ALBERT

Description: Eleven of the world's 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r =  0.9) and high spatial correlation for PM2.5 (r =  0.5 and r =  0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6 %), the model overestimates O3 by 35 %. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data.For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the north characterized by a statistically significant increase by up to 4 % (2 ppb) and one in the south by a decrease up to −3 % (−1.4 ppb). This variation in O3 is assumed to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds.Our calculations suggest that PM2.5 will increase by up to 6.5 % over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed.The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13 and 67 % on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in the 2050s, exceeding OM and dust.

Description: Ship emissions constitute a large, and so far poorly regulated, source of air pollution. Emissions are mainly clustered along major ship routes both in open seas and close to densely populated shorelines. Major air pollutants emitted include sulfur dioxide, NOx, and primary particles. Sulfur and NOx are both major contributors to the formation of secondary fine particles (PM2.5) and to acidification and eutrophication. In addition, NOx is a major precursor for ground-level ozone. In this paper, we quantify the contributions from international shipping to European air pollution levels and depositions. This study is based on global and regional model calculations. The model runs are made with meteorology and emission data representative of the year 2017 after the tightening of the SECA (sulfur emission control area) regulations in 2015 but before the global sulfur cap that came into force in 2020. The ship emissions have been derived using ship positioning data. We have also made model runs reducing sulfur emissions by 80 % corresponding to the 2020 requirements. This study is based on model sensitivity studies perturbing emissions from different sea areas: the northern European SECA in the North Sea and the Baltic Sea, the Mediterranean Sea and the Black Sea, the Atlantic Ocean close to Europe, shipping in the rest of the world, and finally all global ship emissions together. Sensitivity studies have also been made setting lower bounds on the effects of ship plumes on ozone formation. Both global- and regional-scale calculations show that for PM2.5 and depositions of oxidised nitrogen and sulfur, the effects of ship emissions are much larger when emissions occur close to the shore than at open seas. In many coastal countries, calculations show that shipping is responsible for 10 % or more of the controllable PM2.5 concentrations and depositions of oxidised nitrogen and sulfur. With few exceptions, the results from the global and regional calculations are similar. Our calculations show that substantial reductions in the contributions from ship emissions to PM2.5 concentrations and to depositions of sulfur can be expected in European coastal regions as a result of the implementation of a 0.5 % worldwide limit of the sulfur content in marine fuels from 2020. For countries bordering the North Sea and Baltic Sea SECA, low sulfur emissions have already resulted in marked reductions in PM2.5 from shipping before 2020. For ozone, the lifetime in the atmosphere is much longer than for PM2.5, and the potential for ozone formation is much larger in otherwise pristine environments. We calculate considerable contributions from open sea shipping. As a result, we find that the largest contributions to ozone in several regions and countries in Europe are from sea areas well outside European waters.

Description: Emissions of most land-based air pollutants in western Europe have decreased in the last decades. Over the same period emissions from shipping have also decreased, but with large differences depending on species and sea area. At sea, sulfur emissions in the SECAs (Sulphur Emission Control Areas) have decreased following the implementation of a 0.1 % limit on sulfur in marine fuels from 2015. In Europe the North Sea and the Baltic Sea are designated as SECAs by the International Maritime Organisation (IMO). Model calculations assuming present (2016) and future (2030) emissions have been made with the regional-scale EMEP model covering Europe and the sea areas surrounding Europe, including the North Atlantic east of 30∘ W. The main focus in this paper is on the effects of ship emissions from the Baltic Sea. To reduce the influence of meteorological variability, all model calculations are presented as averages for 3 meteorological years (2014, 2015, 2016). For the Baltic Sea, model calculations have also been made with higher sulfur emissions representative of year 2014 emissions. From Baltic Sea shipping the largest effects are calculated for NO2 in air, accounting for more than 50 % of the NO2 concentrations in central parts of the Baltic Sea. In coastal zones contributions to NO2 and also nitrogen depositions can be of the order of 20 % in some regions. Smaller effects, up to 5 %–10 %, are also seen for PM2.5 in coastal zones close to the main shipping lanes. Country-averaged contributions from ships are small for large countries that extend far inland like Germany and Poland, and larger for smaller countries like Denmark and the Baltic states Estonia, Latvia, and Lithuania, where ship emissions are among the largest contributors to concentrations and depositions of anthropogenic origin. Following the implementations of stricter SECA regulations, sulfur emissions from Baltic Sea shipping now have virtually no effects on PM2.5 concentrations and sulfur depositions in the Baltic Sea region. Adding to the expected reductions in air pollutants and depositions following the projected reductions in European emissions, we expect that the contributions from Baltic Sea shipping to NO2 and PM2.5 concentrations, and to depositions of nitrogen, will be reduced by 40 %–50 % from 2016 to 2030 mainly as a result of the Baltic Sea being defined as a Nitrogen Emission Control Area from 2021. In most parts of the Baltic Sea region ozone levels are expected to decrease from 2016 to 2030. For the Baltic Sea shipping, titration, mainly in winter, and production, mainly in summer, partially compensate. As a result the effects of Baltic Sea shipping on ozone are similar in 2016 and 2030.

