Maryland Case Study Research Articles

Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland

The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model’s potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.

Estimating genus-specific effects of non-native honey bees and urbanization on wild bee communities: A case study in Maryland, United States

Non-native species have the potential to detrimentally affect native species through resource competition, disease transmission, and other forms of antagonism. The western honey bee (Apis mellifera) is one such species that has been widely introduced beyond its native range for hundreds of years. There are strong concerns in the United States, and other countries, about the strain that high-density, managed honey bee populations could pose to already imperiled wild bee communities. While there is some experimental evidence of honey bees competing with wild bees for resources, few studies have connected landscape-scale honey bee apiary density with down-stream consequences for wild bee communities. Here, using a dataset from Maryland, US and joint species distribution models, we provide the largest scale, most phylogenetically resolved assessment of non-native honey bee density effects on wild bee abundance to date. As beekeeping in Maryland primarily consists of urban beekeeping, we also assessed the relative impact of developed land on wild bee communities. Six of the 33 wild bee genera we assessed showed a high probability (> 90 %) of a negative association with apiary density and/or developed land. These bees were primarily late-season, specialist genera (several long-horned genera represented) or small, ground nesting, season-long foragers (including several sweat bee genera). Conversely, developed land was associated with an increase in relative abundance for some genera including invasive Anthidium and other urban garden-associated genera. We discuss several avenues to ameliorate potentially detrimental effects of beekeeping and urbanization on the most imperiled wild bee groups. We additionally offer methodological insights based on sampling efficiency of different methods (hand netting, pan trapping, vane trapping), highlighting large variation in effect sizes across genera. The magnitude of sampling effect was very high, relative to the observed ecological effects, demonstrating the importance of integrated sampling, particularly for multi-species or community level assessments.

Integrating dial-a-ride with transportation network companies for cost efficiency: A Maryland case study

Dial-a-ride (DAR) is a shared-ride service that provides mobility to transportation-disadvantaged individuals who are unable to use public transit. While most DAR studies focus on optimizing operations, our research explores the feasibility and benefits of outsourcing outlier trips to transportation network companies (TNCs) to minimize the combined service delivery cost. To achieve this goal, we formulate a multi-vehicle DAR problem (DARP) with trip outsourcing to TNCs, which can be solved optimally for small scale instances. To solve larger instances, we propose a two-stage solution framework to improve DAR routes from commercially available software. Firstly, we develop an integer programming model to re-optimize individual routes with trip outsourcing. Secondly, we design a multi-vehicle heuristic that considers reinserting trips initially designated for outsourcing back into the DAR fleet, as well as reinsertion and exchange of remaining trips among routes. We apply the approach to a medium-sized DAR operator in Maryland and achieve cost reductions of 7%-13% depending on the TNC volume discount negotiated by the DAR company.

Net-Zero Target and Emissions from Land Conversions: A Case Study of Maryland’s Climate Solutions Now Act

Many climate change “solution” plans include net-zero goals, which involve balancing the anthropogenic greenhouse gas emissions (GHG) with their removal. Achieving net-zero goals is particularly problematic for soils because they are often excluded from GHG inventories and reduction plans. For example, Maryland’s Climate Solutions Now Act (Senate Bill 528) put forward the goal of lowering emissions of GHG to 60% under 2006 quantities by 2031 and with a target of net-zero emissions by 2045. To achieve these goals, the state of Maryland (MD) needs to quantify GHG emissions from various sources contributing to the state’s total emissions footprint (EF). Soils are currently excluded from MD’s GHG assessments, which raises a question about how the soil impacts the net-zero goal. This study examines the challenges in meeting net-zero goals using an example of carbon dioxide (CO2) as one of the GHG types (net-zero CO2 emissions). The current study quantified the “realized” social costs of CO2 (SC-CO2) emissions for MD from new land developments in the period from 2001 to 2016 which caused a complete loss of 2.2 × 109 kg of total soil carbon (TSC) resulting in $383.8M (where M = million, USD = US dollars). All MD’s counties experienced land developments with various emissions and SC-CO2 monetary values. Most of the developments, TSC losses, and SC-CO2 occurred near the existing urban areas of Annapolis and Baltimore City. These emissions need to be accounted for in MD’s GHG emissions reduction plans to achieve a net-zero target. Soils of MD are limited in recarbonization capacity because 64% of the state area is occupied by highly leached Ultisols. Soil recarbonization potential is further reduced by urbanization with Prince George’s, Montgomery, and Frederick counties experiencing the highest increases in developed areas. In addition, projected sea-level rises will impact 17 of MD’s 23 counties. These losses will generate additional social costs because of migration, costs of relocation, and damages to infrastructure. The state of MD has a high proportion of private land ownership (92.4%) and low proportion of public lands, which will limit opportunities for relocation within the state. Net-zero targets are important but meeting these targets without specific and integrative approaches depending on the source and type of emissions may result in failure. These approaches should also focus on the social costs of emissions, which raises the need for a new concept of integrating net-zero emissions and social costs.

