Daily PM2 Research Articles

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California’s diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989–2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively.Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations.To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)–NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions.Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

The temporal trend and disparity in short-term health impacts of fine particulate matter in California (2006–2019)

Most epidemiological studies assume that the relationship between short-term air pollution exposure and health outcomes is constant over time, which ignores potential changes in population composition and particulate matter emission sources. Limited studies have assessed changes in the relationship between fine particulate matter (PM2.5) and adverse health outcomes over time, with mixed results. Additionally, there is a need to identify which subgroups are disproportionately impacted over time by PM2.5-related health consequences. Therefore, we aimed to examine whether temporal trends exist in the relationships between daily PM2.5 exposure and circulatory and respiratory acute care utilization in California from 2006 to 2019. We further assessed whether certain subpopulations are more susceptible to PM2.5 exposure by demographic characteristics and extreme wildfire frequency. Daily PM2.5 concentrations estimated from a stacked ensemble model and daily cause-specific acute care utilization and demographic data from the California Department of Health Care Access and Information. We analyzed this relationship using modified two-stage Bayesian hierarchical models, where we first did not consider temporal trends, then stratified by two periods, and finally flexibly considered non-linear changes over time. Increases in circulatory (0.56 %; 95 % credible interval (CI): 0.17 %, 0.96 %) and respiratory acute care utilization risk (2.61 %; 95%CI: 2.29 %, 2.94 %) were found with every 10 μg/m3 increase in PM2.5 on the same day and previous two days. These risks were found to increase over time, where 0.13 % (95%CI: 0.02 %, 0.22 %) and 1.40 % (95%CI: 1.24 %, 1.54 %) increases were identified for circulatory and respiratory acute care utilizations, respectively, from the first (2006–2012) to second period (2013–2019). Differences by age, sex, race/ethnicity, and extreme wildfire frequency were noted. These findings confirm that air pollution guidelines should consider the dynamic nature of epidemiological dose-response and can provide insight for targeted air pollution control and adaptation policies designed to reduce PM2.5 exposure, particularly for the most susceptible subpopulations.

High-Resolution Daily PM2.5 Exposure Concentrations in South Korea Using CMAQ Data Assimilation with Surface Measurements and MAIAC AOD (2015–2021)

High-Resolution Daily PM2.5 Exposure Concentrations in South Korea Using CMAQ Data Assimilation with Surface Measurements and MAIAC AOD (2015–2021)

High-accuracy full-coverage PM2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model

ABSTRACT Obtaining precise ground-level fine particulate matter (PM2.5) information is significant for human health. Spatial PM2.5 maps can be obtained by remote sensing technology, but considerable uncertainty exists when suffering from high pollution with complicated aerosol types. To address this issue, we propose using a hierarchical machine learning model to retrieve high-accuracy and daily full-coverage PM2.5 concentrations from 2014 to 2023 in China. Our hierarchical model was validated by the sample-based 10 cross-validation . Results suggest that our model performs better in terms of RMSE of 12.12 µg/m3, MAE of 8.14 µg/m3 and R2 of 0.95 than traditional model with RMSE of 18.18 µg/m3, MAE of 12.21 µg/m3 and R2 of 0.89, showing 27.49–37.41% improvements for RMSE, 21.85–39.26% improvements for MAE and 8.31–15.39% improvements for R2 at three-folds samples. On longer time scales, our model also shows better results than previous studies. Additionally, for high-pollution provinces, our model can capture PM2.5 trends more preciously than the traditional model. Under severe haze, our hierarchical model can also rightly reflect PM2.5 changes. Overall, due to the hierarchical strategy, our ML-based model can obtain daily full-coverage PM2.5 maps in China with high accuracy and can be applied for follow-up studies.

Initial Insights into Teleworking’s Effect on Air Quality in Madrid City

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives

Particulate matter with a diameter of 2.5 microns or less (PM2.5) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM2.5 concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term PM2.5 concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term PM2.5 forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest PM2.5 value (236.00 μg/m3) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate PM2.5 concentration levels between 50 μg/m3 and 80 μg/m3. In Peshawar, the PM2.5 concentration levels were consistently unhealthy, with the highest peak (153.00 μg/m3) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.

