Escherichia Coli and Biophysicochemical Relationships of Seawater and Water Pollution Index in the Jakarta Bay

Problem statement: Relationships between Escherichia coli (E. coli) and biophysicochemical properties of seawater at differ ent seasons and water pollution index were investigated in the Jakarta Bay, Indonesia. Approach: Water quality data taken at different seasons (Early Rainy Season (ERS) in November 2007 and Late Dry Season (LDS) in August 2008) were analyzed. Additionally, to compare pollution level at different seasons, Nemerow-Sumitomo Water Pollution Index (WPI) was used. Results: Significant correlation of E. coli occured with only few parameters in the ERS, but with more parameters in the LDS. This might be due to the rainfall intensity in the ERS that was potential to dilute s eawater and reduce concentration of some parameters , especially along the offshore stations. However, at the same time, the freshwater coming from land had capacity to force out the polluted water in 13 rive systems flowing into the bay; hence it could generate more pollution along the onshore stations. Seawater pollution level slightly increased in the ERS in respect to the addition of polluted water fr om rivers. In this season, none station was clean, 20 stations were slightly polluted, six stations were moderately polluted and six stations were highly polluted. Meanwhile in the LDS, the number of stati ons following the above WPI criteria were 9, 16, 3 and 4, respectively, indicating less pollution leve l. Conclusion/Recommendations: The overall results showed that E. coli exhibited significant correlations with more water parameters in the LDS and the WPI showed a little increase in the ERS.


INTRODUCTION
Like other metropolitan cities in the world, Jakarta city in Indonesia faces up some environmental problems as an impact of rapid development. Being the country's economic, cultural and political center, Jakarta is targeted by young people to finding jobs and better carrier. The population size of Jakarta almost tripled since the last five decades from 2.9 million in 1961-9.5 million in 2010, based on the tabulation of 2010 National Census.
Rapid development of Jakarta city especially during the centralization period where Indonesian GDP reaching an incredible increase of 5.7 percent per year between 1980and 1992(World Resources Institute, 1996 has made Jakarta city growing very fast. With such an economic growth, Jakarta embodies many of the contradictory forces at play in rapidly industrializing megacities of the world. Of course this "engines of growth" can play a vital role in economic development, however at the same time; worsening environmental problems may threaten economic prosperity and human health (World Resources Institute, 1996).
Some issues, such as air and water pollution (Sato and Harada, 2004;World Resources Institute, 1996) and urban waste (Steinberg, 2007), are among the impact of environmental aspects being faced by Jakarta Provincial Government. Garbage such as plastics, woods, bottles and other solid wastes are easily found in the canal and river systems, worsening the water quality. The wastes are drifted to the coastal zone of the Jakarta Bay as the final destination. The massive dead of fishes in 2004 in the Jakarta Bay could be an evidence of pollution level in 13 river systems in Jakarta City (Steinberg, 2007). Jakarta Bay received three important sources of water pollution, i.e., industrial waste, household discharge and solid trash/garbage. The condition is worsened by poor drainage systems and weak law enforcement (Colbran, 2009;Willoughby et al., 1997). William et al. (2000) reported that high concentrations of heavy metals were found in the water column and sediment bed of the Jakarta Bay. This condition threatens the population of some biodiversities in the bay, such as molluscan fauna (Van der Meij et al., 2009). The pollution level also gives impact on the economic loss of the fisheries in the area as described by Anna and Fauzi (2008).
There are numerous water indicators that can be used to evaluate water quality level, including physical, chemical and biological parameters. Each parameter has associations with other environmental attributes, for example salinity with precipitation, turbidity with sedimentation rate, pH with alkalinity, etc. Among these water parameters, Escherichia coli (E. coli) concentration has been widely used as bioindicator to quantify water quality condition, for example in ground water (UNESCO, 2000) river water (Kido et al., 2009;Yisa and Jimoh, 2010) and seawater (Costa et al., 2000). E. coli is widely known as biological indicator of soil and water pollution. It is one type of fecal coliform bacteria that is commonly found in the intestines of warm-blooded animals and human. Most E. coli strains are actually harmless, but some like O157:H7 can cause serious poisoning in human body. Besides human excrements, cattle faeces are among the important sources of this pathogen strain in the environment (Campbell et al., 2001). Like other bacteria, E. coli prefers to live in the water containing high nutrious elements and organic materials; therefore the presence in water is a strong indication of recent sewage or animal waste contamination (Jalal et al., 2010). One important factor that can exacerbate the high presence of E. coli in the environment is poor management of city sewage systems (Brussow et al., 1992).
Beside single indicator such as E. coli, scientists developed multi-parameter pollution indicators oftenly called Water Quality Index (WQI) and Water Pollution Index (WPI). Both indices are almost similar in use. WQI is used to evaluate water condition especially for consumable water, while WPI is more applicable for evaluating pollution level of a water ecosystem. WQI/WPI is calculated from several water parameters with a set of equations and circumstances. Terrado et al. (2010) lists about 55 different WQI and WPI introduced by many scientists in the world.
This paper attempts to 1) compare the relationships between E. coli concentration and biophysicochemical properties of seawater at different seasons in the Jakarta Bay; and 2) calculate and compare the WPI between offshore and onshore stations at different seasons.

