Urban Particulate Matter Research Articles

A method for measuring the bioaccessibility of polycyclic aromatic hydrocarbons in cell culture media

Airborne polycyclic aromatic hydrocarbons (PAHs) and their derivatives are of particular concern for population health due to their abundance and toxicity via inhalation. Lung toxicity testing includes exposing lung epithelial cell lines to PAHs in a culture medium containing inorganic species, lipids, proteins, and other biochemicals where the cell response is influenced among others by the toxic chemical accessibility in the medium. While inhalation bioaccessibility of PAHs and other toxicants was previously studied in surrogate lung fluids, studies measuring bioaccessibility in cell culture media are rare. In this work, a method was developed to characterize PAH bioaccessibility in a culture medium used for mouse lung epithelial (FE1) cells. Further, the optimised method was tested using commercially available standard reference material of urban particulate matter (PM) as well as polyurethane foam passive air samplers (PUF-PAS). The method provided a high precision and recovery of analytes, indicating no losses during sample processing and analysis. PAHs had non-linear concentration-responses, with the culture medium approaching saturation with PM concentration of 500 μg mL−1. The results showed that phenanthrene, a 3-ring PAH, was significantly more bioaccessible than ≥4-ring congeners in the culture medium (up to ∼2.5 folds; p < 0.05). Finally, using pre-deployed PUF-PAS from a residential and an industrial site, five PAHs were found in the culture medium, including naphthalene, phenanthrene, anthracene, fluoranthene, and pyrene. This work provides a proof of concept to enable future studies to assess the inhalation bioaccessibility of polycyclic aromatic compounds and other airborne pollutants collected using PUF-PAS.

Effects of urban tree pruning on ecosystem services performance

Pruning represents the main tree care activity in cities, where it is carried out to reduce wind-break risk, to ensure road visibility, to reduce infrastructure interactions and damages, and last but not least, for aesthetic reasons. However, pruning is also proven to increase tree susceptibility to disease and it can potentially affect their capacity to provide ecosystem services (ES). This study aims to assess the effect of three different life-long pruning scenarios on urban trees PM10 capture and CO2 stock performance. The total carbon balance of twelve tree species (Acer platanoides L., Cedrus spp., Celtis australis L., Cupressus arizonica Greene, Cupressus sempervirens L., Pinus halepensis Mill., Pinus nigra J.F. Arnold, Pinus pinea L. Platanus hybrida Brot., Populus nigra L. var. 'Italica', Quercus ilex L., and Tilia cordata Mill.) was evaluated, including the disposal of the pruning residues of the trees. In all management scenarios studied, pruning represented an opportunity to increase the crown Carbon stock up to 65 %, taking into account the full reestablishment of the crown. The results suggest considering more frequent and less intense ordinary pruning scenarios as a preferable tree management strategy to optimise the tree ES performance and minimise branch dieback occurrence. In addition, the analysis carried out on four different waste wood disposal methods showed that the open dump scenario was the lowest CO2 eq re-emission choice.

Synergistic interplay of UV radiation and urban particulate matter induces impairment of autophagy and alters cellular fate in senescence-prone human dermal fibroblasts.

Skin aging is a complex process influenced by intrinsic factors and environmental stressors, including ultraviolet (UV) radiation and air pollution, among others. In this study, we investigated the effects of UVA and UVB radiation, combined with urban particulate matter (UPM), on human dermal fibroblasts (HDF). We show here that treatment of HDF with a subcytotoxic dose of UVA/UVB results in a series of events leading to mitochondrial dysfunction, increased ROS levels, and DNA damage. These effects are known to trigger either cellular senescence or cell death, depending on the cells' ability to clear damage by activating autophagy. Whereas UPM treatment in isolation did not affect proliferation or survival of HDF, of note, simultaneous UPM treatment of UV-irradiated cells selectively inhibited autophagic flux, thereby changing cell fate of a fraction of the cell population from senescence to apoptotic cell death. Our findings highlight the synergistic effects of UV radiation and UPM on skin aging, emphasizing the need to consider these factors in assessing the impact of environmental stressors on human health and opening opportunities for developing comprehensive approaches to protect and preserve skin integrity in the face of growing environmental challenges.

