Naturally-occurring colloids, particles of diameter < 10μm, are ubiquitous in geo-environments and can potentially facilitate transport of numerous contaminants in soil including heavy metals, pesticides, pathogens etc. via Colloid-Facilitated Transport (CFT). The CFT of contaminants to groundwater is still an underrepresented transport domain and may lead to significant environmental and health problems related to groundwater contamination. Colloid mobilization, transport and CFT in various geomedia are highly sensitive to physico-chemical perturbations. This study investigated colloid transport and colloid-facilitated heavy metal transport in saturated porous media with a series of column experiments using soil colloids extracted from two areas affected by Chronic Kidney Disease of Unknown Etiology (CKDu) in North Central Province of Sri Lanka. Colloid breakthrough curves were obtained from the column studies to observe the colloid transport under different flow rates (0.5±0.05, 1.65±0.05, 4.10±0.05 cm3/s) and ionic strengths (NaCl - 0.01 M, 0.05 M, 0.1 M). The CFT was studied using Cadmium (Cd(II)) as a model contaminant together with colloidal suspension under selected scenarios for high colloid deposition. Elevated colloid concentrations were observed in high CKDu affected area compared to the low endemic area. The experimental results were numerically simulated on an advection-diffusion/dispersion modelling framework coupled with first-order attachment, detachment and straining parameters inversely estimated using HYDRUS 1D software. Experimental and simulated colloid breakthrough curves showed a good agreement, and recognized colloid attachment as the key mechanism for colloid immobilization in selected soil. Both colloids and CFT of Cd(II) showed pronounced deposition under low flow rate and high ionic strength.
Fossil fuel CO2 emissions (ffCO2) constitute the majority of greenhouse gas emissions and are the main determinant of global climate change. The COVID-19 pandemic caused wide-scale disruption to human activity and provided an opportunity to evaluate our capability to detect ffCO2 emission reductions. Quantifying changes in ffCO2 levels is especially challenging in cities, where climate mitigation policies are being implemented but local emissions lead to spatially and temporally complex atmospheric mixing ratios. Here, we used direct observations of on-road CO2 mole fractions with analyses of the radiocarbon (14C) content of annual grasses collected by community scientists in Los Angeles and California, USA to assess reductions in ffCO2 emissions during the first two years of the COVID-19 pandemic. With COVID-19 mobility restrictions in place in 2020, we observed a significant reduction in ffCO2 levels across California, especially in urban centers. In Los Angeles, CO2 enhancements on freeways were 60 ± 16% lower and ffCO2 levels were 43-55% lower than in pre-pandemic years. By 2021, California’s ffCO2 levels rebounded to pre-pandemic levels, albeit with substantial spatial heterogeneity related to local and regional pandemic measures. Taken together, our results indicate that a reduction in traffic emissions by ~60% (or 10-24% of Los Angeles’ total ffCO2 emissions) can be robustly detected by plant 14C analysis and pave the way for mobile- and plant-based monitoring of ffCO2 in cities without CO2 monitoring infrastructure such as those in the Global South.
In Southern Thailand, Prince of Songkla University is the leading public university across five campuses, with Hat Yai being the main one. Science education is strong with several undergraduate programs; among them physics with geophysics electives where students can also do their term project. Groundwater exploration is among the main objectives of the Geophysics Research Center at PSU and undergraduate projects are well integrated into them. During their classes and laboratories, students learn the science behind groundwater and geophysical technologies. This requires working in the field and thus carrying heavy equipment. Female students, often the majority, have to do that in the same way as male students and thus learning it by doing it as a team. Teamwork is one of the essential social skills students learn. Undergraduate term projects in geophysics are always chosen based on a real problem, which is usually coming from outside the department and in most cases from outside the university, with customers ranging from local communities, over companies, to local government agencies. By this, the students learn the importance of their work for other people, thus giving them a motivation. Every project requires the students to initially talk with the customers to identify the problem and explore the area. They then have to decide on the geophysical survey plan with support from their advisor. After finishing the data acquisition and processing, the interpretation will be done in a team with a presentation, usually at the university. After that, students give a shorter presentation and report to the customers, explaining them in a non-scientific language their interpretations and conclusion. They also have to answer questions that came up. For doing this, in most cases the team is going back into the field where the customers are. This overall procedure with community engagement shows the student how important groundwater is in many rural areas and how difficult it can be to find groundwater, thus realizing them the importance of preserving it. If circumstance allow, later field teams might visit earlier projects done and learn about how the customers continued after the undergraduate project was finished. Photo: Student team engaged with local community leaders after a finished project, where a well was drilled.
