A Novel Calendar-Based Method for Visualizing Water Quality Change: the Case of the Yangtze River 2006–2015

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Article A Novel Calendar-Based Method for Visualizing Water Quality Change: The Case of the Yangtze River 2006–2015

Lina Huang 1 ID , Mengyin Zhong 2, Qiyao Gan 3 and Yanfang Liu 1,*

1 School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China; [email protected] 2 The Bartlett Centre for Advanced Spatial Analysis, University College London, London WC1E 6BT, UK; [email protected] 3 Network Information Center, Changjiang Water Resource Committee, Wuhan 430010, China; [email protected] * Correspondence: yﬂ[email protected]; Tel.: +86-027-6877-8386

Received: 17 August 2017; Accepted: 13 September 2017; Published: 15 September 2017

Abstract: Mapping water quality change is helpful in the study of the water environment of a region. Using visual methods, interpretation of water condition and pollution issues can be intuitive and easy to understand. In this paper, we present a map to represent the spatial and temporal variation of water quality change in the Yangtze River during the period from 2006 to 2015. The calendar view was developed to explore the water quality condition and water pollutants for sections of the Yangtze River. A “W” construction was proposed to arrange the weekly water quality data in a continuous logic, and qualitative colors were designed to identify the change in major pollutants throughout the period. This map provides a promising visual analytical approach to investigate the water quality status and reveal the spatial and temporal patterns of water quality change in the Yangtze River.

Keywords: water quality; spatial and temporal variation; visualization; Yangtze River

1. Introduction The change in the water quality of the Yangtze River has attracted extensive attention, as the Yangtze River is the largest river in China and feeds more than 400 million local people living in the basin. During the process of urbanization and industrialization in the Yangtze River basin, human activities, e.g., hydrological alterations, toxic chemical emission, and land use change, have dramatically affected the water’s physicochemical properties [1–4]. Though efforts have been made to improve the treatment of the water and environment, many studies indicate that the water quality tends to deteriorate and raise various environmental problems, such as eutrophication [5,6], endemic diseases [7], and drinking water issues [8,9]. Identifying the water quality status and investigating the spatial and temporal changes are important to promote environmental governance and increase public awareness of water environmental protection. The studies on water quality change are generally based on numerical models and statistical techniques. Various chemical indices, such as ammonia nitrogen (NH3-N), permanganate index (CODMn), phosphorus (P), nitrogen (N), heavy metals, dissolved oxygen (DO), and total organic carbon (TOC), were measured to evaluate the water quality in each period [10–12]. These indices were then used to explore the spatial and temporal patterns of water quality by adopting multivariate statistical methods, such as the Mann–Kendall test to estimate the trend homogeneity of water quality among monitoring sites [13–15], factor analysis and principal component analysis (FA/PCA) to transform the water quality indices into a new group of varifactors and to evaluate the incidence of

Water 2017, 9, 708; doi:10.3390/w9090708 www.mdpi.com/journal/water Water 2017, 9, 708 2 of 13 each group in the overall water quality change [16,17], discriminant analysis (DA) to implement the classification with prior knowledge [17], and hierarchical cluster analysis to evaluate the similarities between sites and divide the sites into groups (clusters) based on similarities within the group and dissimilarities between groups [14,17]. Note that all of these analyses require complex mathematical algorithms and parameters. It is still difficult to identify the status of water quality and its evolution trend, particularly for the public who do not have sufficient domain knowledge [18,19]. Mapping the water quality change can make the interpretation of water environment issues intuitive and readable. One example is the Srivastava’s heat maps [20], which represented the river pollution potentiality based on the spatial interpolation of pre- and post-monsoonal water quality indices. In a number of water environmental information systems, dynamic maps were developed with animation and interaction techniques to visualize water quality over a time span or at any point in time [19,21–24]. However, as most mapping methods present the time-varying data of water quality on either a few canvases or a series of animation frames, the characteristics of water quality change could not be aware of all at once. A map reader has to switch back and forth between different views and then make connections between them. Our study aimed to develop a visual method with a map to explore the status and the spatial and temporal variation of water quality in the Yangtze River. In this paper, a calendar-based visualization is advanced to address the time characteristic of water quality data [25]. Special attention is paid to constructing the calendar hybrid of water quality and major pollutants for sections of the Yangtze River and to implementing qualitative and quantitative analysis on the spatial and temporal changes of water quality using the calendar view.

