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

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.

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.