Atmospheric Environment最新文献

The particle number size distribution (PNSD) is crucial for evaluating air quality and mitigating environmental pollution, as particles of different sizes have diverse effects on human health and climate. However, obtaining a comprehensive understanding of PNSD is challenging due to its inherent complexities and variability. Multi-lognormal distribution models are employed to fit PNSD, as seen in climate models, but discrepancies between model fits and observed PNSD persist. This study adopts hourly data from 2017 to 2020 across eight monitoring sites in diverse environments—rural, urban, mountainous, and polar, and compares the observed PNDS with those simulated by multi-lognormal distribution models. The results demonstrated that the model generally achieved a high correlation with observed PNSD data (r² > 0.75), effectively capturing key characteristics of nucleation and Aitken mode particles. However, the model had a tendency to overestimate the total number concentration by approximately 1.09 times, particularly noticeable under conditions of high concentrations of smaller particles. The model successfully represented prevalent bimodal size distribution patterns in urban areas with high ultrafine particle concentrations, though its performance was slightly less accurate in scenarios involving trimodal distributions. Despite these strong correlations and the model's ability to reflect diurnal and seasonal variations, which suggests its broad applicability and utility, there were notable limitations on smaller time scales and in specific particle size ranges. These limitations were particularly evident in capturing detailed phenomena relevant to new particle formation events, indicating areas where model refinement is necessary. The results highlighted the importance of investigating discrepancies between model predictions and actual observations, which is crucial for refining climate models that utilize PNDS. The uniform comparison facilitated a detailed exploration of particle properties from model results, offering deeper insights into aerosol behavior and its environmental impacts.

Ozone (O₃) pollution is increasing in the Yangtze River Delta (YRD), with significant influences from regional transport during pollution events. However, the specific contributions of different regions remain imprecisely quantified. This study estimated the regional contributions of eight regions to summer O₃ in the YRD using the Community Multiscale Air Quality (CMAQ) model with a source-tagged photochemical mechanism. Non-background O₃ is attributed to nitrogen oxides (NO_X) and volatile organic compounds (VOCs) from different regions. Background O₃ is the predominant contributor with average ratios of 75.0%, 65.3%, 64.8% and 63.0% for Shanghai, Nanjing, Hangzhou and the entire YRD, respectively. Locally formed O₃ are 39.9, 42.0, 39.6, and 36.9 parts per billion (ppb) by volume in the above areas. North Zhejiang and south Jiangsu are the primary sources of heavy pollution episodes as the maximum daily 8-h average (MDA8) O₃ concentrations in these regions increase the most. NO_X is the predominant contributor to heavy pollution episodes with the maximum increment attributed to NO_X (O₃N) of 14.3 ppb, while VOCs only contribute to 2.1 ppb (O₃V) in Jiangsu. Relative humidity is crucial in heavy pollution episodes while high temperature, low planetary boundary layer (PBL) height and the wind field are associated with regional transport. Diurnal variation underscores the importance of understanding O₃ formation in the afternoon (12:00–16:00), which is essential for devising effective mitigation policies.

So far, detection and quantification of bio-aerosols requires genotypic or phenotypic identification of every single particle. Successful use of optical particle counters, as a time- and cost-saving alternative, has not been reported to date, indicating the need for further method development. Previously, such studies have focused on pollen. Here we report on a laboratory test of commercially available low-cost optical particle counters from Alphasense (OPC-N3 and OPC-R2) for the quantification of a known fungal aerosol (Botrytis cinerea). Aerosols were quantified using a Grimm Portable Aerosol Spectrometer (11E). Our measurements reveal that the low-cost sensors almost correctly detect the relative particle size distribution of the spores, as compared with the Grimm reference instrument, and potentially can be used in environmental detection of fungal aerosols.

