scholarly journals The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012

2018 ◽  
Vol 27 (10) ◽  
pp. 684 ◽  
Author(s):  
Joseph L. Wilkins ◽  
George Pouliot ◽  
Kristen Foley ◽  
Wyat Appel ◽  
Thomas Pierce
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Wildland Fire ◽  
Fine Particulate Matter ◽  
Model Performance ◽  
Grid Cell ◽  
Emissions Inventory ◽  
Fire Emissions ◽  
The Mean ◽  
The Impact

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency’s National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008–2012. Adding fires to CMAQ increases the number of ‘grid-cell days’ with PM2.5 above 35 µg m−3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

scholarly journals Impact of wildfires on particulate matter in the Euro-Mediterranean in 2007: sensitivity to the parameterization of emissions in air quality models

2018 ◽  
Author(s):  
Marwa Majdi ◽  
Solene Turquety ◽  
Karine Sartelet ◽  
Carole Legorgeu ◽  
Laurent Menut ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Correlation Coefficients ◽  
World Health ◽  
Fire Emissions ◽  
Fire Plumes ◽  
Emissions Modeling ◽  
Highly Sensitive ◽  
Health Organization ◽  
The Impact

Abstract. This study examines the uncertainties on air quality modeling associated with the integration of wildfire emissions in chemistry-transport models (CTMs). To do so, aerosol concentrations during the summer 2007, which was marked by severe fire episodes in the Euro-Mediterranean region especially in Balkan (20–31 July 2007, 24–30 August 2007) and Greece (24–30 August 2007), are analysed. Through comparisons to observations from surface networks and satellite remote sensing, we evaluate the abilities of two CTMs, Polyphemus/Polair3D and CHIMERE, to simulate the impact of fires on the regional particulate matter (PM) concentrations and optical properties. During the two main fire events, fire emissions may contribute up to 90 % of surface PM2.5 concentrations, with a significant regional impact associated with long-range transport. Good general performances of the models and a clear improvement of PM2.5 and aerosol optical depth (AOD) are shown when fires are taken into account in the models with high correlation coefficients. Two sources of uncertainties are specifically analysed in terms of surface PM concentrations and AOD using sensitivity simulations: secondary organic aerosol (SOA) formation from intermediate and semi-volatile organic compounds (I/S-VOCs) and emissions' injection heights. The analysis highlights that surface PM2.5 concentrations are highly sensitive to injection heights (with a sensitivity that can be as high as 50 % compared to the sensitivity for I/S-VOCs emissions which is lower than 30 %). However, AOD which is vertically integrated is less sensitive to the injection heights (mostly below 20 %), but highly sensitive to I/S-VOCs emissions (with sensitivity that can be as high as 40 %). The maximum dispersion, which quantifies uncertainties related to fire emissions modeling, is up to 75 % for PM2.5 in Balkan and Greece, and varies between 36 and 45 % for AOD above fire regions. The simulated number of daily exceedance of World Health Organization (WHO) recommendations for PM2.5 over the considered region reaches 30 days in regions affected by fires and ∼ 10 days in fire plumes which is slightly underestimated compared to available observations. The maximum dispersion (σ) on this indicator is also large (with σ reaching 15 days), showing the need for better understanding of the transport and evolution of fire plumes in addition to fire emissions.

scholarly journals Satellite-based estimation of the impacts of summertime wildfires on PM<sub>2.5</sub> concentration in the United States

2021 ◽  
Vol 21 (14) ◽  
pp. 11243-11256
Author(s):  
Zhixin Xue ◽  
Pawan Gupta ◽  
Sundar Christopher
Keyword(s):  
United States ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Atmospheric Correction ◽  
Health Assessment ◽  
The United States ◽  
Ancillary Data ◽  
Leave One Out ◽  
The Impact

