The response relationship between the climate and the water quality of the river in the Yellow River Basin of Inner Mongolia

https://doi-001.org/1025/17613791295512

Lei Shi1,3,a, Hongyu Liu 2,b,*

1College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China; shilei_md@126.com

2College of Ecology and Environment, Baotou Teachers’ College, Baotou 014030, China

3Inner Mongolia Research Academy of Eco-Environmental Sciences, Hohhot 010022, China

aEmail：shilei_md@126.com

* bEmail：66271@bttc.edu.cn

Abstract: In order to understand the changes of water quality in the Inner Mongolia of the Yellow River Basin (IM-YRB) and reveal the relationship between climate and water quality, our study was conducted to comprehensively evaluate the water quality of the Yellow River Basin in Inner Mongolia from 2010 to 2020 based on six water quality parameters using the Water Quality Index (WQI) method. The results showed that: 1) Temperature, precipitation and runoff in the IM-YRB showed an increasing trend from 2010 to 2020. Significant negative relationship was detected between temperature and COD, runoff and NH3-N. Significant positive relationships were detected between runoff and F–. 2) The variation of water quality parameters exhibited a decreased trend from 2010 to 2020. The highest concentration of CODMn was 3.47 mg/L in 2011, CODMn showed a decreasing trend since 2011. The concentration of COD exhibited a decreasing trend since 2013(14.89 mg/L), with the lowest value in 2018 (9.69 mg/L). The mean concentration of BOD5 decreased since 2013(3.21 mg/L), reaching the lowest value in 2020(1.36 mg/L). NH3-N showed a decreasing trend since 2011, with the lowest values in 2020(0.14 mg/L). TP showed a decreasing trend since 2012, with a minimum of 0.06 mg/L in 2018. The mean concentration of F– was 0.44 mg/L with a decreasing trend from 2011 and a minimum of 0.32 mg/L in 2019. WQI was significantly improved since 2015. Linear mixed effects models indicated NH3-N,CODMn and TP were major parameters affecting the 2010-2015 WQI. NH3-N,CODMn and COD were major parameters affecting the 2015-2020 WQI. 3) One-way ANOVA showed significant spatial differences in water quality parameters (p < 0.05). The water quality of the Meidaigou river, Wulanmulin river, and Hun river station remained good state. Kundulun river, Xi river, Xiaohei river and Dong river remained poor state.The water quality evaluation results of the WQI provided a basis for understanding the change of water quality in the Inner Mongolia of the Yellow River Basin, and was a guide for the prevention and control of water pollution in the Yellow River Basin.

Keywords: climate; runoff; Water quality index, the Inner Mongolia of the Yellow River Basin

  1. Introduction

The impacts of climate change are currently a major global issue on an unprecedented scale. Global climate change models predict that global precipitation distribution patterns will become more heterogeneous in the second half of the 21st century[1]. In terms of global distribution, precipitation in mid- and high-latitude regions shows an increasing trend, while subtropical regions generally show a decreasing trend in precipitation, presenting a “drier and drier, wetter and wetter” state[2].Good water quality has become an extremely important factor in determining the ecological development of arid regions[3]. Currently, the deterioration of water quality has become a major and growing problem and challenge worldwide.

In recent years, studies on various mechanisms by which climate change affects water quality have become increasingly sophisticated[4-7]. Both natural factors (e.g., climate change, water body area, deposition of atmospheric elements) and human factors (e.g., landscape, land use type) affect water quality changes[8-11]. Climate change affects water quality in three main ways: it exacerbates eutrophication of water bodies; it alters flow, hydrological, and thermal conditions; and it destroys ecosystems and biodiversity[12]. Climate change, as a major natural factor affecting water quality, influences the hydrologic cycle, and consequently the quality of water bodies, by altering precipitation patterns, temperature fluctuations, sea level rise, and the frequency of extreme weather events[13]. Precipitation alters groundwater recharge, and excessive precipitation can lead to sediment resuspension or a large influx of sediment into surface water bodies, deteriorating water quality. This leads to the ultimate deterioration of water quality. For example, studies in semi-arid regions have shown that lack of precipitation leads to deterioration of groundwater quality[14]. Increase in temperature will accelerate the rate of biochemical reactions within the water body, thus affecting the migration and transformation process of pollutants; it will also increase the evaporation of the water body and increase the suspended matter, which prevents the decomposition of internal impurities, resulting in the destruction of water quality[12]. In contrast, Xingkaihu’s study showed that increased precipitation and temperature reduced nutrient content in water bodies and improved local water quality[15]. Therefore, the relationship between climate change and water quality needs further research.

