scholarly journals Model Representation of Local Air Quality Characteristics

2009 ◽  
Vol 48 (5) ◽  
pp. 945-961 ◽  
Author(s):  
Stephen F. Mueller
Keyword(s):  
Air Quality ◽  
Spatial Resolution ◽  
Fine Particles ◽  
Surface Wind ◽  
Model Performance ◽  
Total Carbon ◽  
Quality Data ◽  
Model Skill ◽  
Temporal Variance ◽  
Hourly Data

Abstract Daily (24 h) and hourly air quality data at several sites are used to examine the performance of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)–Community Multiscale Air Quality Model (CMAQ) system over a 3-month period in 2003. A coarse (36 km) model grid was expected to provide relatively poor performance for ozone and comparatively better performance for fine particles, especially the more regional sulfate and carbonaceous aerosols. However, results were different from this expectation. Modeling showed significant skill for ozone at several locations but very little skill for particulate species. Modeling did poorly identifying surface wind directions associated with the highest and lowest pollutant exposures at most sites, although results varied widely by location. Model skill appeared to be better for ozone when spatial–temporal (S–T) patterns were examined, due in part to the ability of the model to reproduce much of the temporal variance associated with the diurnal photochemical cycle. At some sites the modeling even performed well in replicating the directional variability of hourly ozone despite relatively low spatial resolution. MM5–CMAQ spatial (directional) representation of 24-h-average particulate data was not good in most cases, but model skill improved somewhat when hourly data were examined. Modeling exhibited skill for sulfate at only one of nine sites using 24-h data averaged by daily resultant wind direction, at two of six sites when hourly data were averaged by direction, and at four of six sites when the combined spatial and temporal variance of sulfate was examined. Results were generally poorer for total carbon aerosol mass and total mass of particulate matter with diameter of less than 2.5 μm (PM2.5). The primary result of this study is that an S–T analysis of pollutant patterns reveals model performance insights that cannot be realized by only examining model error statistics as is typically done for regulatory applications. Use of this S–T analysis technique is recommended for better understanding model performance during longer simulation periods, especially when using grids of finer spatial resolution for applications supporting local air quality management studies. Of course, using this approach will require measuring semicontinuous fine particle data at more sites and for longer periods.

scholarly journals Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

2010 ◽  
Vol 3 (1) ◽  
pp. 169-188 ◽  
Author(s):  
K. W. Appel ◽  
S. J. Roselle ◽  
R. C. Gilliam ◽  
J. E. Pleim
Keyword(s):  
United States ◽  
Air Quality ◽  
Mesoscale Model ◽  
Model Performance ◽  
Wrf Model ◽  
Total Carbon ◽  
Eastern United States ◽  
Model Simulations ◽  
Mixing Ratios ◽  
Vegetation Fraction

Abstract. This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O3) mixing ratios were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 mixing ratios are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−), similar performance for nitrate (NO3−), and slightly worse performance for nitric acid (HNO3), total carbon (TC) and total fine particulate (PM2.5) mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary causes of the differences in the MM5-CMAQ and WRF-CMAQ simulations appear to be due to differences in the calculation of wind speed, planetary boundary layer height, cloud cover and the friction velocity (u∗) in the MM5 and WRF model simulations, while differences in the calculation of vegetation fraction and several other parameters result in smaller differences in the predicted CMAQ model concentrations. The performance for SO42−, NO3− and NH4+ wet deposition was similar for both simulations for January and August.

scholarly journals Environmental justice and air quality in Santiago de Chile

2015 ◽  
Vol 17 (3) ◽  
pp. 337-350 ◽  
Author(s):  
Richelle Rose Perez
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Justice ◽  
Fine Particles ◽  
Metropolitan Region ◽  
Quality Data ◽  
Policy Makers ◽  
Pollution Levels ◽  
Spatial Differences ◽  
Socioeconomic Data

<p><strong>Objective </strong>The metropolitan region in Santiago, Chile has an air quality problem.  However, the larger issue may lie in the inequities created by the distribution of the air pollution.</p><p><strong>Methods </strong>To assess the inequities created by the spatial differences in air pollution, the author analyzed fine particle pollution levels for 2008-2011 at monitoring stations throughout the region. The author also compared air quality data with socioeconomic data.</p><p><strong>Results </strong>The areas of the Santiago metropolitan region with the worst air quality have lower socioeconomic levels. Pollution in these areas reaches levels higher than the current Chilean 24 hour standard for fine particles. These areas also have longer time periods of unhealthy air and 21 % more days with unhealthy levels of air pollution.</p><p><strong>Discussion </strong>The differences in exposure to pollution create an inequality and environmental injustice among the socioeconomic groups in the metropolitan region. Chilean policymakers have the regulatory tools needed to improve environmental justice. However, they need to improve the implementation of these tools in order to achieve that goal: Chilean policy makers should consider local sources of air pollution in the most polluted municipalities; Government decision makers should make extra efforts to listen to the community and improve access to environmental information; Environmental justice advocates should involve stakeholders from the social justice movement and other related areas; Policy makers should track progress towards environmental justice by evaluating differences in health outcomes related to differential exposure to air pollution in different parts of the Santiago metropolitan area.</p>