Abstract. Frequent and widespread wildfires in the northwestern United States and Canada have become the “new normal” during the Northern Hemisphere summer months, which significantly degrades particulate matter air quality in the United States. Using the mid-visible Multi Angle Implementation of Atmospheric Correction (MAIAC) satellite-derived aerosol optical depth (AOD) with meteorological information from the European Centre for Medium-Range Weather Forecasts (ECMWF) and other ancillary data, we quantify the impact of these fires on fine particulate matter concentration (PM2.5) air quality in the United States. We use a geographically weighted regression (GWR) method to estimate surface PM2.5 in the United States between low (2011) and high (2018) fire activity years. Our results indicate an overall leave-one-out cross-validation (LOOCV) R2 value of 0.797 with root mean square error (RMSE) between 3 and 5 µg m−3. Our results indicate that smoke aerosols caused significant pollution changes over half of the United States. We estimate that nearly 29 states have increased PM2.5 during the fire-active year and that 15 of these states have PM2.5 concentrations more than 2 times that of the inactive year. Furthermore, these fires increased the daily mean surface PM2.5 concentrations in Washington and Oregon by 38 to 259 µg m−3, posing significant health risks especially to vulnerable populations. Our results also show that the GWR model can be successfully applied to PM2.5 estimations from wildfires, thereby providing useful information for various applications such as public health assessment.

Evaluation of NO2, O3, PM10, and PM2.5 in the city of Buenos Aires, Argentina using WRF-Chem model

2021 ◽  
Author(s):  
Ana Isabel Lopez-Noreña ◽  
Lucas Berná ◽  
María Florencia Tames ◽  
Emmanuel Millán ◽  
Enrique Puliafito ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Buenos Aires ◽  
Model Performance ◽  
Performance Criteria ◽  
Quality Analysis ◽  
Modeling Tools ◽  
Pm10 And Pm2.5 ◽  
The Mean ◽  
The Impact

&lt;p&gt;The online-coupled Weather Research and Forecasting model with Chemistry (WRF-Chem v4.0), was applied to evaluate the impact of using different anthropogenic emissions inventories on regional air quality in Argentina. For this purpose, we couple the Argentinian high-resolution emissions inventory (GEAA-AHRI) and the Emissions Database for Global Atmospheric Research &amp;#8211; Hemispheric Transport of Air Pollution (EDGAR-HTAP) and introduce them into the model, with a local optimized configuration considering 3 nested domains with a horizontal grid size of 20 x 20 km, 4 x 4 km, and 1.3 x 1.3 km and the MOZART chemical scheme. The model output for NO2, PM10, PM2.5, and O3 concentrations over the innermost domain was compared against the existing surface and satellite-derived observations for the Buenos Aires Metropolitan Area (AMBA) during austral fall 2018. We found an overall good model performance for all simulations, and large discrepancies between the emission inventories, obtaining an improved urban-scale spatio-temporal representation when the high resolution GEAA-AHRI dataset is considered. Our results show that the daytime concentrations of air pollutants are strongly influenced by the shape and shift of the hourly emissions profile before sunrise and after sunset, especially for NO2 where the inclusion of the temporal profile decreased the mean bias by ~80%. Performance criteria for modeled PM10 and PM2.5 were in general satisfied, despite having an average underestimation of observations. When compared to NO2 tropospheric columns derived from TROPOMI, The general magnitude and spatial pattern of the NO2 tropospheric column is in agreement with the mean TROPOMI columns during the modeled period, obtaining correlation coefficients higher than 0.6 for all simulations. Our results highlight the benefits of using a time-dependent and high-resolution local inventory for addressing the background air quality in AMBA. The implementation and validation of local emissions and static fields with high spatial and temporal resolution carried out in this work, establishes a benchmark for forthcoming studies in other regions of South America where different modeling tools for air quality analysis are currently being used to complement the usually sparse and discontinuous air quality networks.&lt;/p&gt;

scholarly journals Impact of wildfires on particulate matter in the Euro-Mediterranean in 2007: sensitivity to some parameterizations of emissions in air quality models

