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Analysis of urban wind conditions and wildfire smoke dispersion for downtown Montréal using computational fluid dynamics
... After correcting the raw data for the floating effects, their comparison to sonic anemometers was satisfactory. Dyer-Hawes et al. (2024) combined computational fluid dynamics simulations and lidar measurements to investigate wind conditions and smoke dispersion in downtown Montreal, Quebec, Canada. ...
Sea ports play a major role in the transport of goods worldwide, and knowledge of wind characteristics in these areas is vital to maintaining safety. However, coastal wind flow can be highly complex and turbulent, necessitating additional analysis. A Doppler lidar providing continuous wind profiles is deployed in the Port of Genoa, Italy, to characterize the mean wind velocity and turbulence properties within the coastal surface layer (40–250 m above ground level). Weather conditions, reanalysis data, and transient wind profiles are combined to analyze wind field characteristics on a day which experienced a thunderstorm. We also utilize a method to identify and categorize sources of turbulence through analysis of lidar derived quantities such as wind shear, turbulent kinetic energy, and vertical skewness. Seasonal variations in the wind properties are investigated by selecting data from June (summer) and December (winter). Differences are found in dominant wind direction and the associated frequency of convective mixing, with onshore winds most common in summer and offshore winds in winter. The measured lidar velocity is also compared against Monin–Obukhov similarity theory predictions, showing satisfactory agreement at low heights but struggling to reproduce observations of the stable atmospheric conditions present during winter.
In conventional modeling of air pollution dispersion, pollutants are treated as passive scalars or inert species even though most of them are chemically reactive [1]. Chemical reactions contribute to pollutant dispersion via the generation and depletion of pollutants, in addition to other two mechanisms: advection and turbulent diffusion. This study investigated how chemical reactions affect near-field pollution dispersion by integrating the simple NO x-O 3 chemistry into RANS-based computational fluid dynamics (CFD) simulation. CFD simulation was used to model a mixed emission of NO and NO 2 from a short stack attached to a building into ambient O 3 , prompting chemical reactions between the NO, NO 2 , and O 3. Various degrees of chemical reactivity were modeled by varying the Damkhöler number (Da) between 0.073 and 4.363. The results showed significant chemical reactivity for cases where Da [NO] > 1, while cases with Da [NO] < 1 had pollutant dispersion patterns similar to inert species. Noticeable modifications in concentrations were detected at ground level, where the NO concentration was depleted and NO 2 concentration increased significantly. A budget analysis revealed major contributions of chemistry and turbulent diffusion to plume dispersion in the surroundings, while advection mainly carried the pollutants downstream from the source.
We use Doppler lidar wind profiles from six locations around the globe to evaluate the wind profile forecasts in the boundary layer generated by the operational global Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts (ECMWF). The six locations selected cover a variety of surfaces with different characteristics (rural, marine, mountainous urban, and coastal urban).
We first validated the Doppler lidar observations at four locations by comparison with co-located radiosonde profiles to ensure that the Doppler lidar observations were of sufficient quality. The two observation types agree well, with the mean absolute error (MAE) in wind speed almost always less than 1 m s-1. Large deviations in the wind direction were usually only seen for low wind speeds and are due to the wind direction uncertainty increasing rapidly as the wind speed tends to zero.
Time–height composites of the wind evaluation with 1 h resolution were generated, and evaluation of the model winds showed that the IFS model performs best over marine and coastal locations, where the mean absolute wind vector error was usually less than 3 m s-1 at all heights within the boundary layer. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. For example, in Granada, which is near a high mountain range, the IFS model failed to capture a commonly occurring mountain breeze, which is highly dependent on the sub-grid-size terrain features that are not resolved by the model. The uncertainty in the wind forecasts increased with forecast lead time, but no increase in the bias was seen.
At one location, we conditionally performed the wind evaluation based on the presence or absence of a low-level jet diagnosed from the Doppler lidar observations. The model was able to reproduce the presence of the low-level jet, but the wind speed maximum was about 2 m s-1 lower than observed. This is attributed to the effective vertical resolution of the model being too coarse to create the strong gradients in wind speed observed.
Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time is computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.
The 2019–20 bushfire season in south-eastern Australia was one of the most severe in recorded history. Bushfire smoke-related air pollution reached hazardous levels in major metropolitan areas, including the Australian Capital Territory (ACT), for prolonged periods of time. Bushfire smoke directly challenges human health through effects on respiratory and cardiac function, but can also indirectly affect health, wellbeing and quality of life. Few studies have examined the specific health effects of bushfire smoke, separate from direct effects of fire, and looked beyond physical health symptoms to consider effects on mental health and lifestyle in Australian communities. This paper describes an assessment of the health impacts of this prolonged exposure to hazardous levels of bushfire smoke in the ACT and surrounding area during the 2019–20 bushfire season. An online survey captured information on demographics, health (physical and mental health, sleep) and medical advice seeking from 2,084 adult participants (40% male, median age 45 years). Almost all participants (97%) experienced at least one physical health symptom that they attributed to smoke, most commonly eye or throat irritation, and cough. Over half of responders self-reported symptoms of anxiety and/or feeling depressed and approximately half reported poorer sleep. Women reported all symptoms more frequently than men. Participants with existing medical conditions or poorer self-rated health, parents and those directly affected by fire (in either the current or previous fire seasons) also experienced poorer physical, mental health and/or sleep symptoms. Approximately 17% of people sought advice from a medical health practitioner, most commonly a general practitioner, to manage their symptoms. This study demonstrated that prolonged exposure to bushfire smoke can have substantial effects on health. Holistic approaches to understanding, preventing and mitigating the effects of smoke, not just on physical health but on mental health, and the intersection of these, is important. Improved public health messaging is needed to address uncertainty about how individuals can protect their and their families health for future events. This should be informed by identifying subgroups of the population, such as those with existing health conditions, parents, or those directly exposed to fire who may be at a greater risk.
Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.
Affordable ventilation of residential buildings in situations with outdoor PM2.5 pollution is a challenge. This investigation used the validated EnergyPlus software program to model ventilation performance in a newly constructed residential apartment in five different climate zones in China. The evaluated ventilation modes were natural ventilation, balanced mechanical ventilation, and hybrid ventilation, together with indoor particle filtration. Both indoor-generated particles due to cooking and ventilation-drawn particles from the outdoor environment were considered. Different heat recovery modes for mechanical ventilation were also compared. The evaluated parameters included indoor CO2 (carbon dioxide), PM2.5 (fine particulate matter), formaldehyde, and TVOC (total volatile organic compound) concentrations, energy consumption, and operating costs. The natural ventilation mode was found to be the cheapest, but it may provide an insufficient ventilation rate for 27%–79% of the occupied time. Mechanical ventilation can ensure good indoor air quality continuously, but the associated utility cost may be more than twice that for natural ventilation. Indoor particulate filtration should be utilized in situations with severe outdoor PM2.5 pollution. Heat recovery is not recommended in residential buildings due to the high initial investment cost at present. The least energy-consuming ventilation mode for residential buildings with maximum potential to use natural ventilation is recommended.
Significance
Accurate simulation of fluids is important for many science and engineering problems but is very computationally demanding. In contrast, machine-learning models can approximate physics very quickly but at the cost of accuracy. Here we show that using machine learning inside traditional fluid simulations can improve both accuracy and speed, even on examples very different from the training data. Our approach opens the door to applying machine learning to large-scale physical modeling tasks like airplane design and climate prediction.
Climate change is altering vegetation and disturbance dynamics in boreal ecosystems. However, the aggregate impact of these changes on boreal carbon budgets is not well understood. Here we combined multiple satellite datasets to estimate annual stocks and changes in aboveground biomass (AGB) across boreal northwestern North America. From 1984 to 2014, the 2.82 × 10⁶ km² study region gained 434 ± 176 Tg of AGB. Fires resulted in losses of 789 ± 48 Tg, which were mostly compensated by post-fire recovery of 642 ± 86 Tg. Timber harvests contributed to losses of 74 ± 5 Tg, which were partly offset by post-harvest recovery of 32 ± 9 Tg. Earth system models overestimated AGB accumulation by a factor of 3 (+1,519 ± 171 Tg), which suggests that these models overestimate the terrestrial carbon sink in boreal ecosystems and highlights the need to improve representation of fire and other disturbance processes in these models.
Many bottom-mounted offshore wind farms are currently planned for the coastal areas of Japan, in which wind speeds of 6.0–10.0 m/s are extremely common. The impact of such wind speeds is very relevant for the realization of bottom-mounted offshore wind farms. In evaluating the feasibility of these wind farms, therefore, strict evaluation at wind speeds of 6.0–10.0 m/s is important. In the present study, the airflow characteristics of 2 MW-class downwind wind turbine wake flows were first investigated using a vertically profiling remote sensing wind measurement device (lidar). The wind turbines used in this study are installed at the point where the sea is just in front of the wind turbines. A ground-based continuous-wave (CW) conically scanning wind lidar system (“ZephIR ZX300”) was used. Focusing on the wind turbine near-wakes, the detailed behaviors were considered. We found that the influence of the wind turbine wake, that is, the wake loss (wind velocity deficit), is extremely large in the wind speed range of 6.0–10.0 m/s, and that the wake loss was almost constant at such wind speeds (6.0–10.0 m/s). It was additionally shown that these results correspond to the distribution of the thrust coefficient of the wind turbine. We proposed a computational fluid dynamics (CFD) porous disk (PD) wake model as an intermediate method between engineering wake models and CFD wake models. Based on the above observations, the wind speed range for reproducing the behavior of the wind turbine wakes with the CFD PD wake model we developed was set to 6.0–10.0 m/s. Targeting the vertical wind speed distribution in the near-wake region acquired in the “ZephIR ZX300”, we tuned the parameters of the CFD PD wake model (CRC = 2.5). We found that in practice, when evaluating the mean wind velocity deficit due to wind turbine wakes, applying the CFD PD wake model in the wind turbine swept area was very effective. That is, the CFD PD wake model can reproduce the mean average wind speed distribution in the wind turbine swept area.
Extensive, severe wildfires, and wildfire-induced smoke occurred across the western and central United States since August 2020. Wildfires resulting in the loss of habitats and emission of particulate matter and volatile organic compounds pose serious threatens to wildlife and human populations, especially for avian species, the respiratory system of which are sensitive to air pollutions. At the same time, the extreme weather (e.g., snowstorms) in late summer may also impact bird migration by cutting off their food supply and promoting their migration before they were physiologically ready. In this study, we investigated the environmental drivers of massive bird die-offs by combining socioecological earth observations data sets with citizen science observations. We employed the geographically weighted regression models to quantitatively evaluate the effects of different environmental and climatic drivers, including wildfire, air quality, extreme weather, drought, and land cover types, on the spatial pattern of migratory bird mortality across the western and central US during August-September 2020. We found that these drivers affected the death of migratory birds in different ways, among which air quality and distance to wildfire were two major drivers. Additionally, there were more bird mortality events found in urban areas and close to wildfire in early August. However, fewer bird deaths were detected closer to wildfires in California in late August and September. Our findings highlight the important impact of extreme weather and natural disasters on bird biology, survival, and migration, which can provide significant insights into bird biodiversity, conservation, and ecosystem sustainability.