PM2.5 episodes in northern Taiwan under southerly winds in late winter

When northern Taiwan is influenced locally, PM2.5 events occur in specific conditions. If it is also affected by long-range transport from mainland China in the northwest, the probability of these PM2.5 events increases. The current study applies WRF/CMAQ to study such case but focus on the local. From April 7 to 9, 2019, the Pacific high pressure extended westward, causing the prevailing weak southerly wind near Taiwan. The intensity of the Pacific high pressure weakened, and the wind speed near Taiwan also weakened. Therefore, the PM2.5 concentration in Taiwan increased. In northern Taiwan, the hourly PM2.5 concentration will inevitably increase to approximately 70 μg m−3. The ISAM of the CMAQ model demonstrates that local contributions to daily PM2.5 concentrations at the four representative monitoring stations in northern Taiwan are 3.5 to 16.2 μg m−3, greater than those from central, southern, and eastern Taiwan, from 1.8 to 6.7 μg m−3. We use the integrated process rate (IPR) and integrated reaction rate (IRR) of CMAQ to explore the formation mechanisms of PM2.5. The main causes of the increase in PM2.5 concentration are horizontal advection (HADV), aerosol processes (AERO), emissions (EMIS), and a small amount of cloud processes and aqueous chemistry (CLDS). The main source of NO3− is the reaction between OH and NO2 during the day and the reaction between N2O5 and water vapor at night. The gaseous HNO3 concentration reaches a peak near noon or soon after. The aerosol ANO3 concentration is high in the early morning. We also use the Sulfur Tracking Method (STM) of CMAQ to explore the sources of SO42−. Mainly it is from the local reaction of SO2 and H2O2. Finally, we apply AERO7 in CMAQ to explore the main sources of carbon components. The organic carbon (OC) concentration is much greater than the element carbon (EC) concentration, which indicates a strong local photochemical effect in northern Taiwan. OC mainly comes from low-volatility/semivolatile oxidized combustion OC, followed by low-volatility/semivolatile POA. Similar weather conditions occur in autumn, winter, and spring. When the weak southerly wind was around Taiwan, PM2.5 in northern Taiwan was noticed.

A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow

The detrimental impacts of fine particulate matter (PM2.5) on human health, climate, ecosystems, crops, and building materials are well-established. However, there remain unresolved inquiries regarding the precise location of the sources of PM2.5. This study is the first attempt to use a calibrated sensors-based ambient air quality monitoring network (SAAQM network) and regulatory government monitors to train micro-satellite images for high spatial-resolution air pollution field determination of PM2.5 in Lucknow, Uttar Pradesh, India. A hybrid approach is developed to integrate three different datasets that include microsatellite images, PM2.5 ground measurements, and supporting information (meteorological parameters and geographical coordinates), to be fed into a Random Trees-Random Forest- Convolutional Neural Network (RT-RF-CNN) joint model to estimate PM2.5 concentrations at a sub-km level. The RT-RF-CNN joint model can derive PM2.5 concentrations at a spatial resolution of 500 m with statistically significant indicators such as spatial r of 0.9, a low root-mean-square error of 26.9 μg/m3 and a mean absolute error of 17.2 μg/m3. Based on our approach, the PM2.5 prediction maps using micro-satellite images (spatial resolution of 3m/pixel) and RT-RF-CNN joint model were generated for each day throughout the study period (December 2021–December 2022). The inter-grid comparison of these maps revealed the intra-urban local hotspots and coolspots at a fine-granular level seasonally, monthly, and daily. It is observed that the monsoon season has the highest number of coolspots (67%), while winter (0.1%), post-monsoon (0.5%) and summer (11%) have fewer. It is noted that the high temporal-spatial information of PM2.5 estimates from our integrated approach is not achievable by ground-based measurements and other existing satellite-based estimates alone. The findings of this study have potential applications on a diverse array, encompassing near real-time daily PM2.5 predicted maps, specific air pollution hotspot identification, PM2.5 exposure assessment at the neighbourhood level, and integration of remote sensing-based micro-satellite images and ground-based measurements.

