Search Results (776)

This study applies a Long Short-Term Memory (LSTM) model to predict key air pollutants, i.e., sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM_2.5), as well as the Air Quality Index (AQI) across South Africa using satellite-derived observations. The analysis focuses on comparing original pollutant fields with model-generated predictions for two consecutive days, highlighting both spatial patterns and predictive performance. Results reveal a persistent and intense pollution hotspot over the Mpumalanga Highveld, driven by coal-fired power generation and industrial activities. Elevated pollutant concentrations in this region translate into AQI levels ranging from Unhealthy to Very Unhealthy, while most other parts of the country remain within the Good category. Spatial comparison between original and predicted fields shows strong agreement, with only minor deviations in areas characterized by steep emission gradients and localized plumes. Quantitative evaluation using RMSE (0.020390) and MSE (0.000416) confirms the high accuracy of the predictive model, with error values remaining extremely low across all pollutants and AQI outputs. PM_2.5 exhibits the smallest errors (MSE = 4.230169 × 10⁻⁶), while slightly higher values for SO₂ (MSE = 2.628 × 10⁻⁴) and NO₂ (MSE = 1.39541 × 10⁻⁴) reflect the difficulty of capturing sharp spatial transitions associated with point-source emissions. Despite these localized discrepancies, the model demonstrates robust skill in replicating both pollutant magnitudes and AQI classifications. Overall, the findings indicate that machine-learning approaches offer a reliable, high-resolution tool for air-quality prediction in South Africa and have strong potential for supporting operational forecasting, exposure assessment, and environmental policy development. Full article

Mitigating nitrogen dioxide (NO₂) pollution is a critical objective for enhancing urban environmental quality. The spatial structure of urban tree canopies plays a crucial role in influencing NO₂ diffusion and deposition. However, previous studies have focused mainly on the linear relationships between two-dimensional green spaces and NO₂, while the associated nonlinear relationships and threshold effects of three-dimensional urban tree canopy (UTC) spatial structure remain underexplored. To address this gap, we leveraged 1 m resolution satellite-derived data and explainable machine learning (XGBoost, SHAP, PDP) to examine the nonlinear influences and threshold effects of three-dimensional UTC spatial structures on NO₂ in Shenzhen. The results revealed that urban tree canopy spatial structure is associated with NO₂ concentrations. Among the key metrics, the two-dimensional canopy coverage ratio (CCR) emerged as the primary canopy-related correlate of lower NO₂ concentrations, while three-dimensional vertical structure metrics, particularly canopy height variability (CHV) and standard deviation of canopy height (SDCH), acted as critical secondary correlates in modulating the spatial distribution of pollutants. Based on these relationships, we identified potential threshold ranges for key metrics by comparing mathematically identified inflection points with practical urban planning constraints. In summary, this study advances the spatial analysis of “green spaces-NO₂” interactions from a two-dimensional to a three-dimensional perspective. Our findings could provide quantitative guidance for optimizing green space structure in high-density urban areas to inform strategies potentially associated with improved NO₂ outcomes. Full article

The large-scale mobility restrictions implemented worldwide in response to the COVID-19 (SARS-CoV-2) pandemic led to short-term reductions in anthropogenic emissions, providing an opportunity to explore atmospheric pollutant responses to large-scale changes in human activity and mobility patterns. Although numerous studies have reported air quality improvements during lockdowns, most rely on ground-based monitoring networks and focus on developed regions, leaving gaps in less-studied areas such as Central America. This study evaluates spatiotemporal changes in tropospheric nitrogen dioxide (NO₂) across Central America before, during, and after COVID-19 lockdowns using satellite-based remote sensing. High-resolution NO₂ vertical column density (VCD) data from the TROPOMI instrument onboard Sentinel-5P were processed using Google Earth Engine. Percentage variations were calculated using the March–May 2020 lockdown period as a reference within the 2019–2021 analysis period. Results indicate reductions in NO₂ across several high-density departments, particularly in Guatemala, El Salvador, and Honduras, with decreases of 20–30% and localized negative variations below −40%. In contrast, Nicaragua exhibited comparatively limited changes, while a gradual recovery in NO₂ concentrations was observed during 2021. The observed patterns suggest a potential association between NO₂ variability and changes in anthropogenic activity during the COVID-19 period, while also highlighting the importance of considering meteorological influences in regional atmospheric assessments. The results further demonstrate the potential of cloud-based Earth observation platforms for atmospheric monitoring in data-scarce tropical regions. Full article