Operator learning for urban water clarification hydrodynamics and particulate matter transport with physics-informed neural networks

Computational fluid dynamics (CFD) can be a powerful tool for higher-fidelity water infrastructure planning and design. Despite decades of development and demonstration over a wide range of water systems such as clarification basins, activated sludge processes, ozone contactors, etc., CFD remains primarily used in academic research, with limited application in civil and environmental engineering practice. This limitation is contributed by its higher computational cost and demand for specialized user skills. This, however, need not be the case, if a robust and efficient surrogate model can be developed from CFD simulations and independently deployed for engineering purposes. Leveraging the emerging scientific machine learning (ML) techniques of physics-informed ML and operator learning, this study develops a composite neural network (CPNN) for learning the flow hydrodynamics and particulate matter (PM) transport and fate in clarification systems. The CPNN consists of a deep operator network (DeepONet) as an encoder and a physics-informed neural network (PINN) as a decoder. In contrast to common “black box” and lumped ML approaches, the developed CPNN directly incorporates physics principles into its architecture. Furthermore, the CPNN is designed for process-resolved and operator learning, enabling it to predict spatial hydrodynamics and PM concentration distribution (i.e., contours) across different basin geometrics and loading conditions. Compared to CFD simulation, the developed CPNN model has significantly higher computational efficiency (∼ milliseconds) while demonstrating robust predictive capability. For predicting basin hydrodynamics across 10,000 test cases, the trained CPNN model achieves an R2 above 0.8 for 66.4% of cases and an R2 above 0.4 for 89.2% of cases. A similar performance is also demonstrated by the CPNN in predicting basin PM concentration. Further investigation reveals that basin geometrics that trigger bi-modal flow solutions can be particularly challenging for ML. Additionally, this study visualizes the dependency of basin hydrodynamics and PM concentration on basin geometrics and loading conditions, providing valuable insights for optimizing basin configuration. Lastly, the potentials and benefits of web-based applications, e.g., DeepXtorm, as a user-friendly interface for the developed CPNN model is discussed. This study represents the initial step toward achieving real-time higher-fidelity water infrastructure planning, design, optimization, and regulation.

In-vehicle ultrafine and fine particulate matter exposures during commuting in a South Asian megacity: Dhaka, Bangladesh

We measured ultrafine particle number concentration (PNC) and fine particulate matter mass concentration (PM2.5) levels in public buses and private cars under three conditions: windows open, windows closed with air recirculation off, and windows closed with air recirculation on in Dhaka, Bangladesh—an extremely polluted megacity in South Asia. We collected measurements on ∼10 repeated round trips on unique days for each selected transportation mode, covering various routes that spanned multiple arterial roads over several months (January to March 2020 and November to December 2020) and different times of the day to account for the variability in traffic and meteorological conditions. We used a condensation particle counter (CPC) to measure PNC and a PurpleAir sensor to measure PM2.5 mass concentrations. We applied corrections to the raw data and analyzed quality-assured data to investigate exposure differences across modes, pollutants, locations, and times. Public buses and private cars with open windows had mean PNC levels of 112,000 # cm−3 and 117,000 # cm−3, approximately 5–6 times higher than the mean urban background level of around 20,000 # cm−3. PM2.5 levels in these vehicles were approximately ∼100 μg m−3, about 20% higher than the urban background PM2.5 level of 85 μg m−3. With closed windows and air recirculation off, concentrations were 15% lower for PNC and 10% lower for PM2.5. Under closing windows and keeping air recirculation on, mean PNC and PM2.5 levels were 60,000 # cm−3 and 70 μg m−3, respectively, representing a 50% reduction for PNC and a 30% reduction for PM2.5 compared to open windows. Our findings suggest that commuters in public buses and private cars with open windows have higher and similar exposure levels, whereas those commute in private cars with closed windows and air recirculation on have lower exposure levels.

