Ground-level Air Research Articles

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-use regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving CV R2 values of 0.81 (95 % CI: 0.75–0.86) for the long-term PM2.5 prediction model and 0.90 (95 % CI: 0.87–0.91) for the short-term models. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m–225 m) particles, while 21 % was associated with upper-level (825 m–945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan–22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec–24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.

Tainted trust: air pollution and political trust in China

ABSTRACT Adverse health and economic impacts of air pollution in China have been well documented, but few studies have examined how air pollution may affect people’s political trust in their government. From an expectation-performance perspective, we argue that air pollution makes Chinese citizens feel that their basic needs and well-being are under-protected by the government, thereby dampening their political trust. Empirically, we employ a unique dataset (n=3,972) that merges national public opinion survey with satellite-derived ground-level air pollution data to test our argument. Our analyses show that worsening air pollution significantly taints Chinese people’s trust in the government, while the absolute level of local air pollution also erodes their political trust through raised environmental concern and lowered policy satisfaction. Our research adds knowledge to the links from air pollution to people’s political trust in China and sheds new light on future studies of environmental politics and governance elsewhere.

Joint associations of air pollutants during pregnancy, infancy, and childhood with childhood persistent asthma: Nationwide database study in Japan

The joint effect of air pollutants at relatively low levels requires further investigation. Here, a database study was performed to evaluate the effects of exposure to mixtures of air pollutants during pregnancy, infancy, and childhood on childhood persistent asthma. We used the Japan Medical Data Center database, which provides access to family linkages and healthcare provider addresses, and included child-mother dyads in which the child was born between January 2010 and January 2017. The exposure of interest was ground-level air pollutants, and the primary outcome was childhood persistent asthma at 45 years of age, as defined based on outpatient and inpatient asthma disease codes and/or asthma medication dispensing claims. The weighted quantile sum (WQS) regression was used to evaluate the effects of air pollutant mixtures on 52,526 child-mother dyads from 1149 of 1907 municipalities (60.3 %) in Japan. The WQS regression models showed that with every 10th percentile increase in the WQS index, ground-level air pollutants during pregnancy, infancy, and childhood increased the risk of childhood persistent asthma by an odds ratio of 1.04 (95 % CI: 1.02–1.05; p<0.001), 1.02 (95 % CI: 1.01–1.03; p<0.001), and 1.03 (95 % CI: 1.01–1.04; p<0.001), respectively. Moreover, particulate matter with an aerodynamic diameter ≤ 2.5 µm was assigned the highest weight across all three exposure periods. Relatively high weights were assigned to suspended particulate matter and photochemical oxidants during pregnancy, carbon monoxide during infancy, and photochemical oxidants during childhood. Our study showed that a mixture of low-level air pollutants has a detrimental association with childhood persistent asthma.

Design and Construction of an Anaerobic Digester for the Ingestion of Waste from the Cocoa Industry in Nigeria

The Anaerobic Digester designed and constructed for waste ingestion from the Nigerian cocoa industry aims to harness abundant renewable energy from crop residues like cocoa rinds and groundnuts. This study evaluates the biogas potential of these sources in contributing to the country&amp;apos;s overall energy needs, emphasizing the imperative for environmental sustainability. Focusing on reducing fossil energy consumption, greenhouse gas emissions, and minimizing environmental impact, the project advocates for a shift towards biogas for day-to-day energy requirements, presenting direct cost savings. The utilization of fossil fuel-derived energy is known to contribute to temperature increase, greenhouse gas emissions, noise pollution, and ground-level air pollution, all of which can be mitigated through biogas utilization. This initiative involves the design and construction of a 0.24m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; pilot plastic fossil plant for biogas generation, aiming to &amp;quot;green&amp;quot; various applications, including domestic and industrial usage as well as transportation. The digester, constructed from high-density polyethylene (HDPE) plastic, demonstrates leak-free operation, further supporting its potential for long-term sustainability. Results from a 28-day retention period show a cumulative biogas yield, with a daily assessment indicating a gas yield of 0.0496 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; on the 12th day. The study highlights the positive and negative influences of temperature inequality gradients ≥34°C≤38°C on biogas production. This comprehensive research contributes valuable insights for the sustainable management of waste and the utilization of biogas as a viable alternative energy source.

