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Published in: Environmental Earth Sciences 2/2017

Open Access 01-01-2017 | Original Article

Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach

Authors: E. Martinez-Tavera, P. F. Rodriguez-Espinosa, V. C. Shruti, S. B. Sujitha, S. S. Morales-Garcia, N. P. Muñoz-Sevilla

Published in: Environmental Earth Sciences | Issue 2/2017

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Abstract

Spatiotemporal variations of ten physicochemical parameters in the water quality of Atoyac River basin, Central Mexico, were obtained from 22 sampling sites (66 samples in total) located all along the basin for three different seasons (dry, rainy and winter). Multivariate statistical techniques such as correlation matrix, factor analysis (FA) and cluster analysis (CA) were used as a tool to understand the process. Physicochemical parameters such as temperature (T), pH, conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation–reduction potential (ORP), turbidity, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed. Extremely high values of pH (10.24), conductivity (1870 µS/cm) and reduced redox potential (−370.1 mV) were observed in the dry season, whereas elevated TSS of 2996 mg/L was detected during the rainy season. The results elucidated high influence from the adjoining industrial, agricultural and urban zones, making the river unsuitable for life. FA generated varifactors, which accounted for cumulative % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) clearly grouping the external factors responsible for these significant values indicating the source of contamination. Cluster analysis facilitated the ease of classifying the sampling sites based on the similarities of physicochemical parameters. This study carried out in different seasons using multivariate statistical techniques would definitely prove to be an efficient tool for the restoration and establishing the real-time monitoring stations along this important river basin of Mexico.

Introduction

A river is a well-defined geomorphological structure encompassing the main stream and several tributaries with a unidirectional flow of substantial loads of dissolved and suspended matter from both natural and human-induced activities (Shrestha and Kazama 2007). However, the present global scenario of intense demographic growth and urban practices has resulted in degrading water resources, calling for instant remedial actions by construing a precise study of its physical characteristics and water quality. Studies on the physicochemical characteristics of rivers validate the significant processes responsible for its degradation (Bricker and Jones 1995; Bengraine and Marthaba 2003). Influential factors that determine the river water quality include precipitation inputs, erosion and weathering of crustal materials as well as human-induced activities like urban development and expansion, industrial and agricultural practices (Carpenter et al. 1998; Muangthong and Shrestha 2015), whereas many processes like synergies of pollution loads, hydrological characteristics, sediment and metabolic activities in the water are considered to regulate the water quality gradients (Jung et al. 2016).
In the recent decades, assessment of river water quality has gained scientific interests for its high demand in human consumption and aquatic health. Water quality datasets are challenging to interpret because of the perplexing procedures of the river systems with many uncertainties and prospective relationships among its properties and monitoring sites (Gaume et al. 1998). Several studies have been carried out using multivariate statistical techniques to understand the spatial and temporal patterns of river water quality which is useful for interpretation and effective management of these ecosystems (Perkinns and Underwood 2000; Voutsa et al. 2001; Ouyang 2005; Ouyang et al. 2006; Yang et al. 2010; Dobsa et al. 2014; Li et al. 2015). The river water quality is interpreted using various approaches such as computational models and mathematical formulas, and the most efficient one is through the integration of water quality parameters into one single index (Boyacioglu 2007; Sowlat 2011; Bakan et al. 2010; Wanda et al. 2016).
In Mexico, River Atoyac in Puebla is stated as one of the most contaminated rivers of the country (CONAGUA 2010) and it is extremely impacted by the wastewater discharges from urban, agriculture and industrial sources. Puebla and Tlaxcala states ~120 km away from the megalopolis Mexico City in the center of the country are the fourth largest metropolitan area of Mexico, and it forms the major industrial corridor. Globally, these states form the major grounds for automobile manufacturing and they are the foremost site for the origin of textile industry in Latin America which favored an increase in the demographic growth (154.6%) during the last two decades (INEGI 2010). The region also marks its significance by the presence of both active (Popocatepetl) and extinct volcanoes (Iztaccihuatl and La Malinche) leading to increased turbidity levels, alkalinity and other soluble elements, thereby altering its physicochemical properties (Stewart et al. 2006). This highly influential region from both anthropogenic and natural activities with resulting imbalance of its ecosystems is the strategic factors to commence the present study.
The main objective was to evaluate the seasonal variability (dry, rainy and winter) of the physicochemical parameters in 22 sampling sites (66 number of samples in total) of Atoyac River basin, Central Mexico, using multivariate statistical methods. For this study, statistical methods like correlation matrices, factor analysis (FA) and cluster analysis (CA) were carried out to characterize and assess the variations in surface water quality and to estimate the spatial and seasonal differences caused by natural and anthropogenic factors. An attempt has been made for integrating measured water quality parameters into algorithms for easy interpretation of water quality in Atoyac River basin. The database generated from the present study served as a background for implementing real-time monitoring stations along the Atoyac River, first of its kind in Latin America.

Materials and methods

Study area

The Atoyac River basin in Central Mexico constitutes Zahuapan and Atoyac Rivers that flows through the rural, urban, agricultural and industrial regions of Tlaxcala and Puebla states where it finally drains into the Manuel Ávila Camacho (Valsequillo) dam (Fig. 1). The Atoyac River basin has an extension of 4395 km2, flanked by Volcano Iztaccihuatl and Popocateptl (active) in the western side and Volcano Malinche in the eastern side. The Zahuapan River is fed by the runoff from Sierra de Tlaxco in the north, from inland waters and the Malinche volcano. The Atoyac River originates from the snow melts of Iztaccihuatl volcano with an altitude of 3250 m in the source, 2250 m elevation in the confluence zone and finally devalues to a height of 2000 m (above mean sea level) in the Valsequillo dam. The cross section of Atoyac River basin varies in all the three zones (the upstream, the confluence and downstream zones). The width ranges from 20 to 60 m in R. Atoyac, 25 m in R. Zahuapan and 15–30 m in the confluence zone of both the rivers. The depth of the water column is approximately 1–2 m in R. Atoyac, 1.5 m in R. Zahuapan and 1–3 m in the confluence zone (Rodríguez-Espinosa et al. 2014). The width of Valsequillo dam ranges 2–7 km with a depth of 40 m. The river basin experiences sub-humid climate with an average annual precipitation of 800 mm and temperature of 22 °C. The dry season corresponds to the months of March–May, the rainy season from June–September and winter during October–February (National Meteorological Service 2010).
The basement of this river basin constitutes Paleozoic metamorphic rocks of the Acatlan complex and Mesozoic terrigenous and calcareous rocks (Von Erffa et al. 1977; Ortega-Gutierrez 1978, 1993; Mooser et al. 1996; González-Mancera et al. 2009). The most striking feature of this river basin is the presence of Neogene–Quaternary stratovolcanoes and mountain ridges of Upper Cretaceous limestone filled with volcanic tuffs, lahars, lava flows, cinder cones, lacustrine fluvial deposits and reworked glacial-fluvial materials (Morales-Ramírez et al. 2003).
The Atoyac River is well known for its extensive use in residential, agricultural and industrial activities located in the Puebla city with a population of around 3,303,679 inhabitants residing in the river basin (INEGI 2010). The agricultural activity is characterized with production of corn, vegetables, alfalfa, fodder and beans (IMTA 2005), whereas the industries in this zone include metallurgy (43%), machinery, heavy equipment, food sector (25%), textiles (14%), clothing, leather, chemicals (10%), oil, rubber, plastics, timber products (3%) and other industries (1%) (INEGI 2010).

Sampling and analytical techniques

A total number of 66 river water samples were collected from 22 monitoring sites for three different seasons: dry (April), rainy (September) and winter (February) at Zahuapan River (Station nos. 1–3), Atoyac River (Station nos. 4–6), confluence zone (Station nos. 7–15) and Valsequillo dam (Station nos. 16–22) during the period of 2013–2014. The sampling stations were designated in such way that they cover a wide range of factors like residential drains, industrial discharges, natural soil erosion and influence of volcanoes in all the three sections of the Atoyac River basin. Samples were collected in 5-L polyethylene sterilized bottles and stored in an insulated ice cooled container and were transferred to the laboratory on the same day. Sampling, preservation and transportation of the water samples to the laboratory were based on the standard methods prescribed by APHA (1992).
The physicochemical parameters such as temperature (T), hydrogen potential (pH), conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation reduction potential (ORP), turbidity (TURB) were measured in situ, whereas 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed in laboratory. Temperature (°C), pH and conductivity (µS/cm) were measured using the instrument Conductronic/PC18 calibrated at 25 °C, dissolved oxygen (mg/L) with YSI/51B, Spectral Absorption Coefficient (SAC) (A/m at 254 nm) which measures the dissolved organic substances were estimated using a Spectrophotometry UV/Vis (PerkinElmer Lambda 20), oxidation reduction potential (mV) and turbidity (NTU) using HACH (Model no: 2100Q & HQ40D). The accuracy of the parameters was ±0.3 °C for temperature, ±0.02 for pH, −0.03 mg/L for DO and ±1.5 µS/cm for conductivity. BOD5, COD and TSS were determined following the standard protocols of APHA (1992) in laboratory. The analytical data quality was guaranteed through careful standardization, procedural blank measurements, spiked and duplicate samples.

