Color removal for large-scale interbasin water transfer: Experimental comparison of five sorption media.

The increasing needs of drinking water due to population growth requires seeking for new tap water sources. However, these large-scale tap water sources are oftentimes abundant with dissolved natural organic matter (NOM), such as tannic acid issue causing color in water. If not removed at the source locations beforehand, NOM would impact coagulation and flocculation unit, and/or become precursors to prompt the production of disinfectant by-products after chlorination in drinking water treatment. This study focuses on developing and testing a suite of cost-effective, scalable, adaptable, and sustainable sorption media that can be implemented near the source locations of tap water as a pretreatment option to remove color for a long-distance interbasin transfer. Within the five tested sorption media, a media recipe of Zero-valent-Iron and Perlite based Green Sorption Media (ZIPGEM) with ingredients of 85% sand, 5% clay, 6% zero-valent-iron (ZVI) and 4% perlite by volume stood out as the best option for color removal. Findings showed that ZIPGEM can maintain a color removal of ∼77% for about 14,080 min, maintaining the effluent concentration below 40 Pt-Co units given the influent condition of 175 ± 10 Pt-Co units. A recovery on the adsorption capacity of ZIPGEM was observed around 40,000 min due to synergetic effects among several different ingredients of recycled ZVI, clay, sand, and perlites. ZIPGEM can be applied to industrial wastewater treatment for dye removal as well.

Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment.

Data gaps in satellite aerosol optical depth (AOD) retrievals pose a huge challenge in near real-time air quality assessment. Here, we present a multimodal aerosol data fusion approach to integrate multisource AOD and air quality data for the generation of full coverage AOD maps at hourly resolution. Specifically, data gaps in each Himawari-8 AOD snapshot were partially filled by merging all available daytime AOD snapshots, and these partially gap-filled AOD maps were then fused with coarse yet spatially complete numerical AOD simulations to generate full coverage AOD imageries. Ground-based air quality measurements, including concentrations of PM2.5, PM10, NO2, and SO2, were simultaneously assimilated into gridded AOD fields to enhance the overall data accuracy. A practical implementation of the proposed method was illustrated by generating hourly full-coverage AOD maps in China from 2015 to 2020, and the validation results indicate this new AOD dataset agreed well with ground-based AOD measurements (R = 0.83), from which a ubiquitous AOD decreasing trend was revealed, especially during the noontime. Moreover, the hourly resolution and full-coverage advantages of this AOD dataset allow us to better assess spatiotemporal variations of PM10 and PM2.5 pollution that occurred in China. Overall, the proposed method paves a new way as big data analytics to advance regional air pollution assessment given the full coverage capacity and enhanced accuracy of the resulting AOD and PM concentration data.

Synergizing environmental, social, and economic sustainability factors for refuse derived fuel use in cement industry: A case study in Espirito Santo, Brazil.

The cement industry has been under pressure due to the environmental impact of high cement production, which demands a significant amount of energy and results in greenhouse gas (GHG) emissions. In many developed countries, the cement industry has sought to replace conventional fossil fuels with alternatives to minimize GHG emissions; however, Brazil has underexploited this possibility. Considering the potential of refuse-derived fuel (RDF) to reduce the non-recycled waste disposed in landfills, and its suitable performance as an alternative fuel for cleaner cement production, this paper presents a reverse logistics network analysis for RDF production planning with respect to local economic incentives, social euqity and justice, pollution prevention, and global environmental concerns regarding carbon emissions reduction. The reverse logistics network involves important stakeholders related in waste management in Espirito Santo, Brazil, especially harmonizing social sustainability concerns between waste pickers' cooperatives and waste retailers. By considering the waste generated in 78 municipalities in the Espírito Santo state, the possible levels of fuel replacement in cement industries reflects the economic sustainability of the timeframe of the solid waste management policy implementation. The results showed that the RDF to be produced varies from 42,446.5 tonnes in 2024 with a small fuel replacement by cement industries, to 567,092.1 tonnes in 2040 if all non-recyclable waste available can be used to produce RDF. The avoided annual disposal costs via this network analysis vary from $3,855,412.0 in the initial years to $47,822,876.8 in the year 2040 under optimistic conditions, representing around 25% of the total cost in the network. The cost and GHG emitted reduced significantly in all simulated scenarios; however, the financial incentives are essential for achieving the network social sustainability.