Study site:
The study site is located in the Jakarta Bay with a total area of 285 km2, 33 km of the coastline and 8.4 m of the average water depth. There are 13 river systems flowing into the bay with the average water debit of 112.7 m3 sec-1. Some human activities like industries, harbors, fishing ports, marine aquaculture, tourisms, slum areas and luxury settlements are located along the coastline. For the purpose of analysis, the stations are divided into offshore stations, i.e., A, B, C and D and onshore stations, i.e., M1 -M9 (Fig. 1).

Data sources:
Water quality data of the seawater was derived from the Jakarta Environmental Management Board (BPLHD). Two series of water quality data taken in November 2007, representing early rainy season (ERS) and in August 2008, representing late dry season (LDS), were analyzed. A total of 32 stations were defined throughout the Jakarta Bay, where 30 water parameters, including 5 physical, 20 chemical and 5 biological parameters, were measured in each station, as listed in Table 1.   Mercury (Hg) mg l −1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.002 21 Copper (Cu) mg l −1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.005 22 Lead (Pb) mg l −1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 -23 Cadmium (Cd) mg l −1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.002 24 Chromium (Total) mg l −1 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.002 25 Nickel ( Methods: Bivariate correlation and simple regression analysis between E. coli concentration and biophysicochemical properties of seawater were performed using SPSS ver. 16.0. Pearson-R correlation and R-squared linear coefficients were used to evaluate the magnitude and direction of the association between variables. Two-tailed test with a confidence level (α) of 0.05 and 0.1 was used to examine the significancy of the result. Ocean Data View (ODV) software version 4.2.1 was used to create an interpolation image of water transparency distribution by applying DIVA gridding technique. DIVA gridding has been incorporated in the last version of ODV and generally produces better results than Qucik Gridding in cases of sparse and heterogeneous data coverage and in cases the study area is separated by land masses (small islands), ridges or bathymetric barriers such as Jakarta Bay, In order to analyze the pollution level of the Jakarta Bay at different seasons, a WPI was calculated from all measured water properties by using Nemerow and Sumitomo (1970) method. This method, one among numerous water quality indices, was used to measure water pollution index in several studies (Karami et al., 2009;Nemerow, 2007;Prakirake et al., 2008;Terrado et al., 2010). The Nemerow-Sumitomo method became formally used for water quality analysis in Indonesia, since it has been included in the regulation of the Ministry of Environment of Indonesia No. 115/2003 regarding Water Quality Measurement Guideline; therefore it was used in this study.
The function of this method was to standardize the concentrations of all water parameters such that the different concentration ranges for each water parameter were rescaled by the equation to produce a relative value that lies within a comparable range. The WPI is a function of relative values (C i /L i ), where C i represents the concentration of parameter i and L i represents the PV of parameter i defined by a regulation: WPI = a function of (C i /L i )'s (1) = f (C 1 /L 1 , C 2 /L 2 , C 3 /L 3 ,...C n /L n ) (i = 1, 2, 3, ..., n) Then, the WPI for a specific water use j (WPI j ) is further expressed by the following equation: where, C i is the measured concentration of parameter i, L ij is the PV for parameter i determined for water use j, and (C i /L ij ) max and (C i /L ij ) ave are maximum and average values of C i /L ij for water use j, respectively. For the water parameters for which the higher value represents a higher level of pollution, such as nitrate and heavy metals, the values of C i /L ij obtained from field measurements can be directly calculated using the above equation, with a prerequisite. The prerequisite is that if the value of C i /L ij obtained from the measurement is greater than 1.0, then the C i /L ij value must be standardized by applying the following equation: where, k is the free constant (usually 5).
For the parameters where the lower value represents a higher level of pollution, such as Dissolved Oxygen (DO), the C i /L ij values obtained from field measurements must be standardized by using the following equation: where, C im is the saturation value for any parameter at room temperature (e.g., for DO, C im at 25°C is 7). For parameters for which the PV (L ij ) is defined by a range of numbers, such as for pH, where the PV ranges from 6 to 8.5, a standardized value of C i /L ij is required, which is calculated by the following equation.
If C i ≤ average L ij : If C i > average L ij : where, (L ij ) min and (L ij ) max are, respectively, the minimum and maximum values of L ij (e.g., pH: min = 6, max = 8.5). The (L ij ) ave is the average value of L ij (e.g., pH: (6 + 8.5) / 2 = 7.25).
The pollution level is determined in four criteria as classified by the following definitions: According to the above equations, Nemerow-Sumitomo WPI needs a set of PV for each parameter as an input for the equation and this PV is likely to be designed by a government regulation. Table 1 lists the mean value and standard deviation of all measured parameters along with their PVs in seawater designated by the regulation of Ministry of Environment of Indonesia no. 51/2004. This regulation is designed for the purpose of marine tourism activities and marine living organism. Although in total 30 water properties have been measured in the ERS and LDS, but only the parameters having designated PVs were inputted in the WPI equations (Table 1). Some parameters such as TDS, temperature, salinity, were excluded from WPI calculation, because their PVs are not defined by the regulation.