Statistical analysis

The entire data set was computed for multivariate statistical techniques such as correlation matrix, factor analysis and cluster analysis using Statistica software (version 8). In order to evaluate the relationships between water quality parameters at the sampling sites, correlation analysis was conducted for ten parameters in 22 sampling stations for three different seasons (dry, rainy and winter) with p < 0.5, 0.01, 0.001. Factor analysis (FA) involving varimax normalization generated two distinct factors for dry and three distinct factors for rainy and winter season with p values (>0.7) assisting in understanding the processes and associations. In order to detect interrelationships between the sampling sites based on physicochemical parameters, cluster analysis (CA) was performed.

Results and discussion

The results for the physicochemical parameters of three different seasons (dry, rainy and winter) are presented in Fig. 2a–j.

Temperature and pH

Mean temperature values ranged between 16.1 to 23.60 °C all along the stream. High temperatures observed (all values in °C) in the sampling Stations 8 (27.3), 22 (28.5), 13 (22.7) and 14 (22.7) during the dry season are attributed to the influential impact of effluents from the surrounding industrial zones of tannery and leather, metal based and textiles including the domestic sewage from the urban regions, resulting in direct inputs of heat to the river system (El Morhit and Mouhir 2014). Seasonally, the values of temperature varied between 13 and 27.3 °C in the dry season, 16.9–28.5 °C during the rains and 13.8–22.7 °C in the winter season. The low temperatures of 13 °C in dry, 16.9 °C in rainy and 13.8 °C in winter seasons were presented by the stations closer to the origin of Zahaupan and Atoyac Rivers in the highlands.
The pH values were observed to be similar in all the stations except Station nos. 8, 19, 20 and 22. The sudden increase in the pH value (10.24 in dry season) in Station no. 8 is due to the influence of occasional discharge of large amounts of wastewaters from the textile industries that are strongly affected by the dyeing, scouring, bleaching and washing processes. Similar high values of pH (10–11.5) (Chhonkar et al. 2000) are also observed in some of the rivers in the Asian continent (River Pali in India), which is influenced and affected by the different types of dyes and other materials (e.g., cotton, synthetic, etc.) (Nordin et al. 2013; Sharma et al. 2013). High pH values (Station no. 20: 8.08, Station no. 19: 8.13, Station no. 22: 8.28) presented by the dam stations are linked to the presence of warm waters and intense photosynthetic activity of macrophytes resulting in high production of free CO2 and therefore making the waters alkaline in nature (Saxsena and Saksena 2012). Temporally, the average potential ion activity of hydrogen (pH) in the waters of the Atoyac River basin was relatively high in the winter season (8.01) than the pH of 7.81 during dry period and 7.67 through the rainy season.

Conductivity and DO

The confluence zone (Station nos. 6–15) exhibited high average conductivity (1030.5 µS/cm) compared to the upstream (766.54 µS/cm) and dam (733.94 µS/cm) sections attesting the impact of discharges from industrial activities. Extremely high conductivity of 1870 µS/cm observed in Station no. 4 is due to the effect of ionic pollutants from the industrial (textile, petrochemical and automobile) and agricultural activities during the dry season. Variations in the conductivity values also depend upon the bedrock of the area as in places underlined by granites have lesser conductivities as observed in the stations of Zahaupan River underlined by ignimbrites [avg values in µS/cm: (874) dry; (321.67) rainy; (800.67) winter] and in regions of limestones, chlorides, clay minerals, phosphates and nitrate demonstrate higher values as observed in the stations of Atoyac River and its confluence [avg values in µS/cm: (1375) dry; (502.67) rainy; (1322.89) winter]. In the dam section, conductivity values [avg values in µS/cm: (775) dry; (693) rainy; (734) winter] were observed to be lower than the upstream section due to the absence of industrial inputs that lead to increased conductivity values by increased amounts of dissolved ions liberated from organic and inorganic wastes (Wright 1982; Singh et al. 2013). In general, conductivity values (all values in µS/cm) were higher during the dry season (avg. 1094.32) compared to the rainy (avg. 524.82) and winter (avg. 1009.36) seasons, respectively.
Dissolved oxygen conditions ranged from anoxic level (all values in mg/L) 0 in dry period (Station no. 9) and high value 21 in the winter (Station no. 22). In all three seasons, high values (all values in mg/L) of 3.8 (dry), 7.76 (rainy) and 21 (winter) were observed in the dam sites, which is attributed to the high photosynthetic production by the macrophytes, resulting in supersaturated DO levels (Ahmed 2014). Anoxic and low values (all values in mg/L) of DO (dry: 0; winter: 0.58; rainy: 1.8) in the confluence zone of Atoyac and Zahaupan Rivers are due to the effect of high oxygen demand by organic matter in the sewage wastes from domestic and industrial activities (Paerl et al. 1998).
Upstream side demonstrated turbidity values of 58.24 NTU, where Station no. 4 presented high values of 179 NTU which is due to the impact of waste discharges from the adjoining industries (textile, plastic, autoparts and petrochemical), whereas less turbidities (all values in NTU) in all the three seasons (dry: 1.5; rainy: 0.41; winter: 3) were observed in the reservoir due to the decrease in the velocity of waters as suspended sediment particles settle down (Skalak et al. 2013). Dry and winter seasons experienced maximum turbidities of 381.7 NTU and 346 NTU, respectively, when the dilution effect from rains and erosion of soils was minimal increasing the presence of individual particles.

SAC and ORP

SAC measurements are used to identify the aquatic spectral reflectance and validate the photochemical and photobiological characteristics of natural waters (Mitchell et al. 2002). The spatial variation of SAC values along the river showed peak values at the Stations 14, 13, 4, 12, 8, experiencing high influential impact from the industrial and municipal sewages. Seasonally, they were detected to be high during winter (avg. 51.53 A/m) and similar values were observed in dry (avg. 31.37 A/m) and rainy seasons (avg. 38.38 A/m). The higher absorption coefficients in the dry season exhibited by the sampling stations closer to the industrial discharge points are attributed to the impact of raw settled waste water, whereas during rainy seasons, high amounts of attenuation of the waters as a consequence of dilution effect resulted in lower values (Reynolds and Ahmad 1997).
Negative ORP value observed in Station no. 15 (−370.1 mV) during dry season is due to the influence of effluents from textile and automobile industries containing high amounts of metal waste that result in reducing conditions of the substratum and decreasing the ORP value (Horne and Goldman 1994; Kiran Kumar et al. 2016). High positive values in the dam (Station no. 16: 134.70 mV) are attributed to the presence of large number of macrophytes and high DO levels resulting in an oxidizing environment. Temporal variations in the redox potential conditions of the studied river basin were deduced to be (all values in mV) winter: 165.49; rainy: 76.85; dry: −119.25, where high values in winter are due to high amounts of dissolved oxygen.

COD, BOD5 and TSS

COD and BOD5 measurements are used as repetitive proxy tests to estimate the load of organic carbon in the river systems. In the present study, COD values (127.82 mg/L) were observed to be higher than those of the BOD5 values (80.43 mg/L), as BOD5 measures only the biodegradable constituents. Spatially, elevated levels of COD (815–194.87 mg/L) were presented by the stations at the confluence zone, dominated by the presence of industries. High COD in the dry period (avg. 250.36 mg/L) and low values in the rainy season (avg. 144.73 mg/L) signal to the high amounts of oxygen required to oxidize all organic material in an untreated waste effluent (Cieszynska et al. 2012). BOD5 measurements also exhibited similar pattern of COD at Stations 8, 4, 11, 15, 14. However, raised levels of BOD5 in the dry season (avg. 124.41 mg/L) indicate the extent of organic pollution in aquatic systems and the liable organic matter, which undergoes biotic decomposition (Jonnalagadda and Mhere 2001).
Spatial variations of average TSS levels ranged between 13 mg/L in the dam; 188.6 mg/L in the upstream and 456.3 mg/L after the confluence zone. Lower values of TSS observed in the dam section are due to the fact that in lesser velocities, the suspended solids tend to settle down (Davidson and Summerfelt 2004). The seasonal variations of TSS (total suspended solids) observed in the study presented high values in the rainy season (avg. 644.41 mg/L) and was higher (all values in mg/L) at Station nos. 11 (2996), 8 (2009), 14 (1730), 9 (1234) due to intense precipitation, soil erosion and increased generation of industrial (food processing) and domestic runoffs (Nasrabadi et al. 2016). Comparatively, very less values of TSS were observed in the dry (avg. 67.5 mg/L) and winter (avg. 51.73 mg/L) seasons due to less influential impact of erosional processes and the channels bringing in residues from the human-induced activities (Pimentel 2006).