Development of a high-resolution air quality forecasting system, based on WRF-Chem model, for the greater area of Thessaloniki, Greece

2020 ◽  
Author(s):  
Irene Zyrichidou ◽  
Stavros Solomos ◽  
Stylianos Kotsopoulos ◽  
Panagiota Syropoulou ◽  
Evangelos Kosmidis
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Regional Scale ◽  
Surface Wind ◽  
Model Performance ◽  
Urban Pollution ◽  
Atmospheric Conditions ◽  
Pollution Levels ◽  
Implementation Period ◽  
Simulated Surface

&lt;p&gt;Air pollution models play an important role in science because of their capability to give a description of the air quality problem including an analysis of factors and causes (emission sources, meteorological processes, and physical and chemical changes). Real-time forecast of urban air quality is highly important to the public as advanced information for both air quality and safety assessment.&amp;#160;This study presents the development of a regional scale high-resolution modeling system for simulating air quality and forecasting changes in urban pollution levels. The air quality system based on the state-of-the-art Weather Research and Forecasting model coupled with chemistry (WRF-Chem)&amp;#160;has been applied over the greater area of Thessaloniki, Greece. The model performance, in terms of simulated surface major air pollutants&amp;#8217; concentrations, is evaluated using ground-based measurements during the operational implementation period in winter-spring 2020. Our study highlights the importance of resolving local scale atmospheric conditions such as surface wind flow and boundary layer properties for describing the pollutants&amp;#8217; concentrations and the importance of constraining emissions over the study area.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Effects of fireworks on particulate matter concentration in a narrow valley: the case of the Medellín metropolitan area

2019 ◽  
Vol 192 (1) ◽  
Author(s):  
Carlos D. Hoyos ◽  
Laura Herrera-Mejía ◽  
Natalia Roldán-Henao ◽  
Alejandra Isaza
Keyword(s):  
Boundary Layer ◽  
Particulate Matter ◽  
Air Quality ◽  
Metropolitan Area ◽  
Fine Particles ◽  
Microwave Radiometer ◽  
Potential Temperature ◽  
Quality Data ◽  
Convective Boundary ◽  
Concentration Changes

AbstractThe extensive use of fireworks generates large amounts of pollutants, deteriorating air quality and potentially causing adverse health impacts. In Medellín and its metropolitan area, although fireworks are banned during December, their use is widespread during the Christmas season, particularly during the midnight of November 30 (La Alborada) and New Year’s Eve (NYE). It is therefore essential to assess the effects of these celebrations on air quality in the region. Air-quality data from the official monitoring network and a low-cost particulate matter (PM) citizen science project, backscattering intensity (BI) retrievals from a ceilometer network, potential temperature from a microwave radiometer, and information from a radar wind profiler provide an excellent platform to study the spatio-temporal distribution of contaminants resulting from the La Alborada and NYE celebrations. Substantial increases in PM2.5 and PM10 mass concentrations due to La Alborada and NYE, ranging in some cases from 50 to 100 μgm−3, are observed in the Aburrá Valley and particularly in the densely populated communes of Medellín, with most concentration changes corresponding to ultrafine and fine particles. The PM increments resulting from fireworks show almost no increase in the net amount of black carbon in the atmosphere. Ceilometer BI profiles show a substantial change immediately after the La Alborada and NYE midnights, confined to the atmospheric boundary layer (ABL). Strong thermal inversions lead to fairly homogeneous increments in BI within the ABL, lasting until the onset of the convective boundary layer. In contrast, weak thermal inversions lead to rapid dispersion of aerosols, allowing them to episodically escape above the ABL.

scholarly journals Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

2009 ◽  
Vol 2 (2) ◽  
pp. 1081-1114 ◽  
Author(s):  
K. W. Appel ◽  
S. J. Roselle ◽  
R. C. Gilliam ◽  
J. E. Pleim
Keyword(s):  
United States ◽  
Air Quality ◽  
Dry Deposition ◽  
Mesoscale Model ◽  
Model Performance ◽  
Total Carbon ◽  
Weather Research And Forecasting ◽  
Eastern United States ◽  
Ohio Valley ◽  
Vegetation Fraction