2019 ◽  
Vol 19 (2) ◽  
pp. 785-812 ◽  
Author(s):  
Marwa Majdi ◽  
Solene Turquety ◽  
Karine Sartelet ◽  
Carole Legorgeu ◽  
Laurent Menut ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
World Health ◽  
Voc Emissions ◽  
Fire Emissions ◽  
The Balkans ◽  
Fire Plumes ◽  
Highly Sensitive ◽  
Statistical Dispersion ◽  
The Impact

Abstract. This study examines the uncertainties on air quality modeling associated with the integration of wildfire emissions in chemistry-transport models (CTMs). To do so, aerosol concentrations during the summer of 2007, which was marked by severe fire episodes in the Euro-Mediterranean region especially in the Balkans (20–31 July, 24–30 August 2007) and Greece (24–30 August 2007), are analyzed. Through comparisons to observations from surface networks and satellite remote sensing, we evaluate the abilities of two CTMs, Polyphemus/Polair3D and CHIMERE, to simulate the impact of fires on the regional particulate matter (PM) concentrations and optical properties. During the two main fire events, fire emissions may contribute up to 90 % of surface PM2.5 concentrations in the fire regions (Balkans and Greece), with a significant regional impact associated with long-range transport. Good general performances of the models and a clear improvement of PM2.5 and aerosol optical depth (AOD) are shown when fires are taken into account in the models with high correlation coefficients. Two sources of uncertainties are specifically analyzed in terms of surface PM2.5 concentrations and AOD using sensitivity simulations: secondary organic aerosol (SOA) formation from intermediate and semi-volatile organic compounds (I/S-VOCs) and emissions' injection heights. The analysis highlights that surface PM2.5 concentrations are highly sensitive to injection heights (with a sensitivity that can be as high as 50 % compared to the sensitivity to I/S-VOC emissions which is lower than 30 %). However, AOD which is vertically integrated is less sensitive to the injection heights (mostly below 20 %) but highly sensitive to I/S-VOC emissions (with sensitivity that can be as high as 40 %). The maximum statistical dispersion, which quantifies uncertainties related to fire emission modeling, is up to 75 % for PM2.5 in the Balkans and Greece, and varies between 36 % and 45 % for AOD above fire regions. The simulated number of daily exceedance of World Health Organization (WHO) recommendations for PM2.5 over the considered region reaches 30 days in regions affected by fires and ∼10 days in fire plumes, which is slightly underestimated compared to available observations. The maximum statistical dispersion (σ) on this indicator is also large (with σ reaching 15 days), showing the need for better understanding of the transport and evolution of fire plumes in addition to fire emissions.

scholarly journals Satellite-based Estimation of the Impacts of Summertime Wildfires on Particulate Matter Air Quality in United States

2020 ◽  
Author(s):  
Zhixin Xue ◽  
Pawan Gupta ◽  
Sundar Christopher
Keyword(s):  
United States ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Atmospheric Correction ◽  
Health Assessment ◽  
The United States ◽  
Ancillary Data ◽  
Weather Forecasts ◽  
The Impact

Abstract. Frequent and widespread wildfires in North Western United States and Canada has become the new normal during the northern hemisphere summer months, which degrades particulate matter air quality in the United States significantly. Using the mid-visible Multi Angle Implementation of Atmospheric Correction (MAIAC) satellite-derived Aerosol Optical Depth (AOD) with meteorological information from the European Centre for Medium-Range Weather Forecasts (ECMWF) and other ancillary data, we quantify the impact of these fires on fine particulate matter air quality (PM2.5) in the United States. We use a Geographically Weighted Regression method to estimate surface PM2.5 in the United States between low (2011) and high (2018) fire activity years. Our results indicate that smoke aerosols caused significant pollution changes over half of the United States. We estimate that nearly 29 states have increased PM2.5 during the fire active year and 15 of these states have PM2.5 concentrations more than 2 times than that of the inactive year. Furthermore, these fires increased daily mean surface PM2.5 concentrations in Washington and Oregon by 38 to 259 µgm−3 posing significant health risks especially to vulnerable populations. Our results also show that the GWR model can be successfully applied to PM2.5 estimations from wildfires thereby providing useful information for various applications including public health assessment.

