Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

Three-dimensional volumetric monitoring of settling particulate matters on a leaf using digital in-line holographic microscopy

Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously.

Petrodiesel and Waste Grease Biodiesel (B20) Emission Particles at a Rural Recycling Center: Characterization and Effects on Lung Epithelial Cells and Macrophages

Diesel engine emissions are an important source of ultrafine particulate matter (PM) in both ambient air and many occupational settings. Biodiesel is a popular, 'green' alternative to petroleum diesel fuel, but little is known about the impact of 'real world' biodiesel combustion on workplace PM concentrations and particle characteristics including size, morphology, and composition; or on biological responses. The objectives of the present work were to characterize PM workplace concentrations and tailpipe emissions produced by the combustion of commercially purchased low sulfur petrodiesel and a waste grease B20 blend (20% biodiesel/80% petrodiesel by volume) in heavy duty diesel (HDD) nonroad equipment operating in a 'real world' rural recycling center. Furthermore, we assessed the in vitro responses of cell lines representing human lung epithelial cells (BEAS-2B) and macrophages (THP-1) after 24 h of exposure to these real-world particles. Compared to petroleum diesel, use of B20 in HDD equipment resulted in lower mass concentrations of PM2.5, PM<0.25 (particle diameter less than 2.5 and 0.25 micrometer, respectively), and elemental carbon. Transmission electron analysis of PM showed that primary particle size and morphology were similar between fuel types. Metals composition analysis revealed differences between fuels, with higher Fe, Al, V, and Se measured during B20 use, and higher As, Cd, Cu, Mn, Ni and Pb concentrations measured during petrodiesel use. In vitro responses varied between fuels but data supported that waste grease B20 particles elicited inflammatory responses in human macrophages and lung epithelial cells comparable to petrodiesel particles. However, the effects were more pronounced with B20 than petrodiesel at the same mass concentration. Since the primary particle size and morphology were similar between fuels, it is likely that the differential results seen in the in vitro assays points to differences in the composition of the PM. Future research should focus on the organic carbon and metals speciation and potential impact of real world particles on reactive oxygen species generation and mechanisms for differences in the cellular inflammatory responses.