Abstract. This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O3) concentrations were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 concentrations are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−), similar performance for nitrate (NO3−) and total nitrate (TNO3), and slightly worse performance for total carbon (TC) and total fine particulate (PM2.5) mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary cause of the differences in predicted concentrations between the MM5-CMAQ and WRF-CMAQ simulations is due to differences in the calculation of the friction velocity (u*) in MM5 and WRF models, which has a large effect on the dry deposition of NO, NO2 and HNO3. Differences in the calculation of the vegetation fraction and the predicted cloud cover, along with several other minor differences in the simulations also affect the predicted concentrations from CMAQ. The performance for SO42−, NO3− and NH4+ wet deposition was similar for both simulations for January and August.

scholarly journals A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1344
Author(s):  
Peng Wang ◽  
Lyudmila Mihaylova ◽  
Rohit Chakraborty ◽  
Said Munir ◽  
Martin Mayfield ◽  
...  
Keyword(s):  
Air Quality ◽  
Gaussian Process ◽  
Noise Level ◽  
Process Model ◽  
Model Performance ◽  
Neumann Series ◽  
Quality Data ◽  
Comparative Case Study ◽  
Atmospheric Pollutant ◽  
Pollutant Concentrations

The monitoring and forecasting of particulate matter (e.g., PM2.5) and gaseous pollutants (e.g., NO, NO2, and SO2) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data noises, environmental and other factors. This paper proposes a general solution to analyse how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty, with a comparative case study of atmospheric pollutant concentrations prediction in Sheffield, UK, and Peshawar, Pakistan. The Neumann series is exploited to approximate the matrix inverse involved in the Gaussian process approach. This enables us to derive a theoretical relationship between any independent variable (e.g., measurement noise level, hyperparameters of Gaussian process methods), and the uncertainty and accuracy prediction. In addition, it helps us to discover insights on how these independent variables affect the algorithm evidence lower bound. The theoretical results are verified by applying a Gaussian processes approach and its sparse variants to air quality data forecasting.

scholarly journals Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7206
Author(s):  
Fabienne Reisen ◽  
Jacinta Cooper ◽  
Jennifer C. Powell ◽  
Christopher Roulston ◽  
Amanda J. Wheeler
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Calibration Curve ◽  
Good Accuracy ◽  
Fine Particles ◽  
Absolute Error ◽  
Sensor Technology ◽  
Similar Response ◽  
Data Set ◽  
Hourly Data

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93–0.99) and good accuracy (mean absolute error (MAE) < 2 μg m−3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87–95) and good accuracy (MAE of 2.5–3.0 μg m−3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

scholarly journals Spatial and temporal variations of air quality and six air pollutants in China during 2015–2017

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hong Guo ◽  
Xingfa Gu ◽  
Guoxia Ma ◽  
Shuaiyi Shi ◽  
Wannan Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Resolution ◽  
Air Pollutants ◽  
Control Policy ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Air Pollution Control

Abstract Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO2, and CO all decreased year by year during 2015–2017. However, the annual mean NO2 concentrations were almost unchanged, while the annual mean O3 concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO2 played an important role in the formation of PM2.5 and O3. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.

scholarly journals Improving Cloud Simulation for Air Quality Studies through Assimilation of Geostationary Satellite Observations in Retrospective Meteorological Modeling

2017 ◽  
Vol 146 (1) ◽  
pp. 29-48 ◽  
Author(s):  
Andrew T. White ◽  
Arastoo Pour-Biazar ◽  
Kevin Doty ◽  
Bright Dornblaser ◽  
Richard T. McNider
Keyword(s):  
United States ◽  
Air Quality ◽  
Surface Wind ◽  
Model Performance ◽  
Surface Wind Speed ◽  
Shortwave Radiation ◽  
Percentage Increase ◽  
Meteorological Model ◽  
Average Percentage ◽  
Space And Time

Abstract Development of clouds in space and time within numerical meteorological models as observed in nature is essential for producing an accurate representation of the physical atmosphere for input into air quality models. In this study, a new technique was developed to assimilate Geostationary Operational Environmental Satellite (GOES)-derived cloud fields into the Weather Research and Forecasting (WRF) meteorological model to improve the placement of clouds in space and time within the model. The simulations were performed on 36-, 12-, and 4-km grid-size domains covering the contiguous United States, the south-southeastern United States, and eastern Texas, respectively. The technique was tested over the month of August 2006. The results indicate that the assimilation technique significantly improves the agreement between the model-predicted and GOES-derived cloud fields. The daily average percentage increase in the cloud agreement was determined to be 14.02%, 11.29%, and 4.96% for the 36-, 12-, and 4-km domains, respectively. This was accomplished without degrading the model performance with respect to surface wind speed, temperature, and mixing ratio, which are important parameters for air quality applications; in some cases these variables were even slightly improved. The assimilation technique also produced improvements in the model-predicted precipitation and predicted downwelling shortwave radiation reaching the surface.

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