Abstract
The South Asian island nation of Sri Lanka did not have any permanent PM2.5 monitors sharing data publicly in near-real-time until the U.S. Embassy installed a Beta Attenuation Monitor (BAM) in September 2017. This research aims to better understand the PM2.5 distribution in Sri Lanka by analyzing data collected by that BAM, and leveraging low-cost sensors, model and remote sensing data. BAM data show PM2.5 levels were “Unhealthy for Sensitive Groups” or “Unhealthy” according to the U.S. classification system, for at least 50% of the time between each November and the following February. This coincides with the northeast monsoon when stable air masses reduce dispersion of pollutants. Back trajectory analyses suggest long range transport also contributes to elevated PM2.5 during these months. Although slightly cleaner than regional embassies, this location has exceeded the Sri Lankan 24-hr standard for PM2.5 (50 µg m–3) every year since 2018. The area has met Sri Lanka’s annual standard (25 µg m–3) since 2019. We used PurpleAir (PA) and Atmos low-cost PM2.5 sensors co-located with the Embassy BAM, to develop correction factors to transform raw sensor data to BAM-like data. The influence of meteorological variables and the performance of different statistical models were considered and the regression coefficients of the most applicable models are presented. We also compared our PA correction factor against user-selectable options on the PurpleAir.com website. The Australian “Woodsmoke” correction can be applied to quickly visualize a reasonably accurate estimate of PM2.5 concentrations. We applied our PA correction factor to six other PurpleAir sensors operated around the country, to understand Sri Lanka’s PM2.5 distribution. With these corrected data, we interpolated satellite and model-derived PM2.5 annual averages at 1 km intervals. The most populated Western Province had the highest concentrations with elevated levels extending offshore. The sparsely populated southeast had the cleanest air.
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