Atmospheric Pollution Research最新文献

Accurate prediction of air pollutant concentrations, specifically concerning inhalable particulate matter such as PM_2.5, is crucial for proactive measures to safeguard the well-being of urban residents. This paper focuses on addressing the perceptible latency effect for long-term PM_2.5 predictions produced by existing statistical models. We emphasize the importance of numerical computations in capturing substantial changes, and enhance prediction accuracy by integrating them with high-dimensional, diverse urban data. Specifically, our approach collects data from a global-to-meso-scale atmospheric dispersion model named System for Integrated modeLling of Atmospheric coMposition (SILAM), along with numerical weather forecasts, traffic congestion measurement, meteorological factors and static sources (road network and points of interest). We find that existing deep learning models are prone to overfitting when applied to complex datasets, primarily due to their uniform treatment of diverse data types as time series without adapting to the specific characteristics of each data type. To counter this, we propose a simple yet transferable deep learning architecture, focusing on the proper use of various data types. Additionally, our comparative analysis, through a case study in Shenzhen, China, shows our model not only enhances SILAM dispersion accuracy for 24h-ahead PM_2.5 forecasts by a significant 30.3%, but also mitigates the noticeable latency effect of existing models by 19.5%. Finally, an ablation study further validates the importance of each data source and module of our approach.

Cities occupy only 3% of the earth's land area, yet they contribute over 70% of anthropogenic CO₂ emissions. Accurately quantifying the corresponding uncertainty of CO₂ emissions at the city scale is the first step in simulating global/regional greenhouse gas concentration and implementing effective emission reduction policies. As the world's largest emitter, China has committed to reaching its CO₂ emissions peak by 2030, and cities are facing substantial pressure to reduce CO₂ emissions. The Yangtze River Delta (YRD) region is treated as the largest urban agglomeration in China and globally. Despite the availability of multiple emission inventories, comprehensive assessments of city-level CO₂ emissions uncertainty within this region are still lacking. This study focused on the YRD region, compared six inventories and used nighttime light intensity, GDP, population, and satellite-based xCO₂ concentrations as proxy data to identify potential sources of emission bias. The findings are as follows: (1) The city-level CO₂ emissions in the YRD region ranged from tens of Mt to approximately 600 Mt. From 2010 to 2019, emissions in the region increased slowly. However, emission intensity (calculated by dividing the CO₂ emissions by the Gross Domestic Product) showed a declining trend. The relatively low proportion of point source emissions in EDGAR inventory is attributed to its reliance on the point source CARMA, which overlooks smaller point sources. (2) The average uncertainties for low emission (0–50 Mt), medium emission (50–100 Mt) and high emission (>100 Mt) cities were 51.1%, 34.0%, and 45.0%, respectively. The absolute errors of emissions showed strong positive correlations with proxy data. There exists a logarithmic relationship between them and uncertainty, which can assist in estimating emission uncertainty in other cities in the future. (3) The ratios of biological CO₂ flux to the ten-year average of CO₂ emissions varied between-6.79% and −0.02%, which are much smaller than the uncertainty of anthropogenic emissions, the comparisons indicate that recent large biases in estimating anthropogenic CO₂ emissions hinder the evaluation of carbon neutrality ability.

Air pollution poses a substantial barrier to global environmental sustainability and citizen well-being. However, there is a lack of research that specifically examines the effects of short-term exposure to PM_2.5 and its components on health outcomes in developing nations in Asia, regions often cited as having some of the most severe urban air quality issues globally. The present study evaluated the associations between PM_2.5 components and hospital admissions (HA) and emergency room visits (ERV) for respiratory diseases in the megacity of Karachi, Pakistan. We measured PM_2.5 constituents, including black carbon (BC) and ionic species (sulfate - SO₄²⁻; nitrate - NO₃⁻; and ammonium - NH₄⁺) at two sites (August 2008 to August 2009) in Karachi. The HA and ERV, respiratory disease outcome data, were collected from three local, large tertiary care hospitals. We assessed the lag structure of excess risk (ER) of the pollutants-outcomes association (0–6 single and cumulative lag days) using time-series quasi-Poisson models after adjusting for temperature, humidity, and day of the week. Among a total 13,827 patients, the highest ERs for all respiratory urgent care use (UCU) were observed for PM_2.5 (10.3, 95% CI: 2.59%-18.59), NH₄⁺ (9.58%, 95% CI: 1.50%–18.30%), air quality index (9.11%, 95%CI: 2.54%–16.09%) and SO₄²⁻ (7.26%, 95% CI: 1.03%–13.87%) within 0–4 lag days. Additionally, patients with chronic obstructive pulmonary disease (COPD), tuberculosis (TB), or other pulmonary diseases and older or male patients were more vulnerable to these pollutants. This first study in Pakistan found that PM_2.5 and its constituents were associated with respiratory HAs and ERVs for the inhabitants of the megacity of Karachi. These associations varied by different PM constituents, disease subtypes, age, and gender. Our results provide important information to policymakers for developing regulations for improving air quality and public health. Further studies are urgently needed in other developing countries to disentangle the air pollution health effects.

