Application to Portugal and Macao

Air pollution is a major concern issue for most countries in the world. In Portugal and Macao, the values of nitrogen dioxide (NO2), particulate matter (PM) and ozone (O3) are frequently above the concentration thresholds accepted as “good air quality.” Portugal follows the European Union (EU) legislation (Directive 2008/50/EC) on air quality and Macao the air quality guidelines (AQG) from the WHO. Air quality forecasts are very important mitigation tools because of their ability to anticipate pollution events, and issue early warnings, allowing to take preventive measures and reduce impacts, by avoiding exposure. The work presented here refers to the statistical forecast of air pollutants for three regions: Greater Lisbon Area, Madeira Autonomous Region (both located in Portugal), and Macao Special Administrative Region (in Southern China). The presented statistical approach combines Classification and Regression Tree (CART) and multiple regression (MR) analysis to obtain optimized regression models. This consolidated methodology is now in operation for more than a decade in Portugal, and is subject to regular updates that reflect the ongoing research and the changes in the air quality monitoring network. Recently, the same methodology was applied to Macao in collaboration with the Macao Meteorological and Geophysical Bureau (SMG). Here, a statistical approach for air quality forecasting is described that has been proven to be successful, being able to forecast PM10, PM2.5, NO2, and O3 concentrations, for the next day, with a good performance. In general, all the models have shown a good agreement between the observed and forecasted concentrations (with R2 from 0.50 to 0.89), and were able to follow the concentration evolution trend. For some cases, there is a slight delay in the prediction trend. Moreover, the results obtained for pollution episodes have proven that statistical forecast can be an effective way of protecting public health.publishersversionpublishe

Macao air quality forecast using statistical methods

UID/AMB/04085/2019The levels of air pollution in the cities of Greater Bay Area in Southern China, including Macao, are extremely high and often exceeded the levels recommended by World Health Organization Air Quality Guidelines. In order for the population to take precautionary measures and avoid further health risks un- der high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression analysis were developed successfully for Macao to predict the next-day concentrations of particulate matter (PM10 and PM2.5) for Taipa Ambient, a background representative station located within the area of Macao (32.9 km2), at Taipa Grande, the headquarter of Macao Meteorological and Geophysical Bureau. The two developed models were statistically significantly valid, with a 95% confidence level with high coefficients of determination. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. The meteorological variables such as geopotential height and relative humidity at different vertical levels were selected from an extensive list of variables. The air quality variables that translate the resilience of the recent past concentrations of each pollutant were the ones selected. The models were based in meteorological and air quality variables with five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes, with high coefficients of determination between predicted and observed daily average concentrations (0.92 and 0.89 for PM10 and PM2.5 , respectively). The results are expected to be the basis for an operational air quality forecast for the region.publishersversionpublishe

Statistical forecast of pollution episodes in Macao during national holiday and COVID-19

UID/AMB/04085/2019Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 µg/m3 and 400 µg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 µg/m3 and O3 levels at 50 µg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.publishersversionpublishe

Carbon neutrality pathways effects on air pollutant emissions: The Portuguese case

Funding Information: Funding: This research was based on the outcomes from the Portuguese Carbon Neutrality Roadmap 2050, a project supported by the Ministry of Environment and Energy Transition of the Portuguese Republic. The research work developed at CENSE is financed by the Portuguese Foundation for Science and Technology (FCT) through the strategic project UIDB/04085/2020.Air pollution and climate change are closely interlinked, once both share common emission sources, which mainly arise from fuel combustion and industrial processes. Climate mitigation actions bring co-benefits on air quality and human health. However, specific solutions can provide negative trade-offs for one side. The Portuguese Carbon Neutrality Roadmap was developed to assess conceivable cost-effective pathways to achieve zero net carbon emissions by 2050. Assessing its impacts, on air pollutant emissions, is the main focus of the present work. The bottom-up linear optimization energy system the integrated MARKAL-EFOM system (TIMES) model was selected as a modeling tool for the decarbonization scenarios assessment. The estimation of air pollutant emissions was performed exogenously to the TIMES model. Results show that reaching net zero greenhouse gas (GHG) emissions is possible, and technologically feasible, in Portugal, by 2050. The crucial and most cost-effective vector for decarbonizing the national economy is the end-use energy consumption electrification, renewable based, across all end-use sectors. Decarbonization efforts were found to have strong co-benefits for reducing air pollutant emissions in Portugal. Transport and power generation are the sectors with the greatest potential to reduce GHG emissions, providing likewise the most significant reductions of air pollutant emissions. Despite the overall positive effects, there are antagonistic effects, such as the use of biomass, mainly in industry and residential sectors, which translates into increases in particulate matter emissions. This is relevant for medium term projections, since results show that, by 2030, PM2.5 emissions are unlikely to meet the emission reduction commitments set at the European level, if no additional control measures are considered.publishersversionpublishe

Air quality forecasting by statistical methods: model evaluation for Lisbon and Oporto

Ponencia presentada en: XXXII Jornadas Científicas de la AME y el XIII Encuentro Hispano Luso de Meteorología celebrado en Alcobendas (Madrid), del 28 al 30 de mayo de 2012

A Case Study in Macao

Funding Information: This research was funded by Fundação para a Ciência e Tecnologia, I.P., Portugal, grant number UID/AMB/04085/2020, and the APC was funded by CENSE. Funding Information: The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). Publisher Copyright: © 2022 by the authors.Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.publishersversionpublishe

