scholarly journals Spatiotemporal variability of NO<sub>2</sub> and PM<sub>2.5</sub> over Eastern China: observational and model analyses with a novel statistical method

2018 ◽  
Vol 18 (17) ◽  
pp. 12933-12952 ◽  
Author(s):  
Mengyao Liu ◽  
Jintai Lin ◽  
Yuchen Wang ◽  
Yang Sun ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Eastern China ◽  
Ground Level ◽  
Vertical Gradient ◽  
Spatiotemporal Variability ◽  
Model Errors ◽  
Diurnal Variability ◽  
Near Surface ◽  
Synchronous Mode ◽  
The North

Abstract. Eastern China (27–41∘ N, 110–123∘ E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 µm (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF–EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall–winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north–south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another. We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 µg m−3 and PM2.5 by 35 µg m−3 on average over fall–winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north–south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30 µg m−3 and PM2.5 by 60 µg m−3. For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north–south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF–EEMD package is freely available for noncommercial uses.

scholarly journals Spatiotemporal variability of NO<sub>2</sub> and PM<sub>2.5</sub> over Eastern China: observational and model analyses with a novel statistical method

2018 ◽  
Author(s):  
Mengyao Liu ◽  
Jintai Lin ◽  
Yuchen Wang ◽  
Yang Sun ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Eastern China ◽  
Ground Level ◽  
Vertical Gradient ◽  
Spatiotemporal Variability ◽  
Model Errors ◽  
Diurnal Variability ◽  
Near Surface ◽  
Synchronous Mode ◽  
The North

Abstract. Eastern China (27° N–41° N, 110° E–123° E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 μm (PM2.5) and other air pollutants. These pollutants vary in a variety of temporal and spatial scales, with many temporal scales non-periodic and non-stationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis-visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in Fall–Winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China-wide synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or buildup of pollutants from one day to another. We further evaluate simulations of GEOS-Chem and WRF/CMAQ in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 μg/m3 and PM2.5 by 35 μg/m3 on average. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China-synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dusts. CMAQ overestimates the diurnal cycle of pollutants due to too weak boundary layer mixing – especially in the nighttime, CMAQ overestimates NO2 by about 30 μg/m3 and PM2.5 by 60 μg/m3. For the day-to-day variability, CMAQ reproduces the observed Eastern-China synchronous mode but not the north-south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF-EEMD package is freely accessible.

scholarly journals Estimating ground-level PM<sub>2.5</sub> in Eastern China using aerosol optical depth determined from the GOCI Satellite Instrument

2015 ◽  
Vol 15 (12) ◽  
pp. 17251-17281 ◽  
Author(s):  
J. Xu ◽  
R. V. Martin ◽  
A. van Donkelaar ◽  
J. Kim ◽  
M. Choi ◽  
...  
Keyword(s):  
Organic Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Transport Model ◽  
Eastern China ◽  
Ground Level ◽  
East China ◽  
Chemical Transport Model ◽  
Near Surface ◽  
Significant Agreement

Abstract. We determine and interpret fine particulate matter (PM2.5) concentrations in East China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean Geostationary Ocean Color Imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5/AOD ratio exhibits high consistency with ground-based measurements (MFB = −0.52–8.0 %). We evaluate the satellite-derived PM2.5 vs. the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in-situ observations in both annual averages (r = 0.81, N = 494) and monthly averages (MFB = 13.1 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical speciation of GOCI-derived PM2.5 reveals that secondary inorganics (SO42−, NO3−, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating are responsible for an increase in the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over East China for 2013 is 53.8 μg m−3, threatening the health and life expectancy of its 600 million residents.

scholarly journals The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth

2021 ◽  
Vol 21 (24) ◽  
pp. 18375-18391
Author(s):  
Qingqing He ◽  
Mengya Wang ◽  
Steve Hung Lam Yim
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Scales ◽  
Pearson Correlation ◽  
Eastern China ◽  
Atmospheric Correction ◽  
Mainland China ◽  
Ground Level ◽  
Central China ◽  
The Relationship

Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring the surface-based PM2.5 concentration due to their extensive spatial and temporal coverage. Satellite-derived PM2.5 estimations strongly rely on an accurate representation of the relationship between ground-level PM2.5 and satellite aerosol optical depth (AOD). Due to the limitations of satellite AOD data, most studies have examined the relationship at a coarse resolution (i.e., ≥ 10 km); thus, more effort is still needed to better understand the relationship between “in situ” PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Therefore, considerable uncertainty still exists in satellite-derived PM2.5 estimations due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD as well as its plausible impact factors, including meteorological parameters and topography, in mainland China during 2019, at various spatial and temporal scales. The coefficient of variation, the Pearson correlation coefficient and the slope of the linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China, and the linear slope was larger on average in northern inland regions than in other areas. Temporally, the PM2.5–AOD correlation peaked at noon and in the afternoon, and reached a maximum in winter. Simultaneously, considering relative humidity (RH) and the planetary boundary layer height (PBLH) in the relationship can improve the correlation, but the effect of RH and the PBLH on the correlation varied spatially and temporally with respect to both strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as the Sichuan Basin, the Shanxi–Shaanxi basins and the Junggar Basin. MAIAC 1 km AOD can better represent the ground-level fine particulate matter in most domains with exceptions, such as in very high terrain (i.e., Tibetan Plateau) and northern central China (i.e., Qinghai and Gansu). The findings of this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status of mainland China.