scholarly journals Characterization and Trends of Fine Particulate Matter (PM2.5) Fire Emissions in the Brazilian Cerrado during 2002–2017

2019 ◽  
Vol 11 (19) ◽  
pp. 2254 ◽  
Author(s):  
Guilherme Augusto Verola Mataveli ◽  
Maria Elisa Siqueira Silva ◽  
Daniela de Azeredo França ◽  
Nathaniel Alan Brunsell ◽  
Gabriel de Oliveira ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Land Cover ◽  
Anthropogenic Activities ◽  
Fine Particulate Matter ◽  
Grid Cell ◽  
Northern Region ◽  
Brazilian Cerrado ◽  
Annual Average ◽  
Fire Emissions ◽  
Moderate Resolution Imaging Spectroradiometer

Fire occurrence is a major disturbance in the Brazilian Cerrado, which is driven by both natural and anthropogenic activities. Despite increasing efforts for monitoring the Cerrado, a biome-scale study for quantifying and understanding the variability of fire emissions is still needed. We aimed at characterizing and finding trends in Particulate Matter with diameter less than 2.5 µm (PM2.5) fire emissions in the Brazilian Cerrado using the PREP-CHEM-SRC emissions preprocessing tool and Moderate Resolution Imaging Spectroradiometer (MODIS) active fires datasets for the 2002–2017 period. Our results showed that, on average, the Cerrado emitted 1.08 Tg year−1 of PM2.5 associated with fires, accounting for 25% and 15% of the PM2.5 fire emissions in Brazil and South America, respectively. Most of the PM2.5 fire emissions were concentrated in the end of the dry season (August, 0.224 Tg month−1 and September, 0.386 Tg month−1) and in the transitional month (October, 0.210 Tg month−1). Annually, 66% of the total emissions occurred over the savanna land cover; however, active fires that were detected in the evergreen broadleaf land cover tended to emit more than active fires occurring in the savanna land cover. Spatially, each 0.1° grid cell emitted, on average, 0.5 Mg km−2 year−1 of PM2.5 associated with fires, but the values can reach to 16.6 Mg km−2 year−1 in a single cell. Higher estimates of PM2.5 emissions associated with fires were mostly concentrated in the northern region, which is the current agricultural expansion frontier in this biome. When considering the entire Cerrado, we found an annual decreasing trend representing -1.78% of the annual average PM2.5 emitted from fires during the period analyzed, however, the grid cell analysis found annual trends representing ± 35% of the annual average PM2.5 fire emissions.

scholarly journals The impact of measures to reduce ambient air PM<sub>10</sub> concentrations originating from road dust, evaluated for a street canyon in Helsinki

2019 ◽  
Vol 19 (17) ◽  
pp. 11199-11212 ◽  
Author(s):  
Ana Stojiljkovic ◽  
Mari Kauhaniemi ◽  
Jaakko Kukkonen ◽  
Kaarle Kupiainen ◽  
Ari Karppinen ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Street Canyon ◽  
Dust Emission ◽  
Road Dust ◽  
Ambient Air ◽  
Air Quality Measurements ◽  
Emission Models ◽  
The Impact ◽  
Quality Measurements

