Search Results (23)

Nitrogen oxides (NO_X) are important precursors of ozone and secondary aerosols. Accurate and timely NO_X emission estimates are essential for formulating measures to mitigate haze and ozone pollution. Bottom–up and satellite–constrained top–down methods are commonly used for emission inventory compilation; however, they have limitations of time lag and high computational demands. Here, we propose a machine learning model, WOA-XGBoost (Whale Optimization Algorithm–Extreme Gradient Boosting), to retrieve NO_X emissions. We constructed a dataset incorporating satellite observations and conducted model training and validation in the Shandong region with severe NO_X pollution to retrieve high spatiotemporal resolution of NO_X emission rates. The 10–fold cross–validation coefficient of determination (R²) for the NO_X emission retrieval model was 0.99, indicating that WOA-XGBoost has high accuracy. Validation of the model for the other year (2019) showed high agreement with MEIC (Multi–resolution Emission Inventory for China), confirming its strong robustness and good temporal transferability. The retrieved NO_X emissions for 2021–2022 revealed that emission rate hotspots were located in areas with heavy traffic flow. Among 16 prefecture–level cities in Shandong, Zibo exhibited the highest NO_X rate (>1 μg/m²/s), explaining its high NO₂ pollution levels. In the future, priority areas for emission reduction should focus on heavy industry clusters such as Zibo and high traffic urban centers. Full article

The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant potential to enhance the efficiency and effectiveness of data processing in the GST. This paper develops a method for detecting carbon dioxide (CO₂) emission hotspots using a convolutional neural network (CNN) with short-lived and co-emitted nitrogen dioxide (NO₂) as a proxy. To address the data gaps in model parameter training, we constructed a dataset comprising over 210,000 samples of NO₂ plumes and emissions based on atmospheric dispersion models. The trained model performed well on the test set, with most samples achieving an identification accuracy above 80% and more than half exceeding 94%. The trained model was also applied to the NO₂ column data from the TROPOspheric Monitoring Instrument (TROPOMI) for hotspot detection, and the detections were compared with the MEIC inventory. The results demonstrate that in high-emission areas, the proposed method successfully identifies emission hotspots with an average accuracy of over 80%, showing a high degree of consistency with the emission inventory. In areas with multiple observations from TROPOMI, we observed a high degree of consistency between high NO₂ emission areas and high CO₂ emission areas from the Global Open-Source Data Inventory for Anthropogenic CO₂ (ODIAC), indicating that high NO₂ emission hotspots can also indicate CO₂ emission hotspots. In the future, as hyperspectral and high spatial resolution remote sensing data for CO₂ and NO₂ continue to grow, our methods will play an increasingly important role in global data preprocessing and global emission estimation. Full article

The accurate quantification of anthropogenic carbon dioxide (CO₂) emissions in urban areas is hindered by high uncertainties in emission inventories. We assessed the spatial distributions of three anthropogenic CO₂ emission inventories in Shanghai, China—MEIC (0.25° × 0.25°), ODIAC (1 km × 1 km), and a local inventory (LOCAL) (4 km × 4 km)—and compared simulated CO₂ column concentrations (XCO₂) from WRF-CMAQ against OCO-3 satellite Snapshot Mode XCO₂ observations. Emissions differ by up to a factor of 2.6 among the inventories. ODIAC shows the highest emissions, particularly in densely populated areas, reaching 4.6 and 8.5 times for MEIC and LOCAL in the central area, respectively. Emission hotspots of ODIAC and MEIC are the city center, while those of LOCAL are point sources. Overall, by comparing the simulated XCO₂ values driven by three emission inventories and the WRF-CMAQ model with OCO-3 satellite XCO₂ observations, LOCAL demonstrates the highest accuracy with slight underestimation, whereas ODIAC overestimates the most. Regionally, ODIAC performs better in densely populated areas but overestimates by around 0.22 kt/d/km² in relatively sparsely populated districts. LOCAL underestimates by 0.39 kt/d/km² in the center area but is relatively accurate near point sources. Moreover, MEIC’s coarse resolution causes substantial regional errors. These findings provide critical insights into spatial variability and precision errors in emission inventories, which are essential for improving urban carbon inversion. Full article

