Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 200...Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 2003–2008 to investigate spatio-temporal variations and identify potential pollution sources.Using cluster analysis,the twelve months of the year were classified into three periods of lowflow （LF）,normal-flow （NF）,and high-flow （HF）;and the 37 monitoring sites were divided into low pollution （LP）,moderate pollution （MP）,and high pollution （HP）.Dissolved oxygen （DO）,potassium permanganate index （COD Mn ）,and ammonia-nitrogen （NH 4 ＋-N） were identified as significant variables affecting temporal and spatial variations by non-parametric tests.Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF,heavy metals during LF,and petroleum during HF.In the MP region,the identified pollutants primarily included organic matter and heavy metals year-around,while in the LP region,organic pollution was significant during both NF and HF,and nutrient and heavy metal levels were high during both LF and HF.The main sources of pollution came from domestic wastewater and agricultural activities and runoff;however,they contributed differently to each region in regards to pollution levels.For the HP region,inputs from wastewater treatment plants were significant;but for MP and LP regions,water pollution was more likely from the combined effects of agriculture,domestic wastewater,and chemical industry.These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area.展开更多
Understanding the temporal and spatial variation of hydrochemical components in large freshwater lakes is crucial for effective management and conversation.In this study,we identify the temporalspatial characteristics...Understanding the temporal and spatial variation of hydrochemical components in large freshwater lakes is crucial for effective management and conversation.In this study,we identify the temporalspatial characteristics and driving factors of the hydrochemical components in Baiyangdian Lake using geochemical methods(Gibbs diagram,Piper diagram and End-element diagram of ion ratio)and multivariate statistical techniques(Principal component analysis and Correlation analysis).16 sets of samples were collected from Baiyangdian Lake in May(normal season),July(flood season),and December(dry season)of 2022.Results indicate significant spatial variation in Nat,ci,SO and NO,,suggesting a strong influence of human activities.Cation concentrations exhibit greater seasonal variation in the dry season compared to the flood season,while the concentrations of the four anions show inconsistent seasonal changes due to the combined effects of river water chemical composition and human activities.The hydrochemical type of Baiyangdian Lake is primarily HCO,Cl-Na.Ca,Mg*and HCO,originate mainly from silicate and carbonate rock dissolution,while Kt,Nat and CI originate mainly from sewage and salt dissolution in sediments.SO42 may mainly stem from industrial wastewater,while NO,primarily originates from animal feces and domestic sewage.Through the use of Principal Component Analysis,it is identified that water-rock interaction(silicate and carbonate rocks dissolution,and dissolution of salt in sediments),carbonate sedimentation,sewage,agricultural fertilizer and manure,and nitrification are the main driving factors of the variation of hydrochemical components of Baiyangdian Lake across three hydrological seasons.These findings suggest the need for effective control of substandard domestic sewage discharge,optimization of agricultural fertilization strategies,and proper management of animal manure to comprehensively improve the water environment in Baiyangdian Lake.展开更多
We investigated the phytosociology, structure and dynamics of Pinus roxburghii in 40 stands in northern areas of Pakistan by using cluster analysis （Ward’s agglomerative clustering） and ordination （Non-metric Mult...We investigated the phytosociology, structure and dynamics of Pinus roxburghii in 40 stands in northern areas of Pakistan by using cluster analysis （Ward’s agglomerative clustering） and ordination （Non-metric Multidimensional Scaling）. Cluster analysis revealed three major groups associated with specific environmental characteristics： （1） P. roxburghii （2） Pinus-Quercus baloot and （3） Pinus-Olea ferruginea community types. NMS-ordination showed the major gradient as an amalgam of elevation （r2=0.441, p＆lt;0.01） and slope （r2=0.391, p＆lt;0.05） as the two topographic factors correlated with species distribution. The first ordination axis also showed positive correlation with soil variables like pH and electrical conductivity, suggesting that soil chemistry was related to topographic characteristics and probably acted as a secondary gradient. We also examined size class distributions, age structures and growth rates of the three communities in order to describe community development and dynamics. Total tree density was 14700 plants/ha, with P. roxburghii having a relative density of 82%to 100%. Density of juve-nile and total density and basal area of the subordinate tree species were low. The low