Search Results (77)

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

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

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM_2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning. 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

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

To promote the sustainable use of land resources and improve air pollution control, this study investigates the spatiotemporal dynamics of industrial land development and the heterogeneity of PM_2.5 concentrations across regions. Based on national land transaction data and PM_2.5 raster datasets, the analysis employs Moran’s I, a hot and cold spot analysis, and multivariate linear regression to examine how the transaction frequency, transaction area, and total transaction price of industrial land influence PM_2.5 concentrations in 286 cities from 2010 to 2021. The study focuses on quantifying the impact of industrial land development on PM_2.5 concentrations. The main findings are as follows: (1) the frequency of industrial land transactions varies significantly across regions, with clear intra-regional differences. The transaction area and total transaction price decrease in the following order: “East-West-Central-North-East” and “East-Central-West-North-East”, respectively. (2) The spatial clustering of PM_2.5 concentrations has intensified, with hot spots concentrated in Eastern and Central cities. Cold spots are distributed in bands along the Southern coast and scattered patterns in Heilongjiang Province. (3) The influence of industrial land development on PM_2.5 concentrations has generally weakened nationwide, with the strongest effects observed in the Eastern region. Among the development indicators, the impact of the transaction area is increasing, while those of the transaction frequency and total price are declining, showing clear regional disparities. Therefore, integrating sustainable development principles into the adjustment of the industrial land market is essential for effective air pollution prevention. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article

Based on the air-quality monitoring data of Zaozhuang City from 2018 to 2022, this study systematically analyzed the spatio-temporal variation characteristics of multiple pollutants by comprehensively applying Kriging interpolation, time-series decomposition, wavelet transform, and DBSCAN spatial clustering methods. The key findings include: (1) Overall, air pollutant concentrations in Zaozhuang decrease from 2018 to 2022, with NO₂, SO₂, PM_2.5, and PM₁₀ concentrations declining by 17.3%, 52.2%, 28.9%, and 33.6%, respectively. However, O₃ concentration increases by 2.5% in 2022 compared to 2018. Seasonally, SO₂, PM_2.5, and PM₁₀ concentrations are the highest in winter and lowest in summer, while CO, NO₂, and O₃ follow a winter > autumn > spring > summer pattern. Weekly variations show that daily average concentrations of CO, NO₂, SO₂, PM_2.5, and PM₁₀ peak on Mondays, with concentrations slightly higher on weekdays than weekends. (2) Spatially, CO, NO₂, PM_2.5, and PM₁₀ concentrations are higher in the southern region, while O₃ and SO₂ concentrations are elevated in Shizhong District, Xuecheng District, and Tengzhou City. (3) Correlation analysis reveals that meteorological parameters, such as precipitation, significantly influence pollutant concentrations, with precipitation playing a role in reducing pollutant levels. This study highlights the effectiveness of the Kriging method in analyzing the complex spatio-temporal dynamics of air pollutants, offering valuable insights for environmental policy and urban planning. Full article

Air pollution is a major trigger for chronic respiratory and circulatory diseases. As a key component of air pollution, fine particulate matter (PM_2.5) exposure is largely determined by land use type and population density. However, simultaneous consideration of their spatiotemporal distribution is lacking in existing studies on PM_2.5 exposure. In this paper, we first assess the dynamic evolution of land use patterns in Gansu Province, China, from 2000 to 2020, using a land use transfer matrix and dynamic degree. Population-weighted exposure (PWE) to PM_2.5 is then evaluated for each land use type at provincial, city, and county levels, with seasonal variations analyzed. Spatial autocorrelation analysis is finally performed to explore the spatiotemporal evolution of PM_2.5 exposure, whereas standard deviation ellipses and gravity center migration models highlight spatial distribution characteristics and shifting trends. Experimental results showed that 2010 was a turning point for annual PM_2.5 exposure at the provincial level in Gansu Province, with an initial increase followed by a decrease. Construction land had the highest annual PM_2.5 exposure, whereas forest had the lowest exposure (except in 2005). Exposure levels showed a seasonal pattern: higher in winter and spring and lower in summer and autumn. At city and county levels, southern Gansu indicated a continuous decline in annual PM_2.5 exposure across all land use types since 2000. Exposure levels exhibited a strong spatial positive correlation, with a fluctuating spatial convergence. This study comprehensively analyzes the multi-scale differences and spatiotemporal evolution patterns of PM_2.5 exposure across various land use types, contributing to provide scientific evidence and decision-making support for mitigating air pollution and enhancing coordinated air pollution control at multi-scale administrative levels. Full article