Description: We present a computationally inexpensive method for individually quantifying the contributions from different sources to local air pollution. It can explicitly distinguish between regional–background and local–urban air pollution, allowing for fully consistent downscaling schemes. The method can be implemented in existing Eulerian chemical transport models and can be used to distinguish the contribution of a large number of emission sources to air pollution in every receptor grid cell within one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence, it can be used for time-critical operational services by providing scientific information as input for local policy decisions on air pollution abatement. The main limitation in its current version is that nonlinear chemical processes are not accounted for and only primary pollutants can be addressed. In this paper we provide a technical description of the method and discuss various applications for scientific and policy purposes.

Description: Eleven of the world’s 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no more policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to calculate changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r=0.9) and high spatial correlations for PM2.5 (r=0.5 and r=0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6%), the model overestimates O3 by 35%. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given inevitable uncertainties in much of the input data For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the North characterized by a statistically significant increase by up to 4% (2 ppb) and one in the South by a decrease up to -3% (-1.4 ppb). This variation in O3 is found to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic NMVOCs. Our calculations suggest that PM2.5 will increase by up to 6.5% in the 2050s, driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIA), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13% and 67% in average in 2050s, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in 2050s, exceeding OM and dust.

Description: Emissions of most land based air pollutants in western Europe have decreased in the last decades. Over the same period emissions from shipping have also decreased, but with large differences depending on species and sea area. At sea, sulphur emissions in the SECAs (Sulphur Emission Control Areas) have decreased following the implementation of a 0.1 % limit on sulphur in marine fuels from 2015. In Europe the North Sea and the Baltic Sea are designated as SECAs by the International maritime Organisation (IMO). Model calculations assuming present (2016) and future (2030) emissions have been made with the regional scale EMEP model covering Europe and the sea areas surrounding Europe including the North Atlantic east of 30 degrees west. The main focus in this paper is on the effects of ship emissions from the Baltic Sea. To reduce the influence of meteorological variability, all model calculations are presented as averages for 3 meteorological years (2014, 2015, 2016). For the Baltic Sea, model calculations have also been made with higher sulphur emissions representative of year 2014 emissions. From Baltic Sea shipping the largest effects are calculated for NO2 in air, but effects are also seen for PM2.5 and depositions of oxidised nitrogen, mainly in coastal zones close to the main shipping lanes. As a result country averaged contributions from ships are small for large countries that extend far inland like Germany and Poland, and larger for smaller countries like Denmark and the Baltic states Estonia, Latvia and Lithuania, where ship emissions are among the largest contributors to concentrations and depositions of anthropogenic origin. Following the implementations of stricter SECA regulations, sulphur emissions from ships in the Baltic Sea shipping now have virtually no effects on PM2.5 concentrations and sulphur depositions in the Baltic Sea region. Following the expected reductions in European emissions, model calculated NO2 and PM2.5 concentrations, depositions of oxidised nitrogen, and partially also surface ozone levels, in the Baltic Sea region are expected to decrease in the next decade. Parts of these reductions are caused by reductions in the Baltic Sea ship emissions mainly as a result of the Baltic Sea being defined as a Nitrogen Emission Control Area from 2021.

Description: An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible website (http://www.marcopolo-panda.eu, last access: 7 December 2018). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an intercomparison of the different forecasts performed during a given period of time (1–15 March 2017) and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

Description: An operational multi-model forecasting system for air quality including 9 different chemical transport models has been developed and is providing daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible web site (http://www.marcopolo-panda.eu). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an inter-comparison of the different forecasts performed during a given period of time (1–15 March 2017), and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

Description: An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

Description: An operational multi-model forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multi-model ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multi-model ensemble predictions for the next 72 hours are displayed as hourly outputs on a publicly accessible web site (www.marcopolo-panda.eu). In this paper, the performance of the predictions system (individual models and the multi-model ensemble) for the first operational year (April 2016 until June 2017) has been analysed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate a) the seasonal behavior, b) the geographical distribution and c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide alert warning (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.