Biophysicochemical properties of seawater at different seasons:
For comparison, the sampling stations were divided into offshore (23 stations) and onshore (9 stations) area (Fig. 1). In general, the mean value of biophysicochemical water parameters in the onshore area was several times higher (in case of Dissolved Oxygen [DO], lower) than the mean value of water parameters in the offshore area, both in early rainy and late dry season. In the onshore area, 12 parameters have exceeded the PVs in both seasons, i.e., Total Suspended Solid (TSS), turbidity, ammonia, nitrate, DO, phosphate, phenol, sulfide, blue methylene, Biological Oxygen Demand (BOD), E. coli and fecal coliforms. Meanwhile in the offshore area, only five parameters in both seasons have exceeded the PVs (Table 1 and Fig. 2).

Relationships between E. coli and water parameters:
In this study, we focused on E. coli concentration and its relation to physical, chemical and biological properties of seawater (Fig. 3). Table 2 summarizes the results of bivariate correlation and simple regression analysis between E. coli and other water parameters at different seasons in the Jakarta Bay. Table 3 summarized number of stations in the offshore and onshore stations in both seasons that were classified based on the WPI criteria.

Water Pollution Index:
The results indicate that most of the sampling stations fall within SP criteria with 20 stations (62.5%) in the ERS and 16 stations (50%) in the LDS. Overall, the water tended to be more polluted in the ERS (C = 0, SP = 20, MP = 6 and HP = 6) compared to that of in the LDS (C = 9, SP = 16, MP = 3, and HP = 4).

Biophysicochemical properties of seawater at different seasons:
In respect to seasonal variability, water parameters were responsive to precipitation. For example, in the ERS, although rainfall intensity in this period was not as much as in mid rainy season (Fig. 4), but the presence of rainwater in this period was sufficient to slightly dilute seawater as can be observed from most of water parameters. Therefore some parameters showed relatively lower mean values than those of in the LDS, especially in the onshore area, for example turbidity, potassium permanganate (KMnO 4 ), nitrate, salinity, phosphate, sulfide, blue methylene, TSS, Chemical Oxygen Demand (COD) and BOD.  (Suwandana et al., 2011) However, at the same time, rainfall intensity has also capacity to force out the polluted river water flowing into the bay. Therefore few parameters also showed higher values, i.e. ammonia, phenol, DO, E. coli and fecal coliforms. The resultant of water current from rivers that meets with the waves from open sea could also be the explanatory why the polluted water was more concentrated in the coastal area.