Objectives
To determine healthcare service utilisation for cardiorespiratory presentations and outpatient salbutamol dispensation associated with 2.5 months of severe, unabating wildfire smoke in Canada’s high subarctic.
Design
A retrospective cohort study using hospital, clinic, pharmacy and environmental data analysed using Poisson regression.
Setting
Territorial referral hospital and clinics in Yellowknife, Northwest Territories, Canada.
Participants
Individuals from Yellowknife and surrounding communities presenting for care between 2012 and 2015.
Main outcome measures
Emergency room (ER) presentations, hospital admissions and clinic visits for cardiorespiratory events, and outpatient salbutamol prescriptions
Results
The median 24-hour mean particulate matter (PM 2.5 ) was fivefold higher in the summer of 2014 compared with 2012, 2013 and 2015 (median=30.8 µg/m ³ ), with the mean peaking at 320.3 µg/m ³ . A 10 µg/m ³ increase in PM 2.5 was associated with an increase in asthma-related (incidence rate ratio (IRR) (95% CI): 1.11 (1.07, 1.14)) and pneumonia-related ER visits (IRR (95% CI): 1.06 (1.02, 1.10)), as well as an increase in chronic obstructive pulmonary disease hospitalisations (IRR (95% CI): 1.11 (1.02, 1.20). Compared with 2012 and 2013, salbutamol dispensations in 2014 increased by 48%; clinic visits for asthma, pneumonia and cough increased; ER visits for asthma doubled, with the highest rate in females, in adults aged ≥40 years and in Dene people, while pneumonia increased by 57%, with higher rates in males, in individualsaged <40 years and in Inuit people. Cardiac variables were unchanged.
Conclusions
Severe wildfires in 2014 resulted in extended poor air quality associated with increases in health resource utilization; some impacts were seen disproportionately among vulnerable populations, such as children and Indigenous individuals. Public health advisories asking people to stay inside were inadequately protective, with compliance possibly impacted by the prolonged exposure. Future research should investigate use of at-home air filtration systems, clean-air shelters and public health messaging which addresses mental health and supports physical activity.
Recent increases in the frequency and scale of wildfires worldwide have raised concerns about the influence of climate change and associated socioeconomic costs. In the western United States, the hazard of wildfire has been increasing for decades. Here, we use a combination of physical, epidemiological and economic models to estimate the economic impacts of California wildfires in 2018, including the value of destroyed and damaged capital, the health costs related to air pollution exposure and indirect losses due to broader economic disruption cascading along with regional and national supply chains. Our estimation shows that wildfire damages in 2018 totalled 27.7 billion (19%) in capital losses, 88.6 billion (59%) in indirect losses (all values in US$). Our results reveal that the majority of economic impacts related to California wildfires may be indirect and often affect industry sectors and locations distant from the fires (for example, 52% of the indirect losses—31% of total losses—in 2018 were outside of California). Our findings and methods provide new information for decision makers tasked with protecting lives and key production sectors and reducing the economic damages of future wildfires.
The accuracy and reliability of 3D steady RANS CFD simulations of wind flow in urban environments can be affected by numerical settings including the turbulence model and the imposed roughness heights. In that regard, various k-ε and k-ω turbulence models and roughness height (ks) values are commonly used when predicting wind flow in urban environments. However, it is insufficiently known to which extent the CFD results may be influenced by these settings when simulating wind flows in complex urban environments with large changes in surface roughness. This is the scope of the present paper, for which wind-tunnel (WT) measurements and CFD simulations were performed on a reduced-scale model (1:300) of a district of Livorno (Italy). Mean wind speed (U), turbulent kinetic energy (k) and turbulence dissipation rate (ε) profiles from WT measurements and CFD simulations were compared at 25 positions and deviations between experimental and numerical results were quantified by three metrics: fractional bias, correlation coefficient and fraction of data within a factor of 1.3. The turbulence model selection had a larger impact compared to the surface roughness selection on U, k and ε values. The best and worst performing turbulence models (e.g. for α = 240° at 0.02 m above the bottom) showed a deviation in terms of correlation (0.89 and 0.61, respectively) of about 0.28. Conversely, the best and worst performing roughness set, (e.g. for α = 240° at 0.02 m above the bottom), showed a deviation in terms of correlation (0.77 and 0.78, respectively) of only 0.01.
Computational fluid dynamics (CFD) has become a reference tool for the investigation of pollutant emission and dispersion in urban areas, and for the assessment of the associated risk. In this framework, specific focus is given to the estimation of the downwind and ground level concentration of air pollutants coming from emission sources such as vehicular traffic, industrial plants or accidental events. Pollutant dispersion in the Atmospheric Boundary Layer (ABL) is strongly impacted by the turbulence. This leads to a complex coupled problem, considering the reciprocal influence of the two phenomena. A key role in pollutant dispersion is played by the turbulent Schmidt number Sct which directly affects the turbulent dispersion coefficient and, consequently, the concentration field. No universally-accepted formulation for the turbulent Schmidt number exists in the literature, although its impact on the prediction of pollutant dispersion is recognised. Stemming from a brief review of the existing literature and knowledge on the topic, this paper aims to propose a novel approach for the optimal determination of Sct, also through the use of uncertainty quantification. The proposed Sct is based on the local turbulence level, and is validated on different idealized test-cases, representative of typical urban configurations.
