Air Quality Atmosphere and Health最新文献

This study analyzed the characteristics and sources of aerosols in the Mt. Everest region from May to October 2022 based on instrumental observations and Potential Source Contribution Function (PSCF) analysis. Aerosols were classified into background-, coarse-, and fine-mode dominated types according to the PM₂.₅/PM₁₀ mass-ratio. Both coarse- and fine-mode aerosol increases were linked to low temperature and humidity, strong winds, and high single-scattering albedo (SSA), with black carbon (BC) showing the strongest positive correlation, indicating enhanced transport and reduced wet removal. The scattering and absorption Ångström exponents (SAE and AAE) effectively describe particle size and absorption feature, showing clear spectral signatures of dust-dominated scattering and carbonaceous absorption. The absorption and scattering coefficients of different aerosol modes decreased with increasing wavelength, whereas SSA increased with increasing wavelength, which was consistent with the optical behavior of dust in this region. PSCF analysis showed distinct source regions for each aerosol mode: coarse-mode aerosols mainly originated from northeastern and central India, whereas fine-mode aerosols were primarily transported from the Bay of Bengal. Although the Mt. Everest atmosphere is generally clean, it is strongly influenced by transboundary aerosols from South Asia. These results provide new insights into the physical, optical, and meteorological mechanisms governing regional and long-range pollution over high-altitude environments of the Tibetan Plateau.

The Salton Sea region faces significant air quality challenges, with particulate matter ≤10 μm (aerodynamic diameter, i.e., PM₁₀) concentrations exceeding regulatory standards for 24-hour mean values established by the US Environmental Protection Agency (150 µg m⁻³) and the State of California (50 µg m⁻³). The lake’s receding shoreline has exposed areas of lakebed, creating potential sources for wind-blown dust. In response, the Salton Sea Management Plan aims to construct habitats and implement dust suppression strategies for areas expected to be exposed by 2028. A key component of this effort is to use roughness elements superposed on the exposed lakebed to mitigate dust emissions by reducing wind shear and inhibiting saltation. This study uses computational fluid dynamics to analyze array effectiveness across multiple wind directions for a defined roughness element size, density (number per hectare), and distribution (nominally a staggered array). In contrast to other studies of roughness arrays, we utilize roughness element locations and orientations from an existing array at the Salton Sea and perform a surface integral over the exposed surface to predict emissions. A relation between mass flux of PM₁₀ and shear stress was used in the post-processing of simulated wind shear on the surface, to estimate the total emissions of PM₁₀ before and after the installation of the roughness array. Our findings demonstrate a significant reduction in both shear stress on the intervening surface and dust emissions compared to the same area without the roughness, a result that is further supported by measurements. This research underscores the potential of engineered roughness arrays to improve air quality in regions affected by dust emissions.

This study investigates how environmental policy interventions influence the Environmental Kuznets Curve (EKC) in India, an emerging economy, using annual data from 1980 to 2023 and the Dynamic Autoregressive Distributed Lag (DARDL) model. It focuses on major initiatives including the Perform, Achieve and Trade (PAT) scheme, the FAME programs, and the National Hydrogen Energy Mission. By incorporating interaction terms between GDP and policy variables, the analysis measures how these policies moderate the impact of economic growth on CO₂ emissions. The results confirm an inverted U-shaped EKC for India, with economic growth initially increasing emissions but declining after a threshold income. Importantly, the policies significantly flatten the curve: a 1% increase in electric vehicle adoption and hydrogen use reduces CO₂ emissions by approximately 1.5% in the short term and 0.16% in the long term. Policy interventions also bring forward the EKC turning point from around USD 28,000 per capita to roughly USD 1,800 per capita, demonstrating that strategic environmental policies can accelerate emission reductions without hindering growth. These findings emphasize that India’s environmental improvements are driven by proactive policy measures rather than market forces alone, offering valuable guidance for policymakers in other emerging economies seeking sustainable growth.