Establishing the case for a May 2010 low-yield, unannounced nuclear test in North Korea

AbstractNew data, analyses and modelling are presented concerning the unprecedented mid-May 2010 series of fission product detections in ground level air on and around the Korean Peninsula. For the first time Ba-140 is revealed at Ussuriysk, for which only La-140 had been reported. Thus aerosol particles containing the same parent-daughter pair Ba-140/La-140 were detected at both Ussuriysk and Okinawa, establishing beyond reasonable doubt that their physical, spatial and temporal origins are the same. Together with Ce-141 and Cs-137, all with short-lived xenon isotope parents, a supercritical fission excursion, which experienced a near prompt filtered vent, is the only viable scenario for their explanation. New modelling suggests that the vent occurred around 9 s after the excursion and that the CTBT-relevant xenon isotopes Xe-133 and Xe-135 were ‘quenched’ around 25 min later and released some 10–20 h afterwards. Published corroborating seismic and infrasound data of an event at the North Korean nuclear test site 8 min and 45 s past midnight on 12 May 2010 is subsequently reviewed. These papers adopted a conventional depth of the event although the data suggested a shallower one. Despite arguments in the seismic community about its exact nature, it is prudent to test how well the waveform signals marry the radionuclide detection pattern. Thus the location and time are input into a new atmospheric transport model. The advanced software suite MATCH was used in forward mode with prompt and delayed releases, revealing the presence of plumes at each detection site at the time of their first detection and extending over the observed timeframe. Thus a very consistent picture of a shallow low yield nuclear test is obtained.

Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD

High-resolution multi-component estimates of ground-level air pollutants are necessary for assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a high-resolution fusion and downscaling approach over South Korea for May 2016 and May 2021. Daily 1 km fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) concentrations are calculated at ground level using a random forest machine learning (ML) algorithm, with predictors including reanalysis meteorology, satellite aerosol optical depth (AOD), and gridded surface fields from chemical transport models (CTM). The ML model is tested for May 2016, coinciding with the Korea-United States Air Quality Study (KORUS-AQ) intensive field campaign, and for May 2021, to allow incorporation of observations from the Geostationary Environment Monitoring Spectrometer (GEMS). In the tests for May 2016, the correlation coefficients (R) and root mean squared errors (RMSE) relative to withheld observations of daily-averaged pollutants in 10-fold cross-validation are promising: 0.93 (5.5 μg/m3), 0.90 (5.5 ppbv), and 0.95 (4.7 ppbv) for PM2.5, O3, and NO2, respectively. Relative performance is assessed for alternate choices of predictors: (a) 80-km global reanalysis Copernicus Atmosphere Monitoring Service (CAMS) vs. 4-km regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem); (b) AOD polar-orbiting Moderate Resolution Image Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) vs. geostationary GEMS; and (c) variations in surface observation density. This study is among the very first to incorporate both CTM and GEMS AOD for building surface high resolution multiple air pollution predictions over South Korea.

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse and thus have limited coverage due to high costs. Satellite and reanalysis data represent an alternative with high spatio-temporal resolution. The idea of this work is to build an Artificial Intelligence model for the estimation of surface-level daily concentrations of air pollutants over the entire Italian territory using satellite, climate reanalysis, geographical and social data. As ground truth we use data from the monitoring stations of the Regional Environmental Protection Agency (ARPA) covering the period 2019–2022 at municipal level. The analysis compares different models and applies an Explainable Artificial Intelligence approach to evaluate the role of individual features in the model. The best model reaches an average R2 of 0.84 ± 0.01 and MAE of 5.00 ± 0.01 μg/m3 across all pollutants which compare well with the body of literature. The XAI analysis highlights the pivotal role of satellite and climate reanalysis data. Our work can facilitate One Health surveys and help researchers and policy makers.