2. Materials and Methods

2.1. Materials The Yangtze River starts in the Tanggula Mountains, Qinghai-Tibet Plateau, in the southwest of China and ﬂows generally through the middle part and then east of the mainland into the East China Sea. As shown in Figure1, the hydrographic network of the Yangtze River consists of more than 40 tributaries, including the Yalong River, the Min River, the Jialing River, the Wu River, the Han River, the Xiang River, and the Gan River. The total catchment area is approximately 1.8 million km2 and covers 17 provinces, involving Sichuan, Yunnan, Hubei, Hunan, Jiangxi, Anhui, and Jiangsu, and two municipalities, Chongqing and Shanghai. With the sustainable economic growth in China, there are three national urban agglomerations developing along the Yangtze River, i.e., Yangtze River Delta Urban Agglomerations, including the cities of Shanghai, Nanjing, and Yangzhou; the Triangle of Central China, including the cities of Wuhan, Changsha, NanChang, and Jiujiang; and the Chengyu city group, including the cities of Chengdu, Chongqing, Luzhou, and Yibin. The National Environmental Monitoring Centre of China has published data on the weekly water quality of the Yangtze River for decades. Several physicochemical indices, including temperature, pH, DO, electrical conductivity, turbidity, CODMn, TOC, NH3-N, and two reservoir indices, total phosphorus (TP) and total nitrogen (TN), are measured and uploaded from 42 monitoring sites located in the Yangtze River basin. Among them, pH, DO, CODMn and NH3-N are provided as the four primary water pollutant indices by the National Surface Water Quality Automatic Monitoring System. To facilitate understanding of the water quality issue by the public, the general water quality level (WQL) is graded based on the Chinese Environmental Quality Standards for Surface Water (GB 3838–2002). The WQL is deﬁned as follows: n o WQL = LOWEST WQL_i, WQL_j,..., WQL_n (1) where WQL_i is the water quality level of index i. The grading thresholds for the water quality indices are listed in Table1. WaterWater 20172017,, 99,, 708 708 33 of of 13 13

FigureFigure 1. 1. LocationLocation and and the the main main topographi topographicc features features of of the the Yangtze Yangtze River. River.

Water 2017, 9, 708 4 of 13

Water 2017, 9, 708 Table 1. The grading thresholds for several water quality indices. 4 of 13

WQL Category pH DO (≥ mg/L) CODMn (≤ mg/L) NH3-N (≤ mg/L) TP (≤ mg/L) TN (≤ mg/L) I Excellent Table 1. The grading7.5 thresholds2 for several water 0.15 quality indices.0.02 0.2 II Lightly polluted 6 4 0.5 0.1 0.5 Moderately ≥ ≤ ≤ ≤ ≤ WQLIII Category pH DO5 ( mg/L) COD6 Mn ( mg/L) NH 1.03-N ( mg/L) TP0.2 ( mg/L) TN (1.0mg/L) Ipolluted Excellent 6~9 7.5 2 0.15 0.02 0.2 IISeriously Lightly polluted 6 4 0.5 0.1 0.5 IV 3 10 1.5 0.3 1.5 III Moderatelypolluted polluted6~9 5 6 1.0 0.2 1.0 VIV Terrible Seriously polluted polluted 2 3 15 10 2.0 1.5 0.4 0.3 2.0 1.5 V Terrible polluted 2 15 2.0 0.4 2.0 Poor V Cannot be used - 1 <2 >15 >2.0 >0.4 >2.0 Poor V Cannot be used - 1 <2 >15 >2.0 >0.4 >2.0 Notes: 1 In our study, the Poor V was defined based on the Chinese Environmental Quality Standards Notes: 1 In our study, the Poor V was defined based on the Chinese Environmental Quality Standards for Surface forWater, Surface which Water, only provideswhich only the provides grading thresholds the grading corresponding thresholds tocorrespondingWQL I–V. As theto WQL pH thresholds I–V. As the are pH not thresholdsclassified in are the standers,not classified we didn’t in the define standers, a specific we pH didn’t value define for the a Poor specific V. pH value for the Poor V.