One of the main challenges for surface water quality is the restoration of surface water with deteriorated trophic status (eutrophication) due to excess external nutrients, mainly phosphorus[16]. Changes in precipitation, runoff, and snowmelt regimes can alter sediment, nutrient, and pollutant concentrations in streams, leading to problems such as eutrophication and loss of biodiversity in water bodies[4]. Nutrients such as nitrogen and phosphorus in sediments are re-released to the overlying water body when the external conditions at the sediment-water interface change[15]. Total phosphorus and NH3-N are important factors affecting the water quality of rivers in China[8]. Domestic discharges and agricultural activities are the main threats to the water quality of the mountainous areas of the lower Yellow River and the Yellow River reservoirs, and runoff and atmospheric deposition make TP and SO4 the main pollutants in the mountainous reservoirs. Water quality management remains a challenge due to the complex interactions between multiple influencing factors.

The Inner Mongolia of the Yellow River Basin (IM-YRB) is located in the northernmost part of the Yellow River Basin and is rich in land use types. The Hetao Irrigation District is the largest designed irrigation district in China[17]. The Inner Mongolia section of the Yellow River Basin, as one of the weakest regions in the Inner Mongolia Autonomous Region, has to deal with water pollution, water scarcity, and water resource shortage in addition to the resource-based economic model[18]. The water quality of Chinese inland waters has improved significantly over the period 2006-2016, as the Chinese government has invested in environmental restoration and reforestation[19]. The Inner Mongolia region of the Yellow River Basin showed rising trends in the NDVI, FVC, and NPP between 2000 and 2020[18,20]. Our research analyzed the relationship between climate change and water quality pollution in the Yellow River Basin section of Inner Mongolia from 2010 to 2020 to provide theoretical guidance for the healthy development of the basin, which is of great significance for understanding the current status of surface water quality in the Yellow River Basin.

  1. Materials and Methods

2.1. The Study Area

The Inner Mongolia section of the Yellow River basin (IM-YRB) commences at Bitter Water Gully, situated at the confluence of Shizuishan City and Wuhai City, and terminates at Toudaoguai, Toketo County (106°34′ E~112°78′E, 39°09′~41°84′N). The IM-YRB is located at the northern Yellow River basin, and flows through six cities (Wuhai, Bayannur, Ordos, Baotou, Hohhot, and Ulanqab), accounting for 12.1% of the YRB[17]. The IM-YRB is dominated by the temperate continental monsoon climate. The region is a typical arid and semi-arid climate zone, with average annual precipitation mainly concentrated in the months of June-August, ranging from 120 to 450 mm, with the eastern part of the region receiving more precipitation than the western part. The average annual temperature is about 6.6 ℃.

Figure 1.The location of the study area

The IM-YRB is located in the upper reaches of the Yellow River, with an elevation of 799-2343 m. The Yinshan Mountains and the Ordos Plateau are in the north and south respectively, while the Hetao Plain and the Tumochuan Plain are in the central part of the area, with a lower elevation. The main vegetation types are non-zonal meadow vegetation and swamp plants. The main soil types are salt soil, meadow soil and sandy soil.

2.2. Determination of Water Quality

The water quality data of the Inner Mongolia of the Yellow River Basin are from Inner Mongolia Environmental Quality Bulletin. The water quality data includes 6 indicators including COD, CODMn, BOD5, TP, NH3-N, F– from 2010 to 2020. The precipitation and temperature data are from the China Meteorological Science Data Sharing Service Network (http://www. escience.gov.cn).