Due to the limited understanding of the physical/chemical processes and large uncertainties in emissions, ozone prediction task becomes more difficult with numerical models. Deep learning provides an alternative way. However, most of the deep learning ozone prediction models only consider temporality and have limited capacity. Exist spatiotemporal deep learning models generally suffer from model complexity and inadequate spatiality learning. Thus, we propose a novel spatiotemporal model, namely the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU), which employs double attention mechanism and Gated Recurrent Unit (GRU) to capture spatiotemporal information. We compare STAGRU with Seq2Seq and their single attention version on nine monitoring stations in Nanjing. The results show that STAGRU outperforms other competitors in terms of RMSE, R2, and SMAPE. In addition, we make an interpretability discussion for STAGRU. The discussion reveals that wind direction plays an important role in ozone transmission and temporality mainly involves short-term and periodical dependency.
This mass balance study was intended to provide up-to-date information about the water quality of the headwater streams draining to the Mohican and Walhonding rivers. This data will be used to define target locations for conservation practices, including agricultural and stormwater management practices. During the study, 124 sites were sampled twice in 2021: during spring high-flow conditions (May) and fall low-flow conditions (August).
Rooting depth is an ecosystem trait that determines the extent of soil development and carbon (C) and water cycling. Recent hypotheses propose that human-induced changes to Earth’s biogeochemical cycles propagate deeply due to rooting depth changes from agricultural and climate-induced land cover changes. Yet, the lack of a global-scale quantification of rooting depth responses to human activity limits knowledge of hydrosphere-atmosphere-lithosphere feedbacks in the Anthropocene. Here we use land cover datasets to demonstrate that root depth distributions are changing globally as a consequence of agricultural expansion truncating depths above which 99% of root biomass occurs (D99) by ~60 cm, and woody encroachment linked to anthropogenic climate change extending D99 in other regions by ~38 cm. The net result of these two opposing drivers is a global reduction of D99 by 5%, or ~8 cm, representing a loss of ~11,600 km3 of rooted volume. Projected land cover scenarios in 2100 suggest additional future D99 shallowing of up to 30 cm, generating further losses of rooted volume of ~43,500 km3, values exceeding root losses experienced to date and suggesting that the pace of root shallowing will quicken in the coming century. Losses of Earth’s deepest roots — soil-forming agents — suggest unanticipated changes in fluxes of water, solutes, and C. Two important messages emerge from our analyses: dynamic, human-modified root distributions should be incorporated into earth systems models, and a significant gap in deep root research inhibits accurate projections of future root distributions and their biogeochemical consequences.
Hillslopes are responsible for the production and transport of sediments within a landscape (Gilbert 1877). Since the hillslope gradient and morphology tend to vary across a landscape, it is expected that the erosion and sediment delivery would also be non-uniform. In this study, we explore the probability of the flux at a particular point in the catchment reaching the river mouth using connectivity and the Revised Universal Soil Loss Equation (RUSLE) in the Pranmati river catchment (a small 3rd order Himalayan river catchment within the Ganga River system). Methodology involves characterising the hillslopes of Pranmati river catchment centered on land use and land cover units. Using RUSLE, the sediment yielding capacity of various land cover units are estimated based on which potential source areas are marked. The sediment connectivity within the basin is also calculated by generating a sediment connectivity map of the area using method given by Borcelli et al. (2008). The catchment is categorized into four classes – (A) Highly connected zones with high sediment yielding capacity (B) highly connected zones but low yielding capacity (C) poorly connected zones but high yielding capacity (D) poorly connected zones and low yielding capacity. The area is then mapped on the basis of the defined classes and potential areas of erosion and storage are identified. Our results show that about 62% of the catchment area has low connectivity implying sediment flux generated in these zones have a low probability of leaving the catchment. Only 11% of the catchment area has sediment yield greater than the mean yield per hectare. The sediment generated from this small area of the catchment contributes 93% of the total sediment production of the catchment. References Borselli, L., Cassi, P., & Torri, D. (2008). Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment. Catena, 75(3), 268-277. Gilbert, G. K. (1877). Geology of the Henry mountains (pp. i-160). Government Printing Office.
