Air Quality Atmosphere and Health最新文献

As a light-absorbing component of organic aerosols, Brown Carbon (BrC) significantly influences radiative forcing and regional air quality. To investigate the optical properties, seasonal variations, and sources of BrC in an urban environment, this study conducted online measurements using an AE33 aethalometer in Tianjin, China, during the non-heating (September 2023) and heating (December 2023) seasons. Positive Matrix Factorization (PMF) was employed to apportion the absorption coefficient of BrC (Abs) and its mass absorption efficiency (MAE). Results showed that the average BrC Abs at 370 nm in the heating season (27.02 ± 23.15 Mm⁻¹) was 4.3 times higher than in the non-heating season (6.31 ± 5.32 Mm⁻¹), accounting for 33.8% and 13.2% of the total aerosol absorption, respectively. The MAE of BrC increased from 3.03 ± 3.69 m²/g (non-heating) to 8.37 ± 5.84 m²/g (heating), indicating enhanced light-absorbing capacity per unit mass. Diurnal analysis revealed that secondary BrC accounted for 42% of total BrC absorption in non-heating season, but was strongly correlated with primary emissions (r = 0.95 with BC) in heating season. Correlation analyses further revealed distinct formation mechanisms: secBrC was moderately correlated with NO₂ at night (r = 0.45) in the non-heating season, but with ammonium/nitrate species (r = 0.29–0.41) in the heating season. PMF identified secondary processes (42%) and traffic (47.3%) as major sources in non-heating season, while coal combustion (67.5%) and biomass burning (14.0%) dominated in heating season. These findings underscore that heating activities markedly enhance BrC radiative effects, while secondary formation prevails in the non-heating season, providing critical insights for targeted mitigation of carbonaceous aerosols in northern Chinese cities.

Sand and dust storms (SDSs) pose a significant threat to air quality, health, and transportation safety across Northern Africa (NA). This study adopts an impact-based, dust-induced visibility (V) threshold to categorize dust haze (DH) into Thick (TDH; V ≤ 1000 m), Moderate (MDH; 1000 m < V≤ 5000 m), and Light (LDH; 5000 m < V< 10,000 m) based on surface observations from stations across NA from 2013 to 2024. We analyzed the spatiotemporal patterns of DH and quantified the skill of the World Meteorological Organization Sand and Dust Storm Warning Advisory and Assessment System (WMO SDS-WAS) Multi-Model Ensemble (MME) dust forecast products (DFPs) in simulating these patterns. The results revealed distinct seasonal patterns of DH, with prevalent TDH downwind of the Bodélé depression and towards the West African coast in winter, while TDH is more spatially homogenous in summer. A marked absence of DH south of 10°N in summer is attributed to the moist monsoon incursion. Overall, DH showed moderate correlation with MME DFPs, notably stronger with dust surface concentration (SCON_DUST; ρ≈-0.4), compared to dust optical depth (OD550_DUST; ρ≈-0.3). The correlations exhibit variability across stations, seasons, and forecast lead time, being strongest in winter. Leveraging this relationship, we developed an empirical model to calibrate DFPs into operational visibility alerts. This study provides a valuable step toward integrating dust forecasts into early warnings for improved public safety.

Accurate prediction of carbon emissions is essential for enabling government agencies to set scientific emission reduction targets and formulate targeted green development strategies. To enhance the accuracy and reliability of carbon emission forecasting, this study proposes a deep learning framework based on multi-dimensional feature fusion. By leveraging feature enhancement techniques, a multi-dimensional feature space is constructed, integrating the local feature extraction capability of Convolutional Neural Network(CNN) with the temporal dependency modeling strength of Bidirectional Long Short-Term Memory (BiLSTM). This enables collaborative spatio-temporal feature extraction, while an attention mechanism dynamically allocates weights to key time-series nodes. Using China’s daily carbon emission data from 2019 to 2025 as the research subject, a multi-dimensional feature analysis framework is established. Through feature engineering, three types of variables, temporal, statistical, and time-series features, are systematically extracted to preliminarily explore emission fluctuation patterns and periodic trends. The integrated feature set is then fed into the hybrid model, and feature perturbation analysis is applied to quantify the contribution of each feature. Experimental results highlight the central role of time-series differences, rolling statistics, and lag features in carbon emission prediction. To validate the model’s effectiveness, a comparative experiment is designed using CNN, BiLSTM, and BiLSTM-Attention as benchmark models under identical test conditions. The proposed model achieves a Root Mean Square Error of 0.865 on the test set, representing an average reduction of 30% compared to baseline models, confirming its superior performance. Based on the findings, strategic recommendations are offered to guide optimized carbon emission control.

