scholarly journals An investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosol properties

2008 ◽  
Vol 8 (22) ◽  
pp. 6627-6654 ◽  
Author(s):  
C. H. Song ◽  
M. E. Park ◽  
K. H. Lee ◽  
H. J. Ahn ◽  
Y. Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Chemical Species ◽  
Temporal Variations ◽  
Aerosol Formation ◽  
Model Simulations ◽  
Aerosol Retrieval ◽  
Modis Aod ◽  
The Us ◽  
Us Epa

Abstract. In this study, the spatio-temporal and seasonal distributions of EOS/Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) over East Asia were analyzed in conjunction with US EPA Models-3/CMAQ v4.3 modeling. In this study, two MODIS AOD products (τMODIS: τM-BAER and τNASA) retrieved through a modified Bremen Aerosol Retrieval (M-BAER) algorithm and NASA collection 5 (C005) algorithm were compared with the AOD (τCMAQ) that was calculated from the US EPA Models-3/CMAQ model simulations. In general, the CMAQ-predicted AOD values captured the spatial and temporal variations of the two MODIS AOD products over East Asia reasonably well. Since τMODIS cannot provide information on the aerosol chemical composition in the atmosphere, different aerosol formation characteristics in different regions and different seasons in East Asia cannot be described or identified by τMODIS itself. Therefore, the seasonally and regionally varying aerosol formation and distribution characteristics were investigated by the US EPA Models-3/CMAQ v4.3 model simulations. The contribution of each particulate chemical species to τMODIS and τCMAQ showed strong spatial, temporal and seasonal variations. For example, during the summer episode, τMODIS and τCMAQ were mainly raised due to high concentrations of (NH4)2SO4 over Chinese urban and industrial centers and secondary organic aerosols (SOAs) over the southern parts of China, whereas during the late fall and winter episodes, τMODIS and τCMAQ were higher due largely to high levels of NH4NO3 formed over the urban and industrial centers, as well as in areas with high NH3 emissions. τCMAQ was in general larger than τMODIS during the year, except for spring. The high biases (τCMAQ>τMODIS) may be due to the excessive formation of both (NH4)2SO4 (summer episode) and NH4NO3 (fall and winter episodes) over China, possibly from the use of overestimated values for NH3 emissions in the CMAQ modeling. According to CMAQ modeling, particulate NH4NO3 made a 14% (summer) to 54% (winter) contribution to σext and τCMAQ. Therefore, the importance of NH4NO3 in estimating τ should not be ignored, particularly in studies of the East Asian air quality. In addition, the accuracy of τM-BAER and τNASA was evaluated by a comparison with the AOD (τAERONET) from the AERONET sites in East Asia. Both τM-BAER and τNASA showed a strong correlation with τAERONET around the 1:1 line (R=0.79), indicating promising potential for the application of both the M-BAER and NASA aerosol retrieval algorithms to satellite-based air quality monitoring studies in East Asia.

scholarly journals An investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosol properties

2008 ◽  
Vol 8 (3) ◽  
pp. 8661-8713 ◽  
Author(s):  
C. H. Song ◽  
M. E. Park ◽  
H. J. Ahn ◽  
K. H. Lee ◽  
Y. Lee ◽  
...  
Keyword(s):  
East Asia ◽  
Chemical Species ◽  
Temporal Variations ◽  
Aerosol Formation ◽  
Model Simulations ◽  
Aerosol Retrieval ◽  
Modis Aod ◽  
The Us ◽  
Us Epa ◽  
Spatio Temporal

