A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data

applied sciences

Article A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data

Yong Sun 1 , Fengxiang Jin 2, Yan Zheng 1, Min Ji 1,* and Huimeng Wang 2,3

1 College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; [email protected] (Y.S.); [email protected] (Y.Z.) 2 College of Surveying and Geo-Informatics, Shandong Jianzhu University, Ji’nan 250101, China; [email protected] (F.J.); [email protected] (H.W.) 3 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China * Correspondence: [email protected]

Abstract: Severe air pollution problems have led to a rise in the Chinese public’s concern, and it is necessary to use monitoring stations to monitor and evaluate pollutant levels. However, monitoring stations are limited, and the public is everywhere. It is also essential to understand the public’s awareness and behavioral response to air pollution. Air pollution complaint data can more directly reﬂect the public’s real air quality perception than social media data. Therefore, based on air pollution complaint data and sentiment analysis, we proposed a new air pollution perception index (APPI) in this paper. Firstly, we constructed the emotional dictionary for air pollution and used sentiment analysis to calculate public complaints’ emotional intensity. Secondly, we used the piecewise function to obtain the APPI based on the complaint Kernel density and complaint emotion

Kriging interpolation, and we further analyzed the change of center of gravity of the APPI. Finally, we used the proposed APPI to examine the 2012 to 2017 air pollution complaint data in Shandong Province, China. The results were veriﬁed by the POI (points of interest) data and word cloud Citation: Sun, Y.; Jin, F.; Zheng, Y.; Ji, M.; Wang, H. A New Indicator to analysis. The results show that: (1) the statistical analysis and spatial distribution of air pollution Assess Public Perception of Air complaint density and public complaint emotion intensity are not entirely consistent. The proposed Pollution Based on Complaint Data. APPI can more reasonably evaluate the public perception of air pollution. (2) The public perception Appl. Sci. 2021, 11, 1894. https:// of air pollution tends to the southwest of Shandong Province, while coastal cities are relatively weak. doi.org/10.3390/app11041894 (3) The content of public complaints about air pollution mainly focuses on the exhaust emissions of enterprises. Moreover, the more enterprises gather in inland cities, the public perception of air Academic Editor: Elza Bontempi pollution is stronger.

Received: 25 January 2021 Keywords: air pollution; public perception; sentiment analysis; Kernel density; Kriging interpolation; Accepted: 18 February 2021 air pollution perception index Published: 21 February 2021

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional afﬁl- iations. Air pollution is a major environmental problem that affects every country and every- one. To solve the problem of air pollution, local monitoring and practical evaluation of pollution levels are needed [1]. Many countries have established air quality monitoring stations to measure pollution levels in real time [2]. However, monitoring stations are limited, and the public is everywhere. As the direct feeler of air quality and the assessor Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. of environmental protection performance, the public is crucial to improving air pollu- This article is an open access article tion. Therefore, it is essential to study further the public’s attitudes and opinions on air distributed under the terms and quality [3–5]. conditions of the Creative Commons Public perception of air pollution assessment can be summarized as the public’s Attribution (CC BY) license (https:// subjective evaluation of air pollution events based on subjective feelings and risk percep- creativecommons.org/licenses/by/ tions [6,7]. It is a supplement to objective air quality monitoring, although there is a certain 4.0/). deviation between them [8,9]. According to the different methods of obtaining data, the

Appl. Sci. 2021, 11, 1894. https://doi.org/10.3390/app11041894 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 1894 2 of 17

existing research on public perception of air pollution can be classified into two categories: one based on questionnaires and the second one based on the social media platform. A questionnaire survey records people’s feelings and emotions about air pollution in the form of a questionnaire according to different research purposes [10–12]. The questionnaire survey forms include offline and online; offline is through on-site interviews and paper questionnaires, online is through the mobile devices or professional questionnaire survey platform [13,14]. For example, Oltra et al. [15] collected data through a questionnaire survey of 1202 residents in four cities. They found that there are moderate differences in subjective evaluation of local air pollution, as well as the degree of worry and pain caused by the air pollution. Huang et al. [16] selected three cities in China: Beijing, Nanjing, and Guangzhou, by the questionnaire survey and found that the public’s awareness of the health effects and familiarity of air pollution was significantly higher in winter than in summer. The haze weather is also considerably higher than the typical weather. Chen et al. [17] explored public satisfaction with air pollution and found that the public’s air pollution views would be affected by industrial emissions. However, the perception of air quality based on the questionnaire survey will be affected by many factors, such as investigators and cities’ differences. Moreover, due to the high costs, the questionnaire survey is challenging to perform frequently and regularly. With the development of information and communication technology, people actively express their attitudes and feelings through social media. Some scholars used the social media data such as Tweet, Facebook, or Sina Weibo in China to extract public responses and abundant information on air quality or air pollution [18–20]. For example, Gurajala et al. (2019) explored the text content of Twitter data to understand the public’s response to air quality changes over time and their understanding of air quality issues [18]. Wang et al. [19] proposed a method to infer air quality in Chinese cities based on Sina Weibo and meteorological data. Hswen et al. [20] used Tweet data and sentiment analysis methods to evaluate the feasibility of using social media to monitor outdoor air pollution in London from public perception. Ryu and Tao et al. [21] proposed the air quality perception index (AQPI) based on the Internet search volume data to evaluate air quality views. The above research provides new methods and technologies for the public perception of air quality and expands subjective public emotion on air quality assessment. However, the social media data are complex and need to analyze the positive, negative, neutral, mixed, and other emotion types in the text. There may also be bias, and users may express unreal emotions for social purposes [22]. Unlike the questionnaire and social media data, air pollution complaint data form the real dissatisfaction (negative emotion) expressed by the public, which can most directly reflect the public’s actual air pollution perception. Only a few studies have utilized the complaint data to investigate how people perceive and respond to environmental or air pollution [23–25]. These studies mainly analyzed the potential pollution problems implied in the complaint data or identified potential pollution sources through the public complaint data on environment and odor [24,25]. However, the above studies mostly analyzed the content of complaint data from the perspective of statistics and time and space and rarely investigate the subjective feelings of the contents of public complaints. It is imperative to understand the personal emotion of public complaint data, because the change and intensity of public sentiment in complaint data can reflect the severity of air pollution and the effectiveness of governance to a certain extent. Therefore, this paper proposed a new index to evaluate public perception of air pollution, namely air pollution perception index (APPI), by using the air pollution complaint data and emotion analysis from subjective public emotion. Firstly, we obtained public complaints about air pollution through the network and constructed an air pollution’s emotional dictionary. Then we calculated the emotional intensity of public complaints by using the constructed emotional dictionary and the emotional analysis method of text mining. Secondly, we used the address-matching method to match the geographical location of complaint data. We analyzed the spatial distribution of complaint density and emotion Appl. Sci. 2021, 11, 1894 3 of 17