Nitrate contamination in Maryland drinking water and policy reforms to safeguard private wells

Forty-two million Americans get their drinking water from unregulated water sources, primarily private wells. In lieu of federal action, several U.S. states have adopted policies and programs to protect private well owners, who tend to live in rural, underserved areas. The presentation shares the findings of a national policy review of key programs adopted by states, such as providing free water quality test kits and establishing a public database of private well water quality results, to safeguard public health. The presentation will also share a case study of Maryland, where policymakers have sought to enhance resources for well users and transparency regarding well water quality. These strategies provide a model for other states and nations seeking to alleviate disparities in access to safe drinking water. Keywords: drinking water, nitrate, water access, water pollution

The Impact of Payment Reform on Transcatheter Aortic Valve Replacement Use and Readmissions: a Maryland Case Study

Yesantharao, Pooja S. MS; Etchill, Eric W. MD; Schena, Stefano MD, PhD Author Information

Developing a Multicriteria Decision Analysis Framework to Evaluate Reclaimed Wastewater Use for Agricultural Irrigation: The Case Study of Maryland

Groundwater is the main source of irrigation and residential use in the Eastern Shore Maryland, which is experiencing challenges regarding overuse, saltwater intrusion, and diminishing productivity. The Chesapeake Bay is also facing the problem of water pollution due to pollutant loading from agricultural fields and wastewater treatment plants (WWTPs). Using recycled water for irrigation has the potential to alleviate the pressure on groundwater and reduce pollutant loading. The objective of this study was to develop a decision tool to explore the use of recycled water for agricultural irrigation in Maryland using Multicriteria Decision Analysis (MCDA) integrated with Geographical Information Systems (GIS). Four main evaluation criteria were included in the GIS-MCDA framework: agricultural land cover, climate, groundwater vulnerability, and characteristics of the WWTPs as sources of recycled water. Groundwater vulnerability zones were developed using the groundwater well density, water extraction data, and the aquifer information. Then, the most suitable areas for irrigation using recycled water were identified. About 13.5% and 32.9% of agricultural land was, respectively, found to be “highly” and “moderately” suitable for irrigation with recycled water when WWTPs were categorized based on their treatment process information. The results provide a useful decision tool to promote the use of recycled water for agricultural irrigation.

Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

Annual mean wind speed distribution models for power generation based on regional wind resource maps are limited by spatial and temporal resolutions. These models, in general, do not consider the impact of local terrain and atmospheric circulations. In this study, long-term five-year wind data at three sites on the North, East, and West of the Baltimore metropolitan area, Maryland, USA are statistically analyzed. The Weibull probability density function was defined based on the observatory data. Despite seasonal and spatial variability in the wind resource, the annual mean wind speed for all sites is around 3 m/s, suggesting the region is not suitable for large-scale power generation. However, it does display a wind power capacity that might allow for non-grid connected small-scale wind turbine applications. Technical and economic performance evaluations of more than 150 conventional small-scale wind turbines showed that an annual capacity factor and electricity production of 11% and 1990 kWh, respectively, are achievable. It results in a payback period of 13 years. Government incentives can improve the economic feasibility and attractiveness of investments in small wind turbines. To reduce the payback period lower than 10 years, modern/unconventional wind harvesting technologies are found to be an appealing option in this region. Key contributions of this work are (1) highlighting the need for studying the urban physics rather than just the regional wind resource maps for wind development projects in the build-environment, (2) illustrating the implementation of this approach in a real case study of Maryland, and (3) utilizing techno-economic data to determine suitable wind harnessing solutions for the studied sites.

