This study utilizes backward trajectory cluster analysis, the Potential Source Contribution Function (PSCF), Concentration Weighted Trajectory (CWT), and a random forest model to investigate the pollution characteristics of PM2.5 and PM10 in the “Urumqi-Changji Hui Autonomous Prefecture-Shihezi-Wujiaqu (U-C-S)” urban agglomeration. Findings indicate that on an annual basis, higher PM2.5 concentrations are observed in the central part of the “U-C-S” urban agglomeration, southern Wujiaqu, and the Shihezi area, whereas PM10 concentrations are lower in the high-altitude regions of the Tianshan and Bogda Mountains. Seasonally, both PM2.5 and PM10 concentrations significantly increase during winter, with summer exhibiting the best air quality. On a monthly scale, Urumqi’s central urban area shows a marked rise in PM2.5 concentrations during winter, attributed to coal heating and stable weather conditions. Weekly patterns reveal higher pollution levels on weekdays compared to weekends. Daily data show that PM2.5 concentrations are notably higher in winter compared to other periods, while elevated PM10 levels in spring are primarily due to dust storms. Cluster analysis indicates that seasonal airflow paths significantly influence particulate matter concentrations. PSCF and CWT analyses demonstrate that the most severe PM2.5 pollution in winter is concentrated in the northern part of the Bayingolin Mongol Autonomous Prefecture, southern Yining City, and across all areas of Urumqi. The random forest model provides robust predictions of particulate matter concentrations, aiding in the understanding and mitigation of future pollution trends. This study offers valuable insights for atmospheric particulate matter pollution research in the Xinjiang region and serves as a reference for similar urban agglomerations.

Irreversible Land Use Land Cover changes around iconic heritage sites in south Asian countries, precipitated by an ever-increasing urban density build up, is a serious cause of concern. Some of the monuments are UNESCO world heritage sites and are currently under threat. Heavy vehicular traffic and unregulated encroachments over several south Asian cities, including Indian cities have led to particulate pollution build up around iconic heritage structures. This study shows that emissions from diesel engines operating around a network of roads around the 2000-year-old Meenakshi Amman Temple complex, emit soot and black carbon particles in the accumulation mode (0.2-0.5μm) which remain airborne for over a week and eventually soil and mar the beauty of the temple's iconic tower gates (Gopurams). Over this size range the scavenging efficiency from natural rain showers is ~ 20%. Efficient scavenging depends not only on the rain drop size distribution, but also on the collected aerosol particle size distribution. The region of Madurai remains largely dry for 9 months in a year and municipal authorities must look for the use of pressure calibrated sprinklers optimised with the drop-size distribution of the airborne particles. Quantifying particle size distribution requires the use of in situ scanning mobility particle sizer spectrometers-SMPS -which is prohibitively expensive for developing nations. Creating artificial rain showers is an even more expensive process and is resorted to only in dire situations. From an analysis of European data repositories and with the use of the state-of-the-art Weather Research and Forecasting (WRF) model we show (i) a progressive loss of vegetative cover (and hence a decrease in the leaf area index, LAI) (ii) decrease in precipitation (iii) decrease in soil moisture and (iv) an increase in fractions of non-precipitating clouds over the region over a time period of 1940-2050. It is therefore clear that any economically affordable engineered solution must involve smart roadside sprinklers yielding a fine spray mist which will be able to capture these particles through efficient collision and coalescence until non-polluting vehicles are used. It is shown that when vehicles are in an idling mode (as is very often the case) the median size is at its broadest and progressively decreases with increasing vehicular operating loads. This study proposes a quick AI mediated method to specify the range of Sauter Mean Radii (SMR) for varying vehicular operations, completely circumventing the use of SMPS and geoengineered artificial rain showers. The drop size distribution (DSD) range will help in the choice of sprinkler attributes-a sprinkler yielding an assortment of millimetre sized droplets should scavenge much of the soot particles with moderate spray durations of the order of a few hours. Mask Recurrent-Convolutional Neural Networks(R-CNN) modelling was used to ascertain particle SMR. Future predictions spanning the years 2020-2050 using the Stacked Transformer model show a progressive decrease in the LAI and soil moisture and an aggravated increase in heat stress during the Representation Concentration Pathway (RCP) 8.5 high emissions scenario. Simulations are contrasted for business-as-usual scenarios (using polluting vehicles), with scenarios using a phased-out transition to electric vehicles (EVs) and it was ascertained that if municipal agencies replace diesel vehicles with EVs, then the above indicators significantly improved. These quick protocols can be used by government officials and other stakeholders to provide smart forecasts for Impact analyses (EIA) and cost savings assessments for calculating paybacks resulting from a partial transition to cleaner vehicular transport in the interim period. Estimates suggest benefits to the tune of millions of USD over a 5-year period vis-à-vis facade restoration ravaged by soot deposition.

