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- ItemNear-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic([London] : Nature Publishing Group UK, 2020) Liu, Zhu; Ciais, Philippe; Deng, Zhu; Lei, Ruixue; Davis, Steven J.; Feng, Sha; Zheng, Bo; Cui, Duo; Dou, Xinyu; Zhu, Biqing; Guo, Rui; Ke, Piyu; Sun, Taochun; Lu, Chenxi; He, Pan; Wang, Yuan; Yue, Xu; Wang, Yilong; Lei, Yadong; Zhou, Hao; Cai, Zhaonan; Wu, Yuhui; Guo, Runtao; Han, Tingxuan; Xue, Jinjun; Boucher, Olivier; Boucher, Eulalie; Chevallier, Frédéric; Tanaka, Katsumasa; Wei, Yiming; Zhong, Haiwang; Kang, Chongqing; Zhang, Ning; Chen, Bin; Xi, Fengming; Liu, Miaomiao; Bréon, François-Marie; Lu, Yonglong; Zhang, Qiang; Guan, Dabo; Gong, Peng; Kammen, Daniel M.; He, Kebin; Schellnhuber, Hans JoachimThe COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.
- ItemEURODELTA III exercise: An evaluation of air quality models’ capacity to reproduce the carbonaceous aerosol(Amsterdam : Elsevier, 2019) Mircea, Mihaela; Bessagnet, Bertrand; D'Isidoro, Massimo; Pirovano, Guido; Aksoyoglu, Sebnem; Ciarelli, Giancarlo; Tsyro, Svetlana; Manders, Astrid; Bieser, Johannes; Stern, Rainer; Vivanco, Marta García; Cuvelier, Cornelius; Aas, Wenche; Prévôt, André S.H.; Aulinger, Armin; Briganti, Gino; Calori, Giuseppe; Cappelletti, Andrea; Colette, Augustin; Couvidat, Florian; Fagerli, Hilde; Finardi, Sandro; Kranenburg, Richard; Rouïl, Laurence; Silibello, Camillo; Spindler, Gerald; Poulain, Laurent; Herrmann, Hartmut; Jimenez, Jose L.; Day, Douglas A.; Tiitta, Petri; Carbone, SamaraThe carbonaceous aerosol accounts for an important part of total aerosol mass, affects human health and climate through its effects on physical and chemical properties of the aerosol, yet the understanding of its atmospheric sources and sinks is still incomplete. This study shows the state-of-the-art in modelling carbonaceous aerosol over Europe by comparing simulations performed with seven chemical transport models (CTMs) currently in air quality assessments in Europe: CAMx, CHIMERE, CMAQ, EMEP/MSC-W, LOTOS-EUROS, MINNI and RCGC. The simulations were carried out in the framework of the EURODELTA III modelling exercise and were evaluated against field measurements from intensive campaigns of European Monitoring and Evaluation Programme (EMEP) and the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI). Model simulations were performed over the same domain, using as much as possible the same input data and covering four seasons: summer (1–30 June 2006), winter (8 January – 4 February 2007), autumn (17 September- 15 October 2008) and spring (25 February - 26 March 2009). The analyses of models’ performances in prediction of elemental carbon (EC) for the four seasons and organic aerosol components (OA) for the last two seasons show that all models generally underestimate the measured concentrations. The maximum underestimation of EC is about 60% and up to about 80% for total organic matter (TOM). The underestimation of TOM outside of highly polluted area is a consequence of an underestimation of secondary organic aerosol (SOA), in particular of its main contributor: biogenic secondary aerosol (BSOA). This result is independent on the SOA modelling approach used and season. The concentrations and daily cycles of total primary organic matter (TPOM) are generally better reproduced by the models since they used the same anthropogenic emissions. However, the combination of emissions and model formulation leads to overestimate TPOM concentrations in 2009 for most of the models. All models capture relatively well the SOA daily cycles at rural stations mainly due to the spatial resolution used in the simulations. For the investigated carbonaceous aerosol compounds, the differences between the concentrations simulated by different models are lower than the differences between the concentrations simulated with a model for different seasons. © 2019 The Authors
- ItemA human development framework for CO 2 reductions(San Francisco, CA : Public Library of Science (PLoS), 2011) Costa, L.; Rybski, D.; Kropp, J.P.Although developing countries are called to participate in CO 2 emission reduction efforts to avoid dangerous climate change, the implications of proposed reduction schemes in human development standards of developing countries remain a matter of debate. We show the existence of a positive and time-dependent correlation between the Human Development Index (HDI) and per capita CO 2 emissions from fossil fuel combustion. Employing this empirical relation, extrapolating the HDI, and using three population scenarios, the cumulative CO 2 emissions necessary for developing countries to achieve particular HDI thresholds are assessed following a Development As Usual approach (DAU). If current demographic and development trends are maintained, we estimate that by 2050 around 85% of the world's population will live in countries with high HDI (above 0.8). In particular, 300 Gt of cumulative CO 2 emissions between 2000 and 2050 are estimated to be necessary for the development of 104 developing countries in the year 2000. This value represents between 20 % to 30 % of previously calculated CO 2 budgets limiting global warming to 2°C. These constraints and results are incorporated into a CO 2 reduction framework involving four domains of climate action for individual countries. The framework reserves a fair emission path for developing countries to proceed with their development by indexing country-dependent reduction rates proportional to the HDI in order to preserve the 2°C target after a particular development threshold is reached. For example, in each time step of five years, countries with an HDI of 0.85 would need to reduce their per capita emissions by approx. 17% and countries with an HDI of 0.9 by 33 %. Under this approach, global cumulative emissions by 2050 are estimated to range from 850 up to 1100 Gt of CO 2. These values are within the uncertainty range of emissions to limit global temperatures to 2°C.
