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Tourism offers many economic benefits but can have long-lasting ecological effects when improperly managed. Tourism can cause overwhelming pressure on wastewater treatment systems, as in Belize, where some of the over 400 small islands (cayes) that were once temporary sites for fishermen have become popular tourist destinations. An overabundance of nitrogen, in part as a result of incomplete wastewater treatment, threatens human health and ecosystem services. The tourism industry is a complex and dynamic industry with many sectors and stakeholders with conflicting goals. In this study, a systems thinking approach was adopted to study the dynamic interactions between stakeholders and the environment at Laughing Bird Caye National Park in Belize. The project centered on nutrient discharges from the caye’s onsite wastewater treatment system. An archetype analysis approach was applied to frame potential solutions to nutrient pollution and understand potential behaviors over time. “Out of control” and “Underachievement” were identified as system archetypes; “Shifting the Burden” and ‘‘Limits to Success’’ were used to model specific cases. Based on these results, upgrading of the wastewater treatment system should be performed concurrently with investments in the user experience of the toilets, education on the vulnerability of the treatment system and ecosystem, and controls on the number of daily tourists. 

Urbanization and population growth in coastal communities increase demands on local food and water sectors. Due to this, urban communities are reimagining stormwater pond infrastructure, asking whether the stormwater can be used to irrigate food and grow fish for local consumption. Studies exploring this feasibility are limited in the literature. Driven by a community’s desire to co-locate community gardens with stormwater pond spaces, this research monitored the water quality of a 23.4-hectare stormwater pond located in East Tampa, Florida over one year using the grab sample technique and compared the results with U.S. Environmental Protection Agency (EPA) reuse recommendations, EPA national recommended water quality criteria for aquatic life, and human health. pH and conductivity levels were acceptable for irrigating crops. Heavy metal (arsenic, cadmium, copper, lead, and zinc) concentrations were below the maximum recommended reuse levels (100, 10, 200, 5000 and 2000 µg/L, respectively), while zinc and lead were above the criteria for aquatic life (120 and 2.5 µg/L, respectively). E. coli concentrations ranged from 310 to greater than 200,000 MPN/100 mL, above the 0 CFU/100 mL irrigation requirements for raw food consumption and 200 CFU/100 mL requirements for commercial food processing. Synthetic organic compounds also exceeded criteria for human health. 

The Strategic Partnership for Alignment of Community Engagement in STEM (SPACES) is a collaborative research effort under the National Science Foundation’s ADVANCE program. The overarching goal of SPACES is to build an inclusive academic culture to address intersectional gender-race-ethnicity inequities in Environmental Engineering (EnvE) via the application of evidence-based strategies for systemic change. The two main thrusts of the project are to address systemic problems that cause: (1) underrepresented minority women faculty (URMWF) experiences of isolation in and/or departures from STEM academia and (2) the devaluation of research conducted by URMWF, especially community-engaged research (CER). SPACES is a collaborative effort of faculty and administrators from 11 universities with four leading professional societies. SPACES is adapting evidence-based practices to support women’s intersectional identities and catalyze an attitudinal change among individuals and institutional leaders. This process involves the pursuit of 12 objectives crossing the micro, meso, and macro levels and is being operationalized through 11 activities. An overview of the motivations for this project and activities to date are provided in the paper. 

Life cycle assessment (LCA), a tool used to assess the environmental impacts of products and processes, has been used to evaluate a range of aquaculture systems. Eighteen LCA studies were reviewed which included assess- ments of recirculating aquaculture systems (RAS), flow-through systems, net cages, and pond systems. This re- view considered the potential to mitigate environmental burdens with a movement from extensive to intensive aquaculture systems. Due to the diversity in study results, specific processes (feed, energy, and infrastructure) and specific impact categories (land use, water use, and eutrophication potential) were analyzed in-depth. The comparative analysis indicated there was a possible shift from local to global impacts with a progression from extensive to intensive systems, if mitigation strategies were not performed. The shift was partially due to increased electricity requirements but also varied with electricity source. The impacts from infrastructure were less than 13 % of the environmental impact and considered negligible. For feed, the environmental impacts were typically more dependent on feed conversion ratio (FCR) than the type of system. Feed also contributed to over 50 % of the impacts on land use, second only to energy carriers. The analysis of water use indicated intensive recirculating systems efficiently reduce water use as compared to extensive systems; however, at present, studies have only considered direct water use and future work is required that incorporates indirect and consumptive water use. Alternative aquaculture systems that can improve the total nutrient uptake and production yield per material and energy based input, thereby reducing the overall emissions per unit of feed, should be further investigated to optimize the overall of aquaculture systems, considering both global and local environmental impacts. While LCA can be a valuable tool to evaluate trade-offs in system designs, the results are often location and species specific. Therefore, it is critical to consider both of these criteria in conjunction with LCA results when developing aquaculture systems. 

Communities of color are disproportionately burdened by environmental pollution and by obstacles to influence policies that impact environmental health. Black, Hispanic, and Native American students and faculty are also largely underrepresented in environmental engineering programs in the United States. Nearly 80 participants of a workshop at the 2019 Association of Environmental Engineering and Science Professors (AEESP) Research and Education Conference developed recommendations for reversing these trends. Workshop participants identified factors for success in academia, which included adopting a broader definition for the impact of research and teaching. Participants also supported the use of community-based participatory research and classroom action research methods in engineering programs for recruiting, retaining, and supporting the transition of underrepresented students into professional and academic careers. However, institutions must also evolve to recognize the academic value of community-based work to enable faculty, especially underrepresented minority faculty, who use it effectively, to succeed in tenure promotions. Workshop discussions elucidated potential causal relationships between factors that influence the co-creation of research related to academic skills, community skills, mutual trust, and shared knowledge. Based on the discussions from this workshop, we propose a pathway for increasing diversity and community participation in the environmental engineering discipline by exposing students to community-based participatory methods, establishing action research groups for faculty, broadening the definition of research impact to improve tenure promotion experiences for minority faculty, and using a mixed methods approach to evaluate its impact. 

In this empirical study, a framework was developed for binary and multi-class classification of Twitter data. We first introduce a manually built gold standard dataset of 4000 tweets related to the environmental health hazards in Barbados for the period 2014 - 2018. Then, the binary classification was used to categorize each tweet as relevant or irrelevant. Next, the multiclass classification was then used to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. Results indicate that (combination of TF-IDF, psychometric, linguistic, sentiment and Twitter-specific features) using a Random Forest algorithm is the best feature for detecting and predicting binary classification with (87% F1 score). For multi-class classification, TF-IDF using Decision Tree algorithm was the best with (74% F1 score).