G protein-coupled receptors (GPRs), including pro-inflammatory GPR4 and ovarian cancer GPR1 (OGR1/GPR68), are involved in the pH sensing of the extracellular space and have been implicated in inflammatory bowel disease (IBD). Previous data show that a loss of GPR4 or OGR1 independently is associated with reduced intestinal inflammation in mouse models of experimental colitis. In the present manuscript, we investigated the impact of the simultaneous loss of GPR4 and OGR1 in animal models of IBD. To study the effects of combined loss of Gpr4 Ogr1 in IBD we used the well-established acute dextran sodium sulfate (DSS) and spontaneous Il10^−/− murine colitis models. Disease severity was assessed using multiple clinical scores (e.g., body weight loss, disease activity score, murine endoscopic index of colitis severity (MEICS) and histological analyses). Real-time quantitative polymerase chain reaction (qPCR), Western blot, and flow cytometry were used to investigate changes in pro-inflammatory cytokines expression and immune cells infiltration. We found that a combined loss of GPR4 and OGR1 significantly reduces colon inflammation in IBD relative to single deficiencies as evidenced by reduced body weight loss, disease score, CD4/CD8 ratio, and Il1β, Il6, and Tnf in the colon. Similarly, in the II10 deficiency model, the inflammation was significantly ameliorated upon the simultaneous deletion of GPR4 and OGR1, evidenced by a reduction in the MEICS score, colon length, Tnf and Il1β measurements, and a decrease in the number of macrophages in the colon, as compared to single deletions. Importantly, hydroxyproline levels were decreased close to baseline in Il10^−/− × Gpr4^−/− × Ogr1^−/− mice. Our findings demonstrate that the simultaneous loss of GRP4 and OGR1 functions exerts an additive effect on multiple parameters associated with colonic inflammation. These results further reinforce the hypothesis that chronic inflammatory acidosis is a driver of fibrosis and is dependent on GPR4 and OGR1 signaling. The inhibition of both GPR4 and OGR1 by pH-sensing receptor modulators may constitute as a potential therapeutic option for IBD, as both pH-sensing receptors appear to sustain inflammation by acting on complementary pro-inflammatory pathways. Full article

Synergizing air pollution control and climate change mitigation has been of significant academic and policy concern. The synergy between air pollution and carbon emissions is one of the measures to understand the characteristics and process of the air pollution–carbon synergistic control, which will also provide valuable information for collaboratively achieving Sustainable Development Goals (SDGs) (such as SDGs 11 and 13). This study establishes a systematic framework integrating emissions inventory and projection models, correlation mining and typology analysis methods to predictively evaluate the synergy and comprehensive coordination between air pollution and carbon dioxide (CO₂) emissions in Chinese cities by 2030, 2050, and 2060 under different policy scenarios for air pollution and CO₂ emissions control. The results reveal the significant effects of synergistically implementing clean air and aggressive carbon-reducing policies on mitigating air pollution and CO₂ emissions. Under the On-time Peak-Net Zero-Clean Air and Early Peak-Net Zero-Clean Air scenarios, the total reduction and synergy for air pollution and CO₂ emissions will be more significant, particularly by 2050 and 2060. This study is the first to integrate scenario projection and synergy evaluation in air pollution and CO₂ research, providing a novel supplement to the air pollution–climate change synergy methodology based on co-benefit estimation. The methods and findings will also contribute to measuring the achievement and analyzing the interaction of the SDGs. Full article

Optimization algorithms have a wide range of applications in symmetry problems, such as graphs, networks, and pattern recognition. In this paper, a dynamic periodic multi-depot multi-trip vehicle routing model for scheduling test samples is constructed, which considers the differences in testing unit price and testing capacity of various agencies and introduces a cross-depot collaborative transport method. Both the cost and the testing time are minimized by determining the optimal sampling routes and testing agencies, subjecting to the constraints of vehicle capacity, number of vehicles, and delivery time. To solve the model, a knowledge-guided multi-objective shuffled frog leaping algorithm (KMOSFLA) is proposed. KMOSFLA adopts a convertible encoding mechanism to realize the diversified search in different search spaces. Three novel strategies are designed: the population initialization with historical information reuse, the leaping rule based on the greedy crossover and genetic recombination, and the objective-driven enhanced search. Systematic experimental studies are implemented. First, feasibility analyses of the model are carried out, where effectiveness of the cross-depot collaborative transport is validated and sensitivity analyses on two parameters (vehicle capacity and proportion of the third-party testing agencies) are performed. Then, the proposed algorithm KMOSFLA is compared with five state-of-the-art algorithms. Experimental results indicate that KMOSFLA can provide a set of non-dominated schedules with lower cost and shorter testing time in each scheduling period, which provides a reference for the dispatcher to make a final decision. Full article

