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.

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.

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.

Unintended effects of urban policies on the risk of arbovirus transmission.

Cities across the USA are implementing urban policies to mitigate unwanted effects of urbanization. These policies may inadvertently promote the proliferation of mosquito vectors. Limited evidence exists regarding how urban policies impact arbovirus transmission risk. Models can evaluate public health interventions aimed at reducing arbovirus-related risks caused by urban policies.

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review.

Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.

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.

Green sorption media for the removal of perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) from water.

This study investigated the removal of selected per- and polyfluoroalkyl substances (PFAS) from water via two green sorption media (IFGEM-7 and AGEM-2). Both selected green sorption media recipes contain sand (85-91%) and clay (3-4%), in addition to recycled iron (Fe) (5-7.5%) or aluminum (Al) (4.5% in AGEM-2 only). Batch and column studies were integrated and performed using the prescribed green sorption media recipes to determine their efficiencies in removing two most targeted PFAS, including perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). In the batch test, while the removal efficiencies of PFOS ranged from 27 to 46% and 23 to 42%, those for PFOA ranged from 6 to 16% and 5 to 18% when using IFGEM-7 and AGEM-2, respectively. The higher removal of PFOS than PFOA observed in both IFGEM-7 and AGEM-2 batch tests could be attributed to higher media affinity for sulfonate groups of PFOS when compared to the carboxylate groups of PFOA. In the column study, the initial removal (within 1 h) by IFGEM-7 was greater than 99% for PFOS and 28% for PFOA. When comparing different dynamic adsorption models, it appears that the non-linear equations could better describe the trend of experimental data compared to the linear forms of the Modified Dose Response model. Life expectancy calculations, performed for demonstration purposes of field applications, suggested that if IFGEM-7 were to be applied in a downflow filter box to treat a hypothetical volume of 60,000 L of water during an emergency response, and it may last for 1506 h (62.8 d) and 4.2 h for a target removal of 80% of PFOS and PFOA, respectively.

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.

Per and poly-fluoroalkyl substances (PFAS) as a contaminant of emerging concern in surface water: A transboundary review of their occurrences and toxicity effects.

Per and poly-fluoroalkyl substances (PFAS) have been recognized as contaminants of emerging concerns by the United States Environmental Protection Agency (US EPA) due to their environmental impact. Several advisory guidelines were proposed worldwide aimed at limiting their occurrences in the aquatic environments, especially for perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). This review paper aims to provide a holistic review in the emerging area of PFAS research by summarizing the spatiotemporal variations in PFAS concentrations in surface water systems globally, highlighting the possible trends of occurrences of PFAS, and presenting potential human health impacts as a result of PFAS exposure through surface water matrices. From the data analysis in this study, occurrences of PFOA and PFOS in many surface water matrices were observed to be several folds higher than the US EPA health advisory level of 70 ng/L for lifetime exposure from drinking water. Direct discharge and atmospheric deposition were identified as primary sources of PFAS in surface water and cryosphere, respectively. While global efforts focused on limiting usages of long-chain PFAS such as PFOS and PFOA, the practices of using short-chain PFAS such as perfluorobutanoic acid (PFBA) and perfluorobutane sulfonic acid (PFBS) and PFAS alternatives increased substantially. These compounds are also potentially associated with adverse impacts on human health, animals and biota.

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.

Integration of machine learning classifiers and higher order tensors for screening the optimal recipe of filter media in stormwater treatment.

Filter media have oftentimes been used in fixed-bed column tests to examine their removal efficiencies for various pollutants, such as nutrients in stormwater runoff. With limited data sets from column studies, a response surface method (RSM), such as the Box-Behnken Design (BBD), and machine learning methods, can be used to transition from discrete mode assessment to continuous mode optimization, from which the key ingredients of filter media can be better synergized. In this study, similarly to drug discovery via chemometrics, RSM is used to generate meta-models and identify the optimum ratio between clay and iron-filings contents in Iron-filings-based Green Environmental Media (IFGEM) for nutrient removal in stormwater treatment. To achieve the continuous mode optimization, artificial neural network (ANN), deep belief network (DBN), and extreme learning machine (ELM) were selected as machine learning models to compare with BBD to explore the limited column data sets and improve the data science. While separate RSM can help realize the removal efficiencies of total nitrogen (TN), total phosphorus (TP), and ammonia based on varying ratios of clay and iron-filings contents in IFGEM, heterogeneous and inconsistent response surfaces generated from the four learners or classifiers (ANN, ELM, DBN, and BBD) complicate the selection of the final optimal recipe. The power of higher order singular value decomposition (HOSVD) helps synergize the optimal clay and iron filings matrixes of IFGEM in the context of continuous mode optimization via ANN, ELM, DBN, and BBD. With the aid of HOSVD, the optimal recipe for a holistic nutrient removal of TN, TP, and ammonia was determined to be 5% clay, 10% iron filings, 10% tire crumb, and 75% sand.

