###### Abstract  
Air pollution is a prominent problem all over the world, seriously endangering human life. To protect the environment and human health, timely and accurate air quality evaluations are imperative. Recently, with the increasing focus on air pollution, an evaluation tool that can offer intuitive air quality information is especially needed. Though the Air Quality Index (AQI) has played this role over the years, its intrinsic limitations discussed in this study (sharp boundary, biased evaluation, conservative strategy and incomplete criterion) are gradually apparent, limiting its air quality evaluation capability. Therefore, a novel paradigm, the Air Quality Fuzzy Comprehensive Evaluation (AQFCE), is proposed. In the preprocessing module, missing and reversal data are handled by a least square piecewise polynomial fitting and linear regression. An improved fuzzy comprehensive evaluation model is adopted to solve the AQI's above limitations in the evaluation module. The early warning module provides a timely alert and recommendation. To validate the performance of the AQFCE, Beijing, Shanghai and Xi'an in China are selected for case studies, and daily and hourly concentration data of six conventional air pollutants from September 2018 to August 2019 are employed. For daily reports, the AQFCE and AQI have a high consistent rate and correlation coefficient regarding chief pollutants and levels, respectively, while examples show the level of the AQFCE is more reasonable. For hourly reports, AQI has antinomies and cannot reflect actual pollution, but the AQFCE is still effective. Current major pollutants, \"weekend and holiday effect\" and \"peak type\" of pollution are also revealed by the AQFCE. Experiment results prove that the AQFCE is accurate under different pollution conditions and an important supplement to the AQI. Furthermore, the AQFCE can provide health guideline for the public and assist the government in making environmental decisions and development policies. air quality evaluation; fuzzy math; abnormal data processing; pollution early warning; government decision-making +
Footnote †: journal: Applied Soft  
AQI after revision in 1999 [6]. The European Union (EU) has begun to support the project of Common Information to European Air (CITEAIR) from March 2004, and the Common Air Quality Index (CAQI) was subsequently developed to support European cities to reach the air quality standard [7]. In 2012, the Ministry of Environmental Protection of China also issued the \"Ambient Air Quality Standards\" and \"Technical Regulation on Ambient Air Quality Index (on trial)\", which came into force in 2016 [8]. Although the AQI has been adopted by many countries and under implementation for many years, its limitations are gradually prominent with the increasingly higher demand for precision in air quality evaluation and pollution control. For example, actual air quality depends on the integrated effects of all air pollutants but the evaluation result of the AQI is decided only by the pollutant with the highest subindex. Thus, the evaluation based on the most severe pollutant may overestimate total pollution and bring unnecessary social panic and economic loss from severe control measures, which is biased and too conservative. Therefore, some scholars tried to develop new methods. [PERSON] et al. [9] proposed a multisite-multipollutant AQI, which considers the conjoint effect of pollutants. [PERSON] et al. [10] developed a fuzzy-based AQI, and a fuzzy inference system evaluates the impacts of ten pollutants. Moreover, a traffic AQI was established by [PERSON] for assessing air quality near roadways [11]. Others have also made attempts to either revise the AQI or propose a new index [12; 13; 14; 15; 16; 17; 18]. However, these new methods have their shortcomings and do not deal with AQI limitations well. Revisions based on the similar principle of AQI cannot avoid its intrinsic limitations.