## 4 Discussion
In the present work, the decision was based on the statistical relation of each biomarker alongside a gradient of contaminant concentrations [5]. This is the only empirical step of the IBR index, and can also be its weakness, putting at stake the index's reliability as it is highly dependent on a user's expert judgement and on data availability. As was previously observed, the same biomarker can have different responses depending on the species and on the tested compound and thus this attribution of a positive or negative influence has to be greatly based on experimental data [54, 55]. As the IBR index integrates multiple biomarker scores through standardization of diverse biological responses, it enables direct comparisons among contaminants and performs suitable scores for the set of selected biomarkers. In the end, the complex collected data are presented within a scoring framework, where high values represent environmental stress and low values represent a lack of stress or inhibition. The results of IBR data analysis in this study were consistent and reflected the overall oxidative stress conditions induced in _P. tricornutum_ cells by the exogenous contaminants, translating the expected response of the cell physiological mechanisms to overcome stressful contaminated conditions. Higher index values were attributed to higher pollutant concentrations, which conforms with the general assumption of IBR, revealing statistically significant dose-response trends for four out of the five harmful compounds examined. Our considerations on IBR's advantages and disadvantages must consider some factors: (i) a relatively large set of biomarkers is recommended as the higher the number of biomarkers, the more significant the index becomes [35, 44]; (ii) it is recommended to classify which markers can be combined for IBR, differentiating effect and defense responses [35]; (iii) to compare spatial and temporal series of datasets, new data should be normalized and incorporated together with previously obtained numerical values, since the numerical values of IBR are comparable only within each dataset, [35]; (iv) IBR values must be interpreted in combination with individual biomarker scores [7,47]; and (v) IBR is appropriate for qualitative assessments [50]. In this context, IBR constitutes a robust and practical tool to assess the susceptibility of _P. tricornutum_ cells to contaminants using multiple biomarker responses. The BAI index came into existence as a modification of the \"health assessment index\" (HAI) [56], intending to assess contamination levels for coastal areas. It integrates only biomarkers of general toxicity, leading to a more holistic approach since the use of biomarkers that are non-specific to toxic effects allow the index to respond to a wider set of chemicals [19]. This is a valuable characteristic for an environmental health index, as the anthropogenic sources of contamination comprise a complex mixture of pollutants and tend to show an exponential worldwide increase [16]. The BAI index has confirmed its suitability to address alterations at different bio-organizational levels, reflecting general toxicity patterns and revealing health status under polluted conditions, capturing degradative and restorative information on routine monitoring programs and allowing comparison between geographically distinct areas with differing contamination conditions [6,19,34,35]. The BAI index, a univariate statistical approach, transforms complex alterations of biomarkers into single classes of values, losing some of the original data variability, so the combination of BAI analysis with other integrative index approaches is frequently applied for assessment protocols [6,45]. The BAI index revealed low sensitivity against our data set, confirming its scope of applicability as more adequate for general toxicity biomarkers and parameters of non-specific stress, as suggested in the literature [35], while present results were constructed from specific exposure oxidative stress biomarkers.