Effect of multiscale plant traits on particulate matter resuspension.

Combined effects of particulate and gaseous pollutants on Alzheimer's disease-related brain imaging markers and cognitive function.

Diesel particle filter regeneration: Impact on the physicochemical composition and toxicity of diesel exhaust emissions.

Ultrafine particles and black carbon concentrations and determinants in aircraft cabins of a French airline: Paris-aircraft study.

Unveiling the drivers of PM2.5 pollution in an industrial inland city in China during heating seasons (2021-2024) by an integrated machine learning method.

Tracing the concerted modulations in the metabolome and related metabolic pathways of tree species under simulated particulate matter stress through high-resolution NMR spectroscopy.

Growth inhibition in lettuce callus exposed to particulate matter: Cellular injury linked to intracellular accumulation.

Progress in the study of the emission characteristics of intermediate and semivolatile organic compounds from motor vehicles.

Characterization of brake wear particle emissions from passenger cars: A case study of particle agglomeration and fragmentation.

Seasonal and spatial variations of naphthalene in central western Taiwan: The interaction of meteorology and emission sources.

Impact of flooding events on microplastic distribution from rivers to coastal areas: a case study from Tuticorin, Southeast India.

Sand and dust storms exacerbate the toxicity of particle pollution on mortality: A cohort study among 1.5 million Chinese older adults.

ABSTRACT Airborne particulate matter (PM) pollution increasingly threatens sub‐Saharan tropical cities, yet physiological thresholds for plant resilience remain unclear. This study quantifies dust deposition effects on Psidium guajava L. across urban (PK14), residential (Entrée Kotto), and suburban (Lendi) sites in Douala, Cameroon, over 12 months. Key biochemical traits—dust load (%), chlorophyll, ascorbic acid (AsA), relative water content (RWC), and leaf extract pH, were measured to assess stress responses and the air pollution tolerance index (APTI). Results reveal a nonlinear, threshold‐based response: low‐to‐moderate dust loads (≤ 2.4%) stimulate chlorophyll biosynthesis ( β = 1.607, p = 0.017) and optimize leaf pH (4.76–5.2), enhancing photosynthesis. AsA increases under moderate dust highlighting antioxidant defense activation. Beyond this threshold, excessive dust impairs water content (RWC; r = −0.74, p = 0.01), degrades chlorophyll, and produces APTI values (6.0–9.7) categorizing P. guajava as a sensitive bioindicator. Seasonal‐spatial trends show the moderating role of rainfall and the exacerbating role of urbanization (dust load 3.9% at PK14 vs. 2.4% at Lendi). This pioneering quantitative threshold for a tropical evergreen integrates physiological, biochemical, and tolerance measures, informing targeted urban greening strategies to mitigate pollution impact in tropical cities.

ABSTRACT Evaporative (“swamp”) coolers (ECs) are used for cooling of homes in hot and arid climates. Because ECs draw in large volumes of outdoor air, they can introduce substantial amounts of ambient airborne particulate matter (PM) and other pollutants indoors, which is especially exacerbated during wildfire smoke events. We measured airborne metals and PM inside and outside farmworkers’ homes with ECs in the California San Joaquin Valley, U.S.A. Sampling was conducted as part of a larger study (FRESSCA, Filtration for Respiratory Exposure to wildfire Smoke from Swamp Cooler Air) to develop and evaluate an affordable EC filtration solution. All homes received portable air cleaners (PACs), and approximately half the homes received filters on their EC air intakes. Indoor and outdoor airborne metals were actively measured using 24-hr air sampling. Airborne metals were dominated by Fe, with some detections of Cu, Mn, Zn, and Se. Indoor/outdoor (I/O) ratios for Fe were mostly <1 and were lower for homes with EC filters. Metals analyses of the EC filters themselves revealed additional metals, likely due to the longer sampling time. These metals were partially removed from the air entering participants’ homes by EC filters. Passive PM samplers were deployed for 70 days and analyzed using electron microscopy. PM samples revealed crustal, carbonaceous, and agriculture-related particle types, including Cu-rich fungicides. PM levels were lower indoors compared to outdoors in all homes (30%-70%). Some evidence of greater PM reductions in homes with EC filters was observed, with 25% reductions in coarse PM10–2.5 in homes with EC filters that used their ECs more frequently (p < 0.05). Few smoke events during the study limited our ability to detect differences in PM from wildfires. However, the measured reductions in coarse PM are important for communities heavily impacted by dust, both for home cleanliness and for protection from upper-airway health impacts. Implications: Do-it-yourself evaporative cooler filters are a promising and affordable method of reducing dust infiltration in homes burdened by heat stress and poor outdoor air quality. Further evaluation during wildfire smoke events and optimization of evaporative cooler filter interventions could eventually reduce respiratory-related health impacts in communities affected by a changing climate.

