Particulate Pollution Research Articles

How does PM2.5 affect forest phenology? Integrating PM2.5 into phenology models for warm-temperate forests in China

Vegetation can regulate particulate matter (PM) through various mechanisms, such as facilitating the deposition of gases and particulates and purifying the air via photosynthesis. Conversely, PM directly damages leaves through dry deposition, while it also indirectly affects plant growth by altering weather conditions. However, the ways in which PM influence vegetation growth patterns, and the driving factors behind these impacts, remain unclear. In this study, we primarily focused on the start of the growing season (SOS) of warm-temperate zone forests in China with severe PM. SOS exhibited a trend of advancing at a rate of 0.15 days/yr during the study period from 2004 to 2022. We assessed the impact of satellite-derived fine PM (PM2.5) and coarser PM (PM10) on forest SOS across warm temperate forest regions in China using partial correlation analysis methods. After removing the effects of PM, we found that the correlation between temperature and SOS weakened. Additionally, PM exhibited a positive correlation with SOS in most pixels. Linear regression analysis revealed a significant negative correlation between relative humidity (RH) and the relationship between PM2.5 and SOS. However, in areas where RH exceeds 60.38%, this effect becomes unstable, presumably due to increased aerosol hygroscopicity or the saturation of aerosol particles. We also found that as road network density increased, the relationship between PM2.5 and SOS strengthened, whereas the impact of nightlight on this relationship was relatively weak. It is important to note that while the observed correlations reveal mechanisms by which PM2.5 affects SOS, they do not directly imply causation, as the complex interactions between environmental factors may influence these relationships. Finally, we incorporated PM2.5 into the phenology model and optimized its parameters using the least squares method, which improved the accuracy of SOS simulations and provided insights for predicting vegetation phenology in areas with severe PM pollution.

Fingerprinting to trace sources of suspended solids in the transport of heavy metals in urban stormwater runoff

Suspended solids are an important pollutant in urban stormwater runoff. Past studies have mainly focused on a single transport stage of pollutants, constraining source identification of suspended solids at the catchment scale. Therefore, identifying the sources of suspended solids in stormwater runoff for the formulation of effective pollution mitigation measures is an effective way to manage suspended solids pollution in receiving waters. Sediment source fingerprinting is a widely used technique to trace the sources of river sediments, which can accurately identify the source of sediment through widely used tracers. This study used six heavy metals including Cd, Cr, Ni, Cu, Zn and Pb as tracers to quantify the sources of suspended solids in stormwater runoff from urban catchments. The spatial and temporal distribution characteristics of suspended solids during stormwater transport were investigated. The study results showed that the concentration of suspended solids was the highest in road runoff and sewer flow, especially particles <44 μm. In addition, relatively large rainfall depth, high rainfall intensity and long antecedent dry periods can lead to higher concentrations of suspended solids in roof and road runoff whereas longer rainfall duration can result in more suspended solids in sewer runoff. Sediment source fingerprinting and principal component analysis confirmed that coarse (>105 μm) particles primarily originate from road deposited sediments (63.80%), while fine (<105 μm) particles primarily originate from stormwater grate sediments and soil. The outcomes derived can help to comprehensively understand the sources of suspended solids and provide guidance for the management of urban stormwater particulate pollution, as well as being a technical reference for pollutant source traceability in urban stormwater runoff.

Gaseous emissions from brake wear can form secondary particulate matter

Road traffic is an important source of urban air pollutants. Due to increasingly strict controls of exhaust emissions from road traffic, their contribution to the total emissions has strongly decreased over time in high-income countries. In contrast, non-exhaust emissions from road vehicles are not yet legislated and now make up the major proportion of road traffic emissions in many countries. Brake wear, which occurs due to friction between brake linings and their rotating counterpart, is one of the main non-exhaust sources contributing to particle emissions. Since the focus of brake wear emission has largely been on particulate pollutants, little is currently known about gaseous emissions such as volatile organic compounds from braking and their fate in the atmosphere. This study investigates the oxidative ageing of gaseous brake wear emissions generated with a pin-on-disc tribometer, using an oxidation flow reactor. The results demonstrate, for the first time, that the photooxidation of gaseous brake wear emissions can lead to formation of secondary particulate matter, which could amplify the environmental impact of brake wear emissions.