Effects of Exposure to Urban Atmospheric Particulate Matter Suspended in Seawater on the Mussel Mytilus galloprovincialis

Atmospheric particulate matter (PM) contains numerous constituents, including organic molecules, inorganic ions, and metals, with some of them possessing hazardous properties. Although mainly associated with air pollution, PM can rapidly be transferred from air and land to aquatic ecosystems, and consequently poses a risk to marine biota. The aim of this work was to evaluate how urban atmospheric PM (a standard reference mixture of urban PM, known to contain various organic and inorganic contaminants), suspended in seawater, may cause toxicity in marine organisms. To this purpose, mussels (Mytilus galloprovincialis) were exposed to two concentrations of suspended PM: 5.7 and 11.4 mg/L. After 7, 14, and 21 days, the animals were collected and the gills and digestive gland were analysed for stress biomarkers (CAT, SOD, GPX, GST, MDA, and Ubi). In general, the results show that exposure to different concentrations of PM caused an increase in GST, UBI, and GPx activities compared to their respective controls. The average activities of GST (87.65 ± 30.23 nmol/min/mg of total protein) in the gills of the animals exposed to 11.4 mg/L of PM increased after 21 days of exposure, and the activity of GPx (8.04 ± 3.09 nmol/min/mg of total protein) in the gills increased after 14 days in the animals exposed to 5.7 mg/L of PM. MDA results also provided information on cellular damage, with the most pronounced effects being found in the gills of exposed mussels. This study confirms that mussels are useful as “early warning” indicators of environmental contamination and provides important information on the effects of PM on marine biota.

Modeling the impacts of 2D/3D urban structure on PM2.5 at high resolution by combining UAV multispectral/LiDAR measurements and multi-source remote sensing images

Understanding the impacts of 2D/3D urban structure on PM2.5 is critical for protecting resident health and sustainable development. However, accurate understanding is limited by the lack of high-resolution PM2.5 simulation in cities with sparse monitoring stations and the simultaneously consideration of different landscape types both in 2D and 3D directions. Using Yinchuan City as a case study, an integrated simulation model combining satellite AOD, NDVI and meteorological data was developed using random forest (RF) algorithm to simulate city-scale PM2.5 variation at 30 m resolution. Then, the most critical 2D/3D urban metrics in altering PM2.5 and their impacts were explored through RF analysis. Finally, optimal urban structure was identified using Bayesian Network and multi-scenario analysis. The results indicated that 1) the established RF model could effectively simulate within-city variation of PM2.5 at 30 m with an R2 of 0.75. Thus, it is feasible to use the abundant temporal information and high-resolution AOD to map city-scale PM2.5 and overcome the limitation of spare monitoring stations. 2) The emission-related metric of distance to polluting enterprise and city center had the largest impacts on PM2.5, followed by NDVI, building height, impervious surface area and shape, and UGS green volume, area, shape and connectivity. Optimized 3D urban structure for reducing PM2.5 was proposed from the three aspects of emission source location, UGS pattern and impervious surface. This study deepens the understanding of how 2D/3D urban structure impact PM2.5 and provides scientific references for optimization possibility of urban air quality in both horizontal and vertical directions.

Urban ambient PM10 analysis: Characterization, sources, health risk and sustainable solutions

This is the first inclusive research concerning PM10 samples collected from 3 cities of Maharashtra State of India which categorized into 9 microenvironments. This study presents insights to chemical and morphological analysis, health risks, source apportionment, with sustainable solutions to improve air quality. These samples were analyzed for elemental composition as well as morphological features. Average PM10 concentration at industrial area of Jalgaon city was found to be maximum (70.9 ± 2.7 μg/m3) while minimum at residential area of Pachora city (43.1 ± 5.2 μg/m3). The mean concentration of 8 elements followed the order of (Co < Cd < Ni < Cr < Pb < Mn < Zn < Fe). These particles showed varying morphologies such as spherical, rectangular, cluster, porous shape. Principal Component Analysis (PCA) revealed that elements mainly originate from vehicular exhaust, construction activities, coal combustion and re-suspension of crustal elements due to anthropogenic activities. Health risk assessment showed that the incremental excess lifetime cancer risk (IELCR) for Pb was below threshold limit. Nonetheless, the exposed population is highly susceptible to Cr, Ni, and Co causing high risk of cancer at all sites.