The role of ecosystem services in the decision to grow oysters: A Maryland case study

Ecosystem services provided by oysters are regularly cited to gain support for the continued development of oyster aquaculture, but we have limited understanding of whether and how these benefits influence those who grow oysters. Participant observation and semi-structured interviews occurred with 57 oyster growers in Maryland (United States) to detail factors motivating entry into the oyster aquaculture industry. Results, framed under a lens of ecosystem services, indicate that cultural services are more likely to motivate aquaculture participation than provisioning, regulating or supporting services. This study emphasizes the significance of cultural ecosystem services and defines the need to better understand those provided by oysters and other farmed shellfish. A more complete description of the ecosystem services provided through shellfish aquaculture could serve to enhance support of farmed shellfisheries. In addition, this participatory approach highlights challenges within the ecosystem services framework regarding linked services and their complexity.

Bridge condition rating data modeling using deep learning algorithm

This paper presents a deep learning-based bridge condition rating data modeling approach using selected data from the National Bridge Inventory (NBI) database. The objective of this research is to develop a data-driven approach that enables prediction of future conditions of highway bridge components from historical inspection data. The problem is solved by training a Convolutional Neural Network (CNN) model with online available NBI data. One prominent feature of the CNN model is that if well-trained it can represent the high dimensional data in the dataset abstractions for which conventional mathematical models may be difficult to describe. A case study of Maryland and Delaware highway bridges using historical data (1992–2017) sourced from the NBI database has been performed to demonstrate the proposed method. CNN models for three primary components of these highway bridges including the deck, superstructure, and substructure have been established. Optimization of model parameters is achieved through a parametric study. Research findings suggest that the deep learning model offers a promising tool as a data-driven condition forecasting approach for bridge components with a demonstrated prediction accuracy over 85%.

The Policy Ecology of Behavioral Health Homes: Case Study of Maryland's Medicaid Health Home Program.

Behavioral health homes, shown to improve receipt of evidence-based medical services among people with serious mental illness in randomized clinical trials, have had limited results in real-world settings; nonetheless, these programs are spreading rapidly. To date, no studies have considered what set of policies is needed to support effective implementation of these programs. As a first step toward identifying an optimal set of policies to support behavioral health home implementation, we use the policy ecology framework to map the policies surrounding Maryland's Medicaid behavioral health home program. Results suggest that existing policies fail to address important implementation barriers.

Applications of Trajectory Data From the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study

Transportation agencies have an opportunity to leverage increasingly available trajectory datasets to improve their analyses and decision-making processes. However, this data is typically purchased from vendors, which means agencies must understand its potential benefits beforehand in order to properly assess its value relative to the cost of acquisition. While the literature concerned with trajectory data is rich, it is naturally fragmented and focused on technical contributions in niche areas, which makes it difficult for government agencies to assess its value across different transportation domains. To overcome this issue, this paper explores trajectory data from the perspective of a road transportation agency interested in acquiring trajectories to enhance its analysis. This paper provides a literature review illustrating applications of trajectory data in six areas of road transportation systems analysis: demand estimation, modeling human behavior, designing public transit, traffic performance measurement and prediction, environment, and safety. In addition, it visually explores 20 million GPS traces in Maryland, USA, illustrating the existing and suggesting new applications of trajectory data.