Large Eddy Simulation (LES) undeniably has the potential to provide more accurate and more reliable results than simulations based on the Reynolds-averaged Navier-Stokes (RANS) approach. However, LES entails a higher simulation complexity and a much higher computational cost. In spite of some claims made in the past decades that LES would render RANS obsolete, RANS remains widely used in both research and engineering practice. This paper attempts to answer the questions why this is the case and whether this is justified, from the viewpoint of building simulation, both for outdoor and indoor applications. First, the governing equations and a brief overview of the history of LES and RANS are presented. Next, relevant highlights from some previous position papers on LES versus RANS are provided. Given their importance, the availability or unavailability of best practice guidelines is outlined. Subsequently, why RANS is still frequently used and whether this is justified or not is illustrated by examples for five application areas in building simulation: pedestrian-level wind comfort, near-field pollutant dispersion, urban thermal environment, natural ventilation of buildings and indoor airflow. It is shown that the answers vary depending on the application area but also depending on other—less obvious—parameters such as the building configuration under study. Finally, a discussion and conclusions including perspectives on the future of LES and RANS in building simulation are provided.
The atmospheric mixing layer height (MLH) is an important parameter of the planetary boundary layer (PBL) because it affects the transportation and dispersion processes of pollutants emitted from different sources. This study investigated the relationship between the surface temperature inversion, elevated temperature inversion, and MLH within the PBL of the Kuwait by collecting and analyzing measurements of the temperature and the air quality of upper air during 2013. The upper air temperature and the MLH were derived using a microwave temperature profiler. Hourly concentrations of SO2, O3, particulate matter (PM10), NO2, CO, NOx , and non-methane hydrocarbons (NMHCs) in ambient air were measured by air quality monitoring stations. The collected data were used to estimate the hourly MLH for the transportation and dispersion of critical pollutants. The results showed that concentrations of SO2 and PM10 have direct correlation with MLH during the day, whereas they have the reverse relationship at night. Conversely, concentrations of CO, NMHCs, and NOx showed negative correlation with MLH during both day and night, whereas concentrations of O3 showed direct correlation with MLH during both day and night. In addition, the relationship between the PBL and concentrations of critical pollutants in residential areas was clarified. These findings indicate the influence of the MLH on SO2 and PM10 is much greater during the day than at night. The findings of the present study could help improve our understanding of the effects of MLH on air quality.
Computational Fluid Dynamics (CFD) is widely used to predict wind conditions for wind energy production purposes. However, as wind power development expands into areas of even more complex terrain and challenging flow conditions, more research is needed to investigate the ability of such models to describe turbulent flow features. In this study, the performance of a hybrid Reynolds-Averaged Navier-Stokes (RANS)/Large Eddy Simulation (LES) model in highly complex terrain has been investigated. The model was compared with measurements from a long range pulsed Lidar, which first were validated with sonic anemometer data. The accuracy of the Lidar was considered to be sufficient for validation of flow model turbulence estimates. By reducing the range gate length of the Lidar a slight additional improvement in accuracy was obtained, but the availability of measurements was reduced due to the increased noise floor in the returned signal. The DES model was able to capture the variations of velocity and turbulence along the line-of-sight of the Lidar beam but overestimated the turbulence level in regions of complex flow.
Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.
All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.
Significance
Increased forest fire activity across the western United States in recent decades has contributed to widespread forest mortality, carbon emissions, periods of degraded air quality, and substantial fire suppression expenditures. Although numerous factors aided the recent rise in fire activity, observed warming and drying have significantly increased fire-season fuel aridity, fostering a more favorable fire environment across forested systems. We demonstrate that human-caused climate change caused over half of the documented increases in fuel aridity since the 1970s and doubled the cumulative forest fire area since 1984. This analysis suggests that anthropogenic climate change will continue to chronically enhance the potential for western US forest fire activity while fuels are not limiting.
Planetary boundary-layer (PBL) structure was investigated using observations from a Doppler lidar and the 325-m Institute of Atmospheric Physics (IAP) meteorological tower in the centre of Beijing during the summer 2015 Study of Urban-impacts on Rainfall and Fog/haze (SURF-2015) field campaign. Using six fair-weather days of lidar and tower data under clear to cloudy skies, we evaluate the ability of the Doppler lidar to probe the urban boundary-layer structure, and then propose a composite method for estimating the diurnal cycle of the PBL depth using the Doppler lidar. For the convective boundary layer (CBL), a threshold method using vertical velocity variance is used, since it provides more reliable CBL depths than a conventional maximum wind-shear method. The nocturnal boundary-layer (NBL) depth is defined as the height at which decreases to 10 % of its near-surface maximum minus a background variance. The PBL depths determined by combining these methods have average values ranging from 270 to 1500 m for the six days, with the greatest maximum depths associated with clear skies. Release of stored and anthropogenic heat contributes to the maintenance of turbulence until late evening, keeping the NBL near-neutral and deeper at night than would be expected over a natural surface. The NBL typically becomes more shallow with time, but grows in the presence of low-level nocturnal jets. While current results are promising, data over a broader range of conditions are needed to fully develop our PBL-depth algorithms.
A plume rise algorithm for wildfires was included in WRF-Chem, and applied to look at the impact of intense wildfires during the 2004 Alaska wildfire season on weather simulations using model resolutions of 10 km and 2 km. Biomass burning emissions were estimated using a biomass burning emissions model. In addition, a 1-D, time-dependent cloud model was used online in WRF-Chem to estimate injection heights as well as the vertical distribution of the emission rates. It was shown that with the inclusion of the intense wildfires of the 2004 fire season in the model simulations, the interaction of the aerosols with the atmospheric radiation led to significant modifications of vertical profiles of temperature and moisture in cloud-free areas. On the other hand, when clouds were present, the high concentrations of fine aerosol (PM2.5) and the resulting large numbers of Cloud Condensation Nuclei (CCN) had a strong impact on clouds and cloud microphysics, with decreased precipitation coverage and precipitation amounts during the first 12 h of the integration. During the afternoon, storms were of convective nature and appeared significantly stronger, probably as a result of both the interaction of aerosols with radiation (through an increase in CAPE) as well as the interaction with cloud microphysics.