Relationships between E coli and water parameter:
From five physical parameters, three parameters showed moderate correlation, i.e., water transparency, TSS and turbidity, both in the ERS and LDS. These parameters are associated with sedimentation rate in the water. Suspended solid in the water body provides suitable media for bacterial microorganisms, such as coliforms, to grow (Narkis et al., 1995). Relationship between E. coli and turbidity is also essential especially for the raw material of drinking water, where the median of turbidity should be below 0.1 NTU (Nephelometric Turbidity Unit) (Allen et al., 2008).
The impact of rainwater to the physical parameters can be observed from Fig. 3a-c where the value of each parameter, in general, was lower than that of the LDS. Temperature did not show strong correlation with E. coli (not shown in Table 2) because there was not significant difference in temperature between ERS and LDS.
Among the chemical parameters, pH exhibited a very strong correlation with E. coli in both seasons. Such strong negative correlation indicated that E. coli preferred to grow in a normal to an acidic environment. A laboratory experiment done by Jordan et al. (1999) proved that E. coli concentration was very high at pH 3.0 after 24-h incubation, and even some survivals could still be found after 3-days of experiment.
Among oxygen-related parameters like DO, BOD and COD, two parameters, i.e. COD and BOD, showed high positive correlation with E. coli in the LDS. COD is a very important indicator for E. coli growth as it measures the capacity of water to consume oxygen during the decomposition of organic matter and the oxidation of inorganic chemicals such as ammonia and nitrate. BOD also showed a strong positive correlation with E. coli. Figure 3e and f show that BOD and COD concentrations in the LDS were linearly correlated. However, in the ERS, the relationship between E. coli and BOD/COD was not so clear. The relationship between COD and BOD is actually not necessarily to be linear in nature. However, the study done by Jin et al. (2009) concluded that in the water containing relatively high concentration of sewage contamination, a linear correlation could exist.
The relationships between E. coli and other chemical parameters like phosphate, nitrate and ammonia exhibited from low to moderate correlations based on pearson-r coefficients as presented in Table 2. The correlation of these parameters was not clearly understood and the role of rainwater to these parameters was not clear either. Suppostedly, E. coli should have a strong linear relationship with those three elements. The presence of high organic matter and nutrients, such as phosphorus and nitrites in the seawater can increase the bacterial colony, e.g., E. coli, as reported by Jalal et al. (2010) and Gauthier et al. (1993). Therefore, more field surveys are required, especially in the extreme conditions like in mid rainy and mid dry season, in order to get more precise data.
Beside physicochemical parameters, which their contibution is very important in creating a suitable enviroment for E. coli growth, some biological parameters were also analyzed in this study. There were three biological indicators measured during the survey, i.e., fecal coliforms, phytoplankton and zooplankton. The results revealed that E. coli showed strong correlations with fecal coliforms in both seasons (R 2 = 0.967-0.968, P < 0.001), because in fact E. coli is one type of fecal coliforms. The environmental conditions which are suitable for E. coli growth are also suitable for other fecal coliform bacteria, hence the relationship between those two bioindicators was nearly perfect (see Fig. 3j. On the contrary, the relationship with macrobenthos was insignificant. As organisms living on sediment, macrobenthos is not easily influenced by the changes in the seawater properties. An interesting fact can be observed in the phytoplankton and zooplankton relationships to E. coli. In the LDS, though the correlation coefficient was only 0.261 for phytoplankton and 0.274 for zooplankton, but the trend line was able to describe their association in nature. The negative linears shown in see Fig. 3k and (l) explain that the more the water got polluted, the less the number of phytoplankton and zooplankton was found. A study done by Fachrul and Syach (2006) in the Jakarta Bay reported that biodiversity index of phytoplankton in the polluted area was around 0.26, similarity index was close to 0, and dominance index is nearly 1, meaning that only one species was dominating the polluted area.
Different situation occured in the ERS, where a positive correlation occurred both for phytoplankton and zooplankton. The average concentration of phytoplankton in the onshore area was higher (x̄ = 7.94 ± σ = 0.53) compared to the one in the offshore area (x̄ =7.04 ± σ = 0.47). The same situation was performed by zooplankton, where the average concentration was x̄ = 3.94 ± σ = 0.58 for the onshore and x̄ = 3.34 ± σ = 0.37 for the offshore area.
The reason for high concentration of phytoplankton and zooplankton found in the onshore area during the ERS could be related with the occurrence of high precipitation. Rainfall intensity and nutrients upload from land and river systems might have triggered phytoplankton to start multiplying their population. Within this period, upwelling often occurs, nutrient enrichment takes place and sometimes this may lead to the alga bloom phenomenon (Sellner et al., 2003). Many studies have reported that, with this kind of circumstances, phytoplankton, and then followed by zooplankton, is very sensitive to the increase of nutrient elements introduced by rainwater (Sellner et al., 2003;Lee et al., 2009) and the growth of some phytoplankton species respond very quickly to the rainfall (Al-Homaidan and Arif, 1998). Water pollution Index: Although E. coli concentration in water column can itself be used as indicator for pollution level, scientists developed numerous water quality indices calculated from multi-parameters of water properties. Instead of relying only on a single pollution indicator, these WPI can better explain the association of whole water properties because all parameters are incorporated in the calculation. Although, in general the average value of water parameters in the ERS was lower compared to that of in the LDS, but in some stations the concentration was very high due to the influence of water input from river systems, producing high WPI on those stations. This was the reason why, to some extents, it was necessary to conduct a multiple-parameter pollution index, instead of depending only on one pollution indicator such as E coli. According to Terrado et al. (2010), the presence or absent of certain organisms in water, which is used as a single bioindicator, has been introduced since 1848 in Germany, but sometimes it is not sufficient because it does not take into account other toxicological effects nor contaminant substances.
The results of this study could not answer clearly why the WPI in the ERS was more polluted compared to that of in the LDS. However, the supply of rainwater, which started to increase in November 2007 as the onset of the rainy season, could be one reason, because rainwater supply was able to force out the polluted water in 13 river systems flowing into the bay. Some literatures reported that most of the river systems in Jakarta have been classified into highly polluted (Colbran, 2009;Steinberg, 2007;UNESCO, 2000). Therefore, the existence of sufficient rainwater in this period could be an explanatory for the increase of water pollution in the Jakarta Bay.
Unfortunately, the amount of water debit from all river systems in the ERS was unknown; hence it was difficult to statistically measure the impact of rainfall intensity to the increase of water pollution in the bay. However, an attempt was made to overcome this situation by creating a water transparency distribution map from the water transparency point data using DIVA gridding interpolation method (Fig. 5). The water transparency point data was measured by using secchi disk, where the deeper the secchi disk can be visually seen from the water surface, the more transparently (clearer) the seawater is.
It is clearly seen from Fig. 5, there is a significant supply of fresh water from river systems in the ERS (Fig. 5a), as shown by the expansion of purple and blue colors over green and orange colors. The water transparency in this season was more turbid especially in the onshore area (x̄ = 0.58 ± σ = 0.36) compared to that of in the LDS (x̄ = 0.74 ± σ = 0.66).

