Comparing Standard to Feature-Based Meteorological Model Evaluation Techniques in Bogotá, Colombia

AbstractStandard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

Download Full-text

Evaluate of air pollution dispersion and propose planing scenerios to reduce air pollution for livestock activities in Tan Thanh district, Ba Ria – Vung Tau province

Science & Technology Development Journal - Science of The Earth & Environment ◽

10.32508/stdjsee.v2i2.491 ◽

2019 ◽

Vol 2 (2) ◽

pp. 26-37 ◽

Cited By ~ 2

Author(s):

Dung Minh Ho ◽

Bang Quoc Ho ◽

Thang Viet Le

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutants ◽

Agricultural Sector ◽

Ambient Air ◽

Air Quality Model ◽

Quality Model ◽

Meteorological Model ◽

Pollution Dispersion ◽

Air Pollution Dispersion

Livestock is one of the main activities of the agricultural sector in Tan Thanh district, Ba Ria – Vung Tau province. Beside of pollution sources such as waste water, solid waste, livestock activity in Tan Thanh district, Ba Ria - Vung Tau province in recent years has caused air pollution in the livestock area and surrounding area. This research was carried out to evaluate the process of air pollution dispersion from livestock activities based on applying the TAPM meteorological model and AERMOD air quality model. The results showed that the maximum concentrations of air pollutants from livestock area such as NH3, H2S and CH3SH exceeded the National Technical Regulation on Ambient Air Quality (average hour) in the centre of Tan Thanh district, such as Toc Tien commune, part of Tan Phuoc and Phuoc Hoa communes, is 505 μg/m3; 57.4 μg/m3 and 111 μg/m3, respectively. Phu My district and other suburban communes (Hac Dich, Song Xoai, Chau Pha, Tan Hoa, Tan Hai, My Xuan, etc.) have distribution of lower concentrations of air pollutants. Base on the present results of modeling, the authors have proposed livestock development scenarios to control air pollution from this activity, contributing to environmental protection for Tan Thanh district.

Download Full-text

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

10.5194/acp-2017-182 ◽

2017 ◽

Cited By ~ 1

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.

Download Full-text

Supplementary material to "How much does traffic contribute to benzene and PAH air pollution? Results from a high-resolution North American air quality model centered on Toronto, Canada"

10.5194/acp-2019-871-supplement ◽

2019 ◽

Author(s):

Cynthia H. Whaley ◽

Elisabeth Galarneau ◽

Paul A. Makar ◽

Michael D. Moran ◽

Junhua Zhang

Keyword(s):

Air Pollution ◽

Air Quality ◽

High Resolution ◽

North American ◽

Air Quality Model ◽

Quality Model ◽

Supplementary Material

Download Full-text

Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽

10.28974/idojaras.2021.4.5 ◽

2021 ◽

Vol 125 (4) ◽

pp. 625-646

Author(s):

Zita Ferenczi ◽

Emese Homolya ◽

Krisztina Lázár ◽

Anita Tóth

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Weather Prediction ◽

Horizontal Resolution ◽

Model System ◽

Air Quality Model ◽

Quality Model ◽

Near Surface ◽

The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

Download Full-text

One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

10.5194/acp-2016-148 ◽

2016 ◽

Cited By ~ 2

Author(s):

Jianlin Hu ◽

Jianjun Chen ◽

Qi Ying ◽

Hongliang Zhang

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Model Performance ◽

Monitoring Network ◽

Organic Aerosol ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Low Concentration ◽

Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research & Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

Download Full-text

High Resolution Chemistry Transport Modeling with the On-Line CHIMERE-WRF Model over the French Alps—Analysis of a Feedback of Surface Particulate Matter Concentrations on Mountain Meteorology

Atmosphere ◽

10.3390/atmos11060565 ◽

2020 ◽

Vol 11 (6) ◽

pp. 565 ◽

Cited By ~ 2

Author(s):

