Search Results (7)

Understanding aerosol chemical components’ roles in light extinction is critical for air quality management and climate mitigation. This study compared PM₁ optical properties and chemical compositions in Shanghai (southern China) and Dezhou (northern China) during winter using high-resolution aerosol mass spectrometers and optical instruments. Results showed PM₁ scattering coefficients (10.9–549.8 Mm⁻¹) in Shanghai were dominated by traffic-related organic aerosols (OA) (45.2%), with ammonium sulfate and nitrate contributing 60.5% of extinction. In Dezhou, higher scattering coefficients (3.5–2635.1 Mm⁻¹) were driven by heating/biomass burning, with OA accounting for 57.8% and ammonium nitrate 27.2%. Mass scattering efficiencies (MSEs) in Dezhou were significantly higher (sulfate: 10.75 m²/g; nitrate: 10.15 m²/g; OA: 4.9 m²/g) than those in Shanghai (4.2/3.85/3.00 m²/g). Pollution episodes revealed distinct mechanisms: high-humidity OA accumulation for Shanghai vs. nitrate-organic synergy for Dezhou. The IMPROVE model systematically underestimated scattering coefficients, emphasizing the need for region-specific parameterization. OA was identified as the primary scattering contributor in both cities, though inorganic species became critical under high-pollution conditions. These findings suggest targeted strategies: reducing VOC emissions in southern China and controlling NOx in northern industrial areas to improve winter visibility and air quality. Full article

Understanding the spatio-temporal evolution of farmland carbon emissions, disentangling their underlying driving forces, and exploring the decoupling relationship between these emissions and economic development are pivotal to advancing low-carbon and high-quality agricultural development in Shandong Province, China. Using the Logarithmic Mean Divisia Index (LMDI) and Tapio decoupling model, this study conducted a comprehensive analysis of panel data from 16 cities in Shandong Province spanning 2004–2023. This research reveals that the total farmland carbon emissions in Shandong Province followed a trajectory of “initial fluctuating increase and subsequent steady decline” during the study period. The emissions peaked at 29.4 million tons in 2007 and then declined to 20.2 million tons in 2023, representing a 26.0% reduction compared to the 2004 level. Farmland carbon emission intensity in Shandong Province showed an overall downward trend over the period 2004–2023, with the 2023 intensity registering a 68.9% decline compared to 2004. The carbon emission intensity, agricultural structure, and labor effects acted as inhibiting factors on farmland carbon emissions in Shandong Province, while the economic development effect exerted a positive driving impact on the growth of such emissions. Over the 20-year period, these four factors cumulatively contributed to a reduction of 2.1 × 10⁵ tons in farmland carbon emissions. During 2004–2013, the farmland carbon emissions in Zaozhuang, Yantai, Jining, Linyi, Dezhou, Liaocheng, and Heze showed a weak decoupling state, while in 2014–2023, the farmland carbon emissions and economic development in all cities of Shandong Province showed a strong decoupling state. In the future, it is feasible to reduce farmland carbon emissions in Shandong Province by improving agricultural resource utilization efficiency through technological progress, adopting advanced low-carbon technologies, and promoting the transformation of agricultural industrial structures towards “high-value and low-carbon” designs. Full article

