Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches
Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dada, Ayokunle Christopher [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © Springer International Publishing Switzerland 2016 |
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| Schlagwörter: |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: Water, air & soil pollution - Dordrecht : Springer, 1971, 227(2016), 9, Seite 1-21 |
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| Übergeordnetes Werk: |
volume:227 ; year:2016 ; number:9 ; pages:1-21 |
| Links: |
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| DOI / URN: |
10.1007/s11270-016-3033-6 |
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OLC1981851798 |
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| 520 | |a Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. | ||
| 540 | |a Nutzungsrecht: © Springer International Publishing Switzerland 2016 | ||
| 650 | 4 | |a Environment | |
| 650 | 4 | |a Inland beaches | |
| 650 | 4 | |a Environment, general | |
| 650 | 4 | |a Beach advisory | |
| 650 | 4 | |a Hydrogeology | |
| 650 | 4 | |a Water Quality/Water Pollution | |
| 650 | 4 | |a Climate Change/Climate Change Impacts | |
| 650 | 4 | |a Lake inflows | |
| 650 | 4 | |a Water quality | |
| 650 | 4 | |a Soil Science & Conservation | |
| 650 | 4 | |a Water quality model | |
| 650 | 4 | |a Indicator bacteria | |
| 650 | 4 | |a Atmospheric Protection/Air Quality Control/Air Pollution | |
| 700 | 1 | |a Hamilton, David P |4 oth | |
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10.1007/s11270-016-3033-6 doi PQ20161012 (DE-627)OLC1981851798 (DE-599)GBVOLC1981851798 (PRQ)c1323-3f7a8889fdc07e775d4d6557c277e5807bab5c4108366c98bf9534d25fd455d70 (KEY)0054442620160000227000900001predictivemodelsfordeterminationofecoliconcentrati DE-627 ger DE-627 rakwb eng 570 333.7 DE-600 BIODIV fid ZC 7520 AVZ rvk Dada, Ayokunle Christopher verfasserin aut Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. Nutzungsrecht: © Springer International Publishing Switzerland 2016 Environment Inland beaches Environment, general Beach advisory Hydrogeology Water Quality/Water Pollution Climate Change/Climate Change Impacts Lake inflows Water quality Soil Science & Conservation Water quality model Indicator bacteria Atmospheric Protection/Air Quality Control/Air Pollution Hamilton, David P oth Enthalten in Water, air & soil pollution Dordrecht : Springer, 1971 227(2016), 9, Seite 1-21 (DE-627)12929134X (DE-600)120499-3 (DE-576)014472643 0049-6979 nnns volume:227 year:2016 number:9 pages:1-21 http://dx.doi.org/10.1007/s11270-016-3033-6 Volltext http://search.proquest.com/docview/1814858282 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-TEC SSG-OLC-FOR SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4313 ZC 7520 AR 227 2016 9 1-21 |
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10.1007/s11270-016-3033-6 doi PQ20161012 (DE-627)OLC1981851798 (DE-599)GBVOLC1981851798 (PRQ)c1323-3f7a8889fdc07e775d4d6557c277e5807bab5c4108366c98bf9534d25fd455d70 (KEY)0054442620160000227000900001predictivemodelsfordeterminationofecoliconcentrati DE-627 ger DE-627 rakwb eng 570 333.7 DE-600 BIODIV fid ZC 7520 AVZ rvk Dada, Ayokunle Christopher verfasserin aut Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. Nutzungsrecht: © Springer International Publishing Switzerland 2016 Environment Inland beaches Environment, general Beach advisory Hydrogeology Water Quality/Water Pollution Climate Change/Climate Change Impacts Lake inflows Water quality Soil Science & Conservation Water quality model Indicator bacteria Atmospheric Protection/Air Quality Control/Air Pollution Hamilton, David P oth Enthalten in Water, air & soil pollution Dordrecht : Springer, 1971 227(2016), 9, Seite 1-21 (DE-627)12929134X (DE-600)120499-3 (DE-576)014472643 0049-6979 nnns volume:227 year:2016 number:9 pages:1-21 http://dx.doi.org/10.1007/s11270-016-3033-6 Volltext http://search.proquest.com/docview/1814858282 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-TEC SSG-OLC-FOR SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4313 ZC 7520 AR 227 2016 9 1-21 |
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10.1007/s11270-016-3033-6 doi PQ20161012 (DE-627)OLC1981851798 (DE-599)GBVOLC1981851798 (PRQ)c1323-3f7a8889fdc07e775d4d6557c277e5807bab5c4108366c98bf9534d25fd455d70 (KEY)0054442620160000227000900001predictivemodelsfordeterminationofecoliconcentrati DE-627 ger DE-627 rakwb eng 570 333.7 DE-600 BIODIV fid ZC 7520 AVZ rvk Dada, Ayokunle Christopher verfasserin aut Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. Nutzungsrecht: © Springer International Publishing Switzerland 2016 Environment Inland beaches Environment, general Beach advisory Hydrogeology Water Quality/Water Pollution Climate Change/Climate Change Impacts Lake inflows Water quality Soil Science & Conservation Water quality model Indicator bacteria Atmospheric Protection/Air Quality Control/Air Pollution Hamilton, David P oth Enthalten in Water, air & soil pollution Dordrecht : Springer, 1971 227(2016), 9, Seite 1-21 (DE-627)12929134X (DE-600)120499-3 (DE-576)014472643 0049-6979 nnns volume:227 year:2016 number:9 pages:1-21 http://dx.doi.org/10.1007/s11270-016-3033-6 Volltext http://search.proquest.com/docview/1814858282 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-TEC SSG-OLC-FOR SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4313 ZC 7520 AR 227 2016 9 1-21 |