Aerosol type classification and its temporal distribution in Kanpur using ground-based remote sensing

Based on AERONET version 3 level 2 inversion products, we classify aerosol types and investigate their temporal distribution in the atmosphere using particle linear depolarization ratio (PLDR) and single scattering albedo (SSA) at the wavelength of 1020 nm over Kanpur. It is for the first time over the North Indian region the work has been emphasized. The remarkable findings over Kanpur station indicate that SSA and coarse-mode particles in the atmosphere increased with increasing PLDR at 440, 675, 870, and 1020 nm wavelengths. It is observed in the 2-dimensional histogram that the rate of occurrence of aerosols is high when the fine mode fraction (FMF) is high and the dust ratio (Rd) is low. The atmosphere of Kanpur is partially influenced by dust-dominated mixture (DDM), pollution-dominated mixture (PDM), and pure dust (PD) with 53% whereas, the rest of the dust-free pollution aerosols are 47%. The annual mean occurrence rate for different aerosol types is 5% for Strongly Absorbing (SA), 20% for Moderately Absorbing (MA), 19% for Weakly Absorbing (WA), 3% for Non-Absorbing (NA), 27% for DDM, 22% for PDM, and 4% for PD, ranging from January 2001 to January 2022. There is a variation in the distribution of various types of pollution particles, which is influenced by the changing seasons. The rate of occurrence of dust-free pollution aerosols is 47%, mostly observed throughout the post-monsoon and winter seasons. The PLDR values in the atmosphere of Kanpur are almost balanced equally because it is affected by both (dust and dust-free) pollution aerosols and the changes can be seen due to the frequent occurrence of dust storms and anthropogenic activities.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57µg/m3 and PM10 at 39.58µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Multimodal Hong Kong: A review of policies regarding soundscape and smellscape of Chinese temples

Many people in Hong Kong regularly visit Chinese temples, and see this not necessarily as a religious activity, but as a traditional practice. Temples represent the local intangible cultural heritage. The dense urbanity and mixed population characterise Hong Kong and make it valuable to study its sounds and smells. Most temples existed earlier than modern urban areas, which grew around them. The temple soundscape consists of drums, bells, coin donations, ventilation fans, as well as chanting, especially during festivals. The smellscape consists of ritual burning of incense and paper, fruit offerings, and building materials e.g., wood and cement. The burning causes particle pollution, health concerns, and residential nuisance, that may conflict with the wish for sustaining a valuable heritage. Smells and air quality have recently become debated, raising the question of the need for a specific regulatory framework. There is currently a lack of official provision that directly protects the sounds and smells of these temples in Hong Kong. This paper reviews the current state of Hong Kong policies in regard to soundscape and smellscape, with comparison to China and France, because of the latter's progressive agenda in preserving sensory heritage in the countryside.

Effect of Co-exposure to Additional Substances on the Bioconcentration of Per(poly)fluoroalkyl Substances: A Meta-Analysis Based on Hydroponic Experimental Evidence.

A consensus has yet to emerge regarding the bioconcentration responses of per(poly)fluoroalkyl substances under co-exposure with other additional substances in aqueous environments. This study employed a meta-analysis to systematically investigate the aforementioned issues on the basis of 1,085 published datasets of indoor hydroponic simulation experiments. A hierarchical meta-analysis model with an embedded variance covariance matrix was constructed to eliminate the non-independence and shared controls of the data. Overall, the co-exposure resulted in a notable reduction in PFAS bioaccumulation (cumulative effect size, CES = - 0.4287, p < 0.05) and bioconcentration factor (R2 = 0.9507, k < 1, b < 0) in hydroponics. In particular, the inhibition of PFAS bioconcentration induced by dissolved organic matter (percentage form of the effect size, ESP = - 48.98%) was more pronounced than that induced by metal ions (ESP = - 35.54%), particulate matter (ESP = - 24.70%) and persistent organic pollutants (ESP = - 18.66%). A lower AS concentration and a lower concentration ratio of ASs to PFASs significantly promote PFAS bioaccumulation (p < 0.05). The bioaccumulation of PFASs with long chains or high fluoride contents tended to be exacerbated in the presence of ASs. Furthermore, the effect on PFAS bioaccumulation was also significantly dependent on the duration of co-exposure (p < 0.05). The findings of this study provide novel insights into the fate and bioconcentration of PFAS in aquatic environments under co-exposure conditions.