Fate and transport processes of nitrogen in biosorption activated media for stormwater treatment at varying field conditions of a roadside linear ditch.

Roadside drainage networks can result in changes to watershed hydrology and water quality. By acting as hydrological links between urban development, agricultural fields, and natural streams, roadside ditches may be modified by filling in some green sorption media to control nitrogen pollution. Biosorption activated media (BAM), one of the green sorption media, are composed of sand, tire crumb, and clay, which can remove nitrogen from stormwater and groundwater through integrated hydrological, chemophysical, and microbial processes. The fate and transport processes of interest are complicated by internal microbial processes including ammonification, nitrification, denitrification, and dissimilatory nitrate reduction to ammonium (DNRA), each of which is controlled by different microbial species in addition to some varying field conditions. In this study, BAM was tested in a suite of columns to address site-specific physical, chemical and biological concerns driven by in situ traffic compaction, carbon availability, and animal impact (such as gopher turtles, moles, and ants) all of which impose different impacts on nitrogen fate and transport processes that may be signified by changing dissolved organic nitrogen species (DONs). The traffic compaction condition resulted in the most suitable hydraulic retention time in the hydrological process, which is beneficial for the assimilation of DONs in a long-term carbon rich environment due to biofilm expansion. Denitrifiers were the most predominant microbial population and the microbial species of DNRA were the second most predominant one in all three field conditions. However, the relationship of denitrifiers and DNRA in BAM can be shifted from commensalism to competition or even inhibition after carbon addition in microbial ecology.

Comparative nitrogen removal via microbial ecology between soil and green sorption media in a rapid infiltration basin for co-disposal of stormwater and wastewater.

In this study, a rapid infiltration basin (RIB) designed as green infrastructure for co-disposal of wastewater effluent and stormwater runoff was retrofitted for sustainable groundwater recharge after nitrogen removal. For comparison of nitrogen removal efficiency via different filtration media, the RIB was divided into two sub-basins for different filtration processes. One sub-basin was filled with a native sandy soil with about 2-4% clay (Control RIB), and the other sub-basin was modified with Biosorption Activated Media (BAM) (BAM RIB), for the enhancement of microbial nitrogen removal. The two sub-basins accept an equal amount of excess reclaimed wastewater in non-storm periods, and stormwater during periodic storm events. The infiltrate in both the BAM RIB and the Control RIB eventually reaches the Upper Floridan Aquifer. The seven microbial species involved in this microbial ecology study are nitrite oxidizing bacteria (NOB), ammonia oxidizing bacteria (AOB), anaerobic oxidation of ammonium (anammox) bacteria, complete ammonia oxidizer (Comammox) bacteria, denitrifiers, dissimilatory nitrate reduction to ammonium (DNRA) and ammonia-oxidizing archaea (AOA). The population dynamics study was conducted with the aid of the quantitative polymerase chain reaction (qPCR) for the quantification of the microbial gene population in support of microbial ecology discovery. The qPCR results demonstrated the competition effect between AOA, AOB, and Comammox, the inhibition effect between NOB and DNRA with the presence of anammox, and the complementary effect due to an abundance of NOB and AOB in the microbial ecology. Although, competition between denitrifiers and DNRA was expected to impact population dynamics, both microbial species were found to be the most predominant in both control and BAM RIBs. Research findings indicate that the use of BAM RIB achieves significantly efficient nitrogen removal driven by complementary effects in the microbial ecology.

The interaction of dissolved organic nitrogen removal and microbial abundance in iron-filings based green environmental media for stormwater treatment.