Background:
Epidemiological studies investigating the role of fine particulate matter (PM2.5; aerodynamic diameter <2.5 μm) in triggering acute coronary events, including out-of-hospital cardiac arrests and ischemic heart disease (IHD), during wildfires have been inconclusive.
Methods and results:
We examined the associations of out-of-hospital cardiac arrests, IHD, acute myocardial infarction, and angina (hospital admissions and emergency department attendance) with PM2.5 concentrations during the 2006-2007 wildfires in Victoria, Australia, using a time-stratified case-crossover study design. Health data were obtained from comprehensive health-based administrative registries for the study period (December 2006 to January 2007). Modeled and validated air exposure data from wildfire smoke emissions (daily average PM2.5, temperature, relative humidity) were also estimated for this period. There were 457 out-of-hospital cardiac arrests, 2106 emergency department visits, and 3274 hospital admissions for IHD. After adjusting for temperature and relative humidity, an increase in interquartile range of 9.04 μg/m(3) in PM2.5 over 2 days moving average (lag 0-1) was associated with a 6.98% (95% CI 1.03% to 13.29%) increase in risk of out-of-hospital cardiac arrests, with strong association shown by men (9.05%,95%CI 1.63% to 17.02%) and by older adults (aged ≥65 years) (7.25%, 95% CI 0.24% to 14.75%). Increase in risk was (2.07%, 95% CI 0.09% to 4.09%) for IHD-related emergency department attendance and (1.86%, 95% CI: 0.35% to 3.4%) for IHD-related hospital admissions at lag 2 days, with strong associations shown by women (3.21%, 95% CI 0.81% to 5.67%) and by older adults (2.41%, 95% CI 0.82% to 5.67%).
Conclusion:
PM2.5 exposure was associated with increased risk of out-of-hospital cardiac arrests and IHD during the 2006-2007 wildfires in Victoria. This evidence indicates that PM2.5 may act as a triggering factor for acute coronary events during wildfire episodes.
Wildfires take a heavy toll on human health worldwide. Climate change may increase the risk of wildfire frequency. Therefore, in view of adapted preventive actions, there is an urgent need to further understand the health effects and public awareness of wildfires. We conducted a systematic review of non-accidental health impacts of wildfire and incorporated lessons learned from recent experiences. Based on the literature, various studies have established the relationship between one of the major components of wildfire, particulate matter (particles with diameter less than 10 µm (PM10) and less than 2.5 µm (PM2.5)) and cardiorespiratory symptoms in terms of Emergency Rooms visits and hospital admissions. Associations between wildfire emissions and various subclinical effects have also been established. However, few relationships between wildfire emissions and mortality have been observed. Certain segments of the population may be particularly vulnerable to smoke-related health risks. Among them, people with pre-existing cardiopulmonary conditions, the elderly, smokers and, for professional reasons, firefighters. Potential action mechanisms have been highlighted. Overall, more research is needed to better understand health impact of wildfire exposure.
To calculate the potential wind loading on a tall building in an urban area, an accurate representation of the wind speed profile is required. However, due to a lack of observations, wind engineers typically estimate the characteristics of the urban boundary layer by translating the measurements from a nearby reference rural site. This study presents wind speed profile data obtained from a Doppler lidar in central London, UK, during an 8 month observation period. Used in conjunction with wind speed data measured at a nearby airport, the data have been used to assess the accuracy of the predictions made by the wind engineering tools currently available.
When applied to multiple changes in surface roughness identified from morphological parameters, the non-equilibrium wind speed profile model developed by Deaves (1981) provides a good representation of the urban wind speed profile. For heights below 500 m, the predicted wind speed remains within the 95% confidence interval of the measured data. However, when the surface roughness is estimated using land use as a proxy, the model tends to overestimate the wind speed, particularly for very high wind speed periods. These results highlight the importance of a detailed assessment of the nature of the surface when estimating the wind speed above an urban surface.
Mixed layer depths were derived from potential temperature profiles from aircraft, high-altitude balloon sonde and tethersonde measurements taken during the Pacific '93 field study in the Lower Fraser Valley of southern British Columbia. In general, mixed layer depths derived from these different data sources were closely comparable. An airborne lidar was used to map aerosol depth throughout the valley. These lidar-derived aerosol depths compared well with the meteorologically derived mixed layer depths. During one notable ozone episode, mixed layer depths were low, and in the range 500–800m. Measurements of chemical pollutants such as ozone and nitrogen oxides showed these to be generally well mixed below the top of the mixed layer during daytime. However, at times, layering within and above the inversion layer was observed.
Quality-assured meteorological and tracer data sets are vital for establishing confidence that indoor and outdoor dispersion models used to simulate dispersal of potential toxic agents in urban atmospheres are giving trustworthy results. The U.S. Department of Defense-Defense Threat Reduction Agency and the U.S. Department of Homeland Security joined together to conduct the Joint Urban 2003 atmospheric dispersion study to provide this critically-needed high-resolution dispersion data. This major urban study was conducted from June 28 through July 31, 2003, in Oklahoma City, Oklahoma, with the participation of over 150 scientists and engineers from over 20 U.S. and foreign institutions. The Joint Urban 2003 lead scientist was Jerry Allwine (Pacific Northwest National Laboratory) who oversaw study design, logistical arrangements and field operations with the help of Joe Shinn (Lawrence Livermore National Laboratory), Marty Leach (Lawrence Livermore National Laboratory), Ray Hosker (Atmospheric Turbulence and Diffusion Division), Leo Stockham (Northrop Grumman Information Technology) and Jim Bowers (Dugway Proving Grounds). This report gives a brief overview of the field campaign, describing the scientific objectives, the dates of the intensive observation periods, and the instruments deployed. The data from this field study is available to the scientific community through an on-line database that is managed by Dugway Proving Ground. This report will be included in the database to provide its users with some general information about the field study, and specific information about the instrument coordinates. Appendix A of this document provides the definitive record of the instrument locations during this field campaign, and Appendix B lists all the study principal investigators and participants.
