In today’s urban environments with complex design and configurations, heterogeneous spatial clusters of communities with different socioeconomic characteristics may result in disproportionate exposure of some groups of citizens to natural hazards. The objective of this study was to compare the associations between communities’ socioeconomic characteristics and exposure to different types of natural hazards in New York City (NYC) to examine whether commonly accepted indicators of social vulnerability are associated with similar levels of exposure across various natural hazards. First, we collected socioeconomic data (e.g., population, median income, unemployment rate) at a zip code level of granularity provided by the United States Census Bureau. Next, we identified and gathered spatial data for coastal storms, flooding, extreme heat, and pandemic disease in NYC. We then conducted a pairwise Kendall’s τ-b test to compare the associations. The outcomes showed that the significance and direction of the associations depend on the type of natural hazard. Particularly, the results indicated that zip codes with lower socioeconomic factors and greater percentage of minority ethnicities are exposed disproportionately to extreme heat and COVID-19. On the other hand, zip codes with higher percentage of areas prone to flooding have relatively higher socioeconomic factors. Furthermore, the results did not show any statistically significant association between socioeconomic factors and exposure to coastal storm inundations. The outcomes of this study will help decision makers design and implement better optimized and effective emergency preparedness plans by prioritizing their target areas based on socioeconomic factors in order to enhance social justice.

Multivariate polynomial regression (MPR) models were developed for five macrophyte indices. MPR models are able to capture complex interactions in the data while being tractable and transparent for further analysis. The performance of the MPR modeling approach was compared to previous work using artificial neural networks. The data were obtained from hydromorphologically modified Polish rivers with a widely varying water quality. The modeled indices were the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR), and the River Macrophyte Nutrient Index (RMNI). These indices measure the trophic and ecological status of the rivers. Additionally, two biological diversity indices, species richness (N) and the Simpson index (D), were modeled. The explanatory variables were physico-chemical properties depicting water quality and river hydromorphological status indices. In comparison to artificial neural networks, the MPR models performed similarly in terms of goodness of fit. However, the MPR models had advantages such as model simplicity and ability to be subject to effective visualization of complex nonlinear input–output relationships, as well as facilitating sensitivity analysis using importance ratios to identify effects of individual input variables.

A new method is proposed to compute prediction errors so as to not penalize models for good predictions with a temporal error. Traditional goodness of fit measures are based on model error calculated by pair-wise comparison of observed and simulated values at the same time, (which the authors term isotemporal predictions). However, even good models could have a random temporal error. Relatively small time shifts in predictions could produce large individual errors, especially if there are rapid changes in the system. This could lead to misleadingly poor goodness-of-fit statistics. The authors propose a modification to the calculation of model error that pairs each data point with a prediction that is closest in a Euclidean sense, (which the authors term proxitemporal prediction), instead of the same time. A normalization criterion is proposed to make the prediction and time scales commensurate based on the slope of the model predictions. The method is tested using stochastic simulation for a sinusoidal model and applied to an uncalibrated Escherichia coli water quality model. The stochastic simulation showed that the ability of the new method to accurately capture the true goodness of fit depends on the true prediction error and the true temporal error.

E. coli (EC) concentrations of the upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey, were modeled using multivariate polynomial regression (MPR). Baseflow indexes (BFIs) and river flows from upstream and downstream boundaries of the study area were used as predictors. The MPR models were developed by stepwise addition of the candidate terms. The candidate terms were selected based on their $t$ -statistics and the final term was selected based on the Nash-Sutcliffe efficiency (NSE) of the overall model. The NSE values of the models ranged from 0.61 to 0.88. The boundary concentrations were earlier modeled using symbolic regression without BFI as a predictor, resulting in a set of highly complex models for the same data. This study demonstrates the suitability of BFI as a water quality predictor and the importance of identifying suitable predictors to develop defensible empirical water quality models. Further, the relation between EC concentrations and BFI could be used to infer whether the predominant pollutant source at a location is independent of rainfall or is rainfall driven.