Nonpoint sources pollution from agricultural crop fields and urbanized regions oftentimes have elevated concentrations of dissolved organic nitrogen (DON) in stormwater runoff, which are difficult for microbial communities to decompose. The impact of elevated DON can be circumvented through the use of green sorption media, such as Biosorption Activated Media (BAM) and Iron-Filing Green Environmental Media (IFGEM), which, as integral parts of microbial ecology, can contribute to the decomposition of DON. To compare the fate, transport, and transformation of DON in green sorption media relative to natural soil (control), a series of fixed-bed columns, which contain natural soil, BAM, and two types of IFGEM, respectively, were constructed to compare nutrient removal efficiency under three distinct stormwater influent conditions containing nitrogen and phosphorus. The interactions among six microbial species, including ammonia-oxidizing bacteria, nitrite-oxidizing bacteria, complete ammonia oxidation (comammox) bacteria, anaerobic ammonium oxidation (anammox) bacteria, dissimilatory nitrate reduction to ammonium bacteria, and iron-reducing bacteria, were further analyzed from microbial ecology perspectives to determine the DON impact on nutrient removal in BAM and IFGEM. Natural soil was only able to achieve adequate DON transformation at the influent condition of lower nutrient concentration. However, the two types of IFGEM showed satisfactory nutrient removals and achieved greater transformation of DON relative to BAM when treating stormwater in all three influent conditions.

Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data.

Atmospheric fine particulate matters (PM2.5) have raised global concerns because of their markedly adverse effects on public health and environmental quality. In parallel with technological variations and social changes in the evolving industrialization pathways in China, there is an acute need to evaluate the long-term spatiotemporal trend of PM2.5 concentrations across China after years of elevation. Toward this end, an integrated high-resolution satellite-derived (1998-2016) and ground-measured (2015-2017) PM2.5 data base was applied. Satellite-derived annual mean PM2.5 grids were firstly validated via comparison with collocated surface in situ PM2.5 measurements and were then used for trend analyses. The estimated linear trends from gridded PM2.5 data indicated that PM2.5 concentrations in China increased mainly before 2008 and have decreased since then, with prominent decreases observed primarily in south China. To corroborate the satellite-based PM2.5 trend estimations, surface in situ PM2.5 measurements from the period from 2015 to 2017 were applied to further evaluate the decreasing rate after 2014, at which time the Chinese "Air Pollution Prevention and Control Action Plan" was enforced. The results revealed that the national mean PM2.5 concentrations decreased by about 6.5 µg/m3 from 2015 to 2017, with prominent decreases (by a rate of 5-10 µg/m3 per year) observed primarily associated with large PM2.5 concentrations in Central China, North China, Northeast China, and East China during the period from October to December. Our systematic trend assessment provides a deepened understanding of PM2.5 variations across China in the past few years in association with the newly promoted action plan and offers a brief guideline for relevant policy making in the future.

Reconstruction of sea-land interactions between terrestrial vegetation cover and water quality constituents in the Mattapoisett Harbor area during the 1991 Hurricane Bob event.

Hurricane Bob passed over the New England region in August 1991, causing significant damage to life, property, and the environment, making it one of the costliest hurricanes in New England history. The environmental impact of a hurricane of this magnitude warrants careful assessment to devise preventive measures and mitigation strategies to bolster water resources management programs against future events. In this paper, we show the reconstructed simultaneous impacts of Hurricane Bob on the vegetative cover of the Mattapoisett river watershed and the water quality of the Mattapoisett Harbor with the aid of remote sensing for earth observations. The water quality impacts, especially in terms of Total Organic Carbon (TOC) and Sea Surface Salinity (SSS), can be identified from variations of SSS and TOC near coastal estuaries due to the influx of freshwater from the coastal Mattapoisett River to the continent-ocean transition between natural tides and bay waves. Using the Landsat satellite images, the Normalized Difference Vegetation Index (NDVI) and water quality constitutes (TOC and SSS) were reconstructed and retrieved for the assessment of the sea-land interactions during the Hurricane Bob event in 1991. Results indicate phenomenal interactive patterns between the harbor and the coastal watershed, as well as the riverine system. TOC and NDVI, especially in the upper watershed region, can be strongly correlated with hurricane impacts according to the singular value decomposition analysis.