intensity of air pollution by the Kernel density analysis and Kriging interpolation method, and we constructed the air pollution perception index (APPI) through the piecewise function of the normalized complaint density and emotion intensity. Finally, the proposed APPI was applied to the air pollution complaint data of Shandong Province from 2012 to 2017. This paper’s proposed APPI is a supplement to the existing research on public perception of air pollution. It can provide strong support for air quality management and decision-making. The remainder of this paper is organized as follows: Section2 shows the datasets used in this study and the proposed index APPI. The results of the application study are presented in Section3. Section4 discusses and veriﬁes the application results. Section5 presents the major conclusions, recommendations, and further research.

2. Materials and Methods 2.1. Data 2.1.1. Public Complaint Data The Shandong Environmental Public Prosecution is the public’s primary network platform to complain about environmental pollution in Shandong Province. On the network platform, the crowd ﬁlls in the complaint’s general title, the complaint’s location, the pollution situation, and other details. After the successful submission, the website will automatically generate the number, complaint date, and additional information and update relevant departments’ replies in real time. This paper selected 11,699 complaints about air pollution from January 2012 to Decem- ber 2017 as application examples from the Shandong Environmental Public Prosecution platform. Each example of complaint data includes the number, complaint date, title, content, reply, and other information, as shown in Table1.

Table 1. Sample data of public complaints (Translate data into English).

Number Date Title Content Reply [Environmental Protection Bureau of Zaozhuang City] According to the investigation, Illegal oil refining in Xinzhao there are six small oil refineries Village, Xuzhuang Town, in Xinzhao Village, Xuzhuang Shanting District, Zaozhuang Town, Shanting District, which City, Shandong Province! The uses waste plastics to process 201207180305 2012/7/18 Small polluted refinery water in the river is black with diesel products. The the waste oil discharged. The production equipment is oil factory removes the simple. Shanting District pungent smell, and many Environmental Protection children get sick. Bureau issued a Notice of rectification within a time limit for illegal acts. [Qingdao Municipal Environmental Protection Qingdao Jiaonan Lvyin Bureau] Provincial Environmental Protection Environmental Protection Technology Co., Ltd. secretly Department: Our bureau discharges waste gas at night. immediately organized law As an environmental Exhaust gas and enforcement personnel to 201506052008 2015/6/5 protection enterprise, illegal serious pollution implement it. Our bureau operations have caused the requested that the company surrounding residents to live rectify the entire plant’s links under waste gas pollution for a that could generate odors, and long time, and thus, they the company also reported the require relocation. relevant rectification plan to our bureau. Appl. Sci. 2021, 11, 1894 4 of 17

2.1.2. Point of Interest (POI) Data To analyze whether the areas with a strong public perception of air pollution are related to industrial enterprises, this paper uses POI data from the Gaode map in 2014 to verify and discuss the application results of air pollution complaint data in Shandong Province in 2015. POI data are from a database mainly used for navigation, mainly recording the point’s name, address, and geographical location. In GIS, a POI can represent a building, a shop, a scenic spot, and so on. From the 2014 POI data, we selected a total of 6706 points data with the keyword “enterprises” or “factory” in Shandong Province, which are near-related to air pollution, mainly the steel industry, chemical industry, building material industry, and so on. To avoid interference, we deleted the sites with “company”, because many companies are not related to pollution sources.

2.2. Methods Based on air pollution complaint data and sentiment analysis, this paper proposed a new index to evaluate the public perception of air quality, namely the air pollution Appl. Sci. 2021, 11, 1894 perception index (APPI). Figure1 is the ﬂow chart of the APPI method proposed5 of in 18

this paper.

Air Pollution

Public Complaints Complaint data collection (1) Emotional dictionary (2) Emotional intensity (3) Kriging interpolation Sentiment Density of Analysis Complaints

(1) Address matching Piecewise function (2) Kernel density Normalization

Changes in Air Pollution public perception Perception Index， APPI Center of gravity

Figure 1. Flow chart of air pollution perception index (APPI) based on public complaint data and emotional analysis.

In Figure1, we ﬁrstly collected the public complaint data of air pollution, and we Figure 1. Flow chart of air pollution perception index (APPI) based on public complaint data and emotional analysis. constructed the emotional dictionary for the air pollution complaint data. We analyzed the complaintIn Figure emotional1, we firstly intensity. collected Then, the thepub complaintlic complaint text data data of were air pollution, transformed and into we complaintconstructed points the emotional by the address-matching dictionary for method.the air pollution Secondly, complaint we analyzed data. the We complaint’s analyzed densitythe complaint and the emotional spatial distribution intensity. ofThen, complaint the complaint emotion text intensity data bywere Kernel transformed density andinto Kriging interpolation method. Finally, according to the complaint density and emotional complaint points by the address-matching method. Secondly, we analyzed the com- intensity of air pollution, we obtained the air pollution perception index (APPI) through plaint’s density and the spatial distribution of complaint emotion intensity by Kernel density and Kriging interpolation method. Finally, according to the complaint density and emotional intensity of air pollution, we obtained the air pollution perception index (APPI) through the piecewise function and normalization. The index APPI can reasonably evaluate the public’s perception of air pollution. We also further analyzed the temporal and spatial changes of the center of APPI.

the piecewise function and normalization. The index APPI can reasonably evaluate the public’s perception of air pollution. We also further analyzed the temporal and spatial changes of the center of APPI.