Meteorological variables critically influence air quality by regulating pollutant dispersion, accumulation, and transformation. This study compares meteorological drivers and air quality parameters at two Indo-Gangetic Plain sites urban IIT BHU (Varanasi) and semi-urban REC (Azamgarh) from October 2023 to September 2024. The analysis integrates ground-based monitoring, Aerosol Optical Depth (AOD), back-trajectories, Seasonal-Trend Decomposition (STL), machine learning, and health-risk modelling to capture multi-scale pollution processes. Despite being only ~ 80 km apart, the sites exhibit distinct regimes. REC recorded higher annual mean NO₂ (43.9 µg/m³) and SO₂ (25.8 µg/m³) than IIT BHU (10.6 µg/m³; 3.4 µg/m³), whereas IIT BHU showed higher mean AQI (185 vs. 147). Episodic PM_2.5 and PM₁₀ approached the upper detection limit (1000 µg/m³) at REC during pre-monsoon dust intrusions, while IIT BHU peaks stayed below 400 µg/m³, mainly during winter stagnation. Correlation analysis confirmed strong PM_2.5–PM₁₀ coupling (r = 0.90 at IIT BHU; r = 1.00 at REC) and negative associations with wind speed and temperature, indicating contrasting stagnation and transport influences. Satellite validation showed better AOD–PM_2.5 performance at IIT BHU (R² = 0.46) than REC (R² = 0.41). Prediction skill was higher at IIT BHU (R² = 0.92) than REC (R² = 0.80). Health-risk modelling indicated greater per-capita risk at REC (55 vs. 52 IHD deaths/100k). This integrated dual-site framework reveals the coexistence of stagnation- and transport-driven pollution regimes in eastern Uttar Pradesh, underscoring the need for site-specific monitoring and targeted mitigation across the Indo-Gangetic Plain with relevance to other densely populated, dust-prone regions.

This study examines the decadal trends, source apportionment, and environmental implications of sulphur particulates in West Africa over the period from 1980 to 2024, including sulphur dioxide (SO₂), sulphate aerosols (SO₄²⁻), dimethyl sulfide (DMS), and fine particulate matter (PM_2.5). Using NASA’s MERRA-2 reanalysis data and chemometric techniques, we analyzed spatiotemporal variability, oxidation pathways, and source contributions. SO₂ concentrations increased from 0.19 µg/m³ in the 1980s to 0.26 µg/m³ in the 2020s, while SO₄²⁻ ranged from 0.46 to 0.65 µg/m³. DMS remained relatively low (0.0015–0.0071 µg/m³) but showed variability linked to marine processes. PM₂.₅ levels were consistently high, peaking at 72.8 µg/m³ in the 2000s. Source apportionment revealed anthropogenic bins (EM002, EM003) contributing over 59% across decades, with dry deposition and convective scavenging accounting for > 20%. Residence time analysis indicated SO₂ removal was faster than SO₄²⁻, and Spearman correlations outperformed Pearson in capturing monotonic relationships. These findings highlight significant temporal variations driven by industrialization, biomass burning, and marine emissions, underscoring the need for integrated air quality management and policy interventions in West Africa.