The Impact of Carbon Source as Electron Donor on Composition and Concentration of Dissolved Organic Nitrogen in Biosorption-Activated Media for Stormwater and Groundwater Co-Treatment.

Eutrophication has been a long-term issue in aquatic environments, where dissolved organic nitrogen (DON) recalcitrance is important. Bioavailable nitrogen qualification and quantification for effluents from stormwater and wastewater are always a challenge. The information in this study deepens the understanding of the interactions between carbon addition and DON decomposition through linear-ditch best management practices for stormwater and groundwater cotreatment. By running a laboratory-scale column study for nitrogen removal using green sorption media, the variation in composition and concentration of DON can be further linked to the population dynamics of microbial species that dominate the nitrification and denitrification processes. With the varying levels of influent total nitrogen concentration, the efficacy of nitrogen removal via biosorption activated media may be realized at the molecular level with ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry.

Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models.

Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intra-annual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

Assessing the long-term effects of land use changes on runoff patterns and food production in a large lake watershed with policy implications.

Effects of land use development on runoff patterns are salient at a hydrological response unit scale. However, quantitative analysis at the watershed scale is still a challenge due to the complex spatial heterogeneity of the upstream and downstream hydrological relationships and the inherent structure of drainage systems. This study aims to use the well-calibrated Soil and Water Assessment Tool (SWAT) to assess the response of hydrological processes under different land use scenarios in a large lake watershed (Lake Dongting) in the middle Yangtze River basin in China. Based on possible land use changes, scale-dependent land use scenarios were developed and parameters embedded in SWAT were calibrated and validated for hydrological systems analysis. This approach leads to the simulation of the land use change impacts on the hydrological cycle. Results indicated that evapotranspiration, surface runoff, groundwater flow, and water yield were affected by the land use change scenarios in different magnitudes. Overall, changes of land use and land cover have significant impacts on runoff patterns at the watershed scale in terms of both the total water yield (i.e., groundwater flow, surface runoff, and interflow, minus transmission losses) and the spatial distribution of runoff. The changes in runoff distribution were resulted in opposite impacts within the two land use scenarios including forest and agriculture. Water yield has a decrease of 1.8 percent in the forest-prone landscape scenario and an increase of 4.2 percent in the agriculture-rich scenario during the simulated period. Surface runoff was the most affected component in the hydrological cycle. Whereas surface runoff as part of water yield has a decrease of 8.2 percent in the forest- prone landscape scenario, there is an increase of 8.6 percent in the agriculture-rich landscape scenario. Different runoff patterns associated with each land use scenario imply the potential effect on flood or drought mitigation policy. Based on the results, key areas were identified to show that hydrological extreme mitigation and flood control can be coordinated by some land use regulations.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies.

Fast eutrophication assessment for stormwater wet detention ponds via fuzzy probit regression analysis under uncertainty.

Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds.

Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead.

Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake.

Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain.

Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modeling for a holistic lake water quality assessment with respect to multiple constituents is in acute need. The aim of this paper is to develop an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll a concentrations and water transparency, to be applied for the assessment of the hypertrophic Albufera de Valencia. The Albufera de Valencia is the largest freshwater lake in Spain, which can often present values of chlorophyll a concentration over 200 mg m(-3) and values of transparency (Secchi Disk, SD) as low as 20 cm. Remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) and Enhance Thematic Mapper (ETM+) images were fused to carry out an integrative near-real time water quality assessment on a daily basis. Landsat images are useful to study the spatial variability of the water quality parameters, due to its spatial resolution of 30 m, in comparison to the low spatial resolution (250/500 m) of MODIS. While Landsat offers a high spatial resolution, the low temporal resolution of 16 days is a significant drawback to achieve a near real-time monitoring system. This gap may be bridged by using MODIS images that have a high temporal resolution of 1 day, in spite of its low spatial resolution. Synthetic Landsat images were fused for dates with no Landsat overpass over the study area. Finally, with a suite of ground truth data, a few genetic programming (GP) models were derived to estimate the water quality using the fused surface reflectance data as inputs. The GP model for chlorophyll a estimation yielded a R(2) of 0.94, with a Root Mean Square Error (RMSE) = 8 mg m(-3), and the GP model for water transparency estimation using Secchi disk showed a R(2) of 0.89, with an RMSE = 4 cm. With this effort, the spatiotemporal variations of water transparency and chlorophyll a concentrations may be assessed simultaneously on a daily basis throughout the lake for environmental management.

