Search

The Comrades Marathon is South Africa’s – and the world’s – most recognised and largest ultra-marathon event, with over 15 000 participants from across the globe competing in the 89-km road running event each year. Historically, the event has been held before the start of austral winter (20 May – 17 June). However, in 2022, organisers of the race moved the event to 28 August, when austral spring commences. We explore the climate, in particular the Universal Thermal Comfort Index (UTCI), of past Comrades events (1980-2019) and compare these data to UTCI data of the new proposed date (28 August) for the same period. The climatology for May, June, July and August was determined to identify periods with the lowest risk for ‘strong’ to ‘very strong’ heat stress. Results show that participants’ risk of exposure to ‘strong’ heat stress and ‘very strong’ heat stress periods will be more likely if the event is held in August as compared to the original event dates. Therefore, it is concluded that mid-June to mid-July has the lowest risk of heat stress exposure along the route. Runners and organisers should be aware of the higher risk of exertional heat illness during the 2022 Comrades Marathon to ensure safe participation. Significance:• The new proposed date for the Comrades Marathon will increase the risk of exposure to ‘strong’ and ‘very strong’ heat stress conditions, as defined by the Universal Thermal Comfort Index (UTCI).• The UTCI indicates that mid-June to mid-July has the lowest risk of heat stress exposure at the three reference points along the route.• Organisers should warn runners of the higher risk of exertional heat illness due to the possible exposure to high UTCI values or more unfavourable climatological conditions. Furthermore, runners should be informed of a variety of preventative strategies to ensure safe participation.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required., World Meteorological Organization Global Atmospheric Watch programme is gratefully acknowledged for initiating and coordinating this study and for supporting this publication. We acknowledge the following projects for supporting the analysis contained in this article: Air Pollution and Human Health for an Indian Megacity project PROMOTE funded by UK NERC and the Indian MOES, Grant reference number NE/P016391/1; Regarding project funding from the European Commission, the sole responsibility of this publication lies with the authors. The European Commission is not responsible for any use that may be made of the information contained therein. This project has received funding from the European Commission’s Horizon 2020 research and innovation program under grant agreement No 874990 (EMERGE project). European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Estonian Research Council (project PRG714); Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (KKOBS, project 2014-2020.4.01.20-0281). European network for observing our changing planet project (ERA-PLANET, grant agreement no. 689443) under the European Union’s Horizon 2020 research and innovation program, Estonian Ministry of Sciences projects (grant nos. P180021, P180274), and the Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (3.2.0304.11-0395). Eastern Mediterranean and Middle East—Climate and Atmosphere Research (EMME-CARE) project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856612) and the Government of Cyprus. INAR acknowledges support by the Russian government (grant number 14.W03.31.0002), the Ministry of Science and Higher Education of the Russian Federation (agreement 14.W0331.0006), and the Russian Ministry of Education and Science (14.W03.31.0008). We are grateful to to the following agencies for providing access to data used in our analysis: A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences; Agenzia Regionale per la Protezione dell’Ambiente della Campania (ARPAC); Air Quality and Climate Change, Parks and Environment (MetroVancouver, Government of British Columbia); Air Quality Monitoring & Reporting, Nova Scotia Environment (Government of Nova Scotia); Air Quality Monitoring Network (SIMAT) and Emission Inventory, Mexico City Environment Secretariat (SEDEMA); Airparif (owner & provider of the Paris air pollution data); ARPA Lazio, Italy; ARPA Lombardia, Italy; Association Agréée de Surveillance de la Qualité de l’Air en Île-de-France AIRPARIF / Atmo-France; Bavarian Environment Agency, Germany; Berlin Senatsverwaltung für Umwelt, Verkehr und Klimaschutz, Germany; California Air Resources Board; Central Pollution Control Board (CPCB), India; CETESB: Companhia Ambiental do Estado de São Paulo, Brazil. China National Environmental Monitoring Centre; Chandigarh Pollution Control Committee (CPCC), India. DCMR Rijnmond Environmental Service, the Netherlands. Department of Labour Inspection, Cyprus; Department of Natural Resources Management and Environmental Protection of Moscow. Environment and Climate Change Canada; Environmental Monitoring and Science Division Alberta Environment and Parks (Government of Alberta); Environmental Protection Authority Victoria (Melbourne, Victoria, Australia); Estonian Environmental Research Centre (EERC); Estonian University of Life Sciences, SMEAR Estonia; European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Finnish Meteorological Institute; Helsinki Region Environmental Services Authority; Haryana Pollution Control Board (HSPCB), IndiaLondon Air Quality Network (LAQN) and the Automatic Urban and Rural Network (AURN) supported by the Department of Environment, Food and Rural Affairs, UK Government; Madrid Municipality; Met Office Integrated Data Archive System (MIDAS); Meteorological Service of Canada; Ministère de l'Environnement et de la Lutte contre les changements climatiques (Gouvernement du Québec); Ministry of Environment and Energy, Greece; Ministry of the Environment (Chile) and National Weather Service (DMC); Moscow State Budgetary Environmental Institution MOSECOMONITORING. Municipal Department of the Environment SMAC, Brazil; Municipality of Madrid public open data service; National institute of environmental research, Korea; National Meteorology and Hydrology Service (SENAMHI), Peru; New York State Department of Environmental Conservation; NSW Department of Planning, Industry and Environment; Ontario Ministry of the Environment, Conservation and Parks, Canada; Public Health Service of Amsterdam (GGD), the Netherlands. Punjab Pollution Control Board (PPCB), India. Réseau de surveillance de la qualité de l'air (RSQA) (Montréal); Rosgydromet. Mosecomonitoring, Institute of Atmospheric Physics, Russia; Russian Foundation for Basic Research (project 20–05–00254) SAFAR-IITM-MoES, India; São Paulo State Environmental Protection Agency, CETESB; Secretaria de Ambiente, DMQ, Ecuador; Secretaría Distrital de Ambiente, Bogotá, Colombia. Secretaria Municipal de Meio Ambiente Rio de Janeiro; Mexico City Atmospheric Monitoring System (SIMAT); Mexico City Secretariat of Environment, Secretaría del Medio Ambiente (SEDEMA); SLB-analys, Sweden; SMEAR Estonia station and Estonian University of Life Sciences (EULS); SMEAR stations data and Finnish Center of Excellence; South African Weather Service and Department of Environment, Forestry and Fisheries through SAAQIS; Spanish Ministry for the Ecological Transition and the Demographic Challenge (MITECO); University of Helsinki, Finland; University of Tartu, Tahkuse air monitoring station; Weather Station of the Institute of Astronomy, Geophysics and Atmospheric Science of the University of São Paulo; West Bengal Pollution Control Board (WBPCB).

