2021-10-27 EMC Agenda item 5-b(i) Identifying wastewater management tradeoffs ... in Kona, Hawaii (RG)

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PLOS ONE Identifying wastewater management tradeoffs in Kona, Hawai'i using a composite of two marine drivers known to affect diffusion (depth [m] and wave power [KW/m]) [48, 49]. Then, the spread of nutrient loads into the marine environment from each pourpoint was modeled using a decay function (see Eq 1), which assigned a portion of the remaining nutrient loads from the previous cell to all adjacent cells based on the diffusion fac- tor layer until a maximum distance of 1.2 km from the shoreline was reached [50, 5 1 ]. This threshold was based on measurement of infrared imagery from the study site in ArcGIS and consultation with local experts. Ni = n x e"(—cz/D`) (1) where Ni = Grid cell value for diffused nutrient flux (kg/yr) per pourpoint i, n = Nutrient load (kg/yr) at each pourpoint (obtained from the GW model), c = diffusion factor layer value at each grid cell (unitless), and Dc = distance threshold from the shore for each pourpoint (set to 1.2 km). Then, all the individual nutrient plumes from each pourpoint were summed to obtain the aggregated nutrient plume at the study site scale, per management scenario. Note that this dif- fusive approach to modeling submarine groundwater nutrient discharge accounts for wrap- ping around coastal features and captures the nearshore advection forces that push nutrients in specific directions [35]. For each scenario, the relative change to the current conditions were calculated to identify areas where water quality changes and may impact the marine habi- tat. The results displayed here use the geometrical intervals from the "best" (scenario 3, full upgrade) and "worst" (scenario 2; no upgrade) case scenarios to identify the breaks while also highlighting the differences across scenarios to enable comparison. 2.6 Coral reef habitat potential impact assessment For the coral reef habitat potential impact assessment under the scenarios considered here, the NOAA benthic habitat map [15] was used to identify and quantify areas of coral reef habitat (ha) exposed to a change in coastal water quality (as modeled by the marine quality model), i.e., the response of coral reefs to change in nutrient exposure was not explicitly modeled. For the purpose of this analysis, only live coral cover (879.6 ha) and turf algae cover (332.5 ha) were considered because they are the two most abundant live cover habitat classes in the study area and previous research has shown that they are sensitive to changes in water quality and/or compete with one another when subject to climate change bleaching impacts [5, 39, 40]; the assessment ignored `uncolonized', `coralline algae', `unclassified' and `unknown' classes [38] (Fig I E^,). Aside from abundance relative to other live cover habitat classes, there were two main reasons for the focus on coral and turf algae cover. First, previous research has shown that an increase in nutrient loads will likely result in an increase in turf algal cover and vice versa [52-54]. Second, research has also shown that turf algae respond quickly to environmen- tal changes and are more competitive for space than coral under increased nutrient conditions. These attributes could favor turf algae and hinder corals from recovering after bleaching events [55, 561. 2.7 Economic costs As discussed previously, each cesspool within the model domain is converted to either a septic tank + absorption system or an ATU + absorption system, depending on its risk score. The present value or life cycle cost of each system consists of the one-time installation cost, which varies by capacity (number of bedrooms served), and annual operation and maintenance (O&M) costs, which are assumed to be independent of system capacity. Following a previous study that assessed the costs of cesspool upgrade options in Maui (Hawai`i) [41 ], the septic PLOSONE IIhu�tlps://slams.oirg/n0.t:171/�OLuirirualll,lpoine,0257125 September8,2021 9/26