Dust events are common in Arizona, USA, causing significant impacts on air quality and human health. Several previous studies have compiled an analysis of the dust event climatology for the state of Arizona, yet very few used multiple Meteorological Aerodrome Reports (METARs) to determine their occurrence, seasonality, cause, and duration. In this project, we identified dust events based on measurements taken from nine different METAR Automatic Surface Observation Systems (ASOS) from the greater Phoenix area in Arizona. A total of 531 dust events were identified from 2005 to 2021. Each dust event was examined for the meteorological disturbance (synoptic and convective) that caused it. Dust events were analyzed for different meteorological characteristics (temperature, dew point, relative humidity, wind speed, wind gust, and visibility), temporal patterns, and the impacts of different climatological indices and drought conditions. Each dust event was categorized based on its intensity as either a blowing dust event or a dust storm based on the lowest visibility recorded. Separation based on cause and severity was then performed to further analyze differences in the aforementioned meteorological characteristics and parameters during dust events. Notable seasonality, causality, and temporal trends consistent with the occurrence of the summer monsoon season were found, with differences in meteorological parameters when analyzed by cause and intensity consistent with previous studies. Observations of Particulate Matter (PM) concentrations during dust and non-dust times reveal the impact of these dust events on PM₁₀ and PM_2.5 concentrations.

Wildfires, a persistent environmental menace, are a significant source of harmful gases and particulate emissions. This study leverages the fire radiative power (FRP) method to delineate a comprehensive wildfire emission inventory for Southwest China from 2001 to 2020. Daily fire radiative power data derived from 1 km MODIS Thermal Anomalies/Fire products (MOD14/MYD14) were used to calculate the FRE and combusted biomass. Available emission factors were assigned to three biomass burn types: forest, grass, and shrub fires. Over the span of two decades, we have compiled data and estimated the annual emissions of carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), sulfur dioxide (SO₂), ammonia (NH₃), nitrogen oxides (NO_x), total particulate matter (TPM), black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOC_s) to be 9809.13, 566.82, 25.79, 5.37, 12.25, 16.67, 133.53, 4.16, 41.81, and 97.23 Gg per year (Gg yr⁻¹), respectively. In terms of fire type, forest fires accounted for the largest portion of total CO₂ emissions (59.23%), with grass fires and shrub fires coming in second and third, accounting for 20.41% and 20.36%, respectively. Geographically, Yunnan Province were identified as the major contributor in Southwest China, accounting for 69.67% of the total emissions. Temporally, the maximum emission occurred in 2010 (24263.33 Gg), and the minimum emission occurred in 2017 (2917.66 Gg). And the emissions were mainly concentrated in February (23.33%), March (25.52%), and April (22.61%), which accounted for nearly three-fourths of the total emissions. The results of this study are much higher than those obtained by the burned area method, almost three times as high. In contrast, the results of this study are close to the fire emission data from the GFED4s and GFASv1.2 and QFEDv2.5r1 databases.

Accurate prediction of the mass concentration of atmospheric particulate matter is of great significance for the rational formulation of atmospheric environment management strategies and the study of the spatial and temporal evolution of atmospheric pollutants. In order to solve the problems of low prediction accuracy and low efficiency of traditional prediction models, aiming at the nonlinear and stochastic characteristics of atmospheric particulate matter mass concentration changes, a composite prediction model based on Random Forest (RF) feature selection, Beluga Whale Optimization (BWO) algorithm, Convolutional Neural Network (CNN) and Bidirectional Long and Short-Term Storage Memory Neural Network (BiLSTM) is proposed in this paper. In this composite prediction model, the input variables are adjusted and screened through RF algorithm to reduce the network complexity. The weights and thresholds of CNN-BiLSTM are optimized using BWO to improve the prediction accuracy of the model. The publicly mass concentration data and Aerodynamic Particle Size Spectrometer (APS) measurements are used to train and compare with the predicted data. The experimental results indicate that this composite model has better prediction performance and prediction accuracy compared with the traditional single and the combined model. The fitting coefficient (R²) of PM2.5 prediction using publicly data and APS data can reach 0.8842 and 0.9762, respectively. The R² for the prediction of PM10 using publicly data and APS data can reach 0.8635 and 0.976, respectively. It indicates that the model proposed in this paper has better generalization and robustness.

