Search Results (329)

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

In response to increasingly stringent emission regulations, low-carbon fuels have received significant attention as sustainable energy sources for internal combustion engines. This study investigates four representative low-carbon fuels, methane, methanol, hydrogen, and ammonia, by systematically summarizing their combustion characteristics and emission profiles, along with a review of existing after-treatment technologies tailored to each fuel type. For methane engines, unburned hydrocarbon (UHC) produced during low-temperature combustion exhibits poor oxidation reactivity, necessitating integration of oxidation strategies such as diesel oxidation catalyst (DOC), particulate oxidation catalyst (POC), ozone-assisted oxidation, and zoned catalyst coatings to improve purification efficiency. Methanol combustion under low-temperature conditions tends to produce formaldehyde and other UHCs. Due to the lack of dedicated after-treatment systems, pollutant control currently relies on general-purpose catalysts such as three-way catalyst (TWC), DOC, and POC. Although hydrogen combustion is carbon-free, its high combustion temperature often leads to elevated nitrogen oxide (NO_x) emissions, requiring a combination of optimized hydrogen supply strategies and selective catalytic reduction (SCR)-based denitrification systems. Similarly, while ammonia offers carbon-free combustion and benefits from easier storage and transportation, its practical application is hindered by several challenges, including low ignitability, high toxicity, and notable NO_x emissions compared to conventional fuels. Current exhaust treatment for ammonia-fueled engines primarily depends on SCR, selective catalytic reduction-coated diesel particulate filter (SDPF). Emerging NO_x purification technologies, such as integrated NO_x reduction via hydrogen or ammonia fuel utilization, still face challenges of stability and narrow effective temperatures. Full article

Understanding the differential impacts of emission sources of volatile organic compounds (VOCs) on formaldehyde (HCHO) levels is pivotal to effectively mitigating key photochemical radical precursors, thereby enhancing the regulation of atmospheric oxidation capacity (AOC) and ozone formation. This investigation systematically selected and analyzed year-long VOC measurements across three urban zones in Shenzhen, China. Photochemical age correction methods were implemented to develop the initial concentrations of VOCs before source apportionment; then Positive Matrix Factorization (PMF) modeling resolved six primary sources: solvent usage (28.6–47.9%), vehicle exhaust (24.2–31.2%), biogenic emission (13.8–18.1%), natural gas (8.5–16.3%), gasoline evaporation (3.2–8.9%), and biomass burning (0.3–2.4%). A machine learning (ML) framework incorporating Shapley Additive Explanations (SHAP) was subsequently applied to evaluate the influence of six emission sources on HCHO concentrations while accounting for reaction time adjustments. This machine learning-driven nonlinear analysis demonstrated that vehicle exhaust nearly always emerged as the primary anthropogenic contributor in diverse functional zones and different seasons, with gasoline evaporation as another key contributor, while the traditional reactivity metric method, ozone formation potential (OFP), tended to underestimate the role of the two sources. This study highlights the primacy of strengthening emission reduction of transportation sectors to mitigate HCHO pollution in megacities. Full article

