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Interpolated regulatory monitor measurements that are used to calculate the Air Quality Index (AQI) have low spatial resolution and are less accurate where regulatory monitors are sparsely distributed, especially during episodic events such as wildfires and dust storms. In this paper, an AQI map that is more responsive and accurate than current formulations with 5 km spatial and hourly temporal resolution is constructed for Southern California using interpolated concentration fields. The fine particle mass (PM _2.5 ) fields are calculated by combining regulatory-grade monitored concentrations, low-cost sensor measurements, and Community Multiscale Air Quality Model forecasts, using Residual Kriging (RK) interpolation to account for the uncertainty of each input component. The Ozone fields are calculated by combining regulatory monitor and model forecasted concentrations using RK. The interpolated concentration fields have root mean square interpolation errors (RMSE) of 5.59 µg m ^−3 (PM _2.5 ) and 6.66 ppb (Ozone), smaller than corresponding RMSE of current inverse distance weighting methods used by the U.S. Environmental Protection Agency AirNOW system, and a previous interpolation method used by the South Coast Air Quality Management District. The inclusion of low-cost sensor measurements makes the AQI more responsive to wildfire events, with a PM _2.5 RMSE of 7.73 µg m ^–3 , a significant improvement from the other interpolation methods. The mass of particles with an aerodynamic diameter smaller than 10 micrometers (PM _10 ) calculated using natural neighbor interpolation of the difference, PM _10 —PM _2.5 , and carbon monoxide and nitrogen dioxide fields calculated using natural neighbor interpolation, have small but usually not statistically significant reductions of RMSE relative to the other interpolation methods. Quality control and correction procedures applied to the low-cost sensor measurements are described. Display of the blended AQI data on an interactive map in real time provides an important tool for the public to minimize their exposure to poor air quality in one of the most polluted regions in the United States.

Despite past improvements in air quality, very large parts of the population in urban areas breathe air that does not meet European standards let alone the health-based World Health Organisation Air Quality Guidelines. Over the last 10 years, there has been a substantial increase in findings that particulate matter (PM) air pollution is not only exerting a greater impact on established health endpoints, but is also associated with a broader number of disease outcomes. Data strongly suggest that effects have no threshold within the studied range of ambient concentrations, can occur at levels close to PM background concentrations and that they follow a mostly linear concentration-response function. Having firmly established this significant public health problem, there has been an enormous effort to identify what it is in ambient PM that affects health and to understand the underlying biological basis of toxicity by identifying mechanistic pathways-information that in turn will inform policy makers how best to legislate for cleaner air. Another intervention in moving towards a healthier environment depends upon the achieving the right public attitude and behaviour by the use of optimal air pollution monitoring, forecasting and reporting that exploits increasingly sophisticated information systems. Improving air quality is a considerable but not an intractable challenge. Translating the correct scientific evidence into bold, realistic and effective policies undisputedly has the potential to reduce air pollution so that it no longer poses a damaging and costly toll on public health. [ABSTRACT FROM AUTHOR]