Air quality is an essential factor for human health and ecosystem balance, but in regions like Petroșani Mountain Depression, air pollution continues to be a significant challenge. This area, marked by decades of coal mining, is confronted with high concentrations of pollutants, influenced by human activities and the specific geography and climate. This study aims to compare instrumental air quality measurements with snow sample analysis, as a sustainable alternative method. Specifically, it examines the spatiotemporal distribution and evolution of air pollutants, utilizing long-term monitoring data and an extensive sampling network (42 points) for both air and snow, to provide a thorough understanding of air quality dynamics in the area. The experimental part of this study focused on determining VOCs and PM in the air, and dissolved ions (sulfate, calcium, and magnesium) and suspended solids in snow. The results highlight significant correlations between pollution sources and atmospheric dynamics in mountain depressions, while also analyzing the efficiency of the instruments used for data collection. This study emphasizes that, although instrumental methods provide precise and detailed measurements, their implementation in isolated regions presents significant challenges. Therefore, alternative approaches such as snow analysis can represent a more efficient and sustainable option in these regions. Full article

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing the temporal evolution of PM_2.5 concentrations from multivariate monitoring data, the model demonstrates significant prediction capabilities while maintaining consistency with observed pollutant transport characteristics in the urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance the predictive capability for complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused to achieve high-precision predictions of PM_2.5 concentrations through a multivariate time-series forecasting model. An asymmetric penalty mechanism is added to dynamically optimize the loss function. Experimental results indicate that the proposed E-TSMixer model achieves higher accuracy for the prediction of PM_2.5, which significantly outperforms the traditional models. Additionally, an intelligent dual regulation of fixed and dynamic threshold model is introduced and combined with E-TSMixer for the decision-making model of the real-time adjustments of the frequency, routes, and timing of water truck operation in practice. Full article

With the rapid acceleration of global urbanization, the impact of land use/cover change (LUCC) on the environment and ecosystems has become increasingly prominent, particularly in terms of air quality, which has emerged as a significant issue demanding attention. Focusing on the coastal city of Lianyungang, the spatiotemporal dynamics of land use/cover changes were explored by utilizing land use dynamic degree and land use transfer matrix methods. By integrating a comprehensive historical dataset, multiple linear regression analysis was used to analyze the driving mechanism of land use conversion and to explore the effect of LUCC on the variations in PM₁₀ levels. The results showed an overall decreasing trend in PM₁₀ levels over the 24-year period from 2000 to 2023, with distinct seasonal fluctuations, showing higher concentrations in winter and lower concentrations in summer. The impact of land use on PM₁₀ variations can be categorized into three stages: initial (2000–2006), transitional (2007–2013), and deepening development (2014–2022). Notably, during the third stage, with the involvement of policy interventions and industrial upgrading, a strong negative correlation of −0.97 was identified between urban land expansion and the decrease in PM₁₀ levels. The correlation between LUCC and PM₁₀ levels was insignificant over shorter periods, but the analysis of data from 2000 to 2022 revealed a significant positive correlation of 0.77, emphasizing the importance of adopting a long-term perspective to accurately assess the impact of LUCC on air quality. This research provides valuable insights into the implications of LUCC on air quality during urbanization and establishes a scientific foundation for developing air pollution management strategies in Lianyungang and similar regions. Full article