density of trees in the smallest diameter size-class sug-gested that the recruitment of small P. roxburghii plants into the adult population may be lower than the required replacement rate for the stands. Pooled size-class distributions for the species showed a multimo-dal pattern with some regeneration gaps. Browsing, heavy logging and＆amp;nbsp;other anthropogenic activities were the overriding factors responsible for the poor recruitment of P. roxburghii. We concluded from the age struc-ture that the forests were characterized by the dominance of young trees. Growth rate analysis revealed that P. roxburghii was the fastest growing species among the conifers species in Pakistan. In view of its relatively fast growth and longevity, P. roxburghii seems to be a suitable choice for short-term cultural practices in order to enhance wood production in lesser Himalaya and Hindukush ranges of Pakistan.展开更多
Graveyards or sacred groves are often places of natural vegetation protected by spiritual believers because of their sacred beliefs and indigenous culture.A study of graveyards was conducted to determine their role in...Graveyards or sacred groves are often places of natural vegetation protected by spiritual believers because of their sacred beliefs and indigenous culture.A study of graveyards was conducted to determine their role in species conservation,community formation,and associated indicators and species composition using multivariate statistical approaches.It was hypothesized that variations in the age of graveyards would give rise to diverse plant communities under the impact of various edaphic and climatic factors.Quantitative ecological techniques were applied to determine various phytosociological attributes.All the data were put in MS Excel for analysis in PCORD and CANOCO softwares for cluster analysis(CA),two-way cluster analysis(TWCA),indicator species analysis and canonical correspondence analysis.CA and TWCA through Sorenson distance measurements identified five major graveyard plant communities:(1)FicusBougainvillea-Chenopodium;(2)Acacia-Datura-Convolvulus;(3)Ziziphus-Vitex-Abutilon;(4)Acacia-Lantana-Salsola;and(5)Melia-Rhazya-Peganum.Species such as Capparis decidua,Herniaria hirsuta,Salvadora oliedes and Populus euphratica were only present inside graveyards rather than outside and advocate the role of graveyards in species conservation.The impact of different environmental and climatic variables plus the age of the graveyards were also assessed for comparison of plant communities and their respective indicator species.The results indicate that higher chlorine concentration,age of graveyards,low soil electrical conductivity,lower anthropogenic activities,higher nitrogen,calcium and magnesium concentrations in the soil,and sandy soils were the strong environmental variables playing a significant role in the formation of graveyard plant communities,their associated indicators and species distribution patterns.These results could further be utilized to evaluate the role of edaphic and climatic factors,indicator species and conservation management practices at a greater scale.展开更多
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
基金supported by the National Water Special Project (No.2009ZX07526-005)the Strategic Environmental Assessment Project (No.HP1080901)
文摘Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 2003–2008 to investigate spatio-temporal variations and identify potential pollution sources.Using cluster analysis,the twelve months of the year were classified into three periods of lowflow （LF）,normal-flow （NF）,and high-flow （HF）;and the 37 monitoring sites were divided into low pollution （LP）,moderate pollution （MP）,and high pollution （HP）.Dissolved oxygen （DO）,potassium permanganate index （COD Mn ）,and ammonia-nitrogen （NH 4 ＋-N） were identified as significant variables affecting temporal and spatial variations by non-parametric tests.Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF,heavy metals during LF,and petroleum during HF.In the MP region,the identified pollutants primarily included organic matter and heavy metals year-around,while in the LP region,organic pollution was significant during both NF and HF,and nutrient and heavy metal levels were high during both LF and HF.The main sources of pollution came from domestic wastewater and agricultural activities and runoff;however,they contributed differently to each region in regards to pollution levels.For the HP region,inputs from wastewater treatment plants were significant;but for MP and LP regions,water pollution was more likely from the combined effects of agriculture,domestic wastewater,and chemical industry.These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area.
基金supported by the Natural Science Foundation of China(Grant No.42377232)Natural Science Foundation of Hebei Province of China(Grant No.D2022504015)+1 种基金the Fundamental Research Funds for the Chinese Academy of Geological Sciences(No.YK202310)the open funds of laboratory of water environmental science of Hebei Province,China(Grant No.HBSHJ 202103).