As the sea level rises, it is alarming that the threat from flooding induced by tropical cyclones would cause more severe damages to coastal regions worldwide. In order to address this threat, optimizing coastal protective or mitigation strategies is necessary, given limited resources. The optimization methodology must incorporate feedback from stakeholders for practical use. Multiple interviews were conducted by engineering model developers and social scientists with stakeholders who have first-hand knowledge and varied backgrounds in New York. The protective strategies have been tuned to the critical infrastructure's particular and contextual risks due to flood hazards by engaging and integrating stakeholders' knowledge on the interdependency of the infrastructures and other aspects after the first interview. The second interview was conducted for further model improvement.
Coastal regions are continuously under the threat of flooding induced by tropical cyclones worldwide. These threats continue to increase due to the effects of climate change such as sea-level rise. A number of available protective or mitigation strategies have been examined to address this threat and protect coastlines around the world. However, identifying the most effective strategy given limited resources is a complex question. Optimization methodologies as we have proposed integrate physical analysis and stakeholder feedback to come to a set of best mitigation strategies. This study examines physical and socio-economical aspects of flooding impacts to optimize these strategies. These are then examined including seawalls, elevated promenades, and strategic retreats.
Rain gardens are green stormwater infrastructure that are designed to leverage natural processes to mitigate the impacts of urban stormwater through capturing, infiltrating, and filtering run off. Overtime these systems have the potential to buildup fines and nutrients, impacting their sustainable function. A rain garden’s performance depends on its ability to infiltrate runoff which can be reduced by clogging. Another concern is the potential transport of contaminants from rain gardens to groundwater through deep drainage. This study analyses the spatial and temporal distribution of fines and nutrients in three rain gardens through comprehensive field tests, laboratory testing, and computation analysis. Geomorphic studies were performed by integrating the digital elevation models, derived from Lidar surveys, with the FastMech solver within International River Interface Cooperative (iRIC) software, to model shear stress distribution and sediment transport relative to spatial observations of soil texture and nutrient concentrations within the rain garden. The soil properties were also used in creating models of water infiltration and nutrient sorption using Hydrus 1D. Results show that shear stresses in localized sections of each rain garden can be correlated with fines and nutrient distributions, allowing for prioritizing locations for maintenance. To conclude, LiDAR scans, flow and shear stress models, infiltration and nutrient transport models, field and laboratory soil tests can help us understand the surface dynamics and soil attributes, and gradually gain insight into the GSI performance with time.
Some of the Earth system data products such as those from NASA airborne and field investigations (a.k.a. campaigns), are highly heterogeneous and cross-disciplinary, making the data extremely challenging to manage. For example, airborne and field campaign measurements tend to be sporadic over a period of time, with large gaps. Data products generated are of various processing levels and utilized for a wide range of inter- and cross-disciplinary research and applications. Data and derived products have been historically stored in a variety of domain-specific standard (and some non-standard) formats and in various locations such as NASA Distributed Active Archive Centers (DAACs), NASA airborne science facilities, field archives, or even individual scientists’ computer hard drives. As a result, airborne and field campaign data products have often been managed and represented differently, making it onerous for data users to find, access, and utilize campaign data. Some difficulties in discovering and accessing the campaign data originate from the incomplete data product and contextual metadata that may contain details relevant to the campaign (e.g. campaign acronym and instrument deployment locations), but tend to lack other significant information needed to understand conditions surrounding the data. Such details can be burdensome to locate after the conclusion of a campaign. Utilizing consistent terminology, essential for improved discovery and reuse, is also challenging due to the variety of involved disciplines. To help address the aforementioned challenges faced by many repositories and data managers handling airborne and field data, this presentation will describe stewardship practices developed by the Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) under the NASA’s Earth Science Data systems (ESDS) Program.