Graphical Abstract

Fine particulate matter (PM_2.5) is a well-known risk factor for premature mortality; however, the relative effect size varies between different studies. The current study aims to estimate the concentration–response function (CFR) between all-cause mortality and PM_2.5 while adjusting for differences in population characteristics and exposure assessment method. Separate functions by exposure assessment method by dispersion, land use regression (LUR), and hybrid models as well as monitoring station measurements and by model resolution will be provided. We used the same keywords as those in the review by Chen and Hoek (2020), which included studies up to October 2018. Our updated search covered the period from July 1, 2018, to May 15, 2023, to include more recent publications. A random-effects meta-regression model was developed using the metafor package in R to account for variations in exposure estimates. Exposure assessment methods were categorized into Monitoring, Land-Use Regression (LUR), Dispersion Modeling, and Hybrid approaches, and spatial resolution was classified as high (≤ 1 km) or low (> 1 km). We evaluated linear, logarithmic, inverse, and inverse square root transformations as potential parametric forms of the CRF and selected the one that provided the best fit according to the Akaike Information Criterion (AIC). Additionally, spline-based modeling was employed to test for deviations from this parametric forms in the CFR. The analysis identified 68 eligible studies, incorporating diverse exposure assessment methodologies and geographic regions. In the fully adjusted model, the relative risk (RR) per 10 µg/m³ higher PM_2.5 concentration at a mean exposure of 10 µg/m³ was 1.22 (95% CI: 1.02–1.47) and at a mean exposure of 17.85 µg/m³ 1.15, (95% CI: 0.97–1.36). In the subgroup analysis, the best-fitting functional form for the CRF varied by exposure assessment method: linear for monitoring, logarithmic for LUR, and inverse for dispersion and hybrid models. For resolution, logarithmic fit best for low-resolution models, and inverse square root for high-resolution models. This study underscores the role of modeling choices in quantifying PM_2.5-related health risks. Our analysis offers an updated increased CRF, including the recent evidence, for use in global health risk assessments of particulate air pollution.

Classroom air quality (CAQ) matters greatly for safeguarding children’s health and ensuring a safe learning environment. This study assessed CAQ across 55 schools in Dhaka city, focusing on fine particulate matter (PM_2.5) and heavy metals (HMs) to identify factors that influence classroom PM_2.5 levels and to estimate HMs associated health risks. PM_2.5 was monitored continuously for the entire school day (i.e., 5–7 h) in a single classroom at each school, and settled dust was sampled from 15 classrooms across 15 schools. Using a stepwise multiple linear regression model, the association between classroom factors and classroom PM_2.5 levels was investigated. The result showed, average PM_2.5 concentration in the classroom was 50.5 ± 26.26 µg/m³, over three times above the WHO’s 24-h guideline (15 µg/m³) for ambient air quality. Stepwise multiple linear regression indicated outdoor PM_2.5 (R² = 0.10), board type (R² = 0.10), traffic near school (R² = 0.14), and distance from the major road (R² =0.10) were significant predictors (p < 0.05) of indoor CAQ (full model R²= 0.51). The HMs contents identified through Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) showed Zn (766.81 ± 526.51 mg/Kg) had the highest concentration, followed by Mn (388.78 ± 211.84), Cu (140.73 ± 131.94), Pb (87.18 ± 192.5), Cr (47.06 ± 25.66), Ni (41.78 ± 23.8), As (8.25 ± 6.04), Cd (2.72 ± 1.51). The hazard index (HI) of selected HMs was below 1, and the carcinogenic risk (CR) was found below the safe limit (10^− 6 to 10^− 4) for both children and adults. Findings from this study will serve as evidence to inform policy and school-level actions for ensuring safer classrooms and children’s health.

Current understanding of how airborne particulate pollutants of varying sizes and their chemical constituents relate to conjunctivitis is limited. The aim of this study was to examine the relationship between daily exposure to particulate pollution and conjunctivitis in southeastern coastal China from 2013 to 2023. Data were collected from ten cities on conjunctivitis outpatient visits, air pollution levels, and meteorological conditions. We used conditional logistic regression models to estimate the city-specific effects of particulate matter (PM) of varying diameters (PM_2.5, PM_c, PM₁₀) and five major chemical constituents of PM_2.5 [black carbon (BC), ammonium (NH₄⁺), nitrate (NO₃⁻), sulfate (SO₄²⁻), and organic matter (OM)] on conjunctivitis outpatient visits. Overall estimates were derived through a meta-analysis. A total of 1,023,905 conjunctivitis outpatient visits were included. For each 10 µg/m³ increment in daily mean levels of PM_2.5, PM_c, PM₁₀, BC, OM, SO₄²⁻, NO₃⁻, and NH₄⁺, the risk of conjunctivitis increased by 0.5%, 0.7%, 0.4%, 16.0%, 3.1%, 2.6%, 2.1%, and 3.5%, respectively. These associations did not differ significantly by sex, but were stronger in children under 6 years old (OR = 1.008, 95% CI: 1.003 to 1.013 for PM) and more notable in the warm season (OR = 1.007, 95% CI: 1.003 to 1.012 for PM). In conclusion, our results suggest a link between both the particle size and chemical composition of PM and conjunctivitis. This finding underscores the necessity of directing pollution control efforts specifically towards the most harmful particle sizes and components to prevent conjunctivitis.