Statistical analysis

Correlation analysis

The relationship between water quality parameters for three different seasons (dry, rainy and winter) was evaluated using correlation matrix analysis (Table 1). Dissolved oxygen and the hydrogen potential activity (pH) exhibited a positive correlation [r 2  = 0.44 (dry), 0.78 (rainy) and 0.83 (winter)], which is attributed to the increased decomposition activity in the waters resulting in high carbon dioxide content accounting for a well-defined oxygen status (Araoye 2009). The values of turbidity and total suspended solids were strongly interrelated for all the three seasons (r 2  = 0.75, 0.96, 0.46), where r 2  = 0.96 during the rainy season are slightly high compared to the dry season (r 2  = 0.75) and it is inferred to intense precipitation and consequent soil erosion as well as generation of urban and agricultural runoff (Bakar et al. 2007). Negative correlations of turbidity and dissolved oxygen (r 2  = −0.35) dry, (r 2  = −0.08) rainy, (r 2  = −0.42) winter are inversely related due to the fact that the increase in turbidity might decrease the transparency of water leading to the depletion of DO which affects the river water quality and its aquatic life (Sun et al. 2016). The strong positive correlation between SAC vs BOD5 (r 2  = 0.64, 0.82), COD (r 2  = 0.68, 0.95) and TSS (r 2  = 0.57, 0.81) in dry and winter seasons, respectively, is due to the high impacted loads of biodegradable and non-biodegradable pollutants (Reynolds and Ahmad 1997). The low significant correlations between SAC vs BOD5 (r 2  = −0.16), COD (r 2  = 0.24) and TSS (r 2  = 0.42) during rainy season are due to the dilution effect (Turk et al. 2010). High positive correlations of turbidity with COD (r 2  = 0.80, 0.94, 0.72), BOD5 (r 2  = 0.83, 0.34, 0.88) and TSS (r 2  = 0.75, 0.96, 0.46) in all seasons validated the presence of increased loads of pollutants in the river basin.
Table 1
Correlation matrix of physicochemical parameters for three different seasons (dry, rainy & winter) in Atoyac river basin, Mexico
 
T
pH
COND
DO
TURB
SAC
ORP
COD
BOD5
TSS
Dry season (n = 22)
 T
1.00
         
 pH
0.47*
1.00
        
 COND
1.00
       
 DO
0.44*
−0.42*
1.00
      
 TURB
0.54*
1.00
     
 SAC
0.58*†
0.72*†‡
0.73*†‡
1.00
    
 ORP
−0.54*†
1.00
   
 COD
0.65*†‡
0.72*†‡
0.80*†‡
0.68*†‡
−0.47*
1.00
  
 BOD5
0.59*†
0.73*†‡
0.83*†‡
0.64*†
−0.47*
0.99*†‡
1.00
 
 TSS
0.48*
0.79*†‡
0.75*†‡
0.57*†
−0.69*†‡
0.87*†‡
0.88*†‡
1.00
Rainy season (n = 22)
 T
1.00
         
 pH
1.00
        
 COND
0.56*†
1.00
       
 DO
0.78*†‡
1.00
      
 TURB
−0.58*†
−0.56*†
1.00
     
 SAC
1.00
    
 ORP
−0.49*
0.43*
−0.58*†
1.00
   
 COD
−0.47*
−0.53*
0.94*†‡
1.00
  
 BOD5
−0.49*
−0.54*†
1.00
 
 TSS
−0.47*
−0.50*
0.96*†‡
0.94*†‡
1.00
Winter season (n = 22)
 T
1.00
         
 pH
1.00
        
 COND
1.00
       
 DO
0.83*†‡
−0.46*
1.00
      
 TURB
−0.42*
1.00
     
 SAC
0.82*†‡
0.61*†
1.00
    
 ORP
1.00
   
 COD
0.77*†‡
0.72*†‡
0.95*†‡
1.00
  
 BOD5
0.57*†
0.88*†‡
0.82*†‡
0.89*†‡
1.00
 
 TSS
0.76*†‡
0.46*
0.81*†‡
0.68*†‡
0.59*†
1.00
T Temperature; COND conductivity; DO dissolved oxygen; TURB turbidity; SAC spectral absorption coefficient; ORP oxidation reduction potential; COD chemical oxygen demand; BOD 5 biological oxygen demand; TSS total suspended solids
p < 0.5, p < 0.01, p < 0.001 (only significant values are shown)

Factor analysis

Factor analysis (FA) was carried out for a better inference of the possible influences and associations of the physicochemical characteristics of the water samples. In order to ease the interpretation, the analysis was independently executed for the three different seasons with cumulative  % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) (Table 2). The factor loadings were categorized as strong, moderate and weak corresponding to the absolute values of the variance >0.75, 0.75–0.50 and 0.50–0.30, respectively (Liu et al. 2003). Varifactor 1 (VF1) is explained a total variance of 55.78% which is during the dry period, with strong negative loadings on λ (−0.83), TURB (−0.77), SAC (−0.77), BOD5 (−0.95), COD (−0.96) and TSS (−0.93), moderate negative loading on pH and ORP and a weak negative loading on temperature, respectively. This factor mainly signifies the contribution of pollutants from the untreated sewage flows generated from the domestic, agricultural and industrial activities (Juahir et al. 2011). The total variance of 42.01% in the rainy season for VF1 demonstrated strong positive loading on temperature and conductivity (0.80; 0.80), while turbidity (−0.90), COD (−0.77) and TSS (−0.85) presented strong negative loadings and the associations of TURB, COD and TSS are associated with the erosion of soils and the increase in total suspended solids in the river system during rainy season (Pejman et al. 2009). The negative relationship between temperature, conductivity and other factors like TURB, COD and TSS is due to the presence of large quantities of suspended solids, which adsorb and scatter sunlight determining the extinct of solar radiation (Paaijmans et al. 2008). The VF1 during the winter season explained a total variance of 49.85%, with significant strong negative loadings on λ (−0.84), TURB (−0.94), SAC (−0.79), BOD5 (−0.88), COD (−0.94) and TSS (−0.79), moderate positive loading on DO and a weak loading on ORP. The negative association of DO is due to the high BOD5 levels and presence of organic pollutants that consume the available oxygen for decomposition (Waziri and Ogugbuaja 2010). VF2 explained a total variance of 19.26% (dry), 23.13% (rainy) and 19.64% (winter), all the three seasons denoted a strong negative loading on DO and pH (rainy and winter). As there is decrease in pH, the activity of H+ increases results in the reaction of hydrogen ions and oxygen leading to a depletion of DO (Zang et al. 2011). In the VF3, a total variance of 11.08 and 10.46% was observed in the rainy and winter seasons, respectively. In the rainy season, moderate negative loading was presented by COD, whereas weak positive loading on TURB and SAC is due to the variability in the spectral absorbance for interference by the presence of huge loads of biodegradable and non-biodegradable pollutants. Thus, the factorial analysis supports the fact that the strong negative loadings (>75%) on λ, COD, BOD5, SAC, TURB and TSS affect the health of the river system and significantly contribute to the evaluation of the water quality (Ajorlo et al. 2013).

Cluster analysis

A dendrogram was rendered (Fig. 3a–c) by grouping 22 sampling stations into three statistically significant clusters for three different seasons (dry, rainy and winter). In the dry season (Fig. 3a), three clusters (C1, C2, C3) were obtained and Cluster 1 (C1) presented the groupings of the most contaminated stations (4, 8, 11, 13, 14) with similar characteristics of λ, TURB, SAC, COD, BOD5 and TSS suggested the influence of direct discharges from residential and industrial units that affect the water chemistry and aquatic life (Jingsheng et al. 2006). However, in Cluster 2 (C2) the stations with related negative redox potential conditions were grouped attributing the fact that reductive environments are best sites for the accumulation of contaminants (Chan et al. 2003). In Cluster 3 (C3), the sampling stations of Valsequillo dam were grouped for its less influential impact of toxins in the water column. Majority of sampling sites (13 stations) were linked together in C3 during the rainy season (Fig. 3b) as they presented low values of TSS, whereas the Stations (8, 11, 13, 14) in the urbanized and industrial zones signified high turbidities especially during the rainy season when there is high impact of runoff and flushing of soil salts (Garizi et al. 2011). The dendrogram for winter presented three clusters, where C2 included all the stations as the water holds high amounts of dissolved oxygen (5.91 mg/L) during winter season (Sutherland and O’Neill 2016) resulting in a better water quality conditions than the dry and rainy seasons.

