A comparison of experience sampled hay fever symptom severity across rural and urban areas of the UK.

Hay fever affects people differently and can change over a lifetime, but data is lacking on how environmental factors may influence this. This study is the first to combine atmospheric sensor data with real-time, geo-positioned hay fever symptom reports to examine the relationship between symptom severity and air quality, weather and land use. We study 36145 symptom reports submitted over 5 years by over 700 UK residents using a mobile application. Scores were recorded for nose, eyes and breathing. Symptom reports are labelled as urban or rural using land-use data from the UK's Office for National Statistics. Reports are compared with AURN network pollution measurements and pollen and meteorological data taken from the UK Met Office. Our analysis suggests urban areas record significantly higher symptom severity for all years except 2017. Rural areas do not record significantly higher symptom severity in any year. Additionally, symptom severity correlates with more air quality markers in urban areas than rural areas, indicating that differences in allergy symptoms may be due to variations in the levels of pollutants, pollen counts and seasonality across land-use types. The results suggest that a relationship exists between urban surroundings and hay fever symptoms.

UK daily meteorology, air quality, and pollen measurements for 2016-2019, with estimates for missing data.

In recent years, quantifying the impacts of detrimental air quality has become a global priority for researchers and policy makers. At present, the systems and methodologies supporting the collection and manipulation of this data are difficult to access. To support studies quantifying the interplay between common gaseous and particulate pollutants with meteorology and biological particles, this paper presents a comprehensive data-set containing daily air quality readings from the Automatic Urban and Rural Network, and pollen and weather data from Met Office monitoring stations, in the years 2016 to 2019 inclusive, for the United Kingdom. We describe (1) the sources from which the data were collected, (2) the methods used for the data cleaning process and (3) how issues related to missing values and sparse regional coverage were addressed. The resulting data-set is designed to be used 'as is' by those using air quality data for research; we also describe and provide open access to the methods used for curating the data to allow modification of or addition to the data-set.