2.2.2.2. Calendar Calendar Construction Construction AsAs time time is is the the inherent inherent characteristic characteristic of of the the wa waterter quality quality data, data, data data collection collection can can be be arranged arranged intointo a a calendar calendar structure structure [26,27]. [26,27]. The The rectangle rectangle in in th thee calendar calendar view view can can be be used used for for a a certain certain level level of of temporaltemporal precision, precision, e.g., e.g., daily, daily, weekly, weekly, or or monthly. monthly. Next, Next, the the arrangement arrangement of of rectangles rectangles reasonably reasonably representsrepresents the the duration duration of of our our time time series series data. data. ForFor the purpose ofof exploringexploring the the temporal temporal variations variations in thein the water water quality quality data, data, we propose we propose a cyclic a cyclicstructure structure of time of totime construct to construct the calendar the calendar view. view. As shown As shown in Figure in Figure2, the 2, weeksthe weeks of one of one year year are areassembled assembled in twoin two reverse reverse columns. columns. Thus, Thus, the the weekly weekly collection collection of water of water quality quality from from 2006 2006 to 2015 to 2015 can canbe accommodatedbe accommodated in a in “W” a “W” construction. construction. In such In asuch calendar a calendar view, theview, temporal the temporal schemas schemas of month, of month,season, season, and year and are year also are easy also to easy retrieve. to retrieve.

FigureFigure 2. 2. CyclicCyclic structure structure of of the the calend calendarar view view at at weekly weekly precision. precision.

2.3.2.3. Rendering Rendering Rule Rule QualitativeQualitative colors colors are are designed designed to toencode encode the the water water quality quality based based on Color on Color Brewer Brewer [28]. [28As]. illustratedAs illustrated in Table in Table 2, blue2, blueis used is usedwhen when the general the general WQLs areWQLs at Gradeare at I, Grade implying I, implying that the water that the is anwater excellent is an excellentdrinking drinkingsource and source aquatic and habitat. aquatic For habitat. the other For theWQLs other, sequentialWQLs, sequential colors are colors adopted are correspondingadopted corresponding to various to pollutants. various pollutants.

Water 2017, 9, 708 5 of 13

WaterWaterWaterWater 2017Water 2017 2017 ,2017 9 ,2017 ,9 ,708 ,9 ,708 ,9 ,708 ,9 708 , 708 5 5of 5of 13 5of 135of 13of 13 13 WaterWaterWaterWater 2017Water Water2017Water 2017 ,2017 9 2017,, 9 2017708,2017, 9,708 ,9, 708,9 ,708, , 9 9 708, , 708 708 5 of5 of5 13 5of 13 5 of 135of 5 13 of of13 1313 WaterWaterWaterWater Water2017 2017 2017 ,2017 92017,, 9,708 ,9 ,708 ,9 ,708 ,9 708, 708 Table 2. Qualitative colors for ranking the primary water quality indices. 5 of5 5of 13 5of 135of 13of 13 13 TableTableTableTableTable 2. 2. Qualitative 2. Qualitative2. Qualitative2. Qualitative Qualitative colors colors colors colors colors for for forranking for ranking for ranking ranking ranking the the the primarythe primarythe primary primary primary water water water water water quality quality quality quality quality indices. indices. indices. indices. indices. TableTableTableTableTable 2.TableTable 2. Qualitative 2. Qualitative 2. Qualitative2. 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Data Data Data Data Data pH I I I I I II IIII II II II IIII IIIIIIIIIIIIIIIIIIIII IV IV IV IV -IV IV IV V V V V V V V Poor Poor Poor Poor PoorVNo Poor PoorVNo VNo VNo VNo VNoVNo Data Data Data Data Data Data Data pHpH pH pH pH I I I I I IIII II II II IIIIIIIIIIIIIII IV IV- IV- IV - IV - - V V V V V Poor Poor Poor Poor PoorVNo VNo VNo VNo VNo Data Data Data Data Data pHpHpH pH pH pHpH ------DODOpHDOpHDOpH DOpH pHDO - - - - - DODODO DO DODO DO DODODO DODO CODCODCODCODCODMnMn Mn Mn Mn CODCODCODCODMnCODCODMnCODMnCOD Mn Mn Mn MnMn CODNHNHCODCODNHCODNH3-NCODMnNH3-N3Mn -N3Mn-N Mn3-N Mn NHNHNH3NH-NNH3-NNH3NH -N3-N 3-N 33-N -N NHNH3NH-NNHNH3-NNH3-N3 -N3-N 3-N InIn Inour Inour Inour ourstudy, ourstudy, study, study, study, an an an excessivean excessivean excessive excessive excessive ratio ratio ratio ratio ratiois is introducedis introducedis introducedis introduced introduced to to identifyto toidentify toidentify identify identify the the the themajor themajor major major major pollutant. pollutant. pollutant. pollutant. pollutant. 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Water 2017, 9, 708 6 of 13 Water 2017, 9, 708 6 of 13