In this study, a Long Short-Term Memory (LSTM) network approach is employed to evaluate the prediction performance of PM_2.5 in the Beijing–Tianjin–Hebei region (BTH). The proposed method is evaluated using the hourly air quality datasets from the China National Environmental Monitoring Center, European Center for Medium-range Weather Forecasts ERA5 (ECMWF-ERA5), and Multi-resolution Emission Inventory for China (MEIC) for the years 2016 and 2017. The predicted PM_2.5 concentrations demonstrate a strong correlation with the observed values (R² = 0.871–0.940) in the air quality dataset. Furthermore, the model exhibited the best performance in situations of heavy pollution (PM_2.5 > 150 μg/m³) and during the winter season, with respective R² values of 0.689 and 0.915. In addition, the influence of ECMWF-ERA5’s hourly meteorological factors was assessed, and the results revealed regional heterogeneity on a large scale. Further evaluation was conducted by analyzing the chemical components of the MEIC inventory on the prediction performance. We concluded that the same temporal profile may not be suitable for addressing emission inventories in a large area with a deep learning method. Full article

Cotton production in Xinjiang is increasing year by year, and the improved crop yields have had an impact on the environment. This study investigated the changes in six significant pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) from 2017 to 2022 in Xinjiang. We compiled a biomass burning emission inventory to make the MEIC emission inventory more complete. The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was employed to simulate air quality in different reduction scenarios in 2025, and it explored ways to alleviate air pollution in the main cotton areas of Xinjiang. The result shows that the main pollutant in Xinjiang is particulate matter (PM particles with aerodynamic diameters less than 2.5 µm and 10 µm), and the concentration of particulate matter decreased from the northern mountains toward the south. The concentrations of O₃ (ozone) were highest in summer, while the concentrations of other pollutants were high in autumn and winter. If the pollution is not strictly controlled in terms of emission reduction, it is impossible to achieve the target of a 35 μg/m³ PM_2.5 concentration in the planting area. In the scenario of enhanced emission reduction measures and the scenario of higher intensity emission reduction measures, there was a failure to reach the target, despite the reduction in the PM_2.5 concentration. In the best emission reduction scenario, PM_2.5 in Xinjiang is expected to drop to 22.5 μg/m³ in November and 34 μg/m³ in March, respectively. Therefore, in the optimal emission reduction scenario, the target of 35 μg/m³ will be reached. This study emphasized the importance of future air pollution mitigation and identified a feasible pathway to achieve the target of 35 μg/m³ PM_2.5 concentration by 2025. The research findings provide useful insights for the local government which can be used to develop strategies aimed at mitigating substantial pollution emissions. Full article

To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM_2.5, PM₁₀, and NO₂ in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O₃ increased by 45.1% in comparison; the concentrations of PM_2.5, PM₁₀ and NO₂ emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively; the concentrations of PM_2.5, PM₁₀ and NO₂ emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM_2.5 and PM₁₀ emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO₂. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing. Full article