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10.1007/s11270-016-3033-6 doi PQ20161012 (DE-627)OLC1981851798 (DE-599)GBVOLC1981851798 (PRQ)c1323-3f7a8889fdc07e775d4d6557c277e5807bab5c4108366c98bf9534d25fd455d70 (KEY)0054442620160000227000900001predictivemodelsfordeterminationofecoliconcentrati DE-627 ger DE-627 rakwb eng 570 333.7 DE-600 BIODIV fid ZC 7520 AVZ rvk Dada, Ayokunle Christopher verfasserin aut Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. Nutzungsrecht: © Springer International Publishing Switzerland 2016 Environment Inland beaches Environment, general Beach advisory Hydrogeology Water Quality/Water Pollution Climate Change/Climate Change Impacts Lake inflows Water quality Soil Science & Conservation Water quality model Indicator bacteria Atmospheric Protection/Air Quality Control/Air Pollution Hamilton, David P oth Enthalten in Water, air & soil pollution Dordrecht : Springer, 1971 227(2016), 9, Seite 1-21 (DE-627)12929134X (DE-600)120499-3 (DE-576)014472643 0049-6979 nnns volume:227 year:2016 number:9 pages:1-21 http://dx.doi.org/10.1007/s11270-016-3033-6 Volltext http://search.proquest.com/docview/1814858282 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-TEC SSG-OLC-FOR SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4313 ZC 7520 AR 227 2016 9 1-21 |
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10.1007/s11270-016-3033-6 doi PQ20161012 (DE-627)OLC1981851798 (DE-599)GBVOLC1981851798 (PRQ)c1323-3f7a8889fdc07e775d4d6557c277e5807bab5c4108366c98bf9534d25fd455d70 (KEY)0054442620160000227000900001predictivemodelsfordeterminationofecoliconcentrati DE-627 ger DE-627 rakwb eng 570 333.7 DE-600 BIODIV fid ZC 7520 AVZ rvk Dada, Ayokunle Christopher verfasserin aut Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. Nutzungsrecht: © Springer International Publishing Switzerland 2016 Environment Inland beaches Environment, general Beach advisory Hydrogeology Water Quality/Water Pollution Climate Change/Climate Change Impacts Lake inflows Water quality Soil Science & Conservation Water quality model Indicator bacteria Atmospheric Protection/Air Quality Control/Air Pollution Hamilton, David P oth Enthalten in Water, air & soil pollution Dordrecht : Springer, 1971 227(2016), 9, Seite 1-21 (DE-627)12929134X (DE-600)120499-3 (DE-576)014472643 0049-6979 nnns volume:227 year:2016 number:9 pages:1-21 http://dx.doi.org/10.1007/s11270-016-3033-6 Volltext http://search.proquest.com/docview/1814858282 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-TEC SSG-OLC-FOR SSG-OLC-IBL SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4313 ZC 7520 AR 227 2016 9 1-21 |
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Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches |
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Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches |
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predictive models for determination of e. coli concentrations at inland recreational beaches |
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Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches |
| abstract |
Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. |
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Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. |
| abstract_unstemmed |
Given the 24-h turn-around time before swimming advisories are released, advisories issued to protect public health really only indicates ‘it may be unsafe to swim yesterday’. Predictive modelling for Escherichia coli concentrations at inflow-impacted beaches may be a favourable alternative to the current, routinely criticised monitoring approach. Using a total of 482 sets of meteorological and bacteriological data covering 14 swimming seasons, as well as environmental data of 10 inflow streams, this study developed models that could be used for predicting E. coli concentrations at five Lake Rotorua beaches. The models include predictor variables such as wind speed, antecedent rainfall, suspended solids at Puarenga, Utuhina and Ngongotaha stream inflows and particulate inorganic phosphorus concentration at Puarenga stream inflow. The combined 2011–2012 models had an average-adjusted R 2 of 0.73, root mean square error (RMSE) of 0.33 logCFU/100 mL and captured 38 % of the variance in the validation data when used to predict E. coli concentrations for an additional 2 years (2013–2014). Among the individual beach models, predictive accuracy ranged from 88.89 to 92.31 % for the three beaches considered in the study. The developed models can provide a faster estimation of E. coli condition, potentially assisting local beach managers in the decision process related to swimming advisories issuance. |
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Predictive Models for Determination of E. coli Concentrations at Inland Recreational Beaches |
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