We propose a new approach to the solution of the wave propagation and full waveform inversions (FWIs) based on a recent advance in deep learning called Physics-Informed Neural Networks (PINNs). In this study, we present an algorithm for PINNs applied to the acoustic wave equation and test the model with both forward wave propagation and FWIs case studies. These synthetic case studies are designed to explore the ability of PINNs to handle varying degrees of structural complexity using both teleseismic plane waves and seismic point sources. PINNs’ meshless formalism allows for a flexible implementation of the wave equation and different types of boundary conditions. For instance, our models demonstrate that PINN automatically satisfies absorbing boundary conditions, a serious computational challenge for common wave propagation solvers. Furthermore, a priori knowledge of the subsurface structure can be seamlessly encoded in PINNs’ formulation. We find that the current state-of-the-art PINNs provide good results for the forward model, even though spectral element or finite difference methods are more efficient and accurate. More importantly, our results demonstrate that PINNs yield excellent results for inversions on all cases considered and with limited computational complexity. Using PINNs as a geophysical inversion solver offers exciting perspectives, not only for the full waveform seismic inversions, but also when dealing with other geophysical datasets (e.g., magnetotellurics, gravity) as well as joint inversions because of its robust framework and simple implementation.
Ozone (O3) is an important trace and greenhouse gas in the atmosphere yet, and it threatens the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct measurements whose accuracy and density vary considerably. To overcome these limitations, we employed the ensemble learning method of the extremely randomized trees model by utilizing the spatiotemporal information of a large number of input variables from ground-based observations, remote sensing, atmospheric reanalysis, and model simulation products to estimate ground-level O3. This method yields uniform, long-term and continuous spatiotemporal information of daily maximum eight-hour average (MDA8) O3 over China (called ChinaHighO3) from 2013 to 2020 at a 10 km resolution without any missing values (spatial coverage = 100%). Evaluation against observations indicates that our O3 estimations and predictions are reliable with an average out-of-sample (out-of-station) coefficient of determination (CV-R2) of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 [units here are at standard conditions (273K, 1013hPa)], and are also robust at varying spatial and temporal scales in China. This high-quality and full-coverage O3 dataset allows us to investigate the exposure and trends in O3 pollution at both long- and short-term scales. Trends in O3 concentrations varied substantially but showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020 in China. Most areas show an increasing trend since 2015, especially in summer ozone over the North China Plain. Our dataset accurately captured a recent national and regional O3 pollution event from 23 April to 8 May in 2020. Rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.
Anthropogenic impacts and climate change modify instream flow, altering ecosystem services and impacting on aquatic ecosystems. Alpine rivers and streams on the Qinghai-Tibet Plateau (QTP), are especially vulnerable to disturbance due to a limited taxonomic complexity. The effects of variations in flow have been studied using specific taxa, however, the flow-biota relationships of assemblages are poorly understood. A multi-metric habitat suitability model (MM-HSM) was developed, using biological integrity measures of macroinvertebrate assemblages to substitute for habitat suitability indices (HSI) derived from individual taxa. The MM-HSM was trained using macroinvertebrate data from three representative alpine rivers (the Yarlung Tsangpo, the Nujiang, and the Bai Rivers) on the QTP, and was verified using data from the Lanmucuo River. The model produced reliable predictions using the training dataset (R2 = 0.587) and the verification dataset (R2 = 0.489), and was robust to inter-basin differences and changes in dataset size. By coupling the MM-HSM with hydrodynamic simulations, the relationship between weighted usable area (WUA) and flow variations (0.11–1.99 m3/s) for macroinvertebrates was established, and a unimodal response pattern (optimal flow Q = 1.21 m3/s) was observed for macroinvertebrate assemblages from the Lanmucuo River. This was in contrast to the skewed unimodal or monotonically increasing relationships observed for individual indicator taxa, supporting our hypothesis that biological integrity varies with changing flow and conforms to the intermediate disturbance hypothesis. The MM-HSM provides a novel framework to quantify species-environment relationships, which may be used for integrated river basin management.