The Chinese government introduced regulations to control emissions and reduce the level of NO_x pollutants for the first time with the 12th Five-Year Plan in 2011. Since then, the changes in NO_x emissions have been assessed using various approaches to evaluate the impact of the regulations. Complementary to the previous studies, this study estimates anthropogenic NO_x emissions in 2015 and 2019 over Eastern China using as a reference the Hemispheric Transport of Air Pollution (HTAP) v2.2 emission inventory for 2010 and the new variational inversion system the Community Inversion Framework (CIF) interfaced with the CHIMERE regional chemistry transport model and OMI satellite observations. We also compared the estimated NO_x emissions with the independent Multi-resolution Emission Inventory for China (MEIC) v1.3, from 2015. The inversions show a slight global decrease in NO_x emissions (in 2015 and 2019 compared to 2010), mainly limited to the most urbanized and industrialized locations. In the locations such as Baotou, Pearl River Delta, and Wuhan, the estimations in 2015 compared to 2010 are consistent with the target reduction (10%) of the 12th Five-Year Plan. Comparisons between our emission estimates and MEIC emissions in 2015 suggest that our estimates likely underestimate the emission reductions between 2010 and 2015 in the most polluted locations of Eastern China. However, our estimates suggest that the MEIC inventory overestimates emissions in regions where MEIC indicates an increase of the emissions compared to 2010. Full article

Pollution caused by PM_2.5 and O₃ are common environmental problems which can easily affect human health. Chengdu is a major central city in Western China, and there is little research on the regional emissions and health effects of air pollution in Chengdu. According to the Multi-resolution Emissions Inventory of the Chinese Model, 2017 (MEIC v1.3), this study compiled the air pollutant emission inventory of Chengdu. The results show that the pollutant emission of Chengdu is generally higher in winter than in summer. The southeast area of Chengdu is the key area where emissions of residential and industrial sectors are dominant. Through air quality simulation with a Weather Research and Forecasting model, coupled with the Community Multiscale Air Quality (WRF-CMAQ), the health effects of PM_2.5 and O₃ in winter and summer in Chengdu of 2017 were investigated. The primary pollutant in winter is PM_2.5 and O₃ in summer. PM_2.5 pollution accounted for 351 deaths in January and July 2017, and O₃ pollution accounted for 328 deaths in the same period. There were 276 deaths in rural areas and 413 in urban areas. In January and July 2017, the health economic loss caused by PM_2.5 accounted for 0.0974% of the gross regional product (GDP) of Chengdu in 2017, and the health economic loss caused by O₃ accounted for 0.0910%. Full article

Due to the role of cancer stem cells (CSCs) in tumor resistance and glycosphingolipid (GSL) involvement in tumor pathogenesis, we investigated the effect of a newly synthesized compound (3-amino-N-(3-chloro-2-methylphenyl)-5-oxo-5,6,7,8-tetrahydrothieno[2,3-b]quinoline-2-carboxamide 1 on the percentage of CSCs and the expression of six GSLs on CSCs and non-CSCs on breast cancer cell lines (MDA-MB-231 and MCF-7). We also investigated the effect of 1 on the metabolic profile of these cell lines. The MTT assay was used for cytotoxicity determination. Apoptosis and expression of GSLs were assessed by flow cytometry. A GC–MS-coupled system was used for the separation and identification of metabolites. Compound 1 was cytotoxic for both cell lines, and the majority of cells died by treatment-induced apoptosis. The percentage of CSCs was significantly lower in the MDA-MB-231 cell line. Treatment with 1 caused a decrease of CSC IV⁶Neu₅Ac-nLc₄Cer+ MDA-MB-231 cells. In the MCF-7 cell line, the percentage of GalNAc-GM1b+ CSCs was increased, while the expression of Gg₃Cer was decreased in both CSC and non-CSC. Twenty-one metabolites were identified by metabolic profiling. The major impact of the treatment was in glycolysis/gluconeogenesis, pyruvate and inositol metabolism. Compound 1 exhibited higher potency in MBA-MB-231 cells, and it deserves further examination. Full article