Bertrand Bessagnet ◽

Laurent Menut ◽

Rémy Lapere ◽

Florian Couvidat ◽

Jean-Luc Jaffrezo ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

High Altitude ◽

Wrf Model ◽

Synoptic Situation ◽

Modeling Tools ◽

Meteorological Model ◽

French Alps ◽

On Line

Air pollution is of major concern throughout the world and the use of modeling tools to analyze and forecast the pollutant concentrations in complex orographic areas remains challenging. This work proposes an exhaustive framework to analyze the ability of models to simulate the air quality over the French Alps up to 1.2 km resolution over Grenoble and the Arve Valley. The on-line coupled suite of models CHIMERE-WRF is used in its recent version to analyze a 1 month episode in November–December 2013. As expected, an improved resolution increases the concentrations close to the emission areas and reduced the negative bias for Particulate Matter that is the usual weakness of air quality models. However, the nitrate concentrations seem overestimated with at the same time an overestimation of surface temperature in the morning by WRF. Different WRF settings found in the literature are tested to improve the results, particularly the ability of the meteorological model to simulate the strong thermal inversions in the morning. Wood burning is one of the main contributor of air pollution during the period ranging from 80 to 90% of the Organic Matter. The activation of the on-line coupling has a moderate impact on the background concentrations but surprisingly a change of Particulate Matter (PM) concentrations in the valley will affect more the meteorology nearby high altitude areas than in the valley. This phenomenon is the result of a chain of processes involving the radiative effects and the water vapor column gradients in complex orographic areas. At last, the model confirms that the surrounding glaciers are largely impacted by long range transport of desert dust. However, in wintertime some outbreaks of anthropogenic pollution from the valley when the synoptic situation changes can be advected up to the nearby high altitude areas, then deposited.

Download Full-text

Examining Patterns of Air Quality Perception: A Cluster Analysis for Southern Chilean Cities

SAGE Open ◽

10.1177/2158244019863563 ◽

2019 ◽

Vol 9 (3) ◽

pp. 215824401986356 ◽

Cited By ~ 4

Author(s):

Àlex Boso ◽

Boris Álvarez ◽

Christian Oltra ◽

Álvaro Hofflinger ◽

Arturo Vallejos-Romero ◽

...

Keyword(s):

Health Care ◽

Air Pollution ◽

Cluster Analysis ◽

Air Quality ◽

Health Risks ◽

Public Perception ◽

Emotional Responses ◽

Sociodemographic Factors ◽

Southern Chile ◽

Las Casas

Various medium-sized cities in southern Chile are saturated by air pollution caused by woodsmoke. In this study, we developed a segmentation model to assess the public’s perceptions, understanding of health risks and emotional responses to poor air quality. To date, this is the first segmentation model dealing with public perception in cities contaminated by woodsmoke. A survey ( N = 489) was conducted in Temuco and Padre las Casas, Chile, which included questions regarding attitudes, sociodemographic factors, and health care behaviors, to obtain information for mitigation initiatives. Through a cluster analysis, three population segments were identified that related differently to environmental pollution, which were constructed based on seven psychosocial variables. Different sociodemographic profiles and self-reported behavioral patterns were found, which should guide policies aimed at improving air quality in cities contaminated by pollution from wood-burning stoves.

Download Full-text

Examination of the Physical Atmosphere in the Great Lakes Region and Its Potential Impact on Air Quality—Overwater Stability and Satellite Assimilation

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0355.1 ◽

2018 ◽

Vol 57 (12) ◽

pp. 2789-2816 ◽

Cited By ~ 10

Author(s):

Richard T. McNider ◽

Arastoo Pour-Biazar ◽

Kevin Doty ◽

Andrew White ◽

Yuling Wu ◽

...

Keyword(s):

Boundary Layer ◽

Air Quality ◽

Land Surface ◽

Lake Michigan ◽

Model Performance ◽

Meteorological Model ◽

Mixing Ratios ◽

Chemical Models ◽

Improve Model ◽

Model Mixing

AbstractHigh mixing ratios of ozone along the shores of Lake Michigan have been a recurring theme over the last 40 years. Models continue to have difficulty in replicating ozone behavior in the region. Although emissions and chemistry may play a role in model performance, the complex meteorological setting of the relatively cold lake in the summer ozone season and the ability of the physical model to replicate this environment may contribute to air quality modeling errors. In this paper, several aspects of the physical atmosphere that may affect air quality, along with potential paths to improve the physical simulations, are broadly examined. The first topic is the consistent overwater overprediction of ozone. Although overwater measurements are scarce, special boat and ferry ozone measurements over the last 15 years have indicated consistent overprediction by models. The roles of model mixing and lake surface temperatures are examined in terms of changing stability over the lake. From an analysis of a 2009 case, it is tentatively concluded that excessive mixing in the meteorological model may lead to an underestimate of mixing in offline chemical models when different boundary layer mixing schemes are used. This is because the stable boundary layer shear, which is removed by mixing in the meteorological model, can no longer produce mixing when mixing is rediagnosed in the offline chemistry model. Second, air temperature has an important role in directly affecting chemistry and emissions. Land–water temperature contrasts are critical to lake and land breezes, which have an impact on mixing and transport. Here, satellite-derived skin temperatures are employed as a path to improve model temperature performance. It is concluded that land surface schemes that adjust moisture based on surface energetics are important in reducing temperature errors.

Download Full-text

The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1790.1 ◽

2008 ◽

Vol 47 (7) ◽

pp. 1853-1867 ◽

Cited By ~ 74

Author(s):

Tanya L. Otte

Keyword(s):

Air Quality ◽

Meteorological Conditions ◽

Daily Maximum ◽

Air Quality Model ◽

Quality Model ◽

Meteorological Model ◽

Model Simulations ◽

Dynamic Analyses ◽

The Impact ◽

Over Time

Abstract It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

Download Full-text