The Yangtze River Delta urban agglomeration, characterized by high population density, an advanced transportation system, and a concentration of industrial activity, is one of the regions severely affected by O₃ pollution in central and eastern China. Using data collected from 251 monitoring stations between 2015 and 2025, this paper analyzed the spatio-temporal variation of 8 h O₃ concentrations and instances of exceedance. On the basis of exploring the influence of meteorological factors on regional 8 h O₃ concentration, the potential source contribution areas of pollutants under the exceedance condition were investigated using the HYSPLIT model. The results indicate a rapid increase in the 8 h O₃ concentration at a rate of 0.91 ± 0.98 μg·m⁻³·a⁻¹, with the average number of days exceeding concentration standards reaching 41.05 in the Yangtze River Delta urban agglomeration. Spatially, the 8 h O₃ concentrations were higher in coastal areas and lower in inland regions, as well as elevated in plains compared to hilly terrains. This distribution was significantly distinct from the concentration growth trend characterized by higher levels in the northwest and lower levels in the southeast. Furthermore, it diverged from the spatial characteristics where exceedances primarily occurred in the heavily industrialized northeastern region and the lightly industrialized central region, indicating that the growth and exceedance of 8 h O₃ concentrations were influenced by disparate factors. Local human activities have intensified the emissions of ozone precursor substances, which could be the key driving factor for the significant increase in regional 8 h O₃ concentrations. In the context of high temperatures and low humidity, this has contributed to elevated levels of 8 h O₃ concentrations. When wind speeds were below 2.5 m·s⁻¹, the proportion of 8 h O₃ concentrations exceeding the standards was nearly 0 under almost calm wind conditions, and it showed an increasing trend with rising wind speeds, indicating that the potential precursor sources that caused high O₃ concentrations originated occasionally from inland regions, with very limited presence within the study area. This observation implies that the main cause of exceedances was the transport effect of pollution from outside the region. Therefore, it is recommended that the Yangtze River Delta urban agglomeration adopt economic and technological compensation mechanisms within and between regions to reduce the emission intensity of precursor substances in potential source areas, thereby effectively controlling O₃ concentrations and improving public living conditions and quality of life. Full article

The occurrence of haze pollution significantly deteriorates air quality and threatens human health, yet persistent knowledge gaps in real-time source apportionment of fine particulate matter (PM_2.5) hinder sustained improvements in atmospheric pollution conditions. Thus, this study employed single-particle aerosol mass spectrometry (SPAMS) to investigate PM_2.5 sources and dynamics during winter haze episodes in Yinchuan, Northwest China. Results showed that the average PM_2.5 concentration was 57 μg·m⁻³, peaking at 218 μg·m⁻³. PM_2.5 was dominated by organic carbon (OC, 17.3%), mixed carbonaceous particles (ECOC, 17.0%), and elemental carbon (EC, 14.3%). The primary sources were coal combustion (26.4%), fugitive dust (25.8%), and vehicle emissions (19.1%). Residential coal burning dominated coal emissions (80.9%), highlighting inefficient decentralized heating. Source contributions showed distinct diurnal patterns: coal combustion peaked nocturnally (29.3% at 09:00) due to heating and inversions, fugitive dust rose at night (28.6% at 19:00) from construction and low winds, and vehicle emissions aligned with traffic (17.5% at 07:00). Haze episodes were driven by synergistic increases in local coal (+4.0%), dust (+2.7%), and vehicle (+2.1%) emissions, compounded by regional transport (10.1–36.7%) of aged particles from northwestern zones. Fugitive dust correlated with sulfur dioxide (SO₂) and ozone (O₃) (p < 0.01), suggesting roles as carriers and reactive interfaces. Findings confirm local emission dominance with spatiotemporal heterogeneity and regional transport influence. SPAMS effectively resolved short-term pollution dynamics, providing critical insights for targeted air quality management in arid regions. Full article

Aviation is widely recognised to have global-scale climate impacts through the formation of ozone (O₃) in the upper troposphere and lower stratosphere (UTLS), driven by emissions of nitrogen oxides (NO_X). Ozone is known to be one of the most potent greenhouse gases formed from the interaction of aircraft emission plumes with atmospheric species. This paper follows up on previous research, where a Photochemical Trajectory Model was shown to be a robust measure of ozone formation along flight trajectories post-flight. We use a combination of a global Lagrangian chemistry-transport model and a box model to quantify the impacts of aircraft NO_X on UTLS ozone over a five-day timescale. This work expands on the spatial and temporal range, as well as the chemical accuracy reported previously, with a greater range of NO_X chemistry relevant chemical species. Based on these models, route optimisation has been investigated, through the use of network theory and algorithms. This is to show the potential inclusion of an understanding of climate-sensitive regions of the atmosphere on route planning can have on aviation’s impact on Earth’s Thermal Radiation balance with existing resources and technology. Optimised flight trajectories indicated reductions in O₃ formation per unit NO_X are in the range 1–40% depending on the spatial aspect of the flight. Temporally, local winter times and equatorial regions are generally found to have the most significant O₃ formation per unit NO_X; moreover, hotspots were found over the Pacific and Indian Ocean. Full article