Figure 3. The spatial change of water quality in the Yangtze River basin. (The water quality data of the Yangtze River during 2006–2015 are represented using the Figure 3. The spatial change of water quality in the Yangtze River basin. (The water quality data of the Yangtze River during 2006–2015 are represented using the proposed calendar method. According to the rendering rule defined in Table 2, the colors in calendar view indicate the major water pollutants and the corresponding proposed calendar method. According to the rendering rule deﬁned in Table2, the colors in calendar view indicate the major water pollutants and the corresponding water quality levels qualitatively. water quality levels qualitatively.

Water 2017, 9, 708 7 of 13 Water 2017, 9, 708 7 of 13 A hierarchical cluster analysis was carried on the WQL frequency using the between-groups A hierarchical cluster analysis was carried on the WQL frequency using the between-groups method based on the Euclidean distance. As shown in Figure4, the clustering results are demonstrated method based on the Euclidean distance. As shown in Figure 4, the clustering results are in a dendrogram on the left, and the total days corresponding to each WQL are presented as colored demonstrated in a dendrogram on the left, and the total days corresponding to each WQL are graphs on the right. The ﬁndings from the water quality map are consistent with the results of the presented as colored graphs on the right. The findings from the water quality map are consistent with cluster analysis. All the sections belonging to Groups 2 and 3, in particular the heavily polluted sections, the results of the cluster analysis. All the sections belonging to Groups 2 and 3, in particular the are distributed in the important urban agglomerations along the Yangtze River. This corroborates heavily polluted sections, are distributed in the important urban agglomerations along the Yangtze the hypothesis that the urbanization has dramatically affected the water quality of the Yangtze River. River. This corroborates the hypothesis that the urbanization has dramatically affected the water In general, domestic sewage, agriculture sewage, and industrial wastewater are considered three quality of the Yangtze River. In general, domestic sewage, agriculture sewage, and industrial primary sources of water pollution in these areas [32–34]. wastewater are considered three primary sources of water pollution in these areas [32–34].

Figure 4. The result of hierarchical cluster analysis on spatial pattern of water quality in the Yangtze Figure 4. The result of hierarchical cluster analysis on spatial pattern of water quality in the Yangtze River. (a) Dendrogram for the WQL frequency of water quality monitoring sections in the Yangtze River. (a) Dendrogram for the WQL frequency of water quality monitoring sections in the Yangtze River. River. (b) Cluster viewer for the water quality level of monitoring section belongs to Group 1. (c) (b) Cluster viewer for the water quality level of monitoring section belongs to Group 1. (c) Cluster Cluster viewer for the water quality level of monitoring section belongs to Group 2. (d) Cluster viewer viewer for the water quality level of monitoring section belongs to Group 2. (d) Cluster viewer for the for the water quality level of monitoring section belongs to Group 3. water quality level of monitoring section belongs to Group 3.