Exploring the relationships between 3D urban landscape patterns and PM2.5 pollution using the multiscale geographic weighted regression model

Fine particulate matter (PM2.5) is a major source of air pollution and exerts serious impacts on human health. The 3D urban landscape patterns can significantly affect the diffusion and emissions of PM2.5. However, studies on the relationships between 3D urban landscape patterns and PM2.5 pollution across different seasons remain understudied. With the ground-level air pollutants estimated by the remote sensing and fine-scale building information, this study applied the multiscale geographically weighted regression model to explore such relationships. Wuhan, the largest metropolis in Central China, was selected as the study area for the application of our methodology. The results showed that the direction, degree, and scale of the effect of 3D urban landscape patterns on PM2.5 pollution varied across seasons. For building height, the standard deviation of building height had a significant positive correlation with PM2.5 all year round. For building density, the building count density showed a significant positive correlation with PM2.5 in general, with the bandwidth in winter and autumn smaller than in spring and summer. The building plan area fraction exerted both positive and negative influences on PM2.5, dependent on season and location. The bandwidth of it gradually increased from spring to winter, with the effect changing from local to regional scale. For building volume, the floor area ratio showed a significant negative correlation with PM2.5 in winter and autumn, and a localized effect was found, especially in winter. The findings of this study provide practical implications for urban planning and policy making to mitigate PM2.5 pollution in the rapidly urbanizing regions.

Investigation of 2021 wildfire impacts on air quality in southwestern Turkey

In the summer of 2021, unusually intense wildfires happened in southwestern Turkey, especially in Antalya and Mugla with destroying effects on forests, wildlife, and communities residing there. This study aims to understand the air quality impacts of these wildfires. A time period (June–September 2021) covering pre-, fire, and post-periods was investigated using NO2 ground-based observations and TROPOMI CO, HCHO, and NO2 satellite retrievals along with VIIRS FRP. Eight fire regions were selected for detailed analysis in Antalya (An-1-2), Mugla (Mu-1-4), and Mersin (Me-1-2). The highest fire intensity was found in An-1 and followed by Mu-1. CO also showed strongest signals over An-1 (6.73 × 1018 molecules/cm2) with highest levels (1.44 × 1019 molecules/cm2) in the downwind of the wildfires. NO2 showed fire signals in or in very close proximity to the fire regions with strongest signal and largest impact area in An-1 (>8.75 × 1016 molecules/cm2). HCHO showed a different pattern due to HCHO undergoing chemical production and loss in the wildfire plume. HCHO highest levels were not observed over the fire regions, but inside the transported plume with maximum levels for An-1 (3.60 × 1016 molecules/cm2) and for An-2 (3.23 × 1016 molecules/cm2). CO and NO2 increase continued not only in fire, but also in post-fire period, whereas HCHO levels indicated decreases in post-fire compared to pre-fire period. The increases in column concentrations in fire period ranges from 17.6 to 123.1% for CO, 40.6–116.5% for HCHO and 34.4–294.5% for NO2. Higher increases were observed over the Mediterranean Sea especially for CO. Correlations with FRP indicated highest correlations with CO and NO2 and low correlations with HCHO. This study showed that the capability of sparse, ground-level air quality monitoring stations to capture wildfire impacts are limited in the region, because the plume transport was driven by wind direction and wildfire smoke plumes are elevated due to buoyancy. Satellite retrievals are better for capturing the wildfire plume transport and estimating the overall air quality impacts of wildfires.

Long-term PM2.5 exposure and early-onset diabetes: Does BMI link this risk?