Strong contrasts in daytime mixing height (boundary layer [BL] height or zi) between urban and rural areas were observed during the 1999 Nashville Summer Intensive field campaign of the Southern Oxidants Study. On occasion, the urban mixing height was as much as 45% (700 m) higher than that over the rural areas. The difference was quite persistent, showing strongly in statistical comparisons, with a mean difference over all hours available for comparison of 160 m. Clouds had higher bases and were more common over the urban area as well. In this paper, measurements from wind profiling radars, lidars, and aircraft are used to characterize mixing height and clouds. The urban–rural contrasts have important implications for regional air quality. The mixing height is a first-order control on pollutant concentrations. The urban–rural contrast also results in the venting of urban pollutants, affecting the local concentrations and the regional background. Clouds affect air quality by changing the radiative input for photochemistry and through changes in mixing and venting.
A technique is proposed for the measurement of kinematic properties of a wind field in situations of widespread precipitation, using a single Doppler radar to sense the motion of the precipitation particles. The technique is an extension of ideas put forward by Probert-Jones, Lhermitte, Atlas, Caton and Harrold, and is based upon the Velocity-Azimuth Display (VAD) obtained by scanning the radar beam about a vertical axis at a fixed elevation angle. Harmonic analysis of the VAD permits divergence to be obtained from the magnitude of the `zeroth' harmonic, wind speed and direction to be obtained from the amplitude and phase of the first harmonic, and resultant deformation and the axis of dilatation to be obtained from the amplitude and phase of the second harmonic. Although limitations to the accuracy of this technique are imposed by inhomogeneities in the horizontal distribution of precipitation fall speed and, in the presence of strong vertical wind shear, by elevation angle errors and reflectivity inhomogeneities, the errors resulting from these effects can be made acceptably small by scanning at appropriate elevation angles and ranges. An optimum scanning procedure is suggested. A short case study is also presented to support the view that meaningful estimates of mesoscale divergence and deformation can be obtained using this technique.
The dispersion of exhausted pollutants from a building roof stack situated in the wake of a neighbouring tower has been studied using computational fluid dynamics (CFD) with the realizable k–ɛ turbulence model for closure. Two scales are considered in this work, full-scale (1:1) and wind tunnel scale (1:200).Of primary interest are the distributions of the plume and of the pollutant concentrations on the building roof as well as on the leeward wall of the tower. Two stack heights and pollutant exhaust velocities have been considered for the distribution of pollutant concentrations in the neighbourhood of the building from which the pollutant is emitted. Results are compared with measurements from field and wind tunnel experiments to estimate the accuracy of simulations.
A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude.
This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ε can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.
Increasing trends in biomass burning emissions significantly impact air quality in North America. Enhanced mixing ratios of ozone (O3) in urban areas during smoke-impacted periods occur through transport of O3 produced within the smoke or through mixing of pyrogenic volatile organic compounds (PVOCs) with urban nitrogen oxides (NOx = NO + NO2) to enhance local O3 production. Here, we analyze a set of detailed chemical measurements, including carbon monoxide (CO), NOx, and speciated volatile organic compounds (VOCs), to evaluate the effects of smoke transported from relatively local and long-range fires on O3 measured at a site in Boulder, Colorado, during summer 2020. Relative to the smoke-free period, CO, background O3, OH reactivity, and total VOCs increased during both the local and long-range smoke periods, but NOx mixing ratios remained approximately constant. These observations are consistent with transport of PVOCs (comprised primarily of oxygenates) but not NOx with the smoke and with the influence of O3 produced within the smoke upwind of the urban area. Box-model calculations show that local O3 production during all three periods was in the NOx-sensitive regime. Consequently, this locally produced O3 was similar in all three periods and was relatively insensitive to the increase in PVOCs. However, calculated NOx sensitivities show that PVOCs substantially increase O3 production in the transition and NOx-saturated (VOC-sensitive) regimes. These results suggest that (1) O3 produced during smoke transport is the main driver for O3 increases in NOx-sensitive urban areas and (2) smoke may cause an additional increase in local O3 production in NOx-saturated (VOC-sensitive) urban areas. Additional detailed VOC and NOx measurements in smoke impacted urban areas are necessary to broadly quantify the effects of wildfire smoke on urban O3 and develop effective mitigation strategies.
The computational power available nowadays to industry and research paves the way to increasingly more accurate systems for the wind resource prediction. A promising approach is to support the mesoscale numerical weather prediction (NWP) with high fidelity computational fluid dynamics (CFD). This approach aims at increasing the spatial resolution of the wind prediction by not only accounting for the complex and multiphysics aspects of the atmosphere over a large geographical region, but also including the effects of the fine scale turbulence and the interaction of the wind flow with the sea surface. In this work, we test a set of model setups for both the mesoscale (NWP) and local scale (CFD) simulations employed in a multi-scale modelling framework. The method comprises a one-way coupling interface to define boundary conditions for the local scale simulation (based on the Reynolds Averaged Navier–Stokes equations) using the mesoscale wind given by the NWP system. The wind prediction in an offshore site is compared with LiDAR measurements, testing a set of mesoscale planetary boundary layer schemes, and different model choices for the local scale simulation, which include steady and unsteady approaches for simulation and boundary conditions, different turbulence closure constants, and the effect of the wave motion of the sea surface. The resulting wind is then used for the simulation of a large wind turbine, showing how a realistic wind profile and an ideal exponential law profile lead to different predictions of wind turbine rotor performance and loads.
