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• W4293493493 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Accurately predicting urban <span class=inline-formula>PM_2.5</span> concentrations and composition has proved challenging in the past, partially due to the resolution limitations of computationally intensive chemical transport models (CTMs). Increasing the resolution of <span class=inline-formula>PM_2.5</span> predictions is desired to support emissions control policy development and address issues related to environmental justice. A nested grid approach using the CTM PMCAMx-v2.0 was used to predict <span class=inline-formula>PM_2.5</span> at increasing resolutions of 36 <span class=inline-formula>km</span> <span class=inline-formula>×</span> 36 <span class=inline-formula>km</span>, 12 <span class=inline-formula>km</span> <span class=inline-formula>×</span> 12 <span class=inline-formula>km</span>, 4 <span class=inline-formula>km</span> <span class=inline-formula>×</span> 4 <span class=inline-formula>km</span>, and 1 <span class=inline-formula>km</span> <span class=inline-formula>×</span> 1 <span class=inline-formula>km</span> for a domain largely consisting of Allegheny County and the city of Pittsburgh in southwestern Pennsylvania, US, during February and July 2017. Performance of the model in reproducing <span class=inline-formula>PM_2.5</span> concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Novel surrogates were developed to allocate emissions from cooking and on-road traffic sources to the 1 <span class=inline-formula>km</span> <span class=inline-formula>×</span> 1 <span class=inline-formula>km</span> resolution grid. Total <span class=inline-formula>PM_2.5</span> mass is reproduced well by the model during the winter period with low fractional error (0.3) and fractional bias (<span class=inline-formula>+</span>0.05) when compared to regulatory measurements. Comparison with speciated measurements during this period identified small underpredictions of <span class=inline-formula>PM_2.5</span> sulfate, elemental carbon (EC), and organic aerosol (OA) offset by a larger overprediction of <span class=inline-formula>PM_2.5</span> nitrate. In the summer period, total <span class=inline-formula>PM_2.5</span> mass is underpredicted due to a large underprediction of OA (bias <span class=inline-formula>=</span> <span class=inline-formula>−</span>1.9 <span class=inline-formula>µg m^−3</span>, fractional bias <span class=inline-formula>=</span> <span class=inline-formula>−</span>0.41). In the winter period, the model performs well in reproducing the variability between urban measurements and rural measurements of local pollutants such as EC and OA. This effect is less consistent in the summer period due to a larger fraction of long-range-transported OA. Comparison with total <span class=inline-formula>PM_2.5</span> concentration measurements from low-cost sensors showed improvements in performance with increasing resolution. Inconsistencies in <span class=inline-formula>PM_2.5</span> nitrate predictions in both periods are believed to be due to errors in partitioning between <span class=inline-formula>PM_2.5</span> and <span class=inline-formula>PM₁₀</span> modes and motivate improvements to the treatment of dust particles within the model. The underprediction of summer OA would likely be improved by updates to biogenic secondary organic aerosol (SOA) chemistry within the model, which would result in an increase of long-range transport SOA seen in the inner modeling domain. These improvements are obvious topics for future work towards model improvement. Comparison with regulatory monitors showed that increasing resolution from 36 to 1 <span class=inline-formula>km</span> improved both fractional error and fractional bias in both modeling periods. Improvements at all types of measurement locations indicated an improved ability of the model to reproduce urban–rural <span class=inline-formula>PM_2.5</span> gradients at higher resolutions." @default.
• W4293493493 created "2022-08-29" @default.
• W4293493493 date "2022-08-29" @default.
• W4293493493 modified "2023-10-18" @default.
• W4293493493 title "Comment on gmd-2022-145" @default.
• W4293493493 doi "https://doi.org/10.5194/gmd-2022-145-rc1" @default.
• W4293493493 hasPublicationYear "2022" @default.
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