scholarly journals Impact of Urbanization on the Predictions of Urban Meteorology and Air Pollutants over Four Major North American Cities

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 969 ◽  
Author(s):  
Shuzhan Ren ◽  
Craig Stroud ◽  
Stephane Belair ◽  
Sylvie Leroyer ◽  
Rodrigo Munoz-Alpizar ◽  
...  
Keyword(s):  
Air Quality ◽  
North American ◽  
Air Pollutants ◽  
Model Simulation ◽  
Ground Level ◽  
Early Morning ◽  
Heat Fluxes ◽  
Air Quality Model ◽  
Quality Model ◽  
American Cities

The sensitivities of meteorological and chemical predictions to urban effects over four major North American cities are investigated using the high-resolution (2.5-km) Environment and Climate Change Canada’s air quality model with the Town Energy Balance (TEB) scheme. Comparisons between the model simulation results with and without the TEB effect show that urbanization has great impacts on surface heat fluxes, vertical diffusivity, air temperature, humidity, atmospheric boundary layer height, land-lake circulation, air pollutants concentrations and Air Quality Health Index. The impacts have strong diurnal variabilities, and are very different in summer and winter. While the diurnal variations of the impacts share some similarities over each city, the magnitudes can be very different. The underlying mechanisms of the impacts are investigated. The TEB impacts on the predictions of meteorological and air pollutants over Toronto are evaluated against ground-based observations. The results show that the TEB scheme leads to a great improvement in biases and root-mean-square deviations in temperature and humidity predictions in downtown, uptown and suburban areas in the early morning and nighttime. The scheme also leads to a big improvement of predictions of NOx, PM2.5 and ground-level ozone in the downtown, uptown and industrial areas in the early morning and nighttime.

scholarly journals Forming High Ozone Concentration in the Ambient Air of Southern Taiwan under the Effects of Western Pacific Subtropical High

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Kuo-Cheng Lo ◽  
Chung-Hsuang Hung
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Model Simulation ◽  
Western Pacific Subtropical High ◽  
Ambient Air ◽  
Western Pacific ◽  
Subtropical High ◽  
Air Quality Model ◽  
Quality Model ◽  
Southern Taiwan

Due to the distinct geographical and meteorological conditions of Taiwan, air pollutants concentrations in the ambient air of it may vary with seasons. Accordingly, this study aimed to investigate the formation of high O3concentration in the ambient air of Southern Taiwan during summers. A high O3concentration case occurring between June 28 and July 2, 2013, was modeled and analyzed with WRF-Chem meteorological and air quality model. During the investigated period, a typical western Pacific subtropical high (WPSH) covered most East Asia, including Taiwan and its surrounding areas. The observations showed strong correlations between WPSH invasion and forming high O3concentrations. The dispersion of air pollutants in the ambient air is not sufficient to dilute their concentrations. In the afternoon of June 30, more than 60% of the air quality monitoring stations found O3concentrations exceeding 100 ppb, which were 2~3 times higher than their normal concentrations. Model simulation results verified that the presence of the WPSH hindered the dilution and transportation of air pollutants in ambient air. In addition, the air quality would be getting worse due to the leeward sides caused by the counter clockwise vertex formed in Southwestern Taiwan.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

2016 ◽  
Vol 16 (24) ◽  
pp. 15629-15652 ◽  
Author(s):  
Ioannis Kioutsioukis ◽  
Ulas Im ◽  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Alba Badia ◽  
...  
Keyword(s):  
Air Quality ◽  
Deterministic Model ◽  
Ensemble Methods ◽  
Ground Level ◽  
Chemical Mechanism ◽  
Second Phase ◽  
Air Quality Model ◽  
Quality Model ◽  
Deterministic Models ◽  
Ensemble Mean

Abstract. Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

<p>One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM<sub>2.5</sub> observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.</p><p>In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.</p><p>While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.</p><p> </p><p>Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.</p>

scholarly journals Impact of Bay-Breeze Circulations on Surface Air Quality and Boundary Layer Export

2014 ◽  
Vol 53 (7) ◽  
pp. 1697-1713 ◽  
Author(s):  
Christopher P. Loughner ◽  
Maria Tzortziou ◽  
Melanie Follette-Cook ◽  
Kenneth E. Pickering ◽  
Daniel Goldberg ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Chesapeake Bay ◽  
Surface Ozone ◽  
Early Morning ◽  
Pollution Transport ◽  
Air Quality Model ◽  
Quality Model ◽  
Early Afternoon ◽  
Bay Breeze

AbstractMeteorological and air-quality model simulations are analyzed alongside observations to investigate the role of the Chesapeake Bay breeze on surface air quality, pollutant transport, and boundary layer venting. A case study was conducted to understand why a particular day was the only one during an 11-day ship-based field campaign on which surface ozone was not elevated in concentration over the Chesapeake Bay relative to the closest upwind site and why high ozone concentrations were observed aloft by in situ aircraft observations. Results show that southerly winds during the overnight and early-morning hours prevented the advection of air pollutants from the Washington, D.C., and Baltimore, Maryland, metropolitan areas over the surface waters of the bay. A strong and prolonged bay breeze developed during the late morning and early afternoon along the western coastline of the bay. The strength and duration of the bay breeze allowed pollutants to converge, resulting in high concentrations locally near the bay-breeze front within the Baltimore metropolitan area, where they were then lofted to the top of the planetary boundary layer (PBL). Near the top of the PBL, these pollutants were horizontally advected to a region with lower PBL heights, resulting in pollution transport out of the boundary layer and into the free troposphere. This elevated layer of air pollution aloft was transported downwind into New England by early the following morning where it likely mixed down to the surface, affecting air quality as the boundary layer grew.

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