Tropospheric ozone (O₃) is a critical air pollutant that poses significant risks to human health and ecosystems. While previous studies have primarily focused on O₃ changes in Eastern China, limited attention has been given to Northwest China, where fragile but ecologically important systems may be vulnerable to O₃ pollution. The temporal evolution and driving mechanisms of surface O₃ in this region remain poorly understood. Using the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) datasets and simulations from the Community Atmosphere Model with Chemistry (CAM-Chem), we identified a significant increase in summer surface O₃ concentrations across Northwest China from 1980 to 2020, with the most pronounced rise occurring during 1993–2010. This period accounts for the majority of the long-term upward trend, despite relative declines before and after. The increase in O₃ during 1993–2010 is primarily attributed to rising surface temperatures, which reduce hydroperoxyl radical (HO₂) concentrations and enhance nitrogen dioxide (NO₂) production, leading to elevated nitrogen oxides (NO_x) levels and promoting O₃ formation. The warming trend is closely associated with a concurrent decrease in low cloud cover, which increases surface shortwave radiation and further contributes to surface warming. Further investigation reveals that warming sea surface temperature (SST) in the North Pacific influence atmospheric circulation through wave train processes, amplifying the regional geopotential height field. These circulation changes reinforce the reduction in low cloud cover and the associated increases in surface temperature and O₃ concentrations over Northwest China. The decadal variability of North Pacific SST may therefore serve as an important indicator of long-term surface ozone variability in this region. Full article

To better understand the characteristics and causes of acid rain pollution in Huangshan City, China, in the context of reduced atmospheric pollutant emissions, this study systematically analyzes precipitation monitoring data from Huangshan City for the period 2013–2025. The analytical methods included volume-weighted mean, neutralization factor, and linear regression analysis. The results indicate that, with 2017 as a turning point, acid rain in Huangshan City transitioned from high-level fluctuations to a stabilization phase at medium-to-low levels. However, the annual mean pH remained below 5.6, indicating that the acid rain problem persists. Regarding pollutant emission reductions, sulfur dioxide (SO₂) control has achieved significant results, but nitrogen oxide (NO_x) pollution remains prominent due to factors such as a sharp increase in vehicle ownership. Analysis of the chemical composition of precipitation shows that the SO₄²⁻/NO₃⁻ ratio decreased from 4.09 to 0.92, and the acid rain type has shifted from sulfate-dominated to mixed sulfate-nitrate-dominated. In precipitation, highly specific ion pairings are observed: Ca²⁺ with SO₄²⁻ (r = 0.989) and NH₄⁺ with NO₃⁻ (r = 0.839). These two ion pairs together account for 81.4% of the total cations, forming two independent neutralization mechanisms—below-cloud and in-cloud—which explains the relative stability of precipitation pH despite a decline in total ion concentration. Furthermore, interannual variability in precipitation amount, particularly extreme wet events, is a key external factor driving fluctuations in acid rain frequency under stable emission conditions. The dominant driver of acid rain frequency variability has shifted from emission-dominated to precipitation-dominated. Full article

Nitrogen oxides (NO_x), comprising nitric oxide (NO) and nitrogen dioxide (NO₂), are key precursors of atmospheric nitrate, a major component of fine particulate matter (PM_2.5) that critically affects air quality, human health, and ecosystems. Emission inventories provide detailed spatial and temporal information on NO_x sources, while stable isotope techniques offer an additional constraint for source apportionment. Here, we incorporated stable nitrogen isotopes (¹⁴N, ¹⁵N) into the widely used Multi-resolution Emission Inventory for China (MEIC) over South China, with a focus on the Pearl River Delta (PRD) region, one of the most highly urbanized and industrialized regions in China, using an isotopic mass–balance model. The 2008 MEIC inventory indicated that NO_x emissions across South China were spatially heterogeneous, dominated by transportation sources, and concentrated mainly in the PRD and other urban clusters. We then compared the simulated isotopic composition of emitted NO_x with atmospheric measurements to assess the role of emission sources in controlling atmospheric nitrate (NO₃⁻). The simulated δ¹⁵N(NO_x) values were found to generally underestimate the observed δ¹⁵N(NO₃⁻) values. This discrepancy highlights the need for future ¹⁵N-enabled air quality modeling to better represent both source contributions and atmospheric processing, thereby improving source apportionment, emission inventory evaluation, and our understanding of reactive nitrogen cycling. Full article