Adsorption thermodynamics and kinetics of Advanced Green Environmental Media (AGEM) for nutrient removal and recovery in agricultural discharge and stormwater runoff.

Recycled materials were used in three types of green sorption media for nutrient removal and possible recovery in high nutrient-laden agricultural discharge and stormwater runoff. The three types of green sorption media included in this comparative study were two new aluminum-based green environmental media (AGEM) and one existing iron-filings based green environmental media (IFGEM). The corresponding adsorption isotherm, thermodynamics, and kinetics models were simulated based on isotherm studies to determine their removal efficiency and potential for recovery of nitrate, phosphate, and ammonia when used as a soil amendment in crop fields or in a filter for water treatment. AGEM-2 exhibited the shortest contact time required to achieve nutrient removal above 80% with an average of 7 h, followed by AGEM-1 and IFGEM with 10.6 and 28 h, respectively. Natural soil was included as a control and exhibited minimal nutrient removal. Ammonia, which may be recovered as fertilizer for drop fields in a soil-water-waste nexus, was generated by all three green sorption media mixes, therefore indicating their potential for use as soil amendments in agricultural and forested land after engineering filter applications. The kinetics analysis indicated that nitrate adsorption follows pseudo-first-order kinetics, while phosphate adsorption follows pseudo-second-order kinetics. The Gibbs free energy indicated that most of the adsorption reactions proceeded as exothermic. Lastly, a few equilibrium models, including the Langmuir, Freundlich, First Modified Langmuir, Temkin, Jovanovic, and Elovich models, were ranked and three were selected for use with IFGEM, AGEM-1, and AGEM-2, respectively, as below: (1) Langmuir, (2) Freundlich, and (3) First Modified Langmuir, according to three indices.

Assessing Nutrient Removal in Stormwater Runoff for Urban Farming with Iron filings-based Green Environmental Media.

Ensuring urban areas have access to clean drinking water, safe food supply, and uncontaminated water bodies is essential to the good health of millions of urban residents. This paper presents the functionality of Iron Filings-based Green Environmental Media (IFGEM) in terms of nutrient removal efficiencies to support water quality management and urban farming. IFGEM uses recycled materials such as tire crumb and iron filings to help remove nutrients with essential physicochemical properties. In this study, IFGEM were proven effective and sustainable through an isotherm study, a column study of reaction kinetics, and a microstructure examination under various inlet nutrient concentration levels. IFGEMs exhibited over 90% nitrate removal, as well as 50-70% total phosphorus removal, under most inlet conditions. These promising results make IFGEM suitable for treating stormwater runoff, wastewater effluent, and agricultural discharge via varying ex situ treatment units in flexible landscape environments. In addition, the byproduct of ammonia generation permits possible reuse of spent IFGEM as soil amendments in crop land, gardens and yards, and green roofs for urban farming. Findings may help secure urban food supply chains and harmonize nutrients, soil, water, and waste management in different urban environments.

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.

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.

Copper impact on enzymatic cascade and extracellular sequestration via distinctive pathways of nitrogen removal in green sorption media at varying stormwater field conditions.

Nutrient removal efficiency in green sorption media such as biosorption activated media (BAM) for treating stormwater runoff can be heavily influenced either on a short- or long-term basis by varying field conditions of linear ditches due to the presence of copper in stormwater runoff. It is also noticeable that the linear ditch undergoes physical or mechanical impacts from the traffic compaction, chemical impact of carbon sources from the nearby farmland, and biological impact from potential animal activities (such as gopher tortoises, moles, and ants). In the nitrogen cycle, two denitrification pathways, the dissimilatory nitrate reduction to ammonia and common denitrification, are deemed critical for such assessment. A fixed-bed column study was set up to mimic different linear ditch field conditions for BAM applications and measure the effect of short-and long-term copper addition on microbial dynamics given the varying decomposition of dissolved organic nitrogen (DON). The findings confirm that, as the denitrifiers (in the second pathway) were the dominant species, their population continued to grow and maintain small-sized cells for extracellular sequestration under long-term copper impact. Furthermore, the study indicated that the ammonia oxidizer comammox was found in higher quantities than ammonia oxidizing bacteria or archaea. An enormous amount of DON was released during this process to bind the copper ion and reduce its toxicity as the enzymatic cascade effect appeared. In addition, the long-term copper exposure posed salient inhibitory effects on the microbial community regardless of varying field conditions in BAM. Short-term copper toxicity exerted an important but varying role in the enzymatic cascade effect over different linear ditch field conditions in BAM.