Algorithms for evaluation of water quality in Atoyac River basin

Algorithms were proposed to characterize the water quality of Atoyac River basin based on in situ (T, pH, λ, DO, SAC, ORP, TURB) and laboratory measurements (BOD5, COD and TSS) which assists in easy interpretation of water quality data. They are expressed as follows:
Algorithm for field recorded physicochemical parameters (AFRPP):
$${\text{AFRPP}} = \mathop \sum \limits_{i = 1}^{n} \left( {\left| {\frac{{{\text{pH}}_{0} - {\text{pH}}_{n} }}{{{\text{pH}}_{0} }}} \right|} \right) + \left( {\left| {\frac{{\lambda_{0} - \lambda_{n} }}{{\lambda_{0} }}} \right|} \right) + \left( {\left| {\frac{{{\text{SAC}}_{0} - {\text{SAC}}_{n} }}{{{\text{SAC}}_{0} }}} \right|} \right) + \left( {\left| {\frac{{T_{0} - T_{n} }}{{T_{0} }}} \right|} \right) + \left( {\left| {\frac{{{\text{ORP}}_{0} - {\text{ORP}}_{n} }}{{{\text{ORP}}_{0} }}} \right|} \right) + \left( {\left| {\frac{{{\text{DO}}_{0} - {\text{DO}}_{n} }}{{{\text{DO}}_{0} }}} \right|} \right) + \left( {\left| {\frac{{{\text{TURB}}_{0} - {\text{TURB}}_{n} }}{{{\text{TURB}}_{0} }}} \right|} \right)$$
Algorithm for laboratory-based physicochemical parameters (ALPP)
$${\text{ALPP}} = \mathop \sum \limits_{i = 1}^{n} \left( {\left| {\frac{{{{({\text{BOD}_5})}_0} - {{({\text{BOD}_5})}_{\text{n}}} }}{{{{({\text{BOD}_5})}_0} }}} \right|} \right) + \left( {\left| {\frac{{{\text{COD}}_{0} - {\text{COD}}_{n} }}{{{\text{COD}}_{0} }}} \right|} \right) + \left( {\left| {\frac{{{\text{TSS}}_{0} - {\text{TSS}}_{n} }}{{{\text{TSS}}_{0} }}} \right|} \right)$$
where pH0, λ 0, SAC0, T 0, ORP0, DO0, TURB0, (BOD5)0, COD0 and TSS0 are values of pristine waters collected in upper basin of Atoyac and Zahuapan Rivers (Fig. 1) and pH n , λ n , SAC n , T n , ORP n , DO n , TURB n , (BOD5)n, COD n and TSS n are the values corresponding to the evaluated stations.
The AFRPP and ALPP representing the water quality of the river with the values close to 0 indicate better water quality. The differences observed between the parameters defined for pristine waters and the measured sample are close to 0, only if the measured sample has not been altered by human-induced activities. Therefore, the summation of these parameters close to 0 recommends better water quality and the values greater than 0 refers to deteriorated water quality.
In the present study, these algorithms were applied and the resulting values are represented in Fig. 4a, b, respectively. Regions I and IV of the upstream and Valsequillo dam section of the river basin have similar characteristics to pristine or low contamination sites (COD 40–60 mg/L), whereas moderate contamination (COD 170–400 mg/L) was identified in region III (confluence with Zahuapan River) and in few sites of region II (Atoyac River). During the dry season, Station no. 8 presented significant values of a high alkaline pH of 10.25, BOD5 of 480 mg/L and COD of 1055 mg/L (Fig. 4a, b). A strong positive correlation was observed between AFRPP and ALPP for the 22 sampling stations, where r 2  = 0.84, 0.89 and 0.96 for dry, rainy and winter season, respectively (Fig. 4c). Thus, these generated field- and laboratory-based algorithms for Atoyac River basin enable the interpretation of the water quality data and can be implemented in the real-time monitoring stations for observing natural as well as external discharges.
Table 2
Factor loadings for 10 parameters at 22 sampling sites for three different seasons (dry, rainy & winter) in Atoyac river basin, Mexico
 
Varifactor 1
Varifactor 2
Varifactor 3
Dry season
   
 >75
-λ, -TURB, -SAC, -BOD5, -COD, -TSS
-DO
 75–50
-pH, -ORP,
-T, -pH
 50–30
-T
-ORP, -SAC
 Eigen value
5.58
1.93
 Total variance %
55.78
19.26
Rainy season
   
 >75
T, λ, -TURB, -COD, -TSS
-pH, -DO
 75–50
-SAC, -BOD5
ORP
-COD
 50–30
ORP
-T
-λ, TURB, SAC, -pH
 Eigen value
4.20
2.31
1.11
 Total variance %
42.01
23.13
11.08
Winter season
   
 >75
-λ, -TURB, -SAC, -BOD5, -COD, -TSS
-pH, -DO
-ORP
 75–50
DO
-T
 50–30
ORP
SAC, -TSS
 Eigen value
4.98
1.96
1.04
 Total variance %
49.85
19.64
10.46
T Temperature; COND conductivity; DO dissolved oxygen; TURB turbidity; SAC spectral absorption coefficient; ORP oxidation reduction potential; COD chemical oxygen demand; BOD 5 biological oxygen demand; TSS total suspended solids

Comparison studies

Comparison studies were carried out with the water quality characteristics of other rivers worldwide (Table 3). The Atoyac River basin located in a volcanic summit zone also marked its influence in the water chemistry, where the deposition of airborne volcanic particulate matter (tephra) in the aqueous environments will initiate the dissolution of soluble accumulations resulting in alteration of its physicochemical characteristics (Martin-Del Pozzo et al. 2002). The higher values of pH (10.24), turbidity (381.7 NTU), BOD5 (480 mg/L), COD (1055 mg/L) and TSS (2996 mg/L) observed in the present study than compared to the other rivers are well supported by the high influential impact of the natural processes, volcanic activity, industries and domestic discharges. Distinct high values of SAC and ORP in Atoyac River basin confirmed its depriving state. Based on the water quality standards and comparison with other rivers, Atoyac River in Mexico is unequivocally unfit for domestic usage and aquatic life. The results suggest that the studies on surface water quality drive the need for a regular monitoring program for the improvement in river ecosystem and the identification of the possible sources of pollutants.
Table 3
Worldwide comparison of river water parameters
Location
Temperature (°C)
pH
Conductivity (μS/cm)
DO (mg/L)
SAC
ORP (mV)
Turbidity (NTU)
BOD5 (mg/L)
COD (mg/L)
TSS (mg/L)
References
Worldwide rivers
 Nakdong River, South Korea
14.4–17.7
7.7–8.1
126.9–773.6
10.1–11.2
0.7–3.4
3.3–8.8
7.3–35.5
Jung et al. (2016)
 Guayas River, Ecuador
26.0
7.7
200.0
7.5
9.8
18.0
Damanik-Ambarita et al. (2016)
 Karnaphuli River, Bangladesh
21.2–31.0
7.9–8.2
8.07–9.92
Ali et al. (2016)
 Dez River, Iran
8.2–29.8
7.3–7.6
7.86–8.96
Ramavandi et al. (2015)
 Upper Manyame River, Zimbabwe
19.0–25.6
6.82–6.83
98.8–198.6
3.60–4.81
84.5–50.2
72.78–135.39
11.55–33.01
Kibena et al. (2014)
 U-tapao River, Thailand
27.0–28.0
6.76–6.88
129.0–4304.0
2.86–4.30
1.93–2.74
18.47–37.69
Gyawali et al. (2013)
 Ismailia Canal, Nile River, Egypt
24.0
8.12
423.4
7.85
3.77
6.29
100.7
Goher et al. (2014)
 Songhua River, China
9.85–14.76
7.48–7.60
2.64–22.70
15.29–73.18
Wang et al. (2013)
 Mahanadi River, India
6.7–7.6
7.34
7.45
Sundaray et al. (2006)
Present study
 Atoyac River basin, Mexico (R.Atoyac, R.Zahuapan and Valsequillo dam)
 Dry season
13–27.3
7.36–10.24
684–1870
0.4–3.8
11.09–114.54
−370.1 to 25.5
1.5–381.7
10–480
46–1055
0–240
 
 Rainy season
16.90–28.50
7.23–8.55
138–1185
1.72–7.76
0.09–189.26
18.60–173.80
0.41–148.74
10–55
20.90–449.70
20–2996
 