With the sustained industrial development, air pollution remains a prominent environmental challenge in North China. As a key atmospheric contaminant, nitrogen dioxide (NO₂) is closely associated with significant adverse impacts on both ecological systems and public health. However, existing research regarding the factors related to NO₂ column concentration and the comparative strength of these associations remains limited. To address this research gap, this study employs TROPOMI satellite-based NO₂ data and six categories of influencing factors (meteorology, population density, vegetation coverage, etc.) to characterize the spatiotemporal patterns and the statistical relationships between NO₂ column concentrations and various influencing factors in North China from 2019 to 2023. The results indicate that elevated NO₂ column concentrations are primarily concentrated in central North China, including northern Henan, southern Hebei, and central–western Shandong. During 2019–2023, the regional NO₂ column concentration displayed an overall decreasing trend, accompanied by distinct seasonal variations: peaking in winter, moderate in autumn, and reaching the minimum in summer. Among the evaluated factors, temperature exhibited the strongest correlation with NO₂ variations, followed by surface net solar radiation and Normalized Difference Vegetation Index (NDVI). The relationship between wind and NO₂ was found to vary according to direction, speed, and regional topography. In addition, population density showed a prominent positive association with NO₂ vertical column density. This study identifies key factors linked to NO₂ variability, thereby providing methodological and empirical support for relevant studies in other regions. Full article

Accurate monitoring of nitrogen oxides (NOx) is essential due to their adverse effects on environmental quality and public health. To address the spectral overlap between NO and NO₂ in ultraviolet differential optical absorption spectroscopy (UV-DOAS), we propose a multi-scale dual-branch interaction attention network (MDIAN) for simultaneous concentration retrieval in gas mixtures. The model employs a dual-branch multi-scale convolutional architecture to extract local narrow-band absorption details and broad spectral profile features. A cross-attention mechanism is introduced to enable feature interaction between the NO and NO₂ branches. A bidirectional long short-term memory (Bi-LSTM) network is further incorporated to model contextual dependencies along the wavelength dimension, enabling joint regression of both target gases. Experimental results show that the proposed model achieves mean absolute errors (MAE) of 0.076 ppm for NO and 0.062 ppm for NO₂, with coefficients of determination (R²) of 0.9998 for both gases, outperforming traditional regression methods and baseline deep learning models. The uncertainties are 0.69% and 0.76%, respectively, and the inference time per sample ranges from 48.9 to 74.5 ms. These results indicate that MDIAN achieves a favorable balance among accuracy, stability, and real-time performance, offering a promising approach for intelligent monitoring of complex gas mixtures using UV-DOAS. Full article

Heatwave conditions are increasingly being recognized as important drivers of urban air-quality variability in southern European cities, particularly in inland urban environments exposed to persistent summer warming and atmospheric stagnation. This study examines the long-term variability of O₃, NO₂, and PM_2.5 concentrations in Valladolid, Spain, between 2006 and 2024, focusing particular attention on the occurrence and persistence of heatwave conditions. Ground-level ozone (O₃), nitrogen dioxide (NO₂), and fine particulate matter (PM_2.5) were analyzed to assess temporal variability, seasonal behavior, long-term trends, and exceedance characteristics. Results indicate an increasing persistence of heatwave episodes during the study period, particularly after 2015, with recent events exhibiting longer duration and broader regional extent. O₃ concentrations showed stronger accumulation during warm-season conditions, which is consistent with enhanced photochemical activity under elevated temperatures, while NO₂ concentrations generally declined over time. PM_2.5 variability reflected both local emissions and episodic regional influences, including Saharan dust intrusions. These findings highlight the growing relevance of heatwave conditions in shaping urban air-quality variability in medium-sized inland cities of the Iberian Peninsula. Full article