Water 2017, 9, 708 8 of 13

As observed from the water quality map, the major pollutants in different sections of the Yangtze River basin are diverse. In the drainage network area, NH3-N is the common major pollutant, although high concentrations of DO and CODMn occur from time to time. Scientists have found that the NH3-N in the Yangtze River mainly comes from the decomposition products of organic matter in the sewage, farmland drainage, and industrial wastewater [5]. In the lake area, the pollution by CODMn is much more serious. Accordingly, the lake area can be divided into four groups. The ﬁrst group refers to Dian Lake and Liangzi Lake, including South Dian Lake, Luojia Yin, the Xiyuan tunnel, Guanyin Hill, and Qixing, where the common pollutant is almost always CODMn. Dian Lake has heavier CODMn pollution than does Liangzi Lake, as the CODMn concentration generally levels at Grade V and poor V, implying that the amount of organic compounds in the water has far exceeded the recommended limit. The second group refers to Poyang Lake, Chao Lake, and Tai Lake, including Hama Stone, Hubin, Yuxi Mouth, Shazhu, Yilan Hill Mouth, Xi Hill, and Xintang Port, where CODMn and NH3-N are indicated as the common major pollutants. The third group mainly refers to Dongting Lake, including South Mouth, Yueyang Lou, and Lujiao. In these areas, CODMn is rarely the major pollutant, but NH3-N pollutions emerge most of the time. Then, several areas in Poyang Lake and Tai Lake, including Duchang, Kan Hill, Jishui Port, Wangjiangjing, and Xielu Port, can be viewed as the fourth group, where pollution by CODMn and NH3-N appears alternately, making the water environments of these areas quite different from those of the other groups.

3.2. Temporal Change of the Water Quality The water quality map also demonstrates an intuitive overview of the temporal variation in the water environment of the Yangtze River. Figure5 shows the temporal change of water quality in a several sections of the Yangtze River. Note that there is obvious water degradation in Danjiang Mouth, which is one of the essential water sources for the famous South-to-North Water Diversion Project in China. During the period from 2006 to 2011, the water quality generally leveled at Grade I, but in the consequent years from 2012 to 2015, the water leveled at Grade II most of the time, with light pollution of CODMn and NH3-N. Comparatively, there is a clear and encouraging sign of water restoration in the Nanjingguan area, where the water quality consistently leveled at Grade II from 2006 to 2011, suffering CODMn and NH3-N pollutions, and then improved gradually and has frequently leveled at Grade I since 2012. According to the water quality map, the change of major pollutants had remarkable seasonal and annual patterns in many sections of the Yangtze River. A signiﬁcant seasonal pattern can be found when DO and NH3-N are present as the major pollutants. The DO pollution always concentrates in the summer and autumn, while the NH3-N pollution tends to be serious in the spring and winter. Some of the examples include the First Minjiang Bridge, Xin Port, Duchang, and Chucha. However, such a seasonal pattern may not be common to the CODMn pollution. In several sections, i.e., Dragon Cave and Qixing, the high CODMn concentrations are more likely to rise in the autumn. However, in most other sections, the high CODMn concentrations remain present throughout the year. Generally, three types of annual patterns can be observed from the temporal distribution of major pollutants in different sections. One is characterized in the First Minjiang Bridge and Linshan, where the major pollutant transition shows a reduction tendency with DO. During the period of 2006–2015, the major pollutant in these sections generally alternated between DO and NH3-N at ﬁrst, until NH3-N became the common major pollutant. Another annual pattern is characterized in Xin Port and Jishui Port, illustrating an increasing tendency with DO in contrast. NH3-N was the major pollutant in 2006, having much heavier concentration in the beginning and end of the year. By the year 2015, DO and NH3-N became the two major pollutants, while NH3-N always appeared in the beginning and end of the year and DO in the middle. In addition, a distinct conversion from NH3-N to CODMn pollution was observed in the sections of the Hexi Water Plant, the Wanhe Mouth, the Second Tuojiang Bridge, Yueyang Lou, and Hama Stone; the major pollutant was NH3-N in around 2006, then the major pollutant became CODMn by around 2015. A statistical analysis was carried out as well Water 2017, 9, 708 8 of 13 sewage, farmland drainage, and industrial wastewater [5]. In the lake area, the pollution by CODMn is much more serious. Accordingly, the lake area can be divided into four groups. The first group refers to Dian Lake and Liangzi Lake, including South Dian Lake, Luojia Yin, the Xiyuan tunnel, Guanyin Hill, and Qixing, where the common pollutant is almost always CODMn. Dian Lake has heavier CODMn pollution than does Liangzi Lake, as the CODMn concentration generally levels at Grade V and poor V, implying that the amount of organic compounds in the water has far exceeded the recommended limit. The second group refers to Poyang Lake, Chao Lake, and Tai Lake, including Hama Stone, Hubin, Yuxi Mouth, Shazhu, Yilan Hill Mouth, Xi Hill, and Xintang Port, where CODMn and NH3-N are indicated as the common major pollutants. The third group mainly refers to Dongting Lake, including South Mouth, Yueyang Lou, and Lujiao. In these areas, CODMn is rarely the major pollutant, but NH3-N pollutions emerge most of the time. Then, several areas in Poyang Lake and Tai Lake, including Duchang, Kan Hill, Jishui Port, Wangjiangjing, and Xielu Port, can be viewed as the fourth group, where pollution by CODMn and NH3-N appears alternately, making the water environments of these areas quite different from those of the other groups.