 Winter season
13.8–22.7
7.27–9.06
573–1672
0.58–21
15.2–94.5
−42.6 to 270.6
3–346
6–340
18–612
14–200
 
Water quality standards
 Drinking water quality
8.5
10
WHO (2011)
 Irrigation water quality
8.5
3000
FAO (1994)
 CONAGUA acceptable limits
6 < BOD5 ≤ 30
20 < COD ≤ 40
75 < TSS ≤ 150
 
 SEMANART maximum permissible limits
           
 Irrigation
150
150
NOM-001-SEMARNAT- (1996)
 Public urban use
40
75
75
NOM-001-SEMARNAT- (1996)
 Protection of aquatic life
40
30
40
NOM-001-SEMARNAT- (1996)

Conclusion

Increasing concerns on the degrading river water quality worldwide in recent years, it is mandatory to develop wide range of multivariate statistical techniques to analyze, use and interpret the datasets of river quality management. In this study, various approaches of correlation matrices, factor and cluster analyses had been integrated for the valuation, restoration and protection of the regional water quality. The physicochemical characteristics of the river basin revealed the influence of the small-scale changes associated with the active Popocatepetl Volcano, also supplemented by intense human activities. Hierarchical cluster analysis grouped the 22 sampling sites based on its similar water quality characteristics, which offers a reliable classification for designing an optimal future monitoring strategy. In addition, factor analysis assisted in identification of the pollution sources with strong relationships between λ, TURB, SAC, BOD5, COD and TSS. The results of factor analysis also evidently indicated that the governing factors which were responsible for the deterioration of the water quality include the pollutants from soluble salts (natural- erosion and runoff), organic, heavy metals (point sources: industries), toxic pollution (pharmaceuticals, textile) and from the domestic and agricultural activities. Deduced algorithms of field and laboratory measurements proved to be a tool in fast and economic evaluation of the river water quality. The studies also mentioned the fact that the sites with high degree of contamination are the ones that pass through the urban sector or are directly affected by the raw settled sewage. Comparison studies also presented the fact that this river basin with high fluctuations in its water quality is attributed for its industrial and volcanic setting. Thus, the present study illustrated the efficacy of multivariate statistical technique in validating the status of Atoyac River basin establishing a baseline for the installation of real-time monitoring stations along its course.

