Bas Mijling

Reductions of NO2 detected from space during the 2008 Beijing Olympic Games

During the 2008 Olympic and Paralympic Games in Beijing (from 8 August to 17 September), local authorities enforced strong measures to reduce air pollution during the events. To evaluate the direct effect of these measures, we use the tropospheric NO2 column observations from the satellite instruments GOME-2 and OMI. We interpret these data against simulations from the regional chemistry transport model CHIMERE, based on a 2006 emission inventory, and find a reduction of NO2 concentrations of approximately 60% above Beijing during the Olympic period. The air quality measures were especially effective in the Beijing area, but also noticeable in surrounding cities of Tianjin (30% reduction) and Shijiazhuang (20% reduction). Copyright 2009 by the American Geophysical Union.

Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters

To assess the impact of atmospheric aerosols on health, climate, and air traffic, aerosol properties must be measured with fine spatial and temporal sampling. This can be achieved by actively involving citizens and the technology they own to form an atmospheric measurement network. We establish this new measurement strategy by developing and deploying iSPEX, a low-cost, mass-producible optical add-on for smartphones with a corresponding app. The aerosol optical thickness (AOT) maps derived from iSPEX spectropolarimetric measurements of the daytime cloud-free sky by thousands of citizen scientists throughout the Netherlands are in good agreement with the spatial AOT structure derived from satellite imagery and temporal AOT variations derived from ground-based precision photometry. These maps show structures at scales of kilometers that are typical for urban air pollution, indicating the potential of iSPEX to provide information about aerosol properties at locations and at times that are not covered by current monitoring efforts.

Supporting Earth-Observation Calibration and Validation: A new generation of tools for crowdsourcing and citizen science

Citizens are providing vast amounts of georeferenced data in the form of in situ data collections as well as interpretations and digitization of Earth-observation (EO) data sets. These new data streams have considerable potential for supporting the calibration and validation of current and future products derived from EO. We provide a general introduction to this growing area of interest and review existing crowdsourcing and citizen science (CS) initiatives of relevance to EO. We then draw upon our own experiences to provide case studies that highlight different types of data collection and citizen engagement and discuss the various barriers to adoption. Finally, we highlight opportunities for how citizens can become part of an integrated EO monitoring system in the framework of the European Union (EU) space program, including Copernicus and other monitoring initiatives.

Air-quality modelling in the Lake Baikal region

In this paper, we assess the status of the air quality in the Lake Baikal region which is strongly influenced by the presence of anthropogenic pollution sources. We combined the local data, with global databases, remote sensing imagery and modelling tools. This approach allows to inventorise the air-polluting sources and to quantify the air-quality concentration levels in the Lake Baikal region to a reasonable level, despite the fact that local data are scarcely available. In the simulations, we focus on the month of July 2003, as for this period, validation data are available for a number of ground-based measurement stations within the Lake Baikal region.

Evaluation of modeling NO 2 concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope= 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope= 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

A new method for deriving trace gas emission inventories from satellite observations: The case of SO2 over China

A method is developed that allows the construction of spatial emission inventories. The method is applied for anthropogenic SO2 over China (0.25°×0.25°). The Enhancement Ratio Method (ERM) allows for the calculation of SO2 emissions using relationships between gridded satellite measurements of SO2 and NO2 at low wind speeds, and satellite-based NOx emission estimates. Here, we derive SO2 emissions for five years (2007-2011). A large decrease of emissions during 2007-2009 and a modest increase between 2010 and 2011 is observed. The evolution of emissions over time calculated here is in general agreement with bottom-up inventories, although differences exist, not only between the current inventory and other inventories but also among the bottom up inventories themselves. The ERM-derived emissions are consistent, spatially and temporally, with existing inventories.