- ItemHow the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning(Basel : MDPI AG, 2020) Hempel, Sabrina; Adolphs, Julian; Landwehr, Niels; Janke, David; Amon, ThomasEnvironmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.
- ItemShort term associations of ambient nitrogen dioxide with daily total, cardiovascular, and respiratory mortality: multilocation analysis in 398 cities(London : BMJ Publ. Group, 2021) Meng, Xia; Liu, Cong; Chen, Renjie; Sera, Francesco; Vicedo-Cabrera, Ana Maria; Milojevic, Ai; Guo, Yuming; Tong, Shilu; Coelho, Micheline de Sousa Zanotti Stagliorio; Saldiva, Paulo Hilario Nascimento; Lavigne, Eric; Correa, Patricia Matus; Ortega, Nicolas Valdes; Osorio, Samuel; Garcia, null; Kyselý, Jan; Urban, Aleš; Orru, Hans; Maasikmets, Marek; Jaakkola, Jouni J. K.; Ryti, Niilo; Huber, Veronika; Schneider, Alexandra; Katsouyanni, Klea; Analitis, Antonis; Hashizume, Masahiro; Honda, Yasushi; Ng, Chris Fook Sheng; Nunes, Baltazar; Teixeira, João Paulo; Holobaca, Iulian Horia; Fratianni, Simona; Kim, Ho; Tobias, Aurelio; Íñiguez, Carmen; Forsberg, Bertil; Åström, Christofer; Ragettli, Martina S.; Guo, Yue-Liang Leon; Pan, Shih-Chun; Li, Shanshan; Bell, Michelle L.; Zanobetti, Antonella; Schwartz, Joel; Wu, Tangchun; Gasparrini, Antonio; Kan, HaidongObjective To evaluate the short term associations between nitrogen dioxide (NO2) and total, cardiovascular, and respiratory mortality across multiple countries/regions worldwide, using a uniform analytical protocol. Design Two stage, time series approach, with overdispersed generalised linear models and multilevel meta-analysis. Setting 398 cities in 22 low to high income countries/regions. Main outcome measures Daily deaths from total (62.8 million), cardiovascular (19.7 million), and respiratory (5.5 million) causes between 1973 and 2018. Results On average, a 10 μg/m3 increase in NO2 concentration on lag 1 day (previous day) was associated with 0.46% (95% confidence interval 0.36% to 0.57%), 0.37% (0.22% to 0.51%), and 0.47% (0.21% to 0.72%) increases in total, cardiovascular, and respiratory mortality, respectively. These associations remained robust after adjusting for co-pollutants (particulate matter with aerodynamic diameter ≤10 μm or ≤2.5 μm (PM10 and PM2.5, respectively), ozone, sulfur dioxide, and carbon monoxide). The pooled concentration-response curves for all three causes were almost linear without discernible thresholds. The proportion of deaths attributable to NO2 concentration above the counterfactual zero level was 1.23% (95% confidence interval 0.96% to 1.51%) across the 398 cities. Conclusions This multilocation study provides key evidence on the independent and linear associations between short term exposure to NO2 and increased risk of total, cardiovascular, and respiratory mortality, suggesting that health benefits would be achieved by tightening the guidelines and regulatory limits of NO2.