Presented at the 34th Annual Conference of the South African Society for Atmospheric, 20-21 September 2018,UKZN. Radiosondes are a valuable tool to analyze the vertical profile of the atmosphere. From these soundings we can derive several thermodynamic indices to evaluate the state of the atmosphere and the possibility of certain weather phenomenons. This study examines some of the commonly used indicies over Irene by examining data from the Integrated Global Radiosonde Archive (IGRA) to evaluate the changing nature of the atmosphere over the Highveld. Early results show a marginal increase towards a convective favoring atmosphere when analyzing the 12:00 UTC values of several thermodynamic derived variables including CAPE, CIN, KI, SI and LI possibly indicating an increasingly convective favoring atmosphere. The implications of this include the possibility of an increased frequency in events such as hailstorms, thunderstorms and flash floods.

This paper presents the results of the design and field deployment of multiple autonomous fixed‐wing unmanned aircraft into supercell thunderstorms. As part of a field campaign in Spring 2019, up to three fixed‐wing unmanned aircraft were deployed simultaneously into different regions of supercell thunderstorms, To learn more about the atmospheric conditions that lead to the formation of tornadoes. Successful field deployment is attributed to (a) a nomadic concept of operations that allows the unmanned aircraft system team and science team to work seamlessly together while satisfying all aviation regulations and (b) the ruggedized RAAVEN unmanned aircraft system with modular features that favor rapid, ease‐of‐use over the brute strength of previous designs. The concept of operations and the unmanned aircraft system are described along with results from a 4 day window where four storms were sampled: two of these storms were tornadic (formed tornadoes before, during, or after being sampled) and two were not. These results validate the feasibility of nomadic operation of multiple unmanned aircraft simultaneously in severe weather conditions. Further, the successful field deployments demonstrate the importance of the modular unmanned aircraft design.