A series of carbon dioxide (CO₂) emission inventories with high spatial resolutions covering China have been developed in the last decade, making it possible to assess not only the anthropogenic emissions of large administrational units (countries; provinces) but also those of small administrational units (cities; counties). In this study, we investigate three open-source gridded CO₂ emission inventories (EDGAR; MEIC; PKU-CO₂) and two statistical data-based inventories (CHRED; CEADs) covering the period of 2000–2020 for 16 prefecture-level cities in Shandong province in order to quantify the cross-inventory uncertainty and to discuss potential reasons for it. Despite ±20% differences in aggregated provincial emissions, all inventories agree that the emissions from Shandong increased by ~10% per year before 2012 and that the increasing trend slowed down after 2012, with a quasi-stationary industrial emission proportion being observed during 2008–2014. The cross-inventory discrepancies increased remarkably when downscaled to the city level. The relative differences between two individual inventories for half of the cities exceeded 100%. Despite close estimations of aggregated provincial emissions, the MEIC provides relatively high estimates for cities with complex and dynamic industrial systems, while the CHRED tends to provide high estimates for heavily industrial cities. The CHRED and MEIC show reasonable agreement regarding the evolution of city-level emissions and the city-level industrial emission ratios over 2005–2020. The PKU-CO₂ and EDGAR failed to capture the emissions and their structural changes at the city level, which is related to their point-source database stopping updates after 2012. Our results suggest that cross-inventory differences for city-level emissions exist not only in their aggregated emissions but also in their changes over time. Full article

It is well known that ship emissions contribute significantly to atmospheric pollution. However, the impact on air quality can regionally vary, as influenced by parameters such as the composition of the regional shipping fleet, state of background atmospheric pollution, and meteorological aspects. This study compared two regions with high shipping densities in 2015. These include the North and Baltic Seas in Europe and the Yellow and East China Seas in China. Here, a key focal point is an evaluation of differences and similarities of the impacts of ship emissions under different environmental conditions, particularly between regions with medium (Europe) and high air pollution (China). To assess this, two similarly performed chemical transport model runs were carried out with highly resolved bottom-up ship emission inventories for northern Europe and China, calculated with the recently developed MoSES model, publicly available emissions data for nonshipping sources (EDGAR, MEIC). The performance of the model was evaluated against measurement data recorded at coastal stations. Annual averages at affected coastal regions for ${\mathrm{NO}}_2$, ${\mathrm{SO}}_2$, ${\mathrm{O}}_3$ and ${\mathrm{PM}}_{2.5}$ were modeled in Europe to be 3, below $0.3$, $2.5$, 1 and in China 3, 2, 2–8, $1.5$, respectively, all given in $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$. In highly affected regions, such as large harbors, the contributions of ship-related emissions modeled in Europe were 15%, $0.3$%, $-12.5$%, $1.25$% and in China were 15%, 6%, $-7.5$%, 2%, respectively. Absolute pollutant concentrations from ships were modeled slightly higher in China than in Europe, albeit the relative impact was smaller in China due to higher emissions from other sectors. The different climate zones of China and the higher level of atmospheric pollution were found to seasonally alter the chemical transformation processes of ship emissions. Especially in northern China, high PM concentrations during winter were found to regionally inhibit the transformation of ship exhausts to secondary PM, and reduce the impact of ship-related aerosols, compared to Europe. Full article

In this study, a time change analysis of fine particulate (PM_2.5) emission in multi-resolution emission inventory in China (MEIC) from 2013 to 2016 was conducted. It was found that PM_2.5 emissions showed a decreasing trend year by year, and that the annual total emission of PM_2.5 decreased by 28.5% in 2016 compared with that of 2013. When comparing the observation data of PM_2.5 and ozone (O₃), it was found that both PM_2.5 and O₃ show obvious seasonal changes. The emission of PM_2.5 in autumn and winter is higher than that in summer, while that of O₃ is not. Our study showed that in the 2015–2020 period, annual mean concentrations of PM_2.5 and O₃ in Beijing varied from 80.87 to 38.31 μg m⁻³ and 110.75 to 106.18 μg m⁻³, respectively. Since 2015, the observed value of PM_2.5 has shown an obvious downward trend. Compared with 2015, the average annual PM_2.5 concentrations in Beijing, Shanghai, Xuzhou, Zhengzhou, and Hefei in 2020 had decreased by 52.62%, 40.35%, 22.2%, 46.84%, and 45.11%, respectively, while O₃ showed an upward trend. Compared with the annual averages of 2015 and 2020, Beijing and Shanghai saw a decrease of 4.13% and 8.46%, respectively, while Xuzhou, Zhengzhou, and Hefei saw an increase of 7.08%, 19.46%, and 41.57%, respectively. The comparison shows that PM_2.5 is becoming less threatening in China and that ozone is becoming more difficult to control. Air pollution is a modifiable risk factor. Appropriate sustainable control policies are recommended to protect public health. Full article