ObjectiveLimited studies investigated the association between high-level fine particulate matter (PM2.5) pollution and early-onset diabetes, leaving the possible metabolic mechanisms unclear. We assessed the association of cumulative PM2.5 exposure with diabetes, including early-onset, in high-pollution areas of China and explored whether metabolic factors mediated this association. Methods124,204 participants (≥18 years) from 121 counties in Hunan province, China, were enrolled between 2005 and 2020, with follow-up until 2021. The ground-level air pollution concentrations at each participant's residence were calculated using a high-quality dataset in China. The independent association of PM2.5 with incident diabetes and early-onset diabetes was assessed by Cox proportional hazards models. Restricted cubic splines were utilized to establish the exposure-response relationships. The role of metabolism-related mediators was estimated by mediation analysis. ResultsDuring a median follow-up of 8.47 (IQR, 6.65–9.82) years, there were 3650 patients with new-onset diabetes. Each 1 μg/m3 increase in the level of cumulative PM2.5 exposure was positively related to an increased incidence of diabetes (HR 1.177, 95 % CI 1.172–1.181) among individuals in the PM2.5 > 50 μg/m3 group after adjusting for multiple variables. The relationship of the PM2.5 dose-response curve for diabetes was non-linear. Significant associations between PM2.5 exposure and early-onset diabetes risk were observed, with this risk showing an increase with the earlier age of early diabetes onset. Males, young individuals (≤45 years), and those with a lower body mass index (BMI <24 kg/m2) appeared to be more susceptible to diabetes. Moreover, change in BMI significantly mediated 31.06 % of the PM2.5-diabetes relationship. ConclusionsLong-term cumulative PM2.5 exposure increased the risk of early-onset diabetes, which is partially mediated by BMI. Sustained air pollution control measures, priority protection of vulnerable individuals, and effective management of BMI should be taken to reduce the burden of diabetes.

Increased Impact of Aviation on Air Quality and Human Health in China.

China's civil aviation market has rapidly expanded, becoming the world's second-largest. However, the air quality and health impacts caused by its aircraft emissions have been inadequately assessed. Here, we leverage an updated emission inventory of air pollutants with improved temporal and spatial resolution based on hundreds of thousands of flight trajectories and simulate aviation-attributable contributions to ground-level air pollution in China. We find that in 2017, the annual-average aviation-attributed PM2.5 and O3 concentrations were 0.4-1.5 and 10.6-14.5 μg·m-3, respectively, suggesting that aviation emissions have become an increasingly important source of ambient air pollution. The contributions attributable to high-altitude emissions (climb/cruise/descent) were comparable to those at low altitudes (landing and takeoff). Aviation-attributed ambient PM2.5 and O3 exposures are estimated to have caused about 67,000 deaths in China in 2017, with populous coastal regions in Eastern China suffering the most due to the dense aviation activity. We recommend that industrial and policy stakeholders expedite an agenda of regulating air pollutants harmonized with decarbonization efforts for a more sustainable aviation future.

Questioning whether there was a short-term interaction between the 6 February 2023 earthquakes and air quality parameters in Türkiye

On 6 February 2023, Türkiye was hit by two earthquakes with 7.5 (Eq2) and 7.8 (Eq1) magnitudes. In this study, the hourly ground-level air pollutant concentrations measured in the earthquake areas were examined in detail. In addition, the remote sensing retrievals of CO, NO2, SO2 and CH4 from TROPOMI instrument were also processed and investigated. The hourly mean PM2.5 and CO increased at four stations close to the epicentre several hours prior to the earthquake. The abrupt increase in CO is likely, but PM2.5 is probably related to the degassing activities in the faults. At the day of the earthquake, a significant TROPOMI NO2 increase and a different spatial distribution over the region are observed. This increase coincides with the Eq2 (20 min prior) and a distinctive spatial distribution was observed on that day, indicating this signal can be associated with the earthquake or another process affected by the earthquake. On the other hand, TROPOMI CO indicated a significantly different pattern from NO2 with lowest levels around or prior to the earthquakes, and increased levels afterwards. Limited data from TROPOMI was available for CH4 and SO2 due to meteorological conditions and data quality over the region to draw any conclusive findings. According to the results, it may be helpful to perform special Earth-based observations of NO2, CO and counts of nanoparticles in the ground-based atmosphere near fault lines in seismically active belts. In addition, anomalies can also be monitored via available satellite retrievals, especially in regions with active fault lines.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Influence of atmospheric circulation and local parameters on activity concentration of gross alpha and gross beta in Granada, Spain

