In this plan, we support air pollution inspections to gain higher effectiveness and enable authorities to make decisions with higher quality through the following works: 1. Building AI audit modules to conduct special declaration of data. The goal is to quantitatively analyze and process the existing general rules and directions of the audit experts, and then list down audit target factories to achieve substantial audit results. For all the data declared from central and southern 19 cities in Taiwan between 2018 and 2020, including reports of industrial air pollution prevention and control expenses, raw data of stationary pollution source permits (in specific 64 kinds of processes only) and data of industrial waste reports, we conducted data processing and rationality analysis and interpretation of  declaration. In addition, we processed a total of 2.02 million of data and produced two main modules. The first module, in which we make use of AI supervision and audit to automatically check the amount of declared air pollution fee and permitted amount of different factories. The second module uses AI supervision and audit to check the consistency between the amount of declared air pollution fee and declared waste. With heterogeneous data comparison, we filtered out and pinpointed factories which are suspicious of under-reporting air pollution fees, and then we established a map-oriented data visualization dashboard which contains filtering functions for easier screening of factories. Through screening and with the help of AI, lists of three pinpointed highly suspicious factories were submitted as a follow-up inspection item. 2. Data analysis on microsensors. The goal is to coordinate with the “Serious Air Pollution Period” and conduct a 12-month microsensor data analysis across the year for PM2.5 and VOCs, including data from Oct. 2019 to Mar. 2020 and Oct. 2020 to Mar. 2021, hence to assist actual inspection actions and produce substantial inspection results. Selecting five major industrial areas located in central and southern cities of Taiwan: Douliu industrial park in Yunlin, Linyuan industrial park, Linhai industrial park and Dafa industrial park in Kaohsiung and Pingnan industrial park in Pingtung, we provided analysis and evaluation of potential pollution sources (areas) regarding PM2.5 and VOCs  through historical data from sensors, pinpointed hot-zones with the highest possibilities of air pollution, and listed down suspicious factories to assist air pollution inspectors to narrow down the audit highlights in serious air pollution period. 3. Develop digital governance applications for environmental enforcement systems to provide decision analysis. The goal is to develop a systematic decision-making analysis tool based on internal and external system data matching context integration issues. In this project, we completed the software and hardware configuration as well as the prototype planning for the digital governance of environmental law enforcement. According to two issues including illegal disposal and major cases tracking, we proposed three internal and external systems for data matching and value-added sources when planning related topics in this project. 4. Plan and improve the method and friendliness of environmental enforcement management.  The goal is to divide various functions into business classifications based on business orientation, reducing the ambiguity of users searching for locations of specific functions on their own. The EEMS has been operating for years, the system operation layouts should be integrated. This project provided three system layouts for adjustment. The quick access functions were designed and planned to improve user-friendliness. 5. This project has cooperated with related administrative tasks to improve the effectiveness and quality of project implementation. Cameo assisted this project by performing relative administrative works, consulting services, and organizing, recording and analyzing conference data of related business research and seminars. Last but not least, assisting execution and management of staff work related to this project. For instance, (1) assisting 6 agencies in central and southern cities of Taiwan conducting AI audits. (2) targeting a highly suspicious factory due to precise hot spot analysis. (3) cooperating with reports inspection teams, such as visiting the Southern District inspection team to discuss high-risk pollution hotspots in December.

Audit for heterogeneous data integration using artificial intelligence (AI), Dashboard of visualized data, Restricted and classified factories, Hot-zone analysis, Environmental law enforcement results, Enforcement topic of concern