Acknowledgements

Authors acknowledge the financial assistance awarded to Secretariat of Rural Development, Sustainability and Land Management (Secretaría de Desarrollo Rural, Sustentabilidad y Ordenamiento Territorial, Gobierno del Estado de Puebla, SDRSOT), through the project “Studies for the Network of Monitoring Stations for the Preservation and Improvement of the Water Quality in the Alto Atoyac Basin”, and support from projects SIP-IPN Nos. 20144353 and 20131892. Authors PFRE, SSMG and NPMS wish to express their thanks to Sistema Nacional de Investigadores (SNI), CONACyT and IPN (COFAA, EDI), Mexico.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Literature
go back to reference Ajorlo M, Abdullah RB, Yusoff MK, Halim RA, Hanif AHM, Willms WD et al (2013) Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems. Environ Monit Assess 185(10):8649–8658CrossRef Ajorlo M, Abdullah RB, Yusoff MK, Halim RA, Hanif AHM, Willms WD et al (2013) Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems. Environ Monit Assess 185(10):8649–8658CrossRef
go back to reference Ali MM, Ali ML, Islam MdS, Rahman MdZ (2016) Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh. Environ Nanotechnol Monit Manag 5:27–35CrossRef Ali MM, Ali ML, Islam MdS, Rahman MdZ (2016) Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh. Environ Nanotechnol Monit Manag 5:27–35CrossRef
go back to reference APHA (1992) Standard methods for the examination of water and wastewater, 18th edn. American Public Health Association, Washington, DC APHA (1992) Standard methods for the examination of water and wastewater, 18th edn. American Public Health Association, Washington, DC
go back to reference Araoye PA (2009) The seasonal variation of pH and dissolved oxygen (DO) concentration in Asa Lake Llorin, Nigeria. Int J Phys Sci 4(5):271–274 Araoye PA (2009) The seasonal variation of pH and dissolved oxygen (DO) concentration in Asa Lake Llorin, Nigeria. Int J Phys Sci 4(5):271–274
go back to reference Bakan G, Ozkoc HB, Cuce H (2010) Integrated environmental quality assessment of Kızılırmak River and its coastal environment. Turk. J Fish Aquat Sci 10:453–462 Bakan G, Ozkoc HB, Cuce H (2010) Integrated environmental quality assessment of Kızılırmak River and its coastal environment. Turk. J Fish Aquat Sci 10:453–462
go back to reference Bakar S, Ismail WR, Rahaman ZA (2007) Relationship in two small catchments in Perlis. Malaysia. Malays J Civil Eng 19(2):156–169 Bakar S, Ismail WR, Rahaman ZA (2007) Relationship in two small catchments in Perlis. Malaysia. Malays J Civil Eng 19(2):156–169
go back to reference Bengraine K, Marthaba TF (2003) Using Principal component analysis to monitor spatial and temporal changes in water quality. J Hazard Mater B100:179–195CrossRef Bengraine K, Marthaba TF (2003) Using Principal component analysis to monitor spatial and temporal changes in water quality. J Hazard Mater B100:179–195CrossRef
go back to reference Boyacioglu H (2007) Development of a water quality index based on a European classification scheme. Water SA 33(1):101–106 Boyacioglu H (2007) Development of a water quality index based on a European classification scheme. Water SA 33(1):101–106
go back to reference Bricker OP, Jones BF (1995) Main factors affecting the composition of natural waters. In: Salbu B, Steinnes E (eds) Trace elements in natural waters. CRC Press, Boca Raton, pp 1–5 Bricker OP, Jones BF (1995) Main factors affecting the composition of natural waters. In: Salbu B, Steinnes E (eds) Trace elements in natural waters. CRC Press, Boca Raton, pp 1–5
go back to reference Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8:559–568CrossRef Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8:559–568CrossRef
go back to reference Chan LC, Gu XY, Wong JWC (2003) Comparison of bioleaching of heavy metals from sewage sludge using iron- and sulfur-oxidizing bacteria. Adv Environ Res 7(3):603–607CrossRef Chan LC, Gu XY, Wong JWC (2003) Comparison of bioleaching of heavy metals from sewage sludge using iron- and sulfur-oxidizing bacteria. Adv Environ Res 7(3):603–607CrossRef
go back to reference Chhonkar PK, Datta SP, Joshi HC, Pathak H (2000) Impact of industrial effluents on soil health and agriculture-indian experience: part II-tannery and textile industrial effluents. J Sci Ind Res 59:446–454 Chhonkar PK, Datta SP, Joshi HC, Pathak H (2000) Impact of industrial effluents on soil health and agriculture-indian experience: part II-tannery and textile industrial effluents. J Sci Ind Res 59:446–454
go back to reference Cieszynska M, Wesolowski M, Bartoszewicz M, Michalska M, Nowacki J (2012) Application of physicochemical data for water-quality assessment of watercourses in the Gdansk Municipality (South Baltic coast). Environ Monit Assess 184(4):2017–2029CrossRef Cieszynska M, Wesolowski M, Bartoszewicz M, Michalska M, Nowacki J (2012) Application of physicochemical data for water-quality assessment of watercourses in the Gdansk Municipality (South Baltic coast). Environ Monit Assess 184(4):2017–2029CrossRef
go back to reference Damanik-Ambarita MN, Lock K, Boets P, Everaert G, Nguyen THT, Forio MAE, Musonge PLS, Suhareva N, Bennetsen E, Landuyt D, Dominguez-Granda L, Goethals PLM (2016) Ecological water quality analysis of the Guayas river basin (Ecuador) based on macroinvertebrates indices. Limnologica 57:27–59CrossRef Damanik-Ambarita MN, Lock K, Boets P, Everaert G, Nguyen THT, Forio MAE, Musonge PLS, Suhareva N, Bennetsen E, Landuyt D, Dominguez-Granda L, Goethals PLM (2016) Ecological water quality analysis of the Guayas river basin (Ecuador) based on macroinvertebrates indices. Limnologica 57:27–59CrossRef
go back to reference Davidson J, Summerfelt S (2004) Solid flushing, mixing, and water velocity profiles within large (10 and 150 m3) circular ‘Cornell type’ dual drain tanks. Aqua Eng 32:245–271CrossRef Davidson J, Summerfelt S (2004) Solid flushing, mixing, and water velocity profiles within large (10 and 150 m3) circular ‘Cornell type’ dual drain tanks. Aqua Eng 32:245–271CrossRef
go back to reference Dobsa J, Meznaric V, Tompic T, Legen S, Zeman S (2014) Evaluation of spatial and temporal variation in water contamination along croatian highways by multivariate exploratory analysis. Water Air Soil Pollut 225(10):2083CrossRef Dobsa J, Meznaric V, Tompic T, Legen S, Zeman S (2014) Evaluation of spatial and temporal variation in water contamination along croatian highways by multivariate exploratory analysis. Water Air Soil Pollut 225(10):2083CrossRef
go back to reference El Morhit M, Mouhir L (2014) Study of the physico-chemical properties of waters in the Loukkos river estuary (Larache, Morocco). Environ Syst Res 3:17CrossRef El Morhit M, Mouhir L (2014) Study of the physico-chemical properties of waters in the Loukkos river estuary (Larache, Morocco). Environ Syst Res 3:17CrossRef
go back to reference FAO (1994) Water quality for agriculture, irrigation and drainage, Rome, Paper No. 29. Rev. 1, M-56. ISBN 92-5-102263-1 FAO (1994) Water quality for agriculture, irrigation and drainage, Rome, Paper No. 29. Rev. 1, M-56. ISBN 92-5-102263-1
go back to reference Garizi AZ, Sheikh V, Sadoddin A (2011) Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods. Int J Environ Sci Technol 8(3):581–592CrossRef Garizi AZ, Sheikh V, Sadoddin A (2011) Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods. Int J Environ Sci Technol 8(3):581–592CrossRef
go back to reference Gaume E, Villeneuve JP, Desbordes M (1998) Uncertainty assessment and analysis of the calibrated parameter values of an urban storm water quality model. J Hydrol 210:38–50CrossRef Gaume E, Villeneuve JP, Desbordes M (1998) Uncertainty assessment and analysis of the calibrated parameter values of an urban storm water quality model. J Hydrol 210:38–50CrossRef
go back to reference Goher ME, Hassan AL, Abdel-Moniem IA, Fahmy AH, El-sayed SM (2014) Evaluation of surface water quality and heavy metal indices of Ismailia Canal, Nile River, Egypt. Egypt J Aquat Res 40:225–233CrossRef Goher ME, Hassan AL, Abdel-Moniem IA, Fahmy AH, El-sayed SM (2014) Evaluation of surface water quality and heavy metal indices of Ismailia Canal, Nile River, Egypt. Egypt J Aquat Res 40:225–233CrossRef
go back to reference González-Mancera G, Ortega-Gutiérrez F, Proenza JA, Atudorei V (2009) Petrology and geochemistry of Tehuitzingo serpentinites (Acatlán Complex, SW Mexico). Bol Soc Geol Mex 61(3):419–435 González-Mancera G, Ortega-Gutiérrez F, Proenza JA, Atudorei V (2009) Petrology and geochemistry of Tehuitzingo serpentinites (Acatlán Complex, SW Mexico). Bol Soc Geol Mex 61(3):419–435
go back to reference Gyawali S, Techato K, Monprapussorn S, Yuangyai C (2013) Integrating landuse and water quality for environmental based land use planning for U-tapao River Basin, Thailand. Procedia Soc Behav Sci 91:556–563CrossRef Gyawali S, Techato K, Monprapussorn S, Yuangyai C (2013) Integrating landuse and water quality for environmental based land use planning for U-tapao River Basin, Thailand. Procedia Soc Behav Sci 91:556–563CrossRef
go back to reference Horne AJ, Goldman CR (1994) Limnology, 2nd edn. McGraw-Hill Inc., New York, p 376 Horne AJ, Goldman CR (1994) Limnology, 2nd edn. McGraw-Hill Inc., New York, p 376
go back to reference IMTA (2005) Estudio de clasificación del río Atoyac, Puebla-Tlaxcala. México D.F, CONAGUA IMTA (2005) Estudio de clasificación del río Atoyac, Puebla-Tlaxcala. México D.F, CONAGUA
go back to reference Jingsheng C, Tao Yu, Ongley E (2006) Influence of high levels of total suspended solids on measurement of COD and BOD in the yellow river, China. Environ Monit Assess 116:321–334CrossRef Jingsheng C, Tao Yu, Ongley E (2006) Influence of high levels of total suspended solids on measurement of COD and BOD in the yellow river, China. Environ Monit Assess 116:321–334CrossRef