Towards an improved wind effect assessment for asbestos abatement: A methodology for reduced-scale experiments

During building renovation or demolition, asbestos fibers can be released which can contaminate the environment, leading to potential occupational health and public health concerns. Strict asbestos abatement procedures and regulations are in place to mitigate this risk, which involve sealing the worksite and depressurizing it relative to the outdoor environment (i.e., indoor pressure lower than the outdoor pressure) using mechanical ventilation. However, the depressurization of the containment can be breached by the effects of the wind. Hence, it is essential to identify which wind conditions are problematic and how to prevent their effects. To conduct corresponding laboratory measurements, it is first required to scale down the building and its containment zone including a working mechanical ventilation system. This paper presents an innovative methodology for conducting reduced-scale experiments on buildings equipped with mechanical ventilation systems for contaminant containment. The methodology, here explained in the context of asbestos abatement, could also be applied to any other type of particulate pollutant. The methodology of this research study includes the design of a mechanical ventilation system for depressurization; a method to scale down the building and the ventilation system for testing in a wind tunnel; and 3D steady Reynolds averaged Navier-Stokes (RANS) simulations for optimizing the positioning of the ventilation components. The methodology is applied for a single-zone building including its ventilation system, which is found to achieve the targeted indoor depressurization of −40 Pa, making it ready for wind tunnel tests.

Analysis of the global burden of cardiovascular diseases linked to exposure to ambient particulate matter pollution from 1990 to 2019.

This research endeavors to scrutinize the temporal trends and global burden of cardiovascular diseases (CVDs) associated with ambient particulate matter (PM) pollution spanning from 1990 to 2019. Age-standardized death rates (ASDRs) and age-standardized disability-adjusted life years (DALYs) for CVDs, as well as their estimated annual percentage changes (EAPCs), were calculated using data from the Global Burden of Disease Study 2019 (GBD 2019). The global ASDR and age-standardized DALYs due to CVDs associated with PM pollution increased from 1990 to 2019, with a higher increase in males. The burden was higher among middle-aged and older adults. The ASDR and DALYs increased in low-Socio-demographic Index (SDI), low-middle-SDI, and middle-SDI countries, while they decreased in high-SDI countries. The highest burden was observed in Central Asia, North Africa, the Middle East, East Asia, and South Asia. The highest burdens were reported in Iraq, Egypt, and Uzbekistan at the national level. The burden of CVDs linked to PM pollution has grown significantly from 1990 to 2019, with variations across regions and countries, highlighting the need for targeted prevention and pollution management strategies.

A probabilistic framework for identifying anomalies in urban air quality data.

Just as the value of crude oil is unlocked through refining, the true potential of air quality data is realized through systematic processing, analysis, and application. This refined data is critical for making informed decisions that may protect health and the environment. Perhaps ground-based air quality monitoring data often face quality control issues, notably outliers. The outliers in air quality data are reported as error and event-based. The error-based outliers are due to instrument failure, self-calibration, sensor drift over time, and the event based focused on the sudden change in meteorological conditions. The event-based outliers are meaningful while error-based outliers are noise that needs to be eliminated and replaced post-detection. In this study, we address error-based outlier detection in air quality data, particularly targeting particulate pollutants (PM2.5 and PM10) across various monitoring sites in Delhi. Our research specifically examines data from sites with less than 5% missing values and identifies four distinct types of error-based outliers: extreme values due to measurement errors, consecutive constant readings and low variance due to instrument malfunction, periodic outliers from self-calibration exceptions, and anomalies in the PM2.5/PM10 ratio indicative of issues with the instruments' dryer unit. We developed a robust methodology for outlier detection by fitting a non-linear filter to the data, calculating residuals between observed and predicted values, and then assessing these residuals using a standardized Z-score to determine their probability. Outliers are flagged based on a probability threshold established through sensitivity testing. This approach helps distinguish normal data points from suspicious ones, ensuring the refined quality of data necessary for accurate air quality modeling. This method is essential for improving the reliability of statistical and machine learning models that depend on high-quality environmental data.