Analyses of regional carbon stock dynamics, particularly of spatial and temporal dynamics and their relationship with land use transitions, play a key role in the management of terrestrial ecosystem functions and the optimization of land resource allocation. This study focuses on Shandong Province, an important ecological security barrier along the eastern coast of China, to explore carbon stock changes and how land use modifications contributed to the chrono-spatial distribution of carbon stocks from 1990 to 2020, with additional forecasts up to 2040. Based on Natural Variation Conditions, Ecological Variation Conditions, and the City’s Variation Conditions, the results indicate a downward trend in carbon stocks across Shandong Province, from 2661.87 × 10⁶ t in 1990 to 2380.02 × 10⁶ t in 2020. Carbon stocks exhibit a highly uneven spatial distribution, with concentrations being notably higher in the central and eastern regions. Cities are classified based on their carbon stock level: high carbon stock cities (Linyi, Weifang, Yantai), large carbon stock cities (Jinan, Jining, Qingdao, Dezhou, Binzhou, Liaocheng, Taian, Zibo, Dongying), and cities with general carbon stock levels (Weihai, Rizhao, Zaozhuang). The major driver of carbon stock decline is the conversion of ecological lands into urban areas, with cultivated lands and forests being the primary carbon storage contributors. Projections suggest that under the City’s Variation Conditions, carbon stocks will decrease from 2380.02 × 10⁶ t in 2020 to 1654.16 × 10⁶ t by 2040, while Carbon stocks will rise from 2380.02 × 10⁶ t to 2430.56 × 10⁶ t under the Ecological Variation Conditions. A significant disparity in carbon sink potential is found across cities, which are divided into high carbon sink potential cities (Yantai, Dezhou, Weifang, Qingdao, Jinan), large carbon sink potential cities (Binzhou, Weihai, Zibo, Liaocheng, Dongying, Linyi, Taian, Rizhao, Zaozhuang), and general potential cities (Jining, Heze). The insights gained from this study are essential for promoting the conservation of regional terrestrial ecosystems, directing land use policy development, and supporting sustainable development initiatives in Shandong Province. Full article

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM_2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM_2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m³. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM_2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control. Full article

North China is one of the high-risk areas for destructive and strong earthquakes in mainland China and has experienced numerous strong historical earthquakes. An earthquake of magnitude M_W 5.5 struck Pingyuan County, Dezhou city, in Shandong Province, China, on 6 August 2023. This earthquake was the strongest in the eastern North China Craton since the 1976 Tangshan earthquake. Since the earthquake did not produce surface ruptures, the seismogenic structure for fault responsible for the Pingyuan M_W 5.5 earthquake is still unclear. To reveal the subsurface geological structure near the earthquake epicenter, this study used high-resolution two-dimensional (2D) seismic reflection profiles and constructed a three-dimensional (3D) geometric model of the Tuqiao Fault by interpreting the faults in the seismic reflection profiles. This study further combined focal mechanism solutions, aftershock clusters, and other seismological data to discuss the seismogenic fault of the Pingyuan M_W 5.5 earthquake. The results show that the Tuqiao Fault is not the seismogenic fault of the M_W 5.5 earthquake. The actual seismogenic structure may be related to the NE-oriented high-angle strike-slip blind fault developed in the basement. We further propose three possible fault models for the strong seismogenic structure in North China to discuss the potential seismotectonics in this region. Full article

Clarifying urban-rural spatial explicit structure changes is of great significance for understanding the urban-rural relationship evolution. Previous studies have mostly focused on urban internal spatial structure evolutions and less on the regional scale when it comes to exploring urban and rural evolutions. Nighttime light can timely reflect the human activities in regions and provides great potential for investigating the evolutions of urban and rural spatial explicit structures. Here, taking Dezhou City, a rapidly urbanizing city in China, as a case study, we employed the local contour tree method and nighttime light data to map urban and active rural extents from 2012 to 2020 and further explored their respective development processes. This study showed that unlike in rural regions, the internally explicit structures of urban regions were more complex, and there were often multiple hotspots inside them. The area of the urban-rural region increased significantly by 39.3% from 2012 to 2020 (p < 0.05). Populations were greatly responsible for the spatial explicit structure changes of urban and active rural regions. The urban and rural region rankings of the identified counties were basically consistent with the urban and rural population rankings. Unlike the perspectives of earlier land use (i.e., built-up land or impervious surface), this study underlined urban and active rural regions in view of the scope of active human activities. These results can likely help policymakers understand current active human activity extents and provide a data-based reference for future public services and infrastructure planning. Full article

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province. Full article