Characterising non-linear associations between airborne pollen counts and respiratory symptoms from the AirRater smartphone app in Tasmania, Australia: A case time series approach.

Pollen is a well-established trigger of asthma and allergic rhinitis, yet concentration-response relationships, lagged effects, and interactions with other environmental factors remain poorly understood. Smartphone technology offers an opportunity to address these challenges using large, multi-year datasets that capture individual symptoms and exposures in real time. We aimed to characterise associations between six pollen types and respiratory symptoms logged by users of the AirRater smartphone app in Tasmania, Australia. We analyzed 44,820 symptom reports logged by 2272 AirRater app users in Tasmania over four years (2015-2019). With these data we evaluated associations between daily respiratory symptoms and atmospheric pollen concentrations. We implemented Poisson regression models, using the case time series approach designed for app-sourced data. We assessed potentially non-linear and lagged associations with (a) total pollen and (b) six individual pollen taxa. We adjusted for seasonality and meteorology and tested for interactions with particulate air pollution (PM2.5). We found evidence of non-linear associations between total pollen and respiratory symptoms for up to three days following exposure. For total pollen, the same-day relative risk (RR) increased to 1.31 (95% CI: 1.26-1.37) at a concentration of 50 grains/m3 before plateauing. Associations with individual pollen taxa were also non-linear with some diversity in shapes. For all pollen taxa the same-day RR was highest. The interaction between total pollen and PM2.5 was positive, with risks associated with pollen significantly higher in the presence of high concentrations of PM2.5. Our results support a non-linear response between airborne pollen and respiratory symptoms. The association was strongest on the day of exposure and synergistic with particulate air pollution. The associations found with Dodonaea and Myrtaceae highlight the need to further investigate the role of Australian native pollen types in allergic respiratory disease.

Environmental Hazards and Behavior Change: User Perspectives on the Usability and Effectiveness of the AirRater Smartphone App.

AirRater is a free smartphone app developed in 2015, supporting individuals to protect their health from environmental hazards. It does this by providing (i) location-specific and near real-time air quality, pollen and temperature information and (ii) personal symptom tracking functionality. This research sought to evaluate user perceptions of AirRater's usability and effectiveness. We collected demographic data and completed semi-structured interviews with 42 AirRater users, identified emergent themes, and used two frameworks designed to understand and support behavior change-the Behavior Change Wheel (BCW) and the Protective Action Decision Model (PADM)-to interpret results. Of the 42 participants, almost half indicated that experiencing symptoms acted as a prompt for app use. Information provided by the app supported a majority of the 42 participants to make decisions and implement behaviors to protect their health irrespective of their location or context. The majority of participants also indicated that they shared information provided by the app with family, friends and/or colleagues. The evaluation also identified opportunities to improve the app. Several study limitations were identified, which impacts the generalizability of results beyond the populations studied. Despite these limitations, findings facilitated new insights into motivations for behavior change, and contribute to the existing literature investigating the potential for smartphone apps to support health protection from environmental hazards in a changing climate.

AQVx-An Interactive Visual Display System for Air Pollution and Public Health.

Fine particulate matter emissions (PM2.5) from landscape biomass fires, both prescribed and wild, pose a significant public health risk, with smoke exposure seasonally impacting human populations through both highly concentrated local plumes, and more dispersed regional haze. A range of technologies now exist for mapping and modeling atmospheric particulate concentration, including low-cost mobile monitors, dispersion and chemical transport modeling, multi-spectral earth observation satellites, weather radar, as well as publicly available real-time data feeds from agencies providing information about fire activity on the ground. Ubiquitous smart phone availability also allows instant public reporting of both health symptoms and smoke exposure. We describe a web-based visual display interface, Air Quality Visualization (AQVx), developed to allow the overlaying, synchronization and comparison of a range of maps and data layers, in order to both assess the potential public health impact of landscape fire smoke plumes, and the accuracy of dispersion models. The system was trialed in the state of Victoria, in south-eastern Australia, within the domain of the AQFx chemical transport model, where large-scale annual prescribed burning operations (~11,000 km2 yr) are carried out, and where extreme wildfires frequently occur during the summer months. AQVx, coupled with the ARSmoke smart phone application, allowed managers to rapidly validate modeled smoke transport against satellite imagery, and identify potential exposure risks to populated areas.

Can smartphone data identify the local environmental drivers of respiratory disease?

Asthma and allergic rhinitis (or hay fever) are ubiquitous, chronic health conditions that seasonally affect a sizeable proportion of the population. Both are commonly triggered or exacerbated by environmental conditions including aeroallergens, air quality and weather. Smartphone technology offers new opportunities to identify environmental drivers by allowing large-scale, real-time collection of day-to-day symptoms. As yet, however, few studies have explored the potential of this technology to provide useful epidemiological data on environment-symptom relationships. Here, we use data from the smartphone app 'AirRater' to examine relationships between asthma and allergic rhinitis symptoms and weather, air quality and pollen loads in Hobart, Tasmania, Australia. We draw on symptom data logged by app users over a three-year period and use time-series analysis to assess the relationship between symptoms and environmental co-variates. Symptoms are associated with particulate matter (IRR 1.06, 95% CI: 1.04-1.08), maximum temperature (IRR 1.28, 95% CI: 1.13-1.44) and pollen taxa including Betula (IRR 1.04, 95% CI: 1.02-1.07), Cupressaceae (IRR 1.02, 95% CI: 1.01-1.04), Myrtaceae (IRR 1.06, 95% CI: 1.02-1.10) and Poaceae (IRR 1.05, 95% CI: 1.01-1.09). The importance of these pollen taxa varies seasonally and more taxa are associated with allergic rhinitis (eye/nose) than asthma (lung) symptoms. Our results are congruent with established epidemiological evidence, while providing important local insights including the association between symptoms and Myrtaceae pollen. We conclude that smartphone-sourced data can be a useful tool in environmental epidemiology.

The macroecology of airborne pollen in Australian and New Zealand urban areas.

The composition and relative abundance of airborne pollen in urban areas of Australia and New Zealand are strongly influenced by geographical location, climate and land use. There is mounting evidence that the diversity and quality of airborne pollen is substantially modified by climate change and land-use yet there are insufficient data to project the future nature of these changes. Our study highlights the need for long-term aerobiological monitoring in Australian and New Zealand urban areas in a systematic, standardised, and sustained way, and provides a framework for targeting the most clinically significant taxa in terms of abundance, allergenic effects and public health burden.