Variations of black carbon, particulate matter and nitrogen oxides mass concentrations in urban environment with respect to winter heating period and meteorological conditions

The atmospheric concentrations of particulate pollution are of great scientific concern due to their impact on both human health and environment. This study aimed to investigate the concentration of black carbon (BC), particulate matter with an aerodynamic diameter of less than 10 micrometres (PM10) and nitrogen oxides (NOx) at an urban background environment throughout the year, and understand the impact of winter heating and meteorology on its concentration level. The campaign covered heating and non-heating periods, from 1 June 2021 to 31 May 2022. During the heating period, the mass concentrations of BC, PM10 and NOx were 1.17, 24.9 and 19.4 µg m–3, respectively. The analysis revealed that the mass concentrations of BC and NOx were 1.9 and 1.4 times greater during the heating period, respectively, compared to the non-heating period. In contrast, PM10 remained almost constant during the heating (19.4 µg m–3) and non-heating periods (20.0 µg m–3). Throughout the year, the BC mass concentration was dominated by BCFF (71.2%) originating from fossil fuel combustion with a maximum (8.43 µg m–3) during the heating period. Moreover, wind speed presented a weak negative correlation with BC (r = –0.40), PM10 (r = –0.19) and NOx (r = –0.40) during the heating period.

Global and national burden of type 2 diabetes mellitus attributable to PM2.5 air pollution: An analysis of the GBD study from 1990 to 2019.

Epidemiological studies have established a link between air pollution and an elevated risk of type 2 diabetes mellitus (T2DM). This study aims to measure the impact of T2DM related to fine particulate matter (PM2.5) pollution by examining death rates and disability-adjusted life years (DALYs) from 1990 to 2019 in the United States of America. Using data from the Global Burden of Disease (GBD) database, we examined death and DALY rates per 100,000 populations in T2DM patients, specifically focusing on ambient particulate matter pollution (APMP) and household air pollution (HAP). We assessed average annual percentage change (AAPC) across various age and gender groups, states, and socio-demographic index (SDI) categories. Our findings reveal a significant decline in death rates and DALYs in the United States of America over the last 30 years, with more pronounced decreases among females and older adults. Despite national progress, state-level variations indicate complex interactions between environmental regulations, healthcare access, and socio-economic factors. Some states, such as Oregon, Idaho, and Alaska, exhibited increased AAPC. Our study emphasizes the need for targeted policies and interventions to reduce PM2.5 exposure and further address regional disparities, ensuring continued improvement in public health outcomes.

Impact of biomass burning on air quality: A case study of the agricultural region in South Korea

Various combustion processes occur concurrently during biomass burning events, emitting a complex mixture of particulate and gaseous pollutants into the atmosphere. These emissions undergo chemical transformations facilitated by factors such as solar radiation and cloud formation, thereby altering the composition of aerosols. Additionally, these pollutants can affect the region of origin and neighboring countries, presenting regional and global environmental challenges. Therefore, precise evaluation of the particulate and gaseous pollutants emitted during biomass burning is essential to formulate effective management strategies. This study aimed to assess the concentration and chemical characteristics of particulate matter emitted during biomass burning in South Korea. On June 7, 2021, a research flight was conducted utilizing a High Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and a Single Particle Soot Photometer (SP2) for airborne measurements over South Korea inland areas. For data analysis based on administrative regions, the flight path was divided into four major areas (Areas A, B, C, and D). During the research flight, evidence of biomass burning events was observed primarily in Area C. A positive matrix factorization (PMF) analysis categorized the organic aerosols (OA) into five factors: biomass burning OA (BBOA), hydrocarbon-like OA (HOA1, HOA2), low-oxidized oxygenated OA (LO-OOA), and more-oxidized OOA (MO-OOA). Across all areas, MO-OOA accounted for the highest proportion of aerosols, whereas BBOA dominated in Area C at 23.8%, indicating the significant influence of biomass burning in this region. Instead of running a PMF analysis with all measurement data, a BBOA formula was derived from the previous study and this one. This allows us to estimate BBOA concentration without running PMF.