go back to reference Jonnalagadda SB, Mhere G (2001) Water quality of the Odzi River in the eastern highlands of Zimbabwe. Water Res 35(10):2371–2376CrossRef Jonnalagadda SB, Mhere G (2001) Water quality of the Odzi River in the eastern highlands of Zimbabwe. Water Res 35(10):2371–2376CrossRef
go back to reference Juahir H, Zain SM, Yusoff MK, Hanidza TIT, Armi ASM, Toriman ME, Mokhtar M (2011) Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques. Environ Monit Assess 173:625–641CrossRef Juahir H, Zain SM, Yusoff MK, Hanidza TIT, Armi ASM, Toriman ME, Mokhtar M (2011) Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques. Environ Monit Assess 173:625–641CrossRef
go back to reference Jung KY, Lee K-L, Im TH, Lee IJ, Kim S, Han K-Y, Ahn JM (2016) Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environ Tech Innov 5:67–82CrossRef Jung KY, Lee K-L, Im TH, Lee IJ, Kim S, Han K-Y, Ahn JM (2016) Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environ Tech Innov 5:67–82CrossRef
go back to reference Kibena J, Nhapi I, Gumindoga W (2014) Assessing the relationship between water quality parameters and changes in landuse patterns in the Upper Manyame River, Zimbabwe. Phys Chem Earth 67–69:153–163CrossRef Kibena J, Nhapi I, Gumindoga W (2014) Assessing the relationship between water quality parameters and changes in landuse patterns in the Upper Manyame River, Zimbabwe. Phys Chem Earth 67–69:153–163CrossRef
go back to reference Kiran Kumar A, Ponampalli Nageswara S, Mohan V, Reddy S (2016) Incidence of selected endocrine disrupting estrogens in water bodies of Hyderabad and its relation to water quality parameters. Environ Eng Manage J 15(2):315–325 Kiran Kumar A, Ponampalli Nageswara S, Mohan V, Reddy S (2016) Incidence of selected endocrine disrupting estrogens in water bodies of Hyderabad and its relation to water quality parameters. Environ Eng Manage J 15(2):315–325
go back to reference Li D, Huang D, Guo Ch, Guo X (2015) Multivariate statistical analysis of temporal-spatial variations in water quality of a constructed wetland purification system in a typical park in Beijing, China. Environ Monit Assess 187:4219CrossRef Li D, Huang D, Guo Ch, Guo X (2015) Multivariate statistical analysis of temporal-spatial variations in water quality of a constructed wetland purification system in a typical park in Beijing, China. Environ Monit Assess 187:4219CrossRef
go back to reference Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci Total Environ 313(1–3):77–89CrossRef Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci Total Environ 313(1–3):77–89CrossRef
go back to reference Martin-Del Pozzo AL, Aceves F, Espinosa R, Aguayo A, Inguaggiato S, Morales P, Cienfuegos E (2002) Influence of volcanic activity on spring water chemistry at Popocateptl Volcano, Mexico. Chem Geol 190:207–229CrossRef Martin-Del Pozzo AL, Aceves F, Espinosa R, Aguayo A, Inguaggiato S, Morales P, Cienfuegos E (2002) Influence of volcanic activity on spring water chemistry at Popocateptl Volcano, Mexico. Chem Geol 190:207–229CrossRef
go back to reference Mitchell GB, Kahru M, Wieland J, Stramska M (2002) Determination of spectral absorption co-efficient of particles, dissolved material and phytoplankton for discrete water samples. In: Fargion GS, Mueller JL (eds) Ocean optics protocols for Satellite Ocean color sensor validation, NASA/TM-2002-210004-/Rev.3, vol 2. NASA Goddard Space Flight Center, Greenbelt, pp 231–257 Mitchell GB, Kahru M, Wieland J, Stramska M (2002) Determination of spectral absorption co-efficient of particles, dissolved material and phytoplankton for discrete water samples. In: Fargion GS, Mueller JL (eds) Ocean optics protocols for Satellite Ocean color sensor validation, NASA/TM-2002-210004-/Rev.3, vol 2. NASA Goddard Space Flight Center, Greenbelt, pp 231–257
go back to reference Mooser F, Montiel A, Zúñiga A (1996) Nuevo Mapa Geológico de las Cuencas de México, Toluca y Puebla [new geological map of the Mexico, Toluca and Puebla basins]. Comisión Federal de Electricidad (CFE), Mexico City Mooser F, Montiel A, Zúñiga A (1996) Nuevo Mapa Geológico de las Cuencas de México, Toluca y Puebla [new geological map of the Mexico, Toluca and Puebla basins]. Comisión Federal de Electricidad (CFE), Mexico City
go back to reference Morales-Ramírez JM, Tritla J, Camprubí A, Corona-Esquivel R (2003) Fluid origin of the Ixtacamaxtitlán hydrothermal deposits, Puebla State, Mexico. J Geochem Explor 78–79:653–657CrossRef Morales-Ramírez JM, Tritla J, Camprubí A, Corona-Esquivel R (2003) Fluid origin of the Ixtacamaxtitlán hydrothermal deposits, Puebla State, Mexico. J Geochem Explor 78–79:653–657CrossRef
go back to reference Muangthong S, Shrestha S (2015) Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand. Environ Monit Assess 187:548CrossRef Muangthong S, Shrestha S (2015) Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand. Environ Monit Assess 187:548CrossRef
go back to reference Nasrabadi T, Ruegner H, Sirdari ZZ, Schwientek M, Grathwohl P (2016) Using total suspended solids (TSS) and turbidity as proxies for evaluation of metal transport in river water. Appl Geochem 68:1–9CrossRef Nasrabadi T, Ruegner H, Sirdari ZZ, Schwientek M, Grathwohl P (2016) Using total suspended solids (TSS) and turbidity as proxies for evaluation of metal transport in river water. Appl Geochem 68:1–9CrossRef
go back to reference Nordin N, Amir SFM, Riyanto Othman MR (2013) Textile industries wastewater treatment by electrochemical oxidation technique using metal plate. Int J Electrochem Sci 8(11403):11415 Nordin N, Amir SFM, Riyanto Othman MR (2013) Textile industries wastewater treatment by electrochemical oxidation technique using metal plate. Int J Electrochem Sci 8(11403):11415
go back to reference Norma Oficial Mexicana NOM-001-SEMARNAT (1996) Que establece los límites máximos permisibles de contaminantes en las descargas de aguas residuales en aguas y bienes nacionales Norma Oficial Mexicana NOM-001-SEMARNAT (1996) Que establece los límites máximos permisibles de contaminantes en las descargas de aguas residuales en aguas y bienes nacionales
go back to reference Ortega-Gutierrez F (1978) Estratigrafía del complejo Acatlán en la Mixteca Baja, Estados de Puebla y Oaxaca [stratigraphy of the Acatlan complex in the Lower Mixteca, Puebla and Oaxaca states], vol 2. Universidad Nacional Autonóma de México. Revista do Instituto Geológico, Mexico City, pp 112–131 Ortega-Gutierrez F (1978) Estratigrafía del complejo Acatlán en la Mixteca Baja, Estados de Puebla y Oaxaca [stratigraphy of the Acatlan complex in the Lower Mixteca, Puebla and Oaxaca states], vol 2. Universidad Nacional Autonóma de México. Revista do Instituto Geológico, Mexico City, pp 112–131
go back to reference Ortega-Gutierrez F (1993) Tectonostratigraphic analysis and significance of the Paleozoic Acatlan Complex of southern Mexico. In: Ortega-Gutierrez F, Centeno-Garcia E, Morán-Zenteno DJ, Gomez-Caballero A (eds) Terrane geology of southern Mexico. Universidad Nacional Autónoma de México, Instituto de Geología, Mexico City, pp 54–60 Ortega-Gutierrez F (1993) Tectonostratigraphic analysis and significance of the Paleozoic Acatlan Complex of southern Mexico. In: Ortega-Gutierrez F, Centeno-Garcia E, Morán-Zenteno DJ, Gomez-Caballero A (eds) Terrane geology of southern Mexico. Universidad Nacional Autónoma de México, Instituto de Geología, Mexico City, pp 54–60
go back to reference Ouyang Y (2005) Application of principal component and factor analysis to evaluate surface water quality monitoring network. Water Res 39(12):2621–2635CrossRef Ouyang Y (2005) Application of principal component and factor analysis to evaluate surface water quality monitoring network. Water Res 39(12):2621–2635CrossRef
go back to reference Ouyang Y, Nkedi-Kizza P, Wu QT, Shinde D, Huang CH (2006) Assessment of seasonal variations in surface water quality. Water Res 40(20):3800–3810CrossRef Ouyang Y, Nkedi-Kizza P, Wu QT, Shinde D, Huang CH (2006) Assessment of seasonal variations in surface water quality. Water Res 40(20):3800–3810CrossRef
go back to reference Paaijmans KP, Takken W, Githeko AK, Jacobs AFG (2008) The effect of water turbidity on the near-surface water temperature of larval habitats of the malaria mosquito Anopheles gambiae. Int J Biometeorol 52:747–753CrossRef Paaijmans KP, Takken W, Githeko AK, Jacobs AFG (2008) The effect of water turbidity on the near-surface water temperature of larval habitats of the malaria mosquito Anopheles gambiae. Int J Biometeorol 52:747–753CrossRef
go back to reference Paerl HW, Pinckney JL, Fear JM, Peierls BL (1998) Ecosystem responses to internal and water shed organic matter loading consequences for hypoxia in the eutrophying Neuse River estuary, North Carolina, USA. Mar Ecol Prog Ser 166:17–25CrossRef Paerl HW, Pinckney JL, Fear JM, Peierls BL (1998) Ecosystem responses to internal and water shed organic matter loading consequences for hypoxia in the eutrophying Neuse River estuary, North Carolina, USA. Mar Ecol Prog Ser 166:17–25CrossRef
go back to reference Pejman AH, Bidhendi GRN, Karbassi AR, Mehrdadi N, Bidhendi ME (2009) Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques. Int J Environ Sci Technol 6(3):467–476CrossRef Pejman AH, Bidhendi GRN, Karbassi AR, Mehrdadi N, Bidhendi ME (2009) Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques. Int J Environ Sci Technol 6(3):467–476CrossRef
go back to reference Perkinns RG, Underwood GJC (2000) Gradients of chlorophyll and water chemistry along a eutrophic reservoir with determination of the limiting nutrient by in situ nutrient addition. Water Res 34(3):713–724CrossRef Perkinns RG, Underwood GJC (2000) Gradients of chlorophyll and water chemistry along a eutrophic reservoir with determination of the limiting nutrient by in situ nutrient addition. Water Res 34(3):713–724CrossRef
go back to reference Pimentel D (2006) Soil erosion: a food and environmental threat. Environ Dev Sustain 8:119–137CrossRef Pimentel D (2006) Soil erosion: a food and environmental threat. Environ Dev Sustain 8:119–137CrossRef
