands Through a Theory-Driven and Feedback-Based Nonimmersive Virtual Reality Game: Development and Usability Study

BACKGROUND

The construction, chemical, aviation, medical, and health care industries have used serious games for safety training. To our knowledge, serious games have not been developed focusing on behavioral change to improve safety through the use of verbal commands and instilling players with heightened awareness of their spatial proximity to other people in their surroundings.

OBJECTIVE

We aimed to develop a theory-driven serious game for improving safety behavior using verbal commands and validate the implementation of the theoretical frameworks used for game development. The game developed, KitchenSpeak, was a first-person character (FPC) game where users respond to in-game prompts to use loud verbal commands when they are approaching another employee's blind spot.

METHODS

In addition to using the SERES framework in guiding the general game design and development, and the Reflection, Engagement, Choice, Information, Play, Exposition (RECIPE) framework to inform the design of the game mechanics, we also applied gestalt laws of perception for graphic design to guide the design of the game's user interface. We conducted 2 evaluative tests (alpha and beta) to collect end user and stakeholder feedback on the implementation of the theoretical frameworks, as well as to collect relevant information for full-scale implementation and a future validation study.

RESULTS

The alpha and beta tests had 8 and 40 participants, respectively. The alpha test results revealed that the theoretical frameworks were adequately applied; however, suggestions were also made to modify and improve the game. The beta test results suggested further improvements for the game design and found no differences in the perception of ease of play between participants with and without previous FPC gaming experience (P=.47; Kruskal-Wallis). Results suggested that the game met its design and theoretical requirements, and it would be easily playable by all players regardless of their previous experience in FPC games.

CONCLUSIONS

A theory-driven and evidence-based FPC game titled KitchenSpeak was developed to teach the use of kitchen-speak terms in commercial kitchens. Evaluative tests were conducted to validate the implementation of the theoretical frameworks. Our main contributions are creating and validating game-based training to improve behavioral-based safety in the workplace and the incorporation of gestalt laws of perception for graphic design in the game's user interface.

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks

Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, T2 and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.

Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health

Assessment of the air quality in metropolitan areas is a major challenge in environmental sciences. Issues related include the distribution of monitoring stations, their spatial range, or missing information. In Mexico City, stations have been located spanning the entire Metropolitan zone for pollutants, such as CO, NO2, O3, SO2, PM2.5, PM10, NO, NO x , and PM CO . A fundamental question is whether the number and location of such stations are adequate to optimally cover the city. By analyzing spatio-temporal correlations for pollutant measurements, we evaluated the distribution and performance of monitoring stations in Mexico City from 2009 to 2018. Based on our analysis, air quality evaluation of those contaminants is adequate to cover the 16 boroughs of Mexico City, with the exception of SO2, since its spatial range is shorter than the one needed to cover the whole surface of the city. We observed that NO and NO x concentrations must be taken into account since their long-range dispersion may have relevant consequences for public health. With this approach, we may be able to propose policy based on systematic criteria to locate new monitoring stations.

Strategically Patrolling in a Chemical Cluster Addressing Gas Pollutants' Releases through a Game-Theoretic Model

Chemical production activities in chemical clusters, if not well managed, will pose great threats to the surrounding air environment and impose great burden on emergency handling. Therefore, it is urgent and substantial in a chemical cluster to develop proper and suitable pollution controlling strategies for an inspection agency to monitor chemical production processes. Apart from the static monitoring resources (e.g., monitoring stations and gas sensor modules), patrolling by mobile vehicle resources is arranged for better detecting the illegal releasing behaviors of emission spots in different chemical plants. However, it has been proven that the commonly used patrolling strategies (i.e., the fixed route strategy and the purely randomized route strategy) are non-optimal and fail to interact with intelligent chemical plants. Therefore, we proposed the Chemical Cluster Environmental Protection Patrolling (CCEPP) game to tackle the problem in this paper. Through combining the source estimation process, the game is modeled to detect the illegal releasing behaviors of chemical plants by randomly and strategically arranging the patrolling routes and intensities in different chemical sites. In this game-theoretic model, players (patroller and chemical sites), strategies, payoffs, and game solvers are modeled in sequence. More importantly, this game model also considers traffic delays or bounded cognition of patrollers on patrolling plans. Therefore, a discrete Markov decision process was used to model this stochastic process. Further, the model is illustrated by a case study. Results imply that the patrolling strategy suggested by the CCEPP game outperforms both the fixed route strategy and the purely randomized route strategy.

A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries

Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents. However, results of the current literature strictly rely on several perfect assumptions which rarely hold in real-world domains, especially when dealing with human adversaries. To address bounded rationality and limited observability in human cognition, the CPEP game is extended to generate robust schedules of inspection resources for inspection agencies. The present paper is innovative on the following contributions: (i) The CPEP model is extended by taking observation frequency and observation cost of adversaries into account, and thus better reflects the industrial reality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observation and incomplete information (i.e., the attacker’s parameters) are integrated into the extended CPEP model; (iii) Learning curve theory is employed to determine the attacker’s observability in the game solver. Results in the case study imply that this work improves the decision-making process for environmental protection authorities in practical fields by bringing more rewards to the inspection agencies and by acquiring more compliance from chemical plants.