Background/Objectives: Climate change is a major factor exacerbating non-communicable diseases (NCDs) such as cardiovascular diseases, neoplasms, respiratory diseases, and diabetes, especially in vulnerable Latin American regions. This study analyzes the impact of environmental exposures related to climate change on the NCD burden in eight Latin American countries by quantifying the disability-adjusted life years (DALYs) attributable to these factors. Using Global Burden of Disease (GBD) data (1990–2021), we performed multiple linear regression to assess associations between DALYs and environmental risk factors—air pollution (particulate matter, nitrogen dioxide), radon, lead, and extreme temperatures—in Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru, and Uruguay. The study included major NCDs, and the population was stratified by age and sex. Results: Ischemic heart disease was the leading cause of DALYs in most countries. Particulate matter pollution was the main environmental risk factor contributing to the NCD burden, mainly affecting cardiovascular and respiratory diseases. Mexico showed the highest DALYs from particulate and ozone pollution; temperature and lead exposure also contributed in some countries. Nitrogen dioxide was the primary risk factor for asthma. Statistically significant relationships between environmental factors and DALYs were confirmed. Conclusions: Climate change-related exposures significantly increase the burden of NCDs in Latin America. Targeted interventions in industry, transportation, and energy, along with sustainable urban policies, are essential to mitigate health impacts and reduce disparities. Integrating environmental health into public policies can improve health outcomes amid ongoing climate challenges. Full article

We present coordinated observations from ozone Differential Absorption lidar (DIAL), aerosol lidar, and Doppler wind lidar at the City College of New York (CCNY) in northern Manhattan during the summer 2023 AGES+ campaigns across the New York City (NYC) region and Long Island Sound (LIS) areas. The results highlight significant ozone formation within the planetary boundary layer (PBL) and the concurrent transport of ozone/aerosol plumes aloft and mixing into the PBL during 26–28 July 2023. Especially, 26 July experienced the highest ozone concentration within the PBL during the three-day ozone episode despite having a lower temperature than the following two days. In addition, the onset of the afternoon sea breeze contributed to increased ozone levels in the PBL. A mobile ozone DIAL was also deployed at Columbia University’s Lamont–Doherty Earth Observatory (LDEO) in Palisades, NY, 29 km north of NYC, from 11 August to 8 September 2023. A notable high-ozone episode was observed by both ozone DIALs at the CCNY and the LDEO site during an unusual heatwave event in early September. On 7 September, the peak ozone concentration at the LDEO reached 120 ppb, exceeding the ozone levels observed in NYC. This enhancement was associated with urban plume transport, as indicated by wind lidar measurements, the HRRR (High-Resolution Rapid Refresh) model, and the Copernicus Sentinel-5 TROPOMI (TROPOspheric Monitoring Instrument) tropospheric column NO₂ product. The results also show that, during both heatwave events, those days with slow southeast to southwest winds experienced significantly higher ozone pollution. Full article

Although China has achieved substantial reductions in particulate matter pollution, continually rising ground-level ozone now constitutes the primary challenge to further air-quality improvements. A systematic assessment of the long-term spatiotemporal behavior of ozone (O₃) and its links to meteorology and emissions is still lacking. Here, we develop a novel framework that couples Kolmogorov–Zurbenko (KZ) filtering with an optimized random forest (RF) regression model to examine daily maximum 8 h average ozone (O₃-8h) in 372 Chinese cities from 2013 to 2023. The approach quantitatively disentangles meteorological and emission contributions at the national scale, overcoming the limitations of traditional linear methods in capturing non-linear processes. Long-term components explain, in general, <40% of total O₃ variance. In eastern urban agglomerations, long-term meteorological factors—particularly temperature and surface ultraviolet radiation—account for up to 80% of the trend, whereas long-term emission contributions remain modest (≈5–6%), with pronounced signals in the Beijing–Tianjin–Hebei and Fenwei Plain regions. Empirical orthogonal function analysis further reveals distinct spatial patterns of emission influence: long-term O₃ trends in mega-cities such as Beijing, Tianjin, and Shanghai are driven mainly by local emissions (1.5–3% contribution), while key transport hubs including Xi’an, Tangshan, and Langfang are markedly affected by traffic-related emissions (>2%). These findings clarify the heterogeneous mechanisms governing O₃ formation across China and provide a scientific basis for designing and implementing the next phase of region-specific, joint prevention-and-control policies. Full article