(a) (b)

Water 2017, 9, 708 9 of 13 (c) (d)

(e) (f)

(g) (h)

Figure 5. The temporal change of water quality in several sections of of the the Yangtze River. ( a) Danjiang Mouth; ((bb)) Nanjingguan;Nanjingguan; ( c()c) the the First First Minjiang Minjiang Bridge; Bridge; (d )( Linshan;d) Linshan; (e) Xin(e) Xin Port; Port; (f) Jishui (f) Jishui Port; Port; (g) Hexi (g) WaterHexi Water Plant; Plant; (h) Hama (h) Hama Stone. Stone.

According to the water quality map, the change of major pollutants had remarkable seasonal and annual patterns in many sections of the Yangtze River. A significant seasonal pattern can be found when DO and NH3-N are present as the major pollutants. The DO pollution always concentrates in the summer and autumn, while the NH3-N pollution tends to be serious in the spring and winter. Some of the examples include the First Minjiang Bridge, Xin Port, Duchang, and Chucha. However, such a seasonal pattern may not be common to the CODMn pollution. In several sections, i.e., Dragon Cave and Qixing, the high CODMn concentrations are more likely to rise in the autumn. However, in most other sections, the high CODMn concentrations remain present throughout the year. Generally, three types of annual patterns can be observed from the temporal distribution of major pollutants in different sections. One is characterized in the First Minjiang Bridge and Linshan, where the major pollutant transition shows a reduction tendency with DO. During the period of 2006–2015, the major pollutant in these sections generally alternated between DO and NH3-N at first, until NH3-N became the common major pollutant. Another annual pattern is characterized in Xin Port and Jishui Port, illustrating an increasing tendency with DO in contrast. NH3-N was the major pollutant in 2006, having much heavier concentration in the beginning and end of the year. By the year 2015, DO and NH3-N became the two major pollutants, while NH3-N always appeared in the beginning and end of the year and DO in the middle. In addition, a distinct conversion from NH3-N to CODMn pollution was observed in the sections of the Hexi Water Plant, the Wanhe Mouth, the Second Tuojiang Bridge, Yueyang Lou, and Hama Stone; the major pollutant was NH3-N in around 2006, then the major pollutant became CODMn by around 2015. A statistical analysis was carried out as well to inspect the annual change of major pollutants for relevant sections. As shown in Figure 6, the results illustrate the same temporal patterns as those on the map. Furthermore, it is remarkable that the water pollution in the Dian Lake and Tai Lake areas has been serious for years and presently faces a severe situation. In spite of ongoing efforts for water restoration, the WQLs remain below Grade III. There is a strong need to adopt more effective water protection measures and strengthen the control of wastewater discharge in these areas.

Water 2017, 9, 708 10 of 13

Furthermore, it is remarkable that the water pollution in the Dian Lake and Tai Lake areas has been serious for years and presently faces a severe situation. In spite of ongoing efforts for water restoration, the WQLs remain below Grade III. There is a strong need to adopt more effective water protectionWater 2017, 9,measures 708 and strengthen the control of wastewater discharge in these areas. 10 of 13

(a) (b)

(e) (f)

Figure 6. The annual pattern of major pollutant change in in a few sections of of the the Yangtze Yangtze River. ( a) the First Minjiang Bridge; (b) Linshan; (c) Xin Port; (d) Jishui Port; ((e)) HexiHexi WaterWater Plant;Plant; ((ff)) HamaHama Stone.Stone.

