Assessment of surface water quality using multivariate statistical techniques: A case study of the Nampong River Basin, Thailand

AbstractThis study investigates the spatial water quality pattern of 13 stations located along the main Nampong River. Multivariate statistical methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Sixteen water quality parameters were initially selected and analyzed. Two spatial clusters were formed based on HACA. These clusters are designated as upper part (U/P) of Nampong River and lower part (L/P) of Nampong River regions. Forward and backward stepwise DA managed to discriminate ten water quality variables, from the original 16 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the two clustered regions. Five principal components (PCs) were obtained with 69.806% total variation for the moderate-pollution source region, while five PCs with 69.327% total variances was obtained for the low-pollution source region. The pollution source for the L/P is of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the U/P region, the agricultural runoffs are the main sources of pollution. This study concluded the application of multivariate statistical methods to reduce the large number of water quality parameters down to manageable number

Selected Malaysia air quality pollutants assessment using chemometrics techniques

Air quality played an important role as polluted air quality could harm human health, environment as well as property. Thus, a study of air quality pollutants assessment using chemometrics was performed with the objectives to ensure the air quality data analysis is valid, acceptable and interpreted well. Analysis of PCA, FA, KMO and Bartlett’s test were done on five main air quality pollutants (O3, NO2, SO2, CO and PM10) from all around Malaysia. From the data analysis obtained, the concentrations of air quality pollutants all around Malaysia starting from 2008 to 2011 were acceptable and the most dominant major pollutants had been highlighted. KMO obtained in this study is 0.7760, which show that the results are factor well. While, Bartlett’s test shows that the variables correlated to each other’s. From these tests, air quality data were acceptable for factor analysis.Keywords: air pollution; chemometrics; PCA; F

Assessment of Water Quality in a Subtropical Alpine Lake Using Multivariate Statistical Techniques and Geostatistical Mapping: A Case Study

Concerns about the water quality in Yuan-Yang Lake (YYL), a shallow, subtropical alpine lake located in north-central Taiwan, has been rapidly increasing recently due to the natural and anthropogenic pollution. In order to understand the underlying physical and chemical processes as well as their associated spatial distribution in YYL, this study analyzes fourteen physico-chemical water quality parameters recorded at the eight sampling stations during 2008–2010 by using multivariate statistical techniques and a geostatistical method. Hierarchical clustering analysis (CA) is first applied to distinguish the three general water quality patterns among the stations, followed by the use of principle component analysis (PCA) and factor analysis (FA) to extract and recognize the major underlying factors contributing to the variations among the water quality measures. The spatial distribution of the identified major contributing factors is obtained by using a kriging method. Results show that four principal components i.e., nitrogen nutrients, meteorological factor, turbidity and nitrate factors, account for 65.52% of the total variance among the water quality parameters. The spatial distribution of principal components further confirms that nitrogen sources constitute an important pollutant contribution in the YYL

Validation of the Enterococci indicator for bacteriological quality monitoring of beaches in Malaysia using a multivariate approach

There is currently no established bacteriological beach quality monitoring (BQM) program in place in Malaysia. To initiate cost-effective, sustainable bacteriological BQM schemes for the ultimate goal of protecting public health, policy decision makers need to be provided robust, indigenous empirical findings that validate appropriate water quality parameters for inclusion in such monitoring programs. This is the first study that assesses the validity of enterococci as an ideal indicator for bacteriological BQM in Malaysia using a multivariate approach. Beach water and sand samples from 7 beach locations were analyzed for a total of twenty-one microbial and non-microbial water quality parameters. A multivariate approach incorporating cluster analyses (CA), principal component analyses (PCA), and factor analysis (FA) was also adopted. Apart from the weak correlations of Staphylococcus aureus with concentrations of Vibro species (r = 0.302, p = 0.037) and total coliforms (r = 0.392, p = 0.006) in seawater, no correlation existed between S. aureus concentration and other parameters. Faecal coliforms failed to correlate with any of the tested parameters. Enterococci also correlated with more quality parameters than faecal coliforms or any other indicator. Multiple linear regressions highlighted a significant, best fit model that could predict enterococci concentrations in relation to other parameters with a maximum predictive success of 69.64%. PCA/FA clearly delineated enterococci and faecal coliforms as parameters that weighed strongly for BQM while Staphylococcus aureus, faecal coliforms and enterococci weighed strongly for beach sand quality monitoring. On the whole, higher correlations of enterococci levels with other parameters than was observed for faecal coliforms suggest that the former be considered a preferred parameter of choice for BQM in Malaysia. Our findings provide meaningful evidence particularly as it relates to the correlation of Enterococci with pathogens and other non-microbial parameters. It also provides empirical data to validate the applicability of the enterococci indicator paradigm for bacteriological beach quality monitoring in Malaysia. The current study thus provides policy decision makers evidenced based approach to parameter streamlining for optimized beach sampling and sustainable bacteriological quality monitoring