Temperature variability, particularly diurnal temperature range (DTR), has been linked to mortality, yet evidence from low- and middle-income settings remains limited. We analyzed 188,039 deaths in Ningbo, China (2014–2018) using a time-series design with a distributed lag non-linear model. A J-shaped DTR–mortality curve was observed, with a city-specific minimum-mortality DTR (MMDTR) of 15.3 °C. Both low and high DTR increased mortality risk. Cause-specific effects were largest for endocrine, nutritional, and metabolic diseases (higher excess risk 18.9%), followed by circulatory (5.7%) and respiratory (4.2%) deaths. Females and older adults were more vulnerable. Children showed elevated risks that were not statistically significant and were imprecisely estimated (wide confidence intervals). Overall, 22.7% (~42,711) of deaths were attributable to DTR, largely from the common low-to-moderate DTR range (20.4%;~38,369), despite steeper relative risks at the upper tail. Both low- and high-DTR conditions merit attention; preparedness for high-DTR episodes may need to increase with climate change, while routine protection during frequent low-DTR days could yield substantial benefits, especially for females and older adults, and children, who showed high point estimates despite wide confidence intervals.

The objective of this study is to examine the association between air pollution and health burden in Thailand, Malaysia, and Singapore from 2000 to 2021. PM_2.5 concentrations and life expectancy are measures of air pollution. Health outcomes include death, incidence, and prevalence of respiratory infections and tuberculosis. The data are obtained from the Global Burden of Disease and the Air Quality Life Index. A Generalized Additive Mixed Model is used to analyze spatial and temporal patterns in a panel dataset with repeated annual observations across countries. This method allows for the detection of nonlinear relationships and considers cross-country variation and time series. The results show that Thailand has a significantly lower death rate from respiratory infections and tuberculosis compared to Malaysia, while the difference for Singapore is not statistically significant. Incidence, prevalence, and time are significantly associated with the death rate. PM_2.5 concentrations and life expectancy do not show significant effects in the model. The study contributes to the understanding of how disease-specific indicators and time trends influence death. It provides cross-country evidence from Southeast Asia and supports the use of burden-specific indicators in public health planning and surveillance.

Urban green space (UGS) plays a critical role in mitigating air pollution and enhancing urban sustainability through ecosystem services. However, the complex interactions between UGS and air pollution present both benefits and challenges. By adopting an ecosystem services perspective, the study pursues two main objectives: on the ond hand, to conduct a comprehensive review of the interactions between UGS and air pollution; and on the other hand, to identify current limitations in the field and highlight future research trends. Our study examines this relationship through a comprehensive bibliometric analysis spanning from 2003 to 2025. Using data from the Web of Science, we analyze 422 relevant publications through co-occurrence analysis, keyword clustering analysis, and keyword burst detection, employing CiteSpace and VOSviewer for visualization. The results show that: (1) Our study identifies research hotspots, including the regulatory role of UGS in air pollution control, the impact of air pollution on green space quality, and the interactions between UGS, air quality, and public health. (2) Findings reveal three distinct research phases: initial focus on pollutant removal mechanisms, expansion into urban sustainability frameworks, and recent emphasis on climate adaptation and ecosystem resilience. (3) Future research should pay greater attention to the reciprocal interactions between UGS and air pollution. Our study provides valuable insights to guide future studies and supports the development of more effective strategies for integrating urban greenery into air quality management and sustainable urban planning.

In response to concerns about urban air quality, this study examines particulate matter (PM2.5) exposure and data completeness across diverse demographics in Chicago using low-cost PurpleAir sensors. Chicago’s air quality, periodically worsened by both local emissions and external sources like wildfire smoke, presents health risks that are unevenly distributed across the city. PurpleAir public sensors started to be placed in 2018 with just 7 sensors, the network in Chicago has since expanded to 75 sensors by 2023, with new placements from community members helping to spread spatial and demographic gaps and enabling more detailed air quality data collection across diverse areas. Sensor coverage, data completeness, and correlations with community demographics were assessed over time, revealing that missing data was initially more prevalent in lower-income, majority-Hispanic neighborhoods. Results indicate that increased sensor placement has reduced demographic disparities in data completeness, yet certain communities remain underrepresented. Missingness remains a significant concern, especially during initial deployment and setup, highlighting the need for support to ensure data reliability. While increased coverage has improved spatial representation, targeted placement in high-risk areas and denser neighborhood-level sensor distribution are essential for equitable air quality monitoring. These findings underscore the need for further sensor deployment and maintenance strategies to ensure that air quality data effectively informs public health initiatives in all Chicago neighborhoods.