Aerosol source apportionment uncertainty linked to the choice of input chemical components

For a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices are crucial for the final SA outputs not only in terms of number of sources identified but also, and consequently, in the source contributions estimates. In fact, PMF tends to reproduce most of PM mass measured independently and introduced as a total variable in the input data, regardless of the percentage of PM mass which has been chemically characterized, so that the lack of some specific source tracers (e.g. levoglucosan) can potentially affect the results of the whole source apportionment study. The present study elaborates further on the same concept, evaluating quantitatively the impact of lacking specific sources’ tracers on the whole source apportionment, both in terms of identified sources and source contributions. This work aims to provide first recommendations on the most suitable and critical components to be included in PMF analyses in order to reduce PMF output uncertainty as much as possible, and better represent the most commons PM sources observed in many sites in Western countries. To this aim, we performed three sensitivity analyses on three different datasets across EU, including extended sets of organic tracers, in order to cover different types of urban conditions (Mediterranean, Continental, and Alpine), source types, and PM fractions. Our findings reveal that the vehicle exhaust source resulted to be less sensitive to the choice of analytes, although source contributions estimates can deviate significantly up to 44 %. On the other hand, for the detection of the non-exhaust one is clearly necessary to analyze specific inorganic elements. The choice of not analysing non-polar organics likely causes the loss of separation of exhaust and non-exhaust factors, thus obtaining a unique road traffic source, which provokes a significant bias of total contribution. Levoglucosan was, in most cases, crucial to identify biomass burning contributions in Milan and in Barcelona, in spite of the presence of PAHs in Barcelona, while for the case of Grenoble, even discarding levoglucosan, the presence of PAHs allowed identifying the BB factor. Modifying the rest of analytes provoke a systematic underestimation of biomass burning source contributions. SIA factors resulted to be generally overestimated with respect to the base case analysis, also in the case that ions were not included in the PMF analysis. Trace elements were crucial to identify shipping emissions (V and Ni) and industrial sources (Pb, Ni, Br, Zn, Mn, Cd and As). When changing the rest of input variables, the uncertainty was narrow for shipping but large for industrial processes. Major and trace elements were also crucial to identify the mineral/soil factor at all cities. Biogenic SOA and Anthropogenic SOA factors were sensitive to the presence of their molecular tracers, since the availability of OC alone is unable to separate a SOA factor. Arabitol and sorbitol were crucial to detecting fungal spores while odd number of higher alkanes (C27 to C31) for plant debris.

Loliolide in Sargassum horneri Alleviates Ultrafine Urban Particulate Matter (PM 0.1)-Induced Inflammation in Human RPE Cells.

Owing to increasing air pollution due to industrial development, fine dust has been associated with threatening public health. In particular, ultrafine urban particulate matter (uf-UP, PM 0.1) can easily enter our bodies, causing inflammation-related diseases. Therefore, in the present study, we evaluated the effects of hydrothermal extracts of Sargassum horneri and its bioactive compound, loliolide, on uf-UP-induced inflammation as a potential treatment strategy for retinal disorders. Human retinal pigment epithelial cells (ARPE-19) stimulated with TNF-α or uf-UPs were treated with S. horneri extract and loliolide. S. horneri extracts exhibited anti-inflammatory effects on uf-UP-induced inflammation without cell toxicity through downregulating the mRNA expression of MCP-1, IL-8, IL-6, and TNF-α. UPLC-QTOF/MS analysis confirmed that the hydrothermal extract of S. horneri contained loliolide, which has anti-inflammatory effects. Loliolide effectively reduced the mRNA expression and production of proinflammatory chemokines (IL-8) and cytokines (IL-1β and IL-6) by downregulating the MAPK/NF-ĸB signaling pathway on TNF-α-stimulated inflammatory ARPE-19 cells. These effects were further confirmed in inflammatory ARPE-19 cells after stimulation with uf-UPs. Collectively, these results suggested the application of S. horneri as a functional ingredient for treating ocular disorders caused by particular matters.

Quantitative Estimation of Urban PM2.5 Pollution Baseline and Meteorological Resource Endowment Using Machine Learning in Chinese Yangtze River Economic Belt