Continuous self-circulating up-flow granular sludge fluidized bed process treating low-strength real municipal wastewater at high hydraulic loads

Conditions conducive to aerobic granular sludge (AGS) growth and maintenance are very difficult to realize in continuous-flow biological treatment processes. This study conducted a continuous-flow self-circulating up-flow granular sludge fluidized bed (Zier process) treating real urban wastewater approximately one year. The substantial self-circulating multiple times (RSCMT, 8–15 times) and up-flow velocity (8–15 m/h) generated by aeration, the only power equipment in Zier process, facilitated pollutant removal, particle granulation and stabilization. With hydraulic retention time of 5 h, RSCMT of 9.3–14.4 times and chemical oxygen demand (COD)/total nitrogen (TN) ratio of 5.9 ± 1.0, the effluent COD, ammonia nitrogen and TN were 28.6 ± 7.7, 1.1 ± 1.2, and 13.3 ± 1.7 mg/L, respectively. The median particle size was 150–250 μm and effluent suspended solids concentration was 33.4 ± 14.5 mg/L. It is unnecessary to set up sludge reflux which simplifies the subsequent mud-water separation facilities. The Zier process provides a new process structure for implementation of continuous-flow AGS process.

Toxicity prediction based on PAHs in fruits and vegetables − A machine learning approach

This study analyses the toxicity levels of fruits and vegetables by identifying the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in areas affected by industrial and vehicle pollution. The presence of PAHs on plant surfaces, caused mostly by air pollution particulate matter, raises serious concerns about the nutritional value of these foods. Traditional techniques for measuring PAH levels include Gas Chromatography/Mass Spectrometry (GC/MS) and High-Performance Liquid Chromatography (HPLC). Although successful, these procedures are expensive and time-consuming. Toxicity detection is often based on expert knowledge or experimental analysis relative to the European Food Safety Authority’s (EFSA) limits. In response to these challenges, this work uses artificial intelligence approaches to determine toxicity levels using 16 PAHs. Data on PAH concentrations in fruits and vegetables were collected from a variety of sources, classified as safe or harmful, and verified using statistical analysis. The validated dataset was subsequently classified using several machine learning techniques. The level of toxicity is scaled and compared to the expected outputs using the results obtained from the neural network. An experimental investigation validates the promising findings of level of toxicity classification using artificial intelligence approaches, which are then evaluated statistically. The study shows that machine learning algorithms obtained classification accuracies of over 90 %, including the evaluation of harmfulness. The accuracy for the two-class (Safe and Unsafe) category was 98.27 %, while the accuracy for the four-class (No harm, Low harm, Moderate Harm, and Severe harm) category was 98.68 %. These findings highlight the potential of artificial intelligence in improving food safety and public health by presenting an innovative multidisciplinary method to assessing the impacts of environmental pollutants on the edibility of fruits and vegetables in the context of climate change.

Trends in Lower Respiratory Infections Mortality in Children under 14 from 1990 to 2019: Analysis of the Global Burden of Disease Study 2019

BackgroundLower respiratory infections (LRIs) remain a leading cause of mortality among children, yet previous studies have primarily focused on children under 5 years of age as a whole, with limited research on more specific age groups. This study aimed to analyze the trends in mortality associated with lower respiratory infections (LRIs) among children under 14 years of age from 1990 to 2019, with particular attention to regional and age-specific disparities. MethodsWe collected annual lower respiratory infection (LRI) deaths and mortality rates for six age groups of children under 14 years from 1990 to 2019 using data from GBD 2019. Estimated annual percentage changes (EAPCs) were calculated by fitting a linear regression model to quantify temporal trends. Additionally, EAPCs for 12 risk factors by Socio-Demographic Index（SDI） and age group were calculated to assess their impact on changes in LRI mortality. ResultsGlobally, LRI-related deaths among children under 14 years decreased by 68.81%, from 2,290,115 (95% UI 2,001,328 to 2,687,160) in 1990 to 714,315 (95% UI 588,975 to 875,975) in 2019. The most substantial reduction was observed in East Asia, with an EAPC of -9.68 (95% CI -9.98 to -9.38). The highest mortality occurred in the 28 to 364 days age group, with significant risk factors including child wasting (26.81%), household air pollution from solid fuels (16.14%), and low birth weight (10.15%). In the low and lower-middle SDI regions, there was an upward trend in mortality due to ambient particulate pollution among children aged 10 to 14 years. ConclusionDespite a significant global decline in LRI mortality among children under 14 years from 1990 to 2019, regional disparities persist. Addressing these inequalities and considering the developmental stages of children are crucial for further reducing child mortality rates.

