citizen science
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2022 ◽  
Vol 128 ◽  
pp. 81-93
D. Fraisl ◽  
L. See ◽  
T. Sturn ◽  
S. MacFeely ◽  
A. Bowser ◽  

2022 ◽  
Vol 218 ◽  
pp. 104286
Sebastien Dujardin ◽  
Michiel Stas ◽  
Camille Van Eupen ◽  
Raf Aerts ◽  
Marijke Hendrickx ◽  

2022 ◽  
Vol 128 ◽  
pp. 14-23
Sarah E. Nelms ◽  
Emily Easman ◽  
Nichola Anderson ◽  
Madeleine Berg ◽  
Sue Coates ◽  

2022 ◽  
Vol 807 ◽  
pp. 150742
Jordan Gacutan ◽  
Emma L. Johnston ◽  
Heidi Tait ◽  
Wally Smith ◽  
Graeme F. Clark

2022 ◽  
Vol 21 (1) ◽  
Luca Boniardi ◽  
Federica Nobile ◽  
Massimo Stafoggia ◽  
Paola Michelozzi ◽  
Carla Ancona

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

2022 ◽  
Elizabeth A. Freeman ◽  
Elizabeth J. Carlton ◽  
Sara Paull ◽  
Samuel Dadzie ◽  
Andrea Buchwald

In a rapidly urbanizing region such as West Africa, Aedes mosquitoes pose an emerging threat of infectious disease that is compounded by limited vector surveillance. Citizen science has been proposed as a way to fill surveillance gaps by training local residents to collect and share information on disease vectors. Increasing citizen science efforts can begin to bridge the gaps in our current knowledge of Aedes distribution while engaging locals with mosquito control and public health efforts. Understanding the distribution of disease vectors in West Africa can inform researchers and public health officials on where to conduct disease surveillance and focus public health interventions. We aimed to compare citizen science data to published literature observations of Aedes mosquitoes and to quantify how incorporating citizen science changes our understanding of Aedes mosquito distribution in West Africa. We utilized citizen science data collected through NASAs GLOBE Observer mobile phone application and data from a previously published literature review on Aedes mosquito distribution to examine the contribution of citizen science to understanding the distribution of Ae. aegypti in West Africa using Maximum Entropy modeling. Combining citizen science and literature-derived observations improved the fit of the model compared to models created by each data source alone, but did not alleviate location bias within the models, likely due to lack of widespread observations. Understanding Ae. aegypti distribution will require greater investment in Aedes mosquito surveillance in the region, and citizen science should be utilized as a tool in this mission to increase the reach of surveillance.

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