Abstract. In this study, the spatio-temporal and seasonal distributions of EOS/Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) over East Asia were analyzed in conjunction with US EPA Models-3/CMAQ v4.3 modeling. In this study, two MODIS AOD products (τ MODIS:τM-BAER and τNASA) retrieved through a modified Bremen Aerosol Retrieval (M-BAER) algorithm and NASA collection 5 (C005) algorithm were compared with the AOD (τCMAQ) that was calculated from the US EPA Models-3/CMAQ model simulations. In general, the CMAQ-predicted AOD values captured the spatial and temporal variations of the two MODIS AOD products over East Asia reasonable well. Since τMODIS cannot provide information on the aerosol chemical composition in the atmosphere, different aerosol formation characteristics in different regions and different seasons in East Asia cannot be described or identified by τMODIS itself. Therefore, the seasonally and regionally varying aerosol formation and distribution characteristics were investigated by the US EPA Models-3/CMAQ v4.3 model simulations. The contribution of each particulate chemical species to τM-BAER, τNASA, and τCMAQ showed strong spatial, temporal and seasonal variations. For example, during the summer episode, τM-BAER, τNASA, and τCMAQ were mainly raised due to high concentrations of (NH4)2SO4 over Chinese urban and industrial centers and secondary organic aerosols (SOAs) over the southern parts of China, whereas during the winter episode, τM-BAER, τNASA, and τCMAQ were higher due largely to high levels of NH3NO3 formed over the urban and industrial centers, as well as in areas with high NH3 emissions. In addition, the accuracy of τM-BAER and τNASA was evaluated by a comparison with the AOD (τAERONET) from the AERONET sites in East Asia. Both τM-BAER and τNASA showed a strong correlation with τAERONETR around the 1:1 line (R=0.79), indicating promising potential for the application of both the M-BAER and NASA aerosol retrieval algorithms to satellite-based air quality monitoring studies in East Asia.

scholarly journals Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe – Part 2: Evaluation of chemical concentrations and sensitivity simulations

2013 ◽  
Vol 13 (14) ◽  
pp. 6845-6875 ◽  
Author(s):  
Y. Zhang ◽  
K. Sartelet ◽  
S. Zhu ◽  
W. Wang ◽  
S.-Y. Wu ◽  
...  
Keyword(s):  
Air Quality ◽  
Western Europe ◽  
Model Comparison ◽  
Model Performance ◽  
Chemical Species ◽  
Coupled Model ◽  
Temporal Variations ◽  
Grid Resolution ◽  
Atmospheric Pollutant ◽  
Model Predictions

Abstract. An offline-coupled model (WRF/Polyphemus) and an online-coupled model (WRF/Chem-MADRID) are applied to simulate air quality in July 2001 at horizontal grid resolutions of 0.5° and 0.125° over Western Europe. The model performance is evaluated against available surface and satellite observations. The two models simulate different concentrations in terms of domainwide performance statistics, spatial distribution, temporal variations, and column abundance. WRF/Chem-MADRID at 0.5° gives higher values than WRF/Polyphemus for the domainwide mean and over polluted regions in Central and southern Europe for all surface concentrations and column variables except for the tropospheric ozone residual (TOR). Compared with observations, WRF/Polyphemus gives better statistical performance for daily HNO3, SO2, and NO2 at the European Monitoring and Evaluation Programme (EMEP) sites, maximum 1 h O3 at the AirBase sites, PM2.5 at the AirBase sites, maximum 8 h O3 and PM10 composition at all sites, column abundance of CO, NO2, TOR, and aerosol optical depth (AOD), whereas WRF/Chem-MADRID gives better statistical performance for NH3, hourly SO2, NO2, and O3 at the AirBase and BDQA (Base de données de la qualité de l'air) sites, maximum 1 h O3 at the BDQA and EMEP sites, and PM10 at all sites. WRF/Chem-MADRID generally reproduces well the observed high hourly concentrations of SO2 and NO2 at most sites except for extremely high episodes at a few sites, and WRF/Polyphemus performs well for hourly SO2 concentrations at most rural or background sites where pollutant levels are relatively low, but it underpredicts the observed hourly NO2 concentrations at most sites. Both models generally capture well the daytime maximum 8 h O3 concentrations and diurnal variations of O3 with more accurate peak daytime and minimal nighttime values by WRF/Chem-MADRID, but neither model reproduces extremely low nighttime O3 concentrations at several urban and suburban sites due to underpredictions of NOx and thus insufficient titration of O3 at night. WRF/Polyphemus gives more accurate concentrations of PM2.5, and WRF/Chem-MADRID reproduces better the observations of PM10 concentrations at all sites. The differences between model predictions and observations are mostly caused by inaccurate representations of emissions of gaseous precursors and primary PM species, as well as biases in the meteorological predictions. The differences in model predictions are caused by differences in the heights of the first model layers and thickness of each layer that affect vertical distributions of emissions, model treatments such as dry/wet deposition, heterogeneous chemistry, and aerosol and cloud, as well as model inputs such as emissions of soil dust and sea salt and chemical boundary conditions of CO and O3 used in both models. WRF/Chem-MADRID shows a higher sensitivity to grid resolution than WRF/Polyphemus at all sites. For both models, the use of a finer grid resolution generally leads to an overall better statistical performance for most variables, with greater spatial details and an overall better agreement in temporal variations and magnitudes at most sites. The use of online biogenic volatile organic compound (BVOC) emissions gives better statistical performance for hourly and maximum 8 h O3 and PM2.5 and generally better agreement with their observed temporal variations at most sites. Because it is an online model, WRF/Chem-MADRID offers the advantage of accounting for various feedbacks between meteorology and chemical species. However, this model comparison suggests that atmospheric pollutant concentrations are most sensitive in state-of-the-science air quality models to vertical structure, inputs, and parameterizations for dry/wet removal of gases and particles in the model.

