Publication Archives

Using a mobile laboratory, M. Bauer et al. captured highly resolved spatial patterns of particulate matter across urban areas in Sarajevo, Bosnia and Herzegovina, revealing pollution levels and gradients that are invisible to fixed monitoring stations. The measurements show pronounced neighbourhood-scale variability and extreme wintertime pollution, reflecting the strong influence of local emission sources inContinue reading “From mobile measurements to source apportionment – understanding air pollution in Sarajevo”

G. Chen’s study about the impact of commercial cooking on air pollution has been featured in Nature Magazine’s Research Highlight! Their research shows that COVID-19 restrictions temporarily reduced PM1 pollution in London. However, when dining options such as “Eat Out to Help Out” encouraged restaurants to reopen, emissions from cooking increased significantly. To analyse thisContinue reading “Impact of COVID-19 Related Policies on Air Quality”

L. Liu et al. demonstrated how strongly long-range transport shapes submicron aerosol composition in Jülich, Germany. Back-trajectory analysis showed that distinct air mass origins coincided with distinct PMF-resolved source contributions: spring air masses influenced by wildfires were characterized by enhanced biomass-burning organic aerosol (BBOA), while marine-influenced periods were associated with a clearly resolved MSA-rich organicContinue reading “A More Reliable Way to Resolve Regional Transport”

This study by L.Liu et al. provides a year-long view of submicron aerosols during the 2019 JULIAC campaign in Jülich, Germany. The results show that nighttime chemistry plays a much larger role in organic aerosol formation than previously recognized. Using constrained source apportionment with the ME-2 solver in SoFi, the team analyzed organic and nitrateContinue reading “SoFi Reveals the Key Role of Nighttime Chemistry in Organic Aerosol Formation”

Missing data is a common challenge in air quality research. A recent study compared five methods ( PMF, Random Forest, Denoising Autoencoder, MICE, and kNN) to impute hourly PM2.5 values across 25 Seoul districts without external data. Y. Kim et al. showed that PMF achieved the highest accuracy, outperforming both machine learning and traditional statisticalContinue reading “PMF Outperforms Machine Learning in Imputing Missing PM2.5 Data in Seoul”