Bone formation and resorption markers at 7 years of age: relations with growth and bone mineralization

Purpose We aimed to describe bone formation and resorption markers in generally healthy prepubertal children using total alkaline phosphatase (tALP), osteocalcin (OC) and β-isomerized C-terminal telopeptides of type I collagen (β-CTx) serum concentrations and to estimate markers’ correlations with anthropometric growth (height, weight, body mass index and trajectories of weight gain) as well as bone mineral content (BMC) and areal density (aBMD). Methods We assessed 395 7-year-old children from the Generation XXI cohort with tALP, OC and β-CTx concentrations determined from a fasting venous blood sample and BMC/aBMD measured by dual-energy X-ray absorptiometry. Gender-specific reference intervals for tALP, OC and β-CTx in 7-year-old children were established by calculating the 2.5th and 97.5th percentiles. Pearson and partial correlation coefficients (controlling for sex, age, body size and season) between bone markers and growth measures were computed. Results tALP increased with height (rpartial controlled for sex = 0.26, 95%CI: 0.17, 0.35), was higher in overweight than in healthy weight children, and in children who gained weight above average during infancy. No correlations were found between OC or β-CTx and growth. In girls, OC was slightly correlated with subtotal BMC (rpartial = 0.22, 95%CI: 0.08, 0.35), subtotal aBMD (rpartial = 0.20, 95%CI: 0.06, 0.33) and lumbar spine aBMD (rpartial = 0.23, 95%CI: 0.09, 0.36). tALP and β-CTx were not correlated with any of the DXA-derived bone measures. Conclusion This study contributed to the description of bone turnover at 7 years of age and suggested that bone metabolism markers measured in a single point in time have limited ability to describe anthropometric growth and overall bone status in generally healthy prepubertal children.info:eu-repo/semantics/publishedVersio

The greatest air quality experiment ever: Policy suggestions from the COVID-19 lockdown in twelve European cities

COVID-19 (Coronavirus disease 2019) hit Europe in January 2020. By March, Europe was the active centre of the pandemic. As a result, widespread "lockdown" measures were enforced across the various European countries, even if to a different extent. Such actions caused a dramatic reduction, especially in road traffic. This event can be considered the most significant experiment ever conducted in Europe to assess the impact of a massive switch-off of atmospheric pollutant sources. In this study, we focus on in situ concentration data of the main atmospheric pollutants measured in twelve European cities, characterized by different climatology, emission sources, and strengths. We propose a methodology for the fair comparison of the impact of lockdown measures considering the non-stationarity of meteorological conditions and emissions, which are progressively declining due to the adoption of stricter air quality measures. The analysis of these unmatched circumstances allowed us to estimate the impact of a nearly zero-emission urban transport scenario on air quality in 12 European cities. The clearest result, common to all the cities, is that a dramatic traffic reduction effectively reduces NO2 concentrations. In contrast, each city’s PM and ozone concentrations can respond differently to the same type of emission reduction measure. From the policy point of view, these findings suggest that measures targeting urban traffic alone may not be the only effective option for improving air quality in cities.Peer ReviewedArticle signat per 19 autors/es: Marialuisa Volta 1, Umberto Giostra 2, Giorgio Guariso 3, Jose Baldasano 4, Martin Lutz 5, Andreas Kerschbaumer 5, Annette Rauterberg-Wulff 5, Francisco Ferreira 6, Luısa Mendes 6, Joana Monjardino 6, Nicolas Moussiopοulos 7, Christos Vlachokostas 7, Peter Viaene 8, Janssen Stijn 8, Enrico Turrini 1, Elena De Angelis 1, Claudio Carnevale 1, Martin L. Williams 9, Michela Maione 2,10 // 1 Dipartimento di Ingegneria Meccanica e Civile, Università di Brescia, Brescia, Italy; 2 Dipartimento di Scienze Pure e Applicate, Università di Urbino Carlo Bo, Urbino, Italy; 3 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; 4 Centro Nacional de Supercomputación, Universitat Politècnica de Catalunya, Barcelona, Spain; 5 Senatsverwaltung für Umwelt, Mobilität, Verbraucher-und Klimaschutz, Berlin, Germany; 6 Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciencias e Tecnologia Universidade Nova de Lisboa, Caparica, Portugal; 7 Aristoteleio Panepistemio Thessalonikes, Thessalonike, Greece; 8 VITO, Vision on Technology, Mol, Belgium; 9 Environmental Research Group, Imperial College, London, United Kingdom; 10 Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, Bologna, ItalyPostprint (published version

Upregulation of Inflammatory genes and downregulation of sclerostin gene expression are key elements in the early phase of fragility fracture healing

Peer reviewe

Ar mais Limpo para Lisboa

A qualidade do ar ambiente é um dos fatores ambientais de maior preocupação em zonas urbanas. Em Lisboa, continuam a ocorrer ultrapassagens aos valores limite definidos para determinados poluentes, ainda que menos frequentes nos últimos anos. Para garantir a continuidade das trajetórias de poluição descendentes, há que implementar políticas e medidas já identificadas, aproveitando o fôlego dado pelo vislumbre do que foram as cidades sem tráfego rodoviário (como consequência do confinamento imposto pela COVID-19), com maior usufruto do espaço público e com uma boa qualidade do ar.publishersversionpublishe