scholarly journals An experimental 2DVAR retrieval using AMSR2

2019 ◽  
Author(s):  
David Ian Duncan ◽  
Patrick Eriksson ◽  
Simon Pfreundschuh
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Surface Wind ◽  
Optimal Estimation ◽  
Surface Wind Speed ◽  
Model Errors ◽  
Near Surface ◽  
Noise Characteristics ◽  
Microwave Imager

Abstract. A two-dimensional variational retrieval (2DVAR) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are explicitly simulated to attempt retrieval of near surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters shown by analysis of 2DVAR averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2DVAR for cloud-free retrievals, and opens the possibility of standalone 3DVAR retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

scholarly journals A Climatology of Strong Large-Scale Ocean Evaporation Events. Part I: Identification, Global Distribution, and Associated Climate Conditions

2018 ◽  
Vol 31 (18) ◽  
pp. 7287-7312 ◽  
Author(s):  
Franziska Aemisegger ◽  
Lukas Papritz
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
Past History ◽  
Spatiotemporal Variability ◽  
Western Boundary ◽  
Near Surface ◽  
Cold Air ◽  
The North ◽  
South Indian ◽  
The North Atlantic

This paper presents an object-based, global climatology (1979–2014) of strong large-scale ocean evaporation (SLOE) and its associated climatic properties. SLOE is diagnosed using an “atmospheric moisture uptake efficiency” criterion related to the ratio of surface evaporation and integrated water vapor content in the near-surface atmosphere. The chosen Eulerian identification procedure focuses on events that strongly contribute to the available near-surface atmospheric humidity. SLOE is particularly frequent along the warm ocean western boundary currents, downstream of large continental areas, and at the sea ice edge in polar regions with frequent cold-air outbreaks. Furthermore, wind-driven SLOE occurs in regions with topographically enforced winds. On a global annual average, SLOE occurs only 6% of the time but explains 22% of total ocean evaporation. An analysis of the past history and fate of air parcels involved in cold season SLOE in the North Atlantic and south Indian Oceans shows that cold-air advection is the main mechanism that induces these events. Extratropical cyclones thereby play an important role in setting the necessary equatorward synoptic flow. Consequently, the interannual variability of SLOE associated with the North Atlantic Oscillation and the southern annular mode reveals a very high sensitivity of SLOE with respect to the location of the storm tracks. This study highlights the strong link between transient synoptic events and the spatiotemporal variability in ocean evaporation patterns, which cannot be deduced from thermodynamic steady-state and climate mean state considerations alone.

scholarly journals Topographic drivers of flight altitude over large spatial and temporal scales

The Auk ◽  
2019 ◽  
Vol 136 (2) ◽  
Author(s):  
Adam E Duerr ◽  
Tricia A Miller ◽  
Leah Dunn ◽  
Douglas A Bell ◽  
Peter H Bloom ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Ground Level ◽  
Spatial And Temporal Variation ◽  
Flight Altitude ◽  
Spatial And Temporal Scales ◽  
The North ◽  
Topographic Roughness ◽  
Topographic Characteristics ◽  
Soaring Flight ◽  
East West

Abstract Bird movements vary spatially and temporally, but the primary drivers that explain such variation can be difficult to identify. For example, it is well known that the availability of updraft influences soaring flight and that topography interacts with weather to produce these updrafts. However, the influences of topography on flight are not well understood. We determined how topographic characteristics influenced flight altitude above ground level (AGL) of a large soaring bird, the Golden Eagle (Aquila chrysaetos), over several regions within the State of California, USA. Primary drivers of flight AGL, those to which eagles showed the same response at all spatial scales, were topographic roughness, ground elevation and the east-west component of aspect (eastness). Each of these is related to formation of thermal updrafts. Secondary drivers, those to which eagles showed region-specific patterns, included topographic position, percent slope, and the north-south component of aspect (northness). In contrast to primary drivers, these secondary drivers were related to formation of both thermal and orographic updrafts. Overall, drivers of flight altitudes that were related to thermal updrafts showed different levels of complexity due to spatial and temporal variation of those drivers than did flight altitudes related to orographic updrafts.

scholarly journals Air Quality and Climate Change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III), Part II: aerosol radiative effects and aerosol feedbacks