go back to reference Ramavandi B, Farjadfard S, Ardjmand M, Dobaradaran S (2015) Effect of water quality and operational parameters on trihalomethanes formation potential in Dez River water, Iran. Water Res Ind 11:1–12CrossRef Ramavandi B, Farjadfard S, Ardjmand M, Dobaradaran S (2015) Effect of water quality and operational parameters on trihalomethanes formation potential in Dez River water, Iran. Water Res Ind 11:1–12CrossRef
go back to reference Reynolds DM, Ahmad SR (1997) Rapid and direct determination of waste water BOD values using a fluorescence technique. Water Res 31(8):2012–2018CrossRef Reynolds DM, Ahmad SR (1997) Rapid and direct determination of waste water BOD values using a fluorescence technique. Water Res 31(8):2012–2018CrossRef
go back to reference Rodríguez-Espinosa PF, Valdés SM, Carolina S, Martínez-Tavera E (2014) Determinación del NAME del Río Atoyac, en los sitios de ubicación de las casetas de monitoreo en tiempo real de parámetros fisico-químico del proyecto “Estudios para la red de estaciones de monitoreo para la preservación, conservación y mejoramiento de la calidad del agua en la cuenca de alto Atoyac. Technical report (SDRSOT, Puebla), p 73 Rodríguez-Espinosa PF, Valdés SM, Carolina S, Martínez-Tavera E (2014) Determinación del NAME del Río Atoyac, en los sitios de ubicación de las casetas de monitoreo en tiempo real de parámetros fisico-químico del proyecto “Estudios para la red de estaciones de monitoreo para la preservación, conservación y mejoramiento de la calidad del agua en la cuenca de alto Atoyac. Technical report (SDRSOT, Puebla), p 73
go back to reference Saxsena M, Saksena DN (2012) Water quality and trophic status of Raipur reservoir in Gwalior, Madhya Pradesh. J Nat. Sci. Res. 2(8):82–96 Saxsena M, Saksena DN (2012) Water quality and trophic status of Raipur reservoir in Gwalior, Madhya Pradesh. J Nat. Sci. Res. 2(8):82–96
go back to reference Sharma A, Bora CR, Shukla V (2013) Evaluation of seasonal changes in physico-chemical and bacteriological characteristics of water from the Narmada river (India) using multivariate analysis. Nat Resour Res. doi:10.1007/s11053-013-9204-x Sharma A, Bora CR, Shukla V (2013) Evaluation of seasonal changes in physico-chemical and bacteriological characteristics of water from the Narmada river (India) using multivariate analysis. Nat Resour Res. doi:10.​1007/​s11053-013-9204-x
go back to reference Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw 22:464–475CrossRef Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw 22:464–475CrossRef
go back to reference Singh TA, Meetei NS, Meitei LB (2013) Seasonal variation of some physico-chemical characteristics of three major rivers in Imphal, Manipur: a comparative evaluation. Curr World Environ 8(1):93–102CrossRef Singh TA, Meetei NS, Meitei LB (2013) Seasonal variation of some physico-chemical characteristics of three major rivers in Imphal, Manipur: a comparative evaluation. Curr World Environ 8(1):93–102CrossRef
go back to reference Skalak KJ, Benthem AJ, Schenk ER, Hupp CR, Galloway JM, Nustad RA, Wiche JG (2013) Large dams and alluvial rivers in the Anthropocene: the impacts of the Garrison and Oahe dams on the upper Missouri river. Anthropocene 2:51–64CrossRef Skalak KJ, Benthem AJ, Schenk ER, Hupp CR, Galloway JM, Nustad RA, Wiche JG (2013) Large dams and alluvial rivers in the Anthropocene: the impacts of the Garrison and Oahe dams on the upper Missouri river. Anthropocene 2:51–64CrossRef
go back to reference Sowlat M (2011) A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmos Environ 45:2050–2059CrossRef Sowlat M (2011) A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmos Environ 45:2050–2059CrossRef
go back to reference Stewart C, Johnston DM, Leonard GS, Horwell CJ, Thordarson T, Cronin SJ (2006) Contamination of water supplies by volcanic ashfall: a literature review and simple impact modelling. J Volcanol Geotherm Res 158:296–306CrossRef Stewart C, Johnston DM, Leonard GS, Horwell CJ, Thordarson T, Cronin SJ (2006) Contamination of water supplies by volcanic ashfall: a literature review and simple impact modelling. J Volcanol Geotherm Res 158:296–306CrossRef
go back to reference Sun W, Xia Ch, Xu M, Guo J, Guoping S (2016) Application of modified water quality indices as indicators to assess the spatial and temporal trends of water quality in the Dongjiang River. Ecol Indic 66:306–312CrossRef Sun W, Xia Ch, Xu M, Guo J, Guoping S (2016) Application of modified water quality indices as indicators to assess the spatial and temporal trends of water quality in the Dongjiang River. Ecol Indic 66:306–312CrossRef
go back to reference Sundaray SK, Panda UCh, Nayak BB, Bhatta D (2006) Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of the Mahanadi river-estuarine system (India)—a case study. Environ Geochem Health 28:317–330CrossRef Sundaray SK, Panda UCh, Nayak BB, Bhatta D (2006) Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of the Mahanadi river-estuarine system (India)—a case study. Environ Geochem Health 28:317–330CrossRef
go back to reference Sutherland DA, O'Neill MA (2016) Hydrographic and dissolved oxygen variability in a seasonal Pacific Northwest estuary. Estuar Coast Shelf Sci 172:47–59CrossRef Sutherland DA, O'Neill MA (2016) Hydrographic and dissolved oxygen variability in a seasonal Pacific Northwest estuary. Estuar Coast Shelf Sci 172:47–59CrossRef
go back to reference Turk D, Zappa CJ, Meinen CS, Christian JR, Ho TD, Dickson GA, Mcgillis WR (2010) Rain impacts on CO2 exchange in the western equatorial, Pacific Ocean. Geophys Res Lett. doi:10.1029/2010GL045520 Turk D, Zappa CJ, Meinen CS, Christian JR, Ho TD, Dickson GA, Mcgillis WR (2010) Rain impacts on CO2 exchange in the western equatorial, Pacific Ocean. Geophys Res Lett. doi:10.​1029/​2010GL045520
go back to reference Von Erffa A, Hilger W, Knoblich K, Weyl R (1977) Geologie des hochbecknes von Puebla-Tlaxcala und seiner umgebung [geology of the upper basin of Puebla-Tlaxcala and its surroundings]. Das Mexiko-Project der Deutschen Forschungssemeinschaft Bd. XI. Steiner, Weisbaden Von Erffa A, Hilger W, Knoblich K, Weyl R (1977) Geologie des hochbecknes von Puebla-Tlaxcala und seiner umgebung [geology of the upper basin of Puebla-Tlaxcala and its surroundings]. Das Mexiko-Project der Deutschen Forschungssemeinschaft Bd. XI. Steiner, Weisbaden
go back to reference Voutsa D, Manoli E, Samara C, Sofoniou M, Stratis I (2001) A study of surface water quality in Macedonia, Greece: speciation of nitrogen and phosphorus. Water Air Soil Pollut 129(1–4):13–32CrossRef Voutsa D, Manoli E, Samara C, Sofoniou M, Stratis I (2001) A study of surface water quality in Macedonia, Greece: speciation of nitrogen and phosphorus. Water Air Soil Pollut 129(1–4):13–32CrossRef
go back to reference Wanda EMM, Mamba BB, Msagati TAM (2016) Determination of the water quality index ratings of water in the Mpumalanga and North West provinces, South Africa. Phys Chem Earth 92:70–78CrossRef Wanda EMM, Mamba BB, Msagati TAM (2016) Determination of the water quality index ratings of water in the Mpumalanga and North West provinces, South Africa. Phys Chem Earth 92:70–78CrossRef
go back to reference Wang Y, Wang P, Bai Y, Tian Z, Li J, Shao X, Mustavich LF, Li B-L (2013) Assessment of surface water quality via multivariate statistical techniques: a case study of the Songhua River Harbin region, China. J Hydro Environ Res 7:30–40CrossRef Wang Y, Wang P, Bai Y, Tian Z, Li J, Shao X, Mustavich LF, Li B-L (2013) Assessment of surface water quality via multivariate statistical techniques: a case study of the Songhua River Harbin region, China. J Hydro Environ Res 7:30–40CrossRef
go back to reference Waziri M, Ogugbuaja VO (2010) Interrealtionships between physicochemical water pollution indicators: a case study of River Yobe-Nigeria. Am J Sci Ind Res 1(1):76–80 Waziri M, Ogugbuaja VO (2010) Interrealtionships between physicochemical water pollution indicators: a case study of River Yobe-Nigeria. Am J Sci Ind Res 1(1):76–80
go back to reference WHO (2011) Guidelines for drinking-water quality, 4th edn. WHO, Geneva WHO (2011) Guidelines for drinking-water quality, 4th edn. WHO, Geneva
go back to reference Wright R (1982) Seasonal variation in water quality of a West African river (R. Jong in Sierra Leone). Rev Hydrobiol Trop 15(3):193–199 Wright R (1982) Seasonal variation in water quality of a West African river (R. Jong in Sierra Leone). Rev Hydrobiol Trop 15(3):193–199
go back to reference Yang Y, Zhou F, Guo H, Sheng H, Liu H, Dao X (2010) Analysis of spatial and temporal water pollution patterns in lake Dianchi using multivariate statistical methods. Environ Monit Assess 170(1–4):407–416CrossRef Yang Y, Zhou F, Guo H, Sheng H, Liu H, Dao X (2010) Analysis of spatial and temporal water pollution patterns in lake Dianchi using multivariate statistical methods. Environ Monit Assess 170(1–4):407–416CrossRef
go back to reference Zang C, Huang S, Wu M, Du S, Scholz M, Gao F, Lin C, Guo Y, Dong Yu (2011) Comparison of relationships between pH, dissolved oxygen, chlorophyll a for aquaculture and non-aquaculture waters. Water Air Soil Pollut 219:157–174CrossRef Zang C, Huang S, Wu M, Du S, Scholz M, Gao F, Lin C, Guo Y, Dong Yu (2011) Comparison of relationships between pH, dissolved oxygen, chlorophyll a for aquaculture and non-aquaculture waters. Water Air Soil Pollut 219:157–174CrossRef
Metadata
Title
Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach
Authors
E. Martinez-Tavera
P. F. Rodriguez-Espinosa
V. C. Shruti
S. B. Sujitha
S. S. Morales-Garcia
N. P. Muñoz-Sevilla
Publication date
01-01-2017
Publisher
Springer Berlin Heidelberg
Published in
Environmental Earth Sciences / Issue 2/2017
Print ISSN: 1866-6280
Electronic ISSN: 1866-6299
DOI
https://doi.org/10.1007/s12665-017-6406-2

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