Ambient air pollution remains a major public health concern, contributing to millions of premature deaths worldwide according to the World Health Organization. Regional air quality assessments are commonly performed using chemical-transport models that require substantial computational resources due to their detailed representation of atmospheric processes. This study explores the feasibility of applying the second-generation dispersion model URBAIR^® as a computationally efficient alternative for long-term regional air quality simulations. URBAIR^® was implemented for three European case studies within the DISTENDER project to simulate particulate matter (PM₁₀) and nitrogen dioxide (NO₂) concentrations for 2018 under different spatial and temporal resolutions. Model performance was assessed against background monitoring stations and compared across grid configurations. The results show that the model successfully reproduces annual mean concentration patterns, particularly in urban areas, with R² values ranging mostly between 0.2–0.6, RMSE between 16–36 µg.m⁻³, and mean bias from −8 to 5 µg.m⁻³, indicating overall acceptable statistical performance. Within the specific configurations evaluated in this study, increasing spatial resolution was not consistently associated with improved model performance. However, because spatial resolution covaried with other factors including meteorological temporal resolution, domain characteristics, and monitoring station density, the present analysis does not allow the independent effect of spatial resolution to be isolated. Moreover, a key limitation of the modeling approach is the absence of chemical transformation processes, which may affect the representation of secondary pollutants. Overall, the dispersion-based modeling framework substantially reduces computational demand and input complexity, proving suitable for long-term exposure and climate-related applications when annual average concentrations are the primary objective. In future studies, the modeling approach should be applied to other case studies to consolidate the findings of this exploratory work so that it may contribute to sustainability-oriented decision making by facilitating regional assessments of air quality and potential health impacts related to climate change. Full article

Air and water pollution pose critical threats to public health and environmental stability, particularly in rapidly urbanizing developing nations. This study investigates synergistic interactions between air and water pollutants across 14 cities in Hunan Province, China (2020–2024), using multiparametric statistical approaches. The results show that the coefficient of variation (CV) of particulate matter (PM) with diameters less than 2.5 μm (PM_2.5, CV = 46.9%) and turbidity (TU, CV = 47.4%) showed the highest variability among the air and water quality parameters, respectively. Annual trends revealed significant increases in ozone (O₃) alongside decreases in carbon monoxide (CO) and nitrogen dioxide (NO₂) concentrations. Concurrently, freshwater systems exhibited rising electrical conductivity (EC), water temperature (WT), and pH, paired with declining levels of ammonia nitrogen (NH₃-N), total phosphorus (TP), and turbidity (TU). Principal component analysis (PCA) and Spearman correlation analyses showed significant positive correlations between PM and nitrogen species (TN, NH₃-N), but negative correlations with TP, suggesting potential cross-media pollution interactions. Cross-correlation analysis revealed significant time-lagged relationships (1–5 months) between atmospheric pollutants and aquatic nutrients, suggesting that atmospheric deposition may serve as a contributing pathway for cross-media contamination. The study not only provides empirical evidence for integrated pollution control strategies in urbanizing watersheds, but also offers a transferable framework for addressing similar air–water quality interactions on a global scale. Full article

The use of biodiesel blends in heavy-duty diesel engines changes the relationship between engine operating conditions, fuel properties, and exhaust emissions, which may limit the reliability of data-driven emission models trained under a single fuel condition. This study investigates the cross-fuel transferability of virtual emission sensors for a heavy-duty diesel engine operating on B7 and B20 fuel blends. The analysis was carried out for three target signals: nitrogen oxides concentration, hydrocarbon concentration, and dry carbon dioxide concentration, using data from the World Harmonized Transient Cycle (WHTC) and World Harmonized Stationary Cycle (WHSC) tests. A structured modelling workflow was developed, including signal time alignment, construction of baseline, dynamic, and memory-based features, feature selection, and separate evaluation scenarios: within-domain, cross-cycle, and cross-fuel transfer. Three tree-based regression algorithms were compared: Random Forest (RF), Histogram-Based Gradient Boosting (HGB), and Extreme Gradient Boosting (XGBoost). XGBoost achieved the best predictive performance in the source domain and was selected as the reference model. The results showed that a change in cycle characteristics led to a significant decrease in predictive performance, whereas the transition from B7/WHTC to B20/WHTC resulted in a clearly smaller drop in the evaluation metrics. The relationship between engine operating signals and emission response remained partially transferable across fuels. The highest stability was observed for carbon dioxide, intermediate stability for nitrogen oxides, and the lowest stability for hydrocarbons. The findings support the development of robust data-driven virtual sensing methods for emission monitoring and calibration of heavy-duty diesel engines operating with biodiesel blends. Full article

