Volatile organic compounds (VOCs) are a group of aromatic or chlorinated organic chemicals commonly found in manufactured products that have high vapor pressure, and thus vaporize readily at room temperature. While airshed VOCs are well studied and have provided insights into public health issues, we suggest that belowground VOCs and the related vapor intrusion process could be equally or even more relevant to public health. The persistence, movement, remediation, and human health implications of subsurface VOCs in urban landscapes remain relatively understudied despite evidence of widespread contamination. This review explores the state of the science of subsurface movement and remediation of VOCs through groundwater and soils, the linkages between these poorly understood contaminant exposure pathways and health outcomes based on research in various animal models, and describes the role of these contaminants in human health, focusing on birth outcomes, notably low birth weight and preterm birth. Finally, this review provides recommendations for future research to address knowledge gaps that are essential for not only tackling health disparities and environmental injustice in post-industrial cities, but also protecting and preserving critical freshwater resources.

No abstract

Groundwater plays a significant role in the vitality of the Great Lakes Basin, supplying water for various sections. Due to the interconnection of groundwater and surface water features in this region, the groundwater quality can be affected, leading to potential economic, political, health, and social issues for the region. Groundwater resources have received less emphasis, perhaps due to an “out of sight, out of mind” mentality. The incomplete characterization of groundwater, especially shallow, near-surface waters in urban centers, is an added source of environmental vulnerability for the Great Lakes Basin. This paper provides an improved understanding of urban groundwater to reduce this vulnerability. Towards that end, two approaches for improved characterization of groundwater in southeast Michigan are employed in this project. In the first approach, we construct a regional groundwater model that encompasses four major watersheds to define the large-scale groundwater features. In the second approach, we adopt a local scale and develop a local urban water budget with subsequent groundwater simulation. The results show the groundwater movement in the two different scales, implying the effect of urban settings on the subsurface resources. Both the regional and local scale models can be used to evaluate and mitigate environmental risks in urban centers.

Collecting and analyzing groundwater data are essential to evaluate the regional groundwater flow patterns and quality under existing and future temporal/spatial hydraulic stressors. These data also provide researchers with the information required to calibrate the groundwater models and study the interaction between surface water and groundwater resources. In general, frequent and well-organized groundwater monitoring improves the comprehension of groundwater systems and enhances groundwater resource management. Both surface water and groundwater quality monitoring networks have been extensively studied and presented in literature reviews. In contrast, there is much less literature focused on groundwater level monitoring networks. In many regions, groundwater level monitoring networks are limited and do not provide sufficient data for decision-making purposes. This gap in data availability is due to multiple reasons but includes financial constraints and the limited interest in those areas that rely more heavily on surface water resources for human consumption and industrial purposes. Here, we introduce methods employing K-means clustering and/or Relevance Vector Machine to design an optimal groundwater level monitoring network. The machine learning algorithms utilize the hydrogeological datasets obtained from the initial groundwater MODFLOW models and consider the uncertainties in the aquifer characterization through stochastic simulations. The result of this research is three groundwater level monitoring networks which the optimal one is selected based on the minimum modeling error. The network configurations are demonstrated in terms of the number and location of the observation wells. The proposed monitoring network improves the procedure of groundwater modeling and significantly reduces modeling errors.

No abstract