<b><i>Dataset on water quality parameters, bacteria occurrence and antimicrobial resistance profiles from selected drinking water treatment plants in Oyo State, Nigeria</i></b>.
Domaine:
healthcareenvironment and energyType de record:
dataset
Créateur:
TunOlaAbi
Éditeur:
fig
Hôte:
Overview of the Dataset
This dataset comprises microbiological water quality and antimicrobial resistance data generated from raw and treated water samples collected from five major drinking water treatment plants in Oyo State, Southwestern Nigeria, namely Ogbomoso Waterworks, Erelu Water Scheme, Eleyele Waterworks, Asejire Water Cooperation, and Shaki Waterworks. Sampling was conducted during two rounds, generating datasets on bacterial occurrence, heterotrophic bacterial counts, antibiotic susceptibility patterns, Phenotypic extended-spectrum β-lactamase (ESBL) screening, and multiple antibiotic resistance (MAR) indices. The generated dataset is provided as supplementary spreadsheets and includes culture reports, antimicrobial susceptibility testing (AST) results, resistance gene screening data, and metadata describing sampling locations and water types.
Bacterial Counts and Distribution of Isolates
A total of 28 bacterial isolates were recovered from the collected water samples. Bacterial loads ranged from 110 to 3,760 CFU/100 mL across the investigated treatment plants. Higher bacterial counts were generally observed in raw water samples compared with treated water samples, although detectable bacterial contamination was also observed in some treated water samples.
The recovered isolates belonged predominantly to Gram-negative bacteria of environmental and public health significance. Enterobacter cloacae was the most frequently isolated organism, accounting for 32.1% (9/28) of all isolates. Other recovered organisms included Klebsiella oxytoca (14.3%), Escherichia coli (10.7%), Serratia liquefaciens (10.7%), Klebsiella aerogenes (7.1%), Providencia rettgeri (7.1%), Citrobacter freundii (7.1%), Morganella morganii (3.6%), Proteus mirabilis (3.6%), and Pseudomonas spp. (3.6%).
The distribution of isolates according to sampling location is presented in Table 1, while organism frequencies are summarized in Figure 1.
Antimicrobial Susceptibility Profiles
Antimicrobial susceptibility testing was performed against fifteen antibiotics representing multiple antimicrobial classes, including β-lactams, aminoglycosides, fluoroquinolones, tetracyclines, carbapenems, and folate pathway inhibitors.
Resistance frequencies varied considerably among the tested antibiotics. Ampicillin exhibited the highest resistance frequency (96.6%), followed by Cefixime (75.9%) and Amoxicillin–clavulanate (72.4%). High resistance frequencies were also observed for Cefotaxime (58.6%) and Ceftriaxone (51.7%).
Intermediate resistance levels were recorded for Ciprofloxacin (48.3%), Amikacin (44.8%), Tetracycline (44.8%), Trimethoprim–sulfamethoxazole (44.8%), Meropenem (37.9%), Pefloxacin (37.9%), and Gentamicin (31.0%). The lowest resistance frequencies were observed for Doxycycline (20.7%), Piperacillin–Tazobactam (20.7%), and Levofloxacin (13.8%).
The resistance frequency data are presented in Table 1, 2, and 3.
Multiple Antibiotic Resistance Characteristics
The dataset contains isolate-specific resistance profiles that permitted calculation of multiple antibiotic resistance (MAR) indices. MAR values ranged from 0.20 to 0.80, with a mean MAR index of 0.49.
Most isolates exhibited MAR values greater than the threshold value of 0.20, indicating that they originated from environments exposed to substantial antibiotic selection pressure. The majority of isolates were classified as originating from high-risk contamination sources according to MAR index interpretation criteria.
The distribution of MAR indices among bacterial isolates is presented in Table 3 and Figure 3.
Phenotypic Extended-Spectrum β-Lactamase Screening and Resistance Determinants
The AST dataset includes ESBL screening results and molecular resistance marker information, including blaCTX-M, blaTEM, tetA, sul1, and qnrA. These data provide opportunities for evaluating associations between phenotypic resistance patterns and antimicrobial resistance determinants among waterborne bacterial isolates.