2.2.1. Sentiment Analysis of Air Pollution Complaints Text sentiment analysis, also known as opinion mining and propensity analysis, aims to detect the polarity of emotions (such as negative or positive emotions) or quantify the intensity of emotions [26,27]. Currently, there are three common methods for text sentiment analysis, namely “dictionary-based,” “machine-learning,” and “deep-learning” [26]. Both “machine-learning” and “deep-learning” methods need supervised training. However, sentiment analysis based on a dictionary does not require supervised training, which is the most common and straightforward method. Sentiment analysis based on dictionary refers to calculating sentiment value based on the labeled sentiment dictionary [27]. Therefore, this paper analyzed the public emotion of complaints by constructing the emotional dictionary of air pollution. 1. Construction of the emotional dictionary of air pollution Firstly, we extracted verb (verb), noun (noun), adjective (adj), adverb (adv), idiom, and preposition phrase (prep) as candidate emotional words through Chinese word segmentation and part of speech tagging. Then, the labeling criteria shown in Table2 were formulated to classify air pollution emotion words. The column “Intensity” indicates the emotional intensity of the words in the text. These statements in the column “Description” are summarized from the data of the complaint platform. We labeled the emotion intensity of emotion words after artiﬁcial selection. Combining with the emotional Dictionary of Dalian University of Technology, we removed the duplication and added the modiﬁer dictionary to get the emotional dictionary of air pollution.

Table 2. The labeling criteria of emotion intensity.

Polarity Intensity Description Example 9 Beyond endurance, Cannot survive Pain, cancer 7 The atmosphere was foul, muddy Black smoke, headache, dyspnea -(negative) 5 The dust is rising in clouds Dust storms, grey 3 Has an unpleasant smell Peculiar smell, stench 1 Can feel, but not affected Pollution, not blue

To better understand the air pollution emotion dictionary constructed in this paper, we selected several examples in the dictionary for illustration, as shown in Table3.

Table 3. The examples of the air pollution emotion dictionary.

Emotional Words Parts of Peech Intensity Polarity Privately adv 1 −1 Soot noun 3 −1 Pungent adj 5 −1 Reek to high heaven idiom 7 −1 Killed verb 9 -1

In Table3, there are expressions about its part of speech, emotional intensity, and polarity for each word in the dictionary. The polarity can be divided into neutral (0), positive (+1), and negative (−1). Since the complaint data are usually negative, the polarity is marked “−1”. Appl. Sci. 2021, 11, 1894 6 of 17

2. Sentiment analysis of air pollution complaints Based on the constructed air pollution emotion dictionary, this paper calculated the public complaint emotion value, namely complaint emotion intensity, sentence segmentation, and word segmentation of the complaint text. The specific process is as follows. Text preprocessing: We preprocessed the complaint text by sentence segmentation, word segmentation, and removing stop words. Emotional word matching: We obtained the words after text preprocessing and matched the constructed air pollution emotion dictionary to get the sentence’s emotional words and mark their positions. Modifier matching: We matched the words with the modifier dictionary to get the sentence’s modifiers and mark their positions. Complaint emotion intensity: After matching emotion words and modifiers, we calculated the emotion intensity. Firstly, we used Formula (1) to calculate the emotional intensity of public complaints in short sentences; secondly, we used Formula (2) to calculate the emotional intensity of public complaints in sub-sentences according to the calculation results of short sentences; finally, we used Formula (3) to calculate the emotional intensity E(T) of public complaints on the complaint text according to the calculation results of sub-sentences. n E(Pi) = E(SW) × E(DW) × (−1) (1)

E(Ci) = Min(E(P1), E(P2), ···) (2)

E(T) = Min(E(C1), E(C2), ···) (3) where SW is the emotional word, DW is the degree adverb, E(SW) is the emotional intensity of the emotional word, n is the number of negative words, and E(DW) is the weight of the degree adverb. Moreover, E(Pi) is the emotional value of the ith phrase, E(Ci) is the effective value of the ith clause, and E(T) is the emotional value of the whole text. The complaint data almost reﬂect the negative emotions of the public, so the calculated E(T) is negative. The smaller the value, the greater the intensity of negative emotions. 3. Address matching of complaint data Because the complaint data are not recorded in the form of geographical coordinates, after calculating public complaint emotional intensity, it is necessary to match the address for the geographical location described by each complaint data. Address matching is the process of establishing a correspondence between a textual description address and its spatial geographic location coordinates [28]. The address-matching process mainly includes address extraction, address standardization, and latitude and longitude matching. Address extraction: We analyzed the words after part of speech tagging and detected the address parts of speech in terms including organization name and place name. Address normalization: It is necessary to standardize the extracted address and standardize the address information of each comment data as: [province, city, district/county, and detailed address], since the public often uses abbreviations to represent address information in expression. It is worth noting that when performing detailed address matching, the factory or company name will be used directly in instances where the factory or company name appears; if there is no factory or company name, match ‘town’ + ‘village’; if none of the above, match ‘road name’ + ‘community’. Longitude and latitude matching: This study used the AutoNavi Map API to perform latitude and longitude matching on the address information after address normalization.