Atmospheric circulation anomalies related to the winter PM2.5 mass concentration rapid decline cases in Beijing, China

In this study, we investigate the atmospheric circulation anomalies related to the PM2.5 mass concentration rapid decline cases in Beijing during winter when the PM2.5 concentration is the highest. From 2014 to 2021, 66 PM2.5 concentration rapid decline days (RDDs) are identified by considering the 90 % thresholds of the difference of PM2.5 mass concentration between two adjacent days. The composite evolution of PM2.5 concentration for the rapid decline cases features a slow increase on the accumulation phase but a rapid decline on RDDs with the average daily PM2.5 concentration decreases by 69.1 %. The composite evolution of atmospheric circulation anomalies in the lower troposphere related to these PM2.5 concentration rapid decline cases exhibits the eastward shift of cyclonic/anticyclonic geopotential height anomalies from the Mongolian Plateau/Northeast Asia to the Japan Sea/western Pacific, coinciding with a southerly-to-northerly reverse of wind anomalies in Beijing. The anomalous northerly on RDDs enhances the wind speed and therefore corresponds to the rapid decline of PM2.5 concentration. Further analysis suggests that the above process is responsible for 72.7 % of RDDs, and other about one-third of RDDs display anomalous southerly in Beijing, which also result in comparable PM2.5 reduction amplitude to that resulted from the northerly RDDs. The rapid decline of PM2.5 concentration for the Southerly RDDs cases is related to the near-surface southeasterly, which extends from 125°E to the west of Beijing, bringing the clean air masses from the Bohai Sea to the Beijing region.

Does air pollution cause more car accidents? Evidence from auto insurance claims

Using a proprietary data set of auto insurance claims from May 2014 to December 2016, this paper examines the influence of air pollution on the number and severity of traffic accidents in China. Combining an instrumental variable strategy with high-dimensional fixed effects, we find that air pollution significantly increases the occurrence of traffic accidents, with each 1 μg/m3 increase in the particulate matter 2.5 (PM2.5) resulting in a 0.12 % increase in traffic accident probability and a 0.40 % increase in traffic accident number within one day. A different pattern is revealed in our analysis of accident severity, evidenced by a decrease of 1.20 % in the average claim ratio compared to its mean value and a reduction of 26 yuan in the average claim amount made with an increase of 1 μg/m3 in PM2.5. Combining the effect on the number and severity of traffic accidents, for each 1 μg/m3 increase in daily PM2.5, the district daily claim amount decreases by approximately 34 yuan. Further analysis indicates that this may be related to cautious driving behavior resulting from the driver's increased risk aversion. By exercising caution and care on the road, drivers can reduce the negative influence of air pollution on road safety and avoid non-subjective behavioral biases.

Short-term effects of wildfire-specific fine particulate matter and its carbonaceous components on perinatal outcomes: A multicentre cohort study in New South Wales, Australia

BackgroundEpidemiological evidence on the association between wildfire-specific fine particulate matter (PM2.5) and its carbonaceous components with perinatal outcomes is limited.We aimed to examine the short-term effects of wildfire-specific PM2.5 and its carbonaceous components on perinatal outcomes. MethodsA multicentre cohort of 9743 singleton births during the wildfire seasons from 1 September 2009 to 31 December 2015 across six cities in New South Wales, Australia were linked with daily wildfire-specific PM2.5 and carbonaceous components (organic carbon and black carbon). Adjusted distributed lag Cox regression models with spatial clustering were performed to estimate daily and cumulative adjusted hazard ratios (aHRs) during the last four gestational weeks for preterm birth, stillbirth, nonvertex presentation, low 5-min Apgar score, special care nursery/neonatal intensive care unit (SCN/NICU) admission, and caesarean section. ResultsDaily aHRs per 10 µg/m3 PM2.5 showed nearly inverted ‘U’-shaped positive associations and daily cumulative aHRs that increased with increasing duration of the exposures. The aHRs for lag 0–6 days were 1.17 (95 % CI: 1.04, 1.32) for preterm birth, 1.40 (95 % CI: 1.11, 1.78) for stillbirth, 1.20 (95 % CI: 1.08, 1.33) for nonvertex presentation, 1.12 (95 % CI: 0.93, 1.35) for low 5-min Apgar score, 0.99 (95 % CI: 0.83, 1.19) for SNC/NICU admission, and 1.01 (95 % CI: 0.94, 1.08) for caesarean section. Organic carbon and black carbon components for lag 0–6 days showed positive associations. The highest component-specific aHRs were 1.09 (95 % CI: 1.03, 1.15) and 4.57 (95 % CI: 1.96, 10.68) for stillbirth per 1 µg/m3 organic carbon and black carbon, respectively. The subgroups identified as most vulnerable were female births, births to mothers with low socioeconomic status, and births to mothers with high biothermal exposure. ConclusionsPositive associations of short-term wildfire-specific PM2.5 exposure and its carbonaceous components with adverse perinatal outcomes suggest that policies to reduce exposure would benefit public health.

Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid model within the "Decomposition-Prediction-Integration" (DPI) framework, which combines variational modal decomposition (VMD), causal convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), named as VCBA, for spatio-temporal fusion of multi-site data to forecast daily PM2.5 concentrations in a city. The approach involves integrating air quality data from the target site with data from neighboring sites, applying mathematical techniques for dimensionality reduction, decomposing PM2.5 concentration data using VMD, and utilizing Causal CNN and BiLSTM models with an attention mechanism to enhance performance. The final prediction results are obtained through linear aggregation. Experimental results demonstrate that the VCBA model performs exceptionally well in predicting daily PM2.5 concentrations at various stations in Taiyuan City, Shanxi Province, China. Evaluation metrics such as RMSE, MAE, and R2 are reported as 2.556, 1.998, and 0.973, respectively. Compared to traditional methods, this approach offers higher prediction accuracy and stronger spatio-temporal modeling capabilities, providing an effective solution for accurate PM2.5 daily concentration prediction.

Subway Fine Particles (PM2.5 )-Induced Pro-Inflammatory Response Triggers Airway Epithelial Barrier Damage Through the TLRs/NF-κB-Dependent Pathway In Vitro.

Subways are widely used in major cities around the world, and subway fine particulate matter (PM2.5) is the main source of daily PM2.5 exposure for urban residents. Exposure to subway PM2.5 leads to acute inflammatory damage in humans, which has been confirmed in mouse in vivo studies. However, the concrete mechanism by which subway PM2.5 causes airway damage remains obscure. In this study, we found that subway PM2.5 triggered release of pro-inflammatory cytokines such as interleukin 17E, tumor necrosis factor α, transforming growth factor β, and thymic stromal lymphopoietin from human bronchial epithelial cells (BEAS-2B) in a dose-effect relationship. Subsequently, supernatant recovered from the subway PM2.5 group significantly increased expression of the aforementioned cytokines in BEAS-2B cells compared with the subway PM2.5 group. Additionally, tight junctions (TJs) of BEAS-2B cells including zonula occludens-1, E-cadherin, and occludin were decreased by subway PM2.5 in a dose-dependent manner. Moreover, supernatant recovered from the subway PM2.5 group markedly decreased the expression of these TJs compared with the control group. Furthermore, inhibitors of toll-like receptors (TLRs) and nuclear factor-kappa B (NF-κB), as well as chelate resins (e.g., chelex) and deferoxamine, remarkably ameliorated the observed changes of cytokines and TJs caused by subway PM2.5 in BEAS-2B cells. Therefore, these results suggest that subway PM2.5 induced a decline of TJs after an initial ascent of cytokine expression, and subway PM2.5 altered expression of both cytokines and TJs by activating TLRs/NF-κB-dependent pathway in BEAS-2B cells. The metal components of subway PM2.5 may contribute to the airway epithelial injury.

Observational Constraints on the Aerosol Optical Depth-Surface PM2.5 Relationship during Alaskan Wildfire Seasons.