The interaction between ozone and temperature on cardiovascular biomarkers has not been thoroughly examined. A panel study was conducted among 40 college students with four equal interval follow-ups in Hefei, Anhui Province, China between August and October 2021. Real-time concentrations of ozone were collected from a nearby monitoring device. Temperature variability parameters included diurnal temperature range (DTR), the standard-deviation of temperature (SDT), and temperature variability (TV). A set of cardiovascular biomarkers were measured, including markers of inflammation (Granulocyte-macrophage colony-stimulating factor, GM-CSF and serum amyloid A, SAA), coagulation (D-dimer and ADAMTS13), oxidative stress (Myeloperoxidase, MPO and Growth differentiation factor-15, GDF-15), endothelial function (vascular endothelial growth factor A, VEGFA), and stress hormone (5-hydroxytryptamine, 5-HT). Linear mixed-effect models were conducted to analyze the associations between ozone, temperature variability, and all blood markers. The results showed significant associations among ozone, DTR, SDT, TV, and blood markers, suggesting harmful effects on markers. For instance, a 10-μg/m³ increase in ozone at lag 2d was associated with higher levels of SAA by 19.65% (95%CI: 13.70, 25.60), VEGFA by 10.90% (95%CI: 4.57, 17.22), GDF-15 by 5.33% (95%CI: 0.59, 10.06), and GM-CSF by 2.52% (95%CI: 1.70, 3.34), but 13.09% lower D-dimer (95%CI: 6.99, 19.19). We also found statistically significant interaction between ozone and TV exposures for GM-CSF and SAA. This study shows that ambient ozone and personal TV exposures may independently have acute effects on markers of inflammation, oxidative stress, coagulation, endothelial function, and neuroendocrine stress response. Simultaneous exposure to these factors may also lead to interactive effects on inflammation markers. These findings offer valuable insights for developing comprehensive strategies in cardiovascular disease control and prevention.

Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as “black boxes” that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this paper, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods into two major paradigms: (1) Post-hoc interpretability techniques that explain pre-trained models, such as perturbation-based, game theory based, and gradient-based attribution methods. (2) Designing inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We summarize how each technique provides insights into the predictions, uncovering novel meteorological relationships captured by machine learning. Lastly, we discuss research challenges and provide future perspectives around achieving deeper mechanistic interpretations aligned with physical principles, developing standardized evaluation benchmarks, integrating interpretability into iterative model development workflows, and providing explainability for large foundation models.

Isocyanates are known as widespread hazardous environmental pollutants emitted from various combustion products in our lives because they cause health effects to the nervous system and respiratory systems through sensitization. In this study, to obtain data on the concentration and composition of isocyanates in indoor and outdoor air, we developed a novel passive sampler (PSG-DBA) for monitoring gaseous and particulate isocyanates; e.g. isocyanic acid (ICA), methyl isocyanate (MIC), ethyl isocyanate (EIC), and propyl isocyanate (PIC), and acquired basic data of isocyanates on the behavior in general households. This proposed method allows for continuous sampling over a week, making it possible to calculate not only intermittent data but also low concentrations and average concentrations of isocyanate in our daily lives. In addition, from the results of this monitoring, in particular, ICA and MIC were frequently detected in ambient air. In summer, indoor concentrations were typically higher than those outdoors in most houses, e.g., mean concentrations; ICA; 0.21 ppb (summer), 011 ppb (winter); MIC; 0.0052 ppb (summer), 0.0038 ppb (winter). These were also affected by temperature fluctuations due to climate change during summer. PSG-DBA can monitor a wide range of environments to further investigate the environmental dynamics of isocyanates in the future.

Arising from the Chemical Assessment of Surfaces and Air (CASA) 2022 study at the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF), this paper presents the first evaluation of indoor surface emissions to a house measured with a surface flux chamber coupled to an online, non-targeted volatile organic compound (VOC) mass spectrometric detection. These surface emissions are compared to those assessed using ambient, whole house indoor VOC measurements and the outdoor air change rate. Chamber emission rates varied by almost four orders of magnitude across 35 quantified VOCs. The whole house emissions measured by campaign-long ambient measurements and the flux chamber emissions (when scaled to the painted surface area of the house) are similar, with an average ratio between the two of 1.3 ± 1.0. The general agreement between these two approaches indicates that the flux chamber was not solely measuring primary emissions from building materials located below the chamber. Rather, the results suggest that over the 12-year house lifetime, VOCs have been widely distributed around the house, migrating from their primary sources to secondary surface reservoirs. With the house in a quasi-steady state, the thermodynamic activities (i.e., the vapor pressures) of the VOCs within the different reservoirs become similar. Emissions of aromatics and monoterpenes have declined since the house was built, whereas aldehyde emissions have remained relatively constant.