Considering the influence of baseline values, meteorological conditions, and human activities on PM2.5, quantifying them will facilitate the classification, control, and management of pollution. The machine learning model explained the PM2.5-meteorological nonlinear relationship between PM2.5 and meteorological factors in each city across the Yangtze River Economic Belt, China. Meteorological resource endowments (MRE) are used to quantify the variation on PM2.5 concentration caused by meteorological conditions. Contamination baseline (CB) is used to characterize the lowest limit of anthropogenic impact in PM2.5 contamination without meteorological interference. According to the values of MRE and CB, cities in the Yangtze River economic belt can be divided into four categories (Q1-4). The average value of MRE is −0.41 μg/m3. The average value of CB is 34.05 μg/m3, which is lower than the Chinese Grade II standard (GB 3095-2012). The additional emissions by humans resulted in an increase of 7 μg/m3 in concentration, while the meteorological factors led to a decrease of −0.41 μg/m3. In terms of city classification, Q1 is concentrated in the midstream, and PM2.5 is the most challenging pollutant to control. Q2 is concentrated downstream, with relatively high PM2.5 emissions but favorable meteorological conditions. Q3 is concentrated upstream, and there is surplus environmental capacity even with limited meteorological conditions. Cites in Q4 have the most suitable development potential and exhibit a discrete spatial distribution. The research distinguished various categories of pollution and provided insights into the different characteristics of pollution around the Yangtze River Economic Belt. This information has helped the government classify cities and implement specific policies based on their individual situations.

Characteristics of Suspended Road Dust According to Vehicle Driving Patterns in an Urban Area and PM10 Content in Silt

Characterizing the influence factors of exhaust gas based on the suspended road dust on paved roads, according to the number of vehicles and their distance with regard to driving pattern, is important in order to provide a coefficient for driving patterns to find a model equation. This has been a limitation of previous studies, in which this was difficult to carry out in a large area reflecting various driving patterns because some sections were selected according to empirical measurement results, and only one vehicle measurement was used to find the level of road dust. This study measured the concentration of suspended road dust that could occur, depending on the vehicle’s driving patterns, on an experimental road in Yongin, South Korea, from May to July 2023. The study was conducted to determine the degree of the effect of exhaust gas, according to the concentration of suspended road dust generated, by determining the separation distance based on real-time measurements. This study attempted to determine the changes in suspended road dust based on driving patterns in urban areas and factor in the concentration of suspended road dust with regard to emission characteristics in terms of exhaust gas and particulate matter with a diameter of 10 microns or less (PM10). This was in accordance with conditions evaluated using mobile laboratories, based on suspended-PM10-concentration-measuring equipment. This study mainly focused on the following main topics: (1) increasing the level of suspended particulate matter at less than 10 m intervals produced by exhaust gas; (2) decreasing the level of suspended road dust with an increase in the number of vehicles, with the area measured at a distance of three cars in front showing the lowest level of suspended road dust in the air and a low level for the rear vehicle; (3) demonstrating that PM10 is effective in measuring the generation of suspended road dust; and (4) evaluating suspended road dust levels by road section. Based on the results, this research is necessary to more appropriately set the focus of analyses that aim to characterize suspended road dust according to exhaust gas and PM10 content in silt.

Maternal urban particulate matter (SRM 1648a) exposure disrupted the cellular immune homeostasis during early life: The potential attribution of altered placental transcriptome profile

Ambient fine particular matter (PM2.5) exposure has been associated with numerous adverse effects including triggering functional disorders of the placenta and inducing immune imbalance in offspring. However, how maternal PM2.5 exposure impacts immune development during early life is not fully understood. In the current study, we exposed mice with low-, middle-, and high-dose PM2.5 during pregnancy to investigate the potential link between the transcriptional changes in the placenta and immune imbalance in mice offspring induced by PM2.5 exposures. Using flow cytometry, we found that the proportions of B cells, CD3+CD4+ T cells, CD3+CD8+ T cells, and macrophage (Mφ) cells were altered in the blood of PM2.5-exposed mice pups but not dendritic cells (DCs) and natural killer cells (NKs). Using bulk RNA sequencing, we found that PM2.5 exposure altered the transcriptional profile which indicated an inhibition of the complement and coagulation cascades in the placenta. Weighted gene co-expression network analysis (WGCNA) revealed the potential crosstalk between the perturbation of placental gene expression and the changes of immune cell subsets in pups on postnatal day 10 (PND10). Specifically, WGCNA identified a cluster of genes including Defb15, Defb20, Defb25, Cst8, Cst12, and Adam7 that might regulate the core immune cell types in PND10 pups. Although the underlying mechanisms of how maternal PM2.5 exposure induces peripheral lymphocyte disturbance in offspring still remain much unknown, our findings here shed light on the potential role of placental dysfunction in these adverse effects.