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.

Evaluation of green sorption media blanket filters for nitrogen removal in a stormwater retention basin at varying groundwater conditions in a karst environment.

Removing excess nutrient from stormwater runoffs is necessary to protect the water quality of receiving water bodies such as rivers, lakes, springs, and groundwater aquifers. Silver Springs Springshed, located in the vicinity of Ocala, Florida, has received widespread attention from the local government and residents due to its long-term nutrient impact, which has resulted in eutrophication. Blanket filters containing Bio-sorption Activated Media (BAM) were implemented with different depths of the vadose zone in a stormwater retention basin. The design combined the interaction with groundwater as an innovative Best Management Practice can potentially boost the performance of nutrient removal. Selected storm runoffs were collected at multiple points that cover the runoff timeframe to determine the pollutant load. Infiltrating water samples were collected at various depths within BAM using lysimeters to validate the treatment effectiveness. Significant pollutant load reduction of nutrients was confirmed with highest 99% and 91% removal of nitrate and nitrite (NOx) and total nitrogen (TN) at the deep blanket filter (with more groundwater intrusion impacts) due to more effective denitrification and longer contact time. Yet the highest pollutant load reduction of 93% and 84% removal of NOx and TN was also observed at the shallow blanket filter (with less groundwater intrusion impacts). On the other hand, better pollutant load reduction of ammonia in the BAM layer was found at the shallow blanket filter presumably due to more available oxygen for nitrification.

Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements.

Ground-measured PM2.5 concentration data are oftentimes used as a response variable in various satellite-based PM2.5 mapping practices, yet few studies have attempted to incorporate ground-measured PM2.5 data collected from nearby stations or previous days as a priori information to improve the accuracy of gridded PM2.5 mapping. In this study, Gaussian kernel-based interpolators were developed to estimate prior PM2.5 information at each grid using neighboring PM2.5 observations in space and time. The estimated prior PM2.5 information and other factors such as aerosol optical depth (AOD) and meteorological conditions were incorporated into random forest regression models as essential predictor variables for more accurate PM2.5 mapping. The results of our case study in eastern China indicate that the inclusion of ground-based PM2.5 neighborhood information can significantly improve PM2.5 concentration mapping accuracy, yielding an increase of out-of-sample cross validation R2 by 0.23 (from 0.63 to 0.86) and a reduction of RMSE by 7.72 (from 19.63 to 11.91) µg/m3. In terms of the estimated relative importance of predictors, the PM2.5 neighborhood information played a more critical role than AOD in PM2.5 predictions. Compared with the temporal PM2.5 neighborhood term, the spatially neighboring PM2.5 term has an even larger potential to improve the final PM2.5 prediction accuracy. Additionally, a more robust and straightforward PM2.5 predictive framework was established by screening and removing the least important predictor stepwise from each modeling trial toward the final optimization. Overall, our results fully confirmed the positive effects of ground-based PM2.5 information over spatiotemporally neighboring space on the holistic PM2.5 mapping accuracy.

Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5 /AOD relationship in eastern China using radiosonde data.

Atmospheric stability significantly influences the accumulation and dispersion of air pollutants in the near-surface atmosphere, yet few stability metrics have been applied as predictors in statistical PM2.5 concentration mapping practices. In this study, eleven stability metrics were derived from radiosonde soundings collected in eastern China for the time period of 2015-2018 and then applied as independent predictors to explore their potential in favoring the prediction of PM2.5. The statistical results show that the in situ PM2.5 concentration measurements correlated well with these stability metrics, especially at monthly and seasonal timescales. In contrast, correlations at the daily timescale differed markedly between stability metric and also varied with seasons. Nevertheless, the modeling results indicate that incorporating these stability metrics into the PM2.5 modeling framework rendered small contribution to PM2.5 prediction accuracy, yielding an increase of R2 by < 5% and a reduction of RMSE by < 1 µg/m3 on average. Compared with other stability indices, the inversion depth and intensity appeared to have relative larger benefiting potential. In general, our findings indicate that including these stability metrics would not result in significant contribution to the PM2.5 prediction accuracy in eastern China since their effects could be partially overwhelmed or offset by other predictors such as AOD and boundary layer height.