Outdoor workers perform critical societal functions, often despite higher-than-average on-the-job risks and below-average pay. Climate change is expected to increase the frequency of days when it is too hot to safely work outdoors, compounding risks to workers and placing new stressors on the personal, local, state, and federal economies that depend on them. After quantifying the number of outdoor workers in the contiguous United States and their median earnings, we couple heat-based work reduction recommendations from the US Centers for Disease Control and Prevention with an analysis of hourly weather station data to develop novel algorithms for calculating the annual number of unsafe workdays due to extreme heat. We apply these algorithms to projections of the frequency of extreme heat days to quantify the exposure of the outdoor workforce to extreme heat and the associated earnings at risk under different greenhouse gas emissions mitigation scenarios and, for the first time, different adaptation measures. With a trajectory of modest greenhouse gas emissions reductions (RCP4.5), outdoor worker exposure to extreme heat would triple that of the late 20th century baseline by midcentury, and earnings at risk would reach an estimated $39.3 billion annually. By late century with that same trajectory, exposure would increase four-fold compared to the baseline with an estimated $49.2 billion in annual earnings at risk. Losses are considerably higher with a limited-mitigation trajectory (RCP8.5). While universal adoption of two specific adaptation measures in conjunction could reduce future economic risks by roughly 90%, practical limitations to their adoption suggest that emissions mitigation policies will be critical for ensuring the wellbeing and livelihoods of outdoor workers in a warming climate.
The 2019 Museum Fire burned in a mountainous region near the city of Flagstaff, AZ, USA. Due to the high risk of post-wildfire debris flows and flooding entering the city, we deployed a network of seismometers within the burn area and downstream drainages to examine the efficacy of seismic monitoring for post-fire flows. Seismic instruments were deployed during the 2019, 2020, and 2021 monsoon seasons following the fire and recorded several debris flow and flood events, as well as signals associated with rainfall, lighting and wind. Signal power, frequency content, and wave polarization were measured for multiple events and compared to rain gauge records and images recorded by cameras installed in the study area. We use these data to demonstrate the efficacy of seismic recordings to (1) detect and differentiate between different energy sources, (2) estimate the timing of lightning strikes, (3) calculate rainfall intensities, and (4) determine debris flow timing, size, velocity, and location. This work confirms the validity of theoretical models for interpreting seismic signals associated with debris flows and rainfall in post-wildfire settings and demonstrates the efficacy of seismic data for identifying and characterizing debris flows.
It is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, there is a knowledge gap in finding beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a meso-scale representation model that approximates DWSs with reduced backbone networks, which enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multi-objective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston’s water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The meso-scale modeling approach and the multi-objective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.
Smart stormwater systems equipped with real-time controls are transforming urban drainage management by enhancing the flood control and water treatment potential of previously static infrastructure. Real-time control of detention basins, for instance, has been shown to improve contaminant removal by increasing hydraulic retention times while also reducing downstream flood risk. However, to date, few studies have explored optimal real-time control strategies for achieving both water quality and flood control targets. This study advances a new model-predictive control (MPC) algorithm for stormwater detention ponds that determines the outlet valve control schedule needed to maximize pollutant removal and minimize flooding using forecasts of the incoming pollutograph and hydrograph. We illustrate that, compared to rule-based controls, MPC more effectively prevents overflows, reduces peak discharges, improves water quality, and adapts to changing hydrologic inputs. Moreover, when paired with an online data assimilation scheme based on Extended Kalman Filtering (EKF), we find that MPC is robust to uncertainty in both pollutograph forecasts and water quality measurements. By providing an integrated control strategy that optimizes both water quality and quantity goals while remaining robust to uncertainty in hydrologic and pollutant dynamics, our study paves the way for real-world smart stormwater systems that will achieve improved flood and nonpoint source pollution management.