This study presents a comprehensive Life Cycle Assessment (LCA) of the NENGOUE residence, a multi-occupancy building located in Yaoundé, Cameroon, over an 80-year lifespan. The analysis encompasses four life cycle phases—construction, use, renovation, and deconstruction—and evaluates twelve environmental impact categories. The results reveal that the use phase contributes overwhelmingly to environmental burdens, accounting for over 96% of total impacts. To mitigate this dominance, two alternative scenarios were assessed: a sustainable transport model and the integration of a photovoltaic system. In the first scenario, environmentally friendly commuting strategies, such as increased walking, cycling, and public transport, led to a 17.10% reduction in greenhouse gas (GHG) emissions. In the second, rooftop photovoltaics offset 69.29% of the building’s electricity needs, resulting in a 26.72% GHG reduction. A third, combined scenario demonstrated the highest environmental gains, achieving a 42.97% reduction in GHG emissions, alongside substantial improvements across other impact categories, including acidification (−38.4%), cumulative energy demand (−28.3%), and photochemical ozone formation (−40.18%). In addition to the environmental benefits, the study highlights the importance of considering social acceptance, behavioral change, and economic feasibility for real-world implementation. The willingness of residents to adopt sustainable mobility practices, cultural preferences, safety concerns, and the initial cost barriers associated with photovoltaic technology are identified as critical factors. These findings underscore the need for integrated strategies that combine technological innovation with inclusive urban planning and stakeholder engagement. The proposed approach demonstrates that aligning environmental measures with local socio-economic realities can significantly enhance the sustainability of residential buildings, contributing meaningfully to climate change mitigation in Sub-Saharan African cities. Full article

Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O₃) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O₃ concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O₃ pollution. The results indicate that O₃ pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R² = 0.9043). Feature importance identifies NO₂ as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O₃ pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O₃ transport with local emissions and meteorological conditions further increase the O₃ pollution level. This study highlights the need to strengthen coordinated NO_X and VOCs emission reductions and enhance regional joint prevention and control strategies in China. Full article

Ozone (O₃) and fine particulate matter (PM_2.5) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O₃ and PM_2.5 in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O₃ and PM_2.5 concentrations. The results revealed a distinct inverse relationship between O₃ and PM_2.5, with meteorologically normalized PM_2.5 decreasing significantly (−5.0 μg/m³ compared to the pre-lockdown baseline of 0.6 μg/m³), while O₃ increased substantially (15.2 μg/m³ compared to the baseline of 5.3 μg/m³). Partial dependency analysis revealed that PM_2.5-O₃ relationships evolved from linear to non-linear patterns across lockdown periods, while NO₂-O₃ interactions indicated shifts from VOC-limited to NO_x-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM_2.5 levels while avoiding O₃ increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments. Full article

