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With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change, and mitigate their impacts. However, errors in air quality forecasts limit their value in long-term decision-making processes. Thus, increasing the accuracy of forecasts is of significant importance.In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate air quality forecasts for the CONUS domain for June 1st through September 29th. Our study spans the seven years from 2015 to 2021, and covers the months when there is a high risk of wildfires. CMAQ is an open-source Cartesian modeling system that simulates the concentrations of atmospheric pollutants at regional scales using emission data and meteorological inputs. We generate these meteorological inputs using the Unified Forecast System (UFS) numerical weather prediction model. We create daily 48-hr forecasts of fine particulate matter (PM2.5), ozone, and related species. We have also included a Carbon Monoxide-FIRE (CO-FIRE) tracer in CMAQ, which tracks CO emitted by wildfires.Our study consists of three parts. First, we analyze the performance of the CMAQ air quality and UFS meteorological forecasts over seven years of simulation for every EPA defined region using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs the best in the east of the CONUS with the lowest RMSE (2 µg/m3) while in the west, where there is a high risk of wildfires, the model has the highest RMSE of up to 8 µg/m3. Temporally, the model under/over-estimates the PM2.5 concentrations during the day/night time, respectively. Next, we quantify the uncertainties in the model’s prediction, and we explore the reasons behind the model biases. Finally, we employ the state-of-the-art Analog Ensemble (AnEn) method to improve the accuracy of the forecasts and quantify the forecast improvements by AnEn. To achieve this, AnEn relies on the current deterministic forecasts, here generated from the CMAQ model, and the past archive of analogous predictions with relative prior observations. By considering the history of predictions along with the current forecast, AnEn has previously shown a significant increase in the accuracy of probabilistic forecasts by requiring significantly less computation resources compared to model-based ensembles. Despite the challenges of using AnEn for wildfires, we hypothesize that it will significantly improve the CMAQ model forecast in the proposed scenarios.

Air pollution is considered a leading threat to human health in the United States (U.S.) and worldwide. An important source of air pollution in coastal areas is the globally increasing maritime shipping traffic. In this study, we take a high-resolution modeling approach to investigate the impacts of ship emissions on concentrations of various atmospheric pollutants, under the meteorological conditions and emissions of the year 2018. We utilize the Comprehensive Air Quality Model with extensions (CAMx) to simulate transport, diffusion, and chemical reactions, and the Weather Research and Forecasting (WRF) model to provide the meteorological inputs. We focus on four criteria pollutants – fine particulate matter with a diameter smaller than 2.5 μm (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) – as well as nitrogen oxide (NO), and we calculate their concentrations in the presence and absence of the ship emissions along the U.S. East Coast, particularly in the proximity of major ports. We find that ship emissions increase the PM2.5 concentrations over the ocean and sparse areas inland. The 98-th percentile of the 24-hour average PM2.5 concentrations (the "design value'' used by the U.S. Environmental Protection Agency) increased by up to 3.2 μm m−3 in some coastal areas. In addition, ships contribute significantly to SO2 concentrations, up to 95 % over the Atlantic and up to 90 % over land in coastal states, which represents a ~45 ppb increase in the SO2 design values in some states. The 98-th percentile of the hourly NO2 concentrations also increased by up to 15 ppb at the major ports and along the shore. In addition, we find that the impact of shipping emissions on O3 concentrations is not uniform, meaning that ships affect ozone pollution in both positive and negative ways. Over the ocean, O3 concentrations were significantly higher in the presence of ships, but in major coastal cities, O3 concentrations decreased in the presence of ships. Our simulation results show that ships emit significant amounts of fresh NO in the atmosphere, which then helps scavenge O3 in VOC-limited areas, such as major ports. By contrast, over the ocean (NOx-limited regime), enhanced NOx (NO2 + NO) concentrations due to ships contribute to the formation of O3 and therefore enhance O3 concentrations. Overall, due to the dominant southwesterly wind direction in the region, the impacts of ships on air pollutants mainly remain offshore. However, in coastal states near major ports, the impacts are significantly important.