scholarly journals BIOMASS BURNING RELATED POLLUTIONS AND THEIR CONTRIBUTIONS TO THE LOCAL AIR QUALITY IN HONG KONG

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 29-36
Author(s):  
K. L. Chan ◽  
K. Qin
Keyword(s):  
Hong Kong ◽  
Air Quality ◽  
East Asia ◽  
Black Carbon ◽  
Biomass Burning ◽  
Model Simulations ◽  
Local Monitoring ◽  
Sun Photometer ◽  
Carbon Aerosols ◽  
Black Carbon Aerosols

In this study, we present a quantitative estimation of the impacts of biomass burning emissions from different source regions to the local air quality in Hong Kong in 2014 using global chemistry transport model simulations, sun photometer measurements, satellite observations and local monitoring network data. This study focuses on two major biomass burning pollutants, black carbon aerosols and carbon monoxide (CO). The model simulations of atmospheric black carbon and CO show excellent agreement with sun photometer aerosol optical depth (AOD) measurements, satellite CO columns observations and local monitoring stations data. From the model simulation results, we estimated that biomass burning contributes 12 % of total black carbon and 16 % of atmospheric CO in Hong Kong on annual average. South East Asia shows the largest influence to the black carbon and CO levels in Hong Kong, accounts for 11 % of the total atmospheric black carbon and 8 % of CO. Biomass burning in North East Asia and Africa also show significant impacts to Hong Kong. Elevated levels of atmospheric black carbon aerosols and CO were observed during springtime (March and April) which is mainly due to the enhancement of biomass burning contributions. Black carbon and CO originating from biomass burning sources are estimated to contribute 40 % of atmospheric black carbon and 28 % of CO in Hong Kong during March 2014. An investigation focusing on the biomass burning pollution episode during springtime suggests the intensified biomass burning activities in the Indochinese Peninsula are the major sources of black carbon and CO in Hong Kong during the time.

scholarly journals How emissions, climate, and land use change will impact mid-century air quality over the United States: a focus on effects at National Parks

2014 ◽  
Vol 14 (19) ◽  
pp. 26495-26543 ◽  
Author(s):  
M. Val Martin ◽  
C. L. Heald ◽  
J.-F. Lamarque ◽  
S. Tilmes ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
United States ◽  
Land Use ◽  
Air Quality ◽  
National Parks ◽  
Land Use Changes ◽  
The United States ◽  
Western Us ◽  
The Us ◽  
Us Epa ◽  
Surface O3