Abstract. We have numerically evaluated how effective selected potential measures would be for reducing the impact of road dust on ambient air particulate matter (PM10). The selected measures included a reduction of the use of studded tyres on light-duty vehicles and a reduction of the use of salt or sand for traction control. We have evaluated these measures for a street canyon located in central Helsinki for four years (2007–2009 and 2014). Air quality measurements were conducted in the street canyon for two years, 2009 and 2014. Two road dust emission models, NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and FORE (Forecasting Of Road dust Emissions), were applied in combination with the Operational Street Pollution Model (OSPM), a street canyon dispersion model, to compute the street increments of PM10 (i.e. the fraction of PM10 concentration originating from traffic emissions at the street level) within the street canyon. The predicted concentrations were compared with the air quality measurements. Both road dust emission models reproduced the seasonal variability of the PM10 concentrations fairly well but under-predicted the annual mean values. It was found that the largest reductions of concentrations could potentially be achieved by reducing the fraction of vehicles that use studded tyres. For instance, a 30 % decrease in the number of vehicles using studded tyres would result in an average decrease in the non-exhaust street increment of PM10 from 10 % to 22 %, depending on the model used and the year considered. Modelled contributions of traction sand and salt to the annual mean non-exhaust street increment of PM10 ranged from 4 % to 20 % for the traction sand and from 0.1 % to 4 % for the traction salt. The results presented here can be used to support the development of optimal strategies for reducing high springtime particulate matter concentrations originating from road dust.

scholarly journals The Combined Effects of Fine Particulate Matter and Temperature on Preterm Birth in Seoul, 2010–2016

2021 ◽  
Vol 18 (4) ◽  
pp. 1463
Author(s):  
Youngrin Kwag ◽  
Min-ho Kim ◽  
Shinhee Ye ◽  
Jongmin Oh ◽  
Gyeyoon Yim ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Preterm Birth ◽  
Pregnant Women ◽  
Low Temperatures ◽  
Fine Particulate Matter ◽  
Temperature Data ◽  
Fine Particulate ◽  
The Mean ◽  
Pm2.5 Exposure ◽  
Pm2.5 Concentration

Background: Preterm birth contributes to the morbidity and mortality of newborns and infants. Recent studies have shown that maternal exposure to particulate matter and extreme temperatures results in immune dysfunction, which can induce preterm birth. This study aimed to evaluate the association between fine particulate matter (PM2.5) exposure, temperature, and preterm birth in Seoul, Republic of Korea. Methods: We used 2010–2016 birth data from Seoul, obtained from the Korea National Statistical Office Microdata. PM2.5 concentration data from Seoul were generated through the Community Multiscale Air Quality (CMAQ) model. Seoul temperature data were collected from the Korea Meteorological Administration (KMA). The exposure period of PM2.5 and temperature were divided into the first (TR1), second (TR2), and third (TR3) trimesters of pregnancy. The mean PM2.5 concentration was used in units of ×10 µg/m3 and the mean temperature was divided into four categories based on quartiles. Logistic regression analyses were performed to evaluate the association between PM2.5 exposure and preterm birth, as well as the combined effects of PM2.5 exposure and temperature on preterm birth. Result: In a model that includes three trimesters of PM2.5 and temperature data as exposures, which assumes an interaction between PM2.5 and temperature in each trimester, the risk of preterm birth was positively associated with TR1 PM2.5 exposure among pregnant women exposed to relatively low mean temperatures (<3.4 °C) during TR1 (OR 1.134, 95% CI 1.061–1.213, p < 0.001). Conclusions: When we assumed the interaction between PM2.5 exposure and temperature exposure, PM2.5 exposure during TR1 increased the risk of preterm birth among pregnant women exposed to low temperatures during TR1. Pregnant women should be aware of the risk associated with combined exposure to particulate matter and low temperatures during TR1 to prevent preterm birth.

scholarly journals Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

2021 ◽  
Vol 13 (15) ◽  
pp. 2981
Author(s):  
Jeanné le Roux ◽  
Sundar Christopher ◽  
Manil Maskey
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
Spectral Response ◽  
Fine Particulate Matter ◽  
Daily Basis ◽  
Land Cover Types ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

scholarly journals Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Information Dissemination ◽  
Fine Particulate Matter ◽  
Model Errors ◽  
Air Quality Forecast ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
Forecasting System ◽  
Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

Sign in / Sign up

Export Citation Format

Share Document