The first goal of this study is to quantify the magnitude and spatial variability of air quality changes in the USA during the COVID-19 pandemic. We focus on two pollutants that are federally regulated, nitrogen dioxide (NO2) and fine particulate matter (PM2.5). NO2 and PM2.5 are both primary and secondary pollutants, meaning that they can be emitted either directly into the atmosphere or indirectly from chemical reactions of emitted precursors. NO2 is emitted during fuel combustion by all motor vehicles and airplanes. PM2.5 is emitted by airplanes and, among motor vehicles, mostly by diesel vehicles, such as commercial heavy-duty diesel trucks. Both PM2.5 and NO2 are also emitted by fossil-fuel power plants, although PM2.5 almost exclusively by coal power plants. Observed concentrations at all available ground monitoring sites (240 and 480 for NO2 and PM2.5, respectively) were compared between April 2020, the month during which the majority of US states had introduced some measure of social distancing (e.g., business and school closures, shelter-in-place, quarantine), and April of the prior 5 years, 2015–2019, as the baseline. Large, statistically significant decreases in NO2 concentrations were found at more than 65% of the monitoring sites, with an average drop of 2 parts per billion (ppb) when compared to the mean of the previous 5 years. The same patterns are confirmed by satellite-derived NO2 column totals from NASA OMI, which showed an average drop in 2020 by 13% over the entire country when compared to the mean of the previous 5 years. PM2.5 concentrations from the ground monitoring sites, however, were not significantly lower in 2020 than those in the past 5 years and were more likely to be higher than lower in April 2020 when compared with those in the previous 5 years. After correcting for the decreasing multi-annual concentration trends, the net effect of COVID-19 at the ground stations in April 2020 was a reduction in NO2 concentrations by − 1.3ppb and a slight increase in PM2.5 concentrations by + 0.28 μg/m3. The second goal of this study is to explain the different responses of these two pollutants, i.e., NO2 was significantly reduced but PM2.5 was nearly unaffected, during the COVID-19 pandemic. The hypothesis put forward is that the shelter-in-place measures affected people’s driving patterns most dramatically, thus passenger vehicle NO2 emissions were reduced. Commercial vehicles (generally diesel) and electricity demand for all purposes remained relatively unchanged, thus PM2.5 concentrations did not drop significantly. To establish a correlation between the observed NO2 changes and the extent to which people were actually sheltering in place, thus driving less, we used a mobility index, which was produced and made public by Descartes Labs. This mobility index aggregates cell phone usage at the county level to capture changes in human movement over time. We found a strong correlation between the observed decreases in NO2 concentrations and decreases in human mobility, with over 4 ppb decreases in the monthly average where mobility was reduced to near 0 and around 1 ppb decrease where mobility was reduced to 20% of normal or less. By contrast, no discernible pattern was detected between mobility and PM2.5 concentrations changes, suggesting that decreases in personal-vehicle traffic alone may not be effective at reducing PM2.5 pollution.

Beyond resource efficiencies, green buildings aim to create healthy indoor environments for building occupants. In terms of improving occupant well-being, a unique case emerges for healthcare facilities, whose patients may be at a vulnerable state. In the U.S., the Leadership in Energy and Environmental Design (LEED) rating system has become the most widely recognized certification system for green buildings, including green healthcare facilities and buildings. Hospitals with high total scores in the LEED rating system are green buildings but may not necessarily be the optimal green healthcare environment from a patient’s wellbeing perspective. Certified health-care facilities were analyzed in terms of their credit valuation to assess whether health-care facilities prioritize specific criteria that influence patient wellbeing and recovery time. Analysis of results indicate hospitals may be valuing the level of certification more than those credits that were deemed relevant for patient wellbeing and rate of recovery, either due to lack of information or due to economic constraints. To con-solidate the previous results and to compare the performance of LEED certified green hospitals to the national average, the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey was analyzed for patients’ perspectives on the healthcare facility. Results indicate higher satisfaction in green hospitals’ overall patient care performance as well as a greater tendency to recommend green hospitals to others compared to the national average. No statistical significance was found for hospital cleanliness and quietness between green hospitals and the national average. HIGHLIGHTS • Hospital LEED scores not directly aligned with credits that affect patient wellbeing • HCAHPS survey results compared green hospitals to the national average • Overall patient satisfaction 3.6% higher in green hospitals versus non-green hospitals • Patients self-reported 5.6% more likeliness to recommend green hospitals to others • No statistical difference observed for hospital cleanliness or quietness. © 2020, College Publishing. All rights reserved.

