Surface Particulate Matter Concentrations Research Articles

From Space to Health: The Impact of Earth Observation Research on the Smart4COV19 Telemedicine project

Abstract This study investigates the potential of Earth observation research in improving air quality management and supporting telemedicine initiatives in response to the COVID-19 pandemic. Utilizing ground stations in Sofia and Burgas, we collected hourly measurements of NO2, CO, PM10, and PM2.5. Satellite data from TROPOMI-S5p and ground-based air quality observations were integrated to assess the spatial distribution of surface particulate matter concentrations. Our results demonstrate the feasibility of leveraging satellite-derived atmospheric chemistry data to enhance air pollution modeling and urban-scale air quality management. Furthermore, the technology developed for Burgas has the potential for expansion to other Bulgarian cities and replication in different urban centers. Overall, this research highlights the importance of Earth observation research in addressing critical environmental and public health challenges. Telemedicine via smartphones can help manage these risks by offering assistance to patients with mild symptoms, thereby minimizing their exposure to COVID-19 patients. The study also presents the findings of models used to convert TROPOMI – S5p aerosol data into PM concentrations in Burgas, Bulgaria.

Chemical Data Assimilation With Aqueous Chemistry in WRF‐Chem Coupled With WRFDA (V4.4.1)

AbstractThis study introduces a new chemistry option in the Weather Research and Forecasting model data assimilation (WRFDA) system, coupled with the WRF‐Chem model (Version 4.4.1), to incorporate aqueous chemistry (AQCHEM) in the assimilation of ground‐level chemical measurements. The new DA capability includes the integration of aqueous‐phase aerosols from the Regional Atmospheric Chemistry Mechanism (RACM) gas chemistry, the Modal Aerosol Dynamics Model for Europe (MADE) aerosol chemistry, and the Volatility Basis Set (VBS) for secondary organic aerosol production. The RACM‐MADE‐VBS‐AQCHEM scheme facilitates aerosol‐cloud‐precipitation interactions by activating aerosol particles in cloud water during the model simulation. With the goal of enhancing air quality forecasting in cloudy conditions, this new implementation is demonstrated in the weakly coupled three‐dimensional variational data assimilation (3D‐Var) system through regional air quality cycling over East Asia. Surface particulate matter (PM) concentrations and four gas species (SO2, NO2, O3, and CO) are assimilated every 6 hr for the month of March 2019. The results show that including aqueous‐phase aerosols in both the analysis and forecast can represent aerosol wet removal processes associated with cloud development and rainfall production. During a pollution event with high cloud cover, simulations without aerosols defined in cloud water exhibit significantly higher values for liquid water path, and surface PM10 (PM2.5) concentrations are overestimated by a factor of 10 (3) when wet scavenging processes dominate. On the contrary, AQCHEM proves to be helpful in simulating the wet deposition of aerosols, accurately predicting the evolution of surface PM concentrations without such overestimation.

Can climate indices forecast daily variations of wintertime PM2.5 concentrations in East Asia?

Synoptic meteorological variability plays an important role in determining air quality. In East Asia, the expansion and contraction of the Siberian high-pressure system is an essential mechanism for determining surface particulate matter concentrations (PM2.5) during the winter season. Here, we selected four climate indices that reflected the variability of the Siberian high-pressure system and analyzed their correlation with the daily variability of the observed winter PM2.5 concentrations in China and South Korea over the past six years (2014/15–2019/20). Siberian High Intensity (SHI) and East Asian Winter Monsoon (EAWM) indices were good indicators of daily PM2.5 concentration changes. Two to four days after the daily SHI and EAWM indices exceed the threshold (±1), the daily PM2.5 concentrations in East Asia significantly increased or decreased, up to 40 % compared to the mean winter PM2.5 concentrations. The climate indices associated with the Siberian high-pressure system thus potentially effectively forecast the daily PM2.5 concentrations in East Asia within a period of one week.

Aerosol data assimilation and forecast using Geostationary Ocean Color Imager aerosol optical depth and in-situ observations during the KORUS-AQ observing period

ABSTRACT This study develops an aerosol data assimilation and forecast system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the three-dimensional variational (3D-VAR) data assimilation method. The system assimilates the aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) satellite and surface particulate matter (PM) observations. The simulation domain covers Northeast Asia at 15 km horizontal resolution, and the assimilation and forecast skill is evaluated for the Korea–US Air Quality (KORUS-AQ) intensive observing period. Observing system experiments (OSEs) are conducted to examine the changes in quality of assimilation and forecast skills sensitive to the assimilated observational input data. The baseline model simulation underestimates AOD and surface PM concentration in most regions, in which the assimilation of satellite and in-situ data improves the mean biases and spatial distribution. Moreover, it improves the forecast skill of the surface concentration of PM10 and PM2.5. The results from the OSEs indicate that the assimilation of GOCI AOD only slightly enhances the forecast skill. However, most of the skill improvement comes from the surface PM assimilation, showing a practically useful level of skill until 12 hours from the initial state. The marginal improvement in the PM10 forecasts by the GOCI AOD suggests the non-negligible difference between column-representing AOD and the surface PM concentration.