This work analyses the influence of local meteorology and air mass origin on radioactivity levels measured in Granada (Southern Spain). To the scope, gross α and gross β activities at ground-level air were weekly recorded from 2006 to 2021, by collecting aerosol filters and successively analysing them by low-level proportional counting. Time series decomposition shows a strong correlation between the seasonal components of gross α and gross β and the local meteorological parameters, in particular temperature and wind speed. Back trajectories reaching the study area during the sampling, calculated at three height levels (750 m, 1500 m, and 3000 m a.s.l.), were analysed by three methods with the aim to determine the main characteristics of air masses reaching Granada, and their impact on airborne radioactivity levels. The results of the residence time and clustering analyses showed that an increase in the radioactivity levels in the southeast of Spain is connected with air masses coming from the Mediterranean at 750 m and from the South (Sahara Desert) at 3000 m. In line with the outcomes obtained by the cluster analysis, the Concentration Weighted Trajectory (WCWT) results highlight that the air masses transported from Mediterranean and African regions are a potential source of gross α and gross β. Moreover, the findings of the WCWT bring a new aspect by revealing that the southern France region can also have a significant contribution to gross alpha and beta activities.

A synchronized estimation of hourly surface concentrations of six criteria air pollutants with GEMS data

Machine learning is widely used to infer ground-level concentrations of air pollutants from satellite observations. However, a single pollutant is commonly targeted in previous explorations, which would lead to duplication of efforts and ignoration of interactions considering the interactive nature of air pollutants and their common influencing factors. We aim to build a unified model to offer a synchronized estimation of ground-level air pollution levels. We constructed a multi-output random forest (MORF) model and achieved simultaneous estimation of hourly concentrations of PM2.5, PM10, O3, NO2, CO, and SO2 in China, benefiting from the world’s first geostationary air-quality monitoring instrument Geostationary Environment Monitoring Spectrometer. MORF yielded a high accuracy with cross-validated R2 reaching 0.94. Meanwhile, model efficiency was significantly improved compared to single-output models. Based on retrieved results, the spatial distributions, seasonality, and diurnal variations of six air pollutants were analyzed and two typical pollution events were tracked.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Association between one-year exposure to air pollution and the prevalence of pulmonary nodules in China

PM2.5 is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the computed tomography scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM2.5 exposure and pulmonary nodules was limited. To examine potential associations of exposures to PM2.5 and its major chemical constituents with the prevalence of pulmonary nodules. A total of 16 865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM2.5 and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM2.5 and its components on the risk of pulmonary nodules, respectively. Each 1 mg m−3 increase in PM2.5 (OR 1.011 (95% CI: 1.007–1.014)) was positively associated with pulmonary nodules. Among five PM2.5 components, in single-pollutant effect models, every 1 μg m−3 increase in organic matter (OM), black carbon (BC), and NO3 − elevated the risk of pulmonary nodule prevalence by 1.040 (95% CI: 1.025–1.055), 1.314 (95% CI: 1.209–1.407) and 1.021 (95% CI: 1.007–1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM2.5 components was 1.076 (95% CI: 1.023–1.133) fold. Notably, NO3 − BC and OM contributed higher risks of pulmonary nodules than other PM2.5 components. And the NO3 − particles were identified to have the highest contribution. The impacts of PM2.5 components on pulmonary nodules were consistent across gender and age.These findings provide important evidence for the positive correlation between exposure to PM2.5 and pulmonary nodules in China and identify that NO3 − particles have the highest contribution to the risk.

Association of the components of ambient fine particulate matter (PM2.5) and chronic kidney disease prevalence in China