The ESBL screening results and associated resistance markers are summarized in Table 3. These data may be useful for environmental antimicrobial resistance surveillance and comparative studies involving drinking water systems.
Principal Component Analysis (PCA) loadings and scores are presented in Figure 1 and 3. PCA reduced the dimensionality of the physicochemical dataset and identified major factors influencing water quality variation among sampling locations.
The correlation matrix shown in Figure 3 illustrates relationships among measured physicochemical parameters. Positive correlations were observed among parameters associated with mineralization, whereas inverse relationships were identified between selected treatment-related parameters and microbial indicators.
Hierarchical Cluster Analysis (HCA) results are presented in Figure 3. Sampling locations were grouped according to similarities in water quality characteristics, providing insight into spatial patterns and treatment performance.
Spatial Coverage of the Dataset
The dataset covers five major drinking water treatment facilities distributed across Oyo State, Southwestern Nigeria. Sampling included both raw and treated water points, enabling assessment of treatment effectiveness and persistence of bacterial contaminants through treatment processes.
The largest numbers of isolates were recovered from Ogbomoso Waterworks and Asejire Water Cooperation, while isolates were also recovered from Erelu Water Scheme, Eleyele Waterworks, and Shaki Waterworks. Sampling location metadata and geospatial coordinates are provided to facilitate future geostatistical and comparative analyses.
Potential Applications of the Dataset
The dataset provides baseline information on drinking water microbiological quality and antimicrobial resistance in water treatment systems in Nigeria. Potential applications include:
- Assessment of drinking water treatment performance.
- Environmental antimicrobial resistance surveillance.
- Comparative studies of resistance patterns in water systems.
- Risk assessment of waterborne antimicrobial-resistant bacteria.
- Development of predictive models linking water quality and antimicrobial resistance.
- Regional and global meta-analyses of antimicrobial resistance in aquatic environments.
The availability of culture data, antimicrobial susceptibility profiles, MAR indices, Phenotypic ESBL screening results, and resistance gene information enhances the utility and reusability of the dataset for future environmental microbiology and public health investigations.
This dataset comprises microbiological water quality and antimicrobial resistance data generated from raw and treated water samples collected from five major drinking water treatment plants in Oyo State, Southwestern Nigeria, namely Ogbomoso Waterworks, Erelu Water Scheme, Eleyele Waterworks, Asejire Water Cooperation, and Shaki Waterworks. Sampling was conducted during two rounds, generating datasets on bacterial occurrence, heterotrophic bacterial counts, antibiotic susceptibility patterns, Phenotypic extended-spectrum β-lactamase (ESBL) screening, and multiple antibiotic resistance (MAR) indices. The generated dataset is provided as supplementary spreadsheets and includes culture reports, antimicrobial susceptibility testing (AST) results, resistance gene screening data, and metadata describing sampling locations and water types.
Bacterial Counts and Distribution of Isolates
A total of 28 bacterial isolates were recovered from the collected water samples. Bacterial loads ranged from 110 to 3,760 CFU/100 mL across the investigated treatment plants. Higher bacterial counts were generally observed in raw water samples compared with treated water samples, although detectable bacterial contamination was also observed in some treated water samples.
The recovered isolates belonged predominantly to Gram-negative bacteria of environmental and public health significance. Enterobacter cloacae was the most frequently isolated organism, accounting for 32.1% (9/28) of all isolates. Other recovered organisms included Klebsiella oxytoca (14.3%), Escherichia coli (10.7%), Serratia liquefaciens (10.7%), Klebsiella aerogenes (7.1%), Providencia rettgeri (7.1%), Citrobacter freundii (7.1%), Morganella morganii (3.6%), Proteus mirabilis (3.6%), and Pseudomonas spp. (3.6%).
The distribution of isolates according to sampling location is presented in Table 1, while organism frequencies are summarized in Figure 1.