Quantitative assessment of changes in surface particulate matter concentrations and precursor emissions over China during the COVID-19 pandemic and their implications for Chinese economic activity

Abstract. Sixty days after the lockdown of Hubei Province, where the coronavirus was first reported, China's true recovery from the pandemic remained an outstanding question. This study investigates how human activity changed during this period using observations of surface pollutants. By combining surface data with a three-dimensional chemistry model, the impacts of meteorological variations and variations in yearly emission control are minimized, demonstrating how pollutant levels over China changed before and after the Lunar New Year from 2017 to 2020. The results show that the reduction in NO2 concentrations, an indicator of emissions in the transportation sector, was clearly greater and longer in 2020 than in normal years and started to recover after 15 February. By contrast, PM2.5 emissions had not yet recovered by the end of March, showing a reduction of around 30 % compared with normal years. SO2 emissions were not affected significantly by the pandemic. An additional model study using a top–down emission adjustment still confirms a reduction of around 25 % in unknown surface PM2.5 emissions over the same period, even after realistically updating SO2 and NOx emissions. This evidence suggests that different economic sectors in China may be recovering at different rates, with the fastest recovery in transportation and a slower recovery likely in agriculture. The apparent difference between the recovery timelines of NO2 and PM2.5 implies that monitoring a single pollutant alone (e.g., NOx emissions) is insufficient to draw conclusions on the overall recovery of the Chinese economy.

A complex aerosol transport event over Europe during the 2017 Storm Ophelia in CAMS forecast systems: analysis and evaluation

Abstract. In mid-October 2017 Storm Ophelia crossed over western coastal Europe, inducing the combined transport of Saharan dust and Iberian biomass burning aerosols over several European areas. In this study we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) forecast systems during this complex aerosol transport event and the potential benefits that data assimilation and regional models could bring. To this end, CAMS global and regional forecast data are analysed and compared against observations from passive (MODIS: Moderate Resolution Imaging Spectroradiometer aboard Terra and Aqua) and active (CALIOP/CALIPSO: Cloud-Aerosol LIdar with Orthogonal Polarization aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite sensors and ground-based measurements (EMEP: European Monitoring and Evaluation Programme). The analysis of the CAMS global forecast indicates that dust and smoke aerosols, discretely located on the warm and cold fronts of Ophelia, respectively, were affecting the aerosol atmospheric composition over Europe during the passage of the Storm. The observed MODIS aerosol optical depth (AOD) values are satisfactorily reproduced by the CAMS global forecast system, with a correlation coefficient of 0.77 and a fractional gross error (FGE) of 0.4. The comparison with a CAMS global control simulation not including data assimilation indicates a significant improvement in the bias due to data assimilation implementation, as the FGE decreases by 32 %. The qualitative evaluation of the IFS (Integrated Forecast System) dominant-aerosol type and location against the CALIPSO observations overall reveals a good agreement. Regarding the footprint on air quality, both CAMS global and regional forecast systems are generally able to reproduce the observed signal of increase in surface particulate matter concentrations. The regional component performs better in terms of bias and temporal variability, with the correlation deteriorating over forecast time. Yet, both products exhibit inconsistencies on the quantitative and temporal representation of the observed surface particulate matter enhancements, stressing the need for further development of the air quality forecast systems for even more accurate and timely support of citizens and policy-makers.

Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo

The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of São Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships

Using a combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) mid-visible aerosol optical depth (AOD) product at 0.1 × 0.1-degree spatial resolution and collocated surface PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) monitors, we provide a global five-year (2015–2019) assessment of the spatial and seasonal AOD–PM2.5 relationships of slope, intercepts, and correlation coefficients. Only data from ground monitors accessible through an open air-quality portal that are available to the worldwide community for air quality research and decision making are used in this study. These statistics that are reported 1 × 1-degree resolution are important since satellite AOD is often used in conjunction with spatially limited surface PM2.5 monitors to estimate global distributions of surface particulate matter concentrations. Results indicate that more than 3000 ground monitors are now available for PM2.5 studies. While there is a large spread in correlation coefficients between AOD and PM2.5, globally, averaged over all seasons, the correlation coefficient is 0.55 with a unit AOD producing 54 μgm−3 of PM2.5 (Slope) with an intercept of 8 μgm−3. While the number of surface PM2.5 measurements has increased by a factor of 10 over the last decade, a concerted effort is still needed to continue to increase these monitors in areas that have no surface monitors, especially in large population centers that will further leverage the strengths of satellite data.

Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ

Abstract. KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

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

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.

Comparison of three different methodologies for the identification of high atmospheric turbidity episodes

Abstract The identification and characterization of High Atmospheric Turbidity (HAT) episodes is a key objective of global aerosol monitoring. This study presents a comparison of three different methodologies that were used to identify HAT episodes in the north-central Iberian Peninsula. The first methodology (named C&S inventory) is based on columnar aerosol optical depth (AOD from the Aerosol Robotic Network, AERONET) and surface particulate matter concentrations (PMx from the European Monitoring and Evaluation Programme, EMEP) as well as ancillary information. Another methodology (named SPR) is based on PM surface concentrations levels and ancillary information. Both methods are carefully reviewed by human observers. A third method, based only on fine and coarse mode values of AOD was also analysed. This method (the SDA or Spectral Deconvolution Algorithm) is found to be a good operational candidate for automating the identification of HAT episodes. The three methods allow for the identification of mineral desert dust (coarse type ‘D’) aerosols and aerosols of fine type, ‘A’ (i.e. biomass burning or polluted aerosols): their mixture, categorized as ‘MD’ and ‘MA’ classes (depending of the prevailing ‘D’ or ‘A’ type) is only identified in the C&S and SDA inventories. The three inventories show about 60% coincidence across a 2005–2014 reference period. When the C&S and SDA inventories are compared, the agreement is very high if columnar aerosol data is available: >90% for desert aerosol type and >70% for fine aerosol type. The comparative study of these three aerosol inventories was motivated by the need to automate existing methodologies.

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products

Air quality monitoring across Europe is mainly based on in situ ground stations, which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive models that estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite-derived products to improve particulate matter (PM) estimates. Bayesian geostatistical models addressing confounding between the spatial distribution of pollutants and remotely sensed predictors were developed to estimate yearly averages of both, fine (PM2.5) and coarse (PM10) surface PM concentrations, at 1 km2 spatial resolution over 46 European countries. Model outcomes were compared to geostatistical, geographically weighted and land-use regression formulations. Rigorous model selection identified the Earth observation data which contribute most to pollutants' estimation. Geostatistical models outperformed the predictive ability of the frequently employed land-use regression. The resulting estimates of PM10 and PM2.5, which represent the main air quality indicators for the urban Sustainable Development Goal, indicate that in 2016, 66.2% of the European population was breathing air above the WHO air quality guidelines thresholds. Our estimates are readily available to policy makers and scientists assessing the effects of long-term exposure to pollution on human and ecosystem health.

Toward understanding atmospheric physics impacting the relationship between columnar aerosol optical depth and near-surface PM2.5 mass concentrations in Nevada and California, U.S.A., during 2013

Determining the relationship between columnar aerosol optical depth (τext) and surface particulate matter concentrations (PM2.5) is desired to estimate surface aerosol concentrations over broad spatial and temporal scales using satellite remote sensing. However, remote sensing studies incur challenges when surface aerosol pollution (i.e. PM2.5) is not correlated with columnar conditions (i.e., τext). PM2.5 data fusion models that rely on satellite data and statistical relationships of τext and PM2.5 may not be able to capture the physical conditions impacting the relationships that cause columnar and surface aerosols to not be correlated in the western U.S. Therefore, an extensive examination of the atmospheric conditions is required to improve surface estimates of PM2.5 that rely on columnar aerosol measurements. This investigation uses datasets from both routine monitoring networks and models of meteorological variables and aerosol physical parameters to understand the atmospheric conditions under which surface aerosol pollution can be explained by column measurements in California and Nevada during 2013. A novel quadrant method, that utilizes statistical analysis, was developed to investigate the relationship between τext and PM2.5. The results from this investigation show that τext and PM2.5 had a positive association (τext and PM2.5 increase together) when local sources of pollution or wildfires dominated aerosol pollution in the presence of a deep and well-mixed planetary boundary layer (PBL). Moreover, τext and PM2.5 had no association (where the variables are not related) when stable conditions, long-range transport, or entrainment of air from above the PBL were observed. It was found that seasonal categorization of the relationship between τext and PM2.5, an approach commonly used in statistical models to estimate surface concentrations with satellite remote sensing, may not be enough to account for the atmospheric conditions that drive the relationships between τext and PM2.5. For all stations, winter showed the maximum average PM2.5 concentrations (14.1 μg m−3, σ = 11.6 μg m−3) meanwhile, τext reached minimum values (0.06 μg m−3, σ = 0.04) during the same season. Conversely, spring presented the minimum average PM2.5 concentrations (9.4 μg m−3, σ = 6.9 μg m−3) and the average values of τext during spring had the second highest values (0.11, σ = 0.06) averaged for all stations.

Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory

Abstract. The impact of regional emissions (e.g., domestic and international) on surface particulate matter (PM) concentrations in the Seoul metropolitan area (SMA), South Korea, and its sensitivities to meteorology and emissions inventories are quantitatively estimated for 2014 using regional air quality modeling systems. Located on the downwind side of strong sources of anthropogenic emissions, South Korea bears the full impact of the regional transport of pollutants and their precursors. However, the impact of foreign emissions sources has not yet been fully documented. We utilized two regional air quality simulation systems: (1) a Weather Research and Forecasting and Community Multi-Scale Air Quality (CMAQ) system and (2) a United Kingdom Met Office Unified Model and CMAQ system. The following combinations of emissions inventories are used: the Intercontinental Chemical Transport Experiment-Phase B, the Inter-comparison Study for Asia 2010, and the National Institute of Environment Research Clean Air Policy Support System. Partial contributions of domestic and foreign emissions are estimated using a brute force approach, adjusting South Korean emissions to 50 %. Results show that foreign emissions contributed ∼ 60 % of SMA surface PM concentration in 2014. Estimated contributions display clear seasonal variation, with foreign emissions having a higher impact during the cold season (fall to spring), reaching ∼ 70 % in March, and making lower contributions in the summer, ∼ 45 % in September. We also found that simulated surface PM concentration is sensitive to meteorology, but estimated contributions are mostly consistent. Regional contributions are also found to be sensitive to the choice of emissions inventories.

Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea

Recent changes of surface particulate matter (PM) concentration in the Seoul Metropolitan Area (SMA), South Korea, are puzzling. The long-term trend of surface PM concentration in the SMA declined in the 2000s, but since 2012 its concentrations have tended to incline, which is coincident with frequent severe hazes in South Korea. This increase puts the Korean government’s emission reduction efforts in jeopardy. This study reports that interannual variation of surface PM concentration in South Korea is closely linked with the interannual variations of wind speed. A 12-year (2004–2015) regional air quality simulation was conducted over East Asia (27-km) and over South Korea (9-km) to assess the impact of meteorology under constant anthropogenic emissions. Simulated PM concentrations show a strong negative correlation (i.e. R = −0.86) with regional wind speed, implying that reduced regional ventilation is likely associated with more stagnant conditions that cause severe pollutant episodes in South Korea. We conclude that the current PM concentration trend in South Korea is a combination of long-term decline by emission control efforts and short-term fluctuation of regional wind speed interannual variability. When the meteorology-driven variations are removed, PM concentrations in South Korea have declined continuously even after 2012.

Opposite seasonality of the aerosol optical depth and the surface particulate matter concentration over the north China Plain

Abstract Great difference exists in the aerosol optical depth (AOD) between summer and winter over the North China Plain (NCP). Monthly mean AOD at 550 nm derived from the MODIS (MODerate Resolution Imaging Spectroradiometer) products during 2000–2014 over the area of 30–40° N and 110–125° E exhibits an annual maximum in June (0.855 ± 0.130) and a minimum in December (0.381 ± 0.032). This seasonality of AOD is in the opposite phase with the surface particulate matter (PM) concentration (higher in winter and lower in summer). The possible causes for the higher AOD in June (compared with December) include (a) a higher boundary layer height (BLH) that results in more efficient transport and mixing of aerosol particles to a higher altitude (corresponding to a lower particle concentration near surface) as revealed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations profile, (b) a higher relative humidity (RH) due to the inshore monsoon circulation that leads to enhancement of aerosol extinction, (c) emission from the regional open stalk burning in the summer harvest season (as seen from MODIS fire products), and (d) the typical eastward open topographical basin over NCP. Under the assumption that the aerosol and water vapor are well mixed within the boundary layer, analysis on multi-year average shows that the differences in BLH, RH and surface PM concentration can explain up to 81% of the variance of monthly averaged AOD over NCP. A preliminarily hypothesis is also suggested to interpret the shift of AOD pattern from winter to summer with an abrupt increase of AOD from May to June, as well as an increase of surface PM 2.5 concentration over NCP during the early phase of northward progress of the East Asia summer monsoon front.

Retrieval of aerosol mass load (PM<sub>10</sub>) from MERIS/Envisat top of atmosphere spectral reflectance measurements over Germany

Abstract. Results of a new methodology for retrievals of surface particulate matter concentration (PM10) from satellite reflectance measurements over Germany are presented in this paper. The retrieval derives effective radii from Ångström-α exponents and benefits from the fitting of a smooth spectral slope from seven MERIS spectrometer channels. Comparisons with ground measurements from the air quality surveillance show standard deviations of 33.9% with −18.9% bias over Hamburg. Over rural sites a standard deviation of 17.9% (bias 12.9%) is reached. We discuss critically limitations and potential applications of the retrieval. Additionally, we talk about the aspects at comparing of retrieved particulate matter with ground station measurements.