Abstract. We use a global coupled chemistry-climate-land model (CESM) to assess the integrated effect of climate, emissions and land use changes on annual surface O3 and PM2.5 on the United States with a focus on National Parks (NPs) and wilderness areas, using the RCP4.5 and RCP8.5 projections. We show that, when stringent domestic emission controls are applied, air quality is predicted to improve across the US, except surface O3 over the western and central US under RCP8.5 conditions, where rising background ozone counteracts domestic emissions reductions. Under the RCP4.5, surface O3 is substantially reduced (about 5 ppb), with daily maximum 8 h averages below the primary US EPA NAAQS of 75 ppb (and even 65 ppb) in all the NPs. PM2.5 is significantly reduced in both scenarios (4 μg m−3; ~50%), with levels below the annual US EPA NAAQS of 12 μg m−3 across all the NPs; visibility is also improved (10–15 deciviews; >75 km in visibility range), although some parks over the western US (40–74% of total sites in the US) may not reach the 2050 target to restore visibility to natural conditions by 2064. We estimate that climate-driven increases in fire activity may dominate summertime PM2.5 over the western US, potentially offsetting the large PM2.5 reductions from domestic emission controls, and keeping visibility at present-day levels in many parks. Our study suggests that air quality in 2050 will be primarily controlled by anthropogenic emission patterns. However, climate and land use changes alone may lead to a substantial increase in surface O3 (2–3 ppb) with important consequences for O3 air quality and ecosystem degradation at the US NPs. Our study illustrates the need to consider the effects of changes in climate, vegetation, and fires in future air quality management and planning and emission policy making.

scholarly journals Volatility basis-set approach simulation of organic aerosol formation in East Asia: implications for anthropogenic-biogenic interaction and controllable amounts

2014 ◽  
Vol 14 (5) ◽  
pp. 6203-6260 ◽  
Author(s):  
H. Matsui ◽  
M. Koike ◽  
Y. Kondo ◽  
A. Takami ◽  
J. D. Fast ◽  
...  
Keyword(s):  
East Asia ◽  
Organic Aerosol ◽  
Temporal Variations ◽  
Basis Set ◽  
Anthropogenic Sources ◽  
Aerosol Formation ◽  
Organic Vapors ◽  
Mass Spectrometers ◽  
High Fraction ◽  
Outflow Region

Abstract. Organic aerosol (OA) simulations using the volatility basis-set approach were made for East Asia and its outflow region. Model simulations were evaluated through comparisons with OA measured by aerosol mass spectrometers in and around Tokyo (at Komaba and Kisai in summer 2003 and 2004) and over the outflow region in East Asia (at Fukue and Hedo in spring 2009). The simulations with aging processes of organic vapors reasonably well reproduced mass concentrations, temporal variations, and formation efficiency of observed OA at all sites. As OA mass was severely underestimated in the simulations without the aging processes, the oxidations of organic vapors are essential for reasonable OA simulations over East Asia. By considering the aging processes, simulated OA concentrations increased from 0.24 to 1.28 μg m−3 in the boundary layer over the whole of East Asia. OA formed from the interaction of anthropogenic and biogenic sources was also enhanced by the aging processes. The fraction of controllable OA was estimated to be 87% of total OA over the whole of East Asia, showing that most of the OA in our simulations formed anthropogenically (controllable). Even a large portion of biogenic secondary OA (78% of biogenic secondary OA) was formed through the influence of anthropogenic sources. The high fraction of controllable OA in our simulations is likely because anthropogenic emissions are dominant over East Asia and OA formation is enhanced by anthropogenic sources and their aging processes. Both the amounts (from 0.18 to 1.12 μg m−3) and the fraction (from 75% to 87%) of controllable OA were increased by aging processes of organic vapors over East Asia.

scholarly journals MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia

2019 ◽  
Vol 19 (18) ◽  
pp. 11911-11937 ◽  
Author(s):  
Lei Chen ◽  
Yi Gao ◽  
Meigen Zhang ◽  
Joshua S. Fu ◽  
Jia Zhu ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Model Comparison ◽  
Model Performance ◽  
Temporal Variations ◽  
Phase Iii ◽  
Air Pollution Control ◽  
Similarities And Differences ◽  
Simulation Results ◽  
Oxidation Ratio