Air pollution remains a critical environmental and public health issue, requiring diverse research perspectives, including those related to energy production and consumption. This study examines the relationship between Poland’s energy mix and air pollution trends by integrating national statistical data on primary energy consumption and renewable energy sources over the past 15 years with air pollution measurements from the last eight years. The air pollution data, obtained from reference-grade monitoring stations, focus on particulate matter (PM). To address discrepancies in temporal resolution between daily PM measurements and annual energy sector reports, a bootstrapping method was applied within a regression framework to assess the overall impact of individual energy components on national air pollution levels. Seasonal decomposition techniques were employed to analyze the temporal dynamics of specific energy sources and their contributions to pollution variability. A key aspect of this research is the role of renewable energy sources in air quality trends. This study also investigates regional variations in pollution levels by analyzing correlations between geographic location, industrialization intensity, and the proportion of green areas across Poland’s administrative regions (Voivodeships). This spatially explicit approach provides deeper insights into the linkages between energy production and pollution distribution at a national scale. Poland presents a unique case due to its distinct energy mix, which differs significantly from the EU average, its persistently high air pollution levels, and recent regulatory changes. These factors create an ideal setting to assess the impact of energy sector transitions on environmental quality. By employing high-resolution spatiotemporal big data analysis, this study leverages measurements from over 100 monitoring stations and applies advanced statistical methodologies to integrate multi-scale energy and pollution datasets. From a PM perspective, the regression analysis showed that High-Methane Gas had a neutral impact on PM concentrations, making it a suitable transition energy source, while renewables exhibited negative regression coefficients and coal-based sources showed positive coefficients. The findings offer new perspectives on the long-term environmental effects of shifts in national energy policies. Full article

Mobile source air toxics (MSATs) are major contributors to urban air pollution, especially near high-traffic roadways, where populations face elevated pollutant exposures. Traditional human health risk assessments, based on deterministic methods, often overlook variability in exposure and the vulnerabilities of sensitive subpopulations. This study introduces and applies a new stochastic modeling approach, utilizing Monte Carlo simulations to evaluate cumulative cancer risks from MSATs exposure through inhalation and ingestion pathways. This method captures variability in exposure scenarios, providing detailed health risk assessments, particularly for vulnerable groups such as children and the elderly. This approach was demonstrated in a case study conducted in Saint Paul, Minnesota, using 2019 traffic data. Deterministic models estimated cumulative cancer risks for adults at 6.24E-02 (unitless lifetime cancer risk), while stochastic modeling revealed a broader range, with the 95th percentile reaching 4.98E-02. The 95th percentile, used in regulatory evaluations, identifies high-risk scenarios overlooked by deterministic methods. This research advances the understanding of MSATs exposure risks by integrating spatiotemporal dynamics, identifying high-risk zones and vulnerable subpopulations, and supporting resource allocation for targeted pollution control measures. Future applications of this methodology include expanding stochastic modeling to evaluate ecological risks from mobile emissions. Full article

Better understanding the complex mechanisms underlying the variations in crop residue burning (CRB) intensity and patterns is crucial for evaluating control strategies and developing sustainable policies aimed at the efficient recycling of crop residues. However, the intricate interplay between the CRB practices, climate variability, and human activities poses a significant challenge in this endeavor. Here, we utilize the high spatiotemporal resolution of satellite observations to characterize and explore the dynamics of summer CRB in North China at multiple scales. Between 2003 and 2012, there was a significant intensification of summer CRB in North China, with the annual number of burning spots increasing by an average of 499 (95% confidence interval, 252–1426) spots/year. However, in 2013, China promulgated the stringent Air Pollution Prevention and Control Action Plan, which led to a rapid decrease in the intensity of summer CRB. Local farmers also adjusted their burning practices, shifting from concentrated and intense burning to a more dispersed and uniformly intense approach. Between 2003 and 2020, the onset of summer CRB shifted earlier in North China by 0.75 (0.5–1.1) days/year, which is attributed to the combined effects of climate change and anthropogenic controls. Specifically, the onset time is found to be significantly and negatively correlated with spring temperature anomalies and positively correlated with anomalies in the number of spring frost days. Climate change has led to a shortened crop growing season, resulting in an earlier start to summer CRB. Moreover, the enhanced anthropogenic controls on CRB expedited this process, making the trend of an earlier start time even more pronounced from 2013 to 2020. Contrary to the earlier onset of summer CRB, the termination of local wheat residue burning experienced a notable delay by 1.0 (0.8–1.4) days/year, transitioning from mid-June to early July. Full article