Search Results (950)

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

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

Air pollution, particularly fine (PM_2.5) and coarse (PM₁₀) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R² values of 0.85 for PM_2.5 and 0.89 for PM₁₀, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Environmental comfort and air pollution are among the most important indicators for assessing the population’s quality of life in urban agglomerations. This study aims to explore long-term memory in air pollution time series by analyzing the dynamics of the Hurst exponent and evaluating the predictability index. This type of statistical pre-forecast analysis is essential for developing accurate forecasting models for such time series. The effectiveness of air quality monitoring systems largely depends on the precision of these forecasts. The Ekibastuz coal-mining center, which houses one of the largest coal-fired power stations in Kazakhstan and the world, with a capacity of about 4000 MW, was chosen as an example for the study. Data for the period from 1 March 2023 to 31 December 2024 were collected and analyzed at the Ekibastuz coal-fired power station. During the specified period, 14 indicators (67,527 observations) were collected at 10 min intervals, including mass concentrations of CO, NO, NO₂, SO₂, PM_2.5, and PM₁₀, as well as current mass consumption of CO, NO, NO₂, SO₂, dust, and NO_x. The detrended fluctuation analysis of a time series of air pollution indicators was used to calculate the Hurst exponent and identify long-term memory. Changes in the Hurst exponent in regards to dynamics were also investigated, and a predictability index was calculated to monitor emissions of pollutants in the air. Long-term memory is recorded in the structure of all the time series of air pollution indicators. Dynamic analysis of the Hurst exponent confirmed persistent time series characteristics, with an average Hurst exponent of about 0.7. Identifying the time series plots for which the Hurst exponent is falling (analysis of the indicator of dynamics), along with the predictability index, is a sign of an increase in the influence of random factors on the time series. This is a sign of changes in the dynamics of the pollutant release concentrations and may indicate possible excess emissions that need to be controlled. Calculating the dynamic changes in the Hurst exponent for the emission time series made it possible to identify two distinct clusters corresponding to periods of persistence and randomness in the operation of the coal-fired power station. The study shows that evaluating the predictability index helps fine-tune the parameters of time series forecasting models, which is crucial for developing reliable air pollution monitoring systems. The results obtained in this study allow us to conclude that the method of trended fluctuation analysis can be the basis for creating an indicator of the level of air pollution, which allows us to quickly respond to possible deviations from the established standards. Environmental services can use the results to build reliable monitoring systems for air pollution from coal combustion emissions, especially near populated areas. Full article

Climate change and air pollution are reshaping viral circulation patterns and increasing host vulnerability, amplifying the burden of respiratory illness in early childhood. This narrative review synthesizes current evidence on how environmental exposures, particularly to nitrogen dioxide, ozone, and fine particulate matter, contribute to the incidence and severity of bronchiolitis, with a focus on biological mechanisms, epidemiological trends, and public health implications. Bronchiolitis remains one of the leading causes of hospitalization in infancy, with Respiratory Syncytial Virus (RSV) being responsible for the majority of severe cases. Airborne pollutants penetrate deep into the airways, triggering inflammation, compromising mucosal defenses, and impairing immune function, especially in infants with pre-existing vulnerabilities. These interactions can intensify the clinical course of viral infections and contribute to more severe disease presentations. Children in urban areas exposed to high levels of traffic-related emissions are disproportionately affected, underscoring the need for integrated public health interventions. These include stricter emission controls, urban design strategies to reduce exposure, and real-time health alerts during pollution peaks. Prevention strategies should also address indoor air quality and promote risk awareness among families and caregivers. Further research is needed to standardize exposure assessments, clarify dose–response relationships, and deepen our understanding of how pollution interacts with viral immunity. Bronchiolitis emerges as a sentinel condition at the crossroads of climate, environment, and pediatric health, highlighting the urgent need for collaboration across clinical medicine, epidemiology, and environmental science. Full article

The promotion of new energy vehicles (NEVs) represents a critical strategy for mitigating carbon emissions and air pollution. To evaluate the CO₂ and air pollutant reduction potential of NEVs in the Beijing–Tianjin–Hebei region, this study developed an integrated framework combining gray correlation analysis (GRA) and bidirectional long short-term memory (BiLSTM), referred to as the GRA-BiLSTM model, to forecast the adoption trend of NEVs and calculate the CO₂ and air pollutant emission reduction. The GRA-BiLSTM model developed in this study shows optimal predictive performance. The results indicate that new energy vehicles (NEVs) have great potential for environmental collaborative emission reduction in the transportation sector: it is predicted that by 2035, the total number of NEVs will be nearly 11.88 million, with a cumulative reduction of 2.76 billion tons of carbon emissions and significant reductions in various key air pollutants. This study provides an important quantitative basis for formulating pollution reduction and carbon reduction policies in the transportation sector. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