Smoke from wildfires is a growing health risk across the US. Understanding the spatial and temporal patterns of such exposure and its population health impacts requires separating smoke-driven pollutants from non-smoke pollutants and a long time series to quantify patterns and measure health impacts. We develop a parsimonious and accurate machine learning model of daily wildfire-driven PM2.5 concentrations using a combination of ground, satellite, and reanalysis data sources that are easy to update. We apply our model across the contiguous US from 2006 to 2020, generating daily estimates of smoke PM2.5 over a 10 km-by-10 km grid and use these data to characterize levels and trends in smoke PM2.5. Smoke contributions to daily PM2.5 concentrations have increased by up to 5 μg/m3 in the Western US over the last decade, reversing decades of policy-driven improvements in overall air quality, with concentrations growing fastest for higher income populations and predominantly Hispanic populations. The number of people in locations with at least 1 day of smoke PM2.5 above 100 μg/m3 per year has increased 27-fold over the last decade, including nearly 25 million people in 2020 alone. Our data set can bolster efforts to comprehensively understand the drivers and societal impacts of trends and extremes in wildfire smoke.
The dispersion of traffic pollutants in street canyons takes place under the joint influence of the urban morphology and traffic-induced air motions. Computational fluid dynamics (CFD) are increasingly used to explore air quality problems in cities, however, numerical setup guidance for studies considering moving traffics remains missing. This study presents a systematic evaluation and comparison of steady Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulation (LES) in reproducing the wind flow and pollutant dispersion in a street canyon under traffic flow using the quasi-steady method. The RANS simulations are performed with five turbulence models, and the standard k-ε (SKE) turbulence model is found to yield the best agreement with the wind tunnel data. The LES simulation with the wall adapting local eddy viscosity subgrid-scale model outperforms all RANS models in simulating the mean velocity and mean pollutant concentration, and can accurately predict the turbulent velocity. The counter-gradient turbulent mass flux captured by LES reveals that the gradient-diffusion hypothesis used in steady RANS fails in the regions near moving traffics, which strongly affects the predicted mean concentration. The findings of this study can support future CFD studies on pollutant dispersion in urban environments.
Cities are facing significant challenges due to low outdoor air quality. Hence, a growing number of studies have been conducted to develop strategies for improving air quality. This paper provides a comprehensive and systematic review of the mechanisms and effects of various mechanical measures and urban morphology on pollutant dispersion in an urban context. Moreover, a detailed quantification of the reduction potential of the mechanical measures and urban morphology is provided, followed by the identification of the critical urban factors for the practical urban optimizing strategies. Additionally, the two fundamental processes (mean flow and turbulence) affecting the pollutant dispersion are clarified to understand the dilution mechanism of pollutants. For these purposes, the reviewed papers are categorized into two groups:
(i) Utilizing mechanical measures.
(ii) Designing appropriate urban morphology.
Generally, this study is useful for urban planners and architects who are responsible for decision-making.
This investigation presents computational fluid dynamics (CFD) simulations of carbon dioxide (CO2) dispersion from a natural gas-fueled thermal power plant in an urban environment. The results are compared with experimental measurements of column-averaged dry-air mole fraction (XCO2) on the site, obtaining a good agreement. Different turbulent Schmidt numbers are compared, and we suggest a value for being used in full-scale simulations. The particular characteristics, e.g. azimuth and elevation angles of the XCO2 measurement, are analyzed and taken into account for the comparison with simulation results. The conclusions from this comparison are useful not only for the XCO2 experimental data analysis, but also for the efficient and successful design of future measurement campaigns. The simulation results are also compared with the Gaussian plume model, and a new parametrization (i.e. vertical dispersion parameter) is suggested for being used in the urban environment. Additionally, CO2 concentration maps for an urban area are presented, and the spatial distribution is analyzed.
There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.
In this paper, wake interaction resulting from two stall regulated turbines aligned with the incoming wind is studied experimentally and numerically. The experimental work is based on a full-scale remote sensing campaign involving three nacelle mounted scanning lidars. A thorough analysis and interpretation of the measurements is performed to overcome either the lack of or the poor calibration of relevant turbine operational sensors, as well as other uncertainties inherent in resolving wakes from full-scale experiments. The numerical work is based on the in-house EllipSys3D computational fluid dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation is based on a comparison between wake deficit, wake generated turbulence, turbine power production and thrust force. An excellent agreement between measurement and simulation is seen in both the fixed and the meandering frame of reference. Copyright © 2015 John Wiley & Sons, Ltd.
Our fundamental understanding of the boundary layer comes from measurements. Most measurements are made in the field, some are made in laboratory tank or wind tunnel simulations, and some are samples from numerical simulations. Theories and parameterizations, such as presented in earlier chapters, are valuable only if they describe the observed boundary layer behavior.
Urban physics is the science and engineering of physical processes in urban areas. It basically refers to the transfer of heat and mass in the outdoor and indoor urban environment, and its interaction with humans, fauna, flora and materials. Urban physics is a rapidly increasing focus area as it is key to understanding and addressing the grand societal challenges climate change, energy, health, security, transport and aging. The main assessment tools in urban physics are field measurements, full-scale and reduced-scale laboratory measurements and numerical simulation methods including Computational Fluid Dynamics (CFD). In the past 50 years, CFD has undergone a successful transition from an emerging field into an increasingly established field in urban physics research, practice and design. This review and position paper consists of two parts. In the first part, the importance of urban physics related to the grand societal challenges is described, after which the spatial and temporal scales in urban physics and the associated model categories are outlined. In the second part, based on a brief theoretical background, some views on CFD are provided. Possibilities and limitations are discussed, and in particular, ten tip and tricks towards accurate and reliable CFD simulations are presented. These tips and tricks are certainly not intended to be complete, rather they are intended to complement existing CFD best practice guidelines on ten particular aspects. Finally, an outlook to the future of CFD for urban physics is given.