The Water Quality Index (WQI) calculation was proposed by Pesce and Wunderlin (2000). The Water Quality Index (WQI) objectively evaluates comprehensive water quality through multiple water quality parameters. In this study, WQI was calculated according to six water quality parameters (NH3-N, TP, CODMn , COD, BOD5 , F–). The scores and index weights are determined based on the thresholds of the five classes in the Environmental Quality Standards for Surface Water of the People’s Republic of China (Standard number: GB3838-2002, available at: http://www. cnemc.cn/jcgf/shj /200801/t20080128_647287.shtml), and these weight values have been verified in previous studies[21-24], each water quality parameter and weight are shown in Table1, and the calculation formula is as follows: where:

  (1)

where n is the total number of water quality parameters. Ci is the standardized score of water quality factor i; Pi is the weight of water quality factor i, the minimum value of Pi is 1, the maximum value is 4. WQI ranges from 0 to 100, with high values representing good water quality conditions. According to the WQI score, water quality is divided into 5 levels: excellent [90–100], good [70–90), medium [50–70), poor [25–50), very poor [0–25)[24,25].

Table 1 The weights of water quality parameters used in the WQI calculation based on the Environmental Quality Standards for Surface Water of China (GB 3838-2002).

Parameters Weight (Pi) Standardized scores and water quality classification
100, I 90, II 70, III 50, IV 25, V 0, worse than V
NH3-N 3 0-0.15 0.15-0.5 0.5-1.0 1.0-1.5 1.5-2.0 >2.0
TP 1 0-0.01 0.01-0.025 0.025-0.05 0.05-0.1 0.1-0.2 >0.2
CODMn 3 ≤2 2-4 4-6 6-10 10-15 >15
COD 3 0-15 15-18 18-20 20-30 30-40 >40
BOD5 3 ≤3 3-3.5 3.5-4 4-6 6-10 >10
F– 1 0-1 – – 1-1.5 – >1.5

2.3. Data analyses

Trend analysis examines changes in temperature, precipitation and water quality indicators over time. Spearman correlation analysis was used to investigate the correlation between temperature, precipitation and water quality parameters and the WQI. One-way analysis of variance (ANOVA) for differences in water quality parameters in different stations. A generalized linear mixed model was used to investigate the contribution of different water quality parameters to the quality of water.

  1. Results

3.1. Variation characteristics of precipitation and temperature

From 2010 to 2020, both temperature and precipitation in the IM-YRB show an increasing trend (Figure 2). Precipitation in the study area shows an increasing trend from 2010 to 2020, with the rate of increase of precipitation in the northwest higher than that in the southeast region. Precipitation lowest in 2011 and the highest in 2022. Temperature in the study area shows an increasing trend from 2010 to 2020, with the rate of change in the southwest higher than that in the northeast. The lowest temperature in the study area was recorded in 2012, after which it started increasing and reached its maximum in 2017.

Figure 2. Trends in temperature and precipitation over the period 2010-2020.

3.2. Trends and variations in water quality

The variation of water quality parameters exhibited a decreased trend from 2010 to 2020, reflecting improved water quality.

The mean concentration of CODMn was 2.87 mg/L and the highest values in 2011(3.47 mg/L). CODMn showed a decreasing trend since 2011, with the lowest value in 2018 (2.06 mg/L) (Figure 3a).Mean concentration of COD was 12.75 mg/L. The mean concentration of COD exhibited a decreasing trend since 2013(14.89 mg/L), with the lowest value in 2018 (9.69 mg/L) (Figure 3b).The mean concentration of BOD5 was 2.33 mg/L, which increased from 2010 to the highest value in 2013 (3.21 mg/L), and then began to decrease, reaching the lowest value in 2020 (1.36 mg/L)(Figure 3c).The mean concentration of NH3-N was 0.33 mg/L. NH3-N showed a decreasing trend since 2011, with the lowest values in 2020 (0.14 mg/L) (Figure 3d). The mean concentration of TP was 0.08 mg/L and showed a decreasing trend since 2012, with a minimum of 0.06 mg/L in 2018(Figure 3e).The mean concentration of F– was 0.44 mg/L with a decreasing trend from 2011 and a minimum of 0.32 mg/L in 2019(Figure 3f).