2.2.2. Kernel Density Analysis and Kriging Interpolation After calculating the complaint emotion intensity of air pollution and address matching of complaint data, in this paper, we analyzed the Kernel density of the complaint data Appl. Sci. 2021, 11, 1894 7 of 17

and the spatial–temporal distribution of air pollution complaint emotional intensity by the method of Kriging interpolation. 1. Kernel density analysis of public complaints Complaint density can reflect the public’s satisfaction with air quality in a specific area. In general, the higher the complaint density, the less satisfied the public. The air pollution complaint density (Aird) of the complaint data point i after address matching was obtained by Kernel density analysis.

 !2 1 n 3 dist 2 = × − i Aird 2 ∑ 1  (4) (radius) i=1 π radius

where n is the number of complaint points after the outliers are removed, disti is the distance between point i and other points. radius is the search radius, and the Kernel density method uses the spatial variable of “Silverman experience rule” to calculate the default search radius for the input complaints dataset. 2. Spatial analysis of public complaint emotion based on Kriging interpolation The distribution of complaint emotion intensity can also reflect the public’s satisfaction with a specific area’s air quality. In general, the smaller the air pollution complaint emotional value, the greater the negative emotion, and the more dissatisfied the public is. In this paper, the local air pollution perception Airs was obtained by Kriging interpolation of complaint emotion results, as shown in Equation (5).

n Airs = ∑ λi × zi (5) i=1

where n is the number of complaint points, λi is the weight of complaint point i in space; in this paper, the spherical function is used as the ﬁtting model to calculate λi; zi is the emotion intensity of complaint point i.

2.2.3. Air Pollution Perception Index, APPI Considering the air pollution complaint density or emotional intensity alone cannot fully characterize the public perception of air pollution. For example, in areas with a high complaint density of air pollution, public complaint emotion’s intensity may be low. On the contrary, in areas with low complaint density, the intensity of public complaint emotion may be high. Therefore, this paper proposed the air pollution perception index, APPI. APPI can be used to evaluate the public’s perception of air pollution more reasonably through the piecewise function processing of complaint density Aird (Formula (4)) and complaint emotional intensity Airs (Formula (5)). The specific calculation process of the index is as follows: (1) Normalization We first used the min–max normalization method [29] (Formulae (6) and (7)) to linearly transform the complaint density Aird and complaint emotional intensity Airs to unified ∗ ∗ Aird and Airs , which ranges between 0 and 1.

∗ Aird − Min(Aird) Aird = 1 − (6) Max(Aird) − Min(Aird)

∗ Airs − Min(Airs) Airs = (7) Max(Airs) − Min(Airs) Appl. Sci. 2021, 11, 1894 8 of 17

(2) Piecewise function model Then, we used Formula (8) to construct a piecewise function model f for APPI, accord- ∗ ∗ ing to the normalized complaint density Aird and complaint emotional intensity Airs .

 Air∗ d  ∗ Air∗ ∗ ∗ ∗ ∗  (Airs + 1) s , Airs ≥ Air f (Air , Air ) = ∗ d (8) d s Airs Air∗  ∗ d ∗ ∗ Aird + 1 , Aird > Airs The advantage of using the piecewise function model [30] to measure the public’s perception of air pollution is that if there is a large difference between the complaint density and complaint emotional intensity, it can reduce the deviation value’s impact on the results. (3) APPI Finally, the ﬁnal air pollution perception index (APPI) is obtained by normalizing the piecewise function f, as shown in Formula (9).

f Air∗, Air∗ − Min APPI = d s (9) Max − Min The APPI ranges between 0 and 1. The closer the APPI value is to 1, the stronger the public perception of air pollution.

2.2.4. Perception Center of Gravity Finally, according to the results of APPI, we used the gravity Formula (10) to analyze the temporal and spatial characteristics of the public perception of air pollution. In this paper, the center of gravity for air pollution perception refers to a point in the study area where public perception in all directions can be balanced.

n n ∑i=1 wixi ∑i=1 wiyi X = n Y = n (10) ∑i=1 wi ∑i=1 wi where X and Y are the coordinates of the perceptual center of gravity, n is the total number of complaint points, xi and yi are the coordinates of the complaint point i, wi is the weight of i, expressed as the inverse number of the APPI.