Wildfire is one of the main sources of PM2.5 (particulate matter with aerodynamic diameter < 2.5 μm) in the Alaskan summer. The complexity in wildfire smokes, as well as limited coverage of ground measurements, poses a big challenge to estimate surface PM2.5 during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM2.5 over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD-surface PM2.5 conversion factor (η = PM2.5/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (ηGC) and from observations (ηobs). We show that ηGC is biased high compared to ηobs under smoky conditions, largely because GEOS-Chem assigns the majority of AOD (67%) within the planetary boundary layer (PBL) when AOD > 1, inconsistent with satellite retrievals from CALIOP. The overestimation in ηGC can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for ηobs across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM2.5 measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM2.5 over continental Alaska in the 2021 and 2022 summers. The derived satellite PM2.5 shows a good agreement with corrected PurpleAir PM2.5 in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM2.5 concentrations. This piecewise function, η'obs, shows the capability of providing an observation-based, quick and direct estimation of daily surface PM2.5 over the whole of Alaska during wildfires, without running a 3-D model in real time.

Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a Generalized Additive Model (2018-2023).

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)

Abstract. Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM concentrations are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient-Boosting Machine (LightGBM) was developed to fuse multisource data and estimate long-term (1980–2022) historical daily ground PM concentrations in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation (CV) test R2 values of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 µg m−3) indicate low overfitting risks. With great generalization ability, the openly accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). This showed that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations have significantly increased (p&lt;0.05) in most regions since 2000 (0.34 µgm-3yr-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Programme, and PM2.5 concentrations declined in most regions (−0.78 µgm-3yr-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % confidence interval (CI) [0.65, 0.80]) million in 2000 to 1.22 (95 % CI [1.03, 1.41]) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 million to 0.60 million. LongPMInd has the potential to support multiple applications of air quality management, public health initiatives, and efforts to address climate change. The daily and monthly PM2.5 and PM10 concentrations are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).

Impact of PM2.5 exposure in old age and its interactive effect with smoking on incidence of diabetes

PurposeTo determine the impact of PM2.5 exposure in old age and its interactive effect with smoking on incident diabetes. MethodsA total of 2766 participants aged ≥60 years in China were interviewed at baseline for disease risk factors in 2001–03 and were then followed up for 10 years to document incident diabetes. They were assessed for daily PM2.5 exposure in 2005. Multivariate Cox regression models were used to examine the association of PM2.5 exposure with incident diabetes and interactive effect between PM2.5 and smoking on incident diabetes. ResultsDuring the cohort follow-up, 176 participants developed diabetes. The incidence of diabetes increased with PM2.5 exposure; the multiple-adjusted hazard ratio (HR) of diabetes was 2.27 (95 % CI 1.36–3.77) in participants with PM2.5 at ≥62.0 μg/m3 compared to those at <62.0 μg/m3. There was a significant interaction effect of PM2.5 with smoking on increased risk of diabetes. The adjusted HR for participants exposed to PM2.5 levels ≥62.0 μg/m3 who smoked was 4.39 (95 % CI 1.72–11.21), while for non-smokers it was 1.65 (95 % CI 0.88–3.09), compared to those at <62.0 μg/m3. ConclusionsExposure to PM2.5 in old age was associated with an increased incidence of diabetes and smoking enhanced the impact of PM2.5 on diabetic risk. These findings underscore the urgent need for air quality improvement measures and smoking cessation programs to mitigate the risk of diabetes in aging populations.

The oxidative potential of fine ambient particulate matter in Xinxiang, North China: Pollution characteristics, source identification and regional transport

Atmospheric fine particulate matter (PM2.5) can trigger the production of cytotoxic reactive oxygen species (ROS), which can trigger or exacerbate oxidative stress and pulmonary inflammation. We collected 111 daily (∼24 h) ambient PM2.5 samples within an urban region of North China during four seasons of 2019–2020. PM2.5 samples were examined for carbonaceous components, water–soluble ions, and elements, together with their oxidative potential (represent ROS–producing ability) by DTT assay. The seasonal peak DTTv was recorded in winter (2.86 ± 1.26 nmol min−1 m−3), whereas the DTTm was the highest in summer (40.6 ± 8.7 pmol min−1 μg−1). WSOC displayed the highest correlation with DTT activity (r = 0.84, p < 0.0001), but the influence of WSOC on the elevation of DTTv was extremely negligible. Combustion source exhibited the most significant and robust correlation with the elevation of DTTv according to the linear mixed–effects model result. Source identification investigation using positive matrix factorization displayed that combustion source (36.2%), traffic source (30.7%), secondary aerosol (15.7%), and dust (14.1%) were driving the DTTv, which were similar to the results from the multiple linear regression (MLR) analysis. Backward trajectory analysis revealed that the major air masses originate from local and regional transportation, but PM2.5 OP was more susceptible to the influence of short–distance transport clusters. Discerning the influence of chemicals on health–pertinent attributes of PM2.5, such as OP, could facilitate a deep understanding of the cause–and–effect relationship between PM2.5 and impacts.