4. Conclusions In thisthis study,study, the theMap Map of Waterof Water Quality Quality Change Change in the in Yangtze the Ya Riverngtze over River the Periodover th 2006–2015e Period 2006–2015provided providedan intuitive an interfaceintuitive tointerface understand to understand the water the quality water conditionquality condition of the Yangtze of the Yangtze River from River 2006 from to 20062015. to Using 2015. theUsing calendar the calendar view, theview, spatial the spatial variation variation of water of water quality quality and waterand water pollutants pollutants in the in Yangtzethe Yangtze River River was was explicitly explicitly presented. presented. Qualitative Qualitative colors colors were were designed designed to to identifyidentify waterwater quality levels and major water pollutants. A A “W” “W” construction construction was was also also introduced introduced to make the the arrangement arrangement of the weekly data logical and continuous. It was helpful to investigate both the seasonal and annual temporal pattern of the river’s water quality chan change.ge. Several conclusions can be drawn as follows: (1) TheThe calendar-based calendar-based map map provides provides a a promising promising visual visual analytic approach to identify the water qualityquality issues issues of of the the Yangtze Yangtze River. Using Using the the calendar, calendar, it is possible to reveal the spatial and temporaltemporal patterns patterns in in the the massive massive monitoring monitoring data data without without using using complex complex analysis models. (2) AccordingAccording to thethe map,map, the the upper upper reaches reaches of of the the Yangtze Yangtze River River and and tributaries tributaries have have relatively relatively good goodwater water quality, quality, while thewhile other the sections, other especiallysections, especially where urban where agglomerations urban agglomerations are developing, are developing,e.g., South Dian e.g., andSouth Guanyinshan Dian and Guanyinshan in Kunming, in Xielu Kunming, Port in Xielu Jiaxing, Port and in Jiaxing, Jishui Portand inJishui Qingpu, Port inare Qingpu, seriously are polluted. seriously polluted. (3) The major pollutants in the Yangtze River have multi-scale temporal patterns. At the seasonal scale, DO is the major pollutant in the summer and autumn, and NH3-N tends to be serious in the spring and winter. At the annual scale, at least three patterns can be observed, i.e., from alternating between DO and NH3-N to mainly in NH3-N, from mainly NH3-N to alternating between DO and NH3-N, and switching from NH3-N to CODMn.

Water 2017, 9, 708 11 of 13

(3) The major pollutants in the Yangtze River have multi-scale temporal patterns. At the seasonal scale, DO is the major pollutant in the summer and autumn, and NH3-N tends to be serious in the spring and winter. At the annual scale, at least three patterns can be observed, i.e., from alternating between DO and NH3-N to mainly in NH3-N, from mainly NH3-N to alternating between DO and NH3-N, and switching from NH3-N to CODMn.

So far, we have produced the water quality map through two stages, i.e., collecting the data from the environmental monitoring center and arranging the data in map form. Our future work will focus on the interactive generation of the water quality map online. A real-time mapping system may be developed based on standard web protocols to access the latest water quality data upon demand [35]. It will provide a number of calendar templates to represent either qualitative information, i.e., the WQLs and major pollutants, or the quantitative information, i.e., the concentration of each water quality index and the relative change rate, or both, so that the public can customize the map and better understand the status and change tendency of the water quality of the Yangtze River. In addition, as the current calendar-based method is proposed to visualize the water quality change and make the interpretation of water condition intuitive, the proposed method can also be applied to the water resources community so as to evaluate how effective it is in communicating water quality with the public. Social surveys could be conducted to compare our method with other communicating methods, such as statistical charts [14,17], heat maps [20], dynamic maps [22,24], and information graphs [36], from the aesthetics, efﬁciency, and accuracy aspects.

Supplementary Materials: The following are available online at www.mdpi.com/2073-4441/9/9/708/s1. Acknowledgments: This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFB0503500), the Fund for Fostering Talents in Basic Science of the National Natural Science Foundation of China (Grant No. J1103409), and the Special Foundation for Fundamental Work of Science and Technology of China (Grant No. 2013FY112800). Author Contributions: Lina Huang designed the calendar-based map, developed the web-based application to generate calendar views of water quality change in the Yangtze River, performed the experiments, and wrote the manuscript; Mengyin Zhong contributed the map symbolization and cartographic enhancement; Qiyao Gan prepared the water quality data for the visual analysis and map generation; Yanfang Liu improved the map and modified the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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