Antimicrobial Susceptibility Profiles
Antimicrobial susceptibility testing was performed against fifteen antibiotics representing multiple antimicrobial classes, including β-lactams, aminoglycosides, fluoroquinolones, tetracyclines, carbapenems, and folate pathway inhibitors.
Resistance frequencies varied considerably among the tested antibiotics. Ampicillin exhibited the highest resistance frequency (96.6%), followed by Cefixime (75.9%) and Amoxicillin–clavulanate (72.4%). High resistance frequencies were also observed for Cefotaxime (58.6%) and Ceftriaxone (51.7%).
Intermediate resistance levels were recorded for Ciprofloxacin (48.3%), Amikacin (44.8%), Tetracycline (44.8%), Trimethoprim–sulfamethoxazole (44.8%), Meropenem (37.9%), Pefloxacin (37.9%), and Gentamicin (31.0%). The lowest resistance frequencies were observed for Doxycycline (20.7%), Piperacillin–Tazobactam (20.7%), and Levofloxacin (13.8%).
The resistance frequency data are presented in Table 1, 2, and 3.
Multiple Antibiotic Resistance Characteristics
The dataset contains isolate-specific resistance profiles that permitted calculation of multiple antibiotic resistance (MAR) indices. MAR values ranged from 0.20 to 0.80, with a mean MAR index of 0.49.
Most isolates exhibited MAR values greater than the threshold value of 0.20, indicating that they originated from environments exposed to substantial antibiotic selection pressure. The majority of isolates were classified as originating from high-risk contamination sources according to MAR index interpretation criteria.
The distribution of MAR indices among bacterial isolates is presented in Table 3 and Figure 3.
Phenotypic Extended-Spectrum β-Lactamase Screening and Resistance Determinants
The AST dataset includes ESBL screening results and molecular resistance marker information, including blaCTX-M, blaTEM, tetA, sul1, and qnrA. These data provide opportunities for evaluating associations between phenotypic resistance patterns and antimicrobial resistance determinants among waterborne bacterial isolates.
The ESBL screening results and associated resistance markers are summarized in Table 3. These data may be useful for environmental antimicrobial resistance surveillance and comparative studies involving drinking water systems.
Principal Component Analysis (PCA) loadings and scores are presented in Figure 1 and 3. PCA reduced the dimensionality of the physicochemical dataset and identified major factors influencing water quality variation among sampling locations.
The correlation matrix shown in Figure 3 illustrates relationships among measured physicochemical parameters. Positive correlations were observed among parameters associated with mineralization, whereas inverse relationships were identified between selected treatment-related parameters and microbial indicators.
Hierarchical Cluster Analysis (HCA) results are presented in Figure 3. Sampling locations were grouped according to similarities in water quality characteristics, providing insight into spatial patterns and treatment performance.
Spatial Coverage of the Dataset
The dataset covers five major drinking water treatment facilities distributed across Oyo State, Southwestern Nigeria. Sampling included both raw and treated water points, enabling assessment of treatment effectiveness and persistence of bacterial contaminants through treatment processes.
The largest numbers of isolates were recovered from Ogbomoso Waterworks and Asejire Water Cooperation, while isolates were also recovered from Erelu Water Scheme, Eleyele Waterworks, and Shaki Waterworks. Sampling location metadata and geospatial coordinates are provided to facilitate future geostatistical and comparative analyses.
Potential Applications of the Dataset
The dataset provides baseline information on drinking water microbiological quality and antimicrobial resistance in water treatment systems in Nigeria. Potential applications include:
- Assessment of drinking water treatment performance.
- Environmental antimicrobial resistance surveillance.
- Comparative studies of resistance patterns in water systems.
- Risk assessment of waterborne antimicrobial-resistant bacteria.
- Development of predictive models linking water quality and antimicrobial resistance.
- Regional and global meta-analyses of antimicrobial resistance in aquatic environments.
The availability of culture data, antimicrobial susceptibility profiles, MAR indices, Phenotypic ESBL screening results, and resistance gene information enhances the utility and reusability of the dataset for future environmental microbiology and public health investigations.