One of the main aims of the green building movement has been a desire to create a healthy indoor environment for building occupants. In terms of improving or enhancing occupant well-being, a unique case emerges for healthcare facilities, whose main existence is based on the principles of improving the health of their patients. In the U.S., the Leadership in Energy and Environmental Design (LEED) rating system has become the most widely recognized and used certification system for green buildings, including green hospital buildings. However, hospitals with high total scores may be green buildings but not necessarily the optimal green healthcare environment from a patient's wellbeing perspective. The goal of the study was to identify whether hospitals and healthcare facilities actually value the specific criteria that influence the health of patients and their recovery period. In-depth analysis of LEED healthcare specific credits revealed general tendencies and practices for green healthcare buildings. Based on a statistical analysis, the mean of percent healthcare specific scores were calculated as 48% for those certified under LEED Healthcare, 62% for hospitals certified under LEED New Construction v3, and 52% for hospitals certified under LEED New Construction v2 rating systems. While hospitals included in the Healthcare category were initially expected to be the most successful ones in terms of achieving patient recovery and wellbeing related credits, they proved to earn the lowest percent of relevant points in this analysis. The result may be attributed to heightened restrictions and requirements of credits in the Healthcare scorecard, or hospitals may be valuing the level of certification more than those credits that were deemed relevant for patient wellbeing and rate of recovery, either due to lack of information or due to economic constraints. A high correlation coefficient was calculated among total scores and healthcare specific scores for the Healthcare dataset. On the other hand, correlation coefficients calculated for the other two datasets indicate a more random pattern among the two variables. For hospitals certified under the New Construction rating system, there seem to be weak support to claim that hospitals that receive high total scores and thus certification levels have high healthcare specific points as well.

For most students in higher education, a green residence hall could form their first impressions regarding green buildings. As these students may be expected to make decisions regarding their place of residence upon graduation, if not sooner, the importance of their first experiences with a green building cannot be underestimated. Indoor environmental quality of residence halls may affect students' productivity, which would have far-reaching implications for the student, university, and society. The goal of the study was to assess the students' self-perceived comfort and indoor environmental quality of green residence halls in a higher education institution, together with their willingness to pay to reside in a green building upon graduation. The conducted survey resulted in 1040 complete responses, and so the sample size used in the analysis is one of the largest among studies done on green buildings. Results indicate that, students residing in a green building report higher satisfaction on thermal, lighting, and overall comfort levels, and report higher positive impact of their living environment on the quality of their studying. However, such students were also found to report decreased tendencies to consider green buildings in the future. Analysis towards willingness to pay of students to reside in a green building upon graduation revealed comparable results among the study and the control population, with 35-40% indicating no willingness to pay extra. Students with high self-reported knowledge on environmental issues were found to indicate significantly higher willingness to pay to reside in green buildings. (C) 2020 Elsevier B.V. All rights reserved.

The Northeastern coast of the U.S. is expected to increase its offshore wind capacity from 30 MW today to 86 GW by 2050. Measurements of wind speeds are available near sea level, but not at hub height, thus extrapolation is often required using the surface roughness,z0. The focus of this study is to estimate the surface roughness length off the Northeastern coast of the U.S. using field measurements from Nantucket Sound, MA, with three methods: 1) analytical, dependent on friction velocity and atmospheric stability, 2) the Charnock relationship between zo and friction velocity, and 3) a statistical method, based on wind speed observations at three heights. The main results of this paper are: a comparison of the three methods, a comprehensive error analysis of each method, a regionalz0value of 10–3m, and a new mathematical interpretation of surface roughness.

The International Energy Conservation Code (IECC) is commonly used to improve building energy efficiency, and its multiple iterations have been adopted by numerous countries, as well as most states in the U.S. The study assesses the effects and energy saving potentials of multiple passive and active building design features for new residential construction by using a baseline model designed to mimic existing characteristics of residential buildings in the Northeast region of the U.S. The developed model was verified for the two commonly adopted iterations of IECC codes to attain realistic results, and to quantify potential energy savings calculated beyond those that are provided by the Code. CFD analysis was used to assess indoor temperature variation and air velocity, together with an assessment for occupant comfort. Results indicate annual overall energy savings of 114 kWh/m2 in a detached single family home, which represents a 39% reduction in building total energy consumption in addition to and beyond the energy efficiency gains required by the 2012 IECC. CFD analysis results also indicate that occupant comfort is not compromised by techniques analyzed. For an individual residential building in the studied region, the energy savings over its lifetime would total 500 MWh. At the regional scale, should all new residential construction in the next 6 years in the cold climate region of the U.S. adopt the studied techniques, their cumulative savings would exceed energy generated in the U.S. from solar power in 2016.