Figure 3. Interannual variation of Water quality parameters and runoff in the IM-YRB

From the results of water quality evaluation, the worst year of water quality in the Yellow River Basin during this study period is 2015, with a WQI value of 54.01, in medium status. the water quality was significantly improved since 2015, and the proportion of water quality in excellent status increased year by year, and the WQI reached the maximum value (80.30) in 2020(Figure 4).

   
(a) (b)

Figure 4. Annual water quality index (WQI) from 2010 to 2020 (the straight line indicates the trend line) in the IM-YRB.(a) Annual water quality index (WQI); (b) Water quality classification from 2010 to 2020.

3.3. Spatial variation of water quality

The spatial variation of water quality parameters at different river systems within the IMYR is shown in Figure 5. Using one-way ANOVA, the spatial changes in water quality parameters among the river systems in the IMYR were statistically significant (p < 0.05) (Table 2).

The annual mean WQI values indicated that the water in the Meidaigou river, Wulanmulin river, and Hun river station remained good state. Dusitu river, Zongpaigan river, Sidaosha river, Daheihe river and Longwanggou river remained medium state. Kundulun river, Xi river, Xiaohei river and Dong river remained poor state. The mean CODMn concentrations at the Meidaigou and Hun river stations belonged to Class I and Class II water quality respectively, while the rest of the stations were below the threshold of the Class II standard.The mean COD concentrations at the Meidaigou and Hun river belonged to Class I，Wulanmulin river belonged to Class II, the rest of the stations were below the threshold of the Class II standard. The mean BOD5 concentrations at the Dusitu river, Meidaigou river, Wulanmulun river and Hun river is better than Class II water quality and the rest of the station is below Class II water quality. The mean NH3-N concentrations at the Dusitu river, Meidaigou river, Wulanmulun river and Hun river is better than Class II water quality and the rest of the station is below Class II water quality. The mean F– concentrations at the Zongpaigan river, Daheihe river, Xiaoheihe river, Meidaigou river, Hun river and Longwanggou river belonged to Class I, the rest of the station is below Class IV water quality.

Table 2 One-way ANOVA results for different water quality parameters at the water quality monitoring station.

  WQI CODMn CODcr BOD5 NH3-N TP F-
Dusitu River 68.94±2.74f 5.15±0.33cd 21.19±1.37ab 3.26±0.26ab 0.23±0.03a 0.09±0.02a 1.14±0.07c
Zongpaigan River 50.98±1.71c 8.05±0.27ef 39.62±1.96bc 6.21±0.52ab 0.52±0.05a 0.07±0.00a 0.70±0.02ab
Kundulun River 26.27±3.82b 9.21±0.82fg 49.31±3.98c 14.10±1.26c 22.21±2.00c 0.84±0.10cd 4.25±0.34e
Sidaosha River 51.71±4.08c 7.23±0.95ef 89.51±19.7e 23.08±5.52d 12.06±2.70b 1.02±0.30de 1.17±0.05c
Xi River 13.80±0.96a 10.20±0.49g 91.57±11.8e 23.99±2.69d 24.08±3.64c 1.26±0.13e 2.88±0.15d
Meidaigou River 93.13±1.21g 1.78±0.11a 9.13±0.59a 2.209±0.13a 0.30±0.04a 0.03±0.00a 0.33±0.02a
Dahei River 56.56±1.60cd 5.64±0.21cd 31.12±1.46bc 5.86±0.30ab 3.68±0.34a 0.56±0.04bc 0.51±0.01ab
Xiaohei River 46.55±2.51c 6.69±0.38de 45.71±3.40c 9.32±0.76b 6.18±0.69a 0.80±0.07cd 0.44±0.01ab
Dong River 22.85±1.77b 11.04±0.81g 67.85±6.65d 19.53±2.14d 11.68±1.55b 1.15±0.12de 1.45±0.08c
Wulanmulun  River 73.29±1.19ef 4.51±0.15bc 17.10±0.67ab 2.83±0.11a 0.38±0.04a 0.10±0.01a 1.12±0.04c
Hun River 85.51±0.75g 2.80±0.10ab 13.59±0.34a 2.42±0.09a 0.29±0.04a 0.08±0.00a 0.49±0.01b
Longwanggou River 62.05±1.66de 5.52±0.21cd 21.38±0.98ab 3.82±0.17ab 1.08±0.16a 0.39±0.04ab 0.80±0.02b