Linking pattern shifts of dissolved organic nitrogen fractional removal with microbial species richness in a cascade upflow biofiltration process.

Nutrient pollution has become an important issue to solve in stormwater runoff due to the fast population growth and urbanization that impacts water quality and triggers harmful algal blooms. There is an acute need to link the dissolved organic nitrogen (DON) decomposition with the coupled nitrification and denitrification pathways to realize the pattern shifts in the nitrogen cycle. This paper presented a lab-scale cascade upflow biofiltration system for comparison of nitrate and phosphate removal from stormwater matrices through two specialty adsorbents at three influent conditions. The two specialty adsorbents are denoted as biochar iron and perlite integrated green environmental media (BIPGEM) and zero-valent iron and perlite-based green environmental media (ZIPGEM). An initial condition with stormwater runoff, a second condition with spiked nitrate, and a third condition with spiked nitrate and phosphate were used in this study. To differentiate nitrifier and denitrifier population dynamics associated with the decomposition of DON, integrative analysis of quantitative polymerase chain reaction (qPCR) and 21 tesla Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were performed in association with nitrate removal efficiencies for both media with or without the presence of phosphate. While the qPCR may detect one gene for a single microbe or pathogen and realize the microbial population dynamics in the bioreactors, the 21 T FT-ICR MS can separate and assign elemental compositions to identify organic compounds of DON. Results indicated that ZIPGEM obtained a higher potential for nutrient removal than BIPGEM when the influent was spiked with nitrate and phosphate simultaneously. The sustainable, scalable, and adaptable upflow bioreactors operated in sequence (in a cascade mode) can be expanded flexibly on an as-needed basis to meet the local water quality standards showing process reliability, resilience, and sustainability simultaneously.

Deciphering linkages between DON and the microbial community for nitrogen removal using two green sorption media in a surface water filtration system.

The presence of dissolved organic nitrogen (DON) in stormwater treatment processes is a continuous challenge because of the intertwined nature of its decomposition, bioavailability, and biodegradability and its unclear molecular characteristics. In this paper, 21 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) in combination with quantitative polymerase chain reaction was applied to elucidate the molecular change of DON and microbial population dynamics in a field-scale water filtration system filled with two specialty adsorbents for comparison in South Florida where the dry and wet seasons are distinctive annually. The adsorbents included CPS (clay-perlite and sand sorption media) and ZIPGEM (zero-valent iron and perlite-based green environmental media). Our study revealed that seasonal effects can significantly influence the dynamic characteristics and biodegradability of DON. The microbial population density in the filter beds indicated that three microbial species in the nitrogen cycle were particularly thrived for denitrification, dissimilatory nitrate reduction to ammonium, and anaerobic ammonium oxidation via competition and commensalism relationships during the wet season. Also, there was a decrease in the compositional complexity and molecular weight of the DON groups (CnHmOpN1, CnHmOpN2, CnHmOpN3, and CnHmOpN4), revealed by the 21 T FT-ICR MS bioassay, driven by a microbial population quantified by polymerase chain reaction from the dry to the wet season. These findings indirectly corroborate the assumption that the metabolism of microorganisms is much more vigorous in the wet season. The results affirm that the sustainable materials (CPS and ZIPGEM) can sustain nitrogen removal intermittently by providing a suitable living environment in which the metabolism of microbial species can be cultivated and enhanced to facilitate physico-chemical nitrogen removal across the two types of green sorption media.

