Assessing the relative importance of parameter estimation in stream health based environmental justice modeling

Abstract

Performance of environmental justice models depends on the level of accuracy in measuring or estimating the health of the environment. In the past decades, and especially in the area of stream health modeling, significant improvement has been observed. However, the impacts of these improvements on the robustness of environmental justice models have not been evaluated. Therefore in this study, the relative importance of parameter estimation in stream health based environmental justice modeling was evaluated. The Saginaw River Basin in Michigan was considered as the study area, and four major ecological indices evaluating the response of fish and macroinvertebrates to instream stressors were used for stream health assessment. Seventeen socioeconomic and physiographic indices were evaluated at three census levels of county, census tract, and block group. Then the ecological, socioeconomic, and physiographic indices were used in the development of stream health based environmental justice models. Results showed that incorporating ecologically relevant indices and a using two-phase modeling approach not only improved the performance of stream health predictive models but also reduced the sensitivity of environmental justice models to aggregation at different census levels. In addition, using improved stream health indices reduced the redundancy of the independent variables (socioeconomic and physiographic indices), where the total number of significant parameters was reduced from 171 to 115. Besides that, more robust and meaningful spatial dependencies were observed among stream health measures and environmental justice parameters at different spatial levels. In summary having a reliable stream health information is the key for development of robust environmental justice models as evidence by improving model predictability and eliminating contradictory results compared to previous studies.