Assessment of Water Quality Using Multivariate Statistical Techniques in the Ying River Basin, China

I used multivariate statistical methods, including cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) to evaluate water quality in the Ying River Basin, the largest tributary of Huai River, China. A total of 12 water quality parameters were measured at each of 15 sites from 2008–2010 (540 observations), allowing investigation of temporal and spatial variation and indication of potential pollution sources. Hierarchical CA classified the 15 monitoring sites into three groups, representing heavily, moderately and least polluted sites. Three parameters (temperature, pH and TP) distinguished temporal variation with close to 67.4% correct assignment in the DA, separating summer from winter and spring-fall. In the spatial variation analysis, the DA used eight parameters (temperature, pH, DO, CODMn, CODCr, BOD5, NH4-N, and Hg) and correctly assigned about 85.7% of the sites to spatial clusters. PCA did not result in a significant data reduction in this study, but it did extract and identify significant factors/variables responsible for variation in river water quality at the three groups of sites identified by CA. Sites in Group 1 were mostly correlated with CODCr, NH4-N and volatile phenol, suggesting that they received pollutants mainly from industrial discharge. Group 2 sites correlated most strongly with temperature, pH and DO, which may indicate that these sites were mainly affected by natural processes. Group 3 sites were dominated by CODMn, As and Hg, perhaps indicating influence by both point and non-point pollution sources.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/106539/1/final draft_Lei Lei.pd

AVALIAÇÃO DO USO DE DIFERENTES MODELOS RECEPTORES COM DADOS DE PM2,5: BALANÇO QUÍMICO DE MASSA (BQM) E FATORAÇÃO DE MATRIZ POSITIVA (FMP)

A identificação de fontes para material particulado tem sido um tema de crescente interesse em todo o mundo para auxiliar a gestão da qualidade do ar. Esta classe de estudos é convencionalmente baseada no uso de modelos receptores, que identificam e quantificam as fontes responsáveis a partir da concentração do contaminante no receptor. Existe uma variedade de modelos receptores disponíveis na literatura, este trabalho compara os resultados dos modelos receptores balanço químico de massa (BQM) e fatoração de matriz positiva (FMP) para o banco de dados de PM2,5, da região de Brighton, Colorado, com o intuito de investigar as dificuldades na utilização de cada modelo, bem como suas vantagens e desvantagens. Inicialmente, já é conhecido que o modelo BQM tem a desvantagem de necessitar dos perfis das fontes, determinados experimentalmente, para ser aplicado e também tem limitações quando as fontes envolvidas são similares. Já o modelo FMP não requer os perfis de fontes, mas tem a desvantagem de precisar de elevada quantidade amostral da concentração do contaminante no receptor. Os resultados mostraram, baseados nas medidas de performance que os dois modelos foram aptos para reproduzir os dados do receptor com ajustes aceitáveis. Todavia, resultados diferentes se ajustaram a medidas de performance. O modelo BQM, utilizou 9 tipos de fontes e o modelo FMP encontrou apenas 6 tipos de fontes. Constatou-se com isso que o modelo FMP tem dificuldades em modelar fontes que aparecem ocasionalmente. As fontes sulfato de amônio, solos, veículos a diesel e nitrato de amônio tiverem boas correlações nos resultados dos dois modelos de contribuições de fontes. Os perfis de fontes utilizados no modelo BQM e resultados do modelo FMP que mais se assimilaram foram das fontes nitrato de amônio, solos, sulfato de amônio e combustão de madeira e ou/ veículos desregulados. Verificou-se no modelo FMP que as espécies não características de determinadas fontes aparecem nos resultados dos perfis das fontes, o que torna-se ainda mais complexo a identificação das fontes, requerendo elevado conhecimento sobre a composição de inúmeras fontes