This study investigates the relationships between air quality, social vulnerability, and health outcomes at the census tract-level in Harris County, Texas. Spatial and regression analyses were conducted using sociodemographic data, air quality indicators, including PM2.5, diesel particulate matter (DPM), nitrogen dioxide (NO₂), and ozone, and health metrics, such as coronary heart disease, chronic obstructive pulmonary disease (COPD), asthma, and stroke prevalence. The results indicated variability in sociodemographic challenges, air pollution, and health outcomes. Social vulnerability strongly correlated with increased prevalence of respiratory and cardiovascular diseases, notably COPD, asthma, and stroke. The air quality metrics showed significant geospatial variability: PM2.5 and NO₂ were concentrated centrally near transportation corridors, DPM was elevated near eastern industrial regions, and ozone peaked in western parts of the county, potentially due to atmospheric transport and photochemical processes. PM2.5 exposure significantly correlated with increased cardiovascular and respiratory health outcomes, particularly at elevated concentrations. In contrast, ozone demonstrated a plateauing effect, increasing the health risks but with a diminishing impact at higher concentrations. The correlations between social vulnerability and air quality were modest, suggesting homogenous distributions of PM2.5, NO₂, and DPM across socioeconomically diverse areas, whereas ozone exposure slightly increased with higher social vulnerability. The findings pointed to the complexity of spatial relationships between socioeconomic status, air pollution, and health, highlighting the need for additional monitoring and targeted interventions to improve health outcomes in socio-demographically and economically challenged communities. Full article

The Po Valley is depicted in the literature as a region with one of the most severe air pollution profiles in Europe, frequently exceeding the permitted statutory concentration limits for several air pollutants. The aim of this paper is to present an assessment of the air quality over the Po Valley for the year 2022 as reported by ground-based air quality monitoring stations of the region and assess chemical transport modeling simulations which can enhance the spatiotemporal reporting in air quality levels which cannot be achieved by the sparse in situ monitoring station coverage. To achieve this, the concentration levels of two significant chemical compounds, namely ozone (O₃) and nitrogen dioxide (NO₂), are studied here. Measurements include the surface concentrations of in situ measurements from 28 stations reporting to the European Environment Agency (EEA), while chemical transport simulations from the Long-Term Ozone Simulation—European Operational Smog (LOTOS-EUROS) are employed for a comparative analysis of the relative levels observed in each of the two monitoring methods for air quality. The analysis of the EEA stations reports that, for year 2022, all selected monitoring stations exceeded the EU O₃ level limit for a minimum of 33 days and the World Health Organization (WHO) limit for a minimum of 78 days. The concentrations of surface O₃ and NO₂ studied by both the measurements as well as the simulations exhibit a close correlation with the documented diurnal, monthly, and seasonal variability, as previously reported in the literature. The LOTOS-EUROS CTM ozone simulations demonstrate a strong correlation with the EEA measurements, with a monthly correlation coefficient of R > 0.98 and a diurnal correlation coefficient of R > 0.83, indicating that the model is highly effective at capturing the diverse spatiotemporal patterns. The co-variability between ozone and nitrogen dioxide surface levels reported by the EEA in situ measurements reports high R values from −0.76 to −0.88, while the CTM, due to the spatial resolution of the simulations which disables the identification of local effects, reports higher correlations of −0.96 to −0.99. The CTM simulations are hence shown to be able to close the spatial gaps of the in situ measurements and provide a dependable auxiliary tool for air quality monitoring across Europe. Full article

Due to global warming, ozone depletion and their ramifications on the Arctic and Antarctic snowscapes, there has been an incentivized drive towards net zero-carbon emission policies by several countries. These policies extend to several sectors, including several manufacturing and processing industries and transportation, which are a few of their notable stakeholders. In the transportation sector, this journey towards net zero-carbon emissions is aided by the adoption of battery electric vehicles (BEVs) due to their zero-carbon emissions during operation. However, they might have zero running emissions, but they do have emissions when charging through conventional sources. This research paper looks at the carbon emissions produced by both electric vehicles (EVs) and internal combustion engine (ICE) vehicles during their operational stages and compares them based on a 200,000 km driving range, battery manufacturing emissions and different power production alternatives to draw up some very important recommendations. The analysis presented in this paper helps in drawing conclusions and proposes ideas which, when included in transport policies, will help curb global warming and eventually lead to the sustainable development of the transport sector. The analysis in this study shows that the emissions needed to produce a single battery unit have increased by approximately 258.7% with the change in battery production locations. Furthermore, charging EVs with a fossil-fuel-dominated grid has shown an increase in emissions of 17.98% compared to the least emissive ICE car considered in the study. Finally, policy update recommendations which are essential for the sustainable development of the transport sector are discussed. Full article