Particulate matter (PM)-attributable lower respiratory infections (LRIs) critically impact child and adolescent health worldwide. This study evaluates the global PM-linked LRI burden (1990-2021) and projects trends to 2050. Using data from the Global Burden of Disease (GBD) 2021 study, We assessed the number of deaths, DALYs, YLLs, and YLDs among children and adolescents aged 0-19 years globally. We calculated age-standardized rates (ASRs) to compare regional trends. Historical patterns (1990-2021) were analyzed using linear regression, while future outcomes (2022-2050) were projected using ARIMA and Exponential Smoothing models. From 1990 to 2021, Kuwait saw the sharpest mortality increase (207.14%), while South Korea reduced ASRs by 42.67%. Afghanistan reported rising YLL/YLD ASRs, contrasting with declines in Maldives and Albania. Projections indicate sustained reductions in deaths, DALYs, YLLs, and YLDs by 2050, with male-female burden disparities narrowing. YLL declines surpass YLDs, highlighting persistent non-fatal impacts despite reduced premature mortality. PM pollution disproportionately threatens children in low- and middle-income countries. Although high-income regions show progress, inequities demand tailored strategies. Critical interventions-enhanced air quality regulations, clean energy transitions, and equitable healthcare-are imperative to alleviate PM-related LRIs. Prioritizing these measures is vital to address health disparities and protect vulnerable populations globally.

Abstract. Particulate Matter (PM) and gaseous pollutants can carry or induce the production of Reactive Oxygen Species (ROS) in the lung environment, causing oxidative stress, a key factor in the development of cardiovascular and pulmonary outcomes. Over the past decade, numerous techniques have been implemented to assess the Oxidative Potential (OP) of aerosols, i.e., their ability to oxidise the lung environment as an initial proxy of subsequent biological processes. Offline measurements from filters collected from air samplers are widely assessed but are probably underestimating PM redox activity due to the short lifetime of several ROS and/or the loss of the most volatile compounds on filters in a non-proportional and unsystematic way. This study introduces a new device, called ROS-Online, allowing the automatic and near real time measurement of two complementary OP assays, OP Ascorbic Acid (OPAA) and OP Dithiothreitol (OPDTT), sensitive to ambient PMs at mass concentrations about [PM10] ∼ 20 µg m−3. The ROS-Online device is designed to reproduce the exposure and interaction of airborne particles with the respiratory system. ROS-Online consists of three main modules: (i) an air sampling module using a BioSampler® to collect airborne PM, (ii) a distribution module that transports samples and reagents to (iii) a measurement module that relies on spectrophotometric methods to monitor chemical reactions in real time. Its operation is based on established OPAA and OPDTT protocols, ensuring comparability with existing offline OP measurement methods. Compact and transportable (75 × 65 × 170 cm, 85 kg), ROS-Online is designed for deployment in air quality monitoring stations and allows for autonomous operation over 2 weeks. With a high particle collection efficiency (&gt; 90 % by mass for PM1 and PM2.5) and greater sensitivity than offline methods, it provides accurate and reliable results across a wide range of aerosol concentrations, from urban backgrounds to highly polluted environments. The qualification of the device demonstrated an excellent correlation with offline methods for both OPAA and OPDTT measurements (r &gt; 0.96), over positive controls, confirming the reliability and specificity of ROS-Online for continuous atmospheric aerosol OP monitoring. ROS-Online was deployed in the field, in an urban background site, where OPAA of ambient air was measured for 15 continuous days and OPDTT for 6 continuous days. Results showed a good correlation with ozone (O3) signal (R2 = 0.74), underlying the importance of considering pollutants' interaction in OP measurements, as laboratory experiment showed no OP response when introducing O3 alone into the instrument. Comparison of ROS-Online measurements with established offline methods showed an excellent correlation for both AA and DTT assays (r &gt; 0.96), supporting its reliability for atmospheric monitoring. These preliminary results mark an important step towards establishing ROS-Online as a viable and effective tool for OP assessment in future research and monitoring endeavours.