Urban air quality in megacities such as Delhi is strongly modulated by meteorological variability, yet these interactions remain insufficiently represented in predictive modelling frameworks. This study evaluates the influence of key atmospheric parameters on PM2.5 concentrations using a benchmarking approach that integrates geo-spatial datasets with linear and non-linear machine learning models. Monthly observations from two representative monitoring locations, an industrial site (Bawana) and an urban-residential site (Okhla), selected through a network-wide parametric assessment were analysed to capture spatially distinct pollution-meteorology relationships. Multiple Linear Regression (MLR) was employed as a baseline, while a suite of Artificial Neural Network (ANN) architectures with varying complexity and regularization strategies were developed and optimized through systematic hyperparameter tuning; like the standard three-layer model; two-hidden layers; reduced-complexity with dropout regularization; and with batch normalization. Model evaluation indicated that ANN-based approaches consistently outperform linear regression, achieving higher explanatory power (R² generally > 0.85) and lower prediction errors (RMSE typically reduced by ~ 15–25%) across both Bawana and Okhla stations. These improvements reflect the ability of ANN models to capture non-linear processes governing pollutant dispersion, secondary aerosol formation, and meteorological modulation, particularly in dense urban environments. Site-dependent variations further emphasize the influence of localized emissions and micro-meteorology on the predictive skill. Overall, the findings demonstrate that tailored deep learning frameworks provide a robust and scalable approach for PM2.5 prediction and can meaningfully support anticipatory air-quality management and evidence-based policy interventions in Delhi.

This study investigated the exposure to fine particulate matter (PM_2.5) at residential dwellings in Dhaka, Bangladesh, with the goal of identifying its sources, and estimating the associated health hazards. Indoor PM_2.5 sampling was conducted in 6 households of urban Dhaka using a mini volume sampler. Atomic absorption spectroscopy was used to quantify six heavy metals in PM_2.5 loaded filters. The 24- hour average PM_2.5 concentration in Dhanmondi, Mirpur, Cantonment, Chankharpul, Uttara, and Bashundhara households were 123.0 ± 48.2, 123.0 ± 17.7, 96.3 ± 17.7, 96.3 ± 13.6, 84.8 ± 6.67, and 77.1 ± 6.68 µgm⁻³, respectively, all of which exceeded the WHO threshold. Average concentration of heavy metals followed the order: Zn (2201.0 ± 967.0 ngm⁻³) > Fe (1489.0 ± 955.0 ngm⁻³) > Pb (288.0 ± 137.0 ngm⁻³) > Mn (182.0 ± 365.0 ngm⁻³) > Cu (109.0 ± 92.7 ngm⁻³) > Cr (73.1 ± 49.8 ngm⁻³). Enrichment factor analysis suggested anthropogenic emission of Cr, Pb, Cu, and Zn, whereas Mn and Fe probably had crustal origin. Four sources of indoor PM_2.5 were resolved by Positive Matrix Factorization- Zinc source (44.6%), mixed source (resuspended dust and fugitive lead) (32.9%), crustal sources (17.6%), and vehicular emission (4.8%). Non- carcinogenic risk among children was 2.18 times higher than that among adults. The total cancer risk was 5.18 × 10^–4, implying that 1 in 1930 individuals in Dhaka might develop cancer in his lifetime. Since indoor air quality in Dhaka households was severely compromised, mass awareness should be raised, and policymakers should enact strict regulations to encourage resilient building designs.