Utilization of the Maryland Environmental Justice Screening Tool: A Bladensburg, Maryland Case Study

Maryland residents’ knowledge of environmental hazards and their health effects is limited, partly due to the absence of tools to map and visualize distribution of risk factors across sociodemographic groups. This study discusses the development of the Maryland EJSCREEN (MD EJSCREEN) tool by the National Center for Smart Growth in partnership with faculty at the University of Maryland School of Public Health. The tool assesses environmental justice risks similarly to the U.S. Environmental Protection Agency’s (USEPA) EJSCREEN tool and California’s tool, CalEnviroScreen 3.0. We discuss the architecture and functionality of the tool, indicators of importance, and how it compares to USEPA’s EJSCREEN and CalEnviroScreen. We demonstrate the use of MD EJSCREEN through a case study on Bladensburg, Maryland, a town in Prince George’s County (PG) with several environmental justice concerns including air pollution from traffic and a concrete plant. Comparison reveals that environmental and demographic indicators in MD EJSCREEN most closely resemble those in EPA EJSCREEN, while the scoring is most similar to CalEnviroScreen. Case study results show that Bladensburg has a Prince George’s environmental justice score of 0.99, and that National Air Toxics Assessment (NATA) air toxics cancer risk is concentrated in communities of color.

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.

An innovative model to coordinate healthcare and social services for people with serious mental illness: A mixed-methods case study of Maryland's Medicaid health home program.

We conducted a case study examining implementation of Maryland's Medicaid health home program, a unique model for integration of behavioral, somatic, and social services for people with serious mental illness (SMI) in the psychiatric rehabilitation program setting. We conducted interviews and surveys with health home leaders (N=72) and front-line staff (N=627) representing 46 of the 48 total health home programs active during the November 2015-December 2016 study period. We measured the structural and service characteristics of the 46 health home programs and leaders' and staff members' perceptions of program implementation. Health home program structure varied across sites: for example, 15% of programs had co-located primary care providers and 76% had onsite supported employment providers. Most leaders and staff viewed the health home program as having strong organizational fit with psychiatric rehabilitation programs' organizational structures and missions, but noted implementation challenges around health IT, population health management, and coordination with external providers. Maryland's psychiatric rehabilitation-based health home is a promising model for integration of behavioral, somatic, and social services for people with SMI but may be strengthened by additional policy and implementation supports, including incentives for external providers to engage in care coordination with health home providers.

Pavement sustainability index for highway infrastructures: A case study of Maryland

Pavement deterioration creates conditions that undermine their performances, which gives rise to the need for maintenance and rehabilitation. This paper develops a mathematical multi-linear regression analysis (MLRA) model to determine a pavement sustainability index (PSTI) as dependent variable for flexible pavements in Maryland. Four categories of pavement performance evaluation indicators are subdivided into seven pavement condition indices and analyzed as independent variables for each section of pavement. Data are collected from five different roadways using field evaluations and existing database. Results indicate that coefficient of determination (R2) is correlated and significant, R2 = 0.959. Of the seven independent variables, present serviceability index (PSI) is the most significant with a coefficient value of 0.032, present serviceability rating (PSR) coefficient value = 0.028, and international roughness index (IRI) coefficient value =‒0.001. Increasing each unit value of coefficients for PSI and PSR would increase the value of PSTI; thereby providing a more sustainable pavement infrastructure; which explains the significance of the model and why IRI will most likely impact environmental, economic and social values.

The Impact of State Aid Reform on Property Values: A Case Study of Maryland's Bridge to Excellence in Public Schools Act

A major feature of the school finance landscape over the last two decades has been the reform of state school finance systems. Using the case of Maryland's Bridge to Excellence in Public Schools Act, this paper extends the current literature by developing a conceptual framework for residential bidding and sorting and using it to estimate housing market responses to the Maryland state aid reform. With repeat-sales data and many control variables, we find that an increase of $1,000 in current state aid per pupil induced by the reform is associated with an increase of 5 percent to 13 percent in property values. Moreover, within a district the property-value increases are greater in higher-income tracts, where the demand for school quality is likely to be greater.