Seasonal variations of fine particulate matter (PM) and its chemical components in Suizhou are investigated by collecting PM_2.5 and PM₁ samples from industrial and residential areas between March 2017 and February 2018. The findings reveal more severe PM pollution in industrial areas compared to residential areas. Concentrations and percentages of water-soluble inorganic ions (WSII) in PM are higher in spring and winter. Notably, the high levels of SO₄²⁻ and NO₃⁻ in PM_1-2.5 indicate that secondary transformation of SO₂ and NO_x significantly contribute to the rapid increase in PM_1-2.5. The conversion of NO₂ to NO₃⁻ is a key factor in the winter increase of mass concentrations of PM_2.5 [ρ(PM_2.5)], while photochemical reactions involving NO₂ drive the summer increase of ρ(PM_2.5). Organic carbon (OC) and elemental carbon (EC) are mainly distributed in PM₁, with OC peaking in winter. Chemical fractionation of PM reveals that carbonaceous components have a greater impact on PM₁ concentration than WSII, whereas WSII more significantly affects ρ(PM_2.5) than carbonaceous components. The OC to EC ratio in industrial areas (2.5 ± 1.4) and residential areas (1.9 ± 0.8) suggests more prominent secondary aerosol pollution in industrial areas. Significant correlations between secondary organic carbon (SOC), SOC conversion rate (η_SOC), and concentrations of O₃-8h and NO₂ in Suizhou further indicate that O₃ and NO₂ levels in the atmosphere influence the generation of SOC and η_SOC.

Little evidence has demonstrated the linkage of gaseous air pollution and hospitalization rates for mental diseases among children and adolescents in China. Based on a time-stratified case-crossover design among children and adolescents in nine cities, Sichuan, a conditional logistic regression and a concentration-response (C–R) curve model were applied to investigate mental disorders in relation to gaseous air pollutants exposure at lag 0-lag 7, and lag 01-lag 07. Hospitalization costs were calculated through the attributable risk method. With daily data from official environmental monitoring centers, individual daily mean ambient pollutants estimates were evaluated via Inverse Distance Weighted method. Daily hospitalized records for mental illness were collected from medical organization or/and institutions from January 2016 to December 2019. There were 11479 inpatients suffering depression, anxiety, and/or other mental disorders. In single- and cumulative-day-lag analyses, with each 10 μg/m³ increment of NO₂, SO₂, and O₃, the greatest odds ratio (OR) for all-cause mental disorders were 1.114 (95% confidence interval [CI]: 1.067–1.164) (lag 0), 1.219 (95% CI: 1.040–1.430) (lag 4), and 1.039 (95% CI: 1.009–1.069) (lag 7), separately. Stronger associations were found in inpatients hospitalized in warm days in SO₂ analysis. C–R curve showed that all-cause mental disorders hospitalizations were positively related to SO₂ and O₃ exposure at relative high levels. During study period, the total economic cost of hospitalization for all-cause mental disorders caused by NO₂ pollution was 94.71 million CNY. These findings indicated that gaseous air pollutants exposure may increase the risk and economic burdens of mental disorders among children and adolescents.

Ammonium nitrogen (NH_x) in atmospheric wet deposition is a primary external nitrogen source for reservoir-type water sources, and identifying its sources is crucial for controlling nitrogen pollution in water. This study aimed to examine the NH_x characteristics and sources in wet deposition in the Xichuan reservoir area of the Danjiangkou Reservoir, the South-to-North Water Diversion Project (SNWDP) water source, from September 2019 to August 2020. Major inorganic nitrogen concentrations in precipitation and ammonia-nitrogen isotope (δ¹⁵N–NH₄⁺) values were measured, and the sources were analyzed using the Bayesian mixing model. The results showed that NH₄⁺ was the main inorganic nitrogen form in wet deposition, with a concentration ranged of 0.16–5.26 mg L⁻¹. The δ¹⁵N–NH₄⁺ values ranged from −14.7‰ to +6.3‰, with significant isotopic effects from agricultural sources and climatic conditions, showing higher values in summer and lower values in winter. The release of ammonia (NH₃) from agricultural sources was the primary NH_x source (58.0%–76.6%), with fertilizer application being the most significant contributor (32.1%–46.6%). Seasonally, the relative contribution of agricultural sources to NH₄⁺ in wet deposition was higher in autumn and winter. Spatially, agricultural activities significantly impacted atmospheric nitrogen accumulation across the reservoir area. This study quantified NH_x sources in wet deposition, providing isotopic evidence and scientific references for nitrogen control measures and atmospheric nitrogen cycling studies.

In this study, WRF-Chem was used to analyze the radiative forcing, deposition, emission and transport of mineral dust during three severe dust events in central Iran, specifically in Yazd area. The results showed that central Iran is predominantly influenced by internal dust sources. During these events, the amount of dust emissions and depositions in Iran were about 740–1400 Gg and 50–90 Gg, respectively. In Yazd, the dust emissions and depositions were about 34–104 Gg and 8–16 Gg, respectively, originating from the Ardakan plain and Dasht-e-kavir. In order to analyze the effect of dust on the climate, it was found that dust resulted a surface cooling by shortwave and longwave radiative forcing in the range of −11 to −21 W/m² and 6.7–13.9 W/m², respectively, over Yazd province. In the atmosphere, radiative forcing led to the warming effect with shortwave and longwave radiative forcing in the range of 10.5–19.7 W/m² and -6 to −11 W/m² over Yazd, respectively. Furthermore, it was demonstrated that the radiative forcing associated with dust also had an impact on meteorological parameters. The results showed that this phenomenon influenced microphysical processes and mitigated the climatic effects of dust.