3. Results 3.1. Descriptive Statistical Analysis This paper’s application examples consist of 11,699 air pollution public complaints data in Shandong Province from 2012 to 2017. As of December 2017, this comprises 17 cities in Shandong Province, including Jinan, Qingdao, Zibo, Zaozhuang, Dongying, Yantai, Weifang, Jining, Taian, Weihai, Rizhao, Binzhou, Dezhou, Liaocheng, Linyi, Heze, and Laiwu. Table4 lists statistical results to understand the data characteristics of public complaints about air pollution in Shandong Province from 2012 to 2017. In Table4, the column “Counts” represents the total number of complaints, columns “Mean” and “SD” represent the average and standard deviation of the number of complaints or the Emotional intensity of complaints, respectively. The column “Annual changes” of the number of complaints indicates the total number of complaints changes each year. The column “Annual changes” of the emotional intensity of complaints shows the average complaint intensity changes every year. Table4 shows that the air pollution complaints were identiﬁed with a mean of 1949.83 (SD = 877.74) per year in Shandong Province. From 2012 to 2017, both the number of air pollution complaint data and the public’s complaint emotional intensity in Shandong Province showed a trend of ﬁrst increasing and then decreasing. However, the annual changes in the number of complaints and the intensity of air pollution complaints were not Appl. Sci. 2021, 11, 1894 9 of 17 Appl. Sci. 2021Appl., 11Sci., 1894 2021 , 11, 1894 10 of 18 10 of 18 Appl. Sci. 2021Appl. , 11Sci., 18942021 , 11, 1894 10 of 18 10 of 18 Appl. Sci. 2021 Appl., 11Sci., 1894 2021 , 11, 1894 10 of 18 10 of 18 Appl. Sci. 2021 Appl., 11 Sci., 1894 2021 ,, 1111,, 18941894 10 of 18 10 of 18 Appl. Sci. 2021Appl. , 11 Sci., 1894 2021 , 11, 1894 10 of 18 10 of 18 Appl. 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DescriptiveName Province Counts 11,699 statistics 11,699Counts 1949.83 MeanNumberof public 1949.83 complaintsNumber Meanof Complaints SD1 of about Annual Complaints1 SD air pollutionchanges Annual in changes MeanShandongEmotional2 Mean0.18 ProvinceSD2Emotional plaintsIntensity Annual0.18 from SD plaintsIntensity of 2012 changesCom- Annual to 2017. of changesCom- ShandongNameShandong Province Name Province Counts 11,699 11,699Counts 1949.83 MeanNumber 1949.83 MeanNumberof Complaints SD1 of Annual 1Complaints SD changes Annual changes MeanEmotional2 Mean0.18 SD2Emotional Intensityplaints Annual0.18 SD plaintsIntensity of changesCom- Annual of changesCom- ShandongNameShandong Province Name Province Counts 11,699 11,699Counts 1949.83Number Mean 1949.83 Number Meanof Complaints SD1 of Annual Complaints1 SD 1 changes Annual changes Mean2 Mean0.18 SD2 2 plaints Annual0.18 SD plaints changes Annual changes ShandongNameJinanShandong Province JinanName Province Counts 11,699464 Counts 11,699464 1949.83Number Mean77.33 77.33 1949.83 MeanNumberof Complaints SD 31 of Annual 3Complaints SD 1 changes Annual changes Mean42 Mean0.350.18 SD4 2 plaints Annual0.350.18 SD plaints changes Annual changes ShandongNameJinanShandong Province JinanName Province Counts 11,699464 Counts 11,699464 1949.83 Mean77.33Number 77.33 1949.83 Mean of Complaints SD1 3 Annual3 SD 1 changes Annual changes Mean24 Emotional Mean0.180.35 SD4 2 plaints Intensity Annual0.350.18 SD plaints ofchanges Annual Complaints changes ShandongNameJinanShandong Province Jinan Province Counts 11,699464 Counts 11,699464 1949.83 Mean77.33 77.33 1949.83 Mean SD 31 Annual3 SD 1 changes Annual changes Mean42 Mean0.180.35 SD4 2 Annual0.350.18 SD changes Annual changes ShandongJinanShandong Province Jinan Province Counts 11,699464 Counts 11,699464 1949.83 Mean77.33 77.33 1949.83 Mean SD 153 Annual5 SD 13 changes Annual changes Mean264 Mean0.350.18 SD6 24 Annual0.350.18 SD changes Annual changes ShandongQingdaoJinanShandong Province Qingdao Jinan ProvinceCountsCounts 11,699687464 Counts 11,699687 Mean1949.83464114.50 Mean77.33 114.50 1949.83 Mean77.33 SD SD351 Annual Annual5 SD 13 Changeschanges Annual changes Mean462 Mean Mean0.180.330.35 SD6 24 SD Annual0.330.350.18 SD changes Annual Annual Changeschanges ShandongQingdaoJinanShandong Province Qingdao Jinan Province 11,699464687 11,699687 1949.83464114.5077.33 114.50 1949.8377.33351 5 13 462 0.350.330.186 24 0.330.180.35 ShandongQingdaoJinanShandong Province Qingdao Jinan Province 11,699687464 11,699687 1949.83464114.5077.33 114.50 1949.8377.33153 5 31 264 0.180.330.356 42 0.330.180.35 ShandongShandongQingdaoJinan ProvinceShandong Province Qingdao QingdaoJinan Province 11,699 11,699 687464 11,699687 1949.83 1949.83464687114.5077.33 114.50 1949.83114.5077.33 877.741375 7 513 2486− 6.55 0.180.350.338 624 0.180.330.350.180.33 QingdaoJinanZibo QingdaoZiboJinan 687772464 772687464114.50128.6777.33 128.67114.5077.33573 7 35 684 0.330.250.358 46 0.250.330.35 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Provincefrom air aboutThemethod.pollution 2012 spatial from airto The2017pollution are2012 distribution spatial alare tomost obtained2017 are distribution distributed areal resultsmost obtained(Figure distributed of inresults air 2). all(Figure pollutionFigure regions of in air 2). all2 pollutionshows ofFigure complaintregions 17 cities that2 shows ofcomplaint 17 cities that theindatathe Shandong publicaddress-matchingpublic in Shandongindatathein Shandongcomplaintscomplaints Shandong publicaddress-matchingProvince. in Shandong Province complaints Province. aboutProvince.method.about Provincefrom airair Theaboutmethod. pollutionpollution 2012 spatial from airto The 2017pollution are2012are distribution spatial alareal tomost obtained2017 are distribution distributed areal resultsmost obtained(Figure distributed of inresults air 2). all(Figure pollutionFigure regions of in air 2). all2 pollutionshows ofFigure complaintregions 17 cities that2 shows ofcomplaint 17 cities that indatathe Shandong public in Shandongthedatain complaints Shandong publicProvince. in Shandong Province complaints Province.about Provincefrom air about pollution 2012 from airto 2017pollution are2012 areal tomost obtained2017 are distributed alaremost obtained(Figure distributed in 2). all(Figure Figure regions in 2). all2 showsofFigure regions 17 cities that2 showsof 17 cities that indatathein ShandongShandong public in Shandongthedatain complaints Shandong public Province.Province. in Shandong Province complaints Province.about Provincefrom air about pollution 2012 from airto 2017pollution are2012 areal tomost obtained2017 are distributed alaremost obtained(Figure distributed in 2). all(Figure Figure regions in 2). all2 showsofFigure regions 17 cities that2 showsof 17 cities that thein Shandong publicinthe complaints Shandong publicProvince. complaints Province.about air about pollution air pollution are almost are distributed almost distributed in all regions in all of regions 17 cities of 17 cities thein Shandong publicinthe complaints Shandong publicProvince. complaints Province.about air about pollution air pollution are almost are distributed almost distributed in all regions in all of regions 17 cities of 17 cities in Shandongin Shandong Province. Province. in Shandongin Shandong Province. Province. Appl. Sci. 2021, 11, 1894 10 of 17 Appl. Sci. 2021, 11, 1894 11 of 18

FigureFigure 2.2. SpatialSpatial distributiondistribution ofof airair pollutionpollution complaintcomplaint datadata ininShandong ShandongProvince Province from from 2012 2012 to to 2017. 2017.