Downdraft influences on the differences of PM2.5 concentration: insights from a mega haze evolution in the winter of northern China

A significant haze event occurred in northern China from 16 to 21 November 2022. This study analyzed the haze spatial evolution, and meteorological influences by integrating ground and satellite measurements. Most data were obtained using aerosol lidar and wind lidar observations in suburban (Nanjiao Observation Station, NJOS) and urban Beijing (Haidian Observation Station, HDOS). The observations at NJOS and HDOS indicate the presence of a distinct layer of haze restricted to a height of up to 1500 m above the surface. However, the aerosol intensity at HDOS was comparatively lower (aerosol extinction coefficient: 1.39 ± 0.27 km−1) than at NJOS (1.77 ± 0.38 km−1), with approximately one day of time lag in response to the southerly winds. Though NJOS and HDOS presented a similar wind stratification structure, the downdraft under 1000 m influenced the surface air quality were significantly different. The intense downdraft at the lower height at HDOS prevented the vertical upward diffusion of accumulated ground pollutants, whose effect was similar to that of the inversion layer. That led to a more stable increasing trend of PM2.5 at HDOS, with the shallowest planet boundary layer height of 242 m on 20 November. By contrast, NJOS in the transportation path was more regularly influenced by the southerly flow and presented cyclical PM2.5 concentration. This study shows downdraft in urban environments acting as an accelerator for urban episodic PM2.5 pollution, suggesting the complicated contribution from meteorological factors.

The Characteristics and Impact Factors of Sulfate and Nitrate in Urban PM2.5 over Typical Cities of Hangzhou Bay Area, China

PM2.5 pollution over Hangzhou Bay area, China has received continuous attention. In this study, PM2.5 samples were collected simultaneously in six typical cities in Zhejiang Province from 15 October 2019 to 15 January 2020 (autumn and winter) and from 1 June to 31 August 2020 (summer), and major water-soluble ions were analyzed. Average concentrations of NO3− and SO42− in the six cities were 3.93–15.64 μg/m3 and 4.61–7.58 μg/m3 in autumn and winter, with mass fractions of NO3− and SO42− in PM2.5 up to 19.6–34.2% and 13.6–26.3%, respectively, while in summer, they were 1.23–2.64 μg/m3 and 2.22–4.14 μg/m3, with mass fractions of 7.0–15.0% and 14.7~25.1%. Both NO3− and SO42− were mostly from gas-to-particle transformation of precursors. High relative humidity in the six cities was suggested to significantly promote the formation of NO3− and SO42−, particularly in autumn and winter, while enhanced atmospheric oxidation favored the formation of SO42− in summer. However, the formation of NO3− was inhibited under a high temperature of &gt;15 °C. The concentrations of SO42− and NO3 were mostly correlated with each other among the six cities. Potential source contribution function analysis indicated that both SO42− and NO3− were mostly from local pollution of Hangzhou Bay area in Zhejiang Province and also transported from Shanghai and the southern region of Jiangsu Province. This study contributed to the understanding of regional characteristics of SO42− and NO3− in Hangzhou Bay area and suggested that joint prevention and control efforts should be strengthened to reduce regional PM2.5 pollution.

Exhaled VOC Biomarkers from Rats Injected with PMs from Thirty-One Major Cities in China.

Particulate matter (PMs) of different origins can cause diverse health effects. Here, a homemade box was used to facilitate real-time measurements of breath-borne volatile organic compounds (VOCs) by gas chromatography-ion mobility spectrometry. We have tracked exhaled VOC changes in 228 Wistar rats that were injected with water-soluble PM suspension filtrates (after 0.45 μm) from 31 China cities for 1 h to up to 1-6 days during the experiments. Rats exposed to the filtrates exhibited significant changes in breath-borne VOCs within hours, featuring dynamic fluctuations in the levels of acetone, butan-2-one, heptan-2-one-M, acetic acid-M, and ethanol. Subsequently, on the fifth to sixth day after the injection, there was a notable increase in the proportion of aldehydes (including hexanal-M, hexanal-D, pentanal, heptanal-M, and (E)-2-hexenal). The 10 dynamic VOC fingerprint patterns mentioned earlier showcased the capability to indirectly differentiate urban PM toxicity and categorize the 31 cities into four distinct groups based on their health effects. This study provides valuable insights into the mechanisms of exhaled VOCs and underscores their critical role as biomarkers for differentiating the toxicity of different PMs and detecting the early signs of potential diseases. The results from this work also provide a scientific basis for city-specific air pollution control and policy development.

