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A Novel Air Quality Evaluation Paradigm Based on the Fuzzy Comprehensive Theory

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A Novel Air Quality Evaluation Paradigm Based on the Fuzzy Comprehensive Theory applied sciences Article A Novel Air Quality Evaluation Paradigm Based on the Fuzzy Comprehensive Theory Xinyue Mo 1,2 , Huan Li 3, Lei Zhang 1,2,* and Zongxi Qu 4 1 College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; [email protected] 2 Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China 3 School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; [email protected] 4 School of Management, Lanzhou University, Lanzhou 730000, China; [email protected] * Correspondence: [email protected] Received: 7 November 2020; Accepted: 30 November 2020; Published: 2 December 2020 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. Keywords: air quality evaluation; fuzzy math; abnormal data processing; pollution early warning; government decision-making 1. Introduction In recent years, with the improvement of living standards, people have begun to pay more attention to the living environment and health. Air is an indispensable factor for human survival, and air quality is closely related to human health. However, air pollution is always an anxiety for many countries, which has become the focus of the world [1–5]. To understand the air quality information intuitively, air pollutant concentration can be simplified to a dimensionless index according to the impact on human health and the environment by the Air Quality Index (AQI). The US Environmental Protection Agency firstly proposed the Pollution Standards Index (PSI) in 1976 and renamed it the Appl. Sci. 2020, 10, 8619; doi:10.3390/app10238619 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 8619 2 of 20 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 severer control measures, which is biased and too conservative. Therefore, some scholars tried to develop new methods. Plaia et al. [9] proposed a multisite- multipollutant AQI, which considers the conjoint effect of pollutants. Sowlat 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 Bagie´nskifor assessing air quality near roadways [11]. Others have also made attempts to either revise the AQI or propose a new index [12–18]. 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. The sharp boundary in classification is not reasonable enough, and a biased and conservative evaluation will overstate real pollution [6,9,10,12,19–21]. The evaluation result is highly sensitive to the calculation grid, and it is not suitable for distinct models to share the same calculation grid. So, designing a calculation grid that is scientific and compatible with the algorithm is necessary, but there are few relevant studies. It is essential to compare the new method with the accepted AQI in theory and practice, but it is scarce in previous studies. Furthermore, previous methods are too complicated to be used in practice. Based on the above, an improved air quality evaluation method named Air Quality Fuzzy Comprehensive Evaluation (AQFCE) including preprocessing, evaluation and early warning is proposed in this study. In the preprocessing module, the abnormal data of air pollutants are handled by a least square piecewise polynomial fitting and linear regression. In the evaluation module, the limitations of the AQI (sharp boundary, biased evaluation, conservative strategy and incomplete criterion) are concluded based on sufficient comparison and analysis, and then, inspired by fuzzy math theory, a series of targeted solutions are proposed. Concretely, membership degree can deal with the ambiguity of data near the classification boundary. For the biased evaluation and overly conservative strategy, a principal factor prominent model considering the membership degree and factor weight of all pollutants is adopted to ensure both comprehensive evaluation and public health at the same time. A complete calculation grid based on scientific evidence is established to improve the original evaluation criterion. Moreover, according to the evaluation result, the early warning module can release comprehensive information in a timely manner about air pollution and offer suggestions for the public and government. 2. Current AQI 2.1. Calculation Method of AQI Although the methods of calculating AQI may be slightly different in form among different countries, they are essentially same with linear interpolation between the class borders [19] and the pollutant with the highest subindex decides both the level of air quality and chief pollutant: IH IL Ik = − (Ck CL) + IL (1) CH CL − − AQI = max I , I , I , ::: , In (2) f 1 2 3 g Appl. Sci. 2020, 10, 8619 3 of 20 where: Ik: the subindex of the kth pollutant; Ck: the concentration of the kth pollutant; CH(CL): the concentration breakpoint that is ( ) C ; ≥ ≤ k IH(IL): the index breakpoint corresponding to CH(CL). 2.2. Calculation Criterion of AQI Criterion, namely the calculation grid, is composed of pollutant concentration limits. It depends on specific requirement of air quality and is in accordance with local economic development and pollution control capacity. Therefore, criteria vary among regions and Table1 presents their comparison in three representative regions: the European Union (EU), United States of America (USA) and China (CHN) [7,22,23]. As illustrated by Table1, there are big di fferences in some aspects including level, period and limit value. The EU has five concise levels, but there are six levels and level VI still has two sublevels in the USA and CHN, which seem to be redundant. Six conventional pollutants are necessary items in air quality evaluation, so three criteria reach an agreement on pollutants but differ greatly in terms of the period. In addition, apart from PM2.5 and PM10, the EU has no daily criteria for other pollutants. The USA has neither daily criterion for NO2 nor hourly criteria for CO, PM2.5 and PM10, while CHN has a shortage of hourly limits of particulate matters. Furthermore, the maximum 8 h moving average of CO (EU, USA) and O3 (USA, CHN) are used for daily evaluations. At present, daily and hourly reports of air quality are primary tasks, but generally incompleteness and misuse exist in all criteria. As to limit value, the USA and CHN are relatively close to each other. At the same level, daily limits of CHN are close to or higher than that of the USA, which is similar for hourly limit. However, the EU has the most stringent criterion where all limits are far lower than that of the USA and
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