# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 1 Introduction  
A chemical industrial park refers to a certain area with close connections, a mutual supply of raw and auxiliary materials, common use of public works, unified control of environmental pollution, and efficient logistics supporting services. The concentration of industries in chemical parks, increasing numbers of enterprises, and the expansion of production scales have brought about increasingly significant environmental problems [1, 2], such as regional complex air pollution. This issue has recently been taken seriously. However, such source leaking accidents are still happening every year even with the improvement of safety measures. Due to the suddenness of explosions, it is important to find and locate the source of gas leakage as soon as possible to protect the surrounding residents from danger. The source tracking problem can be divided into a forward diffusion model and a data-based estimation algorithm. As for the forward model, Gaussian diffusion models and CFD (computational fluid dynamics) are both widely in use. Many researchers use CFD to take complex terrains into consideration while ordinary Gaussian diffusion models cannot do such things. However, in this paper, the Gaussian-based model AERMOD is chosen to simulate the gas diffusion process for its advantages including high efficiency and being able to handle building downwash. It is widely used in the world today for the modeling of the emission and dispersion of pollutants. AERMOD is also recommended by both the U.S. Environmental Protection Agency (EPA) and the 'Technical Guidelines for Environmental Impact Assessment Atmospheric Environment' (H) 2.2-2018) released by the Ministry of Ecology and Environment of China. The effectiveness of AERMOD has been evaluated inmany previous works [3; 4; 5]. [PERSON] et al. [6] state that the performance of AERMOD in the prediction of measured values is better than that of CALPUFF. In other words, the performance of AERMOD agrees with the observed values. AERMOD performs well under various terrain conditions. In a study by [PERSON] et al. [7], AERMOD is used to model air quality in Aracaju, Brazil, for the monitoring of ambient pollutant concentrations in the city. [PERSON] et al. [8] estimate the environmental pollutants emitted from a thermal power plant's chimney using AERMOD. [PERSON] et al. [9] improve the performance of AERMOD by modifying AERMET outputs within a major U.S. airport located on a shoreline. To track a leaking source, different models and methods are used in various situations. [PERSON] et al. [10] localized the leak source in an obstacle-free area without the information of source localization, using particle swarm optimization (PSO) [11] and expectation maximization (EM) [12]. However, in most circumstances, the source distribution of the industrial park is registered. Thus, under the condition of knowing the leakage source distribution, the most traditional way to find the leaking source is traversing all source leaking cases with a forward diffusion model and comparing the results with real sensor data. However, this approach is computationally intensive. The past decade has seen the rapid development of machine learning (ML) in many fields. Artificial neural networks (ANNs) appear in many recent works for gas leaking incidents. For example, [PERSON] et al. [13] proposed an evacuation route system using the structure of encoder-decoder to extract the geometric features of the affected area. [PERSON] and [PERSON] discussed a series of prediction models based on different MLAs (machine learning algorithms) including ANN, RBF, and SVM [14]. To cope with the temporal feature of gas diffusion data, [PERSON], [PERSON] et al. [15] applied a long short-term memory recurrent neural network to locate the leaking source with four possible leaking points and 11 monitoring points in a 3-D region. However, the methods mentioned above use the data without obstacles, which means that practical situations are always much more complicated.