Fine-grained PM[formula omitted] prediction in Lanzhou based on the spatiotemporal graph convolutional network

Urban fine-scale PM2.5 concentrations can be predicted that considers both spatial and temporal correlation, owing to the widespread installation of air quality micro-monitoring stations in cities. The traditional method only uses the data of a single monitoring station while ignoring the spatial diffusion characteristics of the influence between stations. Recently, urban PM2.5 predictions have often been insufficiently refined due to coarse-grained air quality data and poor consideration of spatial dependence in modelling. Here, the influence relationship of PM2.5 flow between irregular monitoring stations is abstracted into an unstructured correlation graph, combined with the PMPre-GCN model to forecast the PM2.5 concentration of the city’s intensive monitoring stations. The validity of this method was verified by using air quality monitoring data from 456 dense micro-stations in Lanzhou City as an example. The results illustrate that: (1) The construction of the correlation graph significantly improved the model’s prediction accuracy. Compared with the fully connected and distance-based graphs, the prediction results’ root means square error (RMSE) was reduced by 10.09% and 8.42%, respectively. (2) The density of monitoring sites makes the precision of fine-grained prediction higher. Using data from 456 stations resulted in a 36.31% reduction in RMSE compared to the selection of data from 228 stations to make inferences about air quality in the city. (3) PMPre-GCN showed the best prediction performance when predicting PM2.5 concentration from T+1 to T+8 h at all stations. Especially at T+8 h, RMSE and the mean absolute error are 11.83 μg/m3 and 8.21 μg/m3. Overall, the method is capable of fine-grained predictions of urban PM2.5 concentrations, thereby supporting resident health management and fine-grained government decision-making on the urban air environment.

Urban aerosol, its radiative and temperature response in comparison with urban canopy effects in megacity based on COSMO-ART modeling

The study of urban aerosol and its influence on radiation and meteorological regime is important due to the climate effect. Using COSMO-ART model with TERRA_URB parameterization, we estimated aerosols and their radiative and temperature response at different emission levels in Moscow. Mean urban aerosol optical depth (AOD) was about 0.029 comprising 20–30% of the total AOD. Urban black carbon mass concentration and urban PM10 accounted for 86% and 74% of their total amount, respectively. The urban AOD provided negative shortwave effective radiative forcing (ERF) of −0.9 W m−2 at the top of the atmosphere (TOA) for weakly absorbing aerosol and positive ERF for highly absorbing aerosol. Urban canopy effects decreased surface albedo from 19.1% to 16.9%, which resulted in positive shortwave ERF at TOA, while for longwave irradiance negative ERF was observed due to additional emitting of urban heat. Air temperature at 2 m decreased independently on the ERF sign, partially compensating (up to 0.5 °C) for urban heat island effect (1.5 °C) during daytime. Mean radiative atmospheric absorption over the Moscow center in clear sky conditions reaches 4 W m−2 due to urban AOD. The study highlights the role of urban aerosol and its radiative and temperature effects.

Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.

The Influence of Urbanization and Environmental Factors on the Financial Performance of Retail and Automotive Industries in China

This study probes the intersection of urbanization, environmental degradation, and corporate performance in China’s retail and automotive sectors. Utilizing data from 23 regions spanning 2000–2022, the research elucidates the impacts of urban growth, infrastructure development, and particulate matter 2.5 pollution (PM2.5) on these industries. The study uses panel data from 2000 to 2022 and the corresponding analytical random effect model, along with pre- and post-estimation tests for the main results. The findings reveal that while urban population growth bolsters retail sales, it adversely affects the automotive industry. Urban infrastructure does not influence conventional sectors, yet it negatively impacts online retail sales. PM2.5 pollution depresses retail and automotive sales but boosts online sales, underscoring the rising environmental consciousness among consumers. The urban population significantly moderates the relationship between PM2.5 pollution and sales across sectors, hinting at unique consumption patterns in populated urban areas. This study provides critical insights for policymakers aiming for sustainable economic growth, and encourages further research with more region-specific factors and extended time frames.