Ecosystem attributes related to tidal wetland effects on water quality

Critical Review of Polyphosphate and Polyphosphate Accumulating Organisms for Agricultural Water Quality Management

Despite ongoing management efforts, phosphorus (P) loading from agricultural landscapes continues to impair water quality. Wastewater treatment research has enhanced our knowledge of microbial mechanisms influencing P cycling, especially regarding microbes known as polyphosphate accumulating organisms (PAOs) that store P as polyphosphate (polyP) under oxic conditions and release P under anoxic conditions. However, there is limited application of PAO research to reduce agricultural P loading and improve water quality. Herein, we conducted a meta-analysis to identify articles in Web of Science on polyP and its use by PAOs across five disciplines (i.e., wastewater treatment, terrestrial, freshwater, marine, and agriculture). We also summarized research that provides preliminary support for PAO-mediated P cycling in natural habitats. Terrestrial, freshwater, marine, and agriculture disciplines had fewer polyP and PAO articles compared to wastewater treatment, with agriculture consistently having the least. Most meta-analysis articles did not overlap disciplines. We found preliminary support for PAOs in natural habitats and identified several knowledge gaps and research opportunities. There is an urgent need for interdisciplinary research linking PAOs, polyP, and oxygen availability with existing knowledge of P forms and cycling mechanisms in natural and agricultural environments to improve agricultural P management strategies and achieve water quality goals.

Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques

Dissolved oxygen (DO) concentration is an essential index for water environment assessment. Here, we present a modeling approach to estimate DO concentrations using input variable selection and data-driven models. Specifically, the input variable selection technique, the maximal information coefficient (MIC), was used to identify and screen the primary environmental factors driving variation in DO. The data-driven model, support vector regression (SVR), was then used to construct a robust model to estimate DO concentration. The approach was illustrated through a case study of the Pearl River Basin in China. We show that the MIC technique can effectively screen major local environmental factors affecting DO concentrations. MIC value tended to stabilize when the sample size >3000 and EC had the highest score with an MIC >0.3 at both of the stations. The variable-reduced datasets improved the performance of the SVR model by a reduction of 28.65% in RMSE, and increase of 22.16%, 56.27% in R², NSE, respectively, relative to complete candidate sets. The MIC-SVR model constructed at the tidal river network performed better than nontidal river network by a reduction of approximately 63.01% in RMSE, an increase of 62.36% in NSE, and R² >0.9. Overall, the proposed technique was able to handle nonlinearity among environmental factors and accurately estimate DO concentrations in tidal river network regions.

Does National Wetland Inventory class consistently identify vegetation and edaphic differences in Oregon tidal wetlands?

Accurately mapping, modeling, and managing the diversity of wetlands present in estuaries often relies on habitat classification systems that consistently identify differences in biotic structure or other ecosystem characteristics between classes. We used field data from four Oregon estuaries to test for differences in vegetation structure and edaphic characteristics among three tidal emergent marsh classes derived from National Wetlands Inventory (NWI) data: low marsh, high marsh, and palustrine tidal marsh. Independently of NWI class, we also evaluated the number and types of plant assemblages present and how edaphic variables, non-native plant cover, and plant species richness varied among them. Pore water salinity varied most strongly across marsh classes, with sediment carbon and nitrogen content, grain size and marsh surface elevation showing smaller differences. Cover of common vascular plant species differed between marsh classes and overall vegetation composition was somewhat distinct among marsh types. High marsh had the largest species pools. However, plot-level plant diversity was similar among marsh classes. Non-native species cover was highest in palustrine and high marshes. The marshes in the study contained a large number of plant assemblages with most occurring across more than one marsh class. The more common assemblages occurred along a continuum of tidal elevation, soil salinity, and edaphic characteristics, with varying plant richness and non-native cover. Our data suggest that NWI classes are useful for differentiating several general features of Oregon tidal marsh structure, but that more detailed information on plant assemblages found within those wetland classes would allow more precise characterization of additional wetland features such as edaphic conditions and plant diversity.

Investigating changes in land use cover and associated environmental parameters in Taihu Lake in recent decades using remote sensing and geochemistry

Humans have had a significant impact on the terrestrial pedosphere through activities such as agriculture and urbanization. The effects of human activities on land use and the related environmental changes were investigated through point and areal studies surrounding Meiliang Bay, which is an open area of extreme eutrophication in Taihu Lake, China. This study used remote sensing and environmental-tracer profiles [total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC), grain size, and geochemical parameters] to determine the causes of changes in land use and the associated environmental parameters. The results of LUCCs (Land use/cover changes) indicate that over the past three decades, total farmland decreased by 862.49 km2, with an annual decrement rate of 28.75 km2/year, and total urbanized land increased by 859.71 km2, with an annual growth rate of 28.66 km2/year. The geochemical results indicate that the trophic state of Taihu Lake was persistently intensifying and that the TN, TP, and TOC concentrations increased twofold, threefold, and twofold, respectively, from 1949 to 2010. The sources of TN, TP, and TOC were highly similar after 1975. However, before 1974, TN and TP originated from different sources than TOC. The grassland and woodland around the lake retain nutrients and sand from the land of study area. The increase in urbanized land and tertiary industries significantly increased the sediment concentrations of TN, TP, and TOC after 1980.