Flow and dispersion of traffic pollutants in a generic urban neighborhood with avenue-trees were investigated with Computational Fluid Dynamics (CFD). In Part I of this two-part contribution, quality assessment and assurance for CFD simulations in urban and vegetation configurations were addressed, before in Part II flow and dispersion in a generic urban neighborhood with multiple layouts of avenue-trees were studied. In a first step, a grid sensitivity study was performed that inferred that a cell count of 20 per building height and 12 per canyon width is sufficient for reasonable grid insensitive solutions. Next, the performance of the realizable k-ε turbulence model in simulating urban flows and of the applied vegetation model in simulating flow and turbulence in trees was validated. Finally, based on simulations of street canyons with and without avenue-trees, an appropriate turbulent Schmidt number for modeling dispersion in the urban neighborhood was determined as Sct = 0.5.
Copyright © 2014 Elsevier Ltd. All rights reserved.
Several epidemiological studies have shown that the outbreaks of Saharan dust over southern European countries can cause negative health effects. The reasons for the increased toxicity of airborne particles during dust storms remain to be understood although the presence of biogenic factors carried by dust particles and the interaction between dust and man-made air pollution have been hypothesized as possible causes. Intriguingly, recent findings have also demonstrated that during Saharan dust outbreaks the local man-made particulates can have stronger effects on health than during days without outbreaks. We show that the thinning of the mixing layer (ML) during Saharan dust outbreaks, systematically described here for the first time, can trigger the observed higher toxicity of ambient local air. The mixing layer height (MLH) progressively reduced with increasing intensity of dust outbreaks thus causing a progressive accumulation of anthropogenic pollutants and favouring the formation of new fine particles or specific relevant species likely from condensation of accumulated gaseous precursors on dust particles surface. Overall, statistically significant associations of MLH with all-cause daily mortality were observed. Moreover, as the MLH reduced, the risk of mortality associated with the same concentration of particulate matter increased due to the observed pollutant accumulation. The association of MLH with daily mortality and the effect of ML thinning on particle toxicity exacerbated when Saharan dust outbreaks occurred suggesting a synergic effect of atmospheric pollutants on health which was amplified during dust outbreaks. Moreover, the results may reflect higher toxicity of primary particles which predominate on low MLH days.
PM2.5 and NO2 concentrations were measured simultaneously indoors and outdoors for ten different city centre buildings (shops and offices) in Dublin, Ireland. Outdoor concentrations were measured in two locations either at ground level outside the building or at the air intake of the building's ventilation system. The ratio of indoor to outdoor (I/O) PM2.5 concentrations were all found to be close to or above 1, indicating that either the fabric and/or operational environment of the buildings, whether naturally or mechanically ventilated, was not performing any significant function to reduce particulate concentrations from outdoors or that indoor sources were present. Indoor PM2.5 concentrations during working hours were generally below 25 μg m−3 PM2.5, with naturally ventilated shops showing the highest concentrations. Lower indoor NO2 concentrations were measured during working hours in naturally ventilated buildings compared to the mechanically ventilated buildings, although most buildings showed strong diurnal relationships between outdoor NO2 concentrations and indoor air quality. Indeed, street level concentrations of NO2 seemed to have a stronger influence on the indoor air quality of the mechanically ventilated buildings, than with the corresponding air quality measured at their roof level ventilation intakes. The buildings with the greatest reduction for NO2 were older naturally ventilated offices. I/O ratios of both pollutants, but particularly NO2, increased significantly overnight as outdoor concentrations reduced to a much greater extent than indoors. This would indicate a benefit in promoting increased air exchange between the outdoors and indoors during night–time periods in order to flush out air pollutants.
Re-ingestion of the contaminated exhaust air from the same building is a concern in high-rise residential buildings, and can be serious depending on wind conditions and contaminant source locations. In this paper, we aim to assess the prediction accuracy of three k-ɛ turbulence models, in numerically simulating the wind-induced pressure and indoor-originated air pollutant dispersion around a complex-shaped high-rise building, by comparing with our earlier wind tunnel test results. The building modeled is a typical, 33-story tower-like building consisting of 8-household units on each floor, and 4 semi-open, vertical re-entrant spaces are formed, with opposite household units facing each other in very close proximity. It was found that the predicted surface pressure distributions by the two revised k-ɛ models, namely the renormalized and realizable k-ɛ models agree reasonably with experimental data. However, with regard to the vertical pollutant concentration distribution in the windward re-entrance space, obvious differences were found between the three turbulence models, and the simulation result using the realizable k-ɛ model agreed the best with the experiment. On the other hand, with regard to the vertical pollutant concentration distribution in the re-entrant space oblique to the wind, all the three models gave acceptable predictions at the concentration level above the source location, but severely underestimated the downward dispersion. The effects of modifying the value of the turbulent Schmidt number in the realizable k-ɛ model were also examined for oblique-wind case. It was confirmed that the numerical results, especially the downward dispersion, are quite sensitive to the value of turbulent Schmidt number.
Air quality measurements of urban monitoring stations have a limited spatial representativeness due to the complexity of urban meteorology and emissions distribution. In this work, a methodology based on a set of computational fluid dynamics simulations based on Reynolds-Averaged Navier-Stokes equations (RANS-CFD) for different meteorological conditions covering several months is developed in order to analyse the spatial representativeness of urban monitoring stations and to complement their measured concentrations. The methodology has been applied to two urban areas nearby air quality traffic-oriented stations in Pamplona and Madrid (Spain) to analyse nitrogen oxides concentrations. The computed maps of pollutant concentrations around each station show strong spatial variability being very difficult to comply with the European legislation concerning the spatial representativeness of traffic-oriented air quality stations.