Then,Then, wewe usedused KernelKernel densitydensity andand KrigingKriging interpolationinterpolation toto analyzeanalyze thethe complaintcomplaint densitydensity andand complaintcomplaint emotionemotion intensityintensity ofof airair pollutionpollution inin ShandongShandong ProvinceProvince fromfrom 20122012 toto 2017.2017. TheThe resultsresults areare shownshown inin FigureFigure3 .3. InIn FigureFigure3 3a,a, red red indicates indicates the the area area with with high high public public complaints complaints about about air air pollution. pollution. FigureFigure3 3aa shows shows that that almost almost every every city city in in Shandong Shandong Province Province has has some some high-density high-density areas areas ofof publicpublic complaints. In In Figure Figure 3b,3b, the the emotional emotional intensity intensity of of public public complaints complaints about about air airpollution pollution is negative. is negative. The The redder redder the color, the color, the smaller the smaller the emotional the emotional value value of public of public com- complaints,plaints, and andthe thestronger stronger public’s public’s negative negative emotional emotional intensity. intensity. From the spatial distribution of complaint density and complaint emotion intensity, public complaints’ negative emotion intensity is not very large in areas with high complaint density, such as Jinan, Zibo, and Linyi in 2016. Public complaints’ negative emotion is also very strong in low complaint density areas, such as Binzhou and Yantai inland in 2017. The above results reﬂect that, whether statistical analysis or spatial analysis, only dependent on the public’s complaint density or the public’s complaint emotional intensity, cannot reasonably express the public’s perception of air pollution. Appl. Sci. 2021, 11, 1894 11 of 17 Appl. Sci. 2021, 11, 1894 12 of 18

Figure 3. Spatial distribution of air pollution complaint density (a) and complaint emotion intensity (b) based on Kernel density and Kriging interpolation in Shandong Province from 2012 to 2017. Appl. Sci. 2021, 11, 1894 13 of 18

Figure 3. Spatial distribution of air pollution complaint density (a) and complaint emotion intensity (b) based on Kernel density and Kriging interpolation in Shandong Province from 2012 to 2017.

From the spatial distribution of complaint density and complaint emotion intensity, public complaints’ negative emotion intensity is not very large in areas with high complaint density, such as Jinan, Zibo, and Linyi in 2016. Public complaints’ negative emotion is also very strong in low complaint density areas, such as Binzhou and Yantai inland in 2017. Appl. Sci. 2021, 11, 1894 12 of 17 The above results reflect that, whether statistical analysis or spatial analysis, only dependent on the public’s complaint density or the public’s complaint emotional intensity, cannot reasonably express the public’s perception of air pollution. 3.2.2. Spatiotemporal Analysis of APPI 3.2.2. Spatiotemporal Analysis of APPI To evaluate the public’s perception of air pollution more reasonably and comprehensively,To we evaluate used Formula the public’s 9 to perception calculate theof ai indexr pollution APPI more based reasonably on the public’s and comprehen- complaint densitysively, andwe used complaint Formula emotional 9 to calculate intensity. the in Thedex spatialAPPI based distribution on the public’s results ofcomplaint APPI are showndensity in and Figure complaint4. emotional intensity. The spatial distribution results of APPI are shown in Figure 4.

FigureFigure 4. 4.The The results results of of APPI APPI inin ShandongShandong ProvinceProvince from 2012 to 2017.

As shown in Figure4, the APPI of different years is normalized and comparable. Moreover, to make the interannual changes of public perception comparable, the maximum density of APPI in 2015 was taken as the standard, and the color changes in other years were set. Red is the high-density area of APPI, which indicates the strongest public perception of air pollution. At the same time, green is the low-density area of APPI, which means the weakest public perception of air pollution. Figure4 indicates that, from 2012 to 2015, public perception of air pollution ﬁrst increased and then decreased. For example, the public’s perception of air pollution had increased year by year in Shandong Province, reaching the peak in 2015. Since 2015, the public’s perception of air pollution had been a progressive decrease every year. When coupled with the results in Table4, the increase in the number of public complaints and the perceived intensity of air pollution may be due to the increased public awareness of environmental protection. It may also indicate the aggravation of air pol- Appl. Sci. 2021, 11, 1894 13 of 17

lution. However, the decrease in the number of public complaints and the weakening of public perception of air pollution, to a certain extent, reﬂects that the air quality has been improved. In addition, existing studies have shown that Shandong Province and even China’s air quality have become better in recent years [1]. Figure4 also shows that Zibo, Jinan, Laiwu, Zaozhuang, Linyi, and Qingdao are the areas with the strongest public perception of air pollution. Except for Qingdao, other cities are inland cities with developed industry in Shandong Province. Naturally, air pollution cannot be avoided in cities with developed industries, especially in cities where chemical products mainly by reﬁneries are the critical project, which will also be the birthplace of air pollution. Qingdao is not only a coastal city, but also a tourist city. At the same time, there are many industries and enterprises in Qingdao. Therefore, the public’s strong perception of its air pollution may be due to the public’s higher requirements on air quality in Qingdao or the impact of industrial emissions. That may be related to the distribution of industries or enterprises that are prone to air pollution. This paper will further verify public complaints’ content through word cloud analysis and POI data of industries and enterprises in the discussion section.