2019 ◽  
Author(s):  
Meng Gao ◽  
Zhiwei Han ◽  
Zhining Tao ◽  
Jiawei Li ◽  
Jeong-Eon Kang ◽  
...  
Keyword(s):  
Air Quality ◽  
Radiative Forcing ◽  
Eastern China ◽  
Phase Iii ◽  
Comparison Study ◽  
Near Surface ◽  
Aerosol Radiative Forcing ◽  
The North ◽  
Bth Region ◽  
Aerosol Radiative

Abstract. Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. This paper discusses the estimates of aerosol radiative forcing, aerosol feedbacks, and possible causes for the differences among the models. Over the Beijing-Tianjin-Hebei (BTH) region, the ensemble mean of aerosol direct radiative forcing (ADRF) at the top of atmosphere, inside the atmosphere and at the surface are −1.9, 8.4 and −10.3 W/m2, respectively. Subdivisions of direct and indirect aerosol radiative forcing confirm the dominant roles of direct forcing. During severe haze days (January 17–19, 2010), the averaged reduction in near surface temperature for the BTH region can reach 0.3–3.0 ºC. The responses of wind speeds at 10 m (WS10) inferred from different models show consistent declines in eastern China. For the BTH region, aerosol-radiation feedback induced changes in PM2.5 range from 6.0 to 8.8 µg/m3 (

scholarly journals Estimating ground-level PM<sub>2.5</sub> in eastern China using aerosol optical depth determined from the GOCI satellite instrument

2015 ◽  
Vol 15 (22) ◽  
pp. 13133-13144 ◽  
Author(s):  
J.-W. Xu ◽  
R. V. Martin ◽  
A. van Donkelaar ◽  
J. Kim ◽  
M. Choi ◽  
...  
Keyword(s):  
Organic Matter ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Transport Model ◽  
Eastern China ◽  
Ground Level ◽  
World Health ◽  
Chemical Transport Model ◽  
Near Surface ◽  
Significant Agreement

Abstract. We determine and interpret fine particulate matter (PM2.5) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5 / AOD ratio exhibits high consistency with ground-based measurements in Taiwan (MFB = −0.52 %) and Beijing (MFB = −8.0 %). We evaluate the satellite-derived PM2.5 versus the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in situ observations in both annual averages (r2 = 0.66, N = 494) and monthly averages (relative RMSE = 18.3 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical composition of GOCI-derived PM2.5 reveals that secondary inorganics (SO42-, NO3-, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating increase the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over eastern China for 2013 is 53.8 μg m−3, with 400 million residents in regions that exceed the Interim Target-1 of the World Health Organization.

scholarly journals Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO<sub>2</sub>

2016 ◽  
Vol 16 (18) ◽  
pp. 12005-12038 ◽  
Author(s):  
Saroja M. Polavarapu ◽  
Michael Neish ◽  
Monique Tanguay ◽  
Claude Girard ◽  
Jean de Grandpré ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Transport Model ◽  
Spatial Scales ◽  
Ocean Surface ◽  
Model Error ◽  
Total Carbon ◽  
Boreal Summer ◽  
Model Errors ◽  
Tracer Transport ◽  
Near Surface

Abstract. A new model for greenhouse gas transport has been developed based on Environment and Climate Change Canada's operational weather and environmental prediction models. When provided with realistic posterior fluxes for CO2, the CO2 simulations compare well to NOAA's CarbonTracker fields and to near-surface continuous measurements, columns from the Total Carbon Column Observing Network (TCCON) and NOAA aircraft profiles. This coupled meteorological and tracer transport model is used to study the predictability of CO2. Predictability concerns the quantification of model forecast errors and thus of transport model errors. CO2 predictions are used to compute model–data mismatches when solving flux inversion problems and the quality of such predictions is a major concern. Here, the loss of meteorological predictability due to uncertain meteorological initial conditions is shown to impact CO2 predictability. The predictability of CO2 is shorter than that of the temperature field and increases near the surface and in the lower stratosphere. When broken down into spatial scales, CO2 predictability at the very largest scales is mainly due to surface fluxes but there is also some sensitivity to the land and ocean surface forcing of meteorological fields. The predictability due to the land and ocean surface is most evident in boreal summer when biospheric uptake produces large spatial gradients in the CO2 field. This is a newly identified source of uncertainty in CO2 predictions but it is expected to be much less significant than uncertainties in fluxes. However, it serves as an upper limit for the more important source of transport error and loss of predictability, which is due to uncertain meteorological analyses. By isolating this component of transport error, it is demonstrated that CO2 can only be defined on large spatial scales due to the presence of meteorological uncertainty. Thus, for a given model, there is a spatial scale below which fluxes cannot be inferred simply due to the fact that meteorological analyses are imperfect. These unresolved spatial scales correspond to small scales near the surface but increase with altitude. By isolating other components of transport error, the largest or limiting error can be identified. For example, a model error due to the lack of convective tracer transport was found to impact transport error on the very largest (wavenumbers less than 5) spatial scales. Thus for wavenumbers greater than 5, transport model error due to meteorological analysis uncertainty is more important for our model than the lack of convective tracer transport.

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