文摘Understanding the temporal and spatial variation of hydrochemical components in large freshwater lakes is crucial for effective management and conversation.In this study,we identify the temporalspatial characteristics and driving factors of the hydrochemical components in Baiyangdian Lake using geochemical methods(Gibbs diagram,Piper diagram and End-element diagram of ion ratio)and multivariate statistical techniques(Principal component analysis and Correlation analysis).16 sets of samples were collected from Baiyangdian Lake in May(normal season),July(flood season),and December(dry season)of 2022.Results indicate significant spatial variation in Nat,ci,SO and NO,,suggesting a strong influence of human activities.Cation concentrations exhibit greater seasonal variation in the dry season compared to the flood season,while the concentrations of the four anions show inconsistent seasonal changes due to the combined effects of river water chemical composition and human activities.The hydrochemical type of Baiyangdian Lake is primarily HCO,Cl-Na.Ca,Mg*and HCO,originate mainly from silicate and carbonate rock dissolution,while Kt,Nat and CI originate mainly from sewage and salt dissolution in sediments.SO42 may mainly stem from industrial wastewater,while NO,primarily originates from animal feces and domestic sewage.Through the use of Principal Component Analysis,it is identified that water-rock interaction(silicate and carbonate rocks dissolution,and dissolution of salt in sediments),carbonate sedimentation,sewage,agricultural fertilizer and manure,and nitrification are the main driving factors of the variation of hydrochemical components of Baiyangdian Lake across three hydrological seasons.These findings suggest the need for effective control of substandard domestic sewage discharge,optimization of agricultural fertilization strategies,and proper management of animal manure to comprehensively improve the water environment in Baiyangdian Lake.
文摘We investigated the phytosociology, structure and dynamics of Pinus roxburghii in 40 stands in northern areas of Pakistan by using cluster analysis （Ward’s agglomerative clustering） and ordination （Non-metric Multidimensional Scaling）. Cluster analysis revealed three major groups associated with specific environmental characteristics： （1） P. roxburghii （2） Pinus-Quercus baloot and （3） Pinus-Olea ferruginea community types. NMS-ordination showed the major gradient as an amalgam of elevation （r2=0.441, p＆lt;0.01） and slope （r2=0.391, p＆lt;0.05） as the two topographic factors correlated with species distribution. The first ordination axis also showed positive correlation with soil variables like pH and electrical conductivity, suggesting that soil chemistry was related to topographic characteristics and probably acted as a secondary gradient. We also examined size class distributions, age structures and growth rates of the three communities in order to describe community development and dynamics. Total tree density was 14700 plants/ha, with P. roxburghii having a relative density of 82%to 100%. Density of juve-nile and total density and basal area of the subordinate tree species were low. The low density of trees in the smallest diameter size-class sug-gested that the recruitment of small P. roxburghii plants into the adult population may be lower than the required replacement rate for the stands. Pooled size-class distributions for the species showed a multimo-dal pattern with some regeneration gaps. Browsing, heavy logging and＆amp;nbsp;other anthropogenic activities were the overriding factors responsible for the poor recruitment of P. roxburghii. We concluded from the age struc-ture that the forests were characterized by the dominance of young trees. Growth rate analysis revealed that P. roxburghii was the fastest growing species among the conifers species in Pakistan. In view of its relatively fast growth and longevity, P. roxburghii seems to be a suitable choice for short-term cultural practices in order to enhance wood production in lesser Himalaya and Hindukush ranges of Pakistan.
基金This study is supported by University Research Fund(URF)of Quaid-i-Azam University Islamabad.
文摘Graveyards or sacred groves are often places of natural vegetation protected by spiritual believers because of their sacred beliefs and indigenous culture.A study of graveyards was conducted to determine their role in species conservation,community formation,and associated indicators and species composition using multivariate statistical approaches.It was hypothesized that variations in the age of graveyards would give rise to diverse plant communities under the impact of various edaphic and climatic factors.Quantitative ecological techniques were applied to determine various phytosociological attributes.All the data were put in MS Excel for analysis in PCORD and CANOCO softwares for cluster analysis(CA),two-way cluster analysis(TWCA),indicator species analysis and canonical correspondence analysis.CA and TWCA through Sorenson distance measurements identified five major graveyard plant communities:(1)FicusBougainvillea-Chenopodium;(2)Acacia-Datura-Convolvulus;(3)Ziziphus-Vitex-Abutilon;(4)Acacia-Lantana-Salsola;and(5)Melia-Rhazya-Peganum.Species such as Capparis decidua,Herniaria hirsuta,Salvadora oliedes and Populus euphratica were only present inside graveyards rather than outside and advocate the role of graveyards in species conservation.The impact of different environmental and climatic variables plus the age of the graveyards were also assessed for comparison of plant communities and their respective indicator species.The results indicate that higher chlorine concentration,age of graveyards,low soil electrical conductivity,lower anthropogenic activities,higher nitrogen,calcium and magnesium concentrations in the soil,and sandy soils were the strong environmental variables playing a significant role in the formation of graveyard plant communities,their associated indicators and species distribution patterns.These results could further be utilized to evaluate the role of edaphic and climatic factors,indicator species and conservation management practices at a greater scale.