In this study, we selected the production processes and main products of three typical chemical enterprises in Shanghai, namely SH Petrochemical (part of the oil-refining sector), SK Ethylene, and HS Chlor-Alkali, to quantitatively assess the synergistic effects across technology, policy, and emission mechanisms. The localized air pollutant levels and greenhouse gas emissions of the three enterprises were calculated. The synergistic effects between the end-of-pipe emission reductions for air pollutants and greenhouse gas emissions were analyzed using the pollutant reduction synergistic and cross-elasticity coefficients, including technology comparisons (e.g., acrylonitrile gas incineration (AOGI) technology vs. traditional flare). Based on these data, we used the SimaPro software and the CML-IA model to conduct a life cycle environmental impact assessment regarding the production and upstream processes of their unit products. By combining the life cycle method and the scenario simulation method, we predicted the trends in the environmental impacts of the three chemical enterprises after the implementation of low-carbon development policies in the chemical industry in 2030. We also quantified the synergistic effects of localized air pollutant and greenhouse gas (GHG) emission reductions within the low-carbon development scenario by using cross-elasticity coefficients based on life cycle environmental impacts. The research results show that, for every ton of air pollutant reduced through end-of-pipe treatment measures, the HS Chlor-Alkali enterprise would increase its maximum CO₂ emissions, amounting to about 80 tons. For SK Ethylene, the synergistic coefficient for VOC reduction and CO₂ emissions when using AOGI thermal incineration technology is superior to that for traditional flare thermal incineration. The activities of the three enterprises had an impact on several environmental indicators, particularly the fossil fuel resource depletion potential, accounting for 69.48%, 53.94%, and 34.23% of their total environmental impact loads, respectively. The scenario simulations indicate that, in a low-carbon development scenario, the overall environmental impact loads of SH Petrochemical (refining sector), SK Ethylene, and HS Chlor-Alkali would decrease by 3~5%. This result suggests that optimizing the upstream power structure, using “green hydrogen” instead of “grey hydrogen” in hydrogenation units within refining enterprises, and reducing the consumption of electricity and steam in the production processes of ethylene and chlor-alkali are effective measures in reducing carbon emissions in the chemical industry. The quantification of the synergies based on life cycle environmental impacts revealed that there are relatively strong synergies for air pollutant and GHG emission reductions in the oil-refining industry, while the chlor-alkali industry has the weakest synergies. Full article

Background: Hand, foot, and mouth disease (HFMD) typically exhibits spatiotemporal clustering. This study aimed to analyze the spatiotemporal heterogeneity of HFMD in Fujian Province, China, and to identify the associations of air pollutants and socioeconomic factors with the incidence. Methods: Daily reported HFMD case data, daily air pollutant data, and socioeconomic data in Fujian Province from 2014 to 2023 were collected for analysis. A descriptive analysis was used to describe the epidemiological trends of HFMD. Spatial autocorrelation analysis was applied to explore the spatiotemporal clustering characteristics. The associations between risk factors and HFMD incidence were evaluated using the generalized additive model (GAM). Results: HFMD incidence in Fujian has decreased since 2019, and the peak in each year occurred between May and June. Distinct high–high and low–low clustering areas were identified. The cumulative exposure–response curves for SO₂, NO₂, and CO showed a monotonically increasing trend, with relative risks (RRs) < 1 at concentrations lower than the median levels (SO₂ ≈ 4 μg/m³, NO₂ ≈ 16 μg/m³, CO ≈ 1 mg/m³). In contrast, the curves for O₃ and PM_2.5 showed a decreasing trend, with RR < 1 at concentrations above the median levels (O₃ ≈ 55 μg/m³, PM_2.5 ≈ 20 μg/m³). Among socioeconomic factors, only the proportion of the population under 15 years old was found to be associated with HFMD incidence. Conclusions: HFMD incidence in Fujian exhibited distinct spatiotemporal clustering. The incidence was associated with the concentrations of air pollutants. Targeted interventions should be implemented in high-risk areas to mitigate HFMD transmission, with particular attention given to the environmental and demographic factors. Full article

Fine particulate matter (PM_2.5) plays a major role in haze, and studying its spatio-temporal dynamics and influencing factors is crucial for improving air quality. However, previous studies have often obscured the spatio-temporal interactions of PM_2.5 and neglected local spatio-temporal differences in influencing factors. To address these limitations, this research utilized PM_2.5 concentration data derived from satellite remote sensing and employed exploratory spatio-temporal data analysis (ESTDA) methods to investigate the spatio-temporal evolution patterns of PM_2.5 in Chinese cities from 2000 to 2021. Furthermore, the effects of natural environmental and socioeconomic factors on PM_2.5 were analyzed from both global and local perspectives using a spatial econometric model and the geographically and temporally weighted regression (GTWR) model. Key findings include (1) The annual value of PM_2.5 from 2000 to 2021 ranged between 27.4 and 42.6 µg/m³, exhibiting a “bimodal” variation trend and phased evolutionary characteristics. Spatially, higher concentrations were observed in the central and eastern regions, as well as along the northwestern border, while lower concentrations were prevalent in other areas. (2) The spatial–temporal distribution of PM_2.5 was generally stable, demonstrating a strong spatial dependence during its growth process, with significant path dependence characteristics in local spatial clusters of PM_2.5. (3) Precipitation, temperature, wind speed, and the Normalized Difference Vegetation Index (NDVI) significantly reduced PM_2.5 levels, whereas relative humidity, per capita Gross Domestic Product (GDP), industrialization level, and energy consumption exerted positive effects. These factors exhibited distinct local spatio-temporal variations. These findings aim to provide scientific evidence for the implementation of coordinated regional efforts to reduce air pollution across China. Full article