Association of ambient air pollutants with risk of lung cancer subtypes and survival after diagnosis.

Abstract. The Multi-Angle Imager for Aerosols (MAIA) satellite mission, to be jointly implemented by NASA and the Italian Space Agency, aims to study how different types of particulate matter (PM) pollution affect human health. The investigation will primarily focus on a discrete set of globally distributed Primary Target Areas (PTAs) containing major metropolitan cities, and will integrate satellite observations, ground observations, and chemical transport model (CTM) outputs (meteorology variables and PM concentrations) to generate maps of near-surface total and speciated PM within the PTAs. In addition, the MAIA investigation will provide satellite measurements of aerosols over a set of Secondary Target Areas (STAs), which are useful for studying air quality more broadly. For the CTM, we have developed a Unified Inputs (of initial and boundary conditions) for WRF-Chem (UI-WRF-Chem) modeling framework to support the MAIA satellite mission, building upon the standard WRF-Chem model. The framework includes newly developed modules and major enhancements that aim to improve model simulated meteorology variables, total and speciated PM concentrations as well as AOD. These developments include: (1) application of NASA GEOS FP and MERRA-2 data to provide both meteorological and chemical initial and boundary conditions for performing WRF-Chem simulations at a fine spatial resolution for both forecast and reanalysis modes; (2) application of GLDAS and NLDAS data to constrain surface soil properties such as soil moisture; (3) application of recent available MODIS land data to improve land surface properties such as land cover type; (4) development of a new soil NOx emission scheme – the Berkeley Dalhousie Iowa Soil NO Parameterization (BDISNP); (5) development of a stand-alone emission preprocessor that ingests both global and regional anthropogenic emission inventories as well as fire emissions. Here, we illustrate the model improvements enabled by these developments over four target areas: Beijing in China, CHN-Beijing (STA); Rome in Italy, ITA-Rome (PTA); Los Angeles in the U.S., USA-LosAngeles (PTA), and Atlanta in the U.S., USA-Atlanta (PTA). UI-WRF-Chem is configured as 2 nested domains using an outer domain (D1) and inner domain (D2) with 12 and 4 km spatial resolution, respectively. For each target area, we first run a suite of simulations to test the model sensitivity to different physics schemes and then select the optimal combination based on evaluation of model simulated meteorology with ground observations. For the inner domain (D2), we have chosen to turn off the traditional Grell 3D ensemble (G3D) cumulus scheme. We conducted a case study over USA-Atlanta for June 2022 to demonstrate the impacts of the cumulus scheme on precipitation and subsequent total and speciated PM2.5 concentrations. Our results show that keeping the G3D cumulus scheme turned on results in higher precipitation and lower total and speciated PM2.5 than the simulation with the G3D cumulus scheme turned off. Compared with surface observations of precipitation and PM2.5 concentration, the simulation with the G3D scheme off shows better performance. We focus on two dust intrusion events over CHN-Beijing and ITA-Rome, which occurred in March 2018 and June 2023, respectively. We carried out a suite of sensitivity simulations using UI-WRF-Chem by excluding chemical boundary conditions or including MERRA-2 chemical boundary conditions. Our results show that using MERRA-2 data to provide chemical boundary conditions can help improve model simulation of surface PM concentrations and AOD. Some of the target areas have also experienced significant changes in land cover and land use over the past decade. Our case study over CHN-Beijing in July 2018 investigates the impacts of improved land surface properties with recent available MODIS land data on capturing the urban heat island phenomenon. Model-simulated surface skin temperature shows better agreement with MODIS observed land surface temperature. The updated soil NOx emission scheme in July 2018 also leads to higher NO2 vertical column density (VCD) in rural areas within the CHN-Beijing target area, which matches better with TROPOMI observed NO2 VCD. This in turn affects the simulation of surface nitrate concentration. Lastly, we conducted a case study over USA-LosAngeles to tune dust emissions. These examples illustrate the fine-tuning work conducted over each target area for the purpose of evaluating and improving model performance.