Tire and road wear particles in infiltration pond sediments: Occurrence, spatial distribution, size fractionation and correlation with metals

Stormwater systems, such as infiltration ponds or basins, play a critical role in managing runoff water and reducing particulate pollution loads in downstream environments through decantation. Road runoff carries several pollutants, including trace metals and tire and road wear particles (TRWP). To improve our understanding of infiltration ponds as regards TRWP and their capacity to reduce TRWP loads, we have studied the occurrence, spatial distribution and size distribution of TRWP, as well as their relationship with metals, in considering the input of metals as tire additives, in the sediments of an infiltration pond located along the Nantes urban ring road (Western France), which happens to be a high-traffic roadway site. The sediment was analyzed using pyrolysis coupled with gas chromatography–mass spectrometry to determine the polymeric content of tires, specifically in quantifying the styrene-butadiene rubber (SBR) and butadiene rubber (BR) pyrolytic markers. By applying an SBR + BR-to-TRWP conversion factor, the results showed significant TRWP contamination, up to 65 mg/g, with a spatial enrichment from the entrance to the overflow section of the pond. Size fractionation revealed a bimodal distribution, indicating two distinct types of TRWP. The first type is characterized by small diameters (63–160 μm), suggesting the presence of TRWP less integrated with mineral and organic particles. The second type, characterized by larger diameters (200–500 μm), suggests a more pronounced integration with these same mineral and organic particles. A significant positive correlation between TRWP and metals (As, Cd, Cr, Cu, Li, Mo, Ni, Sb, V, Zn) was found (r > 0.739, p < 0.05). This correlation implies that TRWP and/or their associated phases may act as an indicator of metal contamination in the pond sediments. Lastly, a mass balance between TRWP inputs and the amount retained in the sediments underscores the role of infiltration ponds as “sinks” for TRWP.

Exploring the association between knee osteoarthritis outpatient visits and Asian dust storms: a time-series analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants. However, the relationship between ADS and knee OA remains insufficiently explored. This study investigates the association between ADS occurrences and knee OA outpatient visits from January 2006 to December 2012, aiming to understand the potential health impacts of dust storms on OA patients. Using data from the National Health Insurance Research Database (NHIRD), the Taiwan Environmental Protection Agency (TEPA), and the Taiwan Central Weather Bureau, we conducted a time-series analysis employing the autoregressive moving average with exogenous variables (ARMAX) model. This approach accounted for daily outpatient visits related to knee OA, ADS events, and various environmental and meteorological factors. The results revealed a significant increase in knee OA outpatient visits on days immediately following ADS events, with peaks observed one to two days after the event. This increase was most pronounced among females, individuals aged 61 and above, and residents in the western regions. The study demonstrates an association between ADS and increased knee OA outpatient visits, highlighting the need for public health strategies to mitigate the health impacts of dust storms.

Short-term exposure to PM2.5 constituents, extreme temperature events and stroke mortality