Abstract. A total of 14 chemical transport models (CTMs) participated in the first topic of the Model Inter-Comparison Study for Asia (MICS-Asia) phase III. These model results are compared with each other and an extensive set of measurements, aiming to evaluate the current CTMs' ability in simulating aerosol concentrations, to document the similarities and differences among model performance, and to reveal the characteristics of aerosol components in large cities over East Asia. In general, these CTMs can well reproduce the spatial–temporal distributions of aerosols in East Asia during the year 2010. The multi-model ensemble mean (MMEM) shows better performance than most single-model predictions, with correlation coefficients (between MMEM and measurements) ranging from 0.65 (nitrate, NO3-) to 0.83 (PM2.5). The concentrations of black carbon (BC), sulfate (SO42-), and PM10 are underestimated by MMEM, with normalized mean biases (NMBs) of −17.0 %, −19.1 %, and −32.6 %, respectively. Positive biases are simulated for NO3- (NMB = 4.9 %), ammonium (NH4+) (NMB = 14.0 %), and PM2.5 (NMB = 4.4 %). In comparison with the statistics calculated from MICS-Asia phase II, frequent updates of chemical mechanisms in CTMs during recent years make the intermodel variability of simulated aerosol concentrations smaller, and better performance can be found in reproducing the temporal variations of observations. However, a large variation (about a factor of 2) in the ratios of SNA (sulfate, nitrate, and ammonium) to PM2.5 is calculated among participant models. A more intense secondary formation of SO42- is simulated by Community Multi-scale Air Quality (CMAQ) models, because of the higher SOR (sulfur oxidation ratio) than other models (0.51 versus 0.39). The NOR (nitric oxidation ratio) calculated by all CTMs has larger values (∼0.20) than the observations, indicating that overmuch NO3- is simulated by current models. NH3-limited condition (the mole ratio of ammonium to sulfate and nitrate is smaller than 1) can be successfully reproduced by all participant models, which indicates that a small reduction in ammonia may improve the air quality. A large coefficient of variation (CV > 1.0) is calculated for simulated coarse particles, especially over arid and semi-arid regions, which means that current CTMs have difficulty producing similar dust emissions by using different dust schemes. According to the simulation results of MMEM in six large Asian cities, different air-pollution control plans should be taken due to their different major air pollutants in different seasons. The MICS-Asia project gives an opportunity to discuss the similarities and differences of simulation results among CTMs in East Asian applications. In order to acquire a better understanding of aerosol properties and their impacts, more experiments should be designed to reduce the diversities among air quality models.

scholarly journals A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

2018 ◽  
Vol 11 (1) ◽  
pp. 291-313 ◽  
Author(s):  
Naomi Zimmerman ◽  
Albert A. Presto ◽  
Sriniwasa P. N. Kumar ◽  
Jason Gu ◽  
Aliaksei Hauryliuk ◽  
...  
Keyword(s):  
Air Quality ◽  
Relative Error ◽  
Low Cost ◽  
Personal Exposure ◽  
Minimum Data ◽  
Calibration Models ◽  
The Us ◽  
Forest Models ◽  
Random Forest Models ◽  
Us Epa

Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16–19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

scholarly journals Volatility basis-set approach simulation of organic aerosol formation in East Asia: implications for anthropogenic–biogenic interaction and controllable amounts

2014 ◽  
Vol 14 (18) ◽  
pp. 9513-9535 ◽  
Author(s):  
H. Matsui ◽  
M. Koike ◽  
Y. Kondo ◽  
A. Takami ◽  
J. D. Fast ◽  
...  
Keyword(s):  
East Asia ◽  
Organic Aerosol ◽  
Temporal Variations ◽  
Basis Set ◽  
Anthropogenic Sources ◽  
Aerosol Formation ◽  
Organic Vapors ◽  
Mass Spectrometers ◽  
Oxidation Rates ◽  
Outflow Region

