Relevance of temporal and spatial variability for monitoring the microbiological water quality in an urban bathing area

This paper was all about variability in indicator bacterium being dependent on temporal and spatial sampling. Four beaches (Pastoras, Gondarem, Castelo and Matosinhos) on a 4.5km near-shore stretch were hourly sampled over an 11 hour time period. Sampling occurring during peak bathing season (June-August) and for each beach, the same sampling site with a number of repeated samples was used throughout.

Intestinal enterococci and E. coli were the two indicator organisms used in this study.  Whilst the intestinal enterococci concentrations did not exhibit any relevant temporal patterns with regards to the months E.coli concentrations varies monthly – for example Castelo was most polluted in June, Gondarem showed its highest faecal contamination in July/August, and the two remaining beaches were most polluted in August. 

This variation was statistically significant, however the general trend looks to be that in August months there was the highest faecal contamination (with regards to E. coli,) coincidentally this occurred alongside the highest mean water temperatures recorded. It is important to note that water temperature within the 10km surveying zone could vary considerably within a single survey (e.g. in August, measured temperatures varied from 12.7°C to 21.5°C)

There was a trend of contamination being higher (but not statically so) in morning samples than after noon samples which was explained due to the fact that indicator bacteria is known to exhibit a diel cycle. Also noted was an association between lower salinity and higher bacterial concentration.

Short term (referred to as ‘hourly’) temporal variation was observed at Matosinhos beach, for example in June an 08:00 survey showed a maximum E. coli concentration of 5 100 cfu 100 ml^-1 , but by 15:00 the minimum E. coli concentration was 21 cfu 100 ml^-1. If using classification techniques (the European Directive 2006/7/EC) these two samples would be known as “excellent” and “poor” respectively.

General conclusions were that temporal variation explained most (76%) of the total variance seen in water quality – with monthly variance (44.3% and 46.3%) explaining slightly more of bacterium variability (E. coli and intentional enterococci respectively)  than inconsistencies in sampling hours (32.5%, 30.1%). Lastly, and in this experiment least importantly, the spatial variance explained 23.3% and 23.6% of bacterium concentration differences. Percentages were obtained from a nested ANOVA.

Spatial and monthly variance causing bacterium variations are common sense ideas, but it is nice to have this paper as some hard evidence. However, I feel the most important idea to come from this paper is the effect hourly variation may have on the results of a faecal water quality test – meaning that the time a sample is taken can push a beach from a “poor” to a “good” rating or vice versa.  Due to this reason I am feeling very pro the new EU bathing water directive 2006 idea of continuously testing a water body throughout four years to get a more accurate sample.

Amorim E., Ramon S., & Bordalo A. A. (2014) Relevance of temporal and spatial variability for monitoring the microbiological water quality in an urban bathing area. Ocean & Coastal Management, 91(1), 41-49