Response of SO2 and particulate air pollution to local and regional emission controls: A case study in Maryland

AbstractThis paper addresses the questions of what effect local regulations can have on pollutants with different lifetimes and how surface observations and remotely sensed data can be used to determine the impacts. We investigated the decadal trends of tropospheric sulfur dioxide (SO2) and aerosol pollution over Maryland and its surrounding states, using surface, aircraft, and satellite measurements. Aircraft measurements indicated fewer isolated SO2 plumes observed in summers, a ∼40% decrease of column SO2, and a ∼20% decrease of atmospheric optical depth (AOD) over Maryland after the implementation of local regulations on sulfur emissions from power plants (∼90% reduction from 2010). Surface observations of SO2 and particulate matter (PM) concentrations in Maryland show similar trends. OMI SO2 and MODIS AOD observations were used to investigate the column contents of air pollutants over the eastern U.S.; these indicate decreasing trends in column SO2 (∼60% decrease) and AOD (∼20% decrease). The decrease of upwind SO2 emissions also reduced aerosol loadings over the downwind Atlantic Ocean near the coast by ∼20%, while indiscernible changes of the SO2 column were observed. A step change of SO2 emissions in Maryland starting in 2009–2010 had an immediate and profound benefit in terms of local surface SO2 concentrations but a modest impact on aerosol pollution, indicating that short‐lived pollutants are effectively controlled locally, while long‐lived pollutants require regional measures.

Hazard Ranking Methodology for Assessing Health Impacts of Unconventional Natural Gas Development and Production: The Maryland Case Study.

The recent growth of unconventional natural gas development and production (UNGDP) has outpaced research on the potential health impacts associated with the process. The Maryland Marcellus Shale Public Health Study was conducted to inform the Maryland Marcellus Shale Safe Drilling Initiative Advisory Commission, State legislators and the Governor about potential public health impacts associated with UNGDP so they could make an informed decision that considers the health and well-being of Marylanders. In this paper, we describe an impact assessment and hazard ranking methodology we used to assess the potential public health impacts for eight hazards associated with the UNGDP process. The hazard ranking included seven metrics: 1) presence of vulnerable populations (e.g. children under the age of 5, individuals over the age of 65, surface owners), 2) duration of exposure, 3) frequency of exposure, 4) likelihood of health effects, 5) magnitude/severity of health effects, 6) geographic extent, and 7) effectiveness of setbacks. Overall public health concern was determined by a color-coded ranking system (low, moderately high, and high) that was generated based on the overall sum of the scores for each hazard. We provide three illustrative examples of applying our methodology for air quality and health care infrastructure which were ranked as high concern and for water quality which was ranked moderately high concern. The hazard ranking was a valuable tool that allowed us to systematically evaluate each of the hazards and provide recommendations to minimize the hazards.

Cross-Nested Joint Model of Travel Mode and Departure Time Choice for Urban Commuting Trips: Case Study in Maryland–Washington, DC Region

The aim of this paper is to contribute to describing the simultaneous choice of travel mode and departure time by making use of a cross-nested logit structure that allows for the joint representation of interalternative correlation along the both choice dimensions. Traditional multinomial logit model and nested logit model are formulated respectively. The analysis uses the revealed preference data collected from Maryland-Washington, DC, regional household travel survey during 2007–2008 for commuting trips, considering more work-related characteristics than previous studies. A comparison of the different model results shows that the presented cross-nested logit structure offers significant improvements over multinomial logit and nested logit models. The empirical results of the analysis reveal significant influences on commuter joint choice behavior of travel mode and departure time. Moreover, a Monte Carlo simulation for two groups of scenarios arising from transportation policies, congestion pricing, and improvements to transit service during peak period is undertaken respectively to examine the impact of a change in car travel cost and transit travel time on the travel mode and departure time switching. The simulation results show that US$5 increase in car travel cost during peak period has a similar effect on reducing drive alone in peak hours as 30% saving in transit travel time but only half of the latter policy in the transit ridership increase.