Abstract. Organic aerosol (OA) simulations using the volatility basis-set approach were made for East Asia and its outflow region. Model simulations were evaluated through comparisons with OA measured by aerosol mass spectrometers in and around Tokyo (at Komaba and Kisai in summer 2003 and 2004) and over the outflow region in East Asia (at Fukue and Hedo in spring 2009). The simulations with aging processes of organic vapors reproduced the mass concentrations, temporal variations, and formation efficiencies of observed OA at all of the sites reasonably well. As OA mass was severely underestimated in the simulations without the aging processes, the oxidations of organic vapors are essential for reasonable OA simulations over East Asia. By considering the aging processes, simulated OA concentrations increased from 0.24 to 1.28 μg m−3 in the boundary layer over the whole of East Asia. OA formed from the interaction of anthropogenic and biogenic sources was also enhanced by the aging processes. The fraction of controllable OA was estimated to be 87% of total OA over the whole of East Asia, which indicated that most of the OA in our simulations were formed anthropogenically (from controllable combustion sources). A large portion of biogenic secondary OA (78% of biogenic secondary OA) was formed through the influence of anthropogenic sources. These fractions were higher than the fraction of anthropogenic emissions. An important reason for these higher controllable fractions was higher oxidant concentrations and the resulting faster oxidation rates of OA precursors by considering anthropogenic sources. Both the amounts (from 0.18 to 1.12 μg m−3) and the fraction (from 75 to 87%) of controllable OA were increased by aging processes of organic vapors over East Asia.

scholarly journals Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia

2015 ◽  
Vol 15 (24) ◽  
pp. 35591-35643 ◽  
Author(s):  
X. Dong ◽  
J. S. Fu ◽  
K. Huang ◽  
D. Tong
Keyword(s):  
Soil Moisture ◽  
Air Quality ◽  
East Asia ◽  
Model Development ◽  
Dust Emission ◽  
Temporal Variations ◽  
Heterogeneous Chemistry ◽  
Dust Aerosols ◽  
Meteorological Simulation ◽  
The Impact

Abstract. The Community Multiscale Air Quality (CMAQ) model has been further developed in terms of simulating natural wind-blown dust in this study, with a series of modifications aimed at improving the model's capability to predict the emission, transport, and chemical reactions of dust aerosols. The default parameterization of threshold friction velocity constants in the CMAQ are revised to avoid double counting of the impact of soil moisture based on the re-analysis of field experiment data; source-dependent speciation profiles for dust emission are derived based on local measurements for the Gobi and Taklamakan deserts in East Asia; and dust heterogeneous chemistry is implemented to simulate the reactions involving dust aerosol. The improved dust module in the CMAQ was applied over East Asia for March and April from 2006 to 2010. Evaluation against observations has demonstrated that simulation bias of PM10 and aerosol optical depth (AOD) is reduced from −55.42 and −31.97 % in the original CMAQ to −16.05 and −22.1 % in the revised CMAQ, respectively. Comparison with observations at the nearby Gobi stations of Duolun and Yulin indicates that applying a source-dependent profile helps reduce simulation bias for trace metals. Implementing heterogeneous chemistry is also found to result in better agreement with observations for sulfur dioxide (SO2), sulfate (SO42-), nitric acid (HNO3), nitrous oxides (NOx), and nitrate (NO3-). Investigation of a severe dust storm episode from 19 to 21 March 2010 suggests that the revised CMAQ is capable of capturing the spatial distribution and temporal variations of dust aerosols. Model evaluation indicates potential uncertainties within the excessive soil moisture fraction used by meteorological simulation. The mass contribution of fine mode aerosol in dust emission may be underestimated by 50 %. The revised revised CMAQ provides a useful tool for future studies to investigate the emission, transport, and impact of wind-blown dust over East Asia and elsewhere.

scholarly journals Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance

2017 ◽  
Author(s):  
Naomi Zimmerman ◽  
Albert A. Presto ◽  
Sriniwasa P. N. Kumar ◽  
Jason Gu ◽  
Aliaksei Hauryliuk ◽  
...  
Keyword(s):  
Air Quality ◽  
Relative Error ◽  
Low Cost ◽  
Personal Exposure ◽  
Minimum Data ◽  
Calibration Models ◽  
The Us ◽  
Forest Models ◽  
Random Forest Models ◽  
Us Epa

Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: 1) laboratory univariate linear regression, 2) empirical multivariate linear regression and 3) machine-learning based calibration models using random forests (RF). Calibration models were developed for 19 RAMP monitors using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision was robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing dataset from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error) and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS), and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF model calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low cost air quality sensors.

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