3.3. Change of Center of Gravity of APPI According to the public perception results of air pollution from 2012 to 2017 in Section 3.2.2, the air pollution perception index (APPI) and its geographical location infor- Appl. Sci. 2021, 11, 1894 mation are used to build the center of the gravity model of public perception according15 of to18

Equation (10). The results are shown in Figure5.

Figure 5. Change of center of gravity of APPI (blue points) from 2012 to 2017.

FigureThe red 5 ﬁve-pointedsuggests that star the in general Figure 5trend represents of the thepublic’s center air of pollution Shandong perception Province, andis in thethe southwest blue points of represent Shandong the Province center of from gravity 2012 of to APPI 2017. in There different is little years. difference The ellipses in the in distancedifferent and colors direction represent of thethe directionalcenter of gravity distribution of public of APPI perception in different of air years. pollution The green over theline years, and pink the columnmean offset in the distance statistical is about chart respectively17 km (SD = indicate 2.39), and the the distance mean and offset azimuth angle isangle about change 232° (SD between = 11.09) the per center year of in gravity Shandong of APPI Province. and the center of Shandong Province. ItFigure may5 be suggests that the that northern the general and eastern trend of regions the public’s of Shandong air pollution Province perception are mostly is in coastalthe southwest cities. First of Shandong of all, coastal Province cities gene fromrally 2012 have to 2017. relatively There strong is little winds, difference and the in theair circulatesdistance and quickly. direction Secondly, of the seawater center of will gravity absorb of publicsome of perception the air’s impurities of air pollution due to over the sea and have a specific effect of purifying the air. Moreover, coastal cities generally have more vegetation than inland cities, which can improve the air quality. So coastal cities’ air quality is usually better than inland towns, and the public is more concerned about air pollution. The public’s perception of air pollution is relatively low in the southwestern region of Shandong’s center of gravity, cities such as Jinan, Laiwu, Zibo, Zaozhuang, Linyi, and other towns dominated by industry, mining, and enterprises [31]. The discharge of three industrial wastes, such as exhaust gas, wastewater, solid waste, will pollute the atmosphere and directly endangers human health. Therefore, the public has a stronger perception of air pollution. Moreover, in recent years, the Shandong public’s perception of air pollution has not been very different. It may be due to some inherent air pollution problems related to industrial development that have not been resolved. Specific issues and reasons need to be further developed with the help of refined data.

4. Discussion This paper shows that the areas with the strongest public perception of air pollution are mainly located in the southwest of Shandong Province, including Zibo, Laiwu, Jinan, Zaozhuang, Linyi, and other cities. The cities mentioned above primarily focus on industrial development. Therefore, we used POI data to explore the relationship between the high value of APPI and enterprises’ space distribution. We used word cloud analysis-based complaint data content to discuss the problems of public complaints, because the public’s perception was the strongest in 2015, and we took it as an example. Appl. Sci. 2021, 11, 1894 14 of 17

the years, the mean offset distance is about 17 km (SD = 2.39), and the mean offset angle is about 232◦ (SD = 11.09) per year in Shandong Province. It may be that the northern and eastern regions of Shandong Province are mostly coastal cities. First of all, coastal cities generally have relatively strong winds, and the air circulates quickly. Secondly, seawater will absorb some of the air’s impurities due to the sea and have a specific effect of purifying the air. Moreover, coastal cities generally have more vegetation than inland cities, which can improve the air quality. So coastal cities’ air quality is usually better than inland towns, and the public is more concerned about air pollution. The public’s perception of air pollution is relatively low in the southwestern region of Shandong’s center of gravity, cities such as Jinan, Laiwu, Zibo, Zaozhuang, Linyi, and other towns dominated by industry, mining, and enterprises [31]. The discharge of three industrial wastes, such as exhaust gas, wastewater, solid waste, will pollute the atmosphere and directly endangers human health. Therefore, the public has a stronger perception of air pollution. Moreover, in recent years, the Shandong public’s perception of air pollution has not been very different. It may be due to some inherent air pollution problems related to industrial development that have not been resolved. Specific issues and reasons need to be further developed with the help of refined data.

4. Discussion This paper shows that the areas with the strongest public perception of air pollution are mainly located in the southwest of Shandong Province, including Zibo, Laiwu, Jinan, Zaozhuang, Linyi, and other cities. The cities mentioned above primarily focus on industrial development. Therefore, we used POI data to explore the relationship between the high value of Appl. Sci. 2021, 11, 1894 16 of 18 APPI and enterprises’ space distribution. We used word cloud analysis-based complaint data content to discuss the problems of public complaints, because the public’s perception was the strongest in 2015, and we took it as an example. WordWord cloud cloud was was initially initially proposed proposed by by Rich Rich Gordon, Gordon, associate associate professor professor of of Journalism Journalism atat Northwestern Northwestern University University [32 [32],], a visual a visual display display of ‘keywords’ of ‘keywords’ with with high high frequency frequency in the in text.the Wordstext. Words with increasedwith increased frequency frequency will be will presented be presented in a larger in a form, larger while form, the while words the withwords low with frequency low frequency will be presented will be presented in a smaller in a smaller format. format. Word cloud Word image cloud ﬁltersimage out filters a largeout a number large number of low-frequency of low-frequency and low-quality and low-quality text information text information so that so the that viewer the viewer can appreciatecan appreciate the text’s the text’s theme theme as soon as soon as they as they scan scan the the text. text. The The distribution distribution of of APPI APPI and and industrialindustrial POI POI points points (a) (a) and and the the results results of of complaint complaint content content based based on on word word cloud cloud analysis analy- (b)sis in (b) Shandong in Shandong Province Province in 2015 in 2015 are shown are shown in Figure in Figure6. 6.

FigureFigure 6. 6.Superposition Superposition results results of of industrial industrial sites sites and and APPI APPI in in Shandong Shandong Province Province in in 2015 2015 (a ()a and) and content content analysis analysis of of air air pollutionpollution complaints complaints based based on on word word cloud cloud (b ().b).