CONCLUSION
Most of the biophysicochemical properties of seawater in the Jakarta Bay had significant correlations with E. coli concentration. Some of those parameters were very essential for E. coli growth; hence many significant correlations occurred. The concentration of most water parameters can also be differentiated between offshore and onshore area, where high concentration values occurred mostly in the onshore area, evenmore some already exceeded the PVs. The concentration of water properties was also very responsive to precipitation. The freshwater coming from land (river systems) had two important roles in this environment; one was related its potential in diluting seawater and the other one was related to its capacity in forcing the polluted water in the river systems out into the bay.
Most of the WPI in the sampling stations fall within slightly polluted criteria, with 62.5% and 50% for the ERS and LDS, respectively. More polluted waters were concentrated nearby the onshore area, while the offshore area was relatively cleaner. The results also show that more polluted stations were found in the ERS compared to the LDS. Although, in general, the average value of most parameters reduced during this season, but the capacity of rainwater was able to bring out the polluted river water coming out into the bay, generating more polluted water in the bay.

ACKNOWLEDGEMENT
The researchers would like to thank BPLHD Jakarta, Indonesia, for providing the primary data of water quality parameters to be analyzed in this study. Special thank is addressed to Prof. Reiner Schlitzer at AWI Bremerhaven Germany for ODV software and also to Global Environmental Leader (GEL) Program of Hiroshima University for invaluable supports during the study.