Investigating the correlation between pesticide bioconcentration and human disease through the integration of remote sensing and physical modeling

Abstract

Nowadays, the risk of pesticides to the environment and human health arises with its increasing applications. Bioconcentration Factor (BCF) is utilized to evaluate the potential plant contamination by pesticides from the soil. Large-scale BCF mapping is significant to urban planning, yet it remains challenging. To address this issue, this study integrates the plant uptake model with remote sensing techniques to map BCF considering various land surface properties. Two simplified approaches were developed: the first involves relative humidity and air temperature (RA-model), while the second uses plant transpiration (PT-model). To evaluate these models, BCF mappings generated by each approach were compared and found to be consistent in space and time, with R2 at 0.68 over the continental U.S. Both approaches showed that the eastern part of the U.S. had higher BCF values than the west throughout the year, with an annual cycle of BCF where the highest values occurred in summer and the lowest in winter. To further analyze the impact of BCF on human health, spatial heterogeneity detection was used to examine seven diseases at the county scale. The results suggest that BCF may explain the incidence of spatial heterogeneity of most diseases, specifically high blood pressure (q-value >0.3). Overall, this study demonstrates the potential of integrating plant uptake modeling with remote sensing to map BCF and provides insight into the impact of BCF on human health.

Keywords

• Pesticides
• Bioconcentration Factor (BCF)
• spatial heterogeneity
• human disease
• remote sensing