Short-Term Exposure to Wildfire-Specific PM2.5 and Hospitalization for Diabetes Morbidity: A Study in Multiple Countries and Territories.

To evaluate associations of wildfire fine particulate matter (PM2.5) with diabetes across multiple countries and territories. We collected data on 3,612,135 diabetes hospitalizations from 1,008 locations in Australia, Brazil, Canada, Chile, New Zealand, Thailand, and Taiwan during 2000-2019. Daily wildfire-specific PM2.5 levels were estimated through chemical transport models and machine-learning calibration. Quasi-Poisson regression with distributed lag nonlinear models and random-effects meta-analysis were applied to estimate associations between wildfire-specific PM2.5 and diabetes hospitalization. Subgroup analyses were by age, sex, location income level, and country or territory. Diabetes hospitalizations attributable to wildfire-specific PM2.5 and nonwildfire PM2.5 were compared. Each 10 µg/m3 increase in wildfire-specific PM2.5 levels over the current day and previous 3 days was associated with relative risks (95% CI) of 1.017 (1.011-1.022), 1.023 (1.011-1.035), 1.023 (1.015-1.032), 0.962 (0.823-1.032), 1.033 (1.001-1.066), and 1.013 (1.004-1.022) for all-cause, type 1, type 2, malnutrition-related, other specified, and unspecified diabetes hospitalization, respectively. Stronger associations were observed for all-cause, type 1, and type 2 diabetes in Thailand, Australia, and Brazil; unspecified diabetes in New Zealand; and type 2 diabetes in high-income locations. Relative risks (95% CI) of 0.67% (0.16-1.18%) and 1.02% (0.20-1.81%) for all cause and type 2 diabetes hospitalizations were attributable to wildfire-specific PM2.5. Compared with nonwildfire PM2.5, wildfire-specific PM2.5 posed greater risks of all-cause, type 1, and type 2 diabetes and were responsible for 38.7% of PM2.5-related diabetes hospitalizations. We show the relatively underappreciated links between diabetes and wildfire air pollution, which can lead to a nonnegligible proportion of PM2.5-related diabetes hospitalizations. Precision prevention and mitigation should be developed for those in advantaged communities and in Thailand, Australia, and Brazil.

Composition and Sources of Organic Aerosol in Two Megacities in Western China Using Complementary Mass Spectrometric and Statistical Techniques.

Over 300 daily PM2.5 filter samples were collected in two western Chinese megacities, Xi'an and Chongqing, from October 2019 to May 2020. Their aqueous extracts were nebulized simultaneously to an aerosol mass spectrometer (AMS) and a recently developed extractive electrospray ionization (EESI) mass spectrometer, for bulk and near-molecular organic aerosol (OA) composition, respectively. Carbonate was quantified using EESI and a total organic carbon analyzer to separate inorganic carbon from dust. Via isotopically-labelled internal standards and positive matrix factorization, seven water-soluble sources were quantified separately using the AMS- and EESI-based analyses, with consistent types, concentrations, and correlations. These include dust, solid fuel combustion (SFC)-related, nitrogen- (and sulfur-) containing, summer/winter oxygenated OAs, and a cigarette-related OA only in EESI. When accounting for water-solubility, SFC-related OAs were the largest (53%) sources in Chongqing, while dust (consisting of 77% OA and 23% carbonates) was the largest (30%) source in Xi'an. Overall, this study presents one of the first times that complementary mass spectrometric techniques independently resolved consistent OA sources-with added chemical information-over multiple seasons and locations of complex pollution. The methods and quantified sources are essential for subsequent chemical, modelling, and health studies, and policy making for air pollution mitigation.