Note:Lower case letters then indicate significant differences between sites (p < 0.05).

Figure 5. Spatial distribution of the water quality index (WQI) from 2010 to 2020.

3.4. Relationships between Water quality to precipitation and temperature

According to the results of linear mixed effects models, NH3-N,CODMn and TP were major parameters affecting the 2010-2015 WQI. NH3-N, CODMn and COD were major parameters affecting the 2015-2020 WQI. Compared between the WQI during 2010-2015 and 2015-2020, the main controlling parameters were found to be different. Therefore, linear mixed effects models was performed throughout the year to screen out water quality parameters significantly affecting WQI. The results showed that CODMn was the largest contributor to WQI values, explaining 37.04% variation of the WQI, followed by TP, F–, COD, BOD5, NH3-N, explained 14.89%, 13.51%, 13.28%, 11.97% and 9.32% of variation of the WQI, respectively (Figure 6).

Figure 6. The relative contribution of water quality parameters to water quality index (WQI) during 2010-2020.

Significant negative relationship was detected between temperature and COD, runoff and NH3-N. Significant positive relationships were detected between runoff and F–.

Figure 7. Correlation between climate and water quality parameters from 2010 to 2020(p-value < 0.05)

  1. Discussion

4.1 Impacts of temperature and precipitation on the water quality

The effects of temperature on aquatic ecosystems are complex. River water quality towarded deterioration in the context of droughts and high-temperature heatwaves (as well as heavy precipitation climate change[26]. Studies have shown that increased water temperature alters the content of water quality parameters by affecting a variety of factors such as water evaporation, solubility and microbial activity[27,28]. There’s also research showed that increasing total nitrogen concentration and decreasing dissolved oxygen concentration in the context of extreme arid climate exacerbated the significant deterioration of water quality conditions in the study area[29].

COD is a measure of how much organic matter is in the water. Reducing substances in the water body consume oxygen during the redox process, forming COD. The higher the COD, the more seriously the water body is polluted by organic matter[24]. The results of this study showed a negative correlation between air temperature and COD. Under the background of climate change, the increase of water temperature can help to increase the microbial activity in the water body, and the self-purification ability of the water body will be increased accordingly, which will reduce the COD content in the water body.

Total phosphorus (TP) is an important factor in evaluating the degree of eutrophication of water quality, and changes in air temperature affect the concentration of TP in the water column. The results of this study showed that air temperature was negatively correlated with TP. On the one hand, an increase in temperature leads to higher evaporation, which leads to an increase in pollutant concentration; on the other hand, higher temperature promotes the release of phosphorus from sediments, which leads to an increase in phosphorus concentration in the river, which in turn reduces water quality. Contrary to the findings of Baiyangdian Lake[6], the results of the present study showed a negative correlation between temperature and TP, and this finding is consistent with A Case Study in the Luanhe River Basin[30]. When the temperature increases, evaporation increases, runoff from the basin decreases, and the pollutant degradation coefficient of the water body increases (the pollutant degradation coefficient of the water body increases) reduces the TP content. Higher temperatures can stimulate algal growth and blooms, leading to nutrient depletion, which leads to lower phosphorus concentrations in streams and improved stream water quality[15]. Lower biochemical reaction rates compared to TN made the negative correlation between TP and temperature insignificant.

