Characterising fifteen years of continuous atmospheric radon activity observations at Cape Point (South Africa)

Botha, R.; Labuschagne, Casper; Williams, Alastair G.; Bosman, Gurthwin; Brunke, E.-G.; Rossouw, Arnoux; Lindsay, R.

doi:10.1016/j.atmosenv.2017.12.010

Cited by 20 publications

(15 citation statements)

References 20 publications

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“…During the winter months, the urban-continental proportion could be as high as 60%, while during the summer months it is often as low as 20%. Therefore, the seasonality observed within the mixed dataset is primarily driven by meteorology, which complicates air-chemical interpretations (Botha et al, 2018;Labuschagne et al, 2018). Therefore, it is important to consider this limitation when interpreting composite concentrations of gaseous species determined with passive samplers at CPT GAW.…”

Section: Resultsmentioning

confidence: 99%

“…Rain events during winter are mainly associated with cold fronts moving over the region from the south-west. The CPT GAW station experiences predominantly east-south-easterly to south-easterly winds throughout the year (Botha et al, 2018), as indicated by the wind roses compiled for the wet (April to September) and dry (October to March) seasons from 1995 to 2015 in Fig. 2.…”

Section: Meteorologymentioning

confidence: 95%

“…2) compiled for the wet and dry seasons revealed that inorganic gas concentrations measured during the dry season generally represent Southern Hemispheric background levels CPT GAW, while the higher frequency of air masses passing over the Cape Town conurbation and South African interior during the wet winter months is evident. Botha et al (2018) also attributed higher radon concentrations at CPT GAW (based on 15-year radon measurements) in winter months to an increase in frequency of air masses passing over the continental surface, compared to summer when CPT GAW is more frequently impacted by oceanic air masses. In addition, the monthly carbon monoxide (CO) concentrations at CPT GAW were also indicative of the increased anthropogenic influences from the continent during winter as indicated by higher CO levels measured during winter (Fig.…”

Section: Seasonal Patternsmentioning

confidence: 99%

“…Local and regional factors considered in the model included wind speed (Ws), wind direction (Wd), ambient temperature (T), rain depth (R), relative humidity (RH), planetary boundary layer height (PBL), CO and carbon dioxide (CO 2 ) concentrations, local fire events within 100 km from site (LFE), regional fire events between 100 km and 400 km from site (DFE), and Cape Town metropolitan population (P). Radon data from CPT GAW was used to separate oceanic-(OAM) and continental air mass (CAM) (Botha et al, 2018), which was also included in the model. Global factors considered included TSI, IOD, ENSO, QBO and SAM, as indicated in section 2.2.…”

Section: Modelled Variability and Linear Trend Estimatesmentioning

confidence: 99%

See 3 more Smart Citations

Twenty-one years of passive sampling monitoring of SO2, NO2 and O3 at the Cape Point GAW station, South Africa

Swartz

Zyl

Beukes

et al. 2020

Atmospheric Environment

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Section: Resultsmentioning

confidence: 99%

Section: Meteorologymentioning

confidence: 95%

Section: Seasonal Patternsmentioning

confidence: 99%

Section: Modelled Variability and Linear Trend Estimatesmentioning

confidence: 99%

See 2 more Smart Citations

Twenty-one years of passive sampling monitoring of SO2, NO2 and O3 at the Cape Point GAW station, South Africa

Swartz

Zyl

Beukes

et al. 2020

Atmospheric Environment

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“…Moreover, radon exhalation from inland areas are 2-3 orders of magnitude greater than those from oceanic sources, originating a distinct differentiation between land and marine air masses (Schery and Huang, 2004). This allows the use of radon as a tracer of continental influence as well (Chambers et al, 2016;Botha et al, 2018). For these atmospheric transport studies, radon is considered to have a relatively consistent flux from ice-free terrestrial surfaces, depending on local soil parameters like composition, porosity, moisture and permeability (López-Coto et al, 2013;Karstens et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Radon behavior investigation based on cluster analysis and atmospheric modelling

Gutiérrez-Álvarez

Guerrero

Martin

et al. 2019

Atmospheric Environment

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Radon measurements were performed in Huelva, a city located near a phosphogypsum repository in the SW of the Iberian Peninsula, between March 2015 and March 2016. The mean values of this gas oscillate between 5.6 and 10.9 Bq m −3 and maximum ranges between 36.4 and 53.4 Bq m −3 . Radon shows the expected monthly variation with higher levels in November and December. Typical daily evolutions were also observed, with maximum between 06:00 and 08:00 UTC (Coordinated Universal Time) and minimum around noon. To extract daily radon patterns, the cluster technique of K-means was applied. Based on this classification, four different case study periods were analyzed in detail, describing two events with high radon levels and two with low radon. Local meteorology, back-trajectories computed with the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model and meteorological fields from the WRF (Weather Research and Forecasting) model, were used to analyze the four case study periods selected. Low radon periods are characterized by the occurrence of non-pure breezes and maritime air masses from the Atlantic Ocean, whereas high radon periods occur under pure sea-land breezes affected by Mediterranean air masses. Factors such as meteorology or local emission sources alone may not be enough to explain the high radon events in the area. Other factors could be playing a major role in the radon levels. The obtained results indicate the contribution of radon transported from medium-long range, suggesting that, under specific weather conditions, the Gulf of Cadiz could act as a radon trap and the continental areas around the Western Mediterranean Basin could act as a radon source.

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Using big data for evaluating development outcomes: A systematic map

Rathinam

Khatua²,

Siddiqui³

et al. 2021

Campbell Systematic Reviews

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Background Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data—that is digitally generated, passively produced and automatically collected—offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs. Objectives Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts Search Methods A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist. Selection Criteria The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes. Data Collection and Analysis Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only. Main Results The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine‐generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs. Authors' Conclusions This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficaci...

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Characterising fifteen years of continuous atmospheric radon activity observations at Cape Point (South Africa)

Cited by 20 publications

References 20 publications

Twenty-one years of passive sampling monitoring of SO2, NO2 and O3 at the Cape Point GAW station, South Africa

Twenty-one years of passive sampling monitoring of SO2, NO2 and O3 at the Cape Point GAW station, South Africa

Radon behavior investigation based on cluster analysis and atmospheric modelling

Using big data for evaluating development outcomes: A systematic map

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