Improving 3-day deterministic air pollution forecasts using machine learning algorithms

As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms – random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) – to improve 1, 2, and 3 d deterministic forecasts of PM10, NOx, and O3 at different sites in Greater Stockholm, Sweden. It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NOx is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O3 but is compensated for using the ML models by taking lagged measurements into account. Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NOx at street canyon sites, mean squared error (MSE) decreased by up to 60  %, and seven metrics, such as R2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O3 also modestly increased, with differences between metrics. Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NOx at the station not involved in model training. For PM10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations.</p

Consistent histories of anthropogenic western European air pollution preserved in different Alpine ice cores

Individual high-Alpine ice cores have been proven to contain a well-preserved history of past anthropogenic air pollution in western Europe. The question of how representative one ice core is with respect to the reconstruction of atmospheric composition in the source region has not been addressed so far. Here, we present the first study systematically comparing longer-term ice-core records (1750–2015 CE) of various anthropogenic compounds, such as major inorganic aerosol constituents (NH4+, NO3-, SO42-), black carbon (BC), and trace species (Cd, F−, Pb). Depending on the data availability for the different air pollutants, up to five ice cores from four high-Alpine sites located in the European Alps analysed by different laboratories were considered. Whereas absolute concentration levels can partly differ depending on the prevailing seasonal distribution of accumulated precipitation, all seven investigated anthropogenic compounds are in excellent agreement between the various sites for their respective, species-dependent longer-term concentration trends. This is related to common source regions of air pollution impacting the four sites less than 100 km away including western European countries surrounding the Alps. For individual compounds, the Alpine ice-core composites developed in this study allowed us to precisely time the onset of pollution caused by industrialization in western Europe. Extensive emissions from coal combustion and agriculture lead to an exceeding of pre-industrial (1750–1850) concentration levels already at the end of the 19th century for BC, Pb, exSO42- (non-dust, non-sea salt SO42-), and NH4+, respectively. However, Cd, F−, and NO3- concentrations started surpassing pre-industrial values only in the 20th century, predominantly due to pollution from zinc and aluminium smelters and traffic. The observed maxima of BC, Cd, F−, Pb, and exSO42- concentrations in the 20th century and a significant decline afterwards clearly reveal the efficiency of air pollution control measures such as the desulfurization of coal, the introduction of filters and scrubbers in power plants and metal smelters, and the ban of leaded gasoline improving the air quality in western Europe. In contrast, NO3- and NH4+ concentration records show levels in the beginning of the 21th century which are unprecedented in the context of the past 250 years, indicating that the introduced abatement measures to reduce these pollutants were insufficient to have a major effect at high altitudes in western Europe. Only four ice-core composite records (BC, F−, Pb, exSO42-) of the seven investigated pollutants correspond well with modelled trends, suggesting inaccuracies of the emission estimates or an incomplete representation of chemical reaction mechanisms in the models for the other pollutants. Our results demonstrate that individual ice-core records from different sites in the European Alps generally provide a spatially representative signal of anthropogenic air pollution trends in western European countries.</p

Biofeedback for training balance and mobility tasks in older populations: a systematic review

<p>Abstract</p> <p>Context</p> <p>An effective application of biofeedback for interventions in older adults with balance and mobility disorders may be compromised due to co-morbidity.</p> <p>Objective</p> <p>To evaluate the feasibility and the effectiveness of biofeedback-based training of balance and/or mobility in older adults.</p> <p>Data Sources</p> <p>PubMed (1950-2009), EMBASE (1988-2009), Web of Science (1945-2009), the Cochrane Controlled Trials Register (1960-2009), CINAHL (1982-2009) and PsycINFO (1840-2009). The search strategy was composed of terms referring to biofeedback, balance or mobility, and older adults. Additional studies were identified by scanning reference lists.</p> <p>Study Selection</p> <p>For evaluating effectiveness, 2 reviewers independently screened papers and included controlled studies in older adults (i.e. mean age equal to or greater than 60 years) if they applied biofeedback during repeated practice sessions, and if they used at least one objective outcome measure of a balance or mobility task.</p> <p>Data Extraction</p> <p>Rating of study quality, with use of the Physiotherapy Evidence Database rating scale (PEDro scale), was performed independently by the 2 reviewers. Indications for (non)effectiveness were identified if 2 or more similar studies reported a (non)significant effect for the same type of outcome. Effect sizes were calculated.</p> <p>Results and Conclusions</p> <p>Although most available studies did not systematically evaluate feasibility aspects, reports of high participation rates, low drop-out rates, absence of adverse events and positive training experiences suggest that biofeedback methods can be applied in older adults. Effectiveness was evaluated based on 21 studies, mostly of moderate quality. An indication for effectiveness of visual feedback-based training of balance in (frail) older adults was identified for postural sway, weight-shifting and reaction time in standing, and for the Berg Balance Scale. Indications for added effectiveness of applying biofeedback during training of balance, gait, or sit-to-stand transfers in older patients post-stroke were identified for training-specific aspects. The same applies for auditory feedback-based training of gait in older patients with lower-limb surgery.</p> <p>Implications</p> <p>Further appropriate studies are needed in different populations of older adults to be able to make definitive statements regarding the (long-term) added effectiveness, particularly on measures of functioning.</p

Is the ozone climate penalty robust in Europe?

Ozone air pollution is identified as one of the main threats bearing upon human health and ecosystems, with 25 000 deaths in 2005 attributed to surface ozone in Europe (IIASA 2013 TSAP Report #10). In addition, there is a concern that climate change could negate ozone pollution mitigation strategies, making them insufficient over the long run and jeopardising chances to meet the long term objective set by the European Union Directive of 2008 (Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008) (60 ppbv, daily maximum). This effect has been termed the ozone climate penalty. One way of assessing this climate penalty is by driving chemistry-transport models with future climate projections while holding the ozone precursor emissions constant (although the climate penalty may also be influenced by changes in emission of precursors). Here we present an analysis of the robustness of the climate penalty in Europe across time periods and scenarios by analysing the databases underlying 11 articles published on the topic since 2007, i.e. a total of 25 model projections. This substantial body of literature has never been explored to assess the uncertainty and robustness of the climate ozone penalty because of the use of different scenarios, time periods and ozone metrics. Despite the variability of model design and setup in this database of 25 model projection, the present meta-analysis demonstrates the significance and robustness of the impact of climate change on European surface ozone with a latitudinal gradient from a penalty bearing upon large parts of continental Europe and a benefit over the North Atlantic region of the domain. Future climate scenarios present a penalty for summertime (JJA) surface ozone by the end of the century (2071-2100) of at most 5 ppbv. Over European land surfaces, the 95% confidence interval of JJA ozone change is [0.44; 0.64] and [0.99; 1.50] ppbv for the 2041-2070 and 2071-2100 time windows, respectively

ECLAIRE third periodic report

The ÉCLAIRE project (Effects of Climate Change on Air Pollution Impacts and Response Strategies for European Ecosystems) is a four year (2011-2015) project funded by the EU's Seventh Framework Programme for Research and Technological Development (FP7)