BackgroundFine particulate matter (PM2.5) pollution and extreme temperature events (ETEs) are main environmental threats to human health. Elevated stroke mortality has been growingly linked to PM2.5 mass exposure, while its relationship with PM2.5 constituents was extensively unstudied across the globe. Additionally, no prior assessments have investigated the interactive effects of PM2.5 constituents and ETEs on stroke mortality. MethodsProvince-wide records of 320,372 stroke deaths collected in eastern China during 2016–2019 were analyzed using an individual-level time-stratified case-crossover design. Daily gridded estimates of PM2.5 mass and its major constituents (i.e., black carbon [BC], organic matter [OM], ammonium [NH4+], sulfate [SO42‐], and nitrate [NO3‐]) were assigned to stroke cases on case days and control days at the residential address. We assessed 12 ETEs defined by multiple combinations of air temperature thresholds (2.5–10th percentiles for cold spell, 90–97.5th percentiles for heat wave) and durations (2–4 days). Conditional logistic regression model was applied to investigate associations of short-term exposure to PM2.5 constituents and ETEs with stroke mortality. Odds ratio and its 95% confidence interval (CI) were assessed for an interquartile range (IQR) increase in each PM2.5 constituent and on ETEs days compared with non-ETEs days. Additive interactive effects were quantitatively evaluated via relative excess odds due to interaction (REOI), attributable proportion due to interaction (AP), and synergy index (SI). ResultsElevated overall stroke mortality was significantly related to PM2.5 constituents, with the largest odds observed for NO3‐ (1.04, 95% CI: 1.03–1.04, IQR = 11.25 μg/m3), followed by OM (1.03, 1.03–1.04, IQR = 7.97 μg/m3), NH4+ (1.03, 1.02–1.04, IQR = 6.66 μg/m3), BC (1.03, 1.02–1.03, IQR = 1.41 μg/m3), and SO42‐ (1.03, 1.02–1.03, IQR = 6.67 μg/m3). Overall, higher risks of stroke mortality were identified in analyses using more rigorous thresholds and lengthened durations of ETEs definitions, ranging from 1.19 (1.17–1.21) to 1.55 (1.51–1.60) for heat wave, and 1.03 (1.02–1.05) to 1.11 (1.08–1.15) for cold spell, respectively. We observed consistent evidence for the synergistic effects of heat wave and PM2.5 constituents on both ischemic and hemorrhagic stroke mortality, where compound exposures to heat wave and secondary inorganic aerosols (i.e., NO3‐, SO42‐, and NH4+) posed greater increases in risk (0.23< REOI <0.81, 0.16< AP <0.39, and 2.63< SI <8.19). ConclusionsShort-term exposure to both PM2.5 constituents and ETEs were associated with heightened stroke mortality, and heat wave may interact synergistically with PM2.5 constituents to trigger stroke deaths.

Long term analysis of air quality parameters for Ludhiana, India: sources, trends and health impact.

Ludhiana, a pollution hot spot in North India, has seen a rapid deterioration in air quality over the years due to urbanization and industrialization. This study interprets the variations of particulate matter (PM) and gaseous pollutants (Nitrogen oxide, Nitrogen dioxide, NOX, Sulphur dioxide, Carbon monoxide, Benzene, Toluene, Ozone, and Ammonia) for the data observed from 2017 to 2023 in Ludhiana. This also covers the analysis focused on capturing the changes that occurred at the times of lockdown imposed during the Coronavirus Disease (COVID-19). The maximum 24-h averaged mass concentration values exceeded the National Ambient Air Quality Standards (NAAQS) of 100µg/m3 for PM10 concentration and 60µg/m3 for PM2.5 concentration in 2018 by the factor of 5 and 8. With the onset of the COVID-19 lockdown in 2020year, PM10 and PM2.5 reached the minimum level while CO, T, O3, and NO2 increased by the factor of 3.9, 1.9, 1.4, and 1.3 from their previous year. This NO2 is a precursor of ozone formation, a higher NO2 to NO ratio observed during the lockdown, confirms the role of nitrogen compounds in the higher ozone formation rate. Based on the NO2/NO ratio, the probability rate of ozone formation determined using survival analysis is observed to be 94% from 2017 to 2023. The local sources' contribution to these air pollutants during Pre-Lockdown, Lockdown, and Post-Lockdown are analyzed using principal component analysis. The impact of the lockdown on ozone concentration sources has been observed. During the Pre- and Post-Lockdown phases, three sources (PC1, PC2, and PC3) were positively identified. Ozone levels are linked to PC3 in these phases, but during the lockdown, a negative loading in PC3 and positive loadings in PC1 and PC2 indicate a decrease in ozone from reduced emissions and an increase from secondary reactions involving nitrogen compounds. Moreover, the Toluene to Benzene concentration ratio is > 2, indicating the source of their origin from industrial emission or other non-traffic sources. Health assessment for the years 2017-2019 reveals a significant decrease in the number of cases of all-cause mortality, ischemic heart disease, stroke, and chronic obstructive pulmonary disease associated with reducing PM2.5 concentrations to national and international standards.