Probabilistic modelling is superior to deterministic approaches in the human health risk assessment: an example from a tribal stretch in central India

Abstract

Abstract This case drew national attention in 2018. About 100 people died and more than 300 hospitalized in a span of few years in a village of 1200 people in a tribal stretch in central India. Medical teams visiting the area reported severe renal failure and blamed the local eating and drinking habits as causative factors. This human health assessment based on geochemical investigations finds nitrate (NO 3 − ) and fluoride (F − ) pollution as well in village’s groundwater. Both deterministic and probabilistic techniques are employed to decipher the contamination pathways and extent of contamination. Source apportionments of NO 3 − and F − and their relationship with other ions in groundwater are carried out through chemometric modelling. Latent factors controlling the hydrogeochemistry of groundwater too are explored. While hazard quotients ( $$HQ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>HQ</mml:mi> </mml:mrow> </mml:math> ) of the chemical parameters ( $$HQ_{{{\text{NO}}_{3}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>Q</mml:mi> <mml:msubsup> <mml:mtext>NO</mml:mtext> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mo>-</mml:mo> </mml:msubsup> </mml:msub> </mml:mrow> </mml:math> and $$HQ_{{{\text{F}}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>Q</mml:mi> <mml:msup> <mml:mrow> <mml:mtext>F</mml:mtext> </mml:mrow> <mml:mo>-</mml:mo> </mml:msup> </mml:msub> </mml:mrow> </mml:math> ) identify ingestion as the prominent pathway, the calculated risk certainty levels (RCL) of the hazard index (HI) values above unity are compared between the deterministic and probabilistic approaches. Deterministic model overestimates the HI values and magnify the contamination problems. Probabilistic model gives realistic results that stand at infants ( $$HI_{{{\text{NO}}_{3}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msubsup> <mml:mtext>NO</mml:mtext> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mo>-</mml:mo> </mml:msubsup> </mml:msub> </mml:mrow> </mml:math> = 34.03%, $$HI_{{{\text{F}}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msup> <mml:mrow> <mml:mtext>F</mml:mtext> </mml:mrow> <mml:mo>-</mml:mo> </mml:msup> </mml:msub> </mml:mrow> </mml:math> = 24.17%) &gt; children ( $$HI_{{{\text{NO}}_{3}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msubsup> <mml:mtext>NO</mml:mtext> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mo>-</mml:mo> </mml:msubsup> </mml:msub> </mml:mrow> </mml:math> = 23.01%, $$HI_{{{\text{F}}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msup> <mml:mrow> <mml:mtext>F</mml:mtext> </mml:mrow> <mml:mo>-</mml:mo> </mml:msup> </mml:msub> </mml:mrow> </mml:math> = 10.56%) &gt; teens ( $$HI_{{{\text{NO}}_{3}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msubsup> <mml:mtext>NO</mml:mtext> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mo>-</mml:mo> </mml:msubsup> </mml:msub> </mml:mrow> </mml:math> = 13.17%, $$HI_{{{\text{F}}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msup> <mml:mrow> <mml:mtext>F</mml:mtext> </mml:mrow> <mml:mo>-</mml:mo> </mml:msup> </mml:msub> </mml:mrow> </mml:math> = 2.00%) &gt; adults ( $$HI_{{{\text{NO}}_{3}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msubsup> <mml:mtext>NO</mml:mtext> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> <mml:mo>-</mml:mo> </mml:msubsup> </mml:msub> </mml:mrow> </mml:math> = 11.62%, $$HI_{{{\text{F}}^{ - } }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:msub> <mml:mi>I</mml:mi> <mml:msup> <mml:mrow> <mml:mtext>F</mml:mtext> </mml:mrow> <mml:mo>-</mml:mo> </mml:msup> </mml:msub> </mml:mrow> </mml:math> = 1.25%). Geochemically, about 90% of the samples are controlled by rock-water interaction with Ca 2+ –Mg 2+ –HCO 3 − (~ 56%) as the dominant hydrochemical facies. Chemometric modelling confirms Ca 2+ , Mg 2+ , HCO 3 − , F − , and SO 4 2− to originate from geogenic sources, Cl − and NO 3 − from anthropogenic inputs and Na + and K + from mixed factors. The area needs treated groundwater for human consumption.

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