This study examines the spatio-temporal dynamics of surface ozone (O₃) pollution and the critical role of meteorological conditions in shaping its distribution across a major coastal province in eastern China. Based on high-resolution observational data from 2019 to 2023, we identify distinct spatial gradients and seasonal patterns in O₃ concentrations, with pronounced differences between urban and coastal zones. O₃ pollution frequency peaks at approximately 60% in June, with concentrations reaching ~ 200 µg m⁻³. Although pollution levels generally decline after July, elevated O₃ persists into September, particularly in urban areas south of the Yangtze River. Meteorological drivers exhibit strong regional heterogeneity: near-surface temperature dominates O₃ variability in inland urban areas, whereas solar radiation and wind fields become increasingly influential in coastal and industrialized regions. Crucially, we demonstrate that meteorological variations not only modulate daily pollution intensity but also extend the duration of high-O₃ episodes. This dual modulation effect contributes directly to the prolongation of the ozone season and the amplification of spatial disparities across the region. Our findings highlight the pivotal role of meteorology in exacerbating O₃ pollution in rapidly urbanizing coastal zones. These results are representative of many industrialized coastal cities in East Asia, where the convergence of urbanization, emissions, and complex coastal meteorology shapes evolving air quality challenges. The insights provided are essential for developing region-specific strategies to manage seasonal ozone risks under a changing climate.

O₃ and HCHO (Formaldehyde) exceedances occur periodically in Australian cities such as Sydney, Melbourne, and Brisbane. This study constructed spatio-temporal distribution patterns of pollutants in Australia based on daily O₃-HCHO data from 2012 to 2021. Influencing factors were examined using big data, slope-hurst analysis, and GTWR (geotemporally weighted regression) methods, while the Ben-Map model assessed health risk benefits of ozone reduction in Australia. The findings indicate: (1) Spatially, high-concentration area for both ozone and formaldehyde are distributed across southeastern Australia, with HCHO predominantly classified as Grade 4, Grade 3, and Grade 5.(2) Analysis of time series and trends indicates: ozone concentrations follow the pattern: winter > spring > autumn > summer, with a monthly average of 249.57 DU and an overall increasing trend; The maximum formaldehyde concentration occurs during summer, with a monthly average of 11.31 × 10¹⁵ molec/cm², exhibiting a predominantly decreasing trend. (3) Among natural sources, NDVI (normalised vegetation index), MC(moisture content) and T(temperature) exert the greatest influence on O₃-HCHO; amongst human activities, coal, oil and gas represent the primary contributing sources. (4) There was an overall decreasing trend in premature deaths due to ozone pollution in Australia, with the average number of deaths: all-cause (1105) > cardiovascular disease (703) > respiratory disease (430). The above research provides crucial theoretical support for the Australian government to enhance regional atmospheric environmental quality and public health benefits.

Air pollution is a major contributor to respiratory and cardiovascular diseases, prompting recent studies to adopt AI models for forecasting pollutant levels. In this work, we introduce a hybrid framework—SSA-LSTM-XGBoost—that applies a residual-correction strategy in four stages: (i) data preprocessing, (ii) training an LSTM network optimised with the Sparrow Search Algorithm (SSA), (iii) modelling the residuals with an SSA-optimised XGBoost learner, and (iv) fusing both outputs to obtain the final prediction. The framework is assessed on test and out-of-sample datasets and benchmarked against SVR, BiGRU, random forest, BiLSTM, and GRU. On the test set, SSA-LSTM-XGBoost attains the highest accuracy, recording an R(^{varvec{2}}) of 0.9554 and the lowest errors (RMSE = 2.5194, MAE = 1.4885, MAPE = 0.0635), amounting to an average error reduction of roughly 8.2% relative to the runner-up SSA-SVR. When validated on unseen data, it remains superior (R(^{varvec{2}}) = 0.8830, RMSE = 2.4838), achieving an average error decrease of about 3.5% compared with SSA-SVR despite the harsher evaluation conditions. These findings attest to the robustness and strong generalisability of the proposed framework for reliable PM(_{varvec{2.5}}) forecasting. In practice, such forecasts enable near-real-time hotspot alerts, short-term exposure advisories for vulnerable groups, and preventive traffic or industrial controls, thereby supporting municipal air-quality management and policy decisions in Duitama.