Previous research has implicated PM2.5 as a potential environmental risk factor for CKD, but little is known about the associations between its components and CKD. We conducted a nationwide cross-sectional study using the updated air pollution data in the nationwide population (N = 2,938,653). Using generalized additive models, we assessed the association between long-term exposure to PM2.5 and its components (i.e., black carbon [BC], organic matter [OM], nitrate [NO3−], ammonium [NH4+], sulfate [SO42−]), and CKD prevalence. The air pollution data was estimated using high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. Besides, we adopted a novel quantile-based g-computation approach to assess the effect of a mixture of PM2.5 constituents on CKD prevalence. The average concentration of PM2.5 was 78.67 ± 22.5 μg/m3, which far exceeded WHO AQG. In the fully adjusted generalized additive model, at a 10 km × 10 km spatial resolution, the ORs per IQR increase in previous 1-year average PM2.5 exposures was 1.380 (95%CI: 1.345–1.415), for NH4+ was 1.094 (95%CI: 1.062–1.126), for BC was 1.604 (95%CI: 1.563–1.646), for NO3− was 1.094 (95%CI: 1.060–1.130), for SO42− was 1.239 (95%CI: 1.208–1.272), and for the OM was 1.387 (95%CI: 1.354–1.421), respectively. Subgroup analysis showed females, younger, and healthier were more vulnerable to this effect. In the further exploration of the joint effect of PM2.5 compositions (OR 1.234 [95%CI 1.222–1.246]) per quartile increase in all 5 PM2.5 components, we found that PM2.5SO42− contributed the most. These findings provide important evidence for the positive relationship between long-term exposure to PM2.5 and its chemical constituents and CKD prevalence in a Chinese health check-up population, and identified PM2.5SO42− has the highest contribution to this relationship. This study provides clinical and public health guidance for reducing specific air particle exposure for those at risk of CKD.

Comparative assessment of ground-level air quality in the metropolitan area of Prague using local street canyon modelling

Metropolitan areas have recently been identified as hotspots in terms of air quality and pollution-related diseases caused by gaseous pollutants and particulate matter primarily from road emissions. This study was carried out to investigate the suitability of the air dispersion modelling for predicting the short-term concentration of selected pollutants within the asymmetric street canyon geometries. The geographic projection covered a modelling domain of 0.5 × 1.4 km, with a grid resolution of 2 m mapped to the receptor site of an operational ambient air quality monitoring station in Prague, Czech Republic. Basic street canyon simulation showed the best performance metric for the prediction of PM10 concentration. High spatiotemporal performance was found by the advanced street canyon simulation in predicting hourly averages of NOx and PM10. The underprediction of the modelled NOx concentration corresponded to those of previous studies with FB value of 0.11 and NMSE <4. The slightly overprediction of PM10 concentration with the obtained FB value of −0.19, the NMSE value of 0.04, and the FAC2 of 0.53 were in agreement with previous modelling work. The simulation results provided additional evidence for the underpredicted concentration of PM2.5 in different model configurations. Advanced street canyon simulation improves the accuracy of ADMS-Urban by a maximum of 34%. The underprediction of short-term emission was graphically analysed in time-series profiles. The validation results and significant remarks greatly contribute to the essential optimization of urban street canyon model to better represent observations in the street canyon geometries of historic European building architectures.

Long-term exposure to air pollution and increased risk of atrial fibrillation prevalence in China

BackgroundAtrial fibrillation (AF) is the most common type of treated heart arrhythmia contributing to adverse cardiovascular events. The association between short-term air pollution exposure and AF episodes has been recognized. But the evidence of the association between long-term air pollution exposure and AF was limited, especially in developing countries. MethodsWe performed a nationwide cross-sectional study among 1,374,423 individuals aged ≥35 years from 13 health check-up centers. Using logistic regression models, we assessed the association between long-term exposure to single air pollution and AF prevalence, including particulate matter (PM2.5 and PM10), ozone (O3) and PM2.5 compositions, which were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants for China. The quantile g-computation model was used to explore the joint effect of all exposures to air pollution and the contribution of an individual component to the mixture. ResultsIn single-pollutant models, an increase of 10 μg/m3 in PM2.5 (OR 1.031[95%CI 1.010,1.053]) and PM10 (OR = 1.021 [95%CI 1.009,1.033]) was positively associated with AF prevalence. The stratified analyses revealed that these associations were significantly stronger in females, people <65 years old, and those with hypertension and diabetes. In the further exploration of the joint effect of PM2.5 compositions (OR 1.060 [95%CI 1.022,1.101]) per quintile increase in all five PM2.5 components), we found that PM2.5 sulfate contributed the most. ConclusionsThese findings provide important evidence for the positive relationship between long-term exposure to air pollution and AF prevalence in China and identify sulfate particles of PM2.5 as having the highest contribution to the overall mixture effects among all PM2.5 chemical constituents.