Investigating removal mechanisms of long- and short-chain per- and polyfluoroalkyl substances using specialty adsorbents in a field-scale surface water filtration system.

This study assessed the application of two specialty adsorbents, also known as green sorption media (GSM), including clay-perlite and sand sorption media (CPS) and zero-valent iron and perlite green environmental media (ZIPGEM) to remove long- and short-chain per- and polyfluoroalkyl substances (PFAS) at field scale. The field-scale demonstration employed four GSM filter cells installed near the C-23 Canal (St. Lucie County, FL), which discharges water to the ecologically sensitive St. Lucie River estuary and to the Atlantic Ocean finally. Although prior lab-scale experiments had demonstrated the effectiveness of CPS and ZIPGEM in treating long-chain PFAS, their performance in field-scale application warranted further investigation. The study reveals the critical roles of divalent cations such as Ca2+ and monovalent cations such as ammonium and hydronium ions, as well as other water quality parameters, on PFAS removal efficacy. Ammonia, most likely resulting from photo- and bacterial ammonification, gives rise to elevated ammonium ion formation in the wet season due to the decrease in pH, which ultimately worsens PFAS adsorption. Moreover, there is a strong negative correlation between pH and PFAS removal efficiency in the presence of ammonia, as evidenced by the reduced removal of PFAS during events associated with low pH.

Simultaneous removal of nutrients and biological pollutants via specialty absorbents in a water filtration system for watershed remediation.

To investigate watershed remediation within a Total Maximum Daily Load program, this study examined the field-scale filtration performance of two specialty absorbents. The goal was to simultaneously remove nutrients and biological pollutants along Canal 23 (C-23) in the St. Lucie River Basin, Florida. The filtration system installed in the C-23 river corridor was equipped with either clay-perlite with sand sorption media (CPS) or zero-valent iron and perlite green environmental media (ZIPGEM). Both media were formulated with varying combinations of sand, clay, perlite, and/or recycled iron based on distinct recipes. In comparison with CPS, ZIPGEM exhibited higher average removal percentages for nutrients. Findings indicated that ZIPGEM could remove total nitrogen up to 49.3%, total Kjeldahl nitrogen up to 67.1%, dissolved organic nitrogen (DON) up to 72.9%, total phosphorus up to 79.6%, and orthophosphate up to 73.2%. Both ZIPGEM and CPS demonstrated similar efficiency in eliminating biological pollutants, such as E. coli (both media exhibiting an 80% removal percentage) and chlorophyll a (both media achieving approximately 95% removal). Seasonality effects were also evident in nutrient removal efficiencies, particularly in the case of ammonia nitrogen; the negative removal efficiency of ammonia nitrogen from the fifth sampling event could be attributed to processes such as photochemical ammonification, microbial transformation, and mineralization of DON in wet seasons. Overall, ZIPGEM demonstrated a more stable nutrient removal efficiency than CPS in the phase of seasonal changes.

Scale up urban agriculture to leverage transformative food systems change, advance social-ecological resilience and improve sustainability.

Scaling up urban agriculture could leverage transformative change, to build and maintain resilient and sustainable urban systems. Current understanding of drivers, processes and pathways for scaling up urban agriculture, however, remains fragmentary and largely siloed in disparate disciplines and sectors. Here we draw on multiple disciplinary domains to present an integrated conceptual framework of urban agriculture and synthesize literature to reveal its social-ecological effects across scales. We demonstrate plausible multi-phase developmental pathways, including dynamics, accelerators and feedback associated with scaling up urban agriculture. Finally, we discuss key considerations for scaling up urban agriculture, including diversity, heterogeneity, connectivity, spatial synergies and trade-offs, nonlinearity, scale and polycentricity. Our framework provides a transdisciplinary roadmap for policy, planning and collaborative engagement to scale up urban agriculture and catalyse transformative change towards more robust urban resilience and sustainability.