FigureFigure6a 6a describes describes that that cities cities with with the the strongest strongest public public perception perception of of air air pollution, pollution, suchsuch as as Jinan, Jinan, Zibo, Zibo, Laiwu, Laiwu, Weifang, Weifang, Zaozhuang, Zaozhuang, Linyi, Linyi, and and Qingdao, Qingdao, are are also also the the densest densest enterprises. The word cloud result shows that the most frequent word is “enterprise” (Fig- ure 6b), followed by “pollution,” “villagers,” or “village” in the complaint content. It indicates that the public is more concerned about the impact of enterprises on air pollution. Moreover, the high-frequency vocabulary words of “village” and “villagers” mean that these enterprises may be located in the suburbs, such as the Zhangdian District of Zibo. The more enterprises there are, the more the pollutants, and the stronger the public perception of air pollution will be. Although coastal cities also have high-density industries and enterprises, their self-purification ability is relatively strong, the public perception of air pollution is stable. As an international tourism city, Qingdao mainly focuses on the development of tourism services and enterprises. The public may have higher requirements on its air quality, so the perception of air pollution (APPI) is also stronger. In this section, we can conclude that the public’s strong perception of air pollution is also the high-density area of enterprises. The word cloud analysis of public complaints’ content well confirms this conclusion, indicating that the index APPI proposed in this paper can effectively evaluate the public perception of air pollution.

5. Conclusions This paper put forward the air pollution perception index APPI according to the complaint density and complaint emotion intensity based on the air pollution complaint data. APPI uses the piecewise function model to assess the public’s perception of air pollution comprehensively. The proposed APPI was then applied to the air pollution complaint data of Shandong Province from 2012 to 2017, and the results were also verified by word cloud analysis and POI data. (1) the statistical analysis and spatial distribution of air pollution complaint density and public complaint emotion intensity are not entirely consistent. The proposed APPI can more reasonably evaluate the public perception of air pollution. Appl. Sci. 2021, 11, 1894 15 of 17

enterprises. The word cloud result shows that the most frequent word is “enterprise” (Figure6b), followed by “pollution,” “villagers,” or “village” in the complaint content. It indicates that the public is more concerned about the impact of enterprises on air pollution. Moreover, the high-frequency vocabulary words of “village” and “villagers” mean that these enterprises may be located in the suburbs, such as the Zhangdian District of Zibo. The more enterprises there are, the more the pollutants, and the stronger the public perception of air pollution will be. Although coastal cities also have high-density industries and enterprises, their self-puriﬁcation ability is relatively strong, the public perception of air pollution is stable. As an international tourism city, Qingdao mainly focuses on the development of tourism services and enterprises. The public may have higher requirements on its air quality, so the perception of air pollution (APPI) is also stronger. In this section, we can conclude that the public’s strong perception of air pollution is also the high-density area of enterprises. The word cloud analysis of public complaints’ content well conﬁrms this conclusion, indicating that the index APPI proposed in this paper can effectively evaluate the public perception of air pollution.

5. Conclusions This paper put forward the air pollution perception index APPI according to the complaint density and complaint emotion intensity based on the air pollution complaint data. APPI uses the piecewise function model to assess the public’s perception of air pollution comprehensively. The proposed APPI was then applied to the air pollution complaint data of Shandong Province from 2012 to 2017, and the results were also verified by word cloud analysis and POI data. (1) the statistical analysis and spatial distribution of air pollution complaint density and public complaint emotion intensity are not entirely consistent. The proposed APPI can more reasonably evaluate the public perception of air pollution. (2) The public perception of air pollution tends to the southwest of Shandong Province, and coastal cities are relatively weak. (3) The content of public complaints about air pollution mainly focuses on enterprises’ exhaust emissions. Moreover, in inland cities with dense industrial enterprises, the value of APPI is higher, the public perception of air pollution is stronger. Given the above conclusions, some suggestions can be put forward for the relevant air quality management departments in this paper. Firstly, to maintain economic development. It is necessary to strengthen enterprises’ supervision in the Southwest Shandong Province to meet the pollution discharge standards. Although industries and enterprises drive the city’s economic development, they still have to balance the relationship between industrial development and air quality. Secondly, enhance the public’s enthusiasm to participate in air pollution control and cooperate to optimize air quality. In conclusion, the air pollution perception index (APPI) proposed in this paper has high feasibility and practical application value and can be extended to other cities’ air pollution perception evaluation. It can also test the effectiveness of government air pollution control to a certain extent and has significant supervision value for improving air quality. Moreover, for the objective monitoring station data, it is also a supplement of subjective data analysis. Because where there is pollution, the public will have dissatisfaction and expectation. The complaint data directly reflect the dissatisfaction of the people. The dissatisfaction of the people is an intuitive evaluation of air quality, which is the most convincing. However, it should be noted that the proposed indicator APPI in this paper is primarily meant for the public’s historical perception. In future research, we need to use meteorological data and social media data for in-depth analysis to predict the public’s perception of air pollution more reliably. Appl. Sci. 2021, 11, 1894 16 of 17

Author Contributions: Conceptualization, Y.S.; methodology, Y.S.; software, Y.S.; validation, Y.S., and H.W.; formal analysis, Y.S. and Y.Z.; investigation, Y.S. and H.W.; resources, M.J.; data curation, M.J.; writing—original draft preparation, Y.S.; writing—review and editing, F.J.; visualization, H.W.; supervision, F.J.; project administration, M.J. and H.W.; funding acquisition, F.J. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Major Scientific and Technological Innovation Projects in Shandong Province (2019JZZY020103) and a grant from State Key Laboratory of Resources and Environmental Information System. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also forms part of an ongoing study. Conflicts of Interest: The authors declare no conflict of interest.

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