Changes in water quality in the context of climate change are the result of the interaction of multiple factors. Precipitation and runoff contribute to nutrient transport on the one hand, and soil erosion contributes to the transport of nutrients into water bodies with runoff on the other[31].

Drought increases mineral concentrations in surface and groundwater through evapotranspiration, and extreme arid climates exacerbate the significant deterioration of water quality conditions[27]. For example, Jiang[29] found that increasing total nitrogen concentrations during drought reduced water quality compared to non-drought periods. Moderate precipitation reduced water quality parameter concentrations through dilution effects. Extreme precipitation also improved water quality by purifying and diluting pollutants through the dilution effect[6,15]. However, precipitation may also input more nutrients into the river, leading to eutrophication and increased water pollution[23].

BOD5 is an important indicator of organic matter pollution and ammonia nitrogen is an indicator typical of production and domestic wastewater characteristics[32,33]. In the present study, precipitation and runoff were found to be negatively correlated with BOD5 and ammonia nitrogen. Consistent with the findings of Xingkai Lake[15]. The increase of precipitation and runoff enhanced the purification and dilution of pollutants and reduced the BOD5 and ammonia nitrogen content, indicating that the increase of precipitation can effectively improve the water quality of the river[15]. F– mainly comes from the dissolution of fluorinated minerals[34,35]. Fluoride enrichment may also result from human activities such as sewage, wastewater and fertilizers[36]. Increased runoff promotes mineral dissolution, pollutant transport and transformation, thus runoff is positively correlated with F–.

The results of the study showed an increasing trend of precipitation, temperature and runoff from 2010 to 2020, which is consistent with the results of some scholars in the Yellow River Basin[37-39]. The increase in precipitation and temperature resulted in an increasing trend of vegetation cover in the study area[17], which is favorable to the improvement of water quality in the study area. In addition, the implementation of the ecological protection policy in the study area from 2015 is also an important reason why water quality began to improve.

4.2. Spatial characteristics of water quality

In this study, it was found that Meidaigou and Hun River belong to excellent and good state respectively. Meidaigou is located in tumoteyouqi, and the river originates from Daqingshan Mountain, which is less affected by human activities and has better water quality[40]. The study[41] showed that the vegetation coverage of Hunhe river basin displayed a slightly fluctuating upward trend from 2000 to 2018. Therefore, the rising vegetation coverage of Hunhe river basin may be an important factor in improving water quality.

The water quality of the Kundulun River is in poor condition, and the water quality of the Dong and Xi Rivers is in very poor condition. The Kundulun River, Xi River and Dong River are seasonal rivers. The Kundulun River, the Xi River, the Sidaosha River and the Dong River merge into the Yellow River in Baotou. Baotou is the largest industrial city in Inner Mongolia Autonomous Region. On the one hand, the increase of urban population and construction land area makes the ecological degradation of the Kundulun River more significant[42] and reduces the water quality. On the other hand, the Kundulun River is located in the vicinity of steel mills and residential areas, where industrial pollution sources and human activities reduce water quality, and the lower dilution capacity of seasonal rivers for discharges leads to severe water pollution in the receiving rivers [43].

  1. Conclusions

The Yellow River Basin in Inner Mongolia was used as the study area to investigate the trend of climate change and its impact on water quality from 2010 to 2020. The results showed that both precipitation and air temperature showed an increasing trend, the increase in precipitation and runoff had a diluting effect on river pollutants, and the increased degradation coefficient of pollutants due to the increase in air temperature improved the water quality. In addition, the increase in vegetation cover due to climate change plays an important role in improving water quality.Human activities have reduced water quality, leading to spatial heterogeneity in water quality. In recent years, various ecological protection and environmental management measures have significantly improved water quality. The results of the study provide a basis for the improvement of water quality and ecosystem restoration in the study area.

 

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Original Version Master File-Mapan Journal-Lei ShiDownload
The response relationship between the climate and the water quality of the river in the Yellow River Basin of Inner Mongolia

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