Citizen Science-Informed Community Master Planning: Land Use and Built Environment Changes to Increase Flood Resilience and Decrease Contaminant Exposure

Abstract

Communities adjacent to concentrated areas of industrial land use (CAILU) are exposed to elevated levels of pollutants during flood disasters. Many CAILU are also characterized by insufficient infrastructure, poor environmental quality, and socially vulnerable populations. Manchester, TX is a marginalized CAILU neighborhood proximate to several petrochemical industrial sites that is prone to frequent flooding. Pollutants from stormwater runoff discharge from industrial land uses into residential areas have created increased toxicant exposures. Working with local organizations, centers/institutes, stakeholders, and residents, public health researchers sampled air, water, indoor dust, and outdoor soil while researchers from landscape architecture and urban planning applied these findings to develop a community-scaled master plan. The plan utilizes land use and built environment changes to increase flood resiliency and decrease exposure to contaminants. Using a combination of models to assess the performance, costs, and benefits of green infrastructure and pollutant load impacts, the master plan is projected to capture 147,456 cubic feet of runoff, and create $331,400 of annual green benefits by reducing air pollution and energy use, providing pollution treatment, increase carbon dioxide sequestration, and improve groundwater replenishment. Simultaneously, there is a 41% decrease across all analyzed pollutants, reducing exposure to and transferal of toxic materials.

Creating a Healthy Environment for Elderly People in Urban Public Activity Space

“…Planning principles should be based on the analysis of factors affecting group activities. Specific tasks need to be developed for each discipline, with a focus on how each can build off and integrate with one another [ 29 ]. Factors affecting urban green space include nature, economy, and society, among which natural factors are dominant and include geographical location, landform, sunshine conditions, the river and lake system, climatic conditions, hydrological conditions, and natural resources [ 30 ].…”

Combining Co$ting Nature and Suitability Modeling to Identify High Flood Risk Areas in Need of Nature-Based Services

“…Natural landscapes (such as wetlands) provide ecosystem services such as decreased stormwater runoff amounts, increased groundwater sequestration, and natural phytoremediation of contaminated water [ 9 ]. A decrease in vegetated land cover area, however, also decreases such regulating services [ 10 ].…”

“…NBS apply ideas inspired or supported by nature to the urban environment, thereby incorporating natural features and processes in solving complex environmental problems [ 11 ]. Flooding in coastal communities can result in sediment-laden water and industrial pollution mixed with stormwater runoff [ 9 ]. Such circumstances can be mitigated through the provision and protection of ecosystem services [ 12 ].…”

Gauging the Effects of Potential Chemical Transferal on High Flood-Risk Fenceline Communities

“…The convergence of flood disasters and environmental contamination heightens the potential for mobility and transfer of toxic substances (Newman et al, 2020). These risks are particularly impactful for what are referred to as fenceline communitiesneighborhoods that are immediately adjacent to a company or industry and are directly affected by the noise, odors, chemical emissions, traffic, parking, and/or operations of said company/industry (Johnston and Cushing, 2020).…”

“…Capabilities for visualizing and modeling interactions between flood events and hazardous substance exposure to expose risk have been relatively unachievable until the recent decade; articles lining the subject matter have also been rare (Psyllidis et al, 2015). Traditionally, because cities are complex by nature, computer visualizations will involve synthesizing a variety of complex physical and non-physical digital tasks and algorithms (Newman et al, 2020). Newer, more automated and userfriendly approaches to data visualization and urban analytics have surfaced as a result of deeper understandings of computer science.…”