The urban–rural gradient of surface ozone concentration is closely associated with urban scale and has been widely reported in megacities globally. However, in the Sichuan Basin of southwestern China, a paradoxical asymmetric pattern between the ozone gradient and the physical urban footprint has emerged. By integrating multi-source satellite observations (e.g., TROPOMI), reanalysis data (ERA5-Land), and a concentric-ring spatial gradient analysis, we quantify a dipole-like urban surface ozone trap pattern in two megacities (Chengdu and Chongqing) from 2013 to 2019. We found that the urban–rural ozone gradients in Chongqing were substantially steeper than those in Chengdu, despite Chongqing’s smaller physical urban footprint. Specifically, in winter, the maximum daily average 8 h ozone level in the urban core drops to 27.5 μg m⁻³ in Chongqing and 47.9 μg m⁻³ in Chengdu, with outward radial increasing rates of 6.49% and 1.88% per 10 km, respectively. Conversely, the absolute nitrogen dioxide level in Chengdu is higher, highlighting an asymmetric titration behavior between the two cities. Regarding the chemical regime, analysis of the ratio (β) of nitrogen dioxide to formaldehyde reveals that Chongqing’s core operates under a more severe VOC-limited environment (β is 2.53 and radial gradient is −6.77% per 10 km) compared to Chengdu (β is 2.43 and gradient is −5.34% per 10 km). Furthermore, vertical cross-section analyses indicate that Chongqing’s deep-valley topography induces severe boundary layer compression and aerodynamic stagnation. Thus, rather than acting independently, these localized meteorological constraints function as crucial physical modulators that trap precursor emissions and exacerbate the non-linear chemical titration. This study elucidates how synergistic interactions between basin topography, physical urban footprints, and atmospheric chemistry shape localized ozone traps, providing a referable perspective for assessing complex urban atmospheric environments. Full article

Translating fine-resolution air pollution surfaces into health equity assessments requires aggregating exposure to administrative units, yet the equity implications of this choice are rarely tested. This study links annual 300 m nitrogen dioxide (NO₂) surfaces from the New York City Community Air Survey (2009–2023) with childhood asthma emergency department (ED) visit rates across 42 neighborhoods, comparing area-weighted, population-weighted, and residential-weighted aggregation throughout. Strong spatial convergence was observed in both NO₂ and ED burden (Pearson correlations between 2009 baseline levels and Theil–Sen slopes of −0.96 and −0.95). Panel first-difference estimation yielded a significant within-neighborhood association between NO₂ decline and ED rate decline (coefficient 0.022, p-value below 0.05). The most deprived fifth of neighborhoods received 47% of the total avoided ED burden, four times the share of the least deprived fifth. However, NO₂ reductions were nearly equal across poverty quintiles. The pro-poor distribution of health benefits was driven by baseline health inequality, not by differential pollution reduction. The three aggregation methods produced near-identical results for all metrics because within-neighborhood exposure variability was uncorrelated with poverty (r = −0.14). In cities where baseline disease burden is concentrated in disadvantaged communities, broad-based air quality improvement may contribute to pro-poor health gains without targeted intervention. Full article

The Metropolitan Area of São Paulo (MASP), located in southeastern Brazil, faces significant air quality challenges due to its large vehicle fleet and complex fuel composition, including widespread ethanol use. Air pollution dynamics in this context are investigated, focusing on spatio-temporal variations in formaldehyde (HCHO) and nitrogen dioxide (${\mathrm{NO}}_2$), and their role in ozone (${\mathrm{O}}_3$) formation. High-resolution data from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite are used to analyze HCHO and ${\mathrm{NO}}_2$ vertical column densities (VCDs) over a 5-year period (2019–2023). Results reveal high HCHO and ${\mathrm{NO}}_2$ VCDs over MASP, with spatial patterns related to land use and higher concentrations during the dry season, with HCHO mean VCD reaching 14.21 × ${10}^{15}$ molecules ${\mathrm{cm}}^{-2}$ and ${\mathrm{NO}}_2$ mean VCD reaching 8.91 × ${10}^{15}$ molecules ${\mathrm{cm}}^{-2}$. The Formaldehyde to Nitrogen dioxide Ratio (FNR) thresholds were derived based on observations from 24 CETESB surface ${\mathrm{O}}_3$ monitoring stations, providing region-specific constraints for ${\mathrm{O}}_3$ sensitivity classification in MASP, with lower and upper thresholds of 1.6 and 2.4. Based on these thresholds